About

The experimental flood inundation forecast for the Brahmaputra River Basin is developed using advanced multi model hydrological and hydrodynamic modeling to provide comprehensive flood risk assessments. The forecasts are developed using a combination of observed and forecasted precipitation data, alongside streamflow measurements at various monitoring stations. These inputs are integrated into a sophisticated modeling framework that includes streamlines, representing the flow paths of water throughout the river basin. The modeling system operates by first incorporating real-time observations and meteorological forecasts to simulate streamflow across the basin. This includes both the current state and forecasted conditions, enabling a robust analysis of potential flood events. The forecast process is further refined by generating detailed flood inundation maps, which predict the spatial extent and depth of floodwaters. These maps are critical for identifying vulnerable areas and aiding in the planning and execution of flood mitigation strategies.

This experimental system not only provides real-time data but also includes a forecasting component that projects future flood scenarios. By accounting for observed and forecasted precipitation, as well as current streamflow conditions, the system offers a dynamic view of the flood risk landscape. This dual approach—assessing both present and future conditions—ensures that stakeholders have access to timely and accurate information, which is essential for effective flood preparedness and response.

The long-term data provide us with a record of several floods, which can help in robust estimates of flood risk in different sub-basins. Floods affect a large population in India during almost every summer monsoon season, leaving their mark through human mortality, migration, and agricultural and infrastructure damage. Sub-basins in the Brahmaputra river basins witnessed substantial flood inundation extent during the worst flood in the observational record. Major floods in the sub-basins of the Brahmaputra occur during the late summer monsoon season (August-September). For more information, users may refer to the below cited article.

Forecasted data

Short Range Forecast

IITM developed world’s highest resolution Global Ensemble Forecast System (GEFS) for short range prediction at 12 km using 21 members of the model. The GEFS prediction system provided probabilistic rainfall for next 10 days. The very high (~12 km) resolution Ensemble Prediction System (EPS) with 21 ensemble members for short range forecast system based on GEFS (T1534) has been put in place by IITM and has been handed over to IMD for operational implementation since 1 June 2018. For the benefit of IMD forecasters, various new model diagnostics and products have been developed and added based on GEFS T1534 forecast Presently the GEFS (12km) forecast is utilised to develop the block level forecast of rainfall probability for IMD’s agrimet application.

Observed data

Observed precipitation and maximum & minimum temperatures during the 2001-present (near real time) for the Indian region at 0.25° were obtained from the India Meteorological Department (IMD) [Pai et al., 2015; Srivastava et al., 2009]. A gridded precipitation dataset has been developed using observations from more than 6500 rain gauges located across India. The daily gridded temperature dataset is developed using station-based observations from more than 300 stations located across India (Srivastava et al., 2009). Observed precipitation and temperature data during the 2001-present for the parts of basin that falls outside India (including China, Bhutan, and Bangladesh) is obtained from Global Precipitation Measurement (GPM) and Climate Prediction Center (CPC), respectively.

About Hydrological models

The five hydrological models (HMs) are utilized to assess their performance for the water budget in the Brahmaputra River basin: VIC (Cherkauer et al., 2003; Liang et al., 1996), Noah-MP (Niu et al., 2011; Yang et al., 2011), H08 (Hanasaki et al., 2018), and CWatM (Burek et al., 2020). All models were driven by the same meteorological forcing, with simulations conducted for the period 2001–present at a spatial resolution of 0.25° and a daily temporal resolution.

Variable Infiltration Capacity (VIC) Model

The VIC model is a semi-distributed, physically-based macroscale hydrological model that simulates daily or sub-daily water and energy fluxes using vegetation and soil parameters along with meteorological inputs. ET is calculated using the Penman-Monteith method. Runoff is estimated using the variable infiltration capacity curve and soil moisture using Richard’s equation. Baseflow is determined using the Arno model formulation. The model incorporates land cover data from AVHRR and soil parameters from HWSD. It uses daily precipitation, temperature, and wind speed as inputs, with simulated runoff and baseflow routed using a standalone model.

Noah-Multi Parameterization Land Surface Model (Noah-MP)

Noah-MP, developed by NCAR, NCEP, NASA, and universities, is an advanced model with multiple physics options for land-atmosphere interactions. It simulates water and energy fluxes in each grid cell using soil parameters from STATSGO and FAO and meteorological inputs like precipitation, wind speed, air temperature, humidity, radiation, and pressure. Land use data is from the International Geosphere-Biosphere Program (IGBP). It uses multiple schemes, with the default TOPMODEL, to simulate hydrological variables. Surface runoff is estimated based on infiltration capacity and baseflow using a soil moisture storage-based proportionality function. ET is calculated using the Boreal ecosystem productivity simulator. Soil moisture and runoff/baseflow are estimated using Richard’s equation and SIMTOP schemes. A standalone routing model is used for streamflow routing.

H08 Model

The H08 model is a grid-based global hydrological model with six main modules: land surface, reservoir operation, river routing, environmental flow estimation, crop growth, and anthropogenic water withdrawal. It incorporates cropping intensity, irrigation efficiency, cropland and pasture area, and irrigation data. ET is estimated using the bulk transfer approach, and soil moisture is estimated using the soil water balance approach. Runoff is estimated using saturation excess non-linear flow and baseflow with a leaky bucket model. Streamflow routing is based on Total Runoff Integrated Pathways (TRIP). The model uses various meteorological inputs to simulate hydrological processes and anthropogenic water use on regional and global scales.

Community Water Model (CWatM)

Developed by IIASA, CWatM is an integrated hydrological and channel routing model simulating surface and groundwater processes at 1 km to 50 km grid scales and daily time steps, incorporating human activities like water use and irrigation. It uses physics-based modules for water and energy budgets, estimating ET with the Penman-Monteith method and soil moisture using Van Genuchten's simplification. Runoff is calculated using a triangular weighting function and baseflow with a linear reservoir approach. River routing follows the Saint-Venant equation. Meteorological inputs include daily precipitation, temperature, radiation, wind speed, and humidity. Soil parameters are developed using HWSD.

Flood inundation model

Observed and forecast flood inundation conditions at 0.05°(~5 km) resolution is based on the simulations from the CaMa-Flood model (Yamazaki et al., 2011). The CaMa-Flood is a hydrodynamic model that simulates river–floodplain dynamics. The total runoff output (observed + forecast) from the five hydrological models is forced into the CaMa-Flood model. The flooded depth is further downscaled to 1.67 km for high-resolution flood mapping.

Acknowledgement

This work is supported by the Monsoon Mission III, Ministry of Earth Sciences (MoES). The authors acknowledge the data availability from the India Meteorological Department (IMD) . The Global Ensemble Forecast System (GEFS) forecast products were obtained from the Indian Institute of Tropical Meteorology (IITM), Pune.
The authors acknowledge the availability of the Global Precipitation Measurement (GPM) data provided by NASA and JAXA.
The authors acknowledge the data availability from the CPC (Climate Prediction Center) developed by NOAA.

Dataset Citation

The development of this Brahamaputra Forecast Portal has been supported by various data sources and research efforts. Below, we provide citations for the key datasets and references that were instrumental in its creation. We acknowledge and appreciate the contributions of these sources.

Kushwaha, A. P., Tiwari, A. D., Dangar, S., Shah, H., Mahto, S. S., & Mishra, V. (2021). Multimodel assessment of water budget in Indian sub-continental river basins. Journal of Hydrology, 603, 126977. https://doi.org/10.1016/j.jhydrol.2021.126977
Vegad, U., Pokhrel, Y., and Mishra, V.: Flood risk assessment for Indian sub-continental river basins, Hydrology and Earth System Sciences, 28, 1107–1126,2024. https://doi.org/10.5194/hess-28-1107-2024
Chuphal, D.S., Mishra, V. Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021. Sci Data 10, 717 (2023). https://doi.org/10.1038/s41597-023-02618-w
Nanditha, J. S., & Mishra, V. (2021). On the need of ensemble flood forecast in India. Water Security, 12, 100086. https://doi.org/10.1016/j.wasec.2021.100086

Disclaimer

The data provided is derived from simulations using a calibrated hydrological-hydrodynamic modeling framework. The values may vary from actual observations due to the model's scale and inherent biases.

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