Bangladesh is an extremely flood-prone country due to its geographical location at the downstream end of the Ganges, Brahmaputra and Meghna (GBM) river basin. Flood destroys agricultural products of large areas and causes loss of lives and damage to infrastructures. Heavy rainfall during the monsoon season is the major cause of flooding in this region which occurs almost every year. However, the lack of observations of rainfall in the upper catchment areas outside Bangladesh makes flood forecasting challenging in this region. In addition, errors in rainfall forecasts and lack of high-resolution bathymetry and topographic data put major constraints to flood forecasting in Bangladesh through hydrologic and hydrodynamic models. Currently Flood Forecasting and Warning Centre (FFWC) of Bangladesh Water Development Board (BWDB) is producing short-range flood forecasts with a lead time of up to three days. However, medium-range (3 to 5 days) forecasts are crucial for reducing flood-related losses as they provide more time for decision making and preparation compared to short-range forecasts. In this study, a flood forecast model based on Artificial Neural Network (ANN) has been developed for the Kushiyara river which is one of the major rivers of the northeastern region of Bangladesh. Rainfall data from the fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), daily Terrestrial Water Storage (TWS) from the Global Land Data Assimilation System with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) and daily Surface Soil Moisture data from Soil Moisture Active Passive (SMAP) have been used as input to the model. The model shows reasonable accuracy in forecasting the water level of the Kushiyara river at Sheola station with a lead time of up to seven days. For 1-day lead time, the correlation coefficient (R) between the observed and simulated water levels is 0.97. The performance of the model is also promising for a medium-range forecast (R=0.93 for 7-day lead time). This study indicates that the release of daily GRACE gravity field solutions in near-real-time may enable us to forecast and monitor high volume flood events in this region.

Soil moisture plays an essential role in the complex eco-hydrologic processes, such as infiltration, rainfall-evapotranspiration-runoff circulation, photosynthesis, and groundwater recharge. However, the accurate estimation of soil moisture (SM) at regional or larger scale is difficult because SM varies highly over space and time due to heterogeneous land cover and soil properties, and ground measurements are often time-consuming and expensive. Currently, Bangladesh Meteorological Department (BMD) measures SM only at twelve stations which is quite inadequate for assessing large-scale spatial and temporal variation of SM. Thus, satellite-derived soil moisture data products or Global Land Data Assimilation System simulated (GLDAS-2.2) soil moisture dataset with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) can be promising alternatives to the in-situ measurement for this data-scarce region. In this study, the spatial and temporal variations of SM from GLDAS and Soil Moisture Active Passive (SMAP) satellite were compared against the in-situ measurements from seven agrometeorological stations of Bangladesh. The GLDAS and SMAP products overpredicted the in-situ SM for most of the stations and could capture the temporal dynamics of observed SM with correlation coefficient (R) of 0.36 and 0.17, respectively. Later an Artificial Neural Network model was developed based on soil moisture from both sources (SMAP and GLDAS) and terrestrial water storage from GLDAS to obtain more accurate estimation of SM for this data-scarce region. The ANN model shows an improvement in estimation and predicted SM with R = 0.63 (considering all stations). The results were more promising when separate model is developed for each study site. Incorporating additional climate data (such as precipitation with different lag times) as input improved the accuracy marginally. This study suggests that the release of daily GRACE gravity field solutions in near-real-time may provide a reasonable and continuous estimate of soil moisture in this data-scarce region.

Bangladesh is one of the most vulnerable countries of the world in the event of climate change due to its unique geographical location. This study assessed the impact of climate change on precipitation and temperature extremes over Bangladesh from Coupled Model Intercomparison Project Phase 6 (CMIP6) models under four SSP-RCP (Shared Socioeconomic Pathway-Representative Concentration Pathway) scenarios (SSP126, SSP245, SSP370, and SSP585). At first quantile mapping (QM) method was employed to produce bias-corrected daily data. Then the future changes in climate extremes were assessed using a subset of extreme temperature and precipitation indices devised by the Expert Team on Climate Change Detection and Indices (ETCCDI). For the assessment of precipitation extremes, Consecutive Wet Days (CWD), Number of days with rainfall greater than 10mm (R10mm), Wet Days Precipitation (R95p), total annual rainfall (PRCPTOT), annual maximum 1-day precipitation (Rx1day), and annual maximum 5-day precipitation (Rx5day) have been utilized while for the temperature extremes, frequency of hot days (TX90p) and cool days (TX10p) have been used. The results from the probability density function (PDF) of most of the precipitation indices show rightward shifting in the future indicating a tendency toward wetter conditions. However, the magnitudes of change were different for the selected CMIP6 Global Climate Models (GCMs). The projected increase in CWD is greater over the south-western region of the country while the projected increase in PRCPTOT is greater over the north-eastern region of the country under all scenarios. R10mm shows the highest increase for the SSP585. In response to climate change, the TX90p shows a general increase in this century. However, the frequency of cool days is projected to decrease for most of the SSP scenarios. The results from these analyses present an opportunity to understand the impact of climate change on extreme events in Bangladesh and thus may help the local decision-makers in policy-making, disaster management, and infrastructure planning.

Bangladesh, part of Indo-Gangetic-Brahmaputra Plain, is frequently affected by floods and droughts. As the economy of Bangladesh is still agriculture based, effective measurement of soil moisture will not only strengthen the irrigation management but also improve the hydrological modelling and drought prediction. But only nine agro-meteorological stations of Bangladesh measure the soil moisture four times a month which creates a vacuum to scientifically manage her water resources. SMAP (Soil Moisture Active Passive) satellite of NASA provides an unprecedented opportunity for full scale measurement of soil moisture over this region. Field measurements of soil moisture from April 2015 were used to assess the effectiveness of the SMAP’s measurement over the North West Region of Bangladesh which suffers from frequent dry spells. Initially the Root mean squared error (RMSE) between the SMAP and observed soil moisture were found to vary between 12.28 to 16.72% for the available stations. The results showed a bias in SMAP data and it was significantly reduced using bias correction. Later multiple linear regression, based on supplementary climate data in addition to SMAP observations, was applied to obtain an improved estimate of soil moisture and the RMSE were reduced to 1.19 to 3.18%. Lastly, different machine learning techniques (i.e. ANN, SVR, XGBoost etc.) were used to reduce the bias further. This study demonstrates a promising potential of using the SMAP data in soil moisture estimation over Bangladesh for its effective water resources management.