Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/3419
Title: Product- and Hydro-Validation of Satellite-Based Precipitation Data Sets for a Poorly Gauged Snow-Fed Basin in Turkey
Authors: Uysal, Gökçe
Keywords: neural network model
rainfall-runoff application
satellite precipitation
snowmelt
upper Euphrates basin
water resources
Catchments
Clock and data recovery circuits (CDR circuits)
Rain
Runoff
Satellites
Water management
Multilayers perceptrons
Neural network model
PERSIANN
Product validation
Rainfall runoff
Rainfall-runoff application
Satellite precipitation
Snow melt
Upper euphrates basins
Waters resources
Neural network models
precipitation (climatology)
rainfall-runoff modeling
snowmelt
water resource
Euphrates Basin
Turkey
Issue Date: 2022
Publisher: MDPI
Abstract: Satellite-based Precipitation (SBP) products are receiving growing attention, and their utilization in hydrological applications is essential for better water resource management. However, their assessment is still lacking for data-sparse mountainous regions. This study reveals the performances of four available PERSIANN family products of low resolution near real-time (PERSIANN), low resolution bias-corrected (PERSIANN-CDR), and high resolution real-time (PERSIANN-CCS and PERSIANN-PDIR-Now). The study aims to apply Product-Validation Experiments (PVEs) and Hydro-Validation Experiments (HVEs) in a mountainous test catchment of the upper Euphrates Basin. The PVEs are conducted on different temporal scales (annual, monthly, and daily) within four seasonal time periods from 2003 to 2015. HVEs are accomplished via a multi-layer perceptron (MLP)-based rainfall-runoff model. The Gauge-based Precipitation (GBP) and SBP are trained and tested to simulate daily streamflows for the periods of 2003–2008 and 2009–2011 water years, respectively. PVEs indicate that PERSIANN-PDIR-Now comprises the least mean annual bias, and PERSIANN-CDR gives the highest monthly correlation with the GBP data. According to daily HVEs, MLP provides a compromising alternative for biased data sets; all SBP models show reasonably high Nash–Sutcliffe Efficiency for the training (above 0.80) and testing (0.62) periods, while the PERSIANN-CDR-based MLP (0.88 and 0.79) gives the highest performance. © 2022 by the author.
URI: https://doi.org/10.3390/w14172758
https://hdl.handle.net/20.500.13087/3419
ISSN: 2073-4441
Appears in Collections:İnşaat Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu

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