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

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 modelli...

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Autores principales:	Arman Abdollahipour, Hassan Ahmadi, Babak Aminnejad
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	daily streamflow neural network satellite rainfall product simulation the ghare ghieh river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/bd23627a34174fbfba0be812b434ab31
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spelling	oai:doaj.org-article:bd23627a34174fbfba0be812b434ab312021-11-09T23:59:26ZEvaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran2040-22442408-935410.2166/wcc.2020.050https://doaj.org/article/bd23627a34174fbfba0be812b434ab312021-11-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/7/3018https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In 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.;Arman AbdollahipourHassan AhmadiBabak AminnejadIWA Publishingarticledaily streamflowneural networksatellite rainfall productsimulationthe ghare ghieh river basinEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 7, Pp 3018-3044 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	daily streamflow neural network satellite rainfall product simulation the ghare ghieh river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	daily streamflow neural network satellite rainfall product simulation the ghare ghieh river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Arman Abdollahipour Hassan Ahmadi Babak Aminnejad Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
description	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.;
format	article
author	Arman Abdollahipour Hassan Ahmadi Babak Aminnejad
author_facet	Arman Abdollahipour Hassan Ahmadi Babak Aminnejad
author_sort	Arman Abdollahipour
title	Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
title_short	Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
title_full	Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
title_fullStr	Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
title_full_unstemmed	Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran
title_sort	evaluating the hydrological utility of satellite-based rainfall products using neural network models over the ghare ghieh river basin, iran
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/bd23627a34174fbfba0be812b434ab31
work_keys_str_mv	AT armanabdollahipour evaluatingthehydrologicalutilityofsatellitebasedrainfallproductsusingneuralnetworkmodelsovertheghareghiehriverbasiniran AT hassanahmadi evaluatingthehydrologicalutilityofsatellitebasedrainfallproductsusingneuralnetworkmodelsovertheghareghiehriverbasiniran AT babakaminnejad evaluatingthehydrologicalutilityofsatellitebasedrainfallproductsusingneuralnetworkmodelsovertheghareghiehriverbasiniran
_version_	1718440781110312960

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

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