Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data
Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2472 |
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| author | Zheng Lu Chunying Shen Cun Zhan Honglei Tang Chenhao Luo Shasha Meng Yongkai An Heng Wang Xiaokang Kou |
| author_facet | Zheng Lu Chunying Shen Cun Zhan Honglei Tang Chenhao Luo Shasha Meng Yongkai An Heng Wang Xiaokang Kou |
| author_sort | Zheng Lu |
| collection | DOAJ |
| description | Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10<sup>8</sup> years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. |
| format | Article |
| id | doaj-art-7e15dcbb4e0b4a32884061c656b49e5c |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7e15dcbb4e0b4a32884061c656b49e5c2025-07-25T13:35:23ZengMDPI AGRemote Sensing2072-42922025-07-011714247210.3390/rs17142472Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing DataZheng Lu0Chunying Shen1Cun Zhan2Honglei Tang3Chenhao Luo4Shasha Meng5Yongkai An6Heng Wang7Xiaokang Kou8Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaXi’an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaGroundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10<sup>8</sup> years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments.https://www.mdpi.com/2072-4292/17/14/2472groundwater–climate interactionsremote sensing datacausal relationshipGeographical Convergent Cross Mappinggeneralized additive modelHeihe river basin |
| spellingShingle | Zheng Lu Chunying Shen Cun Zhan Honglei Tang Chenhao Luo Shasha Meng Yongkai An Heng Wang Xiaokang Kou Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data Remote Sensing groundwater–climate interactions remote sensing data causal relationship Geographical Convergent Cross Mapping generalized additive model Heihe river basin |
| title | Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data |
| title_full | Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data |
| title_fullStr | Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data |
| title_full_unstemmed | Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data |
| title_short | Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data |
| title_sort | quantifying multifactorial drivers of groundwater climate interactions in an arid basin based on remote sensing data |
| topic | groundwater–climate interactions remote sensing data causal relationship Geographical Convergent Cross Mapping generalized additive model Heihe river basin |
| url | https://www.mdpi.com/2072-4292/17/14/2472 |
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