Development of a Statistical Predictive Model for Daily Water Table Depth and Important Variables Selection for Inference

Accurately predicting water table dynamics is vital for sustaining groundwater resources,ecological functions, and anthropogenic activities. This study evaluates autoregressive model witha) prediction under sparsity assumption within model coefficients, b) allows lags present in bothdependent and independent variables for estimating daily water table depth using hydroclimaticdata from the USDA Forest Service Santee Experimental Forest(SC) and D1(NC). Data from2006–2019(SC) and 1988–2008(NC) were used, with predictors including soil and air temperature,precipitation, wind, and radiation. For WS80, RMSE during the dormant season was 10.09cm, withdaily testing phase RMSE 14.94cm. The model achieved an R2 0.93 for 2019(dry year) and 0.96for 2016(wet year). Solar radiation, rainfall, and wind direction were among the most influentialvariables. This predictive model can aid forest managers and hydrologists in using water tables forassessing wetland hydrology and related ecosystem functions in management decisions and provideout-of-sample prediction with reasonable accuracy.