Information theory for sustainable management of water resources

水文学和水资源管理是勾勒可持续未来的关键学科，因为除其他外，水与食物、能源、健康和生态系统功能相互作用。环境科学和基础物理学之间的主要区别在于，尽管环境系统受相同的物理定律支配，但它们通常是基本不可观测的复杂系统。感兴趣的时间和空间尺度上的行为通常是从复杂的相互作用和反馈中出现的，这使得它很难预测。因此，不确定性是预测不可避免的一部分，这一点在科学和工程中应该得到承认。 然后，信息--不确定性的对立面--成为环境科学和工程问题中的关键通用货币，在这些问题中，衡量什么规模以及数据所保证的模型复杂性往往远非微不足道。事实上，水资源管理中的所有挑战都可以被视为在某种形式的不确定性下做出的决定，当获得更多信息时，这些决定会得到改善。那么问题是：信息是如何做出决定的？ 原则上，我们所拥有的关于环境的所有信息最终都源于观察。这些观察中的一部分已被浓缩为物理定律。这就是为什么我们不需要访问几个世纪前进行的实验的数据，而是可以在我们的水文模型中使用质量、能量和动量守恒定律。 这种从数据到法律的转变是一种数据压缩形式，其中数据中的模式允许更短的描述。最初被发现用来描述最佳信息传输和存储极限的信息论，因此也描述了我们如何从数据中推导出规律，如何从数字中提取信息。越来越多的物理学家将信息视为一个基本的物理概念。 我打算建立的研究计划将主要专注于使用信息论将信息如何从环境流动到观察，再到模型，在模型中，它们与以前提炼的定律相结合，产生预测，随后改进决策和设计。 其中一个应用领域是水电系统的运行，在从分钟到季节的时间尺度上调查水文和市场信息的作用。这一主题既从信息的角度提出了大量的科学问题，也提出了具有巨大直接好处的实际应用。由于这种巨大的兴趣，有许多可能与工业界和加拿大学术界合作。 综上所述，拟议方案旨在通过将数量“信息”放在中心位置来解决水资源管理中的实际问题。这将有助于简化信息流，使信息流从来源流向与社会有关的决策。

Hydrology and water resources management are key disciplines for outlining a sustainable future, since water interacts, among others, with food, energy, health and ecosystem functioning. The main difference between the environmental science and fundamental physics, is that environmental systems, although governed by the same laws of physics, are generally complex systems that are largely unobservable. The behaviour at temporal and spatial scales of interest is often emergent from complex interactions and feedbacks, which make it hard to predict. Uncertainty is therefore an inevitable part of predictions and this should be acknowledged in science and engineering. Information -the opposite of uncertainty- then becomes a key common currency in environmental science and engineering problems, where it is often far from trivial what to measure at what scale and what model complexity is warranted by the data. In fact, all challenges in water resources management can be seen as decisions made under some form of uncertainty Those decisions improve when more information becomes available. The question then is: How does information get to a decision? In principle, all information we have about our environment ultimately stems from observation. Part of those observations have been condensed into physical laws. This is why we don't need access to data from experiments performed centuries ago, but can use the laws of e.g. mass, energy and momentum conservation in our hydrological models. This going from data to laws is a form of data compression, where patterns in the data allow a shorter description. Information theory, originally found to describe limits of optimal information transmission and storage therefore also describes the way we derive laws from data, how we distill information from numbers. A growing number of physicists sees information as a fundamental physical concept. The research program that I intend to set up will be mainly focused on using information theory to formalize how information flows from the environment into observations, into models, where they are combined with previously distilled laws to yield predictions that subsequently improve decisions and designs. One of the application areas is operation of hydro-electrical systems, investigating the role of hydrological and market information at time scales from minute to seasonal. This topic poses both a large number of science questions from an information perspective, as well as practical applications with large direct benefits. Due to this large interest there are many possible collaborations with industry and Canadian academia. To summarize, the proposed program aims to address practical questions in water resources management by putting the quantity "information" in a central role. This will serve to streamline the information flow from source to decisions relevant for society.