Next Article in Journal
A Spliced Satellite Optical Camera Geometric Calibration Method Based on Inter-Chip Geometry Constraints
Previous Article in Journal
Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot
Article

Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling

by 1,2, 2,*, 3, 1 and 1
1
Institute of Heavy Rain, China Meteorological Administration (CMA), Wuhan 430205, China
2
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
3
NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Jahnebakken 5, NO-5007 Bergen, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Luca Brocca
Remote Sens. 2021, 13(14), 2831; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142831
Received: 2 June 2021 / Revised: 14 July 2021 / Accepted: 14 July 2021 / Published: 19 July 2021
The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets. View Full-Text
Keywords: global precipitation datasets (PPs); precipitation evaluation; hydrological modeling; PPs-specific calibration; bias correction global precipitation datasets (PPs); precipitation evaluation; hydrological modeling; PPs-specific calibration; bias correction
Show Figures

Figure 1

MDPI and ACS Style

Xiang, Y.; Chen, J.; Li, L.; Peng, T.; Yin, Z. Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling. Remote Sens. 2021, 13, 2831. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142831

AMA Style

Xiang Y, Chen J, Li L, Peng T, Yin Z. Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling. Remote Sensing. 2021; 13(14):2831. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142831

Chicago/Turabian Style

Xiang, Yiheng, Jie Chen, Lu Li, Tao Peng, and Zhiyuan Yin. 2021. "Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling" Remote Sensing 13, no. 14: 2831. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142831

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

Article Access Map by Country/Region

1
Back to TopTop