Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption

Abstract

Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10‐year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.

Estimation of annual groundwater changes from InSAR‐derived land subsidence

“…These uncertainties affect the model prediction when the system is heterogeneous from both a spatial and temporal perspective. Chu et al (2020) conducted a study to show the effects of groundwater extraction using pumping and seasonal variations from agricultural usage. Most groundwater in the study region is used for irrigation.…”

“…The SR model does not consider the local effect of human activities or the global effect of hydrological conditions. These uncertainties affect the model prediction when the system is heterogeneous from both a spatial and temporal perspective Chu et al (2020). conducted a study to show the effects of groundwater extraction using pumping and seasonal variations from agricultural usage.…”

Development and Validation of an In Situ Groundwater Abstraction Sensor Network, Hydrologic Statistical Model, and Blockchain Trading Platform: A Demonstration in Solano County, California

“…16 This method has been implemented to estimate groundwater yield in various contexts. Chu et al 200 used electric power consumption records from pumping wells to estimate monthly groundwater yield over a 10 year period across the Choshui River Alluvial Fan in Taiwan and found that a spatio-temporal variability analysis of groundwater yield volumes is an effective method of identifying the patterns of pumped volumes in a region.…”

Human Intervention Will Stabilize Groundwater Storage Across the North China Plain

“…Compared with traditional numerical modeling, these approaches can largely diminish the dependence on forcing data but at the cost of omitting critical physical processes. Due to the lack of observations or adequate information for groundwater modeling (e.g., properties of aquifers), many studies have explored using auxiliary data (e.g., electricity consumption for groundwater pumping) and AI approaches to estimate GWSC based on GWL modeling (Chu et al, 2020;Rajaee et al, 2019;Sun et al, 2020). Shen (2018) showed that AI approaches are more robust than traditional statistical approaches after they are sufficiently trained with large data sets.…”