Principles of equitable and reasonable use underpin international water agreements. Despite the potential for hydrologic information to enhance resilience to extreme events, comparable application of just principles to the distribution of hydrometeorological data is poorly established. Within the Ganges-Brahmaputra-Meghna (GBM) river basin, we find that water allocation agreements are codified into treaties or Memorandums of Understanding (MoUs). Analogous decisions regarding hydrometeorological data sharing are often internalized at the level of river basin organizations and are not upheld as MoUs. This institutional structure provides extremely limited data to the most downstream nation of Bangladesh. Available precipitation and discharge stations are well below the minimum densities recommended by the World Meteorological Organization. Forecasters in Bangladesh therefore contend with vast areas of geopolitically ungauged catchment, precluding the application of basin-wide modelling approaches driven by observed data. Thus, capacity for increasing resilience to extreme events within Bangladesh is obstructed, demonstrating the potential for perceived injustice related to distribution of hydrometeorological data. Consensus that water is a human right warrants the application of equity to water allocation. But is security from water-related disasters also a human right? As hydrometeorological data can be a powerful resource with potential to profoundly affect lives and livelihoods, enhanced awareness of justice related to data sharing is needed.
Bangladesh is home to a network of hundreds of rivers and the world's largest river delta, the Ganges Delta. Historically, the nation has been water rich. But that is changing owing to declining rainfall, more-intensive irrigation and heavier use of water upstream. Contamination from arsenic and sewage is also on the rise.To feed our future planet, it is crucial that water is used more sustainably in agricultural regions such as Bangladesh. Other agricultural hotspots face similar water stresses, including the Western and Central United States, northern India and Brazil 1,2 , where falling water tables punish farmers and grab headlines.Bangladesh has taken some steps to address the problem. In 2018, its Ministry of Planning published the Bangladesh Delta Plan 2100 (BDP; see go.nature.com/3s26anc). This outlines a long-term strategy for the country's sustainable and resilient socio-economic development in a changing climate. Water security is a key part of this plan. Although the BDP rightly identifies the main issues facing the nation's water, it is vague on effective actions. These will require heavy investments and more supporting research.Intensive irrigation and climate change are depleting groundwater reserves in this fast-developing nation. To improve its water security, researchers need more information on water use, quality, flows and forecasts.
The volume of water stored in seasonal wetlands is a fundamental but difficult to measure variable for developing a physical understanding of wetland behavior. For seasonal wetlands that are a major source of water for rice and fish production, this physical understanding is key to planning for water-food security and ecosystem services. This study quantified variations in volumetric storage for the numerous seasonal wetlands of northeastern Bangladesh, locally known as "haors." These haors receive transboundary runoff from densely vegetated and mountainous terrain in India and face persistent monsoonal cloud cover as they become full. We estimated volumetric storage for 13 haors by using extensive remote sensing data on water surface extent and elevation that was complemented with citizen-contributed gauge data. Assuming a trapezoidal bathymetry, an area-volume relationship was developed for selected haors. This relationship was assumed to be valid for extrapolating volumetric estimations over all the haors in the region. Results suggested that as haors get filled with the onset of monsoon rains, total estimated storage relative to the lowest observable level varied from 6.5 (±0.4) km 3 in May to 30.9 (±2.0) km 3 in July (peak of monsoon). Choosing a rectangular bathymetry can lead to 47% higher estimates compared to trapezoidal cross section. Estimating this intra-annual/interannual increase in storage is important for the region to plan water management policies that balance the human and ecosystem needs. Our analytical approach has potential for application to wetlands worldwide in light of the upcoming Surface Water and Ocean Topography (SWOT) mission.
A computationally efficient early warning technique is developed for forecasting flash floods during the pre-monsoon season that are associated with a complex topography and transboundary runoff in northeastern Bangladesh. Locally conditioned topographic and hydrometeorological observations are key forcings to the modeling system that simulate the hydrology and hydraulic processes. The hydrologic model is calibrated and validated using satellite-based observations to estimate the correct amount of transboundary and mountainous inflow into the flash flood-prone plains. Inflow is then forecasted using precipitation forecast from a global numerical weather prediction (NWP) system called the Global Forecasting System (GFS). The forecasted inflows serve as the upstream boundary conditions for the hydrodynamic model to forecast the water stage and inundation downstream in the floodplains. A real-time in-situ data-based error correction methodology is applied to maintain the skill of the system. The simulation grid size and time-step of the hydrodynamic model are also optimized for computational efficiency. Historical performance of the framework revealed at least 60% accuracy at 5-day lead-time in delineating flood inundation when compared against Sentinel-1 synthetic aperture radar (SAR) imagery. The study suggests that higher resolution topographic information and dynamically downscaled meteorological observations can lead to significant improvement in flash flood forecasting skills.
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