GSTDTAP  > 资源环境科学
DOI10.1029/2019WR025228
Seasonal Hydropower Planning for Data-Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming
Koppa, Akash1; Gebremichael, Mekonnen1; Zambon, Renato C.2; Yeh, William W-G1; Hopson, Thomas M.3
2019-11-06
发表期刊WATER RESOURCES RESEARCH
ISSN0043-1397
EISSN1944-7973
出版年2019
文章类型Article;Early Access
语种英语
国家USA; Brazil
英文摘要

In data-scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long-term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scarcity by combining precipitation forecasts from ensemble numerical weather prediction models, spatially distributed hydrologic models, and stochastic programming. We use evapotranspiration as a proxy for streamflow in generating reliable reservoir inflow forecasts. Using the framework, we compare three different formulations of inflow scenario structures and their applicability to data-scarce regions: (1) a single deterministic forecast, (2) a scenario fan with the first stage deterministic, and (3) a scenario fan with all stages stochastic. We apply the framework to a cascade of two reservoirs in the Omo-Gibe River basin in Ethiopia. Future reservoir inflows are generated using a 3-model 30-member ensemble seasonal precipitation forecast from the North American Multimodel Ensemble and the Noah-MP hydrologic model. We then perform deterministic and stochastic optimization for hydropower operation and planning. Comparing the results from the three different inflow scenario structures, we observe that the uncertainty in reservoir inflows is significant only for the dry stages of the planning horizon. In addition, we find that the impact of model parameter uncertainty on hydropower production is significant (0.14-0.18x10(6) MWh).


英文关键词hydropower planning ensemble forecasting remote sensing data-scarce regions taxonomy taxonomy numbers
领域资源环境
收录类别SCI-E
WOS记录号WOS:000494627000001
WOS关键词MONTE-CARLO-SIMULATION ; TO-INTERANNUAL PREDICTION ; LAND-SURFACE SCHEME ; SOIL-MOISTURE ; PROBABILITY-DISTRIBUTION ; RESERVOIR MANAGEMENT ; MODEL ; CLIMATE ; SYSTEM ; CALIBRATION
WOS类目Environmental Sciences ; Limnology ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/223872
专题资源环境科学
作者单位1.Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA 90095 USA;
2.Univ Sao Paulo, Dept Hydraul & Environm Engn, Sao Paulo, Brazil;
3.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA
推荐引用方式
GB/T 7714
Koppa, Akash,Gebremichael, Mekonnen,Zambon, Renato C.,et al. Seasonal Hydropower Planning for Data-Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming[J]. WATER RESOURCES RESEARCH,2019.
APA Koppa, Akash,Gebremichael, Mekonnen,Zambon, Renato C.,Yeh, William W-G,&Hopson, Thomas M..(2019).Seasonal Hydropower Planning for Data-Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming.WATER RESOURCES RESEARCH.
MLA Koppa, Akash,et al."Seasonal Hydropower Planning for Data-Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming".WATER RESOURCES RESEARCH (2019).
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