Though biological soil crusts (biocrusts) form abundant covers in arid and semiarid regions, their competing effects on soil hydrologic conditions are rarely accounted for in models. This study presents the modification of a soil water balance model to account for the presence of biocrusts at different levels of development (LODs) and their impact on one-dimensional hydrologic processes during warm and cold seasons. The model is developed, tested, and applied to study the hydrologic controls of biocrusts in the context of a long-term manipulative experiment equipped with meteorological and soil moisture measurements in a Colorado Plateau ecosystem near Moab, Utah. The climate manipulation treatments resulted in distinct biocrust communities, and model performance with respect to soil moisture was assessed in experimental plots with varying LOD as quantified through a field-based roughness index (RI). Model calibration and testing yielded excellent comparisons to observations and smooth variations of biocrust parameters with RI approximated through simple regressions. The model was then used to quantify how LOD affects soil infiltration, evapotranspiration, and run-off under calibrated conditions and in simulation experiments with gradual modifications in biocrust porosity and hydraulic conductivity. Simulation results show that highly developed biocrusts modulate soil moisture nonlinearly with LOD by altering soil infiltration and buffering against evapotranspiration losses, with small impacts on run-off. The nonlinear and threshold variations of the soil water balance in the presence of biocrusts of varying LOD helps explain conflicting outcomes of various field studies and sheds light on the ecohydrological role of biocrusts in arid and semiarid ecosystems.

Abstract. As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. To better quantify these hydroclimatic changes, it is crucial that the scientific community establishes a reasonably accurate understanding of the spatial patterns associated with the basin hydrologic response. In this study, we employed remotely sensed land surface temperature (LST) and snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess a regional hydrological model applied over the Colorado River Basin between 2003 and 2018. Based on the comparison between simulated and observed LST and SCF spatiotemporal patterns, a stepwise strategy was implemented to enhance the model performance. Specifically, we corrected the forcing temperature data, updated the time-varying vegetation parameters, and upgraded the snow-related process physics. Simulated nighttime LST errors were mainly controlled by the forcing temperature, while updated vegetation parameters reduced errors in daytime LST. Snow-related changes produced a good spatial representation of SCF that was consistent with MODIS but degraded the overall streamflow performance. This effort highlights the value of Earth observing satellites and provides a roadmap for building confidence in the spatiotemporal simulations from regional models for assessing the sensitivity of the Colorado River to climate change.

Accurate characterization of precipitation P at subdaily temporal resolution is important for a wide range of hydrological applications, yet large-scale gridded observational datasets primarily contain daily total P. Unfortunately, a widely used deterministic approach that disaggregates P uniformly over the day grossly mischaracterizes the diurnal cycle of P, leading to potential biases in simulated runoff Q. Here we present Precipitation Isosceles Triangle (PITRI), a two-parameter deterministic approach in which the hourly hyetograph is modeled with an isosceles triangle with prescribed duration and time of peak intensity. Monthly duration and peak time were derived from meteorological observations at U.S. Climate Reference Network (USCRN) stations and extended across the United States, Mexico, and southern Canada at 6-km resolution via linear regression against historical climate statistics. Across the USCRN network (years 2000–13), simulations using the Variable Infiltration Capacity (VIC) model, driven by P disaggregated via PITRI, yielded nearly unbiased estimates of annual Q relative to simulations driven by observed P. In contrast, simulations using the uniform method had a Q bias of −11%, through overestimating canopy evaporation and underestimating throughfall. One limitation of the PITRI approach is a potential bias in snow accumulation when a high proportion of P falls on days with a mix of temperatures above and below freezing, for which the partitioning of P into rain and snow is sensitive to event timing within the diurnal cycle. Nevertheless, the good overall performance of PITRI suggests that a deterministic approach may be sufficiently accurate for large-scale hydrologic applications.

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