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This work integrates streamflow forecasts using radial basis functions and evaluates forecast value across varied lead times and reservoir property types. A Danjiangkou Reservoir case study compares Pareto fronts from forecast-informed versus no-forecast rule sets within multiobjective optimization for power generation and water supply. Sensitivity experiments vary installed hydropower capacity and capacity-inflow ratio.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/sensitivity-of-forecast-value-in-multiobjective-reservoir-operation-forecast-lead-time-and-reservoir-characteristics/148587/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/sensitivity-of-forecast-value-in-multiobjective-reservoir-operation-forecast-lead-time-and-reservoir-characteristics/148587.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-08-26",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",24,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the study define and evaluate forecast value in multiobjective reservoir operation?","Question",{"text":113,"@type":114},"Forecast value is assessed by comparing Pareto fronts from forecast-informed and no-forecast reservoir operation rules within multiobjective optimization focused on power generation and water supply.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which factors are varied to test sensitivity in the Danjiangkou Reservoir case study?",{"text":118,"@type":114},"The study varies installed hydropower plant capacity and the capacity-inflow ratio, defined as the ratio of active reservoir storage capacity to annual reservoir inflow volume.",{"name":120,"@type":111,"acceptedAnswer":121},"What general trends are reported for forecast value with forecast lead time and reservoir characteristics?",{"text":122,"@type":114},"Forecast value increases with longer forecast lead time and generally increases with higher installed capacity while decreasing as capacity-inflow ratio increases; annual magnitude rises, while intra-year distribution is more sensitive to capacity-inflow ratio.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},148587,1787781745,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":36,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","Downloaded on 04/19/21from ascelibrary .org by CASA Institution Identity . Copyright ASCE . For personal use only; all rights reserved .  \nSensitivity of Forecast Value in Multiobjective Reservoir Operation to Forecast Lead Time and Reservoir Characteristics  \nGuang Yang 1 ; Shenglian Guo2 ; Pan Liu 3 ; and Paul Block, M.ASCE4  \nAbstract: Streamflow forecasts can be used to improve reservoir operation decision-making, yet the sensitivity of forecast value to forecast lead time and reservoir characteristics in multiobjective reservoir operations is rarely investigated simultaneously. We incorporate streamflow forecasts into reservoir operation by applying radial basis functions (RBFs) and evaluate the forecast value conditioned on different types of forecast lead time and reservoir characteristics. From a case study of the Danjiangkou Reservoir in the Hanjiang River basin, China, forecast value is assessed by comparing Pareto fronts of forecast-informed and no-forecast reservoir operation rules from multiobjective optimization algorithms maximizing power generation and water supply. Subsequently, we vary the installed hydropower plant capacity and capacity-inflow ratio (ratio of active reservoir storage capacity and annual reservoir inflow volume) of the Danjiangkou Reservoir to investigate corresponding forecast values in terms of magnitude and distribution. The results demonstrate that the inclusion of streamflow forecasts predominantly leads to an increase in power generation in wet years, yet the monthly distribution of generation varies with water supply. Additionally, forecast value increases with forecast lead time, resulting in approximately 10, 15, and 18 million yuan annually for forecast lead times of 10, 20, and 30 days, respectively. Finally, forecast value is demonstrated to generally increase as installed capacity increases and decrease as capacity-inflow ratio increases, however, the distribution of forecast value within a year is more sensitive to the capacity-inflow ratio. DOI: 10.1061/(ASCE)WR.1943-5452.0001384. © 2021 American Society of Civil Engineers.  \nAuthor keywords: Multiobjective reservoir operation; Forecast-informed rules; Forecast value; Sensitivity analysis; Reservoir characteristics.  \nIntroduction  \nThe increase in water demand for societal development often stresses water resources management. Simultaneously, the need to balance competing objectives such as water supply, power generation, and ecosystem protection requires improving the performance of water resources systems. Operating existing water reservoirs efficiently and effectively is one of the most promising options to increase reliability across water demands (BolouriYazdeli et al. 2014 ; Gleick and Palaniappan 2010; Labadie 2004 ; Stedinger et al. 1984 ; Yang et al. 2016 ; Yeh 1985) . In recent decades, numerous methods have been applied to develop reservoir  \n1Postdoctoral Fellow, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin-Madison, Madison, WI 53706 . Email: gyang82@ [gatech.edu](gatech.edu)  \n2Professor, School of Water Resources and Hydropower Engineering, Wuhan Univ., Wuhan, Hubei 430072, China; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan Univ., Wuhan, Hubei 430072, China (corresponding author) . Email: slguo@whu.[edu.cn](edu.cn)  \n3Professor, School of Water Resources and Hydropower Engineering, Wuhan Univ., Wuhan, Hubei 430072, China; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan Univ., Wuhan, Hubei 430072, China. ORCID: [https://orcid.org/0000-0002-3777](https://orcid.org/0000-0002-3777)  \n[-6561](-6561)  \n4Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin-Madison, Madison, WI 53706 .  \nNote. This manuscript was submitted on May 6, 2020; approved on January 5, 2021; published online on April 5, 2021 . Discussion period open until September 5, 2021; separate discussions must be submitted for indivi","cbCaiq1Vq997gVm8","https://ap.wps.com/l/cbCaiq1Vq997gVm8","pdf",3589332,16,"English","# Introduction\n## Reservoir operation objectives and challenges\n## Forecasting for supporting reservoir decisions\n## Forecast value and prior research\n# Method and case study (implicit from abstract)\n## Forecast integration using radial basis functions (RBFs)\n## Forecast-informed vs no-forecast multiobjective optimization\n## Danjiangkou Reservoir experiments and sensitivity design\n# Results and findings (implicit from abstract)\n## Effects on power generation across wet years and monthly distribution\n## Relationship between forecast lead time and forecast value\n## Influence of installed capacity and capacity-inflow ratio on forecast value","[{\"question\":\"How does the study define and evaluate forecast value in multiobjective reservoir operation?\",\"answer\":\"Forecast value is assessed by comparing Pareto fronts from forecast-informed and no-forecast reservoir operation rules within multiobjective optimization focused on power generation and water supply.\"},{\"question\":\"Which factors are varied to test sensitivity in the Danjiangkou Reservoir case study?\",\"answer\":\"The study varies installed hydropower plant capacity and the capacity-inflow ratio, defined as the ratio of active reservoir storage capacity to annual reservoir inflow volume.\"},{\"question\":\"What general trends are reported for forecast value with forecast lead time and reservoir characteristics?\",\"answer\":\"Forecast value increases with longer forecast lead time and generally increases with higher installed capacity while decreasing as capacity-inflow ratio increases; annual magnitude rises, while intra-year distribution is more sensitive to capacity-inflow ratio.\"}]","Sensitivity of Forecast Value in Multiobjective Reservoir Operation - Forecast Lead Time and Reservoir Characteristics | PDF"]