Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China
<p>Maps of the DEM, river network, rain gauge distributions, and streamflow stations over the Beijiang and Dongjiang River Basins.</p> "> Figure 2
<p>Flowchart of Scenarios I–III used in this study. See <a href="#remotesensing-15-05349-t003" class="html-table">Table 3</a> for definitions of other scenarios.</p> "> Figure 3
<p>Characteristics of observed daily discharge during 2006–2018: (<b>a</b>) annual mean discharge and (<b>b</b>) flow duration curve. The dashed lines in Figure a represent the regression lines.</p> "> Figure 4
<p>Scatterplots and associated statistics of (<b>a</b>) 3B42V7, (<b>b</b>) 3B42RT, (<b>c</b>) IMERG-cal, (<b>d</b>) IMERG-uncal, (<b>e</b>) ERA5, (<b>f</b>) ERA-Interim, (<b>g</b>) GSMaP, (<b>h</b>) GSMaP-RNL, (<b>i</b>) PERSIANN-CCS, (<b>j</b>) average Rain 1, and (<b>k</b>) average Rain 2 derived from the basin-average precipitation for the training (calibration, blue dots) and testing (validation, red dots) periods over the Beijiang River Basin.</p> "> Figure 5
<p>Scatterplots and associated statistics of (<b>a</b>) 3B42V7, (<b>b</b>) 3B42RT, (<b>c</b>) IMERG-cal, (<b>d</b>) IMERG-uncal, (<b>e</b>) ERA5, (<b>f</b>) ERA-Interim, (<b>g</b>) GSMaP, (<b>h</b>) GSMaP-RNL, (<b>i</b>) PERSIANN-CCS, (<b>j</b>) average Rain 1, and (<b>k</b>) average Rain 2 derived from the basin-average precipitation for the training (calibration, blue dots) and testing (validation, red dots) periods over the Dongjiang River Basin.</p> "> Figure 6
<p>Comparison of sample proportions at different precipitation intensities for CGDPA and 11 precipitation products over the (<b>a</b>) Beijiang River and (<b>b</b>) Dongjiang River Basins.</p> "> Figure 7
<p>Spatial patterns of KGE′ values for multi-source precipitation datasets against CGDPA at a daily scale from 2006 to 2018 and 0.25° × 0.25° resolution for the two basins.</p> "> Figure 8
<p>Taylor diagrams of (<b>a</b>) calibration over the Beijiang River, (<b>b</b>) validation over the Beijiang River, (<b>c</b>) calibration over the Dongjiang River, and (<b>d</b>) validation over the Dongjiang River (i.e., the centered root-mean-square difference, correlation coefficient, and standard deviation) for the performance of multi-source precipitation products against CGDPA.</p> "> Figure 9
<p>Comparison of (<b>a</b>) modeled streamflow driven by CGDPA-calibrated parameters under Scenario I; (<b>b</b>–<b>j</b>) modeled streamflow with satellite, reanalysis, and two merged precipitation-recalibrated parameters under Scenario II; and (<b>k</b>,<b>l</b>) two merged streamflow series under Scenario III from 2006 to 2018 in the Beijiang River Basin.</p> "> Figure 10
<p>Q–Q plots for (<b>a</b>) modeled streamflow driven by CGDPA-calibrated parameters under Scenario I; (<b>b</b>–<b>j</b>) CREST-simulated streamflow with satellite, reanalysis, and two merged precipitation-recalibrated parameters under Scenario II; and (<b>k</b>,<b>l</b>) two merged streamflow series under Scenario III from 2006 to 2018 in the Beijiang River Basin.</p> "> Figure 11
<p>Comparison of (<b>a</b>) modeled streamflow driven by CGDPA-calibrated parameters under Scenario I; (<b>b</b>–<b>j</b>) modeled streamflow with satellite, reanalysis, and two merged precipitation-recalibrated parameters under Scenario II; and (<b>k</b>,<b>l</b>) two merged streamflow series under Scenario III from 2006 to 2018 in the Dongjiang River Basin.</p> "> Figure 12
<p>Q–Q plots for (<b>a</b>) modeled streamflow driven by CGDPA-calibrated parameters under Scenario I; (<b>b</b>–<b>j</b>) CREST-simulated streamflow with satellite, reanalysis, and two merged precipitation-recalibrated parameters under Scenario II; and (<b>k</b>,<b>l</b>) two merged streamflow series under Scenario III from 2006 to 2018 in the Dongjiang River Basin.</p> "> Figure 13
<p>Statistical metrics of (<b>a</b>) CC, (<b>b</b>) BIAS, (<b>c</b>) MAE, and (<b>d</b>) KGE’ of the daily CREST-simulated and two merged streamflow series versus the observations for five flood categories during 2006–2018 in the Beijiang River Basin.</p> "> Figure 14
<p>Statistical metrics of (<b>a</b>) CC, (<b>b</b>) BIAS, (<b>c</b>) MAE, and (<b>d</b>) KGE’ of the daily CREST-simulated and two merged streamflow series versus the observations for five flood categories during 2006–2018 in the Dongjiang River Basin.</p> "> Figure A1
<p>Core components of the CREST model (CREST User Manual v2.1.0): (<b>a</b>) Vertical profile of a cell including rainfall-runoff generation, evapotranspiration, sub-grid cell routing and feedbacks from routing; (<b>b</b>) variable infiltration curve of a cell; (<b>c</b>) plane view of cells and flow directions; and (<b>d</b>) vertical profile along several cells including sub-grid cell routing, downstream routing, and subsurface runoff redistribution from a cell to its downstream cells.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Availability
2.2.1. Rainfall and Discharge Observations
2.2.2. Satellite and Reanalysis Precipitation Products
2.2.3. Topography and Potential Evapotranspiration
2.3. Methodology
2.3.1. Distributed Hydrological Model
2.3.2. Evaluation Indicators
2.3.3. Hydrological Simulation Scenarios
- Scenario I (calibrated by gauge-observed products)
- 2.
- Scenario II (recalibrated by satellite-based/reanalysis products)
- 3.
- Scenario III (manually controlled ensemble simulation)
3. Results and Discussion
3.1. Hydrometeorological Characteristics
3.2. Comparison of Multi-Satellite and Reanalysis Precipitation Inputs
3.3. Streamflow Simulation Scenarios and Results
3.3.1. Scenario I: Hydrological Model Calibrated by Gauge Observations
- Simulated streamflow forced by CGDPA
Period | Precipitation Input | NSCE | BIAS (%) | CC | RMSE (m3/s) |
---|---|---|---|---|---|
Calibration | CGDPA | 0.89 | −10.47 | 0.95 | 546.91 |
Validation | CGDPA | 0.82 | −9.37 | 0.91 | 662.95 |
3B42V7 | 0.71 | −8.97 | 0.86 | 835.39 | |
3B42RT | 0.61 | 1.69 | 0.84 | 972.68 | |
IMERG-cal | 0.73 | −11.41 | 0.86 | 810.01 | |
IMERG-uncal | 0.63 | −21.94 | 0.81 | 951.71 | |
ERA5 | 0.66 | −5.32 | 0.82 | 902.55 | |
ERA-Interim | 0.45 | 0.85 | 0.70 | 1154.42 | |
GSMaP | 0.78 | −13.23 | 0.89 | 724.98 | |
GSMaP-RNL | 0.40 | −50.94 | 0.77 | 1200.90 | |
PERSIANN-CCS | −0.30 | −83.84 | 0.57 | 1769.10 | |
Average Rain 1 | 0.77 | −12.33 | 0.89 | 746.78 | |
Average Rain 2 | 0.71 | −12.75 | 0.86 | 830.60 |
- 2.
- Streamflow simulations forced by multi-source precipitation
3.3.2. Scenario II: Hydrological Model Calibrated by Different Precipitation Estimates
3.3.3. Scenario III: Comparison of Manually Controlled Ensemble Simulation for the Two River Basins
3.4. Analysis of Merge Data at Five Flood Levels
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
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Dataset | Full Name of the Dataset | Resolution | Period | Reference |
---|---|---|---|---|
Satellite precipitation dataset | ||||
IMERG-cal | Integrated Multi-satellitE Retrievals for GPM Final run V06B | 0.1°/0.5 h | 2006–present | [19] |
IMERG-uncal | Integrated Multi-satellitE Retrievals for GPM Final run | 0.1°/0.5 h | 2006–present | |
TMPA-3B42V7 | TRMM (TMPA) Multi-satellite Precipitation Analysis 3B42V7 | 0.25°/3 h | 1998–present | [18] |
TMPA-3B42RT | TRMM (TMPA) Near Real-Time Precipitation 3B42RT | 0.25°/3 h | 2000–present | |
PERSIANN-CCS | Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System | 0.04°/0.5 h | 2000–present | [16] |
GSMaP | Gauge-adjusted Global Satellite Mapping of Precipitation V6/V7 | 0.1°/1 h | 2000–present | [55] |
GSMaP-RNL | Global Satellite Mapping of Precipitation Project | 0.1°/1 h | 2000–present | [17] |
Reanalysis precipitation dataset | ||||
ERA5 | Fifth generation of ECMWF atmospheric reanalyses of the global climate | ~0.25°/1 h | 1979–present | [56] |
ERA-Interim | ECMWF ReAnalysis Interim | ~0.75°/3 h | 1979–present | [57] |
Merged precipitation product | ||||
Average Rain 1 | The arithmetic mean of 3B42V7, IMERG-cal, and GSMaP | 0.083°/1 d | 2006–2018 | |
Average Rain 2 | The arithmetic mean of 3B42RT, IMERG-uncal, and ERA5 | 0.083°/1 d | 2006–2018 |
Statistical Indicators | Unit | Equation | Perfect Value |
---|---|---|---|
Correlation coefficient (CC) | NA | 1 | |
Relative bias (BIAS) | % | 0 | |
Mean absolute error (MAE) | mm/d | 0 | |
Root mean squared error (RMSE) | mm/d | 0 | |
Probability of detection (POD) | NA | 1 | |
False alarm ratio (FAR) | NA | 0 | |
Critical success index (CSI) | NA | 1 | |
Kling–Gupta efficiency (KGE′) NA | 1 | ||
Nash–Sutcliffe coefficient efficiency (NSCE) | NA | 1 |
Scenario | Period | Time | Precipitation | Parameters |
---|---|---|---|---|
I | Calibration | 2006–2010 | Gauge-observed | Para1 |
Validation | 2011–2018 | Gauge-observed | Para1 | |
7 Satellite | ||||
2 Reanalysis | ||||
2 Merged | ||||
II | Calibration | 2006–2010 | TMPA-3B42V7 | Para2 |
TMPA-3B42RT | Para3 | |||
IMERG-cal | Para4 | |||
IMERG-uncal | Para5 | |||
GSMaP | Para6 | |||
ERA5 | Para7 | |||
ERA-Interim | Para8 | |||
Average Rain 1 | Para9 | |||
Average Rain 2 | Para10 | |||
Validation | 2011–2018 | TMPA-3B42V7 | Para2 | |
TMPA-3B42RT | Para3 | |||
IMERG-cal | Para4 | |||
IMERG-uncal | Para5 | |||
GSMaP | Para6 | |||
ERA5 | Para7 | |||
ERA-Interim | Para8 | |||
Average Rain 1 | Para9 | |||
Average Rain 2 | Para10 |
Period | Precipitation Input | NSCE | BIAS (%) | CC | RMSE (m3/s) |
---|---|---|---|---|---|
Calibration | CGDPA | 0.87 | −2.99 | 0.93 | 305.77 |
Validation | CGDPA | 0.62 | −9.76 | 0.81 | 379.28 |
3B42V7 | 0.65 | −8.46 | 0.83 | 366.67 | |
3B42RT | 0.52 | 12.49 | 0.81 | 427.78 | |
IMERG-cal | 0.69 | −5.53 | 0.86 | 340.65 | |
IMERG-uncal | 0.59 | −19.45 | 0.81 | 392.91 | |
ERA5 | 0.52 | −29.33 | 0.80 | 427.95 | |
ERA-Interim | 0.09 | −47.81 | 0.64 | 588.57 | |
GSMaP | 0.60 | −30.31 | 0.85 | 393.08 | |
GSMaP-RNL | 0.38 | −40.29 | 0.78 | 483.95 | |
PERSIANN-CCS | −0.66 | −82.09 | 0.59 | 793.82 | |
Average Rain 1 | 0.69 | −18.46 | 0.86 | 342.38 | |
Average Rain 2 | 0.68 | −18.76 | 0.86 | 347.07 |
Period | Precipitation Input | NSCE | BIAS (%) | CC | RMSE (m3/s) |
---|---|---|---|---|---|
Calibration | 3B42V7 | 0.84 | −9.59 | 0.92 | 637.68 |
3B42RT | 0.78 | −13.26 | 0.89 | 753.08 | |
IMERG-cal | 0.84 | −10.47 | 0.92 | 651.98 | |
IMERG-uncal | 0.75 | −17.31 | 0.88 | 811.29 | |
ERA5 | 0.79 | −7.92 | 0.89 | 735.02 | |
ERA-Interim | 0.70 | −6.19 | 0.84 | 889.43 | |
GSMaP | 0.87 | −10.01 | 0.94 | 574.55 | |
Average Rain 1 | 0.87 | −10.31 | 0.94 | 587.44 | |
Average Rain 2 | 0.85 | −14.56 | 0.93 | 628.41 | |
Validation | 3B42V7 | 0.73 | −8.17 | 0.86 | 813.05 |
3B42RT | 0.67 | −11.57 | 0.84 | 895.53 | |
IMERG-cal | 0.73 | −9.62 | 0.86 | 802.20 | |
IMERG-uncal | 0.64 | −12.35 | 0.81 | 936.56 | |
ERA5 | 0.67 | −5.90 | 0.83 | 897.44 | |
ERA-Interim | 0.22 | 33.68 | 0.71 | 1372.53 | |
GSMaP | 0.78 | −10.97 | 0.89 | 722.47 | |
Average Rain 1 | 0.78 | −9.61 | 0.89 | 735.70 | |
Average Rain 2 | 0.73 | −15.29 | 0.87 | 809.53 |
Period | Precipitation Input | NSCE | BIAS (%) | CC | RMSE (m3/s) |
---|---|---|---|---|---|
Calibration | 3B42V7 | 0.78 | −3.58 | 0.89 | 389.41 |
3B42RT | 0.76 | −4.98 | 0.87 | 414.30 | |
IMERG-cal | 0.82 | −4.74 | 0.91 | 353.33 | |
IMERG-uncal | 0.76 | −7.60 | 0.88 | 411.61 | |
ERA5 | 0.74 | −3.33 | 0.86 | 431.49 | |
ERA-Interim | 0.65 | −11.02 | 0.82 | 494.52 | |
GSMaP | 0.83 | −1.42 | 0.91 | 346.75 | |
Average Rain 1 | 0.82 | −9.93 | 0.91 | 352.03 | |
Average Rain 2 | 0.82 | −6.18 | 0.91 | 352.15 | |
Validation | 3B42V7 | 0.67 | −5.30 | 0.83 | 354.25 |
3B42RT | 0.64 | −6.52 | 0.82 | 367.73 | |
IMERG-cal | 0.71 | −9.84 | 0.86 | 332.75 | |
IMERG-uncal | 0.61 | −18.44 | 0.81 | 387.06 | |
ERA5 | 0.63 | −8.76 | 0.80 | 372.93 | |
ERA-Interim | 0.16 | 19.93 | 0.67 | 565.66 | |
GSMaP | 0.70 | −2.00 | 0.85 | 335.17 | |
Average Rain 1 | 0.71 | −13.04 | 0.86 | 334.78 | |
Average Rain 2 | 0.69 | −13.62 | 0.85 | 341.06 |
Watershed | Streamflow Simulation | Period | Merged Streamflow | NSCE | BIAS (%) | CC | RMSE (m3/s) |
---|---|---|---|---|---|---|---|
Beijiang | Under Scenario I | Validation | Average Streamflow 01 | 0.77 | −11.20 | 0.89 | 739.01 |
Validation | Average Streamflow 02 | 0.74 | −8.52 | 0.87 | 793.20 | ||
Under Scenario II | Calibration | Average Streamflow 1 | 0.87 | −10.02 | 0.94 | 574.63 | |
Validation | Average Streamflow 1 | 0.78 | −9.59 | 0.89 | 729.99 | ||
Calibration | Average Streamflow 2 | 0.85 | −12.83 | 0.92 | 635.31 | ||
Validation | Average Streamflow 2 | 0.74 | −9.94 | 0.86 | 799.77 | ||
Dongjiang | Under Scenario I | Validation | Average Streamflow 01 | 0.71 | −14.77 | 0.86 | 333.71 |
Validation | Average Streamflow 02 | 0.70 | −12.10 | 0.85 | 335.84 | ||
Under Scenario II | Calibration | Average Streamflow 1 | 0.83 | −3.25 | 0.91 | 343.56 | |
Validation | merged Streamflow 1 | 0.73 | −5.71 | 0.86 | 320.07 | ||
Calibration | Average Streamflow 2 | 0.82 | −5.30 | 0.91 | 358.63 | ||
Validation | Average Streamflow 2 | 0.71 | −11.24 | 0.85 | 334.92 |
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Gao, Z.; Tang, G.; Jing, W.; Hou, Z.; Yang, J.; Sun, J. Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China. Remote Sens. 2023, 15, 5349. https://doi.org/10.3390/rs15225349
Gao Z, Tang G, Jing W, Hou Z, Yang J, Sun J. Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China. Remote Sensing. 2023; 15(22):5349. https://doi.org/10.3390/rs15225349
Chicago/Turabian StyleGao, Zhen, Guoqiang Tang, Wenlong Jing, Zhiwei Hou, Ji Yang, and Jia Sun. 2023. "Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China" Remote Sensing 15, no. 22: 5349. https://doi.org/10.3390/rs15225349