case study of flooding in bangladesh

Bangladesh floods: Experts say climate crisis worsening situation

The densely populated delta nation, one of the world’s most climate-vulnerable, is facing its worst floods in more than a century.

The worst floods in Bangladesh in more than a century have killed dozens of people so far and displaced nearly 4 million people, with authorities warning the water levels would remain dangerously high in the north this week.

Experts say the catastrophic rain-triggered floods , which submerged large part of the country’s northern and northeastern areas, are an outcome of climate change .

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Bangladesh, india floods kill over 100; millions in need of aid, photos: food, drinking water concerns as floods batter bangladesh, ‘children are starving’: a cry for help from flood-hit bangladesh, women in rural bangladesh bear rising cost of climate crisis.

Bangladesh, a densely populated delta nation, is also one of the world’s most climate-vulnerable where the poor are disproportionately impacted as frequent floods threaten livelihoods, agriculture, infrastructure and clean water supply .

A 2015 study by the World Bank Institute said about 3.5 million of Bangladesh’s 160 million people are at risk of river flooding every year.

Saiful Islam, director of the Institute of Water and Flood Management (IWFM) at the Bangladesh University of Engineering and Technology (BUET), analysed 35 years of flooding data and found that rains were getting more unpredictable and many rivers are rising above dangerous levels more frequently than before.

“The last seven years alone brought five major floods, eroding people’s capacity to adapt, especially in the country’s northern and northeastern regions,” Islam told Al Jazeera.

Citing one of his research papers, he said even if average global temperatures increase modestly – by 2 degrees Celsius (3.6 Fahrenheit) over the average for pre-industrial times – flooding along the Brahmaputra river basin in northeastern India and Bangladesh is projected to increase by 24 percent.

With an increase of 4 Celsius (7.2 F), flooding is projected to increase by more than 60 percent, Islam’s research indicated.

‘Clogged system’

Several rivers, including the Brahmaputra, one of Asia’s largest, flow downstream from India’s northeast through the low-lying wetlands of Bangladesh as they drain into the Bay of Bengal.

However, this year, the excess rainwater from India’s Assam and Meghalaya states that flows into Bangladesh’s Meghna and Jamuna Rivers could not drain because the wetlands were already saturated by an earlier pre-monsoon flood last month.

“The siltation of riverbeds caused by deforestation and solid waste dumping has already reduced the water carrying capacity of the rivers in Bangladesh,” Ashiq Iqbal, a researcher at IWFM, told Al Jazeera.

“Besides, excessive sand and stone mining in upstream India has loosened the soil, which ultimately ends up into river bottom and decreases the navigability. As a result, the whole systems get clogged. And this clogged system has lost its ability to drain out water from two quick successive floods in short time,” he said.

Unplanned construction along the northeastern wetland is another reason rivers have become clogged arteries, Mominul Haque Sarkar, senior adviser at the Centre for Environment and Geographic Information Services (CEGIS), told Al Jazeera.

“A lot of pocket roads as well as culverts are being constructed in different places across the wetland. As a result, water flow gets obstructed and it gets swelled when it rains excessively,” Sarkar said.

Most of the towns and villages in northern Bangladesh do not have protection dams. So when the water level in the wetlands or rivers starts rising, it quickly enters the residential areas and inundates them, he said.

To cope with the floods, conventional methods such as building embankments along major rivers were proposed as part of a Flood Action Plan implemented in 1990.

But some experts say structural measures to contain floods are ineffective.

Mohamad Khalequzzaman, geoscientist at Lock Haven University in the United States, told Al Jazeera it is “difficult and undesirable to contain flood with fortified walls”.

“It may be necessary to contain floods in selected places where a high concentration of population and resources are located, such as in big cities,” he said. “But in a geography dominated with wetlands, it is not needed.”

Khalequzzaman said walling off low-lying areas using permanent embankments, or polders, has been a popular intervention in countries such as Bangladesh. “Polders separate rivers from floodplain which in turn intensifies flow in the river and causes riverbank erosion,” he said.

He said water resources in Bangladesh’s major rivers should be managed involving all co-riparian countries in the Ganges-Brahmaputra-Meghna (GBM) basins – Bangladesh, India and Bhutan.

“The problem is only 8 percent of the GBM basins are located within the geographic territory of Bangladesh. So, in reality, without an integrated water resources pact among all countries in the GBM basins, floods cannot be managed properly in Bangladesh,” he said.

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Assessment of flood vulnerability in Jamuna floodplain: a case study in Jamalpur district, Bangladesh

• Original Paper
• Published: 20 October 2022
• Volume 116 , pages 341–363, ( 2023 )

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• Md. Munjurul Haque   ORCID: orcid.org/0000-0001-9802-8842 1 ,
• Sabina Islam 2 ,
• Md. Bahuddin Sikder 1 ,
• Md. Saiful Islam 3 &
• Annyca Tabassum 1  

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Floods are a frequent natural calamity in Bangladesh, where many areas get affected almost every year. An indicator-based vulnerability assessment can help efficiently manage the disaster. Therefore, this study intends to assess the community vulnerability in the Jamuna floodplain, one of the most flood-affected areas, using an indexing method. The index involves many indicators of flood exposure, sensitivity, and adaptive capacity along with their weights, determined based on an extensive literature review. A pretested questionnaire was employed to collect primary data from the study area through 400 household-level interviews. Using multistage sampling techniques, five upazilas from Jamalpur district, i.e., Dewanganj, Islampur, Madarganj, Melandaha, and Sharishabari, were purposefully chosen based on past flood damage reports. The percentage values were derived using SPSS for every variable from the field-level data. The variable vulnerability index (VVI) was computed by dividing the indicator’s weight by its percentage value. Then, exposure, sensitivity, and adaptive capacity indices were calculated using the VVI values. Finally, the composite vulnerability index (CVI) of the five Upazilas has been computed using an established and recognized index formula. The CVI scores for Dewanganj, Islampur, Madarganj, Melandaha, and Sharishabari are 0.86, 0.84, 0.71, 0.70, and 0.65, respectively, which suggest a high overall vulnerability. The scores of the exposure and adaptive capacity indices reveal that Dewanganj and Islampur Upazilas have higher vulnerability than the other three upazilas, especially due to poor socioeconomic conditions, low adaptive capacity, and high exposure. This study recommends some infrastructural development, such as sustainable flood-resistant dams, as the study sites are in a flood-prone zone. Houses should be built using flood-resistant materials like bricks and concrete, which are more resilient than mud. Improvements in education and multiple income sources will help the affected people increase their coping capacity.

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Acknowledgements

The authors express sincere thanks to Abdulla-Al Kafy and Digonresearch.org for their cordial help in proofreading of the manuscript. The first author received funding from the Ministry of Science and Technology, Government of the People’s Republic of Bangladesh, under the NST fellowship program.

The first author received funding from Ministry of Science and Technology, Government of the People’s Republic of Bangladesh under the NST fellowship program. Grant No. 39.00.0000.012.002.03.18.

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Haque, M.M., Islam, S., Sikder, M.B. et al. Assessment of flood vulnerability in Jamuna floodplain: a case study in Jamalpur district, Bangladesh. Nat Hazards 116 , 341–363 (2023). https://doi.org/10.1007/s11069-022-05677-1

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Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

Sjoukje philip, sarah sparrow, sarah f. kew, karin van der wiel, niko wanders, ahmadul hassan, khaled mohammed, hammad javid, karsten haustein, friederike e. l. otto, feyera hirpa, ruksana h. rimi, a. k. m. saiful islam, david c. h. wallom, geert jan van oldenborgh.

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2  ∘ C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed.

In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2  ∘ C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: we find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

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Philip, S., Sparrow, S., Kew, S. F., van der Wiel, K., Wanders, N., Singh, R., Hassan, A., Mohammed, K., Javid, H., Haustein, K., Otto, F. E. L., Hirpa, F., Rimi, R. H., Islam, A. K. M. S., Wallom, D. C. H., and van Oldenborgh, G. J.: Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives, Hydrol. Earth Syst. Sci., 23, 1409–1429, https://doi.org/10.5194/hess-23-1409-2019, 2019.

In August 2017 Bangladesh faced one of the worst river flooding events in recent history, with record high water levels, and the Ministry of Disaster Management and Relief reported that the floods were the worst in at least 40 years. Due to heavy local rainfall, as well as water flow from the upstream hills in India, the various rivers in northern Bangladesh burst their banks. This led to the inundation of river basin areas in the northern parts of Bangladesh, starting on 12 August and affecting over 30 districts. The National Disaster Response Coordination Centre (NDRCC) reported that around 6.9 million people were affected, with 114 people reported dead and at least 297 250 people displaced. Approximately 593 250 houses were destroyed, leaving families displaced in temporary shelters.

Bangladesh is a highly flood-prone country, with flat topography and many rivers that regularly flood and are used to irrigate crops and for fishing. The August 2017 floods were particularly impactful as they followed two earlier flooding episodes in late March and July that year, increasing the vulnerability of people. Nearly 85 % of the rural population in Bangladesh works directly or indirectly with agriculture, and rice is the main staple food, contributing to 95 % of total food production. As is typical after such flooding, farmers started to plant aman , the monsoon rice that is almost entirely rain dependent. However, the August flood was worse than that of July, and areas such as Dinajpur and Rangpur that normally do not flood were also flooded (see Fig.  1 ). These are areas that contain significant rice production. As a result, 650 000 ha of croplands were severely damaged during the August monsoon flooding in the year. Aman rice is historically the most variable, and yields tend to drop dramatically during major flood years ( Yu et al. ,  2010 ) . The flood-induced crop losses in 2017 resulted in the record price of rice, negatively affecting livelihood and food security. Beyond impacts to agriculture, the floods destroyed transport infrastructure such as railways lines, bridges and roads, leaving some areas inaccessible to disaster relief efforts. The rise in water and strong current breached roads and embankments and swept away livestock, houses and assets that may have otherwise been protected. At least 2292 schools were damaged, affecting education for weeks, and 13 035 cases of waterborne illnesses were reported in the aftermath of the floods.

Figure 1 Inundation forecast map of Bangladesh for 16 August 2017 (left panel). Overall flood impact of the August 2017 flooding as stated on 21 August (right panel). The green circle in the northwest of the map denotes the location of Bahadurabad. The Brahmaputra basin is outlined in Fig.  3 ; see the original documents (source: Flood Forecast and Warning Center – FFWC – of BWDB at https://reliefweb.int/sites/reliefweb.int/files/resources/SitRep_2_Bangladesh Flood_16 August 2017.pdf , last access: 8 May 2018 and https://reliefweb.int/sites/reliefweb.int/files/resources/72 hrs-Bangladesh_Flood_Version1_Final 08212017.pdf , last access: 8 May 2018) for more details on the maps and legends.

The 2017 flood was markedly different from previous major flood events in 1988 and 1998, when both the Ganges and Brahmaputra flooded simultaneously ( Webster et al. ,  2010 ) . Based on forecasts it was feared that a similar event would occur in 2017, but in this case, the swelling of the Brahmaputra; its tributary, the Atrai; and the Meghna caused flooding. The worst impacts were along the main reach of the Brahmaputra River (Fig.  1 b).

The first estimates of the return period provided by the Bangladesh Water Development Board (BWDB) for the 2017 flood event range from an event occurring once in 30 years to an event occurring once in 100 years, depending on the data source: water level and discharge data at Bahadurabad (the main station for discharge representing the Brahmaputra in Bangladesh) and the flooding forecast system GloFAS. These estimates, however, were implicitly based on the assumption of a stationary climate and did not account for the possibility that the frequency of such flooding events may be changing.

Extreme rainfall events that subsequently lead to widespread flooding, such as the 2017 event in Bangladesh, are one of the main types of extreme weather events that we are expecting to see more of in a warming climate. But with rainfall not only being driven by thermodynamic processes but also being affected by changing atmospheric processes, it is not clear a priori if such events at a particular location will increase in likelihood or if the dynamic changes will mean that the overall chance of extreme rainfall decreases there ( Otto et al. ,  2016 ) . Furthermore, in the current climate, drivers other than greenhouse gases (GHGs) often play a role that is currently difficult to quantify but likely to mask or exacerbate the effect of greenhouse-gas emissions so far on the occurrence likelihood of extreme rainfall events (e.g. aerosols,  van Oldenborgh et al. ,  2016 ) . Hence regional attribution studies are necessary for identifying whether and to what extent extreme rainfall events are changing and for providing insight into which drivers have been contributing to those changes and whether the trend is likely to continue into the future. Attribution studies require both observational data and models to fully estimate the impact of changes in the climate system. The reported advances in model development for the Brahmaputra region and their success in forecasting gives good confidence in the models' ability to accurately represent the region.

Hydrological models are increasingly used for studies on flooding in Bangladesh. As upstream flow data are absent for Bangladesh, a lot of effort has been made to develop flood forecasting systems based on satellite data and weather predictions. Webster et al. ( 2010 ) , for instance, developed a system that forecasts the Ganges and Brahmaputra discharge into Bangladesh in real time on 1-day to 10-day time horizons. In a recent study Priya et al. ( 2017 ) show that, by using a new long lead flood forecasting scheme for the Ganges–Brahmaputra–Meghna basin, skillful forecasts are provided that inherently not only express a prediction of future water levels but also supply information on the levels of confidence with each forecast. Hirpa et al. ( 2016 ) used reforecasts to improve the flood detection skill of forecasts.

Previous scientific studies generally show an increasing trend in climate projections of extreme rainfall and high discharge in the region. For example, Gain et al. ( 2011 ) use the PCR-GLOBWB model with input from 12 global circulation models (GCMs; 1961–2100) from the CMIP3 ensemble ( Meehl et al. ,  2007 ) in a weighted ensemble analysis. They show that in this ensemble, there is a positive trend in the peak flow at Bahadurabad; in this model configuration and under the SRES B2 scenario, a peak flow that currently occurs every 10 years will occur at least once every 2 years during the time period 2080–2099. Dastagir ( 2015 ) gives an overview of the change in flooding according to the IPCC 5th Assessment Report, using 16 GCMs from the CMIP5 ensemble ( Taylor et al. ,  2012 ) . They state that the warmer and wetter climate predicted for the Ganges–Brahmaputra–Meghna basin by most climate-related research in this region indicates that vulnerability to severe monsoon floods will increase with climate change in the flood-prone areas of Bangladesh. The same conclusion is reached by CEGIS and SEN authors ( 2013 ) , who use GCM projections and a hydrological model to show that in the wet season, an increase in precipitation and annual flow is projected. In line with this, Mohammed et al. ( 2017 ) find that in a 2.0  ∘ C warmer world, floods will be both more frequent and of a greater magnitude than in a 1.5  ∘ C warmer world in Bangladesh, using the hydrological model the Soil and Water Assessment Tool (SWAT) with input from the CORDEX regional model ensemble. Zaman et al. ( 2017 ) use two sets of climate models with climate change runs under the RCP8.5 scenario as input in a basin model that simulates flows in major rivers of Bangladesh, including the Brahmaputra. Using the two climate model runs as input, they find agreement in the basin model runs for Brahmaputra flow in a 2.0  ∘ C warmer world; one run shows a slightly higher impact of climate change compared to the other run, with an overall increase in monsoon flow of approximately 15 % and 10 % in the dry season.

Attribution studies on flooding, using both observational data and models, have often been done with precipitation only. In such studies, (e.g.  Schaller et al. ,  2014 ; van der Wiel et al. ,  2017 ; Philip et al. ,  2018 ; van Oldenborgh et al. ,  2017 ; Risser and Wehner ,  2017 ) it is assumed that precipitation is the main cause of the flooding. For shorter timescales and the relatively small basins involved, this is a reasonable assumption. The major basins in Bangladesh, however, are substantially larger and have longer water travel times than the basins considered in the above studies. Therefore using precipitation alone as a proxy for flooding might not be appropriate. In this paper we explicitly test this assumption by performing an attribution of both precipitation and discharge as a flooding-related measure of climate change. Thus we explore the flood in two different ways – first from a meteorological perspective (using precipitation data) and then from a hydrological perspective (using discharge data). Schaller et al. ( 2016 ) already studied a flooding case in an attribution study using one hydrological model. Yuan et al. ( 2018 ) use observations, GCMs, and one land surface model with and without land cover change to split the changes in observed streamflow and its extremes into anthropogenic and natural climate change, land cover change and human-water withdrawal components. In this paper we do an attribution study for the first time using observational precipitation and discharge data and a combination of GCMs and several hydrological models. To compare the differences between the attribution results for the two variables we calculate the return periods and risk ratios for the August 2017 flooding event in Bangladesh for both precipitation and discharge in observations and models, for past (pre-industrial), present and future (2 ∘ warmer than pre-industrial) conditions.

Bangladesh is influenced by three large river basins: the Ganges basin in the northwest, the Brahmaputra basin in the northeast and the Meghna basin in the east. During the monsoon season the rainfall moves northwest across the country, starting in May–June–July in the Meghna basin. Usually 2–3 weeks after peak rainfall in July, the rivers in the Brahmaputra basin reach their peak discharge. Finally, in August and September the Ganges basin river discharge peaks. The largest impact of flooding in August 2017 was felt in the northern parts of Bangladesh (Fig.  1 ). As this was mainly caused by precipitation in the Brahmaputra basin, the focus in this paper will be on this basin. In the Brahmaputra basin little water originates from precipitation on the northern side of the Himalaya (China–Tibet), with most of the water coming from precipitation in the upstream Assam region in India. Precipitation in Bhutan also contributes to the river water in Bangladesh.

In this paper we use two event definitions: one based on precipitation and one based on discharge. Both observational data and model data can be used for these two event definitions. For precipitation we average over the whole Brahmaputra basin and take a 10-day average, as the largest precipitation volume in the Brahmaputra basin travels to Bangladesh within 10 days; see Fig. 5 in Webster et al. ( 2010 ) . Only precipitation in July–August–September (JAS) is analysed as it is only in these months that precipitation is considered the major cause of flooding. For discharge we simply use the daily maximum discharge at Bahadurabad, a station situated to the north of the confluence point of the Ganges with the Brahmaputra, in JAS.

The data and methods used are described in Sect.  2 . Sections  3 and 4 describe the analysis for observations and models respectively. The results are synthesized in Sect.  5 . A discussion follows in Sect.  6 , and the paper ends with some conclusions.

Observational data are described in Sect.  2.1 , and the models and experiments are described in Sect.  2.2 . The explanation of how these data are used in the analysis is detailed in Sect.  2.3 .

2.1 Observational data

The first observational dataset we use is the 0.5 ∘ gauge-based CPC analysis from 1979 to now ( https://www.cpc.ncep.noaa.gov/products/Global_Monsoons/gl_obs.shtml , last access: 20 March 2018). This is the longest gauge-based daily gridded dataset available that is still being updated. The seasonal cycle of precipitation in the Brahmaputra basin is shown in Fig.  2 a. Monsoon rains start rising slowly, with a maximum in July and August, and become less from September onwards. As precipitation will not, in general, cause flooding before July, we will use the months JAS for the precipitation analysis.

Figure 2 Seasonal cycle of  (a)  precipitation in the Brahmaputra basin for CPC, (b)  discharge at Bahadurabad and (c)  water level at Bahadurabad. The red line shows the mean value, and green lines show the 2.5, 17, 83 and 97.5 percentiles.

The second gauge-based dataset we use for comparison is the combined Full Data and First Guess Daily 1.0 ∘ GPCC dataset (1988–now) ( Schamm et al. ,  2013 , 2015 ). As this is a much shorter dataset we expect the signal-to-noise ratio in the trend to be smaller. We only use this dataset to additionally check the observations. The seasonal cycle can be found in the Supplement Fig. S1.

The third dataset is the reanalysis dataset ERA-interim (ERA-int; 1979–now;  Dee et al. ,  2011 ) . Precipitation of this dataset is analysed directly. As well as precipitation, temperature and potential evapotranspiration (calculated with the Penman–Monteith method) are used to drive one of the hydrological models (see Sect.  2.2.4 ). The seasonal cycle of ERA-int can be found in Fig. S1.

We use discharge and water level data from Bahadurabad. Discharge data are available for the years 1984–2017, and water level data are available for the years 1985–2017 (source: BWDB). For both datasets the seasonal cycle is shown in Fig.  2 b, c. Additionally, we have a discharge dataset for the years 1956–2006 (source: BWDB). As the rating between water level, velocity and discharge is not exactly the same in the two discharge datasets, we consider simply merging the datasets not to be appropriate. The 1984–2017 dataset is used in the analyses, but results are compared to calculations with the 1956–2006 dataset and merged datasets.

2.2 Model descriptions

First the global circulation model and regional model that are used for the analysis of precipitation are listed, including a short description of the model runs. Next a list of hydrological models used in this study is given. Further details of the models, including validation and calibration of the hydrological models, are described in the Supplement.

2.2.1 Precipitation

Ec-earth 2.3.

We use three different ensembles of the coupled atmosphere–ocean general circulation model EC-Earth 2.3 ( Hazeleger et al. ,  2012 ) at T159 ( ∼150  km). The first one is a transient model experiment, consisting of 16 ensemble members covering 1861–2100 (here we use up to 2017), which are based on the historical CMIP5 protocol until 2005 and are based on the RCP8.5 scenario ( Taylor et al. ,  2012 ) from 2006 onwards. The other two EC-Earth 2.3 experiments are two time-slice experiments based on the 16-member transient model experiment above. Two experimental periods are selected in which the model global mean surface temperature (GMST) is as observed in 2011–2015 (“present-day” experiment) and the pre-industrial (1851–1899) +2   ∘ C warming experiment (“2  ∘ C warming” experiment).

weather@home

In addition to the EC-Earth 2.3 experiments, large ensembles of climate model simulations are created using the distributed computing weather@home modelling framework ( Guillod et al. ,  2017 ; Massey et al. ,  2014 ) based on Hadley Centre models. Table  1 describes the experiments used in this study, which are grouped into three sets: (i) ensembles for the historical period 1986–2015, (ii) ensembles for 2017 and (iii) ensembles for assessing possible changes in the future.

See the Supplement for a more detailed description of these runs.

Table 1 Experiments with the weather@home ensemble.

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2.2.2 Discharge

Pcr-globwb 2.

The global hydrological model PCR-GLOBWB 2 ( Sutanudjaja et al. ,  2018 ) was selected because of its ability to simulate the hydrological cycle, including reservoir operations and human–water interactions at continental and global scales. It resolves the water balance at the surface by using precipitation, temperature and potential evaporation inputs from meteorological observations or climate models. We used PCR-GLOBWB to conduct several river discharge simulations, First we used observational data as input to check the performance of the model. Next we used the EC-Earth transient and two time-slice experiments as input to generate a large ensemble.

Second, we use the SWAT, which is a commonly used hydrological model for investigating climate change impacts on water resources at regional scales ( Gassman et al. ,  2014 ) . This model has already been used to simulate impacts of climate change on the flows of the Brahmaputra River ( Mohammed et al. ,  2017 , 2018 ) . The water balance equation used in SWAT consists of daily precipitation, runoff, evapotranspiration, percolation and return flow. The SWAT model was used in this study to simulate flows by taking inputs from both the transient and time-slice EC-Earth experiments and weather@home experiments, using daily maximum and minimum temperatures and precipitation.

The third hydrological model we use is LISFLOOD. This is a fully distributed and semi-physically based model initially developed by the Joint Research Centre (JRC) of the European Commission in 1997. It was subsequently updated to forecast floods and analyse impacts of climate and land-use change ( Burek et al. ,  2013 ) . It has been used for operational flood forecasts as part of the European Flood Awareness System (EFAS) since 2012 ( https://www.efas.eu/en/about , last access: 2 May 2018). The LISFLOOD model was used in this study to simulate the river flow of the Brahmaputra River at the Bahadurabad gauging station with input data from the weather@home model.

River flow model

The fourth and final hydrological model used in the analysis is a fully distributed river flow model (RFM) that estimates the streamflow by discrete approximation of the one-dimensional kinematic wave equation ( Dadson et al. ,  2011 ) . The RFM was used in this study to simulate the river flow of the Brahmaputra River at the Bahadurabad gauging station with input data from the weather@home model.

2.3 Statistical methods

We use a class-based event definition, i.e. we consider all events that are as extreme or more extreme than the observed event on a one-dimensional scale, in this case 10-day averaged precipitation averaged over the Brahmaputra basin or daily runoff at Bahadurabad.

The first step in an attribution analysis is trend detection: fitting the observations to a non-stationary statistical model to look for a trend outside the range of deviations expected by natural variability. In this case we study the trends of extreme high-precipitation and river discharge values. In extreme value analysis, the generalized extreme value (GEV) distribution ( Coles ,  2001 ) is often used to fit and model the tail of the empirical distribution for this type of event, the maximum daily or 10-daily value over the monsoon season. The shape parameter ξ determines the tail behaviour, and negative indicates light tail behaviour while positive indicates heavy tail behaviour. When ξ =0 , the distribution simplifies to the Gumbel distribution. Global warming is factored in by allowing the GEV fit to be a function of the (low-pass filtered) GMST. In the case of precipitation and discharge extremes, it is assumed that the scale in parameter σ (the standard deviation) scales with the position parameter μ (the mean) of the GEV fit. This assumption is also known as the index flood assumption ( Hanel et al. ,  2009 ) and is commonly applied in hydrology to restrain the number of fit parameters. It can be checked in the model experiments where there are enough data to fit both μ and σ independently. These parameters are scaled up or down with the GMST using an exponential dependency similar to Clausius–Clapeyron (CC) scaling: μ = μ 0 exp ( α T / μ 0 ) , σ = σ 0 exp ( α T / μ 0 ) , with T as the smoothed global mean temperature and α as the trend that is fitted together with μ 0 and σ 0 . The shape parameter ξ is assumed to be constant. 95 % confidence intervals are estimated using a 1000-member non-parametric bootstrap. This approach has been used in several previous attribution studies (e.g.  van Oldenborgh et al. ,  2016 ; van der Wiel et al. ,  2017 ; Otto et al. ,  2018 ) . This fit also gives the return periods of the observed event.

The scaling is taken to be an exponential function of the smoothed global mean temperature. This exponential dependence can clearly be seen in the scaling of daily precipitation extremes with local daily temperature in regions with enough moisture availability ( Allen and Ingram ,  2002 ; Lenderink and van Meijgaard ,  2008 ) . It is also expected on theoretical grounds through the first-order dependence of the maximum moisture content on temperature in the Clausius–Clapeyron relations of about 7 % K −1 , which gives rise to an exponential form. Note that we fit the strength of the connection, which is often different from CC scaling. As it is not clear what the relevant local temperature is, but local temperature usually scales linearly with the global mean temperature, we chose the GMST.

The second step in an attribution analysis is the attribution of the detected trend to global warming, natural variability or other factors, such as changes in aerosol concentration or the El Niño–Southern Oscillation; this requires comparing model simulations with and without anthropogenic forcing. There are two approaches. The first is to run two ensembles: one with current conditions and one with conditions as they would have been without anthropogenic emissions. The number of events above the threshold is compared between the two ensembles. In the second approach, we approximate the counterfactual climate by the climate of the late 19th century and fit the same non-stationary GEV that was described above to the model data. The distribution is evaluated for a GMST in the past and the current GMST. These two approaches have been used before for studies of extreme precipitation (e.g.  Schaller et al. ,  2014 ; van Oldenborgh et al. ,  2016 ; van der Wiel et al. ,  2017 ; van Oldenborgh et al. ,  2017 ) . We checked that year-on-year autocorrelations of RX10day (maximum 10-day precipitation amount) are negligible, so serial autocorrelations are not a problem in this analysis.

As a third step, we calculate the risk ratio (RR) or change in probability for different time intervals. These include for instance the difference between the present day and 1979, or between present-day and pre-industrial times. For observations we calculate risk ratios with respect to the beginning of the dataset. If possible, we additionally transform these into risk ratios with respect to pre-industrial conditions, in this case set to be the year 1900, such that we can compare this with model runs for pre-industrial settings. For this transformation we assume that the RR depends exponentially on the covariate, in this case the global mean temperature change. For instance if we find that the probability doubles for 0.5  ∘ C warming, we assume that first ordering it would cause it to double again for 1  ∘ C warming. With future model runs we can also calculate risk ratios between the +2   ∘ C climate and the climate now.

A last step in the analysis is the synthesis of the results into a single attribution statement. Though the method for evaluating risk ratios using a transient model or observations is different from that using ensemble time-slice experiments that are explicitly designed to simulate a +2.0   ∘ C world, we are able to give an average value for all observations and models combined, and we assume that this gives a good first-order estimate of the overall risk ratio.

The differences among the RRs of these ensembles and the observations are due to natural variability, different framings and model spread. The relative contribution of random natural variability can be estimated from a comparison of the uncertainty derived from each fit with the spread of the different estimates of the RR from observations and models. We do this by computing a χ 2 ∕dof , with the number of degrees of freedom (dof) being one less than the number of fits. If this is roughly equal to 1, the variability is compatible with only the natural variability that determines the uncertainty on each separate model estimate of the RR. If it is much larger than 1, the systematic differences between the framings and models contribute significantly.

We choose to use a weighted average, with the weights being the inverse uncertainty squared for each RR (models and observations). The uncertainties are approximated by symmetric errors on log (RR) and added in quadrature ( ϵ 2 = ϵ 1 2 + ϵ 2 2 + … + ϵ N 2 / N ). If there is a significant contribution of χ 2 due to model spread, this has to be propagated to the final result, and the final uncertainty is larger than the spread due to natural variability. In this case we choose to give all models equal weight. The method described here was also used in Eden et al. ( 2016 ) and Philip et al. ( 2018 ) .

3.1 Precipitation

Figure  3 a shows the time series of CPC precipitation averaged over the Brahmaputra basin for 90 days ending on 2 September 2017. The 10-day average at the beginning of July is slightly higher than the 10-day average beginning of August, 14.38 versus 14.20 mm. As we are interested in the August flooding event, we take the precipitation value from the August event, which has a maximum on 5–14 August (see Fig.  3 c). The 10-day average annual maximum precipitation is fitted to a GEV distribution. The return period plots show that the distribution can be described by a GEV by overlaying the data points and fit for the present and a past climate (Fig.  3 d). The return period calculated from this fit is 11 years (95 % CI – confidence interval, 4 to 200 years) for the current climate. There is a positive trend with a risk ratio with respect to 1979 of a factor of 6 ( >0.3 ), although the trend is not significant at p <0.05 when two-sided (the uncertainty range includes 1).

Figure 3 CPC data (a, c) and analysis of the highest observed 10-day mean rainfall in the Brahmaputra basin in July–September  (b, d) . (a)  Time series of precipitation averaged over the Brahmaputra basin; blue is more than average, and red is less than average. (b)  The location parameter μ  (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the 10-day averaged data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years, 2017 and 1950. The purple square denotes the value of 2017 (not included in the fit). (c)  The 10-day averaged precipitation over the Brahmaputra basin. Dark red means heavy precipitation. In red are the contours of the Brahmaputra basin. (d)  The GEV fit of the 10-day averaged data in 2017 (red lines) and 1950 (blue lines). The observations are drawn twice, scaled up with the trend (smoothed global mean temperature) to 2017 and scaled down to 1950. The purple line shows the observed value in 2017.

A similar approach to the one used for CPC data is applied to ERA-int data. In this dataset the July 2017 10-day average was also just slightly higher than the August 2017 10-day average. The return period for the August event with a value of 17.9 mm day −1 was 2 years (95 % CI, 1 to 6 years) in the current climate. This dataset also shows a non-significant positive trend with a risk ratio of 1.9 (0.6 to 7), i.e. doubling the probability of an event like this or higher.

Finally, the shorter GPCC dataset gives similar results as well. Risk ratios are given with respect to 1979 in order to compare this with the other datasets. The August 2017 10-day average is slightly higher than the July 10-day average. The return period is about 20 years (95 % CI, 4 to 800 years). The risk ratio is not significantly different from 1.

The results of return periods and risk ratios based on observations can be found in Table  2 . For analyses with models we use the return period from the CPC dataset of 11 years for this event, as based on local experience we think that this is the best estimate. Due to the shape parameter being close to zero the risk ratio will not have a strong dependence on this choice; for a Gumbel distribution it is independent of the return time.

Table 2 Return periods and risk ratios for observations of precipitation, discharge and water level. The column RR1 gives results wrt 1979 (precipitation), 1984 (discharge) and 1985 (water level). The column RR (wrt 1900) scales the results to the pre-industrial period.

3.2 Discharge

The highest discharge in 2017 was reached on 16 August, with a value of about 78 000 m 3  s −1 . This was clearly higher than any value in July in the same year, as opposed to the precipitation values discussed above. There have been several years in which the discharge was higher than in 2017, including the years 1998 and 1988, which are the two maximum values in the discharge record. The return period is calculated from the discharge dataset since this is our best observational estimate. However it is worth noting that there is a large uncertainty in the accuracy of the discharge measurements from 2012 onwards. We check if the results are robust by comparing the outcomes from the different datasets.

We fitted the discharge time series of Bahadurabad to a GEV distribution. In this distribution we see no trend (95 % CI with respect to – wrt – 1900 is 0.1 to 40; see Fig.  4 ). Therefore we calculate the return period assuming no trend. This results in a return period of the August 2017 event of 4 years (95 % CI, 3 to 6 years). A cross-check with the 1956–2006 dataset or merging the two discharge datasets gives similar results.

Figure 4 Analysis of the highest observed daily discharge at Bahadurabad in July–September. (a)  The location parameter μ (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the discharge data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years, 2017 and 1984. The purple square denotes the value of 2017 (not included in the fit). (b)  The GEV fit of the discharge data, assuming no trend. The purple line shows the observed value in 2017.

3.3 Water level

Although we only have the water level available in observations and not for models, we still analyse the observational water level time series from Bahadurabad. The highest value in 2017 was on 16 August, with a value of 20.83 m. This is 1.33 m higher than the dangerous level of 19.50 m. In contrast to the discharge this was a record level since the beginning of the dataset (1985). It should be noted that the water level is also influenced by factors other than climate change, for instance a raising of the river bed by sedimentation and obstruction of the river channel by man-made constructions. See Sect.  6 for a more detailed discussion on the disentangling of geomorphological changes and climate change.

Under the same assumption as that for precipitation and discharge in which water level scales with GMST, the return period in the current climate is estimated to be 12 years (95 % CI, 3 to 350 years; see Fig.  5 b). However, although the risk ratio between 2017 and 1985 is as large as 170, this is only non-significant with a lower bound of 0.6. This is probably due to the relatively short length of the dataset. In addition, we calculate a return period assuming no trend (see Fig.  5 c). This gives a return period of about 80 years ( >25  years, 95 % CI). This agrees with the estimates from BWDB.

Figure 5 Analysis of the highest observed daily water level at Bahadurabad in July–September. (a)  The location parameter μ (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the discharge data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years, 2017 and 1985. The purple square denotes the value of 2017 (not included in the fit). (b)  The GEV fit of the water level data in 2017 (red lines) and 1985 (blue lines), assuming a trend. The observations are drawn twice, scaled up with the trend (smoothed global mean temperature) to 2017 and scaled down to 1985. (c)  The GEV fit of the same discharge data assuming no trend. The purple line in (b)  and (c)  shows the observed value in 2017.

4.1 Precipitation

In this section we present model validation and analysis results for the precipitation experiments, first for EC-Earth and then for weather@home.

For validation of the EC-Earth 2.3 model we use the years in the transient runs that correspond to the observational years 1979–2017. In the model, as expected, most precipitation falls in the months JJA, with a peak in July, like in observations, though the increase in precipitation is slightly stronger in June than it is in observations (Fig. S1). As it is assumed that the scale parameter σ scales with the position parameter μ of the GEV fit, we check whether the dispersion parameter σ ∕ μ and the shape parameter in this model are similar to those calculated from observations. The parameters of the GEV distribution that is fitted from the precipitation of these model years correspond well to the same parameters for CPC data.

The risk ratio of precipitation is calculated in the same way as that for observations, using the data period 1880–2017 such that we can use the same years for the EC-Earth runs and the PCR-GLOBWB and SWAT runs with EC-Earth input (see Fig.  6 ). The threshold is chosen such that the return period in the current climate is similar to the observed return period when using the same years. The risk ratio between 2017 and pre-industrial conditions is 3.3 (95 % CI, 2.7 to 4.2) in these transient runs. This corresponds to an increase in intensity for the same return period of 10 % (95 % CI, 9 % to 11 %). For the future (figures not shown) we calculate return periods from the present and future distributions separately, again following the same statistical method as that for observations but with two separate GEV fits that do not depend on the GMST. The risk ratio between a 2  ∘ C climate and the present climate follows from this, with a value of 1.8 (95 % CI, 1.7 to 2.1). We thus conclude that in the EC-Earth 2.3 model there is a significant positive trend in the magnitude of precipitation events such as the one in August 2017, both in the past (pre-industrial times up until now) and in the future.

Figure 6 Analysis of the highest 10-day average precipitation in July–September in the EC-Earth model in the years 1880–2017. (a)  The location parameter μ (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the discharge data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years 2017 and 1934. (b)  the GEV fit of the precipitation data in 2017 (red lines) and 1934 (blue lines), assuming a trend. The data are drawn twice, scaled up with the trend (smoothed global mean model temperature) to 2017 and scaled down to 1934. (c)  GEV fits for the present day (PD, red) and +2   ∘ C world (2C, yellow) simulations. The purple lines in (b) and (c)  show the threshold value for which the risk ratio is calculated.

For weather@home, we compare the annual cycle of 10-day running mean precipitation (see Fig. S2) and its spatial pattern in the Brahmaputra basin from historical simulations with CPC and GPCC observational records. As has also been seen in other regions of Bangladesh ( Rimi et al. ,  2019 a ) , weather@home rainfall is too intense in the pre-monsoon season but lies within observational uncertainty during the monsoon season itself. Also the variability of 10-day model precipitation is under-represented by the model for the monsoon season. During the monsoon season the spatial pattern and magnitude of weather@home output agrees well with GPCC and CPC observations (not shown).

Figure  7 shows the return periods of the maximum 10-day precipitation during JAS from the weather@home simulations, see also Table 3. The threshold used in this analysis is defined by taking the magnitude from the historical simulation corresponding to the return period derived from the CPC observational dataset.

Figure 7 Return times of the maximum 10-day precipitation from weather@home simulations. (a)  shows results from the historical, natural, GHG-only and actual 2017, natural 2017, and GHG-only 2017 simulations, and (b)  shows the historical, current, 1.5 and 2 ∘ simulations. Black horizontal lines represent the threshold values derived from the CPC observations. Shaded coloured vertical boxes with solid horizontal lines represent the uncertainty in the return period for the CPC threshold.

Figure  7 a shows the results for the historical and 2017-specific experiments, which we use to analyse how probabilities may have changed in the period from pre-industrial times up until now. There is no statistically significant difference between the historical and natural simulations, with a risk ratio of 0.92 ( 0.84 t o 1.02 ).

The difference in return periods between the historical and actual 2017 experiments gives an indication of the influence of the natural variability of the sea surface temperature (SST) pattern in the precipitation in this region. The historical ensemble is driven by 30 years of differing SST patterns containing different patterns of natural variability such as the El Niño–Southern Oscillation, whereas actual 2017 uses only the observed 2017 Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) SSTs. The SST pattern in 2017 (actual 2017) made extreme precipitation events less likely than the climatological mean (historical) with a risk ratio of 0.25 (95 % CI, 0.2 to 0.31). Within the set of simulations conditioned on 2017 SSTs, the negligible anthropogenic influence found in the full range SST set is confirmed; the actual 2017 and natural 2017 ensembles also do not show a statistically significant difference and have a risk ratio of 0.97 (95 % CI, 0.76 to 1.23), indicating that, if anything, high-precipitation events similar to the amplitude observed are more prevalent in our model in the natural ensemble, whether or not conditioned on 2017 SST conditions.

To understand this result more fully it is useful to look at the “GHG-only” simulations in Fig.  7 a (compare GHG-only with historical simulations and GHG-only 2017 with actual 2017 simulations). The GHG-only simulations show that increased GHG emissions have increased the likelihood of this kind of event (relative to the natural simulations) but that when the sulfate aerosol emissions are taken into account (in the historical and actual 2017 simulations), we find a counterbalancing effect that acts to reduce rainfall, hence reducing the risk for severe flooding. This effect has also been noted by van Oldenborgh et al. ( 2016 ); Rimi et al. ( 2018 b ) . Within the weather@home model sulfate emissions are included, although emissions due to other important aerosols such as black carbon, which can counteract sulfate effects, are not represented. The aerosol effect in HadRM3P is therefore potentially overestimated. The results highlight the non-linear change in risk over time as a function of anthropogenic aerosol emissions. EC-Earth follows the historical+RCP8.5 protocol for aerosols and includes both sulfate emissions and black and organic carbon. It does not include any indirect aerosol effects. The differences in aerosol representation and model handling of aerosols, as well as the influence of the experimental configuration on aerosol concentration, between EC-Earth and weather@home may account for the difference in risk ratios for the past climate period (pre-industrial times up until now) between the two models, whereas the change in risk of future climate scenarios show good agreement.

Figure  7 b shows return periods from the historical, current, natural, 1.5 and 2.0 ∘ simulations, which we use to analyse how probabilities may change in the future with respect to now. The current and historical ensembles are very similar as expected as both are forcing simulations of differing (but overlapping) lengths. Under 1.5 and 2  ∘ C of additional warming, high precipitation within the region is set to increase with risk ratios (compared to current simulation) derived using the CPC observational threshold of 1.46 (95 % CI, 1.27 to 1.69) and 1.74 (95 % CI, 1.52 to 1.99) respectively. In both cases the ERA-int (GPCC) threshold risk ratio is smaller (larger) than the CPC threshold risk ratio (not shown), but with overlapping uncertainty bounds with CPC. For 2  ∘ C of warming these risk ratios show good agreement with the EC-Earth values.

Table 3 Risk ratios for precipitation and discharge for models and observations for both present to pre-industrial times or 1900 and a 2  ∘ C climate to present. 95 % confidence intervals are given as well.

4.2 Discharge

In this section we present model validation and results of the discharge simulations, first for the model PCR-GLOBWB and then for SWAT, LISFLOOD and the RFM.

The runs with the PCR-GLOBWB model are treated in the same way as the EC-Earth runs. The experiment in which the PCR-GLOBWB model is driven by CPC precipitation and ERA temperature and evapotranspiration shows a strong trend in discharge, which was not seen in the discharge observations. The GEV-fit parameters encompass the best estimate from observations when fitted with a trend. However the large discharge events of 1988 and 1998 are not captured in this run (not shown).

The experiment with ERA input, in contrast, shows no trend but clearly shows the strong discharge events of 1988 and 1998 (not shown). The best estimate of the GEV-fit parameter is outside the error margins of the GEV-fit parameters of observations; however, the error margins overlap.

These two model runs show that the PCR-GLOBWB model is able to capture historical flood events, but the magnitude of these events is dependent on the meteorological input data. Furthermore, we find that the statistical properties are a fair representation of the statistical properties of observed discharge.

We perform an additional validation of the transient PCR-GLOBWB run with EC-Earth 2.3 input over the years corresponding to years with observed discharge. With this input the modelled discharge peaks in August but is also high in July and September. We thus use the same months JAS as in observations for further analysis. Different from the observed distribution, the shape parameter ξ is positive, showing higher discharge values in the tail. This is not a problem for this analysis, as the return period of about 4 years that we are interested in is not in the tail of the distribution. When comparing the error margins of the ratio σ ∕ μ with observed statistics we note that the model variability is too large compared to the model mean. This is not the ideal situation, and we note in the discussion how this model bias affects the analysis.

Using the transient model runs, the risk ratio of discharge is calculated in the same way as that for observations, using all data between 1880–2017. The risk ratio between 2017 and pre-industrial times is 2.3 (95 % CI, 1.7 to 2.4; see Fig.  8 ). For the future we calculate return periods from the present and future distributions separately, following the same statistical method as that for precipitation in the EC-Earth 2.3 present and future experiments. The risk ratio between a 2  ∘ C climate and the present follows from this, with a value of 1.3 (95 % CI, 1.2 to 1.4). We thus conclude that in the PCR-GLOBWB model driven by EC-Earth output there is a positive trend in discharge events like the one in August 2017 in both the historical period (pre-industrial times to 2017) and the future period (from current conditions to a +2   ∘ C world).

Figure 8 Analysis of the highest discharge at Bahadurabad in July–September in the PCR-GLOBWB model in the years 1920–2017. (a)  The location parameter μ (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the discharge data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years 2017 and 1934. (b)  The GEV fit of the discharge data in 2017 (red lines) and 1934 (blue lines), assuming a trend. The observations are drawn twice, scaled up with the trend (smoothed global mean model temperature) to 2017 and scaled down to 1934. (c)  GEV fits for the present day (PD, red) and +2   ∘ C world (2C, yellow) simulations. The purple horizontal lines in (b) and (c)  show the threshold value for which the risk ratio is calculated.

The SWAT model calibrated with EC-Earth meteorological data tends to underestimate flows in almost all months of the year (see Fig. S3 in the Supplement). The SWAT model calibrated with weather@home meteorological data, in contrast, tends to underestimate flows in the monsoon months while overestimating flows in the remaining months. Therefore in both cases, flows in our months of interest (JAS) are always slightly underestimated, but the magnitudes of error appear limited enough for the models to be useful in conducting attribution studies. When comparing the error margins of the ratio σ ∕ μ with observed statistics we note that the model variability is too small compared to the model mean, opposite to what was found for the PCR-GLOBWB model. The shape parameter ξ is of the same order as the one in the observed discharge dataset.

The risk ratios are calculated from return period plots for both the EC-Earth runs (see Fig.  9 ) and the weather@home runs (see Fig.  10 ). Using the SWAT model runs with EC-Earth transient data, we see that the discharge shows some decadal variability. The trend in the data therefore depends more strongly on the years used. For consistency we use the same years as in the analyses of EC-Earth and PCR-GLOBWB data (1880–2017), and we note that the error margins do not capture this variability and are underestimated. The risk ratio of discharge between 2017 and pre-industrial times is found to be 1.5 (95 % CI, 1.3 to 1.6). The risk ratio between a 2  ∘ C climate and the current climate is 1.56 (95 % CI, 1.45 to 1.70). Using the SWAT model runs with weather@home actual 2017 and natural 2017 data, the risk ratio between the actual 2017 and natural 2017 scenario is 0.88 (95 % CI, 0.72 to 1.09).

Figure 9 Analysis of the highest discharge at Bahadurabad in July–September in the SWAT flows for EC-Earth. (a)  the location parameter μ (thick line), μ + σ and μ +2 σ (thin lines) of the GEV fit of the discharge data. The vertical bars indicate the 95 % confidence interval on the location parameter μ at the two reference years, 2017 and 1934. (b)  the GEV fit of the discharge data in 2017 (red lines) and 1934 (blue lines), assuming a trend. The observations are drawn twice, scaled up with the trend (smoothed global mean model temperature) to 2017 and scaled down to 1934. (c)  current and future simulations. The purple horizontal line in (b) and dotted line in (c)  show the threshold value for which the risk ratio is calculated.

Figure 10 Return period plots for SWAT flows with weather@home data for the actual 2017 and natural 2017 ensembles.

Calibration and validation graphs for LISFLOOD and the RFM are shown in the Supplement. They show that both LISFLOOD and RFM are able to simulate the seasonality of rise in spring and summer flows correctly. Both models underestimate the river discharge in summer, with an underestimation in the simulated discharge by LISFLOOD.

The return period and risk ratio for the LISFLOOD model and RFM estimated from the weather@home actual 2017 and natural 2017 datasets, as well as the results for the GHG-only 2017 runs, are shown in Fig.  11 .

Figure 11 River flow return periods simulated by (a)  LISFLOOD and (b)  RFM using the actual 2017, natural 2017 and GHG-only 2017 scenarios.

The LISFLOOD model shows that a discharge value with a return period of 4 years in the actual scenario would increase to 5.4 years in the natural climate scenario (risk ratio of 1.35 – 95 % CI, 1.20 to 1.51), while it would reduce to 3.1 years in the GHG-only scenario.

The trend is similar in the results simulated by the RFM, however, the discharge value with a return period of 4 years is slightly greater than the value simulated by LISFLOOD. The return period would increase to 4.5 years under natural climate conditions (risk ratio of 1.13 – 95 % CI, 1.11 to 1.14), while it would reduce to 2.6 years in the GHG-only scenario. Note however that from Fig.  11 b we see that the risk ratio between the different scenarios for RFM becomes larger for larger return periods (e.g. 10 years) than those studied in this analysis.

The shorter return period in the GHG-only 2017 scenario shows that if sulfate aerosols are removed from the atmosphere (which results in increased precipitation), flooding becomes more frequent. This implies that floods can become more frequent in the region if the air pollution levels are reduced in the future.

The risk ratios for the observed threshold from both LISFLOOD and the RFM of 1.35 (95 % CI, 1.20 to 1.51) and 1.13 (95 % CI, 1.11 to 1.14) respectively are in good agreement even though the simulated river flows by the models are different. The mitigation effect due to the aerosols is also comparable between these two different hydrological models.

In observations the uncertainties in return periods and risk ratios are quite large. This is mainly due to the shorter lengths of the time series, and natural variability dominates. In the models, the signal-to-noise ratio is much larger, resulting in smaller uncertainties in the risk ratios. Here, the model spread dominates the signal. As both natural variability and model spread play a role, we use a weighted average with inflated uncertainty range. We do not synthesize the risk ratios for the future, as we only have two model estimates per variable.

In the synthesis we use all available observational datasets that are analysed in this paper and one experiment per model. For weather@home and all hydrological models that use input from weather@home experiments we use the risk ratios calculated from the actual 2017 and natural 2017 experiments. This gives us a fair opportunity to compare the synthesis of precipitation with the synthesis of discharge.

Figure 12 Synthesis of the precipitation  (a) and discharge  (b) results. Dark blue is observations, red is climate model ensembles and the weighted average is shown in purple. The ranges of the models are not compatible with each other, pointing to model uncertainty playing a role over the natural variability. The weighted average has been inflated by factors of 3.89 and 3.45 for precipitation and discharge respectively to account for the model spread.

The synthesis results are shown in Fig.  12 . The synthesis of the precipitation analysis results in a risk ratio between 2017 and pre-industrial times of 1.8 (95 % CI, 0.5 to 9.3). Although the best estimate is above 1, the trend is not significant due to the relatively large error margins. The synthesis of the discharge analysis results in a risk ratio between 2017 and pre-industrial times of 1.1 (95 % CI, 1.0 to 1.3). So for discharge the best estimate is only slightly higher than 1, and due to the smaller error margins in the average, this trend is only significant under the assumptions made in this analysis.

In any event-attribution study, tasks to be carried out include the following:

determining what happened using available observations and defining the event to be studied,

determining how rare the event is in current and pre-industrial conditions,

using models to attribute any changes in likelihood of similar classes of events.

Here we discuss some of the issues encountered in these steps and the interpretation of our results in the light of uncertainties.

First of all, determining the amount of precipitation falling into the Brahmaputra basin from observations (and thus the appropriate precipitation threshold to define this event) is not trivial. As is common in regions with strong topographic gradients, estimating area-averaged rainfall based on observed rainfall is challenging, as rainfall differences between neighbouring locations can be very large in reality, and the orography, which is only partly resolved by a sparse observational network (or model grid), drives these differences. A large part of the Brahmaputra basin has an elevation of over 2000 m; hence unsurprisingly different precipitation datasets show very different spatial and temporal characteristics. They are all likely to underestimate the precipitation at higher elevations, where few weather stations record data ( Immerzeel et al. ,  2015 ) .

For this analysis we used the CPC dataset to provide a single estimate of the event magnitude (i.e. determine what happened) and to define the return period (i.e. determine the rarity of the event) for use in the other datasets and models. Applying this return period, we used three observational datasets to convey the uncertainty related to observations in the resulting risk ratios. However, for the GPCC dataset, the very limited temporal length of the record leads to an uncertainty estimate that is too high for meaningful inference on the change in risk to be made. The longer records do show an increase in the chance of extreme rainfall, but again uncertainties affect a clear signal detection. The intended future availability of high-resolution reanalyses such as ERA5 that will cover the years 1950 onwards at 30 km resolution will potentially improve trend analyses in high-mountain regions in Asia.

From the hydrological perspective, we defined the event as the maximum daily discharge at Bahadurabad in July–September. In contrast to precipitation data, there is only one official discharge observation series, which does not allow for intercomparison. The determination of flood risk, however, appears to be sensitive to the hydrological variable studied. To obtain an impression of this sensitivity, we checked how discharge compares to the water level as a second measure for the likelihood of flooding. The return period of the measured 2017 discharge peak is indeed lower than the return period of the measured 2017 water level peak. Several factors could have influenced this. First of all, the Brahmaputra is a highly braided river, and during severe flood events water enters the floodplain, making it more difficult to accurately relate water level measurements to discharge estimates. Therefore though the water level records are very accurate, the discharge records are unlikely to be of the same accuracy. Based on the observation of the massive spatial extent of the 2017 floods both in Bangladesh and India, we opine that the observed discharges are likely higher than those recorded.

This opinion is supported by the change in correlation between discharge and water level. The correlation between water level and discharge is 0.88 over the whole time series. However, after 2011 this correlation changes to almost 1, with a tendency toward discharges values that are lower for similar water levels than before this change. This change could be due to recalibration of the relationship between discharge and the water level. We therefore expect that the true return period is between the return period calculated from discharge given above and the return period calculated from the water level. As we do not know the exact influence of the change in measurement method of discharge on the discharge values, we cannot give more precise values.

However it should be noted that ongoing morphological changes can introduce additional variability along the river. For instance, higher water levels with lower discharges may be caused by silting and narrowing of the river. McLean and O'Connor ( 2013 ) already showed that, for the years 2006–2011, the relation between discharge and water level changes over time; in 2011 similar discharge values led to higher water levels. This leads to a non-climatic trend in the water level observations.

The disentangling of the influence of climate change and geomorphological changes was beyond the scope of this analysis. On top of qualitatively good observations of the water level, it would require observational data on geomorphological changes, more detailed local hydrological models that can incorporate these and calculate water levels with substantial accuracy, and an additional set of model experiments. In the current analysis we mainly used discharge data and climate model experiments, and from these results it is not possible to conclude whether neglecting geomorphological changes in the models leads to any disagreement with observations given the large uncertainties in the observational analysis.

Climate models, while far from perfect in their representation of reality, are essential for interpreting the results from observations and thereby attributing any observed changes in event frequency to anthropogenic climate change or other factors. Taken at face value, the two climate model simulations of 10-day precipitation maxima in the Brahmaputra basin provide somewhat contradictory results. However, for the weather@home simulations when comparing the natural simulations with GHG-only runs instead of historical simulations, the change in extreme precipitation is significantly positive as well and is therefore more comparable in magnitude to the increase in the two longer observational datasets and EC-Earth simulations. Comparing the GHG-only runs to the historical simulations gives an indication of the impact of aerosol within the weather@home model, which might be slightly overestimated given that black carbon is not included in the models aerosol treatment. Nonetheless, HadRM3P clearly indicates that the increased risk in extreme rainfall due to GHG induced warming has been effectively counterbalanced by aerosol emissions. The EC-Earth model is interpreted as having fewer aerosol effects and hence showing more of the greenhouse-gas-driven increase. Both results are in agreement with the observations due to the large uncertainties in the limited-length observational records.

The counterbalance between the greenhouse-gas and aerosol effects may also be important for clean air policy decisions; as the air is cleaned the already-committed increase in extreme precipitation due to greenhouse gases will be revealed. These results also suggest that the overall signal from long-term climate change, i.e. mainly greenhouse-gas forcing, in the datasets where we cannot separate out the impact of aerosol forcing might be underestimated. The best estimate of the change in risk in extreme rainfall as observed in the Brahmaputra basin in 2017 is therefore likely a rather conservative estimate and hence is of limited use to inform decision-making. In fact, simulations of the near future in both models show a clear increase in the risk of high-precipitation events that lead to flooding in the Brahmaputra.

In extending our multi-method attribution approach to include hydrological modelling, we consequently introduced more degrees of freedom in possible combinations of inputs and models to construct the hydrological response. Time and computational restraints put a limit on the number of combinations that could be explored. We conducted experiments using (i) the same hydrological model (PCR-GLOBWB) run at different resolutions with different input observational and/or modelled meteorological input data, (ii) the same input climate model (weather@home) with different hydrological models, and (iii) the same hydrological model (SWAT) with two different input climate models. Changing the resolution of the PCR-GLOBWB runs with CPC and ERA-int input compared to runs with EC-Earth 2.3 input impacts the dynamics in the hydrological model. In general coarser-resolution simulations respond faster due to the decrease in storage and the shorter connectivity between grid cells. High-resolution models are better able to capture the subsurface and riverine water storage due to their increased heterogeneity ( Sutanudjaja et al. ,  2018 ) . It is therefore more difficult to simulate extreme hydrological events in coarser models ( Samaniego et al. ,  2018 ) . It was beyond the scope of this paper to analyse the differences in detail; however, we use the differences to show the range of possible output within one hydrological model. None of the models or observational datasets are perfect. For instance, in the PCR-GLOBWB model the variability is too high compared to the mean, while RFM and LISFLOOD underestimate the magnitude considerably. This is not the ideal situation however, there is no reason to believe that the order of magnitude of the risk ratios between the current and past climate or between the future and current climate will depend on this very strongly. This is corroborated by the fact that the risk ratios are comparable despite the very different biases.

Despite these strong differences in variables, resolution, simulated processes and input data, the simulated changes in the likelihood of the observed event occurring because of anthropogenic climate change are very comparable. Even when the hydrological models are driven by precipitation from the weather@home simulations the simulated discharge shows a significant increase in likelihood, apart from SWAT, where the change is not significant.

In August 2017, following heavy rains, Bangladesh faced one of their worst river flooding events in recent history, with record high water levels leading to inundation of river basin areas in the northern parts of the country, impacting millions of people who are highly exposed and vulnerable to unusual flooding.

This paper presents an attribution of this precipitation-induced flooding event and, for the first time, extends the multi-method approach of extreme event attribution from a purely meteorological perspective to the more impact-relevant hydrological perspective by employing an ensemble of hydrological models. Firstly, experiments were conducted with three observational datasets and two climate models to estimate changes in extreme precipitation event frequency, in the 10-day Brahmaputra basin average, that have occurred since pre-industrial times. In addition, climate projection experiments were used to indicate if the trends found up until now are likely to continue or become more extreme in the future. The precipitation series were then used in turn as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge. In doing so, a range of possible answers to the attribution question were produced, allowing for comparison between approaches and for the robustness of the attribution results to be assessed.

Specifically, our aims were to (i) determine if precipitation can be used as a measure of the extremity of flooding in the large Brahmaputra basin, or if it is necessary to instead use a hydrological measure such as discharge for the purpose of attributing the flood of August 2017 in Bangladesh, and to (ii) draw conclusions on the attribution of this event, expressed as the change in likelihood of similar or more extreme events, that has occurred since pre-industrial times and which is projected to occur in the future.

From the precipitation perspective, we find that two out of three of the observed series show an increased probability for extreme precipitation like observed in August 2017, but in all three observational datasets the trends are not significant due to the short records. One climate model shows a significant positive influence of anthropogenic climate change, whereas the other simulates a cancellation between the increase due to greenhouse gases and a decrease due to sulfate aerosols. The change in risk of high precipitation that has occurred since pre-industrial times is therefore uncertain. However, both climate models agree that the risk will increase significantly in the future, by more than 1.7, with 2  ∘ C of global heating since pre-industrial times.

Considering discharge rather than precipitation, which corresponds more closely with the hydrological impacts, shows only a slightly different result in that only the increase in risk since pre-industrial times to present-day conditions of high discharge synthesized from both observations and models is just significant, whilst the risk of high precipitation is not. The attribution of the change in discharge is therefore somewhat less uncertain than for precipitation, but the 95 % CI still encompasses no change in risk. For the future, these models project a slightly smaller increase in probability of high discharge than of high 10-day precipitation, being more likely by about a factor of 1.5 in a 2  ∘ C warmer world.

For large basins in orographically diverse regions with complex hydrology, such as the Brahmaputra, we hypothesized that rainfall, river flow and inundation would not be linearly connected and that precipitation would not be an adequate measure of flood intensity. The initial hydrological conditions play an important role in combination with the occurrence of high intensity precipitation events. We therefore anticipated that small changes in the risk of precipitation would lead to disproportionate changes in flood risk, evidenced in differences in the risk ratios of the event calculated from the two perspectives.

Our synthesis, however, produces the best estimate for the past climate that is greater than 1 and of a similar order of magnitude (between 1 and 2) for both methods and a lower bound on the uncertainty range that is less than or about equal to 1, leading to the conclusion that we cannot confidently confirm a significant anthropogenic influence in changes up until now. Projected changes between current conditions and for a world 2  ∘ C warmer than the pre-industrial one were also a similar order of magnitude (between 1 and 2) for 10-day precipitation and discharge, with significant changes found. Thus, in this particular case, studying precipitation alone would have led to the same qualitative conclusion.

Inspecting the individual model outcomes shows that in the study of this particular event, there is an impact of the choice of circulation model used as input for the hydrological model on the amplitude of discharge RRs. Where the EC-Earth model was used, we find a larger positive change in precipitation compared to discharge, but where the weather@home model was used, we find a similar or smaller positive change in precipitation compared to discharge. This highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong.

The use of multiple methods in the attribution of extreme events is the only way to estimate confidence, and hence reliability, in attribution results. As hydrological models are used to simulate impact-relevant variables (such as flood depth) and are in fact used much more for decision-making, it is essential to extend the attribution approach in general to include hydrological models, when possible, for analysis of precipitation-induced flood events. Hydrological models offer further insight into the partitioning of precipitation reaching the ground and thus come closer to the drivers of the impacts observed on people and livelihoods. Climate models, in contrast, allow us to disentangle the potential effects of different atmospheric drivers.

This highlights that only a combination of doing a multi-method attribution analysis of the meteorological drivers with a multi-model approach in hydrological modelling allows for a robust estimate of changing flood hazards under climate change. Therefore we recommend the use of a hydrological variable, such as discharge, for estimating changing flood risk in large basins such as the Brahmaputra, although based on this study, investigating changes in precipitation is also useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions.

Almost all data are available for download and analysis under https://climexp.knmi.nl/selectfield_att.cgi (last access: 20 July 2018) under section “Bangladesh flooding 2017”, including the GPCC data ( Schamm et al. ,  2013 , 2015 ) used in this study.

The supplement related to this article is available online at:  https://doi.org/10.5194/hess-23-1409-2019-supplement .

SP, SS and SFK designed the research. SP, SS and SFK wrote the paper with contributions from all other authors. SP and SFK analysed the observational data, EC-Earth 2.3 data and PCR-GLOBWB data. SS and FELO analysed the weather@home data. HJ and FH provided and analysed the LISFLOOD and RFM data. KM and AKMSI provided and analysed the SWAT data. NW and KvdW provided the PCR-GLOBWB data. KH prepared the weather@home simulations. RHR validated the weather@home data. DCHW managed the weather@home system. AH and KM provided observational water level and discharge data. GJvO supervised the project and contributed analysis tools, and RS and AH contributed with local information.

The authors declare that they have no conflict of interest.

Sarah Sparrow, Hammad Javid, Karsten Haustein, David C. H. Wallom, Friederike E. L. Otto and A. K. M. Saiful Islam were funded as part of the EPSRC GCRF Institutional Sponsorship REBuILD project. Karin van der Wiel was funded as part of the HiWAVES3 project. Niko Wanders acknowledges the funding from NWO 016.Veni.181.049. This work was partially supported by the EUPHEME project, which is part of ERA4CS, an ERA-NET initiated by JPI Climate and co-funded by the European Union (grant 690462). We would like to thank the Met Office Hadley Centre PRECIS team for their technical and scientific support for the development and application of weather@Home. We are grateful to Simon Dadson and Homero Paltan Lopez for sharing the RFM code and for their help in setting it up for the study area. Finally, we would like to thank all of the volunteers who have donated their computing time to climateprediction.net and weather@home. Edited by: Bob Su Reviewed by: Vahid Rahimpour Golroudbary and two anonymous referees

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• Introduction
• Data and methods
• Observational analysis
• Model analysis
• Conclusions
• Data availability
• Author contributions
• Competing interests
• Acknowledgements

Content Search

Bangladesh: floods 2022 - final evaluation (mdrbd028) (10 october 2023), attachments.

EXECUTIVE SUMMARY

Context and Purpose of the Evaluation

In May 2022, heavy monsoon rains and runoff from mountains flooded parts of Bangladesh, affecting shelter and livelihood of 7.2 million people across nine northeastern districts, and resulting immediate food, healthcare and Water, Sanitation, and Hygiene (WASH) needs. The Bangladesh Red Crescent Society (BDRCS), with the support from International Federation of Red Cross and Red Crescent Societies (IFRC) through the Flood Operation 2022, provided immediate food and household item assistances and longer-term shelter, livelihood, WASH and other supports. Upon completion in June, 2023, BDRCS and IFRC initiated a final evaluation for the Flood Operation, 2023, led by an independent consultant and involving representatives from both organizations. The evaluation aimed to assess the effectiveness, efficiency, and relevance of response interventions during the flood operation.

Methodology

The evaluation utilized an adapted IFRC evaluation framework and a mixed-method research methodology. Secondary data encompassed internal IFRC and BDRCS documents, while primary data came from flood operation targeted population and related personnel through Questionnaire Survey (QS), Focus Group Discussions (FGD), Key Informant Interviews (KII) and field observations from physical visits. Data collection was done from July 17 to August 24, 2023.

Relevance and Appropriateness

The selection process for the Flood Operation 2022 target population involved close coordination with local authorities and aimed to support the most disadvantaged in areas with limited humanitarian assistance. Specific criteria were developed for selection in both emergency response and recovery phases. The evaluation team observed that the chosen target population was highly disadvantaged, both in economically and socio-culturally. The target population had limited access to government social security services, basic necessities like safe drinking water, and regular essential services. Thus, the selection of targeted was appropriate for both response and recovery operations. However, in some discrete cases, the selection for recovery operation could have been better had there been stricter verification process. The supports provided focused on meeting immediate food, shelter and WASH needs during response phase and shelter and latrine rebuilding and livelihood restoration during recovery phase. The ET concluded the supports was appropriate and aligned with the need of the affected population.

Adequacy of Support

Approximately 90% of targeted population in the Flood Operation, 2022 found the Multipurpose Cash Grant (MPCG) and food support provided during emergency response and the shelter, WASH and Livelihood supports during recovery phase to be adequate for their households. There were variations by region and gender, with those from Sunamganj expressing higher degree of adequacy. Targeted population from the affected communities made significant personal contributions to rebuilding, averaging BDT 54,094 (CHF 441) per HH for Shelter and BDT 7,746 (CHF 63.16) per for Latrine, driven by their desire to enhance resilience against future floods.

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Article contents

Livelihoods in bangladesh floodplains.

• Parvin Sultana Parvin Sultana Flood Hazard Research Centre, Middlesex University School of Science and Technology
•  and  Paul Thompson Paul Thompson Flood Hazard Research Centre, Middlesex University School of Science and Technology
• https://doi.org/10.1093/acrefore/9780199389407.013.258
• Published online: 28 June 2017

Floodplains are ecologically diverse and important sources of livelihood for rural people. Bangladesh is one of the most floodplain-dominated countries and supports the highest density of rural population in the world. The experience of Bangladesh in floodplain management efforts provides evidence, lessons, and insights on a range of debates and advances in the management of floodplain natural resources, the challenges of climate change, and the role of local communities in sustaining these resources and thereby their livelihoods. Although floodplain areas are primarily used for agriculture, the significance and value of wild common natural resources—mainly fish and aquatic plants—as sources of income and nutrition for floodplain inhabitants has been underrecognized in the past, particularly with respect to poorer households. For example, capture fisheries—a common resource—have been adversely impacted by the building of embankments and sluice gates and by the conversion of floodplains into aquaculture farms, which also exclude poor subsistence users from wetland resources. More generally, an overreliance on engineering “solutions” to flooding that focused on enabling more secure rice cultivation was criticized, particularly in the early 1990s during the Flood Action Plan, for being top down and for ignoring some of the most vulnerable people who live on islands in the braided main rivers. Coastal embankments have also been found to have longer term environmental impacts that undermine their performance because they constrain rivers, which silt up outside these polders, contributing, along with land shrinkage, to drainage congestion. Locals responded in an innovative way by breaking embankments to allow flood water and silt deposition in to regain relative land levels.

Since the early 1990s Bangladesh has adopted a more participatory approach to floodplain management, piloting and then expanding new approaches; these have provided lessons that can be more general applied within Asia and beyond. Participatory planning for water and natural resource management has also been adopted at the local level. Good practices have been developed to ensure that disadvantaged, poor stakeholders can articulate their views and find consensus with other local stakeholders. The management of smaller water-control projects (up to 1,000 ha) has been taken on by community organizations, and in larger water-control projects, there is collaborative management (also called “co-management”) among a hierarchy of groups and associations and the appropriate government agency. In fishery and wetland management, many areas have been managed by community organizations to sustainably restore common resources, although their rights to do this were lost in some cases. Associated with community management are successful experiments in adopting a more system-based approach, called “integrated floodplain management,” which balances the needs of agriculture and common natural resources, for example, by adopting crops with lower water demands that are resilient to less predictable rainfall and drier winters, and enable communities to preserve surface water for wild aquatic resources. Bangladesh also has had success in demonstrating the benefits of systematic learning among networks of community organizations, which enhances innovation and adaptation to the ever-changing environmental challenges in floodplains.

• participation
• embankments

Introduction

Since the early 1990s research and development in the Bangladesh floodplains have given rise to debates, evidence, knowledge, and lessons that have wide international relevance, across several fields, to the challenge of securing and improving livelihoods of the inhabitants of rural floodplains. After introducing the main characteristics of Bangladesh floodplains, this article reviews the debates over the impacts of structural approaches to flood management and public participation in water management at the time of the Flood Action Plan. This is followed by a summary of knowledge on the significance and value of floodplain natural resources, particularly commons, in rural livelihoods of Bangladesh. Those sections set the scene and context for the following two main sections that summarize knowledge and recent research: first, on collaborative management, community-based governance and participatory planning in floodplains; and second, on adaptation and integrated resource-management approaches to the challenges of changing environment and climate. The conclusion highlights some of the contributions to knowledge discussed, gaps, and remaining research questions.

Floodplains

Global floodplains.

A floodplain is made up of flat or nearly flat land that is adjacent to a stream or river and experiences occasional or periodic flooding. It includes the floodway, which consists of the river channel and adjacent areas that carry flood flows, and also the fringing areas, which are covered by floods but do not experience a strong current. Flood waters carry suspended sediment that has been eroded from upstream areas and deposit this as silt on the floodplain. It is widely believed that the sediments or silt deposited by rivers to build up floodplains and deltas are rich in nutrients, and thus form the basis for productive ecosystems and agriculture. Yet even this perceived wisdom—that silt is the main source of nutrients—is a contested simplification of a complex system. Nevertheless, floodplains comprise some of the richest ecological systems on the planet (Ramsar, 2001 ) and can support highly productive agriculture when they are not flooded. Human interventions to change their hydrological regime began early (Hillel, 1992 ), and in the early 21st century floodplains are among the most densely populated areas on the planet. Floodplains provide rich ecosystems and diverse ecosystem services that have supported rural communities for generations. Floodplains are particularly complex systems in terms of biophysical, socioeconomic, institutional, cultural, and political dimensions, but modern development interventions have frequently failed to account for this complexity.

Although all rivers have floodplains, some floodplains are of particular note for their scale or social-ecological systems. Examples of these in the Americas include the Mississippi River floodplain in the United States, with its extensive modifications and embankments; the diverse grass- and marshland-dominated Pantanal floodplain of Brazil, Bolivia, and Paraguay; and the flooded forests of the largest floodplain along the Amazon River in Brazil. Africa is notable for inland floodplain deltas, such as the Okavango in Botswana, and for seasonal floodplains that provide an important source of pastoral livelihoods, as well as for agriculture and fisheries in the Sahel. Many of Asia’s floodplains are cradles of civilization that have been heavily modified over millennia, for example, the combination of a heavy sediment load and embankments present along its length for centuries has resulted in a river bed raised higher than its floodplain along the Yangtze River.

Bangladesh Floodplains

Bangladesh is a floodplain deltaic country formed by the deposits of the three major river systems of the Ganges, Brahmaputra-Jamuna, and Meghna Rivers (Figure 1 ). Bangladesh is one of the most floodplain-dominated countries in the world and also one of the most densely populated, at over 1,100 people per km 2 . Including tributaries and distributaries there are around 700 rivers in Bangladesh stretching over 24,140 km, thousands of smaller channels, floodplain depressions (known as beel s), and extensive seasonally flooded lands that collectively form the floodplain ecosystems (Akonda, 1989 ). Estimates of the area of floodplain in Bangladesh range up to 80% (Brammer, 1990 ), and about 25% to 33% of the country remains under water every year for four to six months during the monsoon (rainy season).

Figure 1. Map of Bangladesh showing significant floodplain locations and place names.

Floods, from slow-rising main rivers, flashy smaller rivers, heavy rainfall, and tidal or storm surges, are a recurring phenomenon. Rural life is well adapted to the normal monsoonal patterns of inundation, known as “borsha”, but if the frequency, timing, intensity, or duration of inundation is atypical and falls outside the usual range, then it is termed a “bonna” (flood) and becomes a serious threat to lives and livelihoods, particularly for the poor floodplain inhabitants. Strong river currents erode croplands and even destroy or damage homes and villages. By the same token, unusually low rainfall, including untimely rains and extended dry seasons or periods of drought, create water stress that negatively impacts agriculture and aquatic life in the floodplains. In addition to floods, erosion, dry-season water stresses, and salinity intrusion (seen in the southern parts of Bangladesh) are the most frequently experienced water-related hazards and stresses in Bangladesh floodplains, as found in a wide-ranging study (Flood Hazard Research Center Bangladesh, 2013 ).

The Bangladesh floodplains are fed by various sources of water (rivers, groundwater, rainfall, and tidal water) and many of them have more than one water source. The contribution of each water source and its impact on livelihoods can change on a seasonal or annual basis, depending on water availability and changes in the water regime. Rains or flash floods that come earlier than normal can damage dry-season crops just before harvest. Even if farmers are able to harvest their crops at such times, there will be a lack of places for drying crops, particularly paddy (rice); moreover, dry land for storing rice straw (used for livestock fodder) becomes scarce. In the regions near the hills on the eastern side of the country, the damage caused by flash floods (despite their short duration) can be more severe than that caused by slower-rising and longer-duration riverine floods because of sudden flooding without warning, high water velocity, and debris load. Nevertheless, it is the large-scale main river floods that draw international attention and with their widescale impact were a significant setback to economic growth in the past when the national economy was heavily agriculture dependent (Benson & Clay, 2002 ). Since 1980 , major floods occurred in Bangladesh in 1987 , 1988 , 1998 , 2004 , and 2007 . Estimates of the numbers of people affected by this flooding vary considerably. The 1988 flood was a 1-in-100-year event that inundated 42% to 57% of the country (Rashid & Pramanik, 1993 ; Miah, 1988 ). Over 2,000 people died; 1,990 km of embankments were damaged; and total losses, including infrastructure and crop damage, were about US$1.3 billion (World Bank, 1990 ). In 1998 about 60% of the country was flooded, with similar impacts—about 1,000 people died, about 2,000 km of embankments damaged, and losses were estimated at up to US$2.8 billion (Brammer, 2004 ; Hossain, 2006 ). However, the relative significance of such major floods has declined over time: the potential reduction in gross domestic product associated with major disasters has gradually become smaller as the national economy has grown and become less dominated by agriculture (Benson & Clay, 2002 ) and as disaster preparedness, including warning systems, have improved.

The 2011 census conducted by the Bangladesh Bureau of Statistics suggests that up to 120 million people lived in the country’s floodplains at that time (see Figure 2 ). Floodplains provide a range of ecosystem services. A large proportion of the rural poor depend on natural water bodies in the floodplains for their livelihoods. Their subsistence is based on food production, fishing, harvesting wetland plants, plying country boats, and other activities that depend on healthy aquatic ecosystems.

Figure 2. A typical seasonal floodplain during the monsoon, Narail district, southwest Bangladesh.

Floodplain fisheries have been of particular significance for generations, and about 11 million people were estimated to depend directly or indirectly for their livelihoods on the Bangladesh fishery sector in 1990 (World Bank, 1991 ). Some 82% of the households that depend on fishing for income are poor (WorldFish Center, 2003 ). Up to 80% of rural people and about half of rural poor households living in the floodplains catch fish and use other aquatic resources, and up to 70% of animal protein consumption in Bangladesh is derived from fish (Minkin, Rahman, & Halder, 1997 ; Muir, 2003 ; Toufique & Gregory, 2008 ). Catches from these fisheries, which may be termed common-pool , capture , or open-water fisheries, have been declining. Fish consumption fell by 11% between 1995 and 2000 (and by 38% for the poorest households), and it is estimated that inland capture-fishery catches fell by 38% between 1995 and 2002 (Muir, 2003 ; however, aquaculture production has risen rapidly; see the section “Commons and Enclosure”). Bangladesh’s floodplain wetlands support over 260 fish species (Rahman, 2005 ). Although there has been no known extirpation in Bangladesh, 54 (21%) of Bangladesh’s freshwater fish species were considered to be threatened with national extinction in 2000 (International Union for the Conservation of Nature [IUCN] Bangladesh, 2000 ), and 64 (25%) of these species were considered to be threatened with national extinction in 2015 (IUCN Bangladesh, 2015 ).

Besides crops and fish, the main provisioning services (i.e., resources directly used by people) in the floodplains are wild plants (aquatic grasses and other plants that are used for grazing and livestock fodder, construction, fuel, and human food), snails (used as feed in duck and fish farms), and minor resources, such as crabs.

Globally, most floodplains have been modified, to a greater or lesser extent, by human interventions as diverse as small-scale agriculture; expansion of villages and towns, constructing bridges; and forest clearing in catchments. However, one of the foundations of ancient and modern civilizations in floodplains, the raising of earthen embankments, also known as “dikes” or “levees,” to change patterns of flooding has a special impact on the livelihood potential in floodplains. This is also true in Bangladesh, where there is a long history of local water-management earthworks. Moreover, since the 1960s, water-resource management to control flooding in Bangladesh has largely relied on a centralized engineering approach.

The normal annual flooding in Bangladesh is considered to provide numerous benefits, such as giving Bangladeshis common access to the large, natural, floodplain fishery; depositing fertile loam on agricultural fields; and flushing stagnant water in low-lying areas. However, the second of these, the common view that floods bring fertile silt to floodplains, appears to apply to only part of the system in Bangladesh—the active floodplain and braided river channels. Brammer ( 2004 ), who has pointed out that silt has a low organic content and documented shallow soils in much of the floodplain, argues instead that high river levels (which result in silt being transported into the Bay of Bengal) trap rainwater, which is the source of normal flooding, in the rest of the floodplain. Floodplain soil fertility is not the result of silt, but because this clearer rain-derived water supports blue-green algae, which contribute significant amounts of nitrogen to the floodplain system, in addition bacteria add to soil nutrients when soil is first flooded, and thirdly leaves decompose during flooding, all of which contribute to fertile conditions supporting crop growth (Brammer, 2004 ). Although the Environment and GIS Support Project for Water Sector Planning report [EGIS], 1997 ) revealed that shallow flooded lands support relatively more wild fish in terms of biomass and diversity than deeper flooded lands and that the heavily silt-laden Jamuna River is of modest importance for fisheries, there is a lack of information comparing fish diversity and catches between clearer and more turbid flood waters.

Local communities have a direct interest in maintaining the floodplain ecosystem services that their livelihoods depend on. However, the complex, dynamic nature of the systems makes managing them a challenge. Bangladesh wetlands have ample water for half the year in the wet season; but the limited amount of surface water in the dry season drives productivity. In the dry season, the surface water that is naturally left in river channels and beels is needed for agriculture, domestic use, and the survival of fish. Agricultural development has largely focused on rice, and much of the rapid growth in rice production since the 1980s has depended on dry-season irrigation of high-yield rice varieties initially pumped in from surface water, in the 1970s, and then, beginning in the 1980s, this irrigation was done using groundwater (which has lowered the water table; Sultana, Johnson, & Thompson, 2008 ; Brammer, 2010 ). Embankments and flood control play an important part in this by protecting rice crops from flooding in the maturing and harvesting periods, while sluices have helped to drain out water from floodplains and beels in the post-monsooon. Despite changes in national policies that call for an end to the draining of remaining wetlands (Ministry of Water Resources, 1999 ), floodplains continue to be converted to sites of agriculture, aquaculture, and urban-related uses.

Issues Raised by the Flood Action Plan

Much of the debate in Bangladesh over how best to enhance the livelihoods of floodplain inhabitants and reduce their vulnerability was catalyzed in the Flood Action Plan of Bangladesh (hereafter, FAP). In response to severe floods in 1987 and 1988 reviews and recommendations for flood and water management in Bangladesh were made by France, Japan, United States, and the United Nations Development Programme [UNDP]. In an attempt to use detailed evidence to consolidate the very divergent views in these reviews, and to develop a comprehensive plan to manage flooding and reduce losses, a consortium of 17 donors responded to requests from the Government of Bangladesh by undertaking the FAP which involved 26 studies and pilot projects undertaken during 1990–1995 . FAP created wide-ranging debate and controversy (Brammer, 2000 ), the initial underlying concept was that the main rivers could be fully embanked as a solution to flooding (World Bank, 1990 ). From the beginning, it drew criticism as a megaproject that would change the socio-ecological character of Bangladesh (Boyce, 1990 ). With the benefit of hindsight, several authors have reflected on the FAP, its legacy and continued relevance; these reflections can be looked at together with the findings and debates during FAP.

Prior to FAP from the 1960s up to late 1980s an engineering coalition or paradigm of structural responses to floods had dominated government and international donor investments in Bangladesh floodplains (Sultana et al., 2008 ). During the early 1990s, nongovernmental organizations (NGOs) and civil society groups publicly questioned the FAP under the newly established parliamentary democracy after the overthrow of General Ershad’s regime in late 1990 . Debate centered on the past and future impact of embankments in Bangladesh in terms of technical performance and feasibility, maintenance, economic returns, negative effects on the environment and fisheries, and inequitable impacts that disadvantaged poorer people.

Impacts of Embankments on Fisheries

At the time of FAP about 7,550 km of flood-control embankments had been built across Bangladesh, including the coastal zone and along main, secondary, and minor rivers. Sluices (water-control structures; see Figure 4 ) of various sizes were constructed in almost all of these embankments to help drain the water, and also to let water in when needed and possible (see Figure 3 ). Locally, the operation of sluices has contributed to draining floodplain wetlands for the benefit of agriculture, but embankments had negative impacts on fisheries because they caused a disruption of migration and the loss of wetlands (Ali, 1990 , 1997 ; Halls, 1998 ; Hughes, Adnan, & Dalal-Clayton, 1994 ). Many fish species follow annual cycles according to the rise and fall of normal floods. Many smaller species travel short-distances from beels to the floodplain to breed and feed; other (often larger) species migrate along the rivers to spawn, and their fry and spawn disperse onto the floodplain to feed (Sultana & Thompson, 1997 ). Thompson and Sultana ( 2000 ) found that fishers reported negative impacts in 12 out of 18 flood-control projects studied. On average, the reported daily catch per person declined 60% from the pre-project levels, compared with a decline of 74% from previous levels in control areas outside the same projects. Studies in the Tangail pilot compartment revealed the strong seasonality of floodplain fisheries, the impacts of intense fishing pressure (which increases at sluices, where fish are concentrated), decline in both catch volume and diversity that would be associated with flood control, and the mortality of fish hatchlings passing through sluice gates (de Graaf, Born, Uddin, & Marttin, 2001 ). The FAP was notable for bringing to the public attention the negative environmental impacts of flood-control works, along with the significance of declining capture fisheries and the nutritional value of fish, including the relative advantages of sustaining the supply of the wild small fish that are caught and eaten by poor floodplain households. Because these fish are eaten whole, they are important sources of micronutrients, containing higher levels of not just calcium but also vitamin A and iron compared with cultured species (Thilsted, Roos, & Hasan, 1997 ; Thompson, Roos, Sultana, & Thilsted, 2002 ).

Figure 3. Women and children collecting aquatic plants (Singra or water chestnut) for food in Hakaluki Haor, northeast Bangladesh.

Active Floodplain and Chars

Given the FAP’s focus on structural flood mitigation, there was concern about the fate of the people living in the active floodplain, including in the chars—the silty-sandy lands and islands within the braided main rivers. These areas were already the most flood prone and could be subjected to worse flooding if stronger complete embankments were built along these rivers. A char is defined as a “mid-channel island that periodically emerges from the riverbed as a result of accretion” (Elahi, Ahmed, & Mafizuddin, 1991 ). Socially and ecologically, the accreted riverine lands that link to the mainland in the dry season were also considered to be chars under the FAP (FAP 16/19, 1993 ) (see Figure 4 ). Chars are inherently hazardous places to live. Public policies might be expected to control settlement in hazardous locations, but there are no such planning regulations in Bangladesh; moreover, erosion and shifting main river courses mean that people on the mainland also lose their land to the rivers, and then they wait for “their” land to reappear when a char accretes in the same area. To adapt to these changing conditions, many char people have been forced to move home several times during their lifetimes (even multiple times a year) and to make use of nearby embankments as important refuges when homes are eroded, and to seasonally migrate to towns for work. The communities living in these chars had largely been neglected in terms of the provision of public services and development projects, and obviously (since they lie within the active river channels) fell outside of water-management investments and benefits, but were also treated as socially distinct and inferior to mainland people.

Figure 4. A typical sluice gate in northwest Bangladesh.

Under the FAP an inventory and related studies focused on these areas (FAP 16/19, 1993 ) highlighting their dynamic character because of frequent erosion and accretion. For example, the Jamuna River widened by 128 meters each year from 1973 to 2000 (EGIS, 2000 ) because mainland along the banks eroded. Although char islands appeared within the main river they have limited productivity. These chars become vegetated in one to two years and are then settled and cultivated despite the hazards and livelihood insecurity, which mostly takes another two years (EGIS, 2000 ). Dry-season crops such as pulses, millets, and groundnuts are major income sources for the people living on the chars; cattle grazing is also common, and grasses are cut for thatching and construction. In 1993 , an estimated 0.6 million people lived in the riverine chars and were arguably the most affected by major floods (EGIS, 2000 ). By highlighting the vulnerability of the livelihoods of the char dwellers, the FAP left a legacy that was seen decade later, when development projects began focusing on “nonstructural” approaches to mitigating the losses from flooding—for example, flood proofing by raising the plinths (floor levels) of houses, and transferring assets to poor households such as cattle to improve income-earning potential.

Debate over FAP

During the FAP there was considerable debate over who should participate in water-management planning and implementation. One of the guiding principles of the plan was that it should “encourage popular support by involving beneficiaries in the planning, design and operation of flood control and drainage works” (World Bank, 1990 ). However, a government task force, in early 1991 , concluded that public-awareness programs had not been incorporated into the plan (Report of the Task Forces on Bangladesh Development Strategies for the 1990s, 1991 ). Other criticisms of flood control emerged as part of the FAP debate. For example, flood-control projects involve acquiring substantial amounts of land to use for embankment construction. Problems of inadequate compensation to the owners of that land and the procedural complexity of the land-acquisition process were found to cause major economic and social suffering to those who lost their land as a result of it, especially small farmers, who lose their crop land, and households, which lose the land where their homes are located. Ultimately, these problems are rooted in poor governance, which in Bangladesh is arguably at its most compromised in land administration, where land acquisition for flagship projects is still found to be inequitable (Atahar, 2013 ), and where reviews commissioned by the government find that the present system enables widespread fraud and malpractice (Hossain, 2015 ).

Civil society questioned the legitimacy of decision-making under the plan, especially in those components of FAP that were based on predefined interventions—notably, the concept of “compartmentalization,” whereby the lands behind a continuous line of embankments along the main rivers would be protected by secondary embankments to form a series of compartments that could be selectively opened to let in floodwater, relieving pressure on other areas). The combination of government and donor pressure to take action resulted in decisions to construct two pilot compartments, but this attracted local opposition and resulted in demonstrations and lawsuits brought by environmental NGOs wanting to halt the project and compensate local people who reported or claimed adverse impacts from embankment and sluice construction. Although the debate was focused on flood infrastructure, it can be seen as part of the greater challenge of finding a balance when economic development and infrastructure bring wide benefits but also result in big losers, who in this case tended to be poor, for whom there was no effective compensation mechanism. Nevertheless, adoption of participatory approaches in the water sector (see the section “Participation in Water Management”) can be traced to Flood Action Plan.

Significance of Floodplain Natural Resources to Livelihoods

Approaches and simplifications to livelihood analysis that assume undifferentiated communities have been criticized (Mehta et al., 1999 ). Bangladesh floodplain inhabitants pursue diverse livelihood strategies that result in competition and conflict, but also the potential to cooperate over access and the use of natural resources (Barr et al., 2000 ).

Floodplains are complex systems. Private land becomes a seasonal commons when it is flooded, and people make use of a multitude of natural resources, all connected through water. Much of the floodplain land is de jure or de facto privately owned and farmed, and water stands there for up to six months. The beels, which hold water year round, and rivers are public or state property. The government leases fishing rights in the beels to the highest bidder, but it has made the use of rivers open access. However, when floodplains are inundated during the wet monsoon season, local people have customary rights to catch fish and collect other aquatic resources (such as plants, used food, fodder, fuel, and building materials, and snails, used as feed for ducks and in shrimp farming) from both public and private lands. Floodplains are vital to capture fisheries, where fish breed and grow out during inundation before moving to deeper areas for the dry season (Shankar, Halls, & Barr, 2004 ). This means that common-pool resources exist during the wet season in areas that are privately owned (Sultana & Thompson, 2008 ). Access to many floodplain natural resources is not clearly defined legally, but the poor have access through custom, local tradition, negotiation, or conflict. The better-off landowners have the right to privatize these resources and prohibit their use by others, but this is only practicable in the dry season. When water levels fall, landowners are able to exclude others from their lands, harvest whatever fish and aquatic plants remain, and then cultivate crops. However, from the perspective of the poor, the aquatic resources present on this land, or in the water above this land when it is flooded, are a common-pool resource. This leads to tensions, for example, when water levels are falling. Moreover, use of floodplain resources is differentiated by gender—it is rare for women to fish for a livelihood, although in some areas poor women and children do catch fish for subsistence, and women collect other aquatic resources (Deba, Haque, & Thompson, 2014 ; Sultana & Thompson, 2008 ). Government interventions often ignore these complexities (see Figure 5 ).

Figure 5. Fishing using a beach seine net in the Jamuna chars.

Since the 1990s, understanding of the significance of commons and of the local institutions that regulate their use has grown rapidly, inspired by research led by Ostrom ( 1990 ), Berkes and Farvar ( 1989 ), and others. The Bangladesh experience adds to this knowledge by highlighting the complexity of floodplain commons in terms of ecological linkages, human use, and institutions encompassing a range of property rights. It confirms that property rights are more complex than being simply private or public and that different types of rights may exist for the same physical area in different seasons and for different components of the suite of natural resources present in that area (see also analysis by Agrawal & Ostrom, 2001 ; Ostrom & Schlager, 1996 ; Meinzen-Dick & Gregorio, 2004 ).

Value of Floodplain Natural Resources

There have been few economic assessments of the value of floodplains in Bangladesh. Thompson and Colavito ( 2007 ) valued uses of Hail Haor in 2000 —haors are large saucer-shaped depressions in northeast Bangladesh that in the monsoon are entirely deeply flooded, but in the dry season become a mosaic of permanent water in beels, marshy areas, waterways, and dry land. At the time, Hail Haor was being overexploited, but the authors estimated that the annual economic value generated from the 12,300 ha that were flooded in 1999 was equivalent to US$7.98 million (almost US$650 per ha). The main human-use value from the site comprised commercial fisheries, 12%; subsistence fisheries, 18%; aquatic plants used by local residents and by tea estates, 28%; dry-season rice, 14%; biodiversity (spending on conservation used as a proxy), 10%; pasture, 9%; flood-control services, 5%; recreation, 2%; and transportation, 2%. Overall, 67% of the annual value of the haor came from wild natural resources. By 2004–2006 , following conservation-based management improvements, the fish catch estimated over the whole haor had increased to 322 kg/ha of wetland, an 88% increase over the 1999 baseline. This yielded, at constant prices, an estimate of the value of direct uses of the haor and its products of US$10.9 million a year (57% derived from fish). High yields of fish were sustained here in 2011 , 2012 , and 2014 , averaging 390 kg/ha. These benefits extend beyond the 3,770 households that fish to earn a living, as many more people catch fish and use aquatic plants for their subsistence.

A similar study (IUCN Bangladesh, 2006 ) of another large floodplain wetland—Hakaluki Haor—found an economic value of about US$414 per ha (reworked to correct calculations in that report). It estimated that 80% of local households used wild natural resources and confirmed the economic significance of noncrop common resources (75% of use value) compared with rice (which contributed only 25% of the use value). Fish and grazing were the main uses.

Commons and Enclosure

A study by Sultana ( 2012 ) investigated a new trend in Bangladesh since the 1990s: the private enclosure of floodplains for aquaculture. Ostensibly, aquaculture use retains their wetland floodplain status; but a number of adverse impacts on poor people, aquatic resources, and the wider floodplain system were revealed. First, the trend of converting seasonal commons to private enterprise use is rapid and widespread: in three study areas with different types of floodplain aquaculture (small individual operations, small group enterprises, and larger enclosures built by local companies) over 500 aquaculture enclosures were documented, and the area of aquaculture increased by 30% to100% annually between 1990 and 2008 (Sultana, 2012 ). Similar growth was found in a fourth more deeply flooded area arising from capture of lands by rich investors (Table 1 ).

Table 1 Growth of area of aquaculture enclosures (in hectares) in four study areas

Source : Narail, Gazipur, Comilla (Sultana, 2012 , reworked data); Hail Haor, unpublished data from the Climate Resilient Ecosystems and Livelihoods project, Winrock International, Dhaka, Bangladesh.

These systems are productive and profitable for the entrepreneurs, although costs are high (Mustafa & Brooks, 2009 ). Toufique and Gregory ( 2008 ) investigated the so-called “Daudkandi approach” (to floodplain aquaculture (where local people in Daudkandi area of Comilla District started to form companies to invest in building bunds around large areas of floodplain to both retain water and keep out floods, so that they could stock the area with fish without fear of the fish escaping). In two villages these authors found qualitative evidence that the benefits of floodplain aquaculture tend to accrue to better-off people, with elite capture of the boards of the companies formed for this enterprise. However, catches of wild fish were reported to have declined to between 44% and 58% of the pre-enclosure levels, and fish diversity also fell. These mostly small wild floodplain fish were caught for food by poor people who are excluded from the aquaculture enclosures, and there was also a loss of aquatic plants, snails, and grazing. All of this mainly impacted on the poor—landless men and women, fishers, and marginal farmers (Sultana, 2012 ).

Participation and Community-Based Management

Floodplain natural resource management in most developing countries has traditionally been top-down and sectoral, led by technical experts, and resulting in low compliance with rules set centrally, limited local contributions to running costs, and plans and designs that do not take into account local circumstances. This led to pressure from NGOs for change. Internationally, a change in approach was signaled by Principle 10 of the Rio Declaration on Environment and Development (United Nations Conference on Environment and Development 1992 ), which states that environmental issues are best handled with the participation of all concerned citizens “at the relevant level” and thereby recognized that the top-down approach was not working and that citizen participation should be encouraged. In floodplain management, the same level of participation is not realistic of all concerned parties. A people-centered approach in which users and government agencies make decisions jointly or when community organizations lead is more difficult when there are larger and more complex natural resource systems and management issues. Participation is often intended to empower the poor. But simply having meetings where all can attend is insufficient to enable local people to take responsibilities, as poor people tend not to be organized to take collective action, Moreover experience at the local level may be insufficient to give local people broader perspective on the scope and complexity of decisions in larger systems unless communities can cooperate with each other and with other experts.

Community-Based Natural Resource Management

Community-based management refers to systems in which local resource users have the main rights and responsibilities for management decisions and actions for a resource or ecosystem rather than government. This may arise either from government recognizing traditional local institutions, or government devolving powers that had been more top-down and centralized to the newly formed community organizations. Community-based management is thought to have several advantages over government-led or top-down management, particularly for common-pool resources (where it is difficult to exclude users, but there is subtractability—for example, people sell or eat the fish they catch—which overcomes the “tragedy” of open access in commons (Hardin, 1968 ). Looking at the management of fisheries, Pinkerton ( 1989 ) identified six benefits of greater community participation:

Users cooperate in planning to enhance or conserve natural resources (sustainability).

Users share the costs and benefits of improved management (economic equity).

It improves conflict resolution among users (intragroup social equity).

The position of users when dealing with other stakeholders is enhanced by their being organized (inter-group social equity).

Users and government share data and their understandings.

Security of tenure for local users over natural resources improves trust between users and government, consequently:

users have an incentive to take a longer term perspective (sustainability), and

enforcement of rules improves as these rules are set by users so compliance improves, reducing “transaction costs” (efficiency).

As part of the worldwide trend toward community-based approaches, from the 1990s onward community-based management has been tested, promoted, and expanded in Bangladesh. Community-based management has been promoted as a means to reduce poverty, conserve natural resources, empower local communities, and promote good governance. Given limited traditional institutions (rules and related organizations regarding access to water or fish for example) in the Bangladesh floodplains, development of community-based organizations (CBOs) for resource management is necessary, in particular to improve and sustain benefits to the poor. CBOs are generally grass-roots organizations of direct local natural-resource users (excluding middlemen or elites, but in some cases including local opinion leaders who champion sustainable practices and a greater role in decision making for the poor; they are formally recognized by the government as having responsibilities for an area, but no government officials are involved as members). Without these formal CBOs, there is a risk that benefits from management of floodplain resources will be lost or more easily captured by local elites.

Community-based management has created opportunities to empower communities, and especially poor resource users. But involving the whole community when the resource is valuable or the CBO has a large budget tends to attract local elites seeking profits. For example, CBOs formed with all villagers in a fisheries project that offered substantial public investment in stocking fish led to domination by elites seeking to profit from subsidies and the approach had to be revised to reform those CBOs to only comprise of fishers (Fourth Fisheries Project, 2005 ). Empowering the poor for livelihood improvement in floodplains involves capacity building; targeting the disadvantaged, including women; and linking participants with services.

Collaborative Management (a.k.a. Co-management)

In community-based management, the CBO has the majority of the powers and responsibilities. Distinct from this is the collaborative management approach (also called “co-management”). Definitions of co-management include “the sharing of power and responsibility between the government and local resource users” (Berkes, George, & Preston, 1991 , p. 12), and “a situation in which two or more social actors negotiate, define and guarantee among themselves a fair sharing of the management functions, entitlements and responsibilities for a given territory, area or set of natural resources” (Borrini-Feyerabend, Taghi Farvar, Nguinguiri, & Ndangang, 2000 , p. 1).

In some of the larger floodplains in Bangladesh (for example, in water-management polders and in the Hail Haor wetlands), stakeholder-participation is achieved through a multitier institutional arrangement in which a committee of government and CBO representatives share a coordinating and oversight role over CBOs which take responsibility for management of local areas and resources within a co-management framework (Sultana & Thompson, 2010 ). In addition, in all cases the CBOs are legal entities registered as social welfare organizations or as cooperateves with the government, and formed under the sponsorship and advice of a government agency, so they have the prospect of advice and oversight from government, although in practice, this depends on the interest of the concerned officials.

Participation in Water Management

In the context of problems associated with past flood-control projects (Thompson & Sultana, 2000 ; see also the earlier discussion on Flood Action Plan (FAP), and experience from rehabilitation of large flood-control works (Soussan & Datta, 1998 ), guidelines on public participation for the water sector were first prepared under FAP (Hanchett, 1997 ). The 1999 National Water Policy called for inclusive water management, taking into consideration the national goal of poverty alleviation. Participation guidelines have since evolved, bringing together initiatives from two government agencies—the Bangladesh Water Development Board (BWDB) and Local Government Engineering Department (LGED). A general guideline was formalized for all publicly funded water-resource projects that brought together good practice from both concerned agencies (Ministry of Water Resources, 2001 ). It aims to ensure local stakeholder participation in identifying of problems, feasibility studies, detailed planning, implementation, operation and maintenance, and monitoring and evaluation. However, it does not enable of participation at the higher levels of agenda setting or developing strategies or polices. Among its main concerns are the long-term institutional arrangements for water and floodplain management. It stipulates that flood-control projects covering up to 1,000 ha (constructed under the LGED) are owned by local government and managed by the communities; those of 1,000 to 5,000 ha (constructed by the BWDB) are owned by government agencies and managed by community organizations together with local governments, and those above 5,000 ha (also constructed by the BWDB) are owned by government agencies but may be co-managed by devolving some powers and responsibilities to CBOs (water user groups).

The number of CBOs rapidly increased under this framework. LGED registered 463 cooperatives to manage floodplain water infrastructure by 2003 created under the Small-Scale Water Resources Development Sector Project, and 600 or more were developed under various subsequent initiatives. Under the BWDB’s multitier model of co-management for polders and projects, local water-management groups are federated into associations; for example, the Blue Gold Project, in 2016 , worked with 339 water management groups and 31 associations in 14 coastal polders. These multitier co-management systems are in many cases not (yet) working as intended. For example, Bernier, Sultana, Bell, and Ringler ( 2016 ) found that water timing and release often depend on local elites (and may require payments), which can reduce the amount of water available locally in the dry season if it is diverted for their interests, at the cost of crops or fisheries in other parts of the system.

Management of Fisheries and Environment

Meanwhile, policies that were supposed to reserve waterbody leases for poor minority fishers have been abused; powerful local people often obtain leases in the name of fisher cooperatives. The failure of this long-established, revenue-based administration of inland floodplain fisheries to secure fisher livelihoods, along with awareness of international initiatives in community-based natural resource management, led to several donor-supported projects involving NGOs and the Department of Fisheries that established community-based fisheries management. Initial efforts demonstrated that natural fishery productivity can recover when silted-up channels between floodplain wetlands and main rivers are re-excavated (Rahman, Capistrano, Minkin, Islam, & Halder, 1999 ) and focused on organizing traditional (Hindu) minority fishers to manage fisheries, but only assured them access for three years (Thompson, Sultana, & Islam, 2003 ). Donor interest was complemented by mutual benefits between the Department of Fisheries and fishing communities. Neither the department nor fishers had secure access or decision-making roles in waterbodies administered by the Ministry of Land, but if the department cooperated with donors, fishers, and NGOs, it could have waterbodies reserved for CBO management, under department oversight, and gain a greater role in the management of fisheries. Formalized recognition and alliance with the Department of Fisheries offered these communities access to fisheries through ten-year agreements reserving these rights, brokered by the Ministry of Fisheries and Livestock and the Ministry of Land. A series of projects from about 1994 onward (supported by the Ford Foundation, UK Aid, the World Bank, USAID, the International Fund for Agricultural Development, and the Government of Bangladesh) helped fishers to organize in individual waterbodies, and, in some cases, to coordinate these activities with the government for larger wetland areas which contain multiple waterbodies. This resulted in the formation of about 300 fisheries-management CBOs by the late 2000s and the introduction of local conservation measures, such as fish sanctuaries and closed seasons (Sultana & Thompson, 2012 ).

A separate co-management approach has developed in wetlands that have been declared ecologically critical areas (ECAs). Under the Bangladesh Environmental Conservation Act 1995 , eight ECAs were, in 1999 , declared threatened or degraded ecosystems, mostly wetlands. Typically, the ECAs, such as Hakaluki Haor (a large floodplain in the northeast), comprise a mix of public lands (waterbodies and other public lands) and private lands. In the ECAs for which project funding was available, the model adopted was to form village conservation groups as local cooperatives, coordinated by tiers of government committees. Most of these groups do not have specific rights over defined areas, but a few groups in Hakaluki Haor have been given the responsibility to protect waterbodies that have been set aside as sanctuaries.

Participatory Planning

One notable methodology that has been developed in Bangladesh for building consensus among diverse stakeholders for sustainable management of natural resources is participatory action plan development (PAPD). It is a process that takes individual and community concerns into account. Participatory approaches have been criticized as a kind of “tyranny” that, for example, reinforces existing social relations or fails to understand local power relations, or has focused on tools rather than empowerment (Cooke & Kothari, 2002 ; Holmes & Scoones, 2000 ; Mosse, 2002 ). PAPD is designed to ensure that poor people’s interests are voiced and represented on an equal footing with those of more powerful stakeholders (Sultana & Thompson, 2004 ).

The principle behind PAPD is that members of any stakeholder category, but especially the disadvantaged, are better able to express their views separately from other (dominant) categories of people. However, separate workshops will fail to develop a shared understanding of common problems and possible win-win solutions. PAPD seeks consensus among the stakeholders on actions that are needed to address common problems. It encourages participants to respect one another’s concerns by having two rounds of divergent and convergent sessions. Since the early 2000s it has been applied in a wide range of projects and locations in Bangladesh. The key steps in a full PAPD are

Problem census (with each stakeholder group separately).

Compilation of stakeholder problem rankings by facilitators.

Plenary with stakeholders and local leaders (to review and agree on main problems for solution analysis).

Solution and impact analysis (with each stakeholder group separately).

Plenary with stakeholders and secondary stakeholders (to present the process, identify feasible solutions, discuss institutional arrangements and next steps).

PAPD actively encourages participation by the poor, empowers disadvantaged stakeholders, and creates scope to evolve an internally driven agenda by ensuring all have a voice. Sultana and Abeyasekera ( 2008 ) studied 36 floodplain fisheries CBOs, half of which were initiated through PAPD and half without PAPD. They found statistically significant differences associated with PAPD, concluding that PAPD led to more efficient formation of CBOs, and saved time and resources in the process leading to actions. Moreover, CBOs that started with PAPD took up more conservation related interventions and faced fewer conflicts than CBOs that did not benefit from PAPD, indicating a greater level of consensus and trust among stakeholders following PAPD. Ultimately, this was associated with benefits for poorer fishers in terms of fish catches and in terms of local social status.

Despite these achievements, participatory floodplain resource management in Bangladesh continues to face challenges:

Biophysical environments are complex—floodplain systems tend to spread across village boundaries, so that different communities are involved in decision-making and management, as well as across administrative and sectoral boundaries which complicates their governance.

Socioeconomic environments are complex, with many different stakeholder groups each with different seasonally shifting livelihoods needs.

Lack of coordination among different government agencies each taking up activities in the same area.

Weak and uneven commitment of government agencies to participatory approaches.

Figure 6. In the dry-season boats have to be moved across the floodplain to remaining beels by other means, northwest Bangladesh.

While there have been significant empowering changes in scattered locations for fishery and wetland management, participation in the water-management sector, particularly in larger polders, has been criticized for being imposed and regimented without actually empowering poorer floodplain households (Dewan, Buisson, & Mukherji, 2014 ).

Adaptation to Environmental and Climate Changes

Bangladesh is widely recognized as being on the front line of climate change (United Nations Development Programme, 2004 ) that is expected to exacerbate many of the natural hazards that already affect agriculture and fisheries. According to Ministry of Environment and Forests ( 2008 , 2009 ), climate change is expected to result in

more frequent and severe tropical cyclones;

increased river bank erosion and sedimentation due to heavier, more erratic monsoon rainfall;

increased flooding because of greater snowmelt in upper catchments, higher monsoon rains, a rise in sea level, and storm surges;

dry-season crop stresses because of lower, more erratic dry-season rainfall, higher evapotranspiration, and increased salinity in the surface and ground water); and

saltwater intrusion into surface freshwater and aquifers and degradation of water quality.

These trends are predicted over a period of decades, to significantly reduce rice and wheat production in Bangladesh unless there are technological changes to adapt to these trends (Ministry of Environment and Forest, 2009 ). Moreover an increase in the middle of the predicted range of increase in average sea level of from 30 cm to 80 cm by 2100 (Ministry of Environment and Forest, 2009 ) would subject about 11% of the country to more regular inundation and adversely impact the lives and livelihoods of millions of people (estimates made in the early 2000s for 2100 are between 10 and 30 million), who it has been claimed could become environmental refugees. However, the extent of these changes has been challenged; for example, Brammer ( 2014 ) argues that the Bangladesh coastal floodplains are highly dynamic, with both sedimentation and land shrinkage and that the impact on livelihoods of any gradual relative sea level or salinity changes will be outweighed by other pressures, such as population.

Coastal Embankments

Coastal Bangladesh has a long history of vulnerability to hazards—cyclones, unusual high tides, floods, and saline water. The government response since the 1960s has been to build embankments and polders to protect agriculture land in a large area of low-lying lands in the coastal delta of the southwest, including areas inland of the Sundarbans mangrove forest. This enabled an expansion of rice farming, and then shrimp farming. Alarms have already been sounded about the fears that large-scale human displacement and mass migration from this area will result from increasing sea levels (Gore, 2009 ). However, physical processes and climatic trends are complex in this area and are less immediate threats compared with direct human pressures (Brammer, 2014 ). Channels outside of embankments continue to silt up as does the mangrove forest, keeping pace with sea level rise, whereas land within polders has shrunk (Auerbach et al., 2015 ). The result has been increasing drainage congestion, and after embankments were broken in cyclones, it left the polders tidally inundated for years, until the embankments were rebuilt. However, opening polders to tidal inflow due either to cyclones or community initiatives has been proven to cause a quite rapid rise of inside land levels from silt and to open up adjacent (outside) waterways.

Local communities proposed keeping tidal flows open, and in 1997 local people cut an embankment to reduce drainage congestion, against the Bangladesh Water Development Board’s wishes, but the measure was found to be effective. Moreover, it built on long-lost but traditional silt-management practices that had been used for smaller local embankments prior to the construction of large polders beginning in the 1960s. So retrospectively, a major project in this region incorporated this initiative in a concept that it formalized as “tidal river management” (Paul, Nath, & Abbas, 2013 ; Tutu, 2005 ). This involves in different years breaking the embankment of different polders to allow a tidal flow between the outside channels and that polder, at the cost of crop production inside that polder that year. This results in silt deposition inside the polder rather than in the outside channels. It was found to be socially acceptable and environmentally friendly solution, but one that requires a high level of coordination among many affected people. In any case, since the late 1990s, as noted, a complex hierarchy of water-management groups and associations has been established inside the polders with the aim of improving participatory water management, which also faces problems of elite influence and coordination (Bernier et al., 2016 ).

Adaptation and Integrated Floodplain Management

Government emphasis on building embankments has had negative impacts, despite having increased rice production. Local planning often creates interest in finding a more balanced approach that will address local needs for diverse floodplain products, as well as uncertainties and hazards. In response, a concept of integrated floodplain management (IFM) has been developed, tested, and promoted through action research with many CBOs in Bangladesh. IFM is intended to address challenges that affect floodplain livelihoods through the following initiatives:

Sluice-gate management—during the early dry season in October and November, sluice gates are opened to maximize quick drainage out of floodplain lands used for cultivation, leaving less water in the beels for fish.

Water abstraction for irrigation is the priority for rice farming in the dry season (January–May), so farmers pump water from beels and canals, but this reduces the critical dry-season habitat of fish and threatens floodplain aquatic life.

Early flash floods can damage dry-season rice crops before the harvest, so farmers keep sluice gates closed to protect crops, preventing migratory fish and their developing larvae from entering the floodplain.

The dry season is the critical time. Scarce water drives the productivity of a system in which water is overabundant in the wet season. Conflicts between farmers and fishers over the use of dry-season water are common. Wealthy, influential farmers tend to win such disputes at the expense of the poor and landless, whose livelihoods depend more on floodplain common-pool resources. Rich, influential farmers living close to structures tend to dominate decisions on opening and closing water-control infrastructure, and do this to support the crops and practices they traditionally use with little concern for impacts on poorer or more distant communities and no incentive to change crops. There are, however, alternative win-win approaches that can benefit farmers and poorer aquatic resource users.

Shankar et al. ( 2004 ) investigated the potential to maximize floodplain productivity and returns by taking a more integrated view of the resource base that would better balance agriculture with fisheries and benefit the poor. Modeling showed the potential to restore floodplain fisheries by modifying water management. Protecting and enhancing open capture fisheries most benefits the poor, but farmers also could gain by adopting profitable alternative crop-management practices that diversify risk and gain from subsistence fishing. This has been refined and brought into practice through work with CBOs.

Example of Operationalizing Integrated Floodplain Management

Linkages between elements of integrated floodplain management are shown in Figure 7 . Within this evolving framework a range of actions have been tested and adopted in Bangladesh:

Cropping pattern management . Promoting dry-season crops that are less water demanding than irrigated rice to conserve water for fish and shorter-duration rice varieties to enable earlier sluice-gate opening to facilitate fish migration.

Modified sluice-gate management . Opening sluice gates in the pre- to early monsoon, when fish are migrating between rivers and floodplains for spawning (provided cropping patterns have changed to avoid crop losses).

Fishing effort control . Reducing fishing pressure, stopping fishing practices that target fish at critical times (e.g., barriers and lift nets close to sluice gates) or catch all fish (e.g., draining out waterbodies for fishing), establishing fish sanctuaries, and observing closed seasons for fishing.

Land retirement . Giving up high-risk crop cultivation in low land, leaving the land for fish.

Improving water quality and ecosystem health . Adopting practices of integrated pest management and enclosed jute processing to replace openwater processing which damages water quality (see Figure 8 ).

Habitat rehabilitation . Re-excavating waterbodies to hold water for overwintering fish, and re-establishing connections between floodplain beels and rivers.

Reintroduction of locally threatened species . Reintroduction and augmentation of low stocks of local rare and threatened fish species.

Watershed management . Implement of soil conservation measures and tree planting to reduce siltation and ensure that floodplain depressions will hold water.

Figure 7. Elements of integrated floodplain management (IFM).

Figure 8. Jute retting in open floodplain areas adversely affects water quality in Narail, southwest Bangladesh.

Local institutions that can engage in collective action are a precondition for integrated floodplain management. In the largest piloting of IFM, Sultana and Thompson ( 2012 ) reported that among 280 existing CBOs, more than 300 farmers from 72 CBOs tried different low-water-demand alternative crops (garlic, potato, maize, sunflower, potato, wheat, and mustard) that were not previously grown in their areas, and found them to be profitable. They also reported that conservation measures (sanctuaries and closed seasons) had restored fish species diversity and catches, including 110 CBOs that established new or improved fish sanctuaries covering on average 6% of their dry-season water area, and that this was associated with improvements in food security. Overall, farmers and fishers in communities adopting IFM benefited from higher catches, higher incomes from crops, and greater community solidarity.

Adaptive Learning

Ideally, the many CBOs in Bangladesh floodplains would strive to continue improving their decisions and management of natural resources after project support ends, in response to environmental and economic variations and trends. Adaptive management and adaptive learning can be thought of as structured processes of “learning by doing” that emphasize systematic reflection on experiences and lessons, and testing management actions and interventions as a way of improving performance and reducing uncertainties. Adaptive co-management incorporates a hierarchy of institutional arrangements for sharing management responsibilities over scales of resource and an explicit commitment to learning among these partners (Armitage, Marschke, & Doubleday, 2008 ).

However, the isolation of each CBO dispersed across Bangladesh limited the scope of adaptive learning, which was limited to adjusting practices based only on its own experience. Networking between CBOs makes it possible to share lessons, accelerating adaptive learning. Adaptive learning networks have been proposed between individuals (Davidson-Hunt, 2006 ), or involved villages focused on technical aspects of resource management (Arthur & Garaway, 2005 ).

In 2007 an innovation was implemented in Bangladesh that has involved action research to test an organized learning network comprising over 250 existing CBOs that are managing floodplain natural resources (Sultana & Thompson, 2012 ). The process evolved through regular workshops and exchange visits. CBOs that had previously concentrated on either culture- or capture-fishery management or water management for rice collectively analyzed their experiences, including both the constraints each had encountered and the potential opportunities perceived, lessons learned, and good practices.

Typically, each CBO has an executive committee, which draws up a management plan and, in most years, updates it based on an informal review of its own experiences, sometimes bringing the general members into the discussion. The Bangladesh innovation was to establish an adaptive learning network to enhance this practice through the multiplier effect of sharing learning across CBOs. The CBOs could then share their experiences collectively, identifying constraints and knowledge gaps, coordinating piloting and changes in practice to address interlinked resource-management issues, and monitoring and assessing these changes.

Based on their experience facilitating this process, Sultana and Thompson ( 2012 ) concluded that face-to-face workshops or meetings of the CBO leaders from the same region were the most effective arrangement. The process that evolved is shown in Figure 9 . Each CBO in a region sends a representative to two workshops a year, covering the cycle of activities shown in the bottom circle of figure 9 . These workshops are used to identify common issues and uncertainties; solutions already proven by some CBOs; potential changes in their draft plans; and other activities that the CBOs want to improve or experiment with. The individual CBOs became more systematic in revising their plans based on their own experience and that of the other CBOs and their experiments. After attending workshops, CBO leaders share options with their members and changes to plans and actions are finalized by the executive committees of each individual CBO ( top circle ). This is coordinated in the network meetings so that alternative views can be tested in the form of comparative “experiments” where appropriate. In the workshops the CBOs also agree common indicators so that they can compare and assess impacts based on their own monitoring.

Figure 9. Concept for an adaptive learning network among community-based organizations.

The outcomes were improvements in governance and natural-resource management. Most of the CBOs adopted win-win practices arising from a system-based view of the links between agriculture, water management, and fisheries—elements of integrated floodplain management—encouraged by sharing lessons. This has enhanced the overall productivity of floodplains and resilience to uncertain environmental conditions. Over the course of a four-year study, 84% of participating CBOs acted to improve fisheries management—mostly through measures such as creating fish sanctuaries and closed seasons that help sustain native wild fish populations and diversity. Moreover 62% of CBOs tested and promoted dry-season crops that were new to their areas (such as maize, sunflower, and garlic) and require a third or less of the water used by irrigated rice. This preserved more surface water in which fish could survive, enhanced the capacity to cope with drought, and yielded better financial returns than rice.

The network also encouraged innovation and improved linkages with service providers and, importantly, generated peer pressure among the CBOs to improve governance and natural-resource management. Improvements in governance included strengthening the internal management role of the disadvantaged within the CBOs, increasing the membership of women and their engagement in decision-making (Sultana & Thompson, 2008 ), further consulting with local stakeholders, and following transparent practices. This federation of CBOs develops capacity, confidence, and strength in numbers enabling communities to enter into dialogue with policy processes and attempt to resist threats to their access to common water resources.

Based on a series of assessments of the participating CBOs, in about 80% of locations where CBOs are active and members of the adaptive learning network, local people report that the CBOs have improved the livelihoods of poor people, and this is consistent across floodplain environments. More CBOs are seen as having protected or improved access for the poor since they became involved in adaptive learning, for example, allowing subsistence fishing by the poor, or ending exclusions imposed by previous leaseholders.

Conclusions

Research and development initiatives since the early 1990s in Bangladesh have revealed the complexity of interactions between ecosystems and livelihoods in the floodplain. The dominance of agricultural use of the floodplain, particularly to grow rice, and interventions to increase rice production have been challenged. Wild aquatic resources, particularly fish, and related ecosystem services have been shown to account for a substantial part of the economic value of floodplains, to contribute more to the livelihoods of the poor than to the livelihoods of larger landowners, and to be under increasing threat from intensification of both crop farming and aquaculture. Relevant to the international livelihood debates are findings as diverse as the nutritional significance of small wild fish as a source dietary micronutrients and the low fertility of silt deposition. Bangladesh has adopted and demonstrated successful methods to facilitate local participation in the planning and governance of floodplains. Empowering local communities and within them the poorer floodplain natural-resource users has been addressed by NGO initiatives, to some extent by policy decisions, and in action research. The Bangladesh experience highlights the role of collective action, provides lessons on building community-based organizations and then sustaining them, and reveals the scope to add value and build adaptation capacity through structured learning between communities.

Gaps and Research Questions

The links between rural floodplains and urban economic opportunities in the livelihood strategies of the poor are becoming increasingly apparent—for example, many floodplain households have members who periodically or seasonally migrate to urban centers or other rural districts to find work, or who leave to find work in times of crisis. The impact on floodplain livelihoods and the influence of such factors as climate stress remain topics for further research. The productivity and value of ecosystem services in a range of floodplain types and conditions (for example, considering flood depth, duration, and silt load) also deserves more research (research thus far has concentrated on only a few, less intensively farmed areas). Ultimately, the sustainability and resilience of floodplain ecosystems, including their natural resources and the rural livelihoods that depend on them remains in question.

In the face of climate change, the lessons and evidence generated in Bangladesh are directly relevant to environments and communities globally that face similar changes (such as salinity changes, flood risks, and unreliable rains). Of particular importance are the generic findings on governance and policies, local natural-resource governance, and the resilience of more diverse local agro-ecological systems and the livelihoods of the poor who depend on them.

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Flooding Case Study: Bangladesh

Rivers of bangladesh.

Three main rivers flow through Bangladesh. They are the Ganges, the Brahmaputra and the Surma (Barak).

• The Ganges.
• The Brahmaputra.
• The Surma (Barak).

• The Ganges is 2,510 km long.
• It is a vital river for the country both financially and sacredly.
• Its source is located in the Himalaya Mountains.
• It then flows downstream through Calcutta (India) and finally out into the Bay of Bengal.
• The citizens of both Bangladesh and India rely on the river to grow crops.

The Brahmaputra River

• The Brahmaputra River is 2,900 km long.
• It has its source in the upland area of Kailash in Tibet.
• It frequently deposits rich soils which benefit farmers.
• However it also can produce devastating flood waters that endanger the local people.

Flooding: Consequences & Actions

Bangladesh suffers from frequent flooding events and some have caused great loss of life.

• This is due to monsoon rains and tropical cyclones.
• This makes Bangladesh more susceptible to flooding after these weather events.

Flood of 2017

• In 2017, heavy rainfall caused an estimated 6 million people to be displaced and killed 93.
• 450,000 hectares of farmland was also flooded and 500,000 homes were destroyed.

Flood of 2004

• One of the most devastating floods in the country occurred in 2004.
• Over 800 people were killed due to the spread of disease.
• An estimated 36 million people displaced.

• In response to the frequent flooding events the government have invested in short and potentially long-term responses to reduce the impact of flooding events in the future.

Short-term responses

• Repairing flood embankments.
• Providing food and drinking water.
• Giving farmers new seeds.

Long-term responses

• Reducing the rate of deforestation.
• Developing technology to implement flood warning systems.
• Construction of dams to store water.

1 Geography Skills

1.1 Mapping

1.1.1 Map Making

1.1.2 OS Maps

1.1.3 Grid References

1.1.4 Contour Lines

1.1.5 Symbols, Scale and Distance

1.1.6 Directions on Maps

1.1.7 Describing Routes

1.1.8 Map Projections

1.1.9 Aerial & Satellite Images

1.1.10 Using Maps to Make Decisions

1.2 Geographical Information Systems

1.2.1 Geographical Information Systems

1.2.2 How do Geographical Information Systems Work?

1.2.3 Using Geographical Information Systems

1.2.4 End of Topic Test - Geography Skills

2 Geology of the UK

2.1 The UK's Rocks

2.1.1 The UK's Main Rock Types

2.1.2 The UK's Landscape

2.1.3 Using Rocks

2.1.4 Weathering

2.2 Case Study: The Peak District

2.2.1 The Peak District

2.2.2 Limestone Landforms

2.2.3 Quarrying

3 Geography of the World

3.1 Geography of America & Europe

3.1.1 North America

3.1.2 South America

3.1.3 Europe

3.1.4 The European Union

3.1.5 The Continents

3.1.6 The Oceans

3.1.7 Longitude

3.1.8 Latitude

3.1.9 End of Topic Test - Geography of the World

4 Development

4.1 Development

4.1.1 Classifying Development

4.1.3 Evaluation of GDP

4.1.4 The Human Development Index

4.1.5 Population Structure

4.1.6 Developing Countries

4.1.7 Emerging Countries

4.1.8 Developed Countries

4.1.9 Comparing Development

4.2 Uneven Development

4.2.1 Consequences of Uneven Development

4.2.2 Physical Factors Affecting Development

4.2.3 Historic Factors Affecting Development

4.2.4 Human & Social Factors Affecting Development

4.2.5 Breaking Out of the Poverty Cycle

4.3 Case Study: Democratic Republic of Congo

4.3.1 The DRC: An Overview

4.3.2 Political & Social Factors Affecting Development

4.3.3 Environmental Factors Affecting the DRC

4.3.4 The DRC: Aid

4.3.5 The Pros & Cons of Aid in DRC

4.3.6 Top-Down vs Bottom-Up in DRC

4.3.7 The DRC: Comparison with the UK

4.3.8 The DRC: Against Malaria Foundation

4.4 Case Study: Nigeria

4.4.1 The Importance & Development of Nigeria

4.4.2 Nigeria's Relationships with the Rest of the World

4.4.3 Urban Growth in Lagos

4.4.4 Population Growth in Lagos

4.4.5 Factors influencing Nigeria's Growth

4.4.6 Nigeria: Comparison with the UK

5 Weather & Climate

5.1 Weather

5.1.1 Weather & Climate

5.1.2 Components of Weather

5.1.3 Temperature

5.1.4 Sunshine, Humidity & Air Pressure

5.1.5 Cloud Cover

5.1.6 Precipitation

5.1.7 Convectional Precipitation

5.1.8 Frontal Precipitation

5.1.9 Relief or Orographic Precipitation

5.1.10 Wind

5.1.11 Extreme Wind

5.1.12 Recording the Weather

5.1.13 Extreme Weather

5.2 Climate

5.2.1 Climate of the British Isles

5.2.2 Comparing Weather & Climate London

5.2.3 Climate of the Tropical Rainforest

5.2.4 End of Topic Test - Weather & Climate

5.3 Tropical Storms

5.3.1 Formation of Tropical Storms

5.3.2 Features of Tropical Storms

5.3.3 The Structure of Tropical Storms

5.3.4 Tropical Storms Case Study: Katrina Effects

5.3.5 Tropical Storms Case Study: Katrina Responses

6 The World of Work

6.1 Tourism

6.1.1 Landscapes

6.1.2 The Growth of Tourism

6.1.3 Benefits of Tourism

6.1.4 Economic Costs of Tourism

6.1.5 Social, Cultural & Environmental Costs of Tourism

6.1.6 Tourism Case Study: Blackpool

6.1.7 Ecotourism

6.1.8 Tourism Case Study: Kenya

7 Natural Resources

7.1.1 What are Rocks?

7.1.2 Types of Rock

7.1.4 The Rock Cycle - Weathering

7.1.5 The Rock Cycle - Erosion

7.1.6 What is Soil?

7.1.7 Soil Profiles

7.1.8 Water

7.1.9 Global Water Demand

7.2 Fossil Fuels

7.2.1 Introduction to Fossil Fuels

7.2.2 Fossil Fuels

7.2.3 The Global Energy Supply

7.2.5 What is Peak Oil?

7.2.6 End of Topic Test - Natural Resources

8.1 River Processes & Landforms

8.1.1 Overview of Rivers

8.1.2 The Bradshaw Model

8.1.3 Erosion

8.1.4 Sediment Transport

8.1.5 River Deposition

8.1.6 River Profiles: Long Profiles

8.1.7 River Profiles: Cross Profiles

8.1.8 Waterfalls & Gorges

8.1.9 Interlocking Spurs

8.1.10 Meanders

8.1.11 Floodplains

8.1.12 Levees

8.1.13 Case Study: River Tees

8.2 Rivers & Flooding

8.2.1 Flood Risk Factors

8.2.2 Flood Management: Hard Engineering

8.2.3 Flood Management: Soft Engineering

8.2.4 Flooding Case Study: Boscastle

8.2.5 Flooding Case Study: Consequences of Boscastle

8.2.6 Flooding Case Study: Responses to Boscastle

8.2.7 Flooding Case Study: Bangladesh

8.2.8 End of Topic Test - Rivers

8.2.9 Rivers Case Study: The Nile

8.2.10 Rivers Case Study: The Mississippi

9.1 Formation of Coastal Landforms

9.1.1 Weathering

9.1.2 Erosion

9.1.3 Headlands & Bays

9.1.4 Caves, Arches & Stacks

9.1.5 Wave-Cut Platforms & Cliffs

9.1.6 Waves

9.1.7 Longshore Drift

9.1.8 Coastal Deposition

9.1.9 Spits, Bars & Sand Dunes

9.2 Coast Management

9.2.1 Management Strategies for Coastal Erosion

9.2.2 Case Study: The Holderness Coast

9.2.3 Case Study: Lyme Regis

9.2.4 End of Topic Test - Coasts

10 Glaciers

10.1 Overview of Glaciers & How They Work

10.1.1 Distribution of Glaciers

10.1.2 Types of Glaciers

10.1.3 The Last Ice Age

10.1.4 Formation & Movement of Glaciers

10.1.5 Shaping of Landscapes by Glaciers

10.1.6 Glacial Landforms Created by Erosion

10.1.7 Glacial Till & Outwash Plain

10.1.8 Moraines

10.1.9 Drumlins & Erratics

10.1.10 End of Topic Tests - Glaciers

10.1.11 Tourism in Glacial Landscapes

10.1.12 Strategies for Coping with Tourists

10.1.13 Case Study - Lake District: Tourism

10.1.14 Case Study - Lake District: Management

11 Tectonics

11.1 Continental Drift & Plate Tectonics

11.1.1 The Theory of Plate Tectonics

11.1.2 The Structure of the Earth

11.1.3 Tectonic Plates

11.1.4 Plate Margins

11.2 Volcanoes

11.2.1 Volcanoes & Their Products

11.2.2 The Development of Volcanoes

11.2.3 Living Near Volcanoes

11.3 Earthquakes

11.3.1 Overview of Earthquakes

11.3.2 Consequences of Earthquakes

11.3.3 Case Study: Christchurch, New Zealand Earthquake

11.4 Tsunamis

11.4.1 Formation of Tsunamis

11.4.2 Case Study: Japan 2010 Tsunami

11.5 Managing the Risk of Volcanoes & Earthquakes

11.5.1 Coping With Earthquakes & Volcanoes

11.5.2 End of Topic Test - Tectonics

12 Climate Change

12.1 The Causes & Consequences of Climate Change

12.1.1 Evidence for Climate Change

12.1.2 Natural Causes of Climate Change

12.1.3 Human Causes of Climate Change

12.1.4 The Greenhouse Effect

12.1.5 Effects of Climate Change on the Environment

12.1.6 Effects of Climate Change on People

12.1.7 Climate Change Predictions

12.1.8 Uncertainty About Future Climate Change

12.1.9 Mitigating Against Climate Change

12.1.10 Adapting to Climate Change

12.1.11 Case Study: Bangladesh

13 Global Population & Inequality

13.1 Global Populations

13.1.1 World Population

13.1.2 Population Structure

13.1.3 Ageing Populations

13.1.4 Youthful Populations

13.1.5 Population Control

13.1.6 Mexico to USA Migration

13.1.7 End of Topic Test - Development & Population

14 Urbanisation

14.1 Urbanisation

14.1.1 Rural Characterisitcs

14.1.2 Urban Characteristics

14.1.3 Urbanisation Growth

14.1.4 The Land Use Model

14.1.5 Rural-Urban Pull Factors

14.1.6 Rural-Urban Push Factors

14.1.7 The Impacts of Migration

14.1.8 Challenges of Urban Areas in Developed Countries

14.1.9 Challenges of Urban Areas in Developing Countries

14.1.10 Urban Sustainability

14.1.11 Case Study: China's Urbanisation

14.1.12 Major UK Cities

14.1.13 Urbanisation in the UK

14.1.14 End of Topic Test- Urbanisation

14.1.15 End of Topic Test - Urban Issues

15 Ecosystems

15.1 The Major Biomes

15.1.1 Distribution of Major Biomes

15.1.2 What Affects the Distribution of Biomes?

15.1.3 Biome Features: Tropical Forests

15.1.4 Biome Features: Temperate Forests

15.1.5 Biome Features: Tundra

15.1.6 Biome Features: Deserts

15.1.7 Biome Features: Tropical Grasslands

15.1.8 Biome Features: Temperate Grasslands

15.2 Case Study: The Amazon Rainforest

15.2.1 Interdependence of Rainforest Ecosystems

15.2.2 Nutrient Cycling in Tropical Rainforests

15.2.3 Deforestation in the Amazon

15.2.4 Impacts of Deforestation in the Amazon

15.2.5 Protecting the Amazon

15.2.6 Adaptations of Plants to Rainforests

15.2.7 Adaptations of Animals to Rainforests

16 Life in an Emerging Country

16.1 Case Studies

16.1.1 Mumbai: Opportunities

16.1.2 Mumbai: Challenges

17 Analysis of Africa

17.1 Africa

17.1.1 Desert Biomes in Africa

17.1.2 The Semi-Desert Biome

17.1.3 The Savanna Biome

17.1.4 Overview of Tropical Rainforests

17.1.5 Colonisation History

17.1.6 Population Distribution in Africa

17.1.7 Economic Resources in Africa

17.1.8 Urbanisation in Africa

17.1.9 Africa's Location

17.1.10 Physical Geography of Africa

17.1.11 Desertification in Africa

17.1.12 Reducing the Risk of Desertification

17.1.13 Case Study: The Sahara Desert - Opportunities

17.1.14 Case Study: The Sahara Desert - Development

18 Analysis of India

18.1 India - Physical Geography

18.1.1 Geographical Location of India

18.1.2 Physical Geography of India

18.1.3 India's Climate

18.1.4 Natural Disasters in India

18.1.5 Case Study: The Thar Desert

18.1.6 Case Study: The Thar Desert - Challenges

18.2 India - Human Geography

18.2.1 Population Distribution in India

18.2.2 Urabinsation in India

18.2.3 The History of India

18.2.4 Economic Resources in India

19 Analysis of the Middle East

19.1 The Middle East

19.1.1 Physical Geography of the Middle East

19.1.2 Human Geography of the Middle East

19.1.3 Climate Zones in the Middle East

19.1.4 Climate Comparison with the UK

19.1.5 Oil & Natural Gas in the Middle East

19.1.6 Water in the Middle East

19.1.7 Population of the Middle East

19.1.8 Development Case Studies: The UAE

19.1.9 Development Case Studies: Yemen

19.1.10 Supporting Development in Yemen

19.1.11 Connection to the UK

19.1.12 Importance of Oil

19.1.13 Oil & Tourism in the UAE

20 Analysis of Bangladesh

20.1 Bangladesh Physical Geography

20.1.1 Location of Bangladesh

20.1.2 Climate of Bangladesh

20.1.3 Rivers in Bangladesh

20.1.4 Flooding in Bangladesh

20.2 Bangladesh Human Geography

20.2.1 Population Structure in Bangladesh

20.2.2 Urbanisation in Bangladesh

20.2.3 Bangladesh's Economy

20.2.4 Energy & Sustainability in Bangladesh

21 Analysis of Russia

21.1 Russia's Physical Geography

21.1.1 Russia's Climate

21.1.2 Russia's Landscape

21.2 Russia's Human Geography

21.2.1 Population of Russia

21.2.2 Russia's Economy

21.2.3 Energy & Sustainability in Russia

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Flooding Case Study: Responses to Boscastle

End of Topic Test - Rivers

• Annual reports
• Annual Report 2022
• Results and Impact
• Project case study Bangladesh

Bangladesh: Dealing with climate change and strengthening resilience

The insurance sector in Bangladesh offers no protection to the rural population from natural disasters. Without access to insurance, farmers must limit their investments in farm implements and are unable to diversify their agricultural activities. A lack of investment and simultaneously low crop yields result in smallholder farmers being unable to free themselves from poverty.

Strengthening resilience

The Bangladesh Microinsurance Market Development Project (BMMDP) was launched in 2017 by the Swiss Agency for Development and Cooperation (SDC). Its objective is to increase the resilience of farmers to withstand climate-related crop failures and improve food security by way of microinsurance products.

Solutions for climate protection

According to the World Bank’s Country Climate and Development Report, Bangladesh may lose up to one-third of its gross domestic product by 2050 due to climate fluctuations and natural disasters. As approximately 38 per cent of the working-age population earns a living in agriculture, income stability and crop security, particularly for smallholder farmers, are a top priority for the country. In order to improve farmer resilience and productivity, the project is working closely with the insurance market in Bangladesh and developing insurance products for crops and farm animals, as well as services to minimize risks.

As part of the programme for sustainable agriculture implemented jointly with the Syngenta Foundation, Swisscontact has introduced a completely new type of insurance to Bangladesh: weather index-based crop insurance. Data is compiled at various weather stations and evaluated over a specific time period. The insurance makes an indexed payout whenever the values of a previously set threshold are exceeded or undercut.

The advantage of this method is that the insurance payout is neither based on the type of crop nor on its effective yield, instead, the payments are independent of the individual farmer’s losses. This means there is no individual damage assessment, and the administrative costs can be decreased significantly.

Weather report via voice calls

The insurance also offers various advisory services, such as voice calls informing farmers of the weather forecast and offering them agricultural advisory services (outbound dialling service, or OBD). Direct voice calls are greatly advantageous over brief news clips because they also reach illiterate people. Given Bangladesh’s literacy rate of 75 per cent in 2020, the use of voice calls has been shown to be significantly more effective than news clips in informing farmers.

Production cost savings

In addition, the farmers receive seasonal advisory services. By getting informed on good agricultural practices such as the use of organic fertilisers or field irrigation for crops such as rice, potatoes, maize, etc., they can undertake the necessary measures to minimise the risk of crop failures from unfavourable weather changes.

One example from the field illustrates this service vividly: a farmer planning to fertilise his crop in the next few days will receive a phone call informing him of upcoming rains. Thus, he decides to delay fertilising until after the rain has ended so that the fertiliser is not washed away. This saves him money and resources.

A lighthouse project for partnership

The programme works together with a dozen partners from the private sector and insurance field on innovative solutions. It is thus a lighthouse project for successful collaboration between international development cooperation and the private sector.

As part of this programme, Swisscontact also helped in the development of the first disease and death insurance policy for cattle, for which farmers file an application and the costs of treatment for insured cattle are reimbursed. This medical insurance product uses the latest technology of machine learning to identify insured cattle by nose prints. Just as every human being has their own fingerprint, each head of cattle has its own unique nose print. Given that in Bangladesh cattle are worshipped and considered sacred, it is forbidden to clip a chip onto them.

From 2017 through 2022, more than 800,000 farmers have taken out crop and cattle insurance as part of the project. Nearly 480,000 of these farmers were women. The volume of financing forwarded to farmers amounted to 166 million Swiss francs. 463,000 farmers benefited from the use of climate-resistant land cultivation methods.

“For many years, we have relied on income from the sale of fish and vegetables. It was difficult to feed a family of four. We earned additional income from planting rice and potatoes. But this was also risky, because downpours, thick fog, drought, and storms destroyed the harvest. In 2021, I found out about crop insurance and soon took one out. I received weekly weather forecasts and agricultural advisory services free-of-charge. This helped me to better manage my crop. In April 2022, I suffered great losses when my potato fields were damaged by a storm. Because I was insured, I was reimbursed for most of my losses. This gave me a feeling of security. By and by, I have started to invest in planting other crops.”

Ms Tohura Khatun, a Bangladeshi farmer

“There is enormous insurance potential in Bangladesh, particularly in agriculture. Without a doubt, we must work toward improving access to insurance and emphasise insurance products tailored to real needs. Now that the first illness and death insurance policies for cattle have been introduced, I can state with confidence that our efforts, along with the support and guidance provided by this project and our partners, have yielded fruit.”

Papia Rahman, Deputy CEO, Chartered Insurer

“We offer crop insurance to smallholder farmers in Africa, Asia, and Latin America. This year, Swisscontact has helped us to enter a new country and develop a customised insurance product called the Area Yield Index Insurance (AYII) for potatoes in Bangladesh. AYII has never been offered on a large scale in Bangladesh; thanks to this project, we were able to develop this new product and launch it on the market.”

Elise Lee, Business Director, Pula Advisors AG

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Towards sustainable flood mitigation strategies: A case study of Bangladesh

2011, Disaster, Risk and Vulnerability Conference 2011 (Volume 1 Number 1)

This paper outlines a part of a research and design project based on work undertaken for the B.Arch. at the Department of Architecture, Bangladesh University of Engineering & Technology in 2008. This study intended to ease the lifestyle & livelihood of rural dwellers beside the river bank areas in respond to natural hazards like floods. Floods in the deltaic valley of Bangladesh is not merely an environmental issue, they play with the very fate of the nation, not to speak havoc they wreak on the economy of these inhabitants besides the bank side areas. Again this climatologic phenomenon not only poses enormous threats to the locality but also moderate floods contribute to the fertility of the land. Flood hazards of bank side areas of rivers are difficult to control through structural measures; Flood proofing through assistance to self help measures to reduce the damage to property and stress are largely accepted preventive efforts that these people have practiced. This paper focuses on formulating future action plans and some immediate incentives to improve the physical environment that are better suited to the people of river bank areas with frequently changing context. To develop a self-sustain community and sustainable mitigation strategies in response to observed or expected changes in climatic stimuli beside the riverbank areas, study goes through the geo-morphological & hydrological analysis and vulnerability assessments in this area. Finally goal is to provide zoning guidelines & few planning solutions along with modified house building techniques through flood level predictions, which would help the peasants at the time of emergency & could be intergrated into official flood management measure.

Related Papers

PWK ITN 2017

Course GEOG6023 ’Physical Geography in Environmental Management’, MSc, University of Southampton, UK

Polina Lemenkova

The presentation describes problem of flooding in Bangladesh: Bangladesh belongs the countries that are affected by flooding the most. The work presents natural hazards happening in Bangladesh, frequent natural disasters causing loss of life, damage to infrastructure and economic assets, impacts on lives and livelihoods. Floods, tropical cyclones, storm surges and droughts are likely to become more frequent and severe in the coming years. Bangladesh lies in the delta of three of the largest rivers in the world – the Brahmaputra, the Ganges and the Meghna and is notable for frequent floods. Social factors are compared. Hence, during the flood hazard the following population groups are at risk: 1) the poor, 2) poor-healthy, 3) women. These groups will suffer much more disproportionately than the group of well-being and healthy men, more so in the coastal and rural areas than elsewhere. The presentation is supported by illustrations, maps and graphs. Presented at the University of Southampton, 2009.

Climate Stricken;Challenges of Flood mitigation in Bangladesh.

Badrul Munir

Abstract: Bangladesh is a riverine country and one of the most flood-prone countries in the world with the greatest risk of being affected by climate change and natural disasters like Flood, Cyclone, Landslide and Lightening. Some 30 to 35% of the total land surface of the country is flooded every year and people use multiple strategies to live with flooding events and associated riverbank erosion. They relocate, evacuate their homes temporarily, change cropping patterns, and supplement their income from migrating household members. The frequency and intensity of natural hazard are increasing day by day parallel to the climate change. The image of Bangladesh as a country that is adapting well stems from its long history of living with floods. Every year the country has been losing a large part of GDP as an economic loss. Regular floods are part of people’s lives, recurring with varying magnitudes and frequencies to which people have adapted. Bangladesh experiences four different types of floods: flash floods, riverine floods, rain floods and storm-surge floods. Floods and cyclone are some of the most destructive hydro meteorological phenomena in terms of their impacts on humans, infrastructure, and economic sectors and also ecosystems in Bangladesh. The Flood Forecasting and Early Warning system is also equipped with experienced and trained personnel. FFWC is capable of issuing forecasts 30 to 72 hours in advance using real time data. In Bangladesh, flood forecasting and warning is conducted with the aid of a hydrological and hydrodynamic mathematical model (MIKE11-GIS) and the NOAA–AVHRR satellite imagery and processing system. The geo-technical work involved in upgrading the embankments to mitigate the flood situation. Soil are used to increase the height and width of the embankments, locally produced concrete blocks or geo-tubes are offer slope reinforcement. In addition to the geo-technical work, sluice gates associated with the existing embankments and their respective drainage channels are being updated. This is now agricultural requirements for the use of the sluice gate," The new sluice gates incorporated into the embankments and sliding gates that is enabling the sluices to function in both ways so that they can be used as part of local water management operations. The flood problem in Bangladesh is extremely complex. The country is an active delta; it has numerous networks of rivers, canals and coast creeks with extensive flood plains through which surface water of about 1.7 million sq-km drains annually. Although the livelihood of the people in Bangladesh is well adapted to normal monsoon flood, the damages due to inundation, riverbank erosion or breach of embankment, etc. still occur in almost every monsoon. The devastating floods often have disastrous consequences: major damage to infrastructure, great loss of property, crops, cattle, poultry etc. With every major flood in Bangladesh, food security and poverty situation adversely affected. Keywords: Water Resource Management, Geo-Technology, Adaptation, Drainage, Monsoon, Mitigation strategy. Early warning.

Dr. Amartya Kumar Bhattacharya

Bristi Basak

Dhaka, the largest and capital city of Bangladesh, is a rapidly growing Mega city. Major environmental concern of Dhaka city is recurring natural disasters. Flood is actually the main natural catastrophic event now days for Dhaka city. To mitigate the flood hazard there is a Flood Action Plan for Dhaka, which was made after 1988 flood. Moreover, there are pre and post mitigation measures taken by Govt. Despite of these measures, Dhaka faces flood hazard in every year and the hazard is becoming more vulnerable day by day. So, there is a need for planning measures to mitigate flood hazard. In this thesis an attempt has been made to arrive at strategies for mitigating floods in the Dhaka city. The goal of the thesis has been achieved under five objectives. The phenomenon and characteristics of flooding in the city of Dhaka has been studied under first objective. The critical flood prone areas, causative factors, existing infrastructure problem that makes worst situation during flood and rainy season have been studied under second and third objective. Under forth objective review of Flood Action Plan of Dhaka has been studied. Suggestion for planning measures to mitigate floods has been studied under objective five. The analysis of the secondary survey data indicates that Dhaka faces two types of flood -monsoon flood and urban flood. This type of floods causes because of local heavy rainfall and blockage of natural drainage of water due to unplanned population settlement. Dhaka has both open and closed drainage system. Most of them are blocked due to solid waste dumping. As population is increasing day by day, slums have been taken place in the retention areas and along the canals & lakes. So, slum population is dumping solid waste as well as sewerage in the canals. It causes environmental hazard and blockage of natural drainage system. Moreover, natural drainage system is getting blocked by the nature (water hyacinth) also. So, storm water cannot be drained out properly during rainy season and it causes urban flood. These are major issues arrived from the analysis. At the end of the thesis some planning measures to mitigate the flood has been recommended. This recommendation has been divided into three categories. In the first category, the proposals of FAP should come true. Zoning of the area has been done also. In second stage the alternative allocation of urbanization has been worked out. The third step covers the preservation of wetlands, rejuvenation of canals, increasing the drainage capacity, increasing the public awareness and improving the situation by leg al instrument. An action plan has been prepared for a flood prone area called Kamrangirchar. Zoning has been done for the area. The zoning covers in three steps. Some other recommendations also have taken for the study area like- conservation of water body, protection of natural drainage system etc.

Anika N. Haque

Imam Abd Sajid

SN Applied Sciences

RABIN CHAKRABORTTY

Sk Nafiz Rahaman

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Dubai’s Extraordinary Flooding: Here’s What to Know

Images of a saturated desert metropolis startled the world, prompting talk of cloud seeding, climate change and designing cities for intensified weather.

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By Raymond Zhong

Scenes of flood-ravaged neighborhoods in one of the planet’s driest regions have stunned the world this week. Heavy rains in the United Arab Emirates and Oman submerged cars, clogged highways and killed at least 21 people. Flights out of Dubai’s airport, a major global hub, were severely disrupted.

The downpours weren’t a freak event — forecasters anticipated the storms several days out and issued warnings. But they were certainly unusual. Here’s what to know.

Heavy rain there is rare, but not unheard-of.

On average, the Arabian Peninsula receives a scant few inches of rain a year, although scientists have found that a sizable chunk of that precipitation falls in infrequent but severe bursts, not as periodic showers.

U.A.E. officials said the 24-hour rain total on Tuesday was the country’s largest since records there began in 1949 . But parts of the nation had experienced an earlier round of thunderstorms just last month.

Oman, with its coastline on the Arabian Sea, is also vulnerable to tropical cyclones. Past storms there have brought torrential rain, powerful winds and mudslides, causing extensive damage.

Global warming is projected to intensify downpours.

Stronger storms are a key consequence of human-caused global warming. As the atmosphere gets hotter, it can hold more moisture, which can eventually make its way down to the earth as rain or snow.

But that doesn’t mean rainfall patterns are changing in precisely the same way across every corner of the globe.

In their latest assessment of climate research , scientists convened by the United Nations found there wasn’t enough data to have firm conclusions about rainfall trends in the Arabian Peninsula and how climate change was affecting them. The researchers said, however, that if global warming were to be allowed to continue worsening in the coming decades, extreme downpours in the region would quite likely become more intense and more frequent.

The role of cloud seeding isn’t clear.

The U.A.E. has for decades worked to increase rainfall and boost water supplies by seeding clouds. Essentially, this involves shooting particles into clouds to encourage the moisture to gather into larger, heavier droplets, ones that are more likely to fall as rain or snow.

Cloud seeding and other rain-enhancement methods have been tried across the world, including in Australia, China, India, Israel, South Africa and the United States. Studies have found that these operations can, at best, affect precipitation modestly — enough to turn a downpour into a bigger downpour, but probably not a drizzle into a deluge.

Still, experts said pinning down how much seeding might have contributed to this week’s storms would require detailed study.

“In general, it is quite a challenge to assess the impact of seeding,” said Luca Delle Monache, a climate scientist at the Scripps Institution of Oceanography in La Jolla, Calif. Dr. Delle Monache has been leading efforts to use artificial intelligence to improve the U.A.E.’s rain-enhancement program.

An official with the U.A.E.’s National Center of Meteorology, Omar Al Yazeedi, told news outlets this week that the agency didn’t conduct any seeding during the latest storms. His statements didn’t make clear, however, whether that was also true in the hours or days before.

Mr. Al Yazeedi didn’t respond to emailed questions from The New York Times on Thursday, and Adel Kamal, a spokesman for the center, didn’t immediately have further comment.

Cities in dry places just aren’t designed for floods.

Wherever it happens, flooding isn’t just a matter of how much rain comes down. It’s also about what happens to all that water once it’s on the ground — most critically, in the places people live.

Cities in arid regions often aren’t designed to drain very effectively. In these areas, paved surfaces block rain from seeping into the earth below, forcing it into drainage systems that can easily become overwhelmed.

One recent study of Sharjah , the capital of the third-largest emirate in the U.A.E., found that the city’s rapid growth over the past half century had made it vulnerable to flooding at far lower levels of rain than before.

Omnia Al Desoukie contributed reporting.

Raymond Zhong reports on climate and environmental issues for The Times. More about Raymond Zhong

What caused Dubai floods? Experts cite climate change, not cloud seeding

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Reporting by Alexander Cornwell; editing by Maha El Dahan and Alexandra Hudson

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