Rapid spatio-temporal pumping volume estimation from electricity consumption big data
DOI:
https://doi.org/10.12974/2311-8741.2023.11.06Keywords:
Groundwater, Pumping, Electricity consumption, Time-dependent spatial regressionAbstract
Land subsidence due to groundwater over-exploitation is a serious problem worldwide. Acquiring total pumping volumes to assess the stresses imposed that lead to subsidence is often difficult to quantify because groundwater extraction is often an unregulated water source. Consequently, pumping volumes represent a critical step for water resource managers to develop a strategic plan for mitigating land subsidence. In this investigation, we develop a time-dependent spatial regression (TSR) model to estimate monthly pumping volume over a ten-year period based on electricity consumption data. The estimated pumped volume is simplified as the spatial function of the electricity consumption and the electric power used by the water pump. Results show that the TSR approach can reduce the errors by 38% over linear regression models. The TSR model is applied to the Choshui alluvial fan in west-central Taiwan, where hundreds of thousands of unregulated pumping wells exist. The results show that groundwater peak extraction across the region occurs from January to May. Monthly pumping volume, and rainfall information are available to provide a better understanding of seasonal patterns and long-term changes of subsidence. Thus, the temporal regional subsidence patterns are found to respond to variations in pumping volume and rainfall.
References
Ali, M. Z., Chu, H. J., & Burbey, T. J. (2020). Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeology Journal, 1-12. https://doi.org/10.1007/s10040-020-02211-0
Bailey, R. T., & Tavakoli Kivi, S. (2017). Method for estimating available groundwater volume of small coral islands. Hydrological Sciences Journal, 62(14), 2381-2392. https://doi.org/10.1080/02626667.2017.1382703
Brunsdon, C., Fotheringham, S., & Charlton, M. (1998). Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), 431-443. https://doi.org/10.1111/1467-9884.00145
Chu, H. J., Ali, M. Z., & He, Y. C. (2020). Spatial calibration and PM 2.5 mapping of low-cost air quality sensors. Scientific reports, 10(1), 1-11. https://doi.org/10.1038/s41598-020-79064-w
Chu, H. J., He, Y. C., Chusnah, W. N. U., Jaelani, L. M., & Chang, C. H. (2021). Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression. Sustainability, 13(11), 6416. https://doi.org/10.3390/su13116416
Chu, H. J., Lin, C. W., Burbey, T. J., & Ali, M. Z. (2020). Spatiotemporal analysis of extracted groundwater volumes estimated from electricity consumption. Groundwater. https://doi.org/10.1111/gwat.13008
Galloway, D. L., & Burbey, T. J. (2011). Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8), 1459-1486. https://doi.org/10.1007/s10040-011-0775-5
Guzy, A., & Malinowska, A. A. (2020). State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water, 12(7), 2051. https://doi.org/10.3390/w12072051
Herrera-García, G., Ezquerro, P., Tomás, R., Béjar-Pizarro, M., López-Vinielles, J., Rossi, M., ... & Ye, S. (2021). Mapping the global threat of land subsidence. Science, 371(6524), 34-36. https://doi.org/10.1126/science.abb8549
Higgins, S. A. (2016). Advances in delta-subsidence research using satellite methods. Hydrogeology Journal, 24(3), 587-600. https://doi.org/10.1007/s10040-015-1330-6
Hurr, R. T., & Litke, D. W. (1989). Estimating pumping time and ground-water withdrawals using energy-consumption data. Water-Resources Investigations Report, 89, 4107.
Jang, C. S., Chen, S. K., & Ching‐Chieh, L. (2008). Using multiple‐variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan. Hydrological Processes: An International Journal, 22(22), 4477-4489. https://doi.org/10.1002/hyp.7037
Konikow, L. F., & Neuzil, C. E. (2007). A method to estimate groundwater depletion from confining layers. Water Resources Research, 43(7). https://doi.org/10.1029/2006WR005597
Liu, C. H., Pan, Y. W., Liao, J. J., Huang, C. T., &Ouyang, S. (2004). Characterization of land subsidence in the Choshui River alluvial fan, Taiwan. Environmental Geology, 45(8), 1154-1166
https://doi.org/10.1007/s00254-004-0983-6
Rodell, M., Velicogna, I., & Famiglietti, J. S. (2009). Satellite-based estimates of groundwater depletion in India. Nature, 460(7258), 999-1002. https://doi.org/10.1038/nature08238
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878-3895. https://doi.org/10.1002/2016WR019933
Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resources Research, 53(5), 3878-3895. https://doi.org/10.1002/2016WR019933
Shao, J., Cui, Y., Hao, Q., Han, Z., & Cheng, T. (2014). Study on the estimation of groundwater withdrawals based on groundwater flow modeling and its application in the North China Plain. Journal of Earth Science, 25(6), 1033-1042. https://doi.org/10.1007/s12583-014-0493-8
Tsanis, I. K., & Apostolaki, M. G. (2009). Estimating groundwater withdrawal in poorly gauged agricultural basins. Water resources management, 23(6), 1097-1123. https://doi.org/10.1007/s11269-008-9317-x
Yu, H. L., &Chu, H. J. (2010). Understanding space-time patterns of groundwater system by empirical orthogonal functions: A case study in the Choshui River alluvial fan, Taiwan. Journal of Hydrology, 381(3-4), 239-247. https://doi.org/10.1016/j.jhydrol.2009.11.046
Zhou, Y., & Li, W. (2011). A review of regional groundwater flow modeling. Geoscience frontiers, 2(2), 205-214. https://doi.org/10.1016/j.gsf.2011.03.003
