In all cases accepted manuscripts should: link to the formal publication via its DOI bear a CC-BY-NC-ND license -this is easy to do, click here to find out how if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.
fellettiD fF nd qri de venizD gF nd tonesD tF nd fizziD F nd f¤ orgerD vF nd egurD qF nd gstellettiD eF nd n de fundD F nd erestrupD uF nd frryD tF nd felkD uF nd ferkhuysenD eF nd firnieEquvinD uF nd fussettiniD wF nd grolliD wF nd gonsuegrD F nd hopioD iF nd peierfeilD F nd pern¡ ndezD F nd pernndez qrridoD F nd qriEzquezD iF nd qrridoD F nd qinnioD qF nd qoughD F nd tepsenD xF nd tonesD FiF nd uempD F nd uerrD tF nd uingD tF nd Lpi¡ nskD wF nd v¡ zroD qF nd vusD wFgF nd wrelloD vF nd wrtinD F nd wqinnityD F nd y9rnleyD tF nd ylivo del emoD F nd rsiewizD F nd inonD qF nd odriguezD gF nd oyteD tF nd hneiderD gFF nd ummersD tFF nd llesiD F nd owlesD eFF nd erspoorD iF nd nningenD rF nd ntzenD uFwF nd ildmnD vF nd lewskiD wF @PHPHA 9wore thn one million rriers frgment iurope9s riversF9D xtureFD SVV F ppF RQTERRIF Further information on publisher's website: httpsXGGdoiForgGIHFIHQVGsRISVTEHPHEQHHSEP Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.
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(2016). Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments. Water Resources Research, 52(6) , University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms RESEARCH ARTICLE 10.1002/2015WR017864Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments Abstract We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20-60% of the total forecast value.
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