Soil loss rates on rangelands are considered one of the few quantitative indicators for assessing rangeland health and conservation practice effectiveness. An erosion model to predict soil loss specific for rangeland applications is needed because existing erosion models were developed from croplands where the hydrologic and erosion processes are different, largely due to much higher levels of heterogeneity in soil and plant properties at the plot scale and the consolidated nature of the soils. The Rangeland Hydrology and Erosion Model (RHEM) was designed to fill that need. RHEM is an event-based derivation of the WEPP model made by removing relationships developed specifically for croplands and incorporating new equations derived from rangeland data. RHEM represents erosion processes under disturbed and undisturbed rangeland conditions, it adopts a new splash erosion and thin sheet-flow transport equation developed from rangeland data, and it links the model hydrologic and erosion parameters with rangeland plant communities by providing a new system of parameter estimation equations based on 204 plots at 49 rangeland sites distributed across 15 western U.S. states. RHEM estimates runoff, erosion, and sediment delivery rates and volumes at the spatial scale of the hillslope and the temporal scale of a single rainfall event. Experiments were conducted to generate independent data for model evaluation, and the coefficients of determination (r 2) for runoff and erosion predictions were 0.87 and 0.50, respectively, which indicates the ability of RHEM to provide reasonable runoff and soil loss prediction capabilities for rangeland management and research needs.

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Extreme hydrologic responses following wildfires can lead to floods and debris flows with costly economic and societal impacts. Process-based hydrologic and geomorphic models used to predict the downstream impacts of wildfire must account for temporal changes in hydrologic parameters related to the generation and subsequent routing of infiltration-excess overland flow across the landscape.However, we lack quantitative relationships showing how parameters change with time-since-burning, particularly at the watershed scale. To assess variations in bestfit hydrologic parameters with time, we used the KINEROS2 hydrological model to explore temporal changes in hillslope saturated hydraulic conductivity (K sh ) and channel hydraulic roughness (n c ) following a wildfire in the upper Arroyo Seco watershed (41.5 km 2 ), which burned during the 2009 Station fire in the San Gabriel Mountains, California, USA. This study explored runoff-producing storms between 2008 and 2014 to infer watershed hydraulic properties by calibrating the model to observations at the watershed outlet. Modelling indicates K sh is lowest in the first year following the fire and then increases at an average rate of approximately 4.2 mm/h/year during the first 5 years of recovery. The estimated values for K sh in the first year following the fire are similar to those obtained in previous studies on smaller watersheds (<1.5 km 2 ) following the Station fire, suggesting hydrologic changes detected here can be applied to lower-order watersheds. Hydraulic roughness, n c , was lowest in the first year following the fire, but increased by a factor of 2 after 1 year of recovery. Post-fire observations suggest changes in n c are due to changes in grain roughness and vegetation in channels. These results provide quantitative constraints on the magnitude of fire-induced hydrologic changes following severe wildfires in chaparral-dominated ecosystems as well as the timing of hydrologic recovery.

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Effective conservation and utilization strategies for natural biological resources require a clear understanding of the geographic distribution of the target species. Tricholoma matsutake is an ectomycorrhizal (ECM) mushroom with high ecological and economic value. In this study, the potential geographic distribution of T. matsutake under current conditions in China was simulated using MaxEnt software based on species presence data and 24 environmental variables. The future distributions of T. matsutake in the 2050s and 2070s were also projected under the RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 climate change emission scenarios described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change (IPCC). The areas of marginally suitable, suitable and highly suitable habitats for T. matsutake in China were approximately 0.22 × 106 km2, 0.14 × 106 km2, and 0.11 × 106 km2, respectively. The model simulations indicated that the area of marginally suitable habitats would undergo a relatively small change under all four climate change scenarios; however, suitable habitats would significantly decrease, and highly suitable habitat would nearly disappear. Our results will be influential in the future ecological conservation and management of T. matsutake and can be used as a reference for studies on other ectomycorrhizal mushroom species.