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Towards forecast-based climate resilience and adaptation in the water sector

水务部门实现基于预测的气候复原力和适应能力

基本信息

批准号：
NE/V010239/1
负责人：
Charles Rougé
金额：
$ 7.42万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FV010239%2F1
关键词：
Towards forecast based climate resilience

项目摘要

Usual applications of forecasts to resilience assessments in water systems ask the question "What are the benefits of forecasting product X for water system Y?". This project proposes to start asking instead: "What are the forecast characteristics that would increase the resilience of a water system to climate-related risks? what variables? what lead times? and with what accuracy?" Such an approach puts the focus on the needs of forecast users. This will enable water managers, government agencies, and communities, to identify more easily which forecasts would be useful to them. It will also help forecast providers such as the Met Office to focus forecast improvement efforts to areas where they would be most beneficial.The work as part of this embedded researcher scheme aims to:A) Start tackling the question of mapping the potential benefits of forecasts depending on their performance, by building a freely available, open-source Python toolbox that does that for a single planned water infrastructure asset (e.g. a storage reservoir with pumps and treatment plant). The toolbox will implement a stress testing procedure to determine which events or combination of events present a risk to the performance of the asset (supply disruption, financial risk, etc.). It will then incorporate a simple synthetic forecast generator to evaluate the ability of forecasts to accurately forewarn of climate-related hazards that can affect system performance. In a final step, the toolbox will be linked with simple multi-objective optimisation algorithms to trade off the benefits of investing in mitigation / adaptation actions to avoid bad performance, vs. the cost of implementing these actions as a result of a false alarm given by the forecast. This will help to understand which forecasts should be used to trigger appropriate mitigation and / or adaptation actions at the asset level, and what forecast precision is required for this.B) Develop a long-term collaboration between the host organisation Anglian Water (AW) and Dr Charles Rougé (CR). The successful implementation of the open-source Python toolbox, and its application to a key asset in AW's long-term adaptation plans, will only be a first step in that direction. Planned activities during with CR embedded at AW will lead to the submission of grant proposals to extend that work, with AW as key partner and beneficiary. 1) A first proposal will (i) design the next generation of synthetic forecast generators to simulate forecasts for several climate variables at once, with different forecast lead times, while reproducing desired statistical properties (precision, correlation between the different forecasts, etc); and (ii) apply this new synthetic forecast generator to the development of flexible forecast-based adaptation plans where new water infrastructure investments decisions would be triggered not only by climate events but also by the availability of new forecast products with the potential to improve how water systems can be managed. This proposal will be submitted during the project and will have the Met Office as its other key partner.2) Further work will scope out how forecast-based resilience tools can help to support the further development of strategic water planning models used by water utilities to make long-term adaptation plans. This work will focus on assessing a new functionality of one such model, which enables return flows (effluents from water treatment plants) to vary dynamically as a function of water demand. Representing them would enable to detect unintended consequences of demand management as it may reduce effluent discharges sustaining environmental flows at key locations. Consequences on adaptation depend on forecast supply and demand during drought conditions, as they are projected to evolve in coming years and decades.

预测在水系统复原力评估中的通常应用会提出这样一个问题：“预测产品X对水系统Y有什么好处？”该项目建议开始发问：“哪些预测特征可以提高水系统对气候相关风险的适应能力？”什么变量?交货期是多少？精确到什么程度？”这种方法把重点放在预测用户的需要上。这将使水资源管理者、政府机构和社区能够更容易地确定哪些预测对他们有用。它还将帮助气象预报提供者（如英国气象局）将预报改进工作的重点放在最有利的领域。作为嵌入式研究人员计划的一部分，这项工作旨在：A)通过构建一个免费可用的、开源的Python工具箱，开始解决根据预测的表现绘制潜在利益的问题，该工具箱可以为单个规划的水基础设施资产（例如，带泵和处理厂的蓄水池）完成该工作。该工具箱将实施压力测试程序，以确定哪些事件或事件组合对资产的性能构成风险（供应中断、财务风险等）。然后，它将纳入一个简单的综合预报生成器，以评估预报准确预警可能影响系统性能的气候相关危害的能力。在最后一步中，工具箱将与简单的多目标优化算法相关联，以权衡投资于缓解/适应行动以避免不良表现的收益与由于预测给出的假警报而实施这些行动的成本。这将有助于了解应使用哪些预测来触发资产一级的适当缓解和/或适应行动，以及为此需要多大的预测精度。B)发展主办机构Anglian Water （AW）和Dr Charles roug<s:1> (CR)之间的长期合作。开源Python工具箱的成功实现，以及它在AW长期适应计划中的关键资产上的应用，将只是朝着这个方向迈出的第一步。在与人权理事会合作期间的计划活动将导致提交赠款提案，以扩大这项工作，人权理事会是主要合作伙伴和受益者。1)第一项建议将(i)设计下一代综合预报生成器，以同时模拟几种气候变量的预报，具有不同的预报前置时间，同时再现所需的统计特性（精度，不同预报之间的相关性等）；（ii）将这种新的综合预测生成器应用于制定灵活的基于预测的适应计划，在这些计划中，新的水基础设施投资决策不仅会受到气候事件的影响，还会受到有可能改善水系统管理方式的新预测产品的影响。该提案将在项目期间提交，并将由英国气象局作为其另一个主要合作伙伴。2)进一步的工作将探讨基于预测的恢复力工具如何帮助支持水务公司用于制定长期适应计划的战略水规划模型的进一步发展。这项工作将侧重于评估一个这样的模型的新功能，它使回流（水处理厂流出的废水）作为水需求的函数动态变化。代表他们将能够发现需求管理的意外后果，因为它可以减少废水排放，在关键地点维持环境流动。对适应的影响取决于干旱条件下的预测供应和需求，因为它们预计将在未来几年和几十年内发生变化。