Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Emergency Storage
2.3. Model Framework
2.4. Cases for Reservoir Operation
2.5. Model Performance Indices
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Dai, A. Drought under global warming: A review. WIREs Clim. Change 2011, 2, 45–65. [Google Scholar] [CrossRef] [Green Version]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2021. [Google Scholar]
- Boo, K.; Kwon, W.; Baek, H. Change of extreme events of temperature and precipitation over Korea using regional projection of future climate change. Geophys. Res. Lett. 2006, 33, 1–4. [Google Scholar] [CrossRef]
- Ministry of Environment. Korean Climate Change Assessment Report 2020. 2020. Available online: http://www.climate.go.kr/home/cc_data/2020/Korean_Climate_Change_Assessment_Report_2020_2_eng_summary.pdf (accessed on 20 September 2022).
- Kwon, H.; Lall, U.; Kim, S. The unusual 2013–2015 drought in South Korea in the context of a multicentury precipitation record: Inferences from a nonstationary, multivariate, Bayesian copula model. Geophys. Res. Lett. 2016, 43, 8534–8544. [Google Scholar] [CrossRef]
- Hong, I.; Lee, J.; Cho, H. National drought management framework for drought preparedness in Korea (lessons from the 2014–2015 drought). Water Policy 2016, 18, 89–106. [Google Scholar] [CrossRef]
- Ngo, L.A.; Masih, I.; Jiang, Y.; Douven, W. Impact of reservoir operation and climate change on the hydrological regime of the Sesan and Srepok Rivers in the Lower Mekong Basin. Clim. Change 2018, 149, 107–119. [Google Scholar] [CrossRef]
- Xu, W.; Zhao, J.; Zhao, T.; Wang, Z. Adaptive reservoir operation model incorporating nonstationary inflow prediction. J. Water Resour. Plan Manag. 2015, 141, 04014099. [Google Scholar] [CrossRef]
- Zhang, J.; Cai, X.; Lei, X.; Liu, P.; Wang, H. Real-time reservoir flood control operation enhanced by data assimilation. J. Hydrol. 2021, 598, 126426. [Google Scholar] [CrossRef]
- Biemans, H.; Haddeland, I.; Kabat, P.; Ludwig, F.; Hutjes, R.W.A.; Heinke, J.; Von Bloh, W.; Gerten, D. Impact of reservoirs on river discharge and irrigation water supply during the 20th century. Water Resour. Res. 2011, 47, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Jamali, A.A.; Montazeri Naeeni, M.A.; Zarei, G. Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS. Remote Sens. Appl. Soc. Environ. 2020, 20, 100428. [Google Scholar] [CrossRef]
- Di Baldassarre, G.; Wanders, N.; AghaKouchak, A.; Kuil, L.; Rangecroft, S.; Veldkamp, T.I.E.; Garcia, M.; van Oel, P.; Breinl, K.; Van Loon, A.F. Water shortages worsened by reservoir effects. Nat. Sustain. 2018, 1, 617–622. [Google Scholar] [CrossRef]
- Jamali, A.A.; Tabatabaee, R.; Randhir, T.O. Ecotourism and socioeconomic strategies for Khansar River watershed of Iran. Environ. Dev. Sustain. 2021, 23, 17077–17093. [Google Scholar] [CrossRef]
- Yi, S.; Kondolf, G.M.; Sandoval-Solis, S.; Dale, L. Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project. Water Resour. Manag. 2022. [Google Scholar] [CrossRef]
- Jeong, J.; Kim, Y.; Seo, S. Evaluating joint operation rules for connecting tunnels between two multipurpose dams. Hydrol. Res. 2020, 51, 392–405. [Google Scholar] [CrossRef]
- Jang, C.; Kim, Y. Improvement of water supply capability of the Nakdong river basin dams with weirs. J. Korean Soc. Civ. Eng. 2016, 36, 637–644. [Google Scholar]
- Kim, K.; Kim, J.-S. Economic assessment of flood control facilities under climate uncertainty: A Case of Nakdong River, South Korea. Sustainability 2018, 10, 308. [Google Scholar] [CrossRef] [Green Version]
- Kim, J.; Park, J.; Jang, S.; Kim, H.; Kang, H. Improving reservoir operation criteria to stabilize water supplies in a multipurpose dam: Focused on Nakdong River Basin in Korea. Water 2018, 10, 1236. [Google Scholar] [CrossRef] [Green Version]
- Eum, H.; Simonovic, S.P. Integrated reservoir management system for adaptation to climate change: The Nakdong River Basin in Korea. Water Resour. Manag. 2010, 24, 3397–3417. [Google Scholar] [CrossRef]
- Lee, D.; Moon, J.; Choi, S. Performance evaluation of water supply for a multi-purpose dam by deficit-supply operation. J. Korea Water Resour. Assoc. 2014, 47, 195–206. [Google Scholar] [CrossRef] [Green Version]
- Ahn, J.; Lee, Y.; Yi, J. Improving the water yield capabilities using reservoir emergency storage and water supply adjustment standard. J. Korea Water Resour. Assoc. 2016, 49, 1027–1034. [Google Scholar] [CrossRef]
- Cha, S.; Park, G. A study on estimate of evaluation indices of water supply capacity for multipurpose dam. J. Environ. Sci. 2004, 13, 197–204. [Google Scholar]
- Kim, K.; Lee, S.; Jin, Y. Forecasting quarterly inflow to reservoirs combining a copula-based bayesian network method with drought forecasting. Water 2018, 10, 233. [Google Scholar] [CrossRef] [Green Version]
- Jin, Y.; Lee, S.; Jeong, T.; Kang, S. Estimation of supplement-reduction amounts of water supply in a reservoir system operation using a monte carlo simulation. J. Korean Soc. Hazard. Mitig. 2019, 19, 87–94. [Google Scholar] [CrossRef]
- Abbas, A.; Baek, S.; Kim, M.; Ligaray, M.; Ribolzi, O.; Silvera, N.; Min, J.-H.; Boithias, L.; Cho, K.H. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks. J. Hydrol. 2020, 590, 125370. [Google Scholar] [CrossRef]
- Kim, J.; Jain, S.; Lee, J.; Chen, H.; Park, S. Quantitative vulnerability assessment of water quality to extreme drought in a changing climate. Ecol. Indic. 2019, 103, 688–697. Available online: https://linkinghub.elsevier.com/retrieve/pii/S1470160X19302997 (accessed on 20 September 2022). [CrossRef]
- Jeong, S.; Yu, I.; Felix, M.L.A.; Kim, S.; Oh, K. Drought assessment for real-time hydrologic drought index of the Nakdong River Basin in Korea. Desalin Water Treat. 2014, 52, 2826–2832. [Google Scholar] [CrossRef]
- Yang, J. Development of drought vulnerability index using delphi method considering climate change and trend analysis in Nakdong River basin. J. Korean Soc. Civ. Eng. 2013, 33, 2245. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.; Park, S.; Kim, J.; Sur, C.; Chen, J. Extreme drought hotspot analysis for adaptation to a changing climate: Assessment of applicability to the five major river basins of the Korean Peninsula. Int. J. Climatol. 2018, 38, 4025–4032. [Google Scholar] [CrossRef]
- Kim, J.; Park, J.; Jang, S.; Kang, H.; Kim, S. Applicability evaluation of real-time standard flow index to develop termination criteria at each drought response stage on multi-purpose dams. Korean Soc. Hazard. Mitig. 2017, 17, 411–420. [Google Scholar] [CrossRef]
- Lee, D.; Moon, J.; Lee, D. Development of water supply capacity index to monitor drought in a reservoir. J. Korea Water Resour. Assoc. 2006, 39, 199–214. [Google Scholar]
- Kim, C.; Park, M.; Lee, J. Analysis of climate change impacts on the spatial and frequency patterns of drought using a potential drought hazard mapping approach. Int. J. Climatol. 2014, 34, 61–80. [Google Scholar] [CrossRef]
- Di Baldassarre, G.; Martinez, F.; Kalantari, Z.; Viglione, A. Drought and flood in the Anthropocene: Feedback mechanisms in reservoir operation. Earth Syst. Dyn. 2017, 8, 225–233. [Google Scholar] [CrossRef] [Green Version]
- Han River Flood Control Office. Water Resources Status and Prospect. 2022. Available online: http://www.hrfco.go.kr/web/riverPage/riverInfo.do (accessed on 22 September 2022).
- Sperber, K.R.; Annamalai, H.; Kang, I.-S.; Kitoh, A.; Moise, A.; Turner, A.; Wang, B.; Zhou, T. The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim. Dyn. 2013, 41, 2711–2744. [Google Scholar] [CrossRef]
- Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747–756. [Google Scholar] [CrossRef]
- Wood, A.W.; Leung, L.R.; Sridhar, V.; Lettenmaier, D.P. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim. Change 2004, 62, 189–216. [Google Scholar] [CrossRef]
- Eum, H.; Cannon, A.J. Intercomparison of projected changes in climate extremes for South Korea: Application of trend preserving statistical downscaling methods to the CMIP5 ensemble. Int. J. Climatol. 2017, 37, 3381–3397. [Google Scholar] [CrossRef]
- Park, J.; Hwang, S.; Song, J.-H.; Kang, M.-S. An Alternative for Estimating the Design Flood Interval of Agricultural Reservoirs under Climate Change Using a Non-Parametric Resampling Technique. Water 2020, 12, 1894. [Google Scholar] [CrossRef]
- Ahn, J.M.; Im, T.H.; Lee, I.J.; Lee, K.-L.; Jung, K.Y.; Lee, J.W.; Cheon, S.U.; Park, I.H. A study on efficiency of water supply through conjunctive operation of reservoirs and multi-function weirs in the Nakdong River. J. Korean Soc. Water Environ. 2014, 30, 138–147. [Google Scholar] [CrossRef]
- Zagona, E.A.; Fulp, T.J.; Shane, R.; Magee, T.; Goranflo, H.M. RiverWare: A generalized tool for complex reservoir system modeling. J. Am. Water Resour. Assoc. 2001, 37, 913–929. [Google Scholar] [CrossRef]
- Fredericks, J.W.; Labadie, J.W.; Altenhofen, J.M. Decision support system for conjunctive stream-aquifer management. J. Water Resour. Plan Manag. 1998, 124, 69–78. [Google Scholar] [CrossRef] [Green Version]
- Che, D.; Mays, L.W. Development of an optimization/simulation model for real-time flood-control operation of river-reservoirs systems. Water Resour. Manag. 2015, 29, 3987–4005. [Google Scholar] [CrossRef]
- Yang, L.; Bai, X.; Khanna, N.Z.; Yi, S.; Hu, Y.; Deng, J.; Gao, H.; Tuo, L.; Xiang, S.; Zhou, N. Water evaluation and planning (Weap) model application for exploring the water deficit at catchment level in beijing. Desalin Water Treat. 2018, 118, 12–25. [Google Scholar] [CrossRef]
- Chandel, A.; Shankar, V.; Jaswal, S. Employing HEC-ResSim 3.1 for Reservoir Operation and Decision Making. In Bound Layer Flows—Model Comput Appl Laminar, Turbul Incompressible Compressible Flows; IntechOpen: London, UK, 2022. [Google Scholar]
- Choi, Y.; Ahn, J.; Ji, J.; Lee, E.; Yi, J. Effects of inter-basin water transfer project operation for emergency water supply. Water Resour. Manag. 2020, 34, 2535–2548. [Google Scholar] [CrossRef]
- Lee, D.; Choi, C.; Yu, M.; Yi, J. Reevaluation of multi-purpose reservoir yield. J. Korea Water Resour. Assoc. 2012, 45, 361–371. [Google Scholar] [CrossRef]
- Yang, W.; Ahn, J.; Yi, J. A study on the measures to use Gunnam flood control reservoir through a reservoir simulation model. J. Korea Water Resour. Assoc. 2017, 50, 407–418. [Google Scholar]
- US Army Corps of Engineers. HEC-ResSim Reservoir System Simulation User’s Manual Version 3.3; US Army Corps of Engineers: Washington, DC, USA, 2021. [Google Scholar]
- Ministry of Land Transport and Maritime Affairs. South Korea Water Plan; Ministry of Land Transport and Maritime Affairs: Sejong City, Korea, 2011. [Google Scholar]
- Hashimoto, T.; Stedinger, J.R.; Loucks, D.P. Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour. Res. 1982, 18, 14–20. [Google Scholar] [CrossRef] [Green Version]
- Sung, J.; Chung, E.-S.; Shahid, S. Reliability–Resiliency–Vulnerability Approach for Drought Analysis in South Korea Using 28 GCMs. Sustainability 2018, 10, 3043. [Google Scholar] [CrossRef]
River Basin | Han | Nakdong | Geum | Yeongsan |
---|---|---|---|---|
Precipitation (mm) | 1366.3 | 1192.3 | 1299.0 | 1437.7 |
Reservoirs | Andong | Gimcheon-Buhang | Gunwi | Hapcheon | Imha | Milyang |
---|---|---|---|---|---|---|
Total storage (MCM) | 1248 | 54.3 | 48.7 | 790 | 595 | 73.6 |
Conservation storage (MCM) | 1000 | 42.6 | 40.1 | 560 | 424 | 69.8 |
Emergency storage (MCM) | 130 | 1.6 | 1.3 | 130 | 84 | 3.6 |
Daily planned supply (MCM) | 2.5 | 0.1 | 0.1 | 1.6 | 1.6 | 0.2 |
Emergency storage/ | 13 | 3.8 | 3.2 | 23.2 | 19.8 | 5.2 |
Conservation storage (%) | ||||||
Emergency storage/ | 52 | 16 | 13 | 81 | 53 | 18 |
Daily planned supply (days) |
Stage | Reduction Scale |
---|---|
Concern | Uncontracted domestic and industrial water |
Caution | Concern reduction + instream flow |
Alert | Caution reduction + Irrigation water (April~June: 20%, July~September: 30%) |
Emergency | Alert reduction + 20% of domestic and industrial water |
No | Scenario | Water Shortage (Days) | Water Shortage (MCM) | Number of Failure Events | Max Shortage Duration (Days) | Max Shortage (MCM) |
---|---|---|---|---|---|---|
1 | RCP 8.5 Canadian Earth System Model 2 (RCP 8.5 CanESM2) | 0 | 0 | 0 | 0 | 0 |
2 | RCP 8.5 Community Earth System Model Biogeochemistry (RCP 8.5 CESM1-BGC) | 225 | 310 | 6 | 63 | 96.1 |
3 | RCP 8.5 Meteorological Research Institute Coupled Global Climate Model 3 (RCP 8.5 MRI-CGCM3) | 110 | 153.1 | 3 | 67 | 99.3 |
4 | RCP 4.5 Hadley Center Global Environmental Model version 2 Anomaly (RCP 4.5 HadGEM2-AO) | 244 | 366.9 | 5 | 70 | 111.2 |
5 | RCP 4.5 MRI-CGCM3 | 186 | 249.4 | 7 | 74 | 103 |
6 | RCP 4.5 CanESM2 | 256 | 379.6 | 8 | 81 | 123.1 |
7 | RCP 4.5 Institut Pierre-Simon Laplace Climate Model 5A Low Resolution (RCP 4.5 IPSL-CM5A-LR) | 1351 | 1946.8 | 33 | 128 | 191.5 |
8 | RCP 4.5 Institute for Numerical Mathematics Climate Model 5 (RCP 4.5 INM-CM4) | 1843 | 2420.6 | 54 | 129 | 171.7 |
9 | RCP 4.5 Centro Euro-Mediterraneo sui Cambiamenti Climatici Climate Model (RCP 4.5 CMCC-CM) | 420 | 597.3 | 10 | 130 | 197.3 |
10 | RCP 8.5 HadGEM2- Earth System (RCP 8.5 HadGEM2-ES) | 1147 | 1653.4 | 24 | 131 | 192.2 |
11 | RCP 4.5 Geophysical Fluid Dynamics Laboratory Earth System Models 2G (RCP 4.5 GFDL-ESM2G) | 727 | 1015.8 | 17 | 134 | 190 |
12 | RCP 8.5 GFDL-ESM2G | 977 | 1421.6 | 16 | 136 | 194.6 |
13 | RCP 8.5 CMCC-CM | 774 | 1110.0 | 21 | 137 | 193.6 |
14 | RCP 8.5 HadGEM2-AO | 521 | 748.1 | 8 | 141 | 185.5 |
15 | RCP 4.5 Community Earth System Model BGC (RCP 4.5 CESM1-BGC) | 353 | 519.8 | 6 | 150 | 238.5 |
16 | RCP 4.5 Norwegian Earth System Model (RCP 4.5 NorESM1-M) | 552 | 772.3 | 20 | 159 | 229.1 |
17 | RCP 4.5 Centre National de Recherches Météorologiques Circulation Model 5 (RCP 4.5 CNRM-CM5) | 684 | 905.8 | 11 | 171 | 236.6 |
18 | RCP 4.5 HadGEM2-ES | 2049 | 2882.0 | 33 | 184 | 277.5 |
19 | RCP 8.5 CMCC- Climate Model System (CMS) | 647 | 936.8 | 9 | 190 | 292.9 |
20 | RCP 8.5 CNRM-CM5 | 402 | 539.7 | 7 | 196 | 270.1 |
21 | RCP 4.5 IPSL-Climate Model 5A—Medium Resolution (RCP 4.5 IPSL-CM5A-MR) | 2417 | 3498.9 | 46 | 201 | 298.6 |
22 | RCP 8.5 IPSL-CM5A-MR | 3377 | 4854.8 | 52 | 251 | 370.5 |
23 | RCP 4.5 CMCC-CMS | 8882 | 12,831.9 | 181 | 267 | 404.3 |
24 | RCP 8.5 IPSL-CM5A-LR | 1598 | 2308.2 | 29 | 296 | 455.2 |
25 | RCP 8.5 INM-CM4 | 2527 | 3605.6 | 64 | 307 | 456.1 |
Scenarios | Max Shortage Duration (Days) | Max Shortage (MCM) |
---|---|---|
RCP 8.5 INM-CM4 | 307 | 456 |
RCP 8.5 IPSL-CM5A-LR | 296 | 455 |
RCP 4.5 CMCC-CMS | 267 | 404 |
Scenario | Case | Andong-Imha | Gimcheon-Boohang | Gunwi | Hapcheon | Milyang | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
P1 | P2 | P3 | P1 | P2 | P3 | P1 | P2 | P3 | P1 | P2 | P3 | P1 | P2 | P3 | ||
RCP 8.5 INM-CM4 | 1 | 153.87 | 152.78 | 149.40 | 182.19 | 187.47 | 189.85 | 198.35 | 197.42 | 195.41 | 162.70 | 164.63 | 159.28 | 181.11 | 188.50 | 179.71 |
2 | 154.56 | 153.90 | 152.22 | 182.67 | 187.47 | 189.85 | 198.36 | 197.57 | 195.69 | 163.68 | 165.36 | 160.81 | 190.70 | 193.16 | 190.42 | |
3 | 154.56 | 153.90 | 152.22 | 182.67 | 187.47 | 189.85 | 198.36 | 197.57 | 195.69 | 163.63 | 165.35 | 160.70 | 190.70 | 193.16 | 190.42 | |
RCP 8.5 IPSL-CM5A-LR | 1 | 153.03 | 154.34 | 154.69 | 187.97 | 189.20 | 188.03 | 197.66 | 198.81 | 198.33 | 161.30 | 165.08 | 164.04 | 183.32 | 189.45 | 193.16 |
2 | 154.46 | 154.96 | 155.79 | 188.02 | 189.20 | 188.12 | 197.79 | 198.85 | 198.44 | 162.62 | 165.72 | 164.81 | 192.53 | 194.45 | 195.98 | |
3 | 154.46 | 154.96 | 155.79 | 188.02 | 189.20 | 188.12 | 197.78 | 198.85 | 198.44 | 162.53 | 165.72 | 164.74 | 192.53 | 194.45 | 195.98 | |
RCP4.5 CMCC-CMS | 1 | 140.51 | 137.60 | 144.87 | 189.08 | 188.77 | 185.93 | 188.03 | 187.78 | 192.84 | 146.45 | 145.44 | 154.92 | 159.42 | 154.89 | 163.60 |
2 | 146.86 | 146.67 | 151.25 | 189.11 | 188.77 | 186.09 | 188.80 | 188.80 | 193.42 | 151.00 | 151.00 | 158.16 | 183.71 | 181.10 | 185.85 | |
3 | 146.86 | 146.67 | 151.25 | 189.11 | 188.77 | 186.09 | 188.78 | 188.77 | 193.41 | 150.50 | 150.63 | 157.88 | 183.71 | 181.10 | 185.85 |
Scenario | Case | Volumetric Reliability (%) | Average Resiliency | Average Vulnerability (MCM) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
P1 | P2 | P3 | P1 | P2 | P3 | P1 | P2 | P3 | ||
RCP 8.5 INM-CM4 | 1 | 93.3 | 97.3 | 88.6 | 0.023 | 0.040 | 0.023 | 60.2 | 34.7 | 63.9 |
2 | 92.1 | 96.3 | 87.4 | - | - | - | - | - | - | |
3 | 92.2 | 96.3 | 87.5 | - | - | - | - | - | - | |
RCP 8.5 IPSL-CM5A-LR | 1 | 93.2 | 97.9 | 95.6 | 0.019 | 0.022 | 0.014 | 76.1 | 62.2 | 104.4 |
2 | 92.5 | 96.6 | 94.4 | - | - | - | - | - | - | |
3 | 92.5 | 96.6 | 94.5 | - | - | - | - | - | - | |
RCP4.5 CMCC-CMS | 1 | 70.2 | 71.8 | 85.4 | 0.018 | 0.021 | 0.024 | 78.9 | 68.7 | 59.9 |
2 | 69.5 | 71.7 | 84.3 | - | - | - | - | - | - | |
3 | 69.6 | 71.7 | 84.4 | - | - | - | - | - | - |
Scenario | Case | Volumetric Reliability (%) | Average Resiliency | Average vulnerability (MCM) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
P1 | P2 | P3 | P1 | P2 | P3 | P1 | P2 | P3 | ||
RCP 8.5 INM-CM4 | 1 | 97.1 | 98.7 | 93.8 | 0.012 | 0.047 | 0.024 | 8.2 | 1.9 | 3.9 |
2 | 97.0 | 98.0 | 93.4 | 0.069 | - | - | 1.4 | - | - | |
3 | 97.0 | 98.0 | 93.4 | 0.059 | - | - | 1.6 | - | - | |
RCP 8.5 IPSL-CM5A-LR | 1 | 94.5 | 99.2 | 96.9 | 0.016 | 0.020 | 0.019 | 5.7 | 4.5 | 4.7 |
2 | 94.3 | 99.1 | 96.7 | 0.333 | - | - | 0.3 | - | - | |
3 | 94.3 | 99.1 | 96.7 | 0.125 | - | - | 0.7 | - | - | |
RCP 4.5 CMCC-CMS | 1 | 77.1 | 79.9 | 92.1 | 0.016 | 0.020 | 0.021 | 5.8 | 4.4 | 4.2 |
2 | 76.7 | 79.4 | 91.1 | 0.032 | 0.091 | 0 | 3.0 | 1.0 | 0 | |
3 | 76.7 | 79.5 | 91.1 | 0.039 | 0.097 | 0.250 | 2.4 | 1.0 | 0.4 |
Scenario | Case | P1 | P2 | P3 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Normal | Concern | Caution | Alert | Emergency | Normal | Concern | Caution | Alert | Emergency | Normal | Concern | Caution | Alert | Emergency | ||
RCP 8.5 INM-CM4 | 1 | 8847 | 116 | 350 | 163 | 1482 | 9677 | 128 | 212 | 149 | 791 | 7432 | 172 | 320 | 207 | 2096 |
2 | 9432 | 421 | 639 | 90 | 376 | 10,127 | 561 | 206 | 37 | 26 | 7965 | 704 | 784 | 140 | 634 | |
3 | 9429 | 407 | 643 | 68 | 411 | 10,123 | 565 | 206 | 36 | 27 | 7927 | 716 | 716 | 158 | 710 | |
RCP 8.5 IPSL-CM5A-LR | 1 | 8821 | 188 | 340 | 198 | 1411 | 10,036 | 128 | 174 | 64 | 555 | 8870 | 173 | 292 | 93 | 799 |
2 | 9473 | 546 | 361 | 130 | 448 | 10,232 | 319 | 337 | 67 | 2 | 9205 | 280 | 276 | 74 | 392 | |
3 | 9442 | 549 | 386 | 48 | 533 | 10,232 | 318 | 338 | 67 | 2 | 9187 | 283 | 275 | 83 | 399 | |
RCP 4.5 CMCC-CMS | 1 | 2994 | 521 | 886 | 414 | 6143 | 2351 | 430 | 938 | 711 | 6527 | 6244 | 304 | 486 | 285 | 2908 |
2 | 5311 | 1279 | 1392 | 470 | 2506 | 5561 | 1434 | 1737 | 512 | 1713 | 7524 | 522 | 750 | 252 | 1179 | |
3 | 5080 | 1226 | 1381 | 559 | 2712 | 5382 | 1483 | 1642 | 483 | 1967 | 7424 | 587 | 672 | 216 | 1328 |
Scenario | Case | P1 | P2 | P3 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Normal | Concern | Caution | Alert | Emergency | Normal | Concern | Caution | Alert | Emergency | Normal | Concern | Caution | Alert | Emergency | ||
RCP 8.5 INM-CM4 | 1 | 10,521 | 8 | 18 | 31 | 380 | 10,558 | 54 | 30 | 57 | 258 | 8864 | 180 | 149 | 168 | 866 |
2 | 10,524 | 60 | 77 | 51 | 246 | 10,614 | 272 | 50 | 21 | - | 9234 | 480 | 225 | 105 | 183 | |
3 | 10,522 | 61 | 77 | 51 | 247 | 10,614 | 272 | 50 | 21 | - | 9231 | 479 | 227 | 106 | 184 | |
RCP 8.5 IPSL-CM5A-LR | 1 | 9872 | 61 | 89 | 101 | 835 | 10,803 | 9 | 12 | 18 | 115 | 9655 | 39 | 35 | 53 | 445 |
2 | 10,077 | 198 | 194 | 107 | 382 | 10,807 | 63 | 41 | 46 | - | 9735 | 182 | 201 | 77 | 32 | |
3 | 10,065 | 200 | 188 | 100 | 405 | 10,807 | 63 | 41 | 46 | - | 9735 | 179 | 194 | 86 | 33 | |
RCP 4.5 CMCC-CMS | 1 | 6602 | 283 | 259 | 238 | 3576 | 6715 | 382 | 245 | 243 | 3372 | 8389 | 172 | 201 | 149 | 1316 |
2 | 7423 | 891 | 899 | 522 | 1223 | 7756 | 941 | 899 | 369 | 992 | 8898 | 553 | 388 | 105 | 283 | |
3 | 7397 | 894 | 882 | 506 | 1279 | 7728 | 952 | 856 | 374 | 1047 | 8890 | 560 | 389 | 104 | 284 |
Period/Scenario | RCP 8.5 INM-CM4 | RCP 8.5 IPSL-CM5A-LR | RCP 4.5 CMCC-CMS |
---|---|---|---|
P1 | 79.73 | 89.15 | 62.15 |
P2 | - | - | 68.33 |
P3 | 105.92 | 50.68 | 26.12 |
Period/Scenario | RCP 8.5 INM-CM4 | RCP 8.5 IPSL-CM5A-LR | RCP 4.5 CMCC-CMS |
---|---|---|---|
P1 | 3.73 | 2.52 | 7.91 |
P2 | - | - | 3.34 |
P3 | 1.49 | 0.23 | 2.17 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chae, H.; Ji, J.; Lee, E.; Lee, S.; Choi, Y.; Yi, S.; Yi, J. Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought. Water 2022, 14, 3242. https://doi.org/10.3390/w14203242
Chae H, Ji J, Lee E, Lee S, Choi Y, Yi S, Yi J. Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought. Water. 2022; 14(20):3242. https://doi.org/10.3390/w14203242
Chicago/Turabian StyleChae, Heechan, Jungwon Ji, Eunkyung Lee, Seonmi Lee, Youngje Choi, Sooyeon Yi, and Jaeeung Yi. 2022. "Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought" Water 14, no. 20: 3242. https://doi.org/10.3390/w14203242