A General Method to Improve Runoff Prediction in Ungauged Basins Based on Remotely Sensed Actual Evapotranspiration Data

Abstract

The availability of remotely sensed (RS) actual evapotranspiration (ET) provides a possibility for improving runoff prediction in ungauged basins. To develop a general practical method to improve runoff prediction by directly incorporating RS-ET into rainfall-runoff (RR) models, two modeling schemes are proposed: (i) using RS-ET as direct input; and (ii) using RS-ET as partial direct input. The principle is to use RS-ET in cases where the runoff prediction can be improved. The two schemes are compared in over 200 basins using three RR models (Xinanjiang model, SIMHYD, and GR4J) and RS-ET inverted from AVHRR, and the modeling results in ungauged basins are assessed using the spatial proximity method. Results show that: (i) it is beneficial to incorporate RS-ET into the Xinanjiang model for over 85% of the basins, but this is not the case for SIMHYD and GR4J models; (ii) further model improvements can be obtained by using RS-ET as partial direct input, and are achieved in 91.1%, 59.0%, and 53.2% of the basins for Xinanjiang, SIMHYD, and GR4J, respectively; and (iii) incorporation of RS-ET is more applicable for Xinanjiang while less so for GR4J, and the efficacy is superior for basins that are relatively arid and were originally poorly simulated. Overall, using RS-ET as partial direct input is recommended.

Keywords

• runoff prediction
• RS data
• evapotranspiration
• RR model
• MOPEX