Assessing runoff and erosion on woodland‐encroached sagebrush steppe using the Rangeland Hydrology and Erosion Model

Abstract

The transition of sagebrush‐dominated (Artemisia spp.) shrublands to pinyon (Pinus spp.) and juniper (Juniperus spp.) woodlands markedly alters resource‐conserving vegetation structure typical of these landscapes. Land managers and scientists in the western United States need knowledge and predictive tools for assessment and effective targeting of tree‐removal treatments to conserve or restore sagebrush vegetation and associated hydrologic function. This study developed modeling approaches to quantify the hydrologic vulnerability and erosion potential of sagebrush rangelands in the later stages of woodland encroachment and in response to commonly applied tree‐removal treatments. Using experimental data from multiple sites in the Great Basin Region, USA, and process‐based knowledge from decade‐long vegetation and rainfall simulation studies at those sites, we (1) assessed the capability of the Rangeland Hydrology and Erosion Model (RHEM) to accurately predict patch‐scale (12 m2) measured runoff and erosion from tree canopy and intercanopy hydrologic functional units in untreated and burned woodlands 9 years postfire, and (2) developed and evaluated multiple RHEM approaches/frameworks to model aggregated effects of tree canopy and intercanopy areas on patch‐ and hillslope‐scale (50 m length) runoff and erosion processes in untreated and treated (burned, cut, and masticated) woodlands. The RHEM accurately predicted measured runoff and sediment yield from patch‐scale rainfall simulations as partitioned on untreated and treated tree canopy and intercanopy areas and effectively parameterized the dominant controls on runoff and erosion process in woodlands. With few exceptions, evaluated hillslope‐scale RHEM frameworks similarly predicted reduced hydrologic vulnerability and erosion potential for conditions 9 years following tree removal by burning, cutting, and mastication treatments. Regressions of RHEM‐predicted hillslope runoff, sediment, and hydraulic/erosion parameters with bare ground and ground cover attributes indicate all RHEM frameworks effectively represented the dominant controls on hydrologic and erosion processes for rangelands and woodlands. The results provide RHEM frameworks and recommendations for assessing hydrologic vulnerability and erosion potential on woodland‐encroached sites and predicting the effectiveness of tree removal to reestablish a water and soil resource‐conserving vegetation structure on sagebrush rangelands. We anticipate our RHEM or similar modeling approaches may be applicable to analogous water‐limited landscapes elsewhere subject to woody plant encroachment.

Informing Sustainable Forest Management: Remote Sensing Strategies for Assessing Soil Disturbance after Wildfire and Salvage Logging

“…There was more nonphotosynthetic vegetation (NPV) in the plots than live vegetation; however, this was likely due to the timing of the field sampling in early fall. Organic ground cover of at least 60% is generally considered to be the threshold for elevated runoff or erosion events [49][50][51]. In the seven units sampled for DSD, the percent of the area that was still found to have detrimental disturbance 2-4 years after salvage logging ranged from 1% to 20% (Table 4).…”

Grassland Resilience to Woody Encroachment in North America and the Effectiveness of Using Fire in National Parks

“…), and oak (Quercus spp.) were the most studied encroaching trees [33][34][35]. The tall mesquite was also defined as encroaching trees in some studies.…”

An Artificial Neural Network to Estimate the Foliar and Ground Cover Input Variables of the Rangeland Hydrology and Erosion Model

“…Models that estimate or predict erosion are critical tools for rangeland management -used in applications such as estimating soil vulnerability and assessing the impacts of soil protection policies (Flanagan et al, 2001). The Rangeland Hydrology and Erosion Model (RHEM) is a process-based hydraulic soil erosion prediction tool specific for rangeland applications that estimates runoff, soil loss, and sediment yield from storm events with sufficient accuracy (RHEM performance has been evaluated by the percent bias (PBIAS) (Gupta et al, 1999) and Root mean squared error-observations standard deviation ratio (RSR) (N. Moriasi et al, 2007)) for a wide range of applications (Nearing et al, 2011;Hernandez et al, 2017;Williams et al, 2022). A RHEM scenario includes four groups of input data (Al-Hamdan et al, 2015): 1-Climate station information, often from the CLImate GENerator (CLIGEN) model (Nicks et al, 1995), 2-Soil features, 3-Slope percentage and shape, and 4-Foliar cover fractions (Annual Forbs, Bunch Grass, Shrubs, Sod Grass) and Ground cover fractions (Litter, Biological Crusts, Basal, Rock).…”

“…Department of Agriculture (USDA), Natural Resources Conservation Service(NRCS) Rangeland National Resource Inventory (NRI) have been used to create real-world RHEM scenarios (Weltz et al, 2014). Running RHEM at a plot scale (tens of square meters) provides insight into hydrologic and erosion patterns (Hernandez et al, 2013;Williams et al, 2022). Currently, running RHEM is mostly done at plot scales.…”