Decision support tool for integrated water monitoring networks design and evaluation

在上个世纪，加拿大内陆沃茨（湖泊、河流、地下水）的状况一直由各种类型的水监测网络进行监测，这些网络被称为地表水网络、地下水网络、水质网络和降水网络，包括雪监测网络。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决策的基本水信息。这包括，仅举几例，提高水生态系统服务，确保安全的社区水系统，洪水和干旱预报，水源保护，并确定环境流量，以保护水生态系统的健康，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有直接的好处。然而，这需要设计适当的最佳水监测网络，以便收集在时空分辨率方面足够的信息，并在代表性和不确定程度方面可靠。到目前为止，这在加拿大是一项非常具有挑战性的任务，因为大多数加拿大流域面积很大。许多流域仍然没有监测站，或由于监测网络的费用高和/或设计不良而测量不准确。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络。这是现在可以实现的诉诸紧急信息处理技术，即双熵分析和“层次贝叶斯优化”技术。拟议的决策支持工具将非常有助于确定水监测站的最佳数量及其在加拿大各地的适当位置，这是2010年审计长关于加拿大水资源监测报告的主要建议之一。拟议的决策支持工具持有的承诺，以降低成本，提高水信息的质量和可靠性，这将是非常有益的，在加拿大所有与水有关的部门。

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.