Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling
Abstract
:1. Introduction
2. Datasets
2.1. Global Precipitation Datasets
2.2. Other Meteorological Datasets
2.3. Observed Streamflow
3. Methods
- (1)
- PPs evaluation was conducted by comparison with the rain gauge observations over the selected catchments. In addition, we applied a bias correction method to PPs and obtained “bias corrected-PPs” (BC-PPs), which were also conducted in the comparison.
- (2)
- The hydrological model calibration was firstly performed driven by rain gauge observations, and the calibrated parameter set was referred to as the ‘‘Reference Parameter-sets’’ (RP). The performance of hydrological model calibration served as a benchmark value for later hydrological modeling driving by the eight PPs.
- (3)
- The performances of hydrological modeling with the PPs were evaluated in the following three steps: in step 1, the reliability of PPs for hydrological modeling was investigated by running the model with RP in the calibration period; in step 2, a hydrological model was calibrated by each PPs, which was called PPs-specific calibration, and then their performances were compared with the benchmark value; in step 3, the BC-PPs were used to drive the hydrological model based on the RP in the calibration period and their performances were compared with the benchmark value.
3.1. XAJ Model
3.2. Bias Correction Method
- (1).
- The LOCI method was used to correct the precipitation occurrence, which ensured that the frequency of the precipitation occurrence estimated by PPs equaled to that of the observed data for a specific month.
- (2).
- The DT method was then used to correct the empirical distribution of PPs-estimated precipitation magnitudes in terms of 100 quantiles from 0.01 to 1 with an interval of 0.01.
3.3. Performance Evaluation Indices
- (1)
- The Pearson linear correlation coefficient (R) is used to assess the agreement between 3-day means of PPs and gauge-observed precipitation as follows:
- (2)
- The relative bias ratio (RB) is used to assess the systematic bias of precipitation estimates of PPs and it is also used to assess the systematic bias of the simulated discharge as follows:
- (3)
- The root mean square error (RMSE) is used to assess the difference between PPs and gauge-observed precipitation as follows:The RMSE ranges from 0 to and a smaller RMSE represents a better performance.
4. Results and Discussion
4.1. Evaluation of Precipitation Estimates
4.2. Evaluation of Hydrological Modeling
4.2.1. Benchmark Performance of Streamflow Simulation with Gauge-Observed Precipitation
4.2.2. Evaluation of Streamflow Simulations with Eight PPs
5. Conclusions
- (1)
- Compared with the gauge-observed precipitation, GPCC provides the best performance overall, followed by MSWEP V2.0, which is merged based on multiple satellite and reanalysis datasets.
- (2)
- Among all the PPs, MSWEP V2.0 and CMORPH BLD, which incorporate daily gauge data 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, due to the sparse rain-gauge networks and the highly non-linear rainfall-runoff response. Uncertainty exists in the regional performances of all the PPs.
- (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. The improvements in hydrological modeling performances are larger for the PPs with poor performances.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Type | Name (Details) | Category | Temporal/Spatial Resolution | Temporal Coverage | Reference or Link |
---|---|---|---|---|---|
Global Precipitation Datasets | GPCC (Global Precipitation Climatology Centre) | G | Daily/global 0.5° | 1982–2016 | Schneider, Fuchs [40] |
CHIRPS V2.0 (Climate-Hazards Group Infrared Precipitation V2.0) | S/R/G | Daily/50N-50S 0.25° | 1981–now | Peterson, Funk [41] | |
CMORPH BLD (Climate Prediction Center Morphing Technique, Gauge Blended dataset) | S/G | 30 min/global 0.25° | 1998–now | Joyce, Janowiak [42] | |
PERSIANN CDR (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks dataset, Climate Data Record) | S/G | Daily/60N-60S 0.25° | 2003–now | Ashouri, Hsu [43] | |
TMPA 3B42RT (Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT) | S/G | 3-hourly/50N-50S 0.25° | 1998–now | Huffman, Bolvin [33] | |
MSWEP V2.0 (Multi-Source Weighted-Ensemble Precipitation V2.0) | S/R/G | 3-hourly/global 0.25° | 1979–now | Beck, Van Dijk [19] | |
ERA5 (European Center for Medium-range Weather Forecast Reanalysis 5) | R | Hourly/global 0.5° | 1979–now | Hersbach, Bell [20] | |
WFDEI (WATCH Forcing Data (WFD) methodology applied to ERA-Interim Data) | R/G | 3-hourly/global 0.5° | 1979–2016 | Weedon, Balsamo [44] | |
Gauge-observed Precipitation, Temperature | CGRD/CGTD (China Ground Rainfall/temperature Daily Value 0.5°×0.5° Lattice Dataset) | — | Daily/0.5° | 1961–2015 | http://data.cma.cn, accessed on 16 July 2021 |
E-obs (European high-resolution gridded dataset) | — | Daily/0.5° | 1950–2017 | Haylock, Hofstra [45] | |
CANOPEX (Canadian model parameter experiment database); Santa Clara database | — | Daily/catchment averaged | — | Arsenault, Bazile [46]; Maurer, Wood [47] | |
Gridded Potential Evaporation Data | GLEAM (Global Land Evaporation Amsterdam Model) | — | Daily/global 0.5° | 1980–2018 | Martens, Miralles [48] |
Observed Streamflow | Streamflow-gauging stations in China | — | Daily/station | — | — |
GRDC (Global Runoff Data Centre) | — | Daily/station | — | http://grdc.bafg.de, accessed on 16 July 2021 | |
CANOPEX; USGS (United States Geological Survey database) | — | Daily/station | — | Arsenault, Bazile [46]; Falcone, Carlisle [49] |
GPCC | CHIRPSV2.0 | CMORPH BLD | MSWEPV2.0 | PERSIANNCDR | TMPA 3B42RT | ERA5 | WFDEI | |
---|---|---|---|---|---|---|---|---|
Step 1 | 0.58 | 0.50 | 0.59 | 0.63 | 0.35 | 0.38 | 0.50 | 0.44 |
Step 2 | 0.71 | 0.67 | 0.72 | 0.76 | 0.58 | 0.63 | 0.67 | 0.61 |
Step 3 | 0.62 | 0.56 | 0.61 | 0.65 | 0.47 | 0.53 | 0.59 | 0.49 |
Step 2’ | 0.13 | 0.17 | 0.13 | 0.12 | 0.24 | 0.25 | 0.17 | 0.17 |
Step 3’ | 0.04 | 0.05 | 0.03 | 0.02 | 0.12 | 0.15 | 0.09 | 0.06 |
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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://doi.org/10.3390/rs13142831
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://doi.org/10.3390/rs13142831
Chicago/Turabian StyleXiang, 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://doi.org/10.3390/rs13142831