Robust and transparent planning and operation of water resource infrastructure

确保可靠和安全的供水对我们社会的社会经济和环境可持续性至关重要。在英国，几家水务公司负责向全国不同地区的工业和家庭用户供应清洁水。水务公司需要估计未来（通常是未来25年）的水需求和可用资源，以便能够规划基础设施发展（例如，建造一个新的水库）或管理上的变化（例如，减少或增加流入现有水库的河流抽水量），无论他们在哪里预测到需求和供应之间的差距。在我们生活的这个快速变化的世界里，做决定变得越来越复杂。在供应方面，在气候和土地利用变化的共同影响下，洪水和干旱等极端事件变得越来越频繁和不可预测。在需求方面，由于人口密度和分布的变化、生活方式和社会经济条件的变化，以及技术发展（例如智能水表的引入），水的需求也变得越来越多变，这些都可能以不同的方式影响不同地方的用水。为了解决所有这些复杂问题，水务行业需要采用创新、灵活和适应性强的规划和管理解决方案，这将提高水务系统的效率和弹性，同时避免增加成本。数学模型可以为实现这一目标作出重要贡献。通过再现水资源系统（如水库、泵站、处理厂等）的主要组成部分的行为，以及它们彼此之间以及与自然环境的联系，数学模型使水务从业者能够预测关键的系统变量(例如，水库未来的储水量、抽水消耗的能量、（一组住宅用户的洁净水供应量），并模拟系统在不同基建/管理情况下的反应。近年来，数学模型在水工业中的应用有所增加，但就其潜力而言，其采用仍然相对有限。水资源从业者面临的一个关键挑战是认识到不可避免地影响所有模型预测的不确定性和错误，同时仍然从中提取有用的信息。如今，它们面临着一个巨大的机遇，那就是从快速发展的传感和计算技术中提取越来越多的有用信息，例如卫星数据、智能传感器和高性能计算机。在这个研究项目中，我的目标是解决不确定性挑战，并利用IT机会开发下一代建模工具，以支持英国更可持续的水资源管理。该项目将开发数学方法和软件工具，以帮助水系统管理人员进行日常决策（例如，从河流或水库中抽取多少水，向处理厂泵送多少水等）以及长期决策（例如，是否建立一个新的水库或连接现有的水库），通过寻找“低遗憾”的解决方案，在一系列可能的未来被证明是有效的。所有方法都将在水务公司提供的案例研究应用程序上进行开发和测试，以确保它们对解决他们面临的最紧迫问题具有实际价值，并将以开源软件包的形式实施，以便除了直接参与项目的人员外，其他水务从业者也能从项目的发现和产出中受益。

Ensuring a reliable and safe supply of water is essential for the socioeconomic and environmental sustainability of our society. In the UK, several water companies are responsible for supplying clean water to industrial and domestic users in different parts of the country. Water companies need to estimate what the water demand and the available resource will be in the future (typically over a 25-years ahead period) so to be able to plan infrastructure development (for example, building a new reservoir) or changes in their management (for example, reducing or increasing river abstractions that feed into an existing reservoir) wherever they anticipate a gap between demand and supply.Making decisions is becoming increasingly complex in the fast-changing world we live in. On the supply side, extreme events such as floods and droughts are becoming more frequent and unpredictable under the combined effect of climate and land-use change. On the demand side, water demand is also becoming more variable due to changes in population density and distribution, changing life-style and socioeconomic conditions, and technological developments (for example, the introduction of smart water meters), which all together may affect water consumption in different ways in different places.To tackle all these complexities, the water industry needs to adopt innovative, flexible and adaptive planning and management solutions, which will increase the efficiency and resilience of water systems while avoiding raising costs. Mathematical models can provide a vital contribution to this end. By reproducing the behavior of the main components of a water resource system (such as reservoirs, pumping stations, treatment plants, etc.) and their connections among each other and with the natural environment, mathematical models enable water practitioners to predict the key system variables (for example, the future storage levels in a reservoir, the amount of energy consumed for pumping, the supply rate of clean water to a group of domestic users) and to simulate the system response under different infrastructural/management scenarios.The use of mathematical models in the water industry has increased in recent years, however their adoption is still relatively limited with respect to their potential. A key challenge water resource practitioners face is in recognising the uncertainty and errors that unavoidably affect all model predictions while still extracting useful information from them. A great opportunity that they are offered today, is to extract more and more useful information from fast growing sensing and computing technology, for example satellite data, smart sensors and high-performance computers. In this research project, I aim to tackle the uncertainty challenge and take the IT opportunity to develop the next-generation modelling tools that will support more sustainable water resource management in the UK.This project will develop mathematical methods and software tools to assist water system managers in their day-to-day decisions (for example, how much water to abstract from a river or a reservoir, how much water to pump to a treatment plant, etc.) as well as long-term decisions (for example, whether to build a new reservoir or connect existing ones) by finding "low-regret" solutions that would prove effective across a range of possible futures. All methods will be developed and tested on case study applications provided by water companies, so to ensure that they are actually valuable to address the most urgent issues they face, and they will be implemented in open-source software packages so that also other water practitioners besides those directly involved in the project will benefit from its findings and outputs.