Assessment of the hydrological and coupled soft computing models, based on different satellite precipitation datasets, to simulate streamflow and sediment load in a mountainous catchment

Research output: Contribution to journalResearch articleContributedpeer-review


This study evaluated the performance and hydrologic utility of four different satellite precipitation datasets (SPDs), including GPM (IMERG_F), PERSIANN_CDR, CHIRPS, and CMORPH, to predict daily streamflow and SL using the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANNs), random forests (SWAT-RFs), and support vector regression (SWAT-SVR), in the mountainous Upper Jhelum River Basin (UJRB), Pakistan. SCMs were developed using the outputs of un-calibrated SWAT models to improve the predictions. Overall, the GPM shows the highest performance for the entire simulation with R2 and PBIAS varying from 0.71 to 0.96 and 13.1 to 0.01%, respectively. For the best GPM-based models, SWAT-RF showed a superior ability to simulate the entire streamflow with R2 of 0.96, compared with the SWAT-ANN (R2 = 0.90), SWAT-SVR (R2 = 0.87), and SWAT-CUP (R2 = 0.71). Similarly, SWAT-ANN presented the best performance capability to simulate the SL with an R2 of 0.71, compared with the SWAT-RF (R2 = 0.66), SWAT-SVR (R2 = 0.52), and SWAT-CUP (R2 = 0.42). Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating hydrological par-ameters, particularly in complex terrain where gauge network density is low or uneven.


Original languageEnglish
Article number610
Pages (from-to)610-632
Number of pages23
JournalJournal of Water and Climate Change
Issue number2
Publication statusPublished - 3 Feb 2023

External IDs

unpaywall 10.2166/wcc.2023.470
Scopus 85149426080
Mendeley 30f45a39-3ac3-31ce-a10a-b6dde3de2661
WOS 000925832300001


Research priority areas of TU Dresden

Subject groups, research areas, subject areas according to Destatis


  • SWAT, artificial neural networks, random forest, satellite precipitation products, support vector regression, Swat, Artificial neural networks, Satellite precipitation products, Support vector regression, Random forest