Abstract
The availability of precipitation data is the key driver in the application of hydrological models when simulating streamflow. Ground weather stations are regularly used to measure precipitation. However, spatial coverage is often limited in low-population areas and mountain areas. To overcome this limitation, gridded datasets from remote sensing have been widely used. This study evaluates four widely used global precipitation datasets (GPDs): The Tropical Rainfall Measuring Mission (TRMM) 3B43, the Climate Forecast System Reanalysis (CFSR), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), against point gauge and gridded dataset observations using multiple monthly water balance models (MWBMs) in four different meso-scale basins that cover the main climatic zones of Peninsular Spain. The volumes of precipitation obtained from the GPDs tend to be smaller than those from the gauged data. Results underscore the superiority of the national gridded dataset, although the TRMM provides satisfactory results in simulating streamflow, reaching similar Nash-Sutcliffe values, between 0.70 and 0.95, and an average total volume error of 12% when using the GR2M model. The performance of GPDs highly depends on the climate, so that the more humid the watershed is, the better results can be achieved. The procedures used can be applied in regions with similar case studies to more accurately assess the resources within a system in which there is scarcity of recorded data available.
Highlights
Precipitation is one of the most important drivers for hydrological modelling because it has a strong impact on the accuracy of hydrological models [1]
The second phase of the research assessed the performance of the four global precipitation datasets (GPDs) and the two rain gauges’ banks of data from the AEMET as input into the monthly water balance models (MWBMs) considered for streamflow simulation in the four studied watersheds
Tropical Rainfall Measuring Mission (TRMM) showed the best performance in three out of the four watersheds among the GPDs studied, because RVA does not exceed 0.33 NSE with Multi-Source Weighted-Ensemble Precipitation (MSWEP), and the lowest REV is over 60%, the rest of the GPDs showed worse errors
Summary
Precipitation is one of the most important drivers for hydrological modelling because it has a strong impact on the accuracy of hydrological models [1]. Intensity, and distribution of precipitation are clearly linked to various processes in the hydrological cycle, this relation is nonlinear. The accurate assessment of precipitation is of the utmost importance for hydrological modelling, as it provides meteorological input for hydrological studies. Reliable and accurate precipitation information at sufficient spatial and temporal resolution is essential for the study of climate trends, and for water resource management [2]. There can be important deviations between point-scale gauge information and true areal precipitation [4,5,6,7]; the use of a grid dataset rather than a single rain gauge is advisable
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