Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products-Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)-were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.