The GEOtop model was used to calculate water flow in mountain soils. Dry soil bulk density was optimized to determine soil hydraulic properties. Soil water contents for two watersheds were used to test the optimization procedure. 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.
Integrated watershed models can be used to calculate streamflow generation in snow‐dominated mountainous catchments. Parameterization of water flow is often complicated by the lack of information on subsurface hydraulic properties. In this study, bulk density optimization was used to determine hydraulic parameters for the upper and lower regolith in the GEOtop model. The methodology was tested in two small catchments in the Dry Creek Watershed in Idaho and the Libby Creek Watershed in Wyoming. Modelling efficiencies for profile‐average soil–water content for the two catchments were between 0.52 and 0.64. Modelling efficiencies for stream discharge (cumulative stream discharge) were 0.45 (0.91) and 0.54 (0.94) for the Idaho and Wyoming catchments, respectively. The calculated hydraulic properties suggest that lateral flow across the upper–lower regolith interface is an important driver of streamflow in both the Idaho and Wyoming watersheds. The overall calibration procedure is computationally efficient because only two bulk density values are optimized. The two‐parameter calibration procedure was complicated by uncertainty in hydraulic conductivity anisotropy. Different upper regolith hydraulic conductivity anisotropy factors had to be tested in order to describe streamflow in both catchments.
The prediction of snowmelt in mountainous forests strongly depends on the accurate description of sensible and latent heat turbulent fluxes. Uncertainty about the withincanopy wind conditions especially poses a challenge, with relatively few studies examining both above-and below-canopy turbulent fluxes. In this study, turbulent flux predictions from a state-of-the-art watershed model GEOtop were verified against eddy covariance data from one above-canopy tower and two below-canopy towers in a snow-dominated coniferous forest in south-eastern Wyoming. The model was applied in one-dimensional vertical mode using field-observed vegetation parameters and laboratory-measured soil water retention data. The model was calibrated by identifying optimum values for the canopy fraction and the within-canopy eddy decay coefficient using the brute-force method. Above-canopy sensible heat flux at the Glacier Lakes Ecosystem Experiments Site was predicted reasonably well (r 2 = .851). The prediction of above-canopy latent heat flux was weaker (r 2 = .426). For latent heat flux, errors in 30-min values offset each other when fluxes were aggregated over time, resulting in realistic mean diurnal trends. Below-canopy turbulent flux at two sites in the Libby Creek Experimental Watershed were predicted with variable success with r 2 = .031-.146 for sensible heat flux and r 2 = .445-.581 for latent heat flux. Modelled below-canopy sensible heat flux was too low due to the underestimation of daytime ground surface temperature, because of not enough solar radiation reaching the soil surface. This study suggests that future work on GEOtop and related models should include better parameterizations of the ground surface energy balance to more reliably predict snowmelt and streamflow from mountainous forests. KEYWORDS forest, modelling, snow cover, surface energy balance 1 | INTRODUCTION Streamflow from snow-dominated mountainous ecosystems is an important source of water in many parts of the world, including the Western United States (Bales et al., 2006). The timing of snowmelt is strongly influenced by the canopy and ground surface energy balances. These energy balances show high spatio-temporal variability with differences in elevation, slope, aspect, and vegetation cover, and diurnal and seasonal fluctuations in solar radiation, temperature, and precipitation, all playing a role. As a result, watershed computer
HighlightsAn international dataset of simulated breakpoint precipitation climate stations was used to overcome the limitations of fixed-interval precipitation in global soil erosion applications.The international simulated breakpoint dataset was validated against collocated high-quality, high-resolution precipitation data from a ground network and other data sources.The process-based Rangeland Hydrology and Erosion Model (RHEM) was used to predict erosion based on global climate classifications and soil properties.Critical precipitation factors in RHEM scenarios were identified based on their ability to predict erosion rates.Abstract. Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data as opposed to breakpoint precipitation data, which better preserves precipitation characteristics such as intensity and duration. Due to their wider availability, erosion modeling applications and risk assessments are typically based on fixed-interval data. However, these applications could be subject to substantial erosion underestimation bias related to time-averaged precipitation. Alternatively, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset of climate parameters from more than 10,000 long-term climate stations in numerous countries was evaluated for use in parameterizing CLIGEN simulations. CLIGEN-simulated event characteristics and derived statistics such as annual rainfall erosivity were compared to high-quality, high-resolution NOAA-ASOS precipitation data where available. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs for simple bare soil scenarios with site-specific soil textures taken from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most critical climate factor for determining sediment yield, and combined CLIGEN precipitation factors had approximately as much predictive power as soil texture. Average annual rainfall erosivity values were calculated for each location and were 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively. This finding is in agreement with the latest research on the topic. Keywords: ASOS, CLIGEN, Erosivity, Global soil erosion, Machine learning, RHEM, Simulated breakpoint precipitation.
Abstract. This dataset contains input parameters for 12 703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature, and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-, 20-, and 10-year record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the US, which were compared to a selection of stations from an existing US CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen (last access: 20 November 2020) and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020a).
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