Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China
"> Figure 1
<p>Elevation map of the Xinfengjiang River reservoir basin.</p> "> Figure 2
<p>The main flowchart of this research.</p> "> Figure 3
<p>Box plots of statistical accuracy indices ((<b>a</b>) CORR, (<b>b</b>) RMSE, (<b>c</b>) BIAS, (<b>d</b>) POD, (<b>e</b>) FAR, and (<b>f</b>) HSS) on a daily scale of the three IMERG products versus rain gauge data at the grid scale.</p> "> Figure 4
<p>Box plots of statistical accuracy indices ((<b>a</b>) CORR, (<b>b</b>) RMSE, (<b>c</b>) BIAS, (<b>d</b>) POD, (<b>e</b>) FAR, and (<b>f</b>) HSS) on a monthly scale of the three IMERG products versus rain gauge data at the grid scale.</p> "> Figure 5
<p>Comparison of observed and simulated hydrographs using daily rain gauge data during the calibration period and validation period in the XRRB (Scenario I).</p> "> Figure 6
<p>Comparison of observed and simulated hydrographs using monthly rain gauge data during the calibration period and validation period in the XRRB (Scenario I).</p> "> Figure 7
<p>Daily streamflow simulations based on the optimal parameter in Scenario I driven by. (<b>a</b>) STA, (<b>b</b>) ER, (<b>c</b>) LR, and (<b>d</b>) FR during the validation period (2014–2017).</p> "> Figure 8
<p>Scatter plots of daily simulated streamflow driven by (<b>a</b>) STA, (<b>b</b>) ER, (<b>c</b>) LR, and (<b>d</b>) FR against the corresponding observed streamflow (***: <span class="html-italic">p</span> < 0.001).</p> "> Figure 9
<p>Monthly streamflow simulations during the validation period based on the optimal parameter in Scenario I driven by the four precipitation datasets (STA, ER, LR, and FR).</p> "> Figure 10
<p>Scatter plots of monthly simulated streamflow driven by (<b>a</b>) STA, (<b>b</b>) ER, (<b>c</b>) LR, and (<b>d</b>) FR against the corresponding observed streamflow (***: <span class="html-italic">p</span> < 0.001).</p> "> Figure 11
<p>Model simulations during the calibration and validation periods at the daily scale (Scenario II).</p> "> Figure 12
<p>Model simulations during the calibration and validation periods at the monthly scale (Scenario II).</p> ">
Abstract
:1. Introduction
2. Study Area and Data Sets
2.1. Study Area
2.2. Data Set Description
2.2.1. Rain Gauge Data
2.2.2. GPM IMERG Products
2.2.3. Data for Hydrologic Model Construction and Implementation
- (1)
- Daily streamflow data (inflow runoff data) from 2000 to 2019 were obtained from the Xinfengjiang Reservoir Administration, and the location of the hydrological station is shown in Figure 1.
- (2)
- Other meteorological data forcing required in the hydrological simulation, including daily maximum and minimum temperatures, solar radiation, wind speed, and relative humidity, over the period of 2000 to 2019 is obtained from the China Meteorological Administration (http://data.cma.cn; accessed on 10 May 2020). These data are used to calculate evapotranspiration and related processes.
- (3)
- The soil types in the XRRB are derived from the soil map in the Harmonized World Soil Database (HWSD) (2009), with a resolution of 1 km (http://www.fao.org; accessed on 10 May 2020).
- (4)
- The land use/cover is collected from the Resource and Environment Science and Data Center (http://www.resdc.cn; accessed on 10 May 2020) and reclassified into four classes for the SWAT model, including forest, grassland, cultivated land, and water body.
3. Methodology
3.1. Technical Framework
3.2. Statistical Evaluation Indices
3.3. SWAT Model
4. Results
4.1. Comparison of IMERG Series Products with Rain Gauge Data
4.2. Hydrological Utility Evaluation of the IMERG Series Products in the Xinfengjiang River Reservoir Basin
4.2.1. Simulation Driven by Rain Gauge Data (Scenario I)
Rain Gauge Calibration and Validation
Simulation Driven by Multiple Precipitation Datasets with the Optimal Parameter Set from Scenario I
4.2.2. Simulation Driven by the IMERG FR Product (Scenario II)
5. Discussion
6. Conclusions
- (1)
- At the daily scale, the FR outperforms the ER and LR products and has better CORR values (0.61/0.71) than the ER (0.59/0.69) and LR (0.59/0.69) at both the grid accumulation scale and the basin scale. At the monthly scale, the FR also has a higher CORR value (0.94/0.99) than the ER (0.88/0.93) and LR (0.88/0.92) products at both the grid accumulation scale and the basin scale. The RMSEs of the FR (48.03/18.99 mm) are also lower than those of the ER (71.50/54.88 mm) and LR (73.13/58.34 mm) products. For the systematic error, the ER and LR products generally tend to underestimate the precipitation, with BIASs of −0.14 and −0.16, respectively, while the FR tends to overestimate the precipitation, with a relatively low BIAS of 0.01. In terms of detection indicators, at the grid accumulation scale, the POD of IMERG series products is above 0.8 at the daily and monthly scales, the FAR is below 0.37, and the HSS is above 0.56. The results at the average scale of the basin show that the detection effect of each product is also satisfactory.
- (2)
- For the hydrological evaluation, two experiments based on different parameter calibration scenarios are conducted over the XRRB. In Scenario I, the IMERG-based simulation shows acceptable hydrological prediction skill in terms of the NSE (0.25–0.39) and CORR (0.50–0.61) at the daily scale and performs fairly well at the monthly scale (an NSE of 0.69–0.72 and a CORR of 0.71–0.87), especially for the FR product.
- (3)
- For the hydrological evaluation in Scenario II, the hydrological simulation performance improved. The hydrological prediction skill of the FR product in the validation period is acceptable (CORR = 0.64, BIAS = 0.01, and NSE = 0.43), and it is significantly better at the monthly scale than the simulation results in Scenario I (CORR = 0.85, BIAS = −0.02, and NSE = 0.84).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Parameter Name | Description | Method | Daily-Best Value | Monthly-Best Value |
---|---|---|---|---|
OV_N | Manning’s “n” value for overland flow | Replace | 6.995 | 22.242466 |
ESCO | Soil evaporation compensation factor | Replace | 0.28764 | 0.144083 |
SOL_AWC(1) | Available water capacity of the soil layer | Relative | 0.3166 | –0.179 |
CN2 | SCS runoff curve number | Relative | 0.279132 | 0.121667 |
GWQMN | Treshold depth of water in the shallow aquifer required for return flow to occur (mm) | Replace | 3552.5 | 1363.644897 |
GW_REVAP | Groundwater “revap” coefficient | Replace | 0.19715 | 0.137424 |
GW_DELAY | Groundwater delay (days) | Replace | 388.6434 | 159.862595 |
ALPHA_BF | Baseflow alpha factor (days) | Replace | 0.561159 | 0.586 |
ALPHA_BNK | Baseflow alpha factor for bank storage | Replace | 0.504893 | 0.633746 |
SOL_K(1) | Saturated hydraulic conductivity | Relative | 0.323612 | 0.553253 |
REVAPMN | Threshold depth of water in the shallow aquifer for “revap” to occur (mm) | Replace | 1.19 | 143.035614 |
CH-K2 | Effective hydraulic conductivity in main channel alluvium | Replace | 21.667183 | 24.792658 |
Appendix B
Parameter Name | Description | Method | Daily-Best Value | Monthly-Best Value |
---|---|---|---|---|
OV_N | Manning’s “n” value for overland flow | Replace | 15.866 | 14.638 |
ESCO | Soil evaporation compensation factor | Replace | 0.104 | –0.301 |
SOL_AWC(1) | Available water capacity of the soil layer | Relative | –0.464 | 0.211 |
CN2 | SCS runoff curve number | Relative | 0.171 | 0.407 |
GWQMN | Treshold depth of water in the shallow aquifer required for return flow to occur (mm) | Replace | 4954.167 | 3077.062 |
GW_REVAP | Groundwater “revap” coefficient | Replace | 0.123 | 0.095 |
GW_DELAY | Groundwater delay (days) | Replace | 167.082 | 291.394 |
ALPHA_BF | Baseflow alpha factor (days) | Replace | 0.446 | 0.605 |
ALPHA_BNK | Baseflow alpha factor for bank storage | Replace | 0.977 | 0.467 |
SOL_K(1) | Saturated hydraulic conductivity | Relative | –0.030 | 0.763 |
REVAPMN | Threshold depth of water in the shallow aquifer for “revap” to occur (mm) | Replace | 295.667 | 498.000 |
CH-K2 | Effective hydraulic conductivity in main channel alluvium | Replace | 66.373 | 26.624 |
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Evaluation Indexes | Equation | Perfect Value |
---|---|---|
Correlation coefficient (CORR) | 1 | |
Relative bias (BIAS) | 0 | |
Root-mean-square error (RMSE) | 0 | |
Probability of detection (POD) | 1 | |
False alarm rate (FAR) | 0 | |
Heidke skill score (HSS) | 1 | |
Nash–Sutcliffe efficiency (NSE) | 1 |
Spatial Scale | Temporal Scales | Products | CORR | RMSE (mm) | BIAS | POD | FAR | HSS |
---|---|---|---|---|---|---|---|---|
Grid accumulation scale | Daily | ER | 0.59 *** | 11.78 | −0.14 | 0.80 | 0.37 | 0.56 |
LR | 0.59 *** | 11.77 | −0.16 | 0.81 | 0.37 | 0.57 | ||
FR | 0.61 *** | 12.16 | +0.01 | 0.81 | 0.37 | 0.57 | ||
Monthly | ER | 0.88 *** | 71.50 | −0.14 | 1.00 | 0.02 | 0.98 | |
LR | 0.88 *** | 73.13 | −0.16 | 1.00 | 0.02 | 0.98 | ||
FR | 0.94 *** | 48.03 | +0.01 | 1.00 | 0.02 | 0.98 | ||
Basin scale | Daily | ER | 0.69 *** | 9.04 | −0.14 | 0.83 | 0.22 | 0.67 |
LR | 0.69 *** | 9.10 | −0.16 | 0.83 | 0.22 | 0.67 | ||
FR | 0.71 *** | 9.56 | +0.01 | 0.84 | 0.22 | 0.67 | ||
Monthly | ER | 0.93 *** | 54.88 | −0.14 | 1.00 | 0.00 | 1.00 | |
LR | 0.92 *** | 58.34 | −0.16 | 1.00 | 0.00 | 1.00 | ||
FR | 0.99 *** | 18.99 | +0.01 | 1.00 | 0.00 | 1.00 |
(H, p-Value) | ER | LR | FR | ||
---|---|---|---|---|---|
Grid accumulation scale | Daily scale | ER | (S, <0.05) | (S, <0.05) | |
LR | (S, <0.05) | ||||
FR | |||||
Monthly scale | ER | (S, <0.05) | (S, <0.05) | ||
LR | (S, <0.05) | ||||
FR | |||||
Basin scale | Daily scale | ER | (S, <0.05) | (S, <0.05) | |
LR | (S, <0.05) | ||||
FR | |||||
Monthly scale | ER | (S, <0.05) | (S, <0.05) | ||
LR | (S, <0.05) | ||||
FR |
Indexes | Daily | Monthly | ||
---|---|---|---|---|
Calibration Period | Validation Period | Calibration Period | Validation Period | |
R2 | 0.91 *** | 0.85 *** | 0.96 *** | 0.90 *** |
NSE | 0.91 | 0.83 | 0.95 | 0.80 |
BIAS | +0.04 | +0.13 | +0.06 | +0.20 |
p-factor | 0.74 | 0.78 | 0.83 | 0.82 |
R-factor | 0.39 | 0.56 | 0.48 | 0.67 |
Indexes | Daily | Monthly | ||
---|---|---|---|---|
Calibration Period | Validation Period | Calibration Period | Validation Period | |
R2 | 0.65 *** | 0.64 *** | 0.85 *** | 0.85 *** |
NSE | 0.65 | 0.43 | 0.84 | 0.84 |
BIAS | −0.03 | +0.01 | +0.06 | −0.02 |
p-factor | 0.74 | 0.69 | 0.76 | 0.75 |
R-factor | 0.69 | 0.70 | 0.57 | 0.36 |
STA | ER | LR | FR | Average (ER, LR, and FR) | |
---|---|---|---|---|---|
Precipitation (mm) | 1938.9 | 1721.5 | 1681.3 | 1975.1 | 1792.6 |
Relative change rate | −0.11 | −0.13 | 0.02 | 0.09 * | |
Discharge (m3/s) | 221.6 | 208.6 | 203.1 | 245.9 | 219.2 |
Relative change rate | −0.06 | −0.08 | 0.11 | 0.08 * |
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Li, X.; Chen, Y.; Deng, X.; Zhang, Y.; Chen, L. Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China. Remote Sens. 2021, 13, 866. https://doi.org/10.3390/rs13050866
Li X, Chen Y, Deng X, Zhang Y, Chen L. Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China. Remote Sensing. 2021; 13(5):866. https://doi.org/10.3390/rs13050866
Chicago/Turabian StyleLi, Xue, Yangbo Chen, Xincui Deng, Yueyuan Zhang, and Lingfang Chen. 2021. "Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China" Remote Sensing 13, no. 5: 866. https://doi.org/10.3390/rs13050866