Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran

Abstract

In recent years, gridded precipitation data derived from satellite rainfall products have become critical data sources for hydrological applications, especially in ungauged basins where rain gauges are sparse or nonexistent. Also, in streamflow simulations, since the existing rainfall–runoff modelling methods require exogenous input with some assumptions, neural networks can be an efficient solution. In this paper, to simulate daily streamflow on the Ghare Ghieh River basin in northwestern Iran, the Levenberg–Marquardt Neural Network (LMNN) and the Particle Swarm Optimization Neural Network (PSONN) models are proposed. These models are trained and tested with different input patterns from ground-based data for water years of 1988–2008. Then, three satellite-based precipitation datasets, including TRMM-3B42V7, TRMM-3B42RT, and PERSIANN with 0.25° × 0.25° resolutions from 2003 to 2008, are used as inputs for the best-trained models which were selected in the testing step. These products are evaluated before and after calibration in streamflow simulation, and the Geographical Difference Analysis method is used to calibrate them. The results showed that the PSONN model performed better than the LMNN model. Also, in both models, before calibration of satellite precipitation products, TRMM-3B42 showed better performance in streamflow simulation, and after calibration, TRMM-3B42RT performed much better. HIGHLIGHTS Three commonly used satellite rainfall products including TRMM-3B42V7, TRMM-3B42RT, and PERSIANN are hydrologically evaluated, before and after calibration, over the Ghare Ghieh River basin.; The hybrid neural network model named PSONN is proposed for simulation of streamflow and it is compared to the basic ANN model, named LMNN.; In general, the PSONN model performed better than the LMNN model.; In both models, before calibration of satellite precipitation products, TRMM-3B42 showed better performance in streamflow simulation and after calibration using the GDA method, 3B42RT performed much better.; After calibration, in simulation with PSONN and use of TRMM-3B42, 3B42RT, and PERSIANN, the Rbias index decreased by 48, 72, and 73%, respectively.;

Keywords

• daily streamflow
• neural network
• satellite rainfall product
• simulation
• the ghare ghieh river basin