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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Integrating remotely sensed surface water extent into continental scale hydrology
Beatriz Revilla-Romero1, Niko Wanders2, Peter Burek3
1European Commission, Joint Research Centre, Ispra, Italy; Utrecht University, Faculty of Geosciences, Utrecht, The Netherlands.
Journal of Hydrology
|January 24, 2017
Summary
This study shows that using satellite-derived surface water extent data can improve hydrological models for flood forecasting, especially in areas with limited ground data. Assimilating this data enhances streamflow simulations and peak flow predictions.
Area of Science:
- Hydrology and Remote Sensing
- Environmental Modeling
- Geospatial Analysis
Background:
- Accurate hydrological forecasting relies on data assimilation to correct model states with observational data.
- Limited ground streamflow data hinders large-scale flood forecasting models, particularly in ungauged regions.
- Remotely sensed surface water extent offers a potential data source to overcome these limitations.
Purpose of the Study:
- To assess the impact of assimilating daily remotely sensed surface water extent data into a global rainfall-runoff model.
- To evaluate the effectiveness of this data assimilation for hydrological simulations in Africa and South America.
- To determine the potential of satellite-derived surface water extent for improving flood forecasting.
Main Methods:
- Utilized daily remotely sensed surface water extent data (0.1° × 0.1° resolution) from the Global Flood Detection System (GFDS).
- Employed the Ensemble Kalman Filter (EnKF) for data assimilation, perturbing precipitation inputs to account for uncertainty in LISFLOOD model simulations.
- Compared assimilation results against baseline simulations and validated using over 100 in situ river gauges across Africa and South America.
Main Results:
- Assimilation of satellite-derived surface water extent improved streamflow simulations at 61 out of 101 validation stations, notably enhancing the timing and volume of flow peaks.
- Metrics such as Nash-Sutcliffe Efficiency (NSE) showed significant improvements.
- Poorest performance was observed in lowland jungle areas due to forest cover interference with satellite signal retrieval.
Conclusions:
- Remotely sensed surface water extent demonstrates significant potential for enhancing rainfall-runoff streamflow simulations.
- This approach can lead to more accurate forecasts of peak river flow, particularly beneficial for flood prediction.
- Further research may be needed to address challenges in specific environments like dense forests.
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