Effects of Climate, Basin Characteristics, and High‐Capacity Wells on Baseflow in the State of Wisconsin, United States

Abstract

Understanding the factors that affect groundwater-surface water interactions will lead to policy decisions that will both protect stream habitats and provide for the needs of the agriculture industry.ABSTRACT: When it comes to water resources management, it is critical to understand the factors that affect baseflow processes. Declines in baseflow due to increased use of the groundwater from unconfined aquifers is well documented, but that is not the case for confined aquifers. Furthermore, since the groundwater basin size and shape can be different than the surface water basin, the use of the surface basin to determine well withdrawal rates can affect baseflow and be problematic. This study used the variables determined to be related to baseflow variability (precipitation, temperature, drainage class, available storage, land use, and slope) and the withdrawal rates of wells located within the study basins to create regression models for the state of Wisconsin, United States. We find that: (1) precipitation and temperature variable are significant in explaining the temporal variability of baseflow, whereas land cover variables are important when the temporal variability is not considered; (2) evaporation and soil drainage are important in basins over unconfined aquifers, whereas precipitation the most significant over confined aquifers; (3) whether to use surface water or groundwater divides to delineate basins matters in particular conditions, and (4) groundwater withdrawal rates do not significantly affect baseflow when using statistical analysis. Therefore, analyzing baseflow should be supplemented by a process-based model for the effects of groundwater withdrawals.

Spatiotemporal Variability of Regional Rainfall Frequencies in South Korea for Different Periods

“…Many studies have combined dynamic and statistical approaches to downscale and perform the spatial interpolation of precipitation [34,[44][45][46][47]. In this study, the probability rainfall data were recorded for 61 stations, along with the latitude and longitudinal coordinates of each station.…”

“…The kriging interpolation tool in ArcGIS was used to create a raster layer with a 1 km resolution, consistent with the resolution applied in previous studies in South Korea [48][49][50]. The tool was used to weight the surrounding measured values and estimate values in unmeasured locations using the ArcGIS kriging function [47].…”