This study aims to assess the impact of climate change on water resources in a large watershed within the Laurentian Great Lakes region, using the fully integrated surface‐subsurface model HydroGeoSphere. The hydrologic model is forced with an ensemble of high‐resolution climate projections from the Weather Research and Forecasting (WRF) model. The latter has been extended with an interactive lake model (FLake) to capture the effect of the Great Lakes on the regional climate. The WRF ensemble encompasses two different moist physics configurations at resolutions of 90, 30, and 10 km, as well as four different initial and boundary conditions, so as to control for natural climate variability. The integrated hydrologic model is run with a representative seasonal cycle, which effectively controls natural climate variability, while remaining computationally tractable with a large integrated model. However, the range of natural variability is also investigated, as are the impacts of climate model resolution and bias correction. The two WRF configurations show opposite climate change responses in summer precipitation, but similar responses otherwise. The hydrologic simulations generally follow the climate forcing; however, due to the memory of the subsurface, the differences in summer propagate throughout the entire seasonal cycle. This results in a set of dry scenarios with reduced streamflow and water availability year‐round and a set of wet scenarios with increased streamflow for all times excluding the spring peak, which does not increase. Most of the analysis focuses on streamflow, but changes in the seasonal cycle of baseflow and groundwater recharge are also analyzed.
This study compares saturated hydraulic conductivities (K s ) of three sandy soils such as coarse, medium and fine sand. The K s was obtained using three different methods: empirical methods based on the grain size analysis, the relative effective porosity model (REPM), and breakthrough curve analyses based on tracer tests. Column drainage tests were performed to characterize the water retention properties of the samples, which are required in the REPM. Bench scale tracer tests with various conditions were conducted to obtain reasonable linear velocities of the samples using breakthrough curve analyses and then K s was estimated using Darcy's law. For the REPM, the differences of K s for the coarse and fine sand soils were less than one order of magnitude; however, the difference of K s values between the empirical methods and the breakthrough curve analyses was larger than one order of magnitude. The comparison results suggest that the REPM can be a reliable method for estimating K s for soil grains, and is cost effective due to its experimental simplicity.
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