Simulation of the shallow hydrologic system in the vicinity of Middle Genesee Lake, Wisconsin, using analytic elements and parameter estimation (original) (raw)
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Simulating the influence of lake position on groundwater fluxes
Water Resources Research, 1994
Groundwater flow around three hypothetical flow-through lakes, located in the upper, middle, and lower portions of a watershed, was numerically simulated in cross section under steady state and transient conditions. Results showed that (1) groundwater fluxes into and out of the lake located in the lowest position were higher and more stable than for lakes farther upgradient, and (2) periods of intense groundwater recharge caused flow reversals and the formation of a stagnation point downgradient of the uppermost lake, thereby causing a net increase in groundwater inflow. These simulation results provide guidance on monitoring network design, including the frequency of monitoring and/or sampling. Introduction Quantifying groundwater fluxes is an important component in every hydrological lake study. Numerical modeling is helpful in analyzing field data and quantifying these fluxes [e.g., Krabbenhoft et al., 1990; Cherkauer and Zager, 1989]. According to Born et al. [1979] there are more than 30,000 lakes in the upper Midwest (parts of Michigan, Minnesota, and Wisconsin). In this region, seepage lakes typically receive precipitation and groundwater inflow and discharge water through evaporation and groundwater seepage. Drainage lakes also receive and/or lose water via surface streams. In humid zones, more water enters the lake by precipitation than is lost by evaporation. Hence lakes are either groundwater flow-through lakes, which receive groundwater inflow through part of the lake bed and discharge to groundwater over the rest of the lake bed, or recharge lakes, which lose water to the groundwater system over the entire lake bed. Numerical simulations of hypothetical groundwater-lake systems by Winter [1976, 1978] showed that when there are groundwater mounds on all sides of a lake and a stagnation point on its downgradient side, the lake will receive groundwater inflow through its entire lake bed so that there is no seepage from the lake. Groundwater discharge lakes have not been widely reported from field studies, although there is some evidence [Anderson and Munter, 1981] that flowthrough lakes can temporarily become discharge lakes in response to high-intensity recharge events. Transient effects are usually neglected in groundwaterlake studies. Field studies that addressed transient effects include early work by Meyboorn [1967] and more recent investigations by Winter [1986], who documented seasonal transience in lake systems in Nebraska, and Anderson and Munter [1981], Cherkauer and Zager [1989], and Anderson and Cheng [1993], who documented the seasonal formation of groundwater mounds near lakes in Wisconsin.
A multiscale investigation of ground water flow at Clear Lake, Iowa
Ground water, 2006
Ground water flow was investigated at Clear Lake, a 1468-ha glacial lake in north-central Iowa, as part of a comprehensive water quality study. A multiscale approach, consisting of seepage meters (and a potentiomanometer), Darcy's law, and an analytic element (AE) model, was used to estimate ground water inflow to and outflow from the lake. Estimates from the three methods disagreed. Seepage meters recorded a median-specific discharge of 0.25 lm/s, which produced a lake inflow rate between 90,750 and 138,200 m 3 /d, but no detectable outflow. A wave-induced Bernoulli effect probably compromised both inflow and outflow measurements. Darcy's law was applied to 11 zones around the lake, producing inflow and outflow values of 10,500 and 5000 m 3 /d, respectively. The AE model, GFLOW, coupled with the parameter estimation model, UCODE, simulated ground water flow in a 700-km 2 region using 31 hydraulic head and base flow measurements as calibration targets. The model produced ground water inflow and outflow rates of 14,300 and 9200 m 3 /d, respectively. Although not a substitute for field data, the model's ability to simulate ground water flow to the lake and the region, estimate uncertainty for model parameters, and calculate a lake stage and associated lake water balance makes it a powerful tool for water quality management and an attractive alternative to the traditional methods of ground water/lake investigation. 100 milliliters (Mason City Globe Gazette 1998c). This precipitated closure of some or all of the three main beaches on the lake and caused an economic loss for the region. Shortly thereafter, a study funded by the Iowa
Journal of Hydrology: Regional Studies, 2015
Study region: Lake Buchanan, a major reservoir for the City of Austin area, the Texas Hydrologic Region 12, USA. Numerical climate models are increasingly being used by climate scientists to inform water management. However, successful transitions from climate models (O(10-100 km)) to water resources studies (O(100 m-1 km)) still need improved data structures and modeling strategies to resolve spatial scale mismatch. In this study, we introduce a mechanistic lake-level modeling framework that consists of a state-of-the-art land surface model-Noah-MP, a vector-based river routing scheme-RAPID, and a lake mass-balance model. By conducting a case study for Lake Buchanan, we demonstrate the capability of the framework in predicting lake levels at seasonal lead times. The experiments take into account different runoff resolutions, model initialization months, and multiple lead times. Uncertainty analyses and sensitivity tests are also conducted to guide future research. New hydrological insights: Different from traditional grid-based solutions, the framework is directly coupled on the vector-based NHDPlus dataset, which defines accurate hydrologic features such as rivers, dams, lakes and reservoirs. The resulting hybrid framework therefore allows for more flexibility in resolving "scaling-issues" between large-scale climate models and fine-scale applications. The presented hindcast results also provide insight into the influences of baseline LSM resolutions, initialization months, and lead times, which would ultimately help improve lake-level forecast skills.