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).

胶合的长期目标是继续进行非常实用的方法，以减少加拿大的弹性，适应性能力和水资源的可持续性（WR），而其他人则面对日益增长的复杂性和气候变化。加拿大，质量和数量的压力，来自城市，行业和气候变化。这种复杂的问题受到许多因素的限制，i）或不合适的WR数据记录在许多分水岭中可以帮助支持WR建模，计划和管理II）在提供WR变量非常短的预测方面的挑战，并为有用的不确定性估算提供了有用的不确定性估算决策者nd iii）我们对TEMS中的社会经济和过程之间的复杂互动和反馈的有限和模型，结合了WR模型中利益相关者的经常缺乏有意义的利益，以应对复杂的挑战。 /主题方法来发展，测试和实施改进的方法：i）水资源数据记录（主题1）。估算的记录是从美国获得的OVED WR数据记录。学习'贝斯机器学习方法介绍了地球，并在短期（例如，提前1-3天）的新方法不确定性估计）WR变量预测。 3）。 S/轨迹和“如果在十年时间跨越的问题”（与短期“操作预测”（例如主题2模型）相反）。