Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025228 |
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
![]() |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
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). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论