Real-time remote sensing driven river basin modeling using radar altimetry
- 1Department of Environmental Engineering, Technical University of Denmark, Miljøvej, Building 113, 2800 Kgs, Lyngby, Denmark
- 2Earth and Planetary Remote Sensing Laboratory, De Montfort University, The Gateway, Leicester, LE19BH, UK
- 3Hydrological Forecasting Department – UzHydromet, Tashkent, Uzbekistan
- 4The Water Center, The Earth Institute, Columbia University, New York, USA
Abstract. Many river basins have a weak in-situ hydrometeorological monitoring infrastructure. However, water resources practitioners depend on reliable hydrological models for management purposes. Remote sensing (RS) data have been recognized as an alternative to in-situ hydrometeorological data in remote and poorly monitored areas and are increasingly used to force, calibrate, and update hydrological models.
In this study, we evaluate the potential of informing a river basin model with real-time radar altimetry measurements over reservoirs. We present a lumped, conceptual, river basin water balance modeling approach based entirely on RS and reanalysis data: precipitation was obtained from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), temperature from the European Centre for Medium-Range Weather Forecast's (ECMWF) Operational Surface Analysis dataset and reference evapotranspiration was derived from temperature data. The Ensemble Kalman Filter was used to assimilate radar altimetry (ERS2 and Envisat) measurements of reservoir water levels. The modeling approach was applied to the Syr Darya River Basin, a snowmelt-dominated basin with large topographical variability, several large reservoirs and scarce hydrometeorological data that is located in Central Asia and shared between 4 countries with conflicting water management interests.
The modeling approach was tested over a historical period for which in-situ reservoir water levels were available. Assimilation of radar altimetry data significantly improved the performance of the hydrological model. Without assimilation of radar altimetry data, model performance was limited, probably because of the size and complexity of the model domain, simplifications inherent in model design, and the uncertainty of RS and reanalysis data. Altimetry data assimilation reduced the mean absolute error of the simulated reservoir water levels from 4.7 to 1.9 m, and overall model RMSE from 10.3 m to 6.7 m. Model performance was variable for the different reservoirs in the system. The RMSE ranged from 10% to 76% of the mean seasonal reservoir level variation.
Because of its easy accessibility and immediate availability, radar altimetry lends itself to being used in real-time hydrological applications. As an impartial source of information about the hydrological system that can be updated in real time, the modeling approach described here can provide useful medium-term hydrological forecasts to be used in water resources management.