Development of novel approaches to improve water resources data records, deep learning based forecasting, and participatory socio-hydrological systems modeling for integrated and adaptive water resources management

开发新方法来改进水资源数据记录、基于深度学习的预测以及用于综合和适应性水资源管理的参与式社会水文系统建模

• 批准号: RGPIN-2020-05325
• 负责人: Adamowski, Jan
• 金额: $ 2.62万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717593
• 关键词: Development; novel; approaches; improve; water

The long-term goal of my research program is to continue developing very original and practical methods to reduce the vulnerability, as well as enhance the resilience, adaptive capacity, and sustainability of water resources (WR) in Canada and elsewhere in the face of increasing complexity and climate change. In many parts of Canada and the world, pressure on WR, both in terms of quality and quantity, from urban areas, agriculture, industry and climate change are increasing, resulting in significant challenges for sustainable WR management. The capacity of communities in Canada and elsewhere to mitigate against, and adapt to, such complex problems is constrained by numerous factors, including: i) a lack of, or unsuitable, WR data records in many watersheds that can help support WR modeling, planning and management; ii) challenges in providing very accurate short-term forecasts of WR variables and providing useful uncertainty estimates for decision makers; and iii) our limited understanding and modeling ability of the complex interactions and feedbacks between socioeconomic and physical processes in WR systems, combined with a frequent lack of meaningful participation of stakeholders in WR modeling, planning and management. To address these complex challenges, my 5 year NSERC DG research program objectives consist of an interlinked three pronged/themed approach (that is a direct continuation of my current DG research) to develop, test, and implement very original and practical methods to improve: i) Water resources data records (Theme 1). We will develop new statistical approaches to: fill in records at stations with scattered missing values; extend records at short gauged stations or records with large missing gaps; and estimate records at completely ungauged sites. Some of these newly developed methods, and the improved WR data records obtained from their use, will also be used in themes 2 and 3 of my research program. ii) Short-term water resources variable forecasting (Theme 2). We will develop new Deep Learning' based machine learning approaches that address nonlinearity, and new approaches to address non-stationarity and uncertainty estimation, in short-term (e.g., 1-3 days ahead) WR variable forecasting. iii) Participatory coupled socio-hydrological systems modeling (Theme 3). We will develop new approaches to address the complex challenges of calibration, validation and user friendly' software for participatory coupled socio-hydrological systems models that represent and dynamically model the interactions and feedbacks between physical (e.g., hydrological and related environmental processes) and socioeconomic processes that govern WR systems. The purpose of these coupled models will be to explore, in conjunction with stakeholders, long-term scenarios/trajectories and what if' questions over decadal time spans (in contrast to short term operational forecasting' such as Theme 2 models).

我的研究计划的长期目标是继续开发非常原始和实用的方法，以减少脆弱性，以及提高恢复力，适应能力和水资源的可持续性（WR）在加拿大和其他地方面对日益复杂和气候变化。在加拿大和世界的许多地方，来自城市地区、农业、工业和气候变化的水资源压力在质量和数量上都在增加，这给可持续水资源管理带来了重大挑战。 加拿大和其他地方的社区缓解和适应这些复杂问题的能力受到许多因素的限制，包括：i）许多流域缺乏或不合适的水资源数据记录，这些数据记录可以帮助支持水资源建模，规划和管理; ii）在提供非常准确的水资源变量短期预测和为决策者提供有用的不确定性估计方面的挑战;我们对水资源系统中社会经济和物理过程之间复杂的相互作用和反馈的理解和建模能力有限，加上利益相关者经常缺乏对水资源建模、规划和管理的有意义的参与。 为了应对这些复杂的挑战，我的5年NSERC DG研究计划目标包括一个相互关联的三管齐下/主题方法（这是我目前DG研究的直接延续），以开发，测试和实施非常原始和实用的方法来改进： （一）水资源数据记录（专题1）。我们将开发新的统计方法，以：在有零散缺失值的台站填写记录;在短测量台站或有大缺失间隙的台站延长记录;在完全无测量的台站估计记录。这些新开发的方法中的一些，以及从它们的使用中获得的改进的WR数据记录，也将用于我的研究计划的主题2和3。 短期水资源变量预测（专题2）。我们将开发新的基于深度学习的机器学习方法来解决非线性问题，以及解决非平稳性和不确定性估计的新方法，在短期内（例如，1-3未来几天）WR变量预测。 （三）预测性耦合社会水文系统建模（专题3）。我们将开发新的方法来解决参与式耦合社会水文系统模型的校准，验证和用户友好的软件的复杂挑战，这些模型代表并动态模拟物理（例如，水文和相关的环境过程）和社会经济过程，支配水资源系统。这些耦合模型的目的是与利益相关者一起探索长期情景/轨迹以及十年时间跨度内的“如果”问题（与短期业务预测相比，如主题2模型）。