Next Article in Journal
A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives
Previous Article in Journal
Optical and Geometrical Properties of Cirrus Clouds over the Tibetan Plateau Measured by LiDAR and Radiosonde Sounding during the Summertime in 2014
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(3), 304; https://doi.org/10.3390/rs11030304

Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin

1
Nanjing Hydraulic Research Institute, Nanjing 210029, China
2
Ministry of Water Resources, Research Center for Climate Change, Nanjing 210029, China
3
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
4
Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Received: 13 January 2019 / Revised: 30 January 2019 / Accepted: 30 January 2019 / Published: 2 February 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Full-Text   |   PDF [2700 KB, uploaded 12 February 2019]   |  

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. View Full-Text
Keywords: SWAT model; AgMERRA; MSWEP; PERSIANN-CDR; TMPA; Uncertainty analysis; Mekong River Basin SWAT model; AgMERRA; MSWEP; PERSIANN-CDR; TMPA; Uncertainty analysis; Mekong River Basin
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Tang, X.; Zhang, J.; Gao, C.; Ruben, G.B.; Wang, G. Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin. Remote Sens. 2019, 11, 304.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top