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。对社会、工业和生态系统的好处取决于我们将水质保持在可接受水平的能力，以及保持水量适当的时间分布的能力。一个重大挑战出现了，因为水的数量和质量都受到压力因素的威胁，包括集约化农业，水需求增加，土地使用变化和气候变化。预计这一趋势将继续下去。申请方一直致力于预测水质和水量的主要压力源（气候变化和土地使用），改进缩小尺度技术，并在水文生态模型中制定更好的水管理程序。在拟议的研究计划中，他的目标是通过开发方法和工具来建立他以前的工作，这些方法和工具可以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）已经尝试使用自上而下的方法来解决这个问题，这种方法主要包括使用有限的一组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，已经出现的情况是，即使使用多模式、多情景预测，也没有涵盖所有可能的情况，因此可能会忽略看似合理的风险。也没有既定的方法来评估特定预测情景的可信度，因此将其纳入决策或设计框架是一项挑战。最近提出的自下而上的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，以解决自上而下方法的局限性（Brown et al. 2011; Brown and Wilby 2012; Prudhomme et al. 2010），但仍有几个问题未得到解决。这个为期五年的研究计划的目标是为那些自下而上的方法还没有得到充分解决的问题做出贡献。计划开展的活动包括：开发和测试一种新的随机方法，该方法将克隆和扰动从气候模型中得出的一套初始气候变化假设情景，并生成新的假设情景，其中将涵盖在历史时期观察到的可变性范围;探索新的方法，以评估自下而上框架中特定天气生成器是否足以发现风险;制定标准，以评估自下而上框架中气候模型和降尺度技术相结合的价值;以及一项案例研究，以展示南方国家流域的改进。虽然应用程序将集中在一个试点流域，该方法将是加拿大和超越利益。

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.