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Improved estimation of global-scale changes in groundwater storage using machine learning.

使用机器学习改进对全球范围地下水储量变化的估计。

基本信息

批准号：
2843347
负责人：
金额：
--
依托单位：
Brunel University London
依托单位国家：
英国
项目类别：
Studentship
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=studentship-2843347
关键词：
Improved estimation global scale changes

项目摘要

The importance of groundwater as a reliable freshwater source is expected to increase amidst climate change, given its greater resilience to short- and mid-term fluctuations in the hydrological cycle. Over the last two decades, the Gravity Recovery and Climate Experiment (GRACE) has enabled large-scale satellite monitoring of changes in total terrestrial water storage globally. Changes in groundwater storage have, to date, been computed from total terrestrial water storage by deducting mass contributions from other terrestrial water stores: soil moisture, surface water, and snow and ice. Estimates of changes in these terrestrial water stores derive primarily from inadequately constrained land surface models (LSMs) and occasionally from in situ observations. Substantial uncertainty persists in the estimated changes in terrestrial water stores from uncalibrated, global-scale LSMs hindering robust monitoring of groundwater storage from GRACE. The project aims to devise a data-driven framework to calculate groundwater storage from total terrestrial water storage, leveraging newly accessible global-scale piezometric observations. Next, as the global process-based models oversimplify water storage components, a surrogate deep learning model for global ground water storage will be created to capture non-linear relationships in predictive variables and groundwater trends, and to transfer the learned knowledge to the regions with limited observations. Improved estimates of groundwater storage will help to inform basin-scale management for sustainable freshwater use.

由于地下水对水文循环的短期和中期波动具有更大的复原力，预计地下水作为可靠的淡水来源的重要性将随着气候变化而增加。在过去的二十年里，重力恢复和气候实验（GRACE）使大规模卫星监测全球陆地水储存总量的变化成为可能。迄今为止，地下水储存量的变化是通过扣除其他陆地水储存量（土壤水分、地表水和冰雪）的质量贡献，从陆地水储存总量中计算出来的。这些陆地水储量的变化估计主要来自不充分约束的陆面模型（LSMs），偶尔从原位观测。大量的不确定性持续存在的估计变化，陆地水存储从未经校准的，全球规模的LSM阻碍强大的监测地下水存储GRACE。该项目旨在设计一个数据驱动的框架，利用新获得的全球范围的测压观测数据，从陆地总蓄水量计算地下水蓄水量。接下来，由于基于全球过程的模型过度简化了水储存组件，因此将创建一个全球地下水储存的替代深度学习模型，以捕获预测变量和地下水趋势中的非线性关系，并将学到的知识转移到观测有限的地区。改进对地下水储存量的估计将有助于为可持续淡水利用的流域规模管理提供信息。