Decision support tool for integrated water monitoring networks design and evaluation

在过去的一个世纪里，加拿大内陆水域(湖泊、河流、地下水)的状况一直受到各种类型的水监测网络的监测，这些网络被称为地表水网络、地下水网络、水质网络和包括降雪监测网络在内的降水网络。这些网络收集的数据和信息是用于所有与水有关的活动和政策决策的基本水信息。这包括加强水生生态系统服务、确保安全的社区水系统、洪水和干旱预报、水源保护、确定环境流量以保护水生生态系统健康，同时优化工业/农业用水等。因此，提高水信息的质量和可靠性将对所有与水有关的活动和决策产生直接好处。然而，这需要适当地设计最佳的水监测网络，以便在时空分辨率方面收集足够的信息，并在代表性和不确定性程度方面可靠。到目前为止，这在加拿大各地一直是一项非常具有挑战性的任务，因为加拿大大多数流域的面积都很大。许多流域仍然没有监测站，或者由于监测网的高成本和/或设计不佳而计量不善。这项研究的目的是开发一种决策支持工具，用于设计成本较低、能够提供更充分和可靠的水信息的最佳综合水监测网。现在可以借助新兴的信息处理技术来实现这一点，即“双熵分析”和“分层贝叶斯优化”技术。拟议的决策支持工具将非常有助于确定加拿大各地水监测站的最佳数量及其适当位置，这是审计长2010年关于加拿大水资源监测的报告的主要建议之一。拟议的决策支持工具承诺降低成本，提高水信息的质量和可靠性，这将对加拿大所有与水相关的部门非常有利。

Over the last century, the status of Canadian inland waters (lakes, rivers, groundwater) has been monitored by various types of water monitoring networks called surface water networks, groundwater networks, water quality networks, and precipitation networks including snow monitoring networks. The data and information collected by these networks are the essential water information used for all water related activities and policy decisions. This includes, to name a few, enhancing aquatic ecosystems services, ensuring secure community water systems, flood and drought forecasting, source water protection, and determining environmental flow to protect aquatic ecosystems health while optimizing industrial/agricultural water use, etc. Therefore, enhancing the quality and reliability of the water information will have direct benefits on all the water related activities and decision-making. However, this requires optimal water monitoring networks properly designed to collect adequate information in terms of space-time resolution and reliable in terms of representativeness and level of uncertainty. So far, this has been a very challenging task across Canada because of the large size of most Canadian watersheds. Many watersheds are still without monitoring stations or poorly gauged because of the high cost and/or the poor design of the monitoring networks. The objective of this research is to develop a decision support tool for the design of optimal integrated water monitoring networks that are less costly and can provide more adequate and reliable water information. This is now achievable by resorting to emergent information processing technologies namely dual entropy analysis' and 'hierarchical Bayesian optimization' techniques. The proposed decision support tool will be highly useful for determining the optimum number of water monitoring stations and their appropriate locations across Canada, which was one of the main recommendations of the Auditor General 2010 Report on the monitoring of Canadian water resources. The proposed decision support tool holds the promise to reduce cost, enhance the quality and reliability of water information, which will be highly beneficial to all water related sectors in Canada.