Rethinking water resources management in a context of environmental change

健康的水资源是加拿大人财富和福祉的关键。社会、工业和生态系统的效益取决于我们是否有能力将水质维持在可接受的水平，并保持适当的水量时间分布。水资源的数量和质量都受到集约化农业、用水需求增加、土地利用转移和气候变化等压力因素的威胁，这是一个重大挑战。而且，这一趋势预计将继续下去。申请人一直致力于预测水质和水量的主要压力源（气候变化和土地利用），改进缩小尺度技术，并在水文生态模型中发展改进的水管理程序。在拟议的研究计划中，他的目标是在他以前的工作的基础上，通过开发方法和工具，将预测的变化简化为适应决策。这个问题并不新鲜，一些研究人员（包括申请人）已经尝试用一种自上而下的方法来解决这个问题，这种方法本质上是利用一组有限的气候变化情景来发现未来的风险。然而，最近的研究发现了这种方法的关键局限性。例如，即使使用多模型、多情景预测，也不能涵盖所有可能的情况，因此可能会忽略可能的风险。也没有既定的方法来评估给定投影场景的可信度，这使得将它们包含在决策或设计框架中成为一项挑战。最近提出了自下而上的方法来解决自上而下方法的局限性（Brown et al. 2011; Brown and Wilby 2012; Prudhomme et al. 2010），该方法包括使用随机产生的气候状态识别风险情况，然后使用预测来评估其未来的可能性，但仍有几个问题尚未解决。这项为期五年的研究计划的目标是为自下而上的方法尚未充分解决的问题做出贡献。预估的活动包括开发和试验一种新的随机方法，这种方法将克隆和干扰从气候模式中得出的一组最初的气候变化情景，并产生新的情景，这些情景将涵盖在历史时期观测到的变率范围；探索在自下而上的框架中评估某一特定天气发生器是否足以发现风险的新方法；制定标准，以评估自下而上框架中气候模式和降尺度技术组合的价值；以及一个案例研究，展示了南部国家流域的改善。虽然该应用将集中在一个试点流域，但其方法将引起加拿大和其他地区的兴趣。

Healthy water resources are key to the wealth and well-being of Canadians. Benefits to society, industry, and ecosystems are dependent on our ability to maintain water quality at acceptable levels as well as maintain an adequate temporal distribution of water quantity. A major challenge arises because both water quantity and quality are threatened by stressors including intensive agriculture, increased water demand, land use shifts, and climate change. And, the trend is expected to continue. The applicant has been working on the projection of the main stressors to water quality and quantity (climate change and land use), on the improvement of downscaling techniques, and the development of improved water management routines in hydro-ecological models. In the proposed research program, he aims to build on his previous work by developing methodologies and tools that can streamline projected changes into adaptation decisions. The problem is not new, and several researchers (including the applicant) have already tried to tackle the problem using a top-down approach, which essentially consists of using of a limited set of climate change scenarios to discover future risks. However, recent research has identified critical limitations in that approach. For instance, it has emerged that even when multi-model, multi-scenario projections are used, not all possible conditions are covered and, therefore, plausible risks may be overlooked. There is also no established way to evaluate the credibility of given projection scenarios, making it a challenge to include them in a decision or design framework. The bottom-up approach, which consists of identifying risky situations using stochastically generated climate states and then using projections to evaluate their likelihood in the future, was recently proposed to address the limitations of the top-down approach (Brown et al. 2011; Brown and Wilby 2012; Prudhomme et al. 2010), but several issues remain unaddressed. The objective of this five-year research program is to contribute toward issues that are yet to be adequately addressed in the bottom-up approach. Projected activities include the development and testing of a novel stochastic method that would clone and perturb an initial set of climate change scenarios derived from climate models and generate new ones that would cover the range of variability observed on the historical period; the exploration of new ways of evaluating the adequacy of a given weather generator for risk discovery in the bottom-up framework; the development of criteria to assess the value of a combination of climate models and downscaling technique in the bottom-up framework; and a case study to showcase the improvements in the South Nation watershed. Although the application will be centered in one pilot watershed, the methodology will be of interest for Canada and beyond.