Salehi, Hossein and Sadeghi, Mojtaba and Golian, Saeed and Nguyen, Phu and Murphy, Conor and Sorooshian, Soroosh (2022) The Application of PERSIANN Family Datasets for Hydrological Modeling. Remote Sensing, 14 (3675). pp. 1-21. ISSN 2072-4292

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Abstract

This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System–Climate Data Record (CCS–CDR), a climatology dataset, and PERSIANN–Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS–CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, EKGE values (Kling–Gupta efficiency) for CCS–CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (EKGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS–CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.

Item Type:	Article
Additional Information:	The research was partially funded by the Center for Western Weather and Water Extremes (CW3E) at the Scripps Institution of Oceanography via AR Program Phase II grant 4600013361 sponsored by the California Department of Water Resources and NASA grant 80NSSC 21K1668.
Keywords:	PERSIANN family; precipitation; VIC hydrologic model; VIC; SMAP; GLEAM;
Academic Unit:	Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	17783
Identification Number:	https://doi.org/10.3390/rs14153675
Depositing User:	Corinne Voces
Date Deposited:	08 Nov 2023 11:28
Journal or Publication Title:	Remote Sensing
Publisher:	MDPI
Refereed:	Yes
URI:	https://www.mdpi.com/2072-4292/14/15/367...
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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