Abstract
Using hydrological simulation to evaluate the accuracy of satellite-based and reanalysis precipitation products always suffer from a large uncertainty. This study evaluates four widely used global precipitation products with high spatial and temporal resolutions [i.e., AgMERRA (AgMIP modern-Era Retrospective Analysis for Research and Applications), MSWEP (Multi-Source Weighted-Ensemble Precipitation), PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), and TMPA (Tropical Rainfall Measuring Mission 3B42 Version7)] against gauge observations with six statistical metrics over Mekong River Basin (MRB). Furthermore, the Soil and Water Assessment Tool (SWAT), a widely used semi-distributed hydrological model, is calibrated using different precipitation inputs. Both model performance and uncertainties of parameters and prediction have been quantified. The following findings were obtained: (1) The MSWEP and TMPA precipitation products have good accuracy with higher CC, POD, and lower ME and RMSE, and the AgMERRA precipitation estimates perform better than PERSIANN-CDR in this rank; and (2) out of the six different climate regions of MRB, all six metrics are worse than that in the whole MRB. The AgMERRA can better reproduce the occurrence and contributions at different precipitation densities, and the MSWEP has the best performance in Cwb, Cwa, Aw, and Am regions that belong to the low latitudes. (3) Daily streamflow predictions obtained using MSWEP precipitation estimates are better than those simulated by other three products in term of both the model performance and parameter uncertainties; and (4) although MSWEP better captures the precipitation at different intensities in different climatic regions, the performance can still be improved, especially in the regions with higher altitude.
Highlights
Hydrological models are vital tools for water resources management and allocation policy development, and to assess the impact of climate change and human activities on water yield [1,2,3]
Comparing CC, Root Mean Square Error (RMSE), Mean error (ME), Probability of detection (POD), False alarm ratio (FAR), and Critical success index (CSI) between daily gauge precipitation and AgMERRA, Multi-Source Weighted-Ensemble Precipitation (MSWEP), PERSIANN-CDR, and TMPA at the whole Mekong River Basin (MRB), it is shown that the agreement between MSWEP and the gauge observations is better than the remaining three products, which are mainly because MSWEP product was developed by taking full advantage of satellite-based and reanalysis precipitation data (e.g., CPC United and Global Precipitation Climatology Center (GPCC), CMORPH, GsMAP-MVK, TMPA, ERA-Interim, and JRA-55), many of these precipitation products were gauge-ajusted [27]
When looking to the parameter uncertainties for different precipitation inputs, we found that the ranges of 10 sensitive parameters of MSWEP are closer to the gauge precipitation data, we can carefully draw a conclusion that MSWEP has a good simulation effect when considering parameter uncertainty
Summary
Hydrological models are vital tools for water resources management and allocation policy development, and to assess the impact of climate change and human activities (e.g., land use change and dam construction) on water yield [1,2,3]. Unlike the lumped models, which treat the entire basin as a homogeneous, requiring input basin average precipitation, temperature, and evaporation, distributed hydrological models often divide the study area into multiple sub-basins based on soil, land use, and topographic data that can better reflect the spatial distribution of precipitation and water cycle in different sub-basins [6] Among these developed distributed models, SWAT (Soil Water Assessment Tool) has become the most widely used model in the hydrology department [7,8]. In the past few decades, different modules have been developed and coupled into SWAT to continuously improve this open sourced model, which have derived many different versions of model, and it has been successfully applied to the research of climate change [9], formulation of water resources management policies [10], and non-point source pollution [11] in various river basins around the world. It has been selected by Mekong River Commission as one of the simulation tools in their model library since 2010 [12]
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