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

预测在水系统恢复力评估中的实际应用提出了这样一个问题：“预测产品X对水系统Y有什么好处？".该项目建议开始问：“什么是预测特征，将增加水系统的弹性，以气候相关的风险？什么变数？交货时间是多少？准确性如何“这种方法把重点放在预测用户的需求上。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。它还将帮助气象局等预报提供商将预报改进工作集中在最有利的领域。作为嵌入式研究计划的一部分，这项工作旨在：A）通过构建一个免费可用的开源Python工具箱，开始解决根据预报的表现绘制预报的潜在效益的问题，该工具箱可以为单个规划的水基础设施资产（例如，带有泵和处理厂的蓄水池）。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合成预报生成器，以评估预报准确预警可能影响系统性能的气候相关灾害的能力。在最后一步中，工具箱将与简单的多目标优化算法相连接，以权衡投资缓解/适应行动以避免不良表现的好处，以及由于预测发出的假警报而实施这些行动的成本。这将有助于了解哪些预测应用于触发资产层面的适当减缓和/或适应行动，以及为此需要何种预测精度。B）在主办组织Anglian Water（AW）和Charles Rougé博士（CR）之间建立长期合作关系。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的应用，只是朝着这个方向迈出的第一步。在CR嵌入AW期间计划的活动将导致提交赠款提案，以扩展该工作，AW是主要合作伙伴和受益人。1)第一项建议将（i）设计下一代综合预报生成器，以便在不同的预报准备时间内同时模拟对若干气候变量的预报，同时再现所需的统计特性（精度、不同预测之间的相关性等）;以及（ii）将这种新的合成预测生成器应用于灵活预测的开发-在这些适应计划中，新的水基础设施投资决定不仅由气候事件触发，而且还由有可能改善水系统管理的新预测产品的可用性触发。该提案将在项目期间提交，并将气象局作为其另一个关键合作伙伴。2）进一步的工作将探讨基于预测的弹性工具如何帮助支持进一步开发水务公司用于制定长期适应计划的战略水规划模型。这项工作将侧重于评估一个这样的模型的新功能，它使返回流量（水处理厂的污水）动态变化的水需求的函数。代表他们将能够检测需求管理的意外后果，因为它可能会减少污水排放维持环境流量在关键地点。适应的后果取决于干旱条件下的预测供需，因为预计未来几年和几十年将发生变化。

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.