Abstract
Saudi Arabia (SA) lies in an arid region where groundwater is the main natural resource; therefore, it is essential to understand the groundwater dynamics for the best groundwater management practice in SA. In Hadat Ash-Sham Farm Experimental Station, SA, water table data from 11 wells and rainfall data were monitored for 16 months. The water table (WT) data is analyzed using the geostatistical method with the ordinary Kriging technique to generate the best WT spatial distribution map for each month and the expected flow direction. The cross-validation technique is used to evaluate the goodness of the developed WT maps. The Kriging maps show two regimes: weak spatial dependence (WSD, the ratio of the nugget to sill > 75%) and strong spatial dependence (SSD, the ratio of the nugget to sill < 25%). The WSD regime happens during dry seasons, while the SSD happens during wet seasons. The SSD gives better results and accuracy when compared to WSD. The root-mean-square error (RMSE) of WT varies between 0.26 and 3.4 m in the case of SSD, while it varies between 0.51 and 4.8 m in the case of WSD. WT maps show that the groundwater flow direction is from south-east to north-west during the wet season (SSD). This direction is in the orientation of surface stream with higher elevation (in the south) to the surface stream with lower elevation (in the north), where the study area is between these surface streams. While during the dry season (WSD), there is no preferred direction since there is almost no flow.
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Acknowledgements
The authors would like to express gratitude to the technical staff of the Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment, and Arid Land Agriculture, King Abdulaziz University, who measured and collected the data at Hadat Ash-Sham Farm Experimental Station in 16 months.
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Budiman, J.S., Al-Amri, N.S., Chaabani, A. et al. Geostatistical based framework for spatial modeling of groundwater level during dry and wet seasons in an arid region: a case study at Hadat Ash-Sham experimental station, Saudi Arabia. Stoch Environ Res Risk Assess 36, 2085–2099 (2022). https://doi.org/10.1007/s00477-021-01971-9
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DOI: https://doi.org/10.1007/s00477-021-01971-9