Illuminating Deep Uncertainties in the Estimation of Irrigation Water Withdrawals

阐明灌溉取水量估算中的深层不确定性

• 批准号: EP/Y02463X/1
• 负责人: Arnald Puy
• 金额: $ 215.53万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY02463X%2F1
• 关键词: Illuminating; Deep; Uncertainties; Estimation; Irrigation

The volume of water globally withdrawn for irrigation agriculture is a measure of the human impact on freshwater resources. Although scientists have attempted to produce an accurate quantification of irrigation water withdrawals through increasingly detailed mathematical models, their estimates do not seem to converge. This discrepancy points towards the existence of deep uncertainties in our conceptualization of irrigation water withdrawals that evade the development of finer-grained algorithms. Without thoroughly exposing and understanding the relevance of these uncertainties, our knowledge of the influence that humans have on the hydrological cycle will remain on fragile grounds.DAWN will assemble a five-member team to unfold, examine and assimilate the deep uncertainties that condition our understanding of irrigation water withdrawals. Firstly, DAWN will unravel the underlying assumptions of all global irrigation water withdrawal models, ponder their effect on the estimations and assess the solidity of the main belief systems grounding the simulations. Secondly, DAWN will retrieve insights on irrigation withdrawals from traditional irrigators, and compare their understandings of the premises that govern irrigation water use with the scientific knowledge of irrigation embedded in global models. Thirdly, DAWN will combine the knowledge systems of scientists and traditional irrigators and develop cost-effective uncertainty/sensitivity analysis methods to explore how their ambiguities impact the modeling of global irrigation water withdrawals. By merging approaches from hydrology, statistics, philosophy and anthropology, DAWN proposes ground-breaking research to dramatically robustify our comprehension of irrigation withdrawals. This will ultimately enhance our capacity to design model-based irrigation policies that deliver under irreducible ambiguities.

全球取水以灌溉农业的水量是对人类对淡水资源的影响的衡量标准。尽管科学家试图通过越来越详细的数学模型对灌溉水抽水进行准确的量化，但它们的估计似乎并没有融合。这种差异表明，我们对灌溉水抽水的概念化的存在，从而逃避了较细粒的算法的发展。如果不彻底揭露和理解这些不确定性的相关性，我们对人类对水文周期的影响的了解将保持在脆弱的地面上。傍晚，将组建一个由五人组成的团队，以展开，检查和吸收我们对灌溉水撤离的理解的深刻不确定性。首先，黎明将揭示所有全球灌溉水模型的基本假设，思考它们对估计的影响，并评估扎根模拟的主要信念系统的坚固性。其次，黎明将检索从传统灌溉者中提取灌溉的见解，并将他们对灌溉用水的前提的理解与全球模型中嵌入的灌溉的科学知识进行比较。第三，黎明将结合科学家和传统灌溉者的知识系统，并开发成本效益的不确定性/敏感性分析方法，以探索其歧义如何影响全球灌溉水抽水的建模。通过合并水文学，统计，哲学和人类学的方法，黎明提出了开创性的研究，以极大地稳健地了解我们对灌溉戒断的理解。这最终将增强我们设计基于模型的灌溉政策的能力，这些政策在不可还原的歧义下提供。