Evaluating Climate Variability and Pumping Effects in Statistical Analyses

Abstract

As development of ground water resources reaches the limits of sustainability, it is likely that even small changes in inflow, outflow, or storage will have economic or environmental consequences. Anthropogenic impacts of concern may be on the scale of natural variability, making it difficult to distinguish between the two. Under these circumstances, we believe that it is important to account for effects from both ground water development and climate variability. We use several statistical methods, including trend analysis, cluster analysis, and time series analysis with seasonal decomposition, to identify climate and anthropogenic effects in regional ground water levels and spring discharge in southern Nevada. We discuss the parameterization of climate and suggest that the relative importance of various measures of climate provides information about the aquifer system response to climate. In our system, which may be characteristic of much of the arid southwestern United States, ground water levels are much more responsive to wet years than to dry years, based on the importance of selected climate parameters in the regression. Using cluster analysis and time series seasonal decomposition, we relate differences in amplitude and phase in the seasonal signal to two major forcings-climate and pumping-and distinguish between a regional recharge response to an extremely wet year and a seasonal pumping/evapotranspiration response that decays with distance from the pumping center. The observed spring discharge data support our hypothesis that regional spring discharge, particularly at higher elevation springs, is sensitive to relatively small ground water level changes.

Tools to Estimate Groundwater Levels in the Presence of Changes of Precipitation and Pumping

“…when SPI values are close to zero). Other precipitation variables such as moving averages or lags will have less influence during dry conditions when precipitation values are close to zero, and more influence under wet conditions as precipitation values get larger [22]. In this study the SPI24 improved the explanation and prediction of groundwater fluctuations in multiple regression models and was able to represent the systematic response to wet and dry periods that occurs at monitoring wells in the study region.…”

“…The 24-month SPI (SPI24) was used for this study based on the work done by Mayer and Congdon (2008) [22]. They found that the 24-month SPI has the least influence in regression equations during normal precipitation periods (i.e.…”

“…To recognize these impacts a "covariate" also plays an important role in this study. A covariate is a statistical term used to identify an interaction which is not measured but is observed in the record [22] [40] [41]. A covariate may be binary and is often referred to as either a hidden, lurking or dummy variable.…”

“…Monthly growing season (May-September) SPI data for Wisconsin climate division 5 were obtained from NCDC. The SPI is a normalized index that quantifies precipitation deficits, can be calculated for any desired duration, and takes into account time scales in the analysis of wet and dry periods for water availability and use [22] [36].…”

“…Despite near average precipitation totals, questions remain about the effects and interactions of precipitation on groundwater levels. Many studies have been conducted throughout the United States that relate groundwater pumping to declines in surface waters or decreases of water levels in monitoring wells [18] [19] [20] [21] [22]. Only recent permitting practices in Wisconsin have required high capacity wells to log pumping rates and volumes, so historic pumping data are rarely available.…”