Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran
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Indian Academy of Sciences (0123456789().,-volV)(0123456789( ).,-volV)
Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran M KHOSHCHEHREH1,* , M GHOMESHI1 and A SHAHBAZI2 1
Department of Water and Hydraulic Structures, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 2 Department of Hydrology and Water Resources, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. *Corresponding author. e-mail: [email protected] MS received 27 July 2019; revised 2 June 2020; accepted 10 June 2020
Accurate estimation of the precipitation characteristics, including the value, temporal pattern, and spatial distribution, plays a significant role in the input uncertainty reduction for rainfall-runoA models. In many basins, the improper spatial distribution of rain gauge stations or their limited historical recorded data causes many challenges, especially in heterogeneous catchments which due to the impact of the drastic geographical alterations on the rainfall distribution pattern, the cover of the ground stations cannot estimate the actual precipitation rate. This challenge can be potentially solved by adopting rainfall products as alternative or complementary data sources. In this research, three rainfall products (PERSIANN-CCS, CMORPH and ERA-Interim), were compared against rain gauge stations for calibration of a daily conceptual lumped rainfall-runoA model (CRFM) in a data-scarce and heterogeneous basin located in southwestern Iran. The results indicated that ERA-Interim has the best performance among other datasets. Better performance of this dataset compared to the in-situ data also suggests a better estimation of the basin average as well as the temporal pattern of precipitation. The KGE value was obtained as 0.8 and 0.74, respectively, for a rainfall-runoA model that utilized the ERA-Interim as input in the calibration and validation periods. The results showed that the performance of satellite-based data of CMORPH and PERSIANN-CCS is not acceptable in simulating the daily Cow. Also, the seasonal assessment showed that ERA-Interim has a better performance compared to other datasets, during fall and winter. However, in the spring, the performance of all datasets significantly reduces, and the range of BIAS variation increases. Generally, all datasets were shown to perform better in simulating the Cow in terms of the transition from dry to wet periods, rather than wet to dry periods. Keywords. Rainfall-runoA; gridded precipitation; satellite-based precipitation; CMORPH; PERSIANNCCS; ERA-Interim.
1. Introduction Flow simulation in basins is always considered as an important challenge. On the other hand, the proper performance of different hydrological
models depends on the quality and accuracy of the input data. Precipitation data are the main input for hydrological models. In this regard, the precipitation data with a high spatiotemporal resolution increases the output accur
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