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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746285
• 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创和实用的方法来减少脆弱性，并增强加拿大和其他地方水资源(WR)面对日益复杂和气候变化的弹性、适应能力和可持续性。在加拿大和世界许多地区，来自城市地区、农业、工业和气候变化的水资源在质量和数量上的压力都在增加，这给水资源的可持续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种复杂问题的能力受到许多因素的制约，包括：i)许多流域缺乏或不适合有助于支持水资源建模、规划和管理的水资源数据记录；ii)在为水资源变量提供非常准确的短期预测和为决策者提供有用的不确定性估计方面存在挑战；iii)我们对水资源系统中社会经济和物理过程之间的复杂互动和反馈的理解和建模能力有限，再加上利益攸关方经常缺乏有意义的水资源建模、规划和管理参与。为了应对这些复杂的挑战，我5年的NSERC DG研究计划目标包括一个相互关联的三管齐下/主题的方法(这是我目前DG研究的直接延续)，以开发、测试和实施非常原创和实用的方法来改进：i)水资源数据记录(主题1)。我们将开发新的统计方法来：在零散缺失值的站点填充记录；在短量程的站点延长记录或在大的遗漏间隙的记录；在完全未测量的站点估计记录。其中一些新开发的方法，以及通过使用它们获得的改进的水资源数据记录，也将用于我的研究计划的主题2和3。二)短期水资源变量预测(主题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，以及在短期(例如提前1-3天)WR变量预测中解决非平稳性和不确定性估计的新方法。(三)参与式社会-水文耦合系统建模(主题3)。我们将开发新的方法来解决复杂的挑战，为参与式耦合社会-水文系统模型提供校准、验证和“用户友好”软件，这些模型代表并动态模拟管理水资源系统的物理过程(例如，水文和相关环境过程)和社会经济过程之间的相互作用和反馈。这些耦合模型的目的将是与利益攸关方一起探索长期情景/轨迹和十年时间跨度上的“如果”问题(与短期“业务预测”，如主题2模型形成对比)。