基于优化深度学习的地下水储量异常预测可改善典型干旱区地下水管理

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL
Xiaoya Deng, Guangyan Wang, Feifei Han, Yanming Gong, Xingming Hao, Guangpeng Zhang, Pei Zhang, Qianjuan Shan
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引用次数: 0

摘要

GRACE卫星为准确表征气候变化和人为干扰背景下区域地下水储量异常(GWSA)的时空变化提供了工具。但其空间分辨率较低,制约了地下水的精细化管理。创新性地引入多尺度地理加权回归(MGWR)残差进行偏差校正,提高了基于grace的GWSA降尺度精度(平均R2 = 0.98)。进一步利用K-means分析了塔里木河干流GWSA的4种空间分布格局,2003 - 2020年GWSA呈下降趋势。然而,在有效的地下水管理(如生态调水、生态闸门引水等)下,下降速度逐渐降低。特征贡献分析表明,土壤水分储存(SMS)、地表温度(LST)和归一化植被指数(NDVI)是GWSA变化的主要驱动因子。利用多策略灰狼优化算法(MSGWO)优化的长短期记忆(LSTM)深度学习模型,对两种共享社会经济路径(ssp,包括SSP245和SSP585)下4种空间格局的GWSA进行了预测。该模型在列车数据集上的最大R/NSE为0.95/0.91,在测试数据集上的最大R/NSE为0.88/0.71,优于同类模型。未来TRM地下水储量将呈现改善趋势,表明地下水管理已取得显著效益。值得注意的是,无政府干预的高排放(SSP585)加剧了地下水资源短缺的风险,未来需要进一步加强精炼水管理。总体而言,基于grace的GWSA降尺度框架和MSGWO-LSTM预测模型为干旱区地下水精细化科学管理提供了工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins
The GRACE satellite provides tools for accurately characterizing the spatiotemporal variations of regional groundwater storage anomalies (GWSA) under the background of climate change and anthropogenic disturbances. However, its low spatial resolution restricts the refined management of groundwater. Multi-scale geographically weighted regression (MGWR) residuals are innovatively introduced for bias correction, which improves the GRACE-based GWSA downscaling accuracy (average R2 = 0.98). Further application of the K-means identifies four spatial distribution patterns of GWSA in the Tarim River mainstream (TRM), which showed a downward trend from 2003 to 2020. However, under effective groundwater management (such as ecological water transfer, ecological gate water diversion, etc.), the decline rate is gradually decreasing. Feature contribution analysis demonstrates that soil moisture storage (SMS), land surface temperature (LST), and normalized difference vegetation index (NDVI) are the primary driving factors of GWSA changes. Using the long short-term memory (LSTM) deep learning model optimized by multi-strategy gray wolf optimization algorithm (MSGWO), the GWSA of four spatial patterns is predicted under two shared socioeconomic pathways (SSPs, including SSP245 and SSP585). The model achieved a maximum R/NSE of 0.95/0.91 on the train dataset and 0.88/0.71 on the test dataset, outperforming similar models. The future groundwater reserves of TRM will show an improving trend, indicating that groundwater management has achieved significant benefits. Notably, high emissions without government intervention (SSP585) have exacerbated the risk of groundwater resource shortages, and refined groundwater management needs to be further strengthened in the future. Overall, the proposed GRACE-based GWSA downscaling framework and MSGWO-LSTM predictive model provide tools for the refined scientific management of groundwater in arid basins.
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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