Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China
"> Figure 1
<p>Location of the Wan’an basin, and distribution of rain stations, stream network, and the DEM in this basin.</p> "> Figure 2
<p>Schematic framework of this study.</p> "> Figure 3
<p>Scatters of forecasted IMERG and gauge rainfall vs. the raw gauge rainfall at each lead time of (<b>a</b>) 6 h, (<b>b</b>) 12 h, (<b>c</b>) 24 h, (<b>d</b>) 48 h, and (<b>e</b>) 72 h and (<b>f</b>) 0 h (the “Raw” data).</p> "> Figure 4
<p>Probability density function (PDF) of 6-hourly precipitation with different intensities, as derived from the raw and the forecasted IMERG and gauge precipitation in 31 storm events during 2014 and 2019 over the Wan’an basin.</p> "> Figure 5
<p>Cumulative distribution function (CDF) of 6-hourly precipitation as derived from the raw and the forecasted (<b>a</b>) gauge and (<b>b</b>) IMERG precipitation in 31 storm events during 2014 and 2019 over the Wan’an basin. Note: the “Raw” represents the raw gauge rainfall and IMERG data.</p> "> Figure 6
<p>Change in (<b>a</b>) <span class="html-italic">r</span>, (<b>b</b>) <span class="html-italic">NSE</span>, (<b>c</b>) <span class="html-italic">KGE,</span> and (<b>d</b>) <span class="html-italic">RMSE</span> indices of GR4H-forecasted flood <span class="html-italic">Q</span> with IMERG and gauge rainfall input over 31 storm-flood events occurring in the Wan’an basin during the period 2014–2019. The box and whisker plot (i.e., box plot) used here displays the five-number summary of a set of gauge rainfall-driven (in yellow) and IMERG satellite rainfall-driven (in purple) flood modeling series. The five-number summary is the minimum, first quartile, median, third quartile, and maximum of modeled flood data. The box from the first quartile to the third quartile is plotted and a vertical line goes through the box at the median.</p> "> Figure 7
<p>CDF curves of flood derived by rainfall–runoff model GR4H driven by the raw and forecasted (<b>a</b>) gauge and (<b>b</b>) IMERG rainfall over the Wan’an basin. Note: “Obs Q” and “Raw” denote CDF curves with the actual flood data and the simulated flood driven by the raw IMERG and gauge rainfall, respectively.</p> "> Figure 8
<p>Reliability dynamics with the varying scale ratio for (<b>a</b>) the gauge rainfall-based and (<b>b</b>) the IMERG-based flood inflow forecast (the scale ratio represents a factor that controls the change of reservoir states about capacity, maximum depth, and maximum surface).</p> "> Figure 9
<p>Resilience dynamics with the varying scale ratios for (<b>a</b>) the gauge rainfall-based and (<b>b</b>) the IMERG-based flood inflow forecast.</p> "> Figure 10
<p>Vulnerability dynamics with the varying scale ratio for (<b>a</b>) the gauge rainfall-based and (<b>b</b>) the IMERG-based flood inflow forecast.</p> "> Figure 11
<p>Flood risk ratio (>95 m) with the varying scale ratio for (<b>a</b>) the gauge rainfall-based and (<b>b</b>) the IMERG-based flood inflow forecast.</p> "> Figure 12
<p>Flood risk (>96 m) with the varying scale ratio states for (<b>a</b>) the gauge rainfall-based and (<b>b</b>) the IMERG-based flood inflow forecast.</p> ">
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area and Reservoir
2.2. Hydro-Meteorological Data
3. Methodology
3.1. Storm Stochastic Generator
3.2. Flood Forecasting Based on GR4H Model
3.3. Reservoir System Construction
3.3.1. Reservoir Optimal Operation Model
3.3.2. Robustness Criteria of Reservoir Operation Assessment
4. Results
4.1. Forecasted IMERG Heavy Rainfall at Sub-Daily and Daily Lead Times
4.2. Event-Based Flood Forecast Analysis
4.3. Flood Inflow Forecast-Informed Reservoir Optimal Operation
4.3.1. Analysis of rrv Indices
4.3.2. Analysis of Flood Risk Ratio Indices
5. Discussion
6. Conclusions
- (1)
- The flood forecast with GR4H forced with IMERG shows slightly lower accuracy than that driven by the gauge rainfall of the Wan’an basin with the median r, NSE, KGE, and RMSE values ranging from 0.86–0.91, 0.67–0.75, 0.68–0.73, and 0.29 mm–0.33 mm for IMERG, respectively, and 0.88–0.91, 0.72–0.74, 0.64–0.77, and 0.26 mm–0.32 mm for gauge measured rainfall, respectively, across varying lead times.
- (2)
- For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The rrv values are more sensitive to IMERG-related rainfall and inflow forecast uncertainty for the smaller reservoir scale.
- (3)
- The pattern of increasing forecast error in rainfall with the lead time increasing is changed in the resultant inflow forecast series and dynamics of four risk-based robustness indices of optimal operation decision, due to the rainfall–runoff model and reservoir operation system partly compensating the original heavy rainfall forecast errors in IMERG and gauge data.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Flood Inflow (Mm3) | Storage (Mm3) | Controlled Release (Mm3) | Regulated Water Level (m) | |||||
---|---|---|---|---|---|---|---|---|
Mean | Range | Mean | Range | Mean | Range | Mean | Range | |
Obs Q | 66.14 | (4.97, 289.44) | 542.79 | (73.11, 1165.90) | 65.31 | (0, 317.09) | 87.46 | (69.52, 96.12) |
Raw Gauge | 66.10 | (8.51, 262.32) | 553.70 | (96.98, 1115.16) | 65.44 | (0, 237.82) | 87.73 | (71.86, 95.62) |
Gauge 6 h | 67.35 | (6.35, 248.59) | 554.13 | (54.53, 1061.21) | 66.95 | (0, 237.82) | 87.73 | (67.18, 95.06) |
Gauge 12 h | 67.25 | (8.01, 236.85) | 556.53 | (61.95, 1071.52) | 66.81 | (0, 317.09) | 87.82 | (68.07, 95.17) |
Gauge 24 h | 67.25 | (5.66, 222.97) | 557.72 | (79.49, 998.43) | 66.54 | (0, 237.82) | 87.82 | (70.21, 94.39) |
Gauge 48 h | 67.14 | (0, 236.91) | 562.88 | (65.32, 980.98) | 66.54 | (0, 237.82) | 87.92 | (68.61, 94.19) |
Gauge 72 h | 66.84 | (0, 239.33) | 564.71 | (93.23, 1130.52) | 66.27 | (0, 237.82) | 87.96 | (71.53, 95.77) |
Raw IMERG | 66.58 | (6.15, 249.63) | 545.45 | (49.24, 1068.15) | 66.27 | (0, 237.82) | 87.52 | (66.38, 95.13) |
IMERG 6 h | 66.63 | (4.98, 253.00) | 550.90 | (45.50, 1047.01) | 66.40 | (0, 237.82) | 87.61 | (65.77, 94.92) |
IMERG 12 h | 66.90 | (6.49, 254.02) | 542.52 | (22.92, 1031.47) | 66.54 | (0, 237.82) | 87.44 | (60.70, 94.75) |
IMERG 24 h | 67.08 | (4.41, 249.32) | 544.12 | (44.65, 978.26) | 67.09 | (0, 237.82) | 87.50 | 65.62, 94.16) |
IMERG 48 h | 66.99 | (10.03, 243.42) | 551.56 | (57.03, 986.46) | 66.40 | (0, 237.82) | 87.64 | (67.53, 94.26) |
IMERG 72 h | 67.12 | (3.22, 252.57) | 555.59 | (52.49, 948.99) | 66.95 | (0, 237.82) | 87.81 | (66.88, 93.83) |
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Ma, Q.; Gui, X.; Xiong, B.; Li, R.; Yan, L. Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China. Remote Sens. 2023, 15, 4741. https://doi.org/10.3390/rs15194741
Ma Q, Gui X, Xiong B, Li R, Yan L. Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China. Remote Sensing. 2023; 15(19):4741. https://doi.org/10.3390/rs15194741
Chicago/Turabian StyleMa, Qiumei, Xu Gui, Bin Xiong, Rongrong Li, and Lei Yan. 2023. "Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China" Remote Sensing 15, no. 19: 4741. https://doi.org/10.3390/rs15194741