Rangeland Hydrology And Erosion Model Research Articles

Snow simulation for the rangeland hydrology and erosion model

In the western US, most rangelands receive snowfall. Yet, a commonly used tool to assess rangeland’s vulnerability to erosion, the USDA’s Rangeland Hydrology and Erosion Model (RHEM) is run using long-term simulated climate inputs that assumes that all precipitation occurs as rainfall. This can be problematic for areas that receive heavy snowfall or substantial rain-on-snow events. In this research, we have developed an efficient snow module for RHEM, called RHEM-Snow, which partitions precipitation between rainfall and snowfall, simulates snowpack accumulation and ablation, and passes net water input (consisting of rainfall, snowmelt, or both) to RHEM. In some areas, the inclusion of the snow module can reduce annual overland flow runoff and erosion estimates by more than 20 % of the total annual overland flow runoff and erosion produced without the snow module (or by as much as 10–50 mm/year for overland flow runoff or >100 kg/ha-yr for erosion). The reclassification of precipitation events from rainfall in RHEM to snowfall in RHEM-Snow tends to reduce overland flow runoff and erosion, but this reduction can be partially counterbalanced by increases from snowmelt and rain-on-snow. However, hydrologic responses to rain-on-snow events can either be enhanced or muted depending on the characteristics of the storm and the snowpack, as sometimes the snowpack can absorb the precipitation inputs, and sometimes snowmelt enhances the precipitation inputs. Because of this mixed impact, the average difference in erosion caused by rain on snow events is relatively small compared to corresponding events where only the liquid phase is considered. Further study is needed of the complex erosion processes under snowpack and frozen soil/variable saturation conditions. Overall, RHEM-Snow provides more realistic timing and magnitude of overland flow runoff and erosion in cold environments, better satisfying the conditions for RHEM applications.

Integrating Erosion Models Into Land Health Assessments to Better Understand Landscape Condition

Wind and water erosion can severely impact natural resources and ecosystem services, making soil erosion management essential to sustaining agroecosystems. Land health assessment protocols, such as Interpreting Indicators of Rangeland Health (IIRH), provide valuable information to make decisions on managing soil erosion in vulnerable drylands. Using quantitative erosion models with land health assessments can further inform management decisions. For example, sediment transport estimates from the Aeolian EROsion (AERO) model and Rangeland Hydrology and Erosion Model (RHEM) can help in understanding the impacts of differences in soil and vegetation on wind and water erosion risk. In this article, we provide a conceptual basis for using AERO and RHEM to support IIRH assessments that are used extensively by managers across United States rangelands. We describe how using erosion models with IIRH can (1) improve understanding about potential erosion rates for different types of storm events; (2) support identifying areas at risk of erosion where erosion evidence is not (yet) significant; (3) increase land health assessment consistency by providing reproducible erosion indicators; (4) provide another line of evidence to support assessment conclusions about land health; and (5) improve understanding about potential erosion rates across ecologically similar sites and over time. Effectively using erosion models to support land health assessments will improve wind and water erosion management in drylands, thus helping to protect and restore these ecosystems.

Predicting the coupling effects of grass and shrub with biological crust on splash and sheet erosion

Vegetation plays an important role in preventing soil erosion in arid and semi-arid regions. However, the current research on soil erosion under vegetation conditions neglected the role of biological crusts (BSC). The impact of different combinations of grass and shrub vegetation with BSC on the soil erosion process were investigated in this study through indoor simulated rainfall experiments and field experiments. Additionally, the calculation formula for the splash and sheet erodibility coefficients (KSS) in the Rangeland Hydrology and Erosion Model (RHEM) model was revised and validated. The results showed that the ability of BSC to reduce erosion was gradually enhanced with an increase in BSC coverage. The significant erosion reduction effect of BSC under different vegetation coverages was found. As the BSC coverage increased to 40 %, the soil erosion process changed from transport-limited to detach-limited. When grass and shrub was combined with BSC, the average flow velocity was smaller than that of the individual vegetation treatments. Moreover, the proportion of BSC resistance was the highest among the various vegetation combinations consistently. When the coverage of the combined BSC exceeded that of the vegetation, the BSC dominated the soil erosion process through its direct physical protection and indirect alteration of soil properties. When the BSC coverage was incorporated into KSS, the determination coefficients of the revised KSS were all above 0.8. This study deepened the understanding of the coupling effects of grass and shrub with BSC on slope erosion processes, and improved the predictive accuracy of the RHEM model.

Estimating Effective Hydraulic Conductivity (Ke) for the Rangeland Hydrology and Erosion Model (RHEM)

Highlights Effective hydraulic conductivity (Ke ) predictive equations in all RHEM versions have satisfactory performance. New Ke predictive equations were developed for RHEM applications over broader rangeland conditions. Ke values on most rangelands can be estimated through readily measurable ground cover and soil texture data. Abstract. Effective hydraulic conductivity (Ke) is an important parameter for the prediction of infiltration and runoff by the Rangeland Hydrology and Erosion Model (RHEM). Three sets of equations to predict Ke have previously been used in RHEM. These equations are mainly based on rainfall simulation data representing undisturbed sites and have not undergone comprehensive evaluation for various rangeland conditions, particularly after disturbances. The goal of this research was to evaluate these equations using independent data obtained from rainfall simulations conducted at multiple rangeland sites. Additionally, we developed and evaluated a new set of Ke predictive equations applying readily measurable cover and soils data spanning a wide range of vegetation, soil textures, and disturbance conditions. The results show that all previous Ke equations in RHEM have a “satisfactory” performance with index of agreement (d) > 0.75 and R2 > 0.4. The new Ke approach resulted in “very good” performance with d > 0.9 and R2 > 0.5. The new set of equations enhances RHEM for applications over broader rangeland conditions, including sparse vegetation cover following disturbances or community transitions. Keywords: Disturbed Rangelands, Hillslope Runoff, Infiltration.

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computational demand. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale. Using traditional models like Rangeland Hydrology and Erosion Model (RHEM) requires too much computation time and resources. In this study, we designed an Artificial Neural Network that is able to recreate the RHEM outputs (annual average runoff, soil loss, and sediment yield and not the daily storm event-based values) with high accuracy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion times faster on average using a GPU). We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them. We also, fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains comprehensive while it works specifically accurately for the real-world cases. We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies. Finally, the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.

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

AbstractThe 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.

A Global Rain-Driven Soil Erosion Investigation Based on Simulated Breakpoint Precipitation

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.

Temporally downscaling precipitation intensity factors for Köppen climate regions in the United States

Model inputs for prediction of runoff and soil erosion commonly require precipitation intensity information. Intensity is often estimated if precipitation data with high temporal resolution are unavailable. However, when intensity is time-averaged for fixed measurement intervals, estimates become increasingly underestimated with longer intervals due to the assumption that event durations begin and end at specified measurement intervals. In this study, adjustment factors were determined for downscaling the temporal resolution of intensity values derived from selected resolutions within the range of 10 to 1,440 min for Köppen-Geiger climate regions in the United States. In this case, monthly mean maximum 30 min intensity (MX.5P) was downscaled, which is a parameter used to generate stochastic meteorological inputs for models that include the Rangeland Hydrology and Erosion Model (RHEM) and the Water Erosion Prediction Project model (WEPP). The adjustment factors were given by regressions of reference MX.5P values derived from data with 5 min resolution against MX.5P values derived from data with lower temporal resolutions (≥10 min). In addition to using a slope coefficient for intensity in the regression equation, permutations of the equation included use of an elevation coefficient and constants, resulting in four total permutations. For the 143 stations and 17 climate regions analyzed, the four regression equations had roughly equal performance, and all gave statistically significant results. Regressions for adjusting hourly data using only an intensity coefficient in the equation had standard error of the estimate ranging from 1.01 to 2.96 mm h^–1 with an average of 2.04 mm h^–1. When downscaling daily values, the error range was 2.50 to 10.20 mm h^–1 with an average of 5.63 mm h^–1. Average time-to-peak intensity probability distributions for each climate region were also determined. Finally, a stochastic weather generator, CLIGEN, was used to test the effectiveness of applying the climate-based factors as an alternative to using subhourly data.

Temporally downscaling a precipitation intensity factor for soil erosion modeling using the NOAA-ASOS weather station network

Precipitation intensity is an important meteorological input for water erosion and runoff applications. A commonly used intensity factor is maximum 30-min intensity (I30), which represents the sustained intensity of a storm. Determining I30 is challenging for two reasons. First, intensity can vary significantly over time, even within very short durations of 5 min or less. Second, the majority of precipitation data sets are limited by their fixed-interval nature, and I30 may not be constant within fixed measurement intervals. When intensity is simply averaged given the accumulation of a measurement interval, the temporal resolution of the precipitation data set biases the result. Therefore, in this study, bias adjustments were determined for a range of selected temporal resolutions and Köppen-Geiger climate regions in the United States. In this case, the intensity factor was monthly mean maximum 30-minute intensity (MX.5P), which is a parameter used to generate stochastic meteorological inputs for models that include the Rangeland Hydrology and Erosion model (RHEM) and the Water Erosion Prediction Project model (WEPP). The adjustment factors were obtained by using linear regression of reference MX.5P values derived from breakpoint data against MX.5P values aggregated from the breakpoint data to represent lower temporal resolutions. The resulting slope coefficients were used to determine bias adjustment factors. In addition, multivariate machine learning regression was used to obtain more complex correlations involving a host of predictor variables that may each be determined from daily precipitation statistics and the spatial location of each station. In total, 609 stations and 16 climate classifications were represented in the regressions. Linear regressions for climate classifications gave RMSE for values of MX.5P derived from hourly data ranging from 0.98 to 3.46 mm hr−1 with an average of 2.18 mm hr−1. For daily data, the error range was 2.83–8.44 mm hr−1 with an average of 5.61 mm hr−1. The multivariate regression using machine learning algorithms improved regressions for coarser resolutions, reducing error to the 3–4 mm hr−1 range for downscaled daily values.

Mapping erosion risk for saline rangelands of the Mancos Shale using the rangeland hydrology erosion model

AbstractThis research used the rangeland hydrology and erosion model (RHEM) to map erosion risks affecting water quality of the Colorado River that originate on the Mancos Shale formation in Utah, Colorado, New Mexico, and Arizona. The Mancos Shale is a significant source of salinity to the river, and a portion of that salt load derives from erosion of rangeland soils. Here we demonstrate that the hillslope‐scale RHEM model can effectively characterize erosion risk across this large, discontinuous region. Inputs to RHEM included digital elevation data, maps of soil properties, the LANDFIRE vegetation map, Landsat and MODIS satellite imagery, field data from the Rangeland National Resource Inventory program of the US Natural Resources Conservation Service, and rainfall data from Atlas 14 of the U.S. National Atmospheric and Oceanographic Administration. RHEM predicted sediment yield at a 30‐m spatial resolution for storms with 30‐ and 60‐min durations whose intensities corresponded to 10‐ and 25‐year return frequencies. Results corresponded reasonably with prior field experiments that used the Walnut Gulch Rainfall Simulator (WGRS), with a Spearman's rank‐order correlation of .76 for cumulative sediment yield after 20 min of rainfall. Issues of input map accuracy were identified for rainfall intensity and estimates for sodium adsorption ratio (SAR) of soils. Correction of erroneous SAR at WGRS sites in one location improved rank‐order correlation to .93, indicating very good model performance where map inputs are accurate. The high‐resolution map of erosion risk developed from RHEM can help to prioritize specific areas for more intensive study and action.

Efficiency of using the rangeland hydrology and erosion model for assessing the degradation of pastures and forage lands in Aydarly, Kazakhstan

This study examined the use of a novel web-tool for Rangeland Hydrology and Erosion Model (RHEM) as a prediction runoff and erosion as a function of vegetation structure and behavior of different plant community phases and the amount of coverage for the different states in the Aydarly village of Jambul district of Almaty province. US Department of Agriculture experts and Kazakhstani scientists jointly conducted this study, where, based on the results, they received recommendations on improving rangeland. Results suggested that the model could be further improved with additional measured experimental data on infiltration, runoff, and soil erosion within key ecological sites in order to better quantify model parameters to reflect ecosystem changes and risk of crossing interdependent biotic and abiotic thresholds. These additions were further improved and implemented in other regions of Kazakhstan on other projects.

Hydrologic model parameterization using dynamic Landsat-based vegetative estimates within a semiarid grassland

The use of hydrologic models to assess long-term watershed condition through repeated simulations of runoff and erosion is one common approach for rangeland health evaluation. However, obtaining vegetative data of appropriate spatiotemporal resolution for model parameterization can be difficult. The goal of this research was to assess the utility of using time-varying, Landsat-derived vegetative values to parameterize an event-based, watershed-scale hydrologic model. This study was conducted on a small, instrumented grassland watershed in the USDA Agricultural Research Service operated Walnut Gulch Experimental Watershed in southeastern, Arizona. Cloud-free Landsat scenes were acquired over the watershed for the years 1996–2014. The Soil Adjusted Total Vegetation Index (SATVI) was calculated for each image and calibrated using ground measured data to produce a time series of satellite-based foliar cover rasters. These values were used to parameterize the Rangeland Hydrology and Erosion Model (RHEM) for 26 rainfall-runoff events with corresponding observed data. Three parameterization scenarios using these data aggregated to different temporal resolutions (static, long-term mean, annual mean, and intra-annual values) were compared to a static literature-based scenario for evaluation. The linear relationship between field-measured foliar cover and SATVI showed statistically significant agreement with R2 = 0.85 and p < 0.05. Simulated runoff volume and peak flow rate using the three remotely sensed parameterization scenarios improved upon that of the literature-based scenario, with the annual mean scenario performing the best of the three temporal aggregations. The methodological framework outlined here provides a means for improved parameterization for watershed-scale modelling where vegetative data may be scarce or unobtainable for long-term analysis.

Geospatial Scaling of Runoff and Erosion Modeling in the Chihuahuan Desert

Abstract. Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (Ke) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (Kss) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that Ke parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of Kss or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent Kss or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of Kss, and, ultimately, arid rangeland soil erosion simulation. Keywords: Erosion, GIS, Hydrologic model, Rangeland, Runoff.

Process-Based Modeling of Infiltration, Soil Loss, and Dissolved Solids on Saline and Sodic Soils

Abstract. The Colorado River is a central socio-economic resource of the western U.S. but is vulnerable to excessive salt load. To improve knowledge of the surface processes controlling salt loading, a series of rainfall simulation experiments were conducted in saline rangelands of the upper Colorado River basin (UCRB). In this study, data from these rainfall simulation experiments were used to develop predictive equations for the process-based Rangeland Hydrology and Erosion Model (RHEM). Runoff and soil loss prediction performances were assessed with the Nash-Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the percent bias (PBIAS). Calibration on 36 individual plots, randomly selected to cover each treatment, yielded improved runoff prediction (NSE = 0.73, R2 = 0.74, and PBIAS = 6.93%) compared to the non-calibrated RHEM parameter estimation equation (NSE = 0.65, R2 = 0.68, and PBIAS = 32.03%) when a refined ground cover coefficient was used to estimate the effective hydraulic conductivity (Ke). Soil loss prediction with the calibration data was also improved compared to the non-calibrated parameter estimation equation (NSE = 0.94, R2 = 0.94, and PBIAS = 4.25% vs. NSE = 0.81, R2 = 0.85, and PBIAS = 6.47%) when the soil sodium adsorption ratio (SAR) was included in estimation of the sheet and splash erosion parameter (Kss). Improvements in runoff and soil loss predictions with the calibration data were maintained with an independent set of 36 plots from the original rainfall simulation dataset not used for calibration. Overall, soil sodicity was an important consideration in the performance of the newly developed Kss parameterization equation in this study. Performance on sodic soils (SAR = 15) gained the most from the inclusion of SAR in the Kss estimation. Salt load was linearly related to soil loss (R2 = 0.94), and this linear model performed well in estimating runoff salt load from RHEM-predicted soil loss. These new developments will provide a physically based modeling scheme for land managers for predicting rainfall-driven soil and salt load to surface waters of the UCRB. Keywords: Erosion modeling, Hydrology, Rangeland, Salinity prediction, Soil erosion, Water quality.

Developing a Parameterization Approach for Soil Erodibility for the Rangeland Hydrology and Erosion Model (RHEM)

Soil erodibility is a key factor for estimating soil erosion using physically based models. In this study, a new parameterization approach for estimating erodibility was developed for the Rangeland Hydrology and Erosion Model (RHEM). The approach uses empirical equations that were developed by applying piecewise regression analysis to predict the differences of erodibility before and after disturbance (i.e., wildfire, prescribed fire, and tree encroachment) and across a wide range of soil textures as a function of vegetation cover and surface slope angle. The approach combines rain splash, sheet flow, and concentrated flow erodibilities into a single parameter for modeling erodibility in most cases. We evaluated the new approach for sites representing different degrees of disturbance associated with burning and tree encroachment. Our results show that the new erodibility approach in RHEM predicts erosion at the plot scale with a satisfactory range of error in all cases. The new approach extends the applications of RHEM to a greater scope of landscapes and soil texture.

Performance of the Rangeland Hydrology and Erosion Model for runoff and erosion assessment on a semiarid reclaimed construction site

The ability to assess the impact of management actions on soil and water resources is crucial to sustainable land management. The Rangeland Hydrology and Erosion Model (RHEM) was developed as an assessment and decision support tool for resource management agencies and has been used to estimate soil erosion at national, regional, and local scales for both disturbed and undisturbed rangeland soil and vegetation conditions. In this paper, runoff, sediment, and microtopography data were collected during a series of rainfall simulation experiments aimed at evaluating the effectiveness of revegetation on a shrub-dominated post-construction hillslope where the soil has had time to reconsolidate. RHEM9s input parameters are simple to collect and consist of soil texture, slope configuration, and canopy and ground cover. Experimental results showed that a mix of the shrub species rabbitbrush (<i>Chrysothamnus nauseosus</i>) and the invasive annual grass cheatgrass (<i>Bromus tectorum</i>) were more effective at reducing runoff and soil loss than rabbitbrush alone. Plots with more than 45% of residue cover produced as much as 2.1 times less runoff and 16 times less sediments than those with less than 15% of litter. Microtopographic changes information allowed partitioning the erosion process into diffuse and concentrated flow processes. Analysis of this 3D data highlighted the central role of concentrated flow erosion in sediment delivery on rangeland hillslopes. RHEM9s performance as expressed by the coefficient of determination, <i>R</i>², and the Nash-Stucliffe Efficiency (NSE) was <i>R</i>² = 0.84 and NSE = 0.27 for runoff and <i>R</i>² = 0.81 and NSE = 0.26 for sediments. This study demonstrates that RHEM can be effectively used by land managers and project managers to estimate soil erosion as a function of precipitation event on construction sites that have been revegetated to rangeland plant communities.

Incorporating Hydrologic Data and Ecohydrologic Relationships into Ecological Site Descriptions

The purpose of this paper is to recommend a framework and methodology for incorporating hydrologic data and ecohydrologic relationships in Ecological Site Descriptions (ESDs) and thereby enhance the utility of ESDs for assessing rangelands and guiding resilience-based management strategies. Resilience-based strategies assess and manage ecological state dynamics that affect state vulnerability and, therefore, provide opportunities to adapt management. Many rangelands are spatially heterogeneous or sparsely vegetated where the vegetation structure strongly influences infiltration and soil retention. Infiltration and soil retention further influence soil water recharge, nutrient availability, and overall plant productivity. These key ecohydrologic relationships govern the ecologic resilience of the various states and community phases on many rangeland ecological sites (ESs) and are strongly affected by management practices, land use, and disturbances. However, ecohydrologic data and relationships are often missing in ESDs and state-and-transition models (STMs). To address this void, we used literature to determine the data required for inclusion of key ecohydrologic feedbacks into ESDs, developed a framework and methodology for data integration within the current ESD structure, and applied the framework to a select ES for demonstrative purposes. We also evaluated the utility of the Rangeland Hydrology and Erosion Model (RHEM) for assessment and enhancement of ESDs based in part on hydrologic function. We present the framework as a broadly applicable methodology for integrating ecohydrologic relationships and feedbacks into ESDs and resilience-based management strategies. Our proposed framework increases the utility of ESDs to assess rangelands, target conservation and restoration practices, and predict ecosystem responses to management. The integration of RHEM technology and our suggested framework on ecohydrologic relations expands the ecological foundation of the overall ESD concept for rangeland management and is well aligned with resilience-based, adaptive management of US rangelands. The proposed enhancement of ESDs will improve communication between private land owners and resource managers and researchers across multiple disciplines in the field of rangeland management.

Estimating Conservation Needs for Rangelands Using USDA National Resources Inventory Assessments

This study presents (1) the overall concept of assessing non-federal western rangeland soil loss rates at a national scale for determining areas of vulnerability for accelerated soil loss using USDA Natural Resources Conserva- tion Service (NRCS) National Resources Inventory (NRI) data and the Rangeland Hydrology and Erosion Model (RHEM) and (2) the evaluation of a risk-based vulnerability approach as an alternative to the conventional average annual soil loss tolerance (T) for assessment of rangeland sustainability. RHEM was used to estimate runoff and soil loss at the hillslope scale for over 10,000 NRCS NRI sample points in 17 western states on non-federal rangelands. The national average annual soil loss rate on non-federal rangeland is estimated to be 1.4 ton ha -1 year -1 . Nationally, 20% of non- federal rangelands generate more than 50% of the average annual soil loss. Over 29.2 × 10 6 ha (18%) of the non-federal rangelands might benefit from treatment to reduce 1559-1570soil loss to below 2.2 ton ha -1 year -1 . National average an- nual soil loss rates combine areas with low and accelerated soil loss. Evaluating data in this manner can misrepresent the magnitude of the soil loss problem on rangelands. Between 23% and 29% of U.S. non-federal rangelands are vulnerable to accelerated soil loss (≥2.2 ton ha -1 event -1 ) if assessed as a function of vulnerability to a runoff event with a return peri- od of ≥25 years. The NRCS has not evaluated potential soil loss risk in national reports in the past, and adaptation of this technique will allow the USDA and its partners to be proactive in preventing accelerated soil loss on rangelands.

Rangeland hydrology and erosion model (RHEM) enhancements for applications on disturbed rangelands

AbstractThe rangeland hydrology and erosion model (RHEM) is a new process‐based model developed by the USDA Agricultural Research Service. RHEM was initially developed for functionally intact rangelands where concentrated flow erosion is minimal and most soil loss occurs by rain splash and sheet flow erosion processes. Disturbance such as fire or woody plant encroachment can amplify overland flow erosion by increasing the likelihood of concentrated flow formation. In this study, we enhanced RHEM applications on disturbed rangelands by using a new approach for the prediction and parameterization of concentrated flow erosion. The new approach was conceptualized based on observations and results of experimental studies on rangelands disturbed by fire and/or by tree encroachment. The sediment detachment rate for concentrated flow was calculated using soil erodibility and hydraulic (flow width and stream power) parameters. Concentrated flow width was calculated based on flow discharge and slope using an equation developed specifically for disturbed rangelands. Soil detachment was assumed to begin with concentrated flow initiation. A dynamic erodibility concept was applied where concentrated flow erodibility was set to decrease exponentially during a run‐off event because of declining sediment availability. Erodibility was estimated using an empirical parameterization equation as a function of vegetation cover and surface soil texture. A dynamic partial differential sediment continuity equation was used to model the total detachment rate of concentrated flow and rain splash and sheet flow. The enhanced version of the model was evaluated against rainfall simulation data for three different sites that exhibit some degree of disturbance by fire and/or by tree encroachment. The coefficient of determination (R2) and Nash–Sutcliffe efficiency were 0.78 and 0.71, respectively, which indicates the capability of the model using the new approach for predicting soil loss on disturbed rangeland. By using the new concentrated flow modelling approach, the model was enhanced to be a practical tool that utilizes readily available vegetation and soil data for quantifying erosion and assessing erosion risk following rangeland disturbance. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Modeling climate change effects on runoff and soil erosion in southeastern Arizona rangelands and implications for mitigation with conservation practices

Climate change is expected to impact runoff and soil erosion on rangelands in the western United States. This study evaluated the potential impacts of precipitation changes on soil erosion and surface runoff in southeastern Arizona using seven General Circulation Model (GCM) models with three emission scenarios for the 2050s and 2090s. A spatial-temporal downscaling process was used to generate daily precipitation series from GCM outputs for runoff and erosion modeling with the Rangeland Hydrology and Erosion Model (RHEM). Results were compared to 1970 through 1999 conditions. Our results suggested no significant changes in annual precipitation across the region under the three emission scenarios, while projected mean annual runoff and soil loss increased significantly, ranging from 79% to 92% and from 127% to 157%, respectively, relative to 1970 to 1999. At the seasonal scale, though an increase of summer precipitation and a reduction of winter precipitation were projected, both runoff and soil loss increased significantly for both periods. The dramatic increases in runoff and soil loss were attributed to the increase in the frequency and intensity of extreme events in the study area. Predicted soil loss from shrub communities increased more than that predicted for other plant communities under the three emission scenarios. Future increases in runoff and soil erosion may accelerate the transitions of grassland to shrublands or to more eroded states due to the positive vegetation-erosion feedback. Rangeland management policies and practices should consider these changes and adapt to the increased risk of runoff and soil erosion.