Evaluation of non-uniform groundwater level data using spatiotemporal modeling

Abstract

Groundwater is one of the main sources of freshwater. To ensure its sustainability, it is important to know its current status and changing pattern over time, through the essential groundwater monitoring program conducted by water management planners, groundwater modelers and urban developers. However, uniformly distributed data is hardly available in most catchments. In this study, the Spatiotemporal Regression Kriging method (Rkriging) was adopted to derive a spatiotemporal pattern for Harvey River Catchment in Western Australia, using the limited groundwater data in the catchment. The accuracy of the estimation was investigated using the Leave-One-Out Cross-Validation approach. Time-series analysis (i.e., auto-correlation and cross-correlation) was then employed to provide a better understanding of the estimated groundwater level change (ΔGWL) over time. To gain insight into the change of groundwater levels, the correlation between groundwater level (GWL) and precipitation pattern with possible time-lag was explored. The results showed that the Rkriging method is satisfactory and the findings were consistent with the previously published results in literature in the area. The estimated decreasing GWL trend matched the precipitation pattern in the catchment. Such shallow groundwater levels in Harvey Catchment resulted in a short time-lag between the precipitation and GWL time-series. The proposed method should be applied to other catchments with limited groundwater data and can be a useful approach for catchments with irregular temporal and spatial data.