Estimating Recharge Rates with Analytic Element Models and Parameter Estimation

Abstract

Quantifying the spatial and temporal distribution of recharge is usually a prerequisite for effective ground water flow modeling. In this study, an analytic element (AE) code (GFLOW) was used with a nonlinear parameter estimation code (UCODE) to quantify the spatial and temporal distribution of recharge using measured base flows as calibration targets. The ease and flexibility of AE model construction and evaluation make this approach well suited for recharge estimation. An AE flow model of an undeveloped watershed in northern Wisconsin was optimized to match median annual base flows at four stream gages for 1996 to 2000 to demonstrate the approach. Initial optimizations that assumed a constant distributed recharge rate provided good matches (within 5%) to most of the annual base flow estimates, but discrepancies of >12% at certain gages suggested that a single value of recharge for the entire watershed is inappropriate. Subsequent optimizations that allowed for spatially distributed recharge zones based on the distribution of vegetation types improved the fit and confirmed that vegetation can influence spatial recharge variability in this watershed. Temporally, the annual recharge values varied >2.5-fold between 1996 and 2000 during which there was an observed 1.7-fold difference in annual precipitation, underscoring the influence of nonclimatic factors on interannual recharge variability for regional flow modeling. The final recharge values compared favorably with more labor-intensive field measurements of recharge and results from studies, supporting the utility of using linked AE-parameter estimation codes for recharge estimation.

The importance of diverse data types to calibrate a watershed model of the Trout Lake Basin, Northern Wisconsin, USA

“…1) and is fed by four streams. Annual precipitation averages about 79 cm/yr (Cheng, 1994) and average terrestrial groundwater recharge is estimated to be 27 cm/yr (Hunt et al, 1998), with slightly higher rates in areas composed of a higher percentage of conifer trees (Dripps et al, 2006). Annual evaporation from the lakes is about 54 cm/yr (Krabbenhoft et al, 1990;Wentz and Rose, 1991); thus, net precipitation on the lakes is about 25 cm/yr.…”

The spatial and temporal variability of groundwater recharge in a forested basin in northern Wisconsin

“…Table I summarizes the model's input parameters. The SWB model strikes the appropriate balance between complexity and accuracy while meeting the needs of practicality and applicability and provides groundwater modellers, planners, and policy makers with an easily applicable method for providing spatially and temporally distributed estimates of recharge for regional groundwater flow models and water resource management. Dripps and Bradbury (2007) have tested the model's performance on two watersheds in Wisconsin and showed that the model yields comparable recharge estimates and spatial recharge arrays relative to values measured in the field and calculated by two significantly more rigorous models, a two-dimensional analytic element groundwater flow model (Haitjema and Kelson, 1994;Dripps et al, 2006) and a comprehensive, modular terrestrial biosphere model (IBIS-2) (Foley et al, 1996;Kucharik et al, 2000;Lenters et al, 2000;Dripps, 2003).…”

Evaluation of remotely sensed data for estimating recharge to an outcrop zone of the Guarani Aquifer System (South America)

“…Because recharge has a complex interaction with other water-budget components (Dripps et al 2006), several methods are suggested for its estimation. In general, the methods used to estimate recharge differ from each other by the source of data input (surface water, unsaturated and saturated zone), the governing hypothesis, and the range of spatial and temporal applicability (Scanlon et al 2002).…”