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

基本信息

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

项目摘要

The volume of water globally withdrawn for irrigation agriculture is a measure of the human impact on freshwater resources.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.

全球用于灌溉农业的取水量是衡量人类对淡水资源影响的指标。尽管科学家们试图通过越来越详细的数学模型对灌溉用水进行准确的量化，但他们的估计似乎并没有趋同。这种差异表明我们的灌溉用水概念化中存在深刻的不确定性，从而阻碍了更细粒度算法的开发。如果不彻底揭示和理解这些不确定性的相关性，我们对人类对水文循环影响的认识将仍然脆弱。DAWN 将组建一个五人团队来揭示、检查和吸收影响我们理解的深层不确定性灌溉用水的取水量。首先，DAWN 将阐明所有全球灌溉取水模型的基本假设，思考它们对估计的影响，并评估支持模拟的主要信念系统的可靠性。其次，DAWN 将检索传统灌溉者对灌溉用水的见解，并将他们对管理灌溉用水的前提的理解与嵌入全球模型中的灌溉科学知识进行比较。第三，DAWN将结合科学家和传统灌溉者的知识体系，开发具有成本效益的不确定性/敏感性分析方法，以探索它们的模糊性如何影响全球灌溉取水的建模。通过融合水文学、统计学、哲学和人类学的方法，DAWN 提出了开创性的研究，以极大地增强我们对灌溉用水的理解。这最终将增强我们设计基于模型的灌溉政策的能力，这些政策可以在不可减少的模糊性下实现。