Abstract. Slope-velocity equilibrium is hypothesized as a state that evolves naturally over time due to the interaction between overland flow and surface morphology, wherein steeper areas develop a relative increase in physical and hydraulic roughness such that flow velocity is a unique function of overland flow rate independent of slope gradient. This study tests this hypothesis under controlled conditions. Artificial rainfall was applied to 2 m by 6 m plots at 5, 12, and 20 % slope gradients. A series of simulations were made with two replications for each treatment with measurements of runoff rate, velocity, rock cover, and surface roughness. Velocities measured at the end of each experiment were a unique function of discharge rates, independent of slope gradient or rainfall intensity. Physical surface roughness was greater at steeper slopes. The data clearly showed that there was no unique hydraulic coefficient for a given slope, surface condition, or rainfall rate, with hydraulic roughness greater at steeper slopes and lower intensities. This study supports the hypothesis of slope-velocity equilibrium, implying that use of hydraulic equations, such as Chezy and Manning, in hillslope-scale runoff models is problematic because the coefficients vary with both slope and rainfall intensity.

In this study, we present the improved Rangeland Hydrology and Erosion Model (RHEM V2.3), a process‐based erosion prediction tool specific for rangeland application. The article provides the mathematical formulation of the model and parameter estimation equations. Model performance is assessed against data collected from 23 runoff and sediment events in a shrub‐dominated semiarid watershed in Arizona, USA. To evaluate the model, two sets of primary model parameters were determined using the RHEM V2.3 and RHEM V1.0 parameter estimation equations. Testing of the parameters indicated that RHEM V2.3 parameter estimation equations provided a 76% improvement over RHEM V1.0 parameter estimation equations. Second, the RHEM V2.3 model was calibrated to measurements from the watershed. The parameters estimated by the new equations were within the lowest and highest values of the calibrated parameter set. These results suggest that the new parameter estimation equations can be applied for this environment to predict sediment yield at the hillslope scale. Furthermore, we also applied the RHEM V2.3 to demonstrate the response of the model as a function of foliar cover and ground cover for 124 data points across Arizona and New Mexico. The dependence of average sediment yield on surface ground cover was moderately stronger than that on foliar cover. These results demonstrate that RHEM V2.3 predicts runoff volume, peak runoff, and sediment yield with sufficient accuracy for broad application to assess and manage rangeland systems.

Abstract. Slope–velocity–equilibrium is hypothesized as a state that evolves naturally over time due to the interaction between overland flow and surface morphology, wherein steeper areas develop a relative increase in physical and hydraulic roughness such that flow velocity is a unique function of overland flow rate independent of slope gradient. This study tests this hypothesis under controlled conditions. Artificial rainfall was applied to 2 m by 6 m plots at 5 %, 12 %, and 20 % slope gradients. A series of simulations were made for each treatment with measurements of runoff rate, velocity, rock cover, and surface roughness. Velocities measured at the end of each experiment were a unique function of discharge rates, independent of slope gradient or rainfall intensity. Physical surface roughness was greater at steeper slopes. The data clearly showed that there was not a unique hydraulic coefficient for a given slope, surface condition, or rainfall rate, with hydraulic roughness greater at steeper slopes and lower intensities. This study supports the hypothesis of slope–velocity–equilibrium, implying that use of hydraulic equations, such as Chezy and Manning, in hillslope scale runoff models is problematic because the coefficients vary with both slope and rainfall intensity.

No abstract

The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets.

The vision of the Long Term Agroecosystem Research (LTAR) network is to enable multi‐decadal, trans‐disciplinary, and cross‐location science to ensure the long‐term sustainability of U.S. agriculture. LTAR's primary goals are to: (1) Intensify agricultural productivity, (2) Improve ecosystem services related to agricultural production, and (3) Improve rural prosperity. The LTAR network includes 18 locations (sites). It includes 10 existing hydrologic observatories from the Agricultural Research Service‐Experimental Watershed Network (ARS‐EWN) that were established before the creation of LTAR. Background and an overview of the network are presented.

No abstract

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-year, 20-year, and 10-year minimum 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 U.S., which were compared to a selection of stations from an existing U.S. 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 and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020c).