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来减少脆弱性，并增强加拿大和其他地方的水资源（WR）的韧性，适应能力和可持续性（WR）复杂性和气候变化。在加拿大和世界的许多地方，在质量和数量方面，城市地区，农业，工业和气候变化的压力都在增加，从而对可持续性WR管理面临重大挑战。 加拿大和其他地方的社区能够减轻和适应这种复杂问题的能力受到许多因素的限制，包括：i）在许多分水岭中缺乏或不合适的WR数据记录，这些数据记录可以帮助支持WR建模，计划和管理； ii）在提供非常准确的WR变量的短期预测以及为决策者提供有用的不确定性估计方面面临的挑战； iii）我们对WR系统中社会经济和物理过程之间复杂相互作用以及反馈的有限理解和建模能力，加上利益相关者在WR建模，计划和管理中经常缺乏有意义的参与。 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种相互联系的三种统治/主题方法（这是我目前的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： i）水资源数据记录（主题1）。我们将开发新的统计方法：填写具有分散缺失值的站点的记录；在短尺寸的电台或记录中扩展记录，并有较大的缺失差距；并在完全未加州的网站上进行估计记录。这些新开发的方法中的一些以及从其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中。 ii）短期水资源变量预测（主题2）。我们将在短期内（例如提前1-3天）开发基于深度学习的机器学习方法，以解决非线性和不确定性估计的新方法WR变量预测。 iii）参与性耦合的社会流程系统建模（主题3）。我们将开发新方法，以应对参与式耦合的社会杂种系统模型的校准，验证和用户友好的软件的复杂挑战，这些模型代表并动态建模物理（例如水文和相关环境过程）与社会经济过程之间的相互作用和反馈控制WR系统。这些耦合模型的目的将是与利益相关者一起探索，长期情景/轨迹以及如果“在十年时间跨度上的问题相比，与短期操作预测相反”，例如主题2模型）。