Real - time remote sensing driven river basin modeling using radar 1 altimetry 2 3 Silvio

17 Remote sensing (RS) data are an alternative to in-situ hydrometeorological data in 18 remote and poorly monitored areas and are increasingly used in hydrological 19 modeling. This study presents a lumped, conceptual, river basin water balance 20 modeling approach based entirely on RS data: precipitation was obtained from the 21 Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 22 (TMPA), temperature from the European Centre for Medium-Range Weather Forecast 23 (ECMWF) global reanalysis dataset and evapotranspiration was derived from 24 temperature data. The Ensemble Kalman Filter was used to assimilate radar altimetry 25 2 (ERS2 and Envisat) measurements of reservoir water surface elevations. The 26 modeling approach is applied to the Syr Darya River Basin, a primarily snowmelt 27 driven basin with large topographical variability and scarce meteorological data that is 28 shared between 4 countries with conflicting water management interests. 29 Assimilation of radar altimetry data improved model results significantly. Without 30 data assimilation, model performance was limited, probably because of the size and 31 complexity of the model domain, simplifications inherent in model design, and the 32 uncertainty of RS data. Data assimilation reduced the mean of the reservoir water 33 surface level residuals from 19.5 to 4.5 meters, and model root mean square error 34 from 16.7 meters to 6.4 meters. 35 By providing an impartial source of information about the hydrological system that 36 can be updated in real time, the modeling approach described here could provide 37 useful hydrological forecast information that could be updated at time scales 38 appropriate for decision-making. The approach has potential to facilitate cooperation 39 in transboundary basins with conflicting management objectives. 40 41