Water Flow Modeling with Dry Bulk Density Optimization to Determine Hydraulic Properties in Mountain Soils

A new method for determining profile-average and depth-wise hydraulic properties in heterogeneous mountain soils is presented using the GEOtop watershed model in 1-D vertical mode. Dry soil bulk density–converted volumetric soil water retention data are used to determine van Genuchten soil water retention parameters, and the Kozeny–Carman equation is used to determine saturated soil hydraulic conductivity. Optimum dry soil bulk densities are identified by minimizing the sum of squared error between measured and calculated soil water content time series. The new method was tested using soil moisture data from soil profiles at the Dry Creek Experimental Watershed, Boise, ID, and the Libby Creek Experimental Watershed, Laramie, WY. Results of different scenarios showed that the optimization of a single profile-average dry soil bulk density is a good option for describing soil water flow in the heterogeneous mountain soils. Soil water content modeling efficiency (ME) values of 0.084 ≤ ME ≤ 0.745 and –2.443 ≤ ME ≤ 0.373 were found for the Dry Creek and Libby Creek sites, respectively. Relatively low ME values for the deepest sensor depths for some scenarios were attributed to the overestimation of soil water freezing and uncertainty in the soil water retention function near saturation. The resulting calibration procedure is computationally efficient because only one parameter (dry soil bulk density) is optimized

A hybrid reduced-order model of fine-resolution hydrologic simulations at a polygonal tundra site

High-resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional- and watershed-scale processes. Direct deterministic simulations of fine-resolution land surface variables present many challenges, including high computational cost. We therefore propose the use of reduced-order modeling techniques to facilitate emulation of fine-resolution simulations. We use an emulator, Gaussian process regression, to approximate fine-resolution four-dimensional soil moisture fields predicted using a three-dimensional surface-subsurface hydrological simulator (PFLOTRAN). A dimension-reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced-order model (ROM). The ROM reduces simulation computational demand to negligible levels compared to the underlying fine-resolution model. In addition, the ROM that we constructed is equipped with an uncertainty estimate, allowing modelers to construct a ROM consistent with uncertainty in the measured data. The ROM is also capable of constructing statistically equivalent analogs that can be used in uncertainty and sensitivity analyses. We apply the technique to four polygonal tundra sites near Barrow, Alaska that are part of the Department of Energy’s Next-Generation Ecosystem Experiments (NGEE)-Arctic project. The ROM is trained for each site using simulated soil moisture from 1998-2000 and validated using the simulated data for 2002 and 2006. The average relative RMSEs of the ROMs are under 1%

Modeling Runoff Generation in a Small Snow-Dominated Mountainous Catchment

Snowmelt in mountainous areas is an important contributor to river water flows in the western United States. We developed a distributed model that calculates solar radiation, canopy energy balance, surface energy balance, snow pack dynamics, soil water flow, snow–soil–bedrock heat exchange, soil water freezing, and lateral surface and subsurface water flow. The model was applied to describe runoff generation in a subcatchment of the Dry Creek Experimental Watershed near Boise, ID. Calibration was achieved by optimizing the soil water field capacity (a trigger for lateral subsurface flow), lateral saturated soil hydraulic conductivity, and vertical saturated hydraulic conductivity of the bedrock. Validation results show that the model can successfully calculate snow dynamics, soil water content, and soil temperature. Modeled streamflow for the validation period was underestimated by 53%. The timing of the streamflow was captured reasonably well (modeling efficiency was 0.48 for the validation period). The model calculations suggest that 50 to 53% of the yearly incoming precipitation in the subcatchment is consumed by evapotranspiration. The model results further suggest that 34 to 36% of the incoming precipitation is transformed into deep percolation into the bedrock, while only 11 to 16% is transformed into streamflow