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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.

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 Automated Geospatial Watershed Assessment Tool (AGWA) and the Rangeland Hydrology Erosion Model (RHEM) were used to evaluate conservation practices on the Cienega Creek watershed (CCW) that were implemented under the Conservation Effects Assessment Project (CEAP) on Grazing Lands. CEAP on Grazing Lands is a multi-agency effort to quantify the environmental effects of conservation practices and programs, and develop the science base for managing rangelands for environmental quality using conservation practices (Spaeth et al. 2013). The evaluation was performed on the CCW and the Empire Ranch, located in southeastern Arizona, where numerous conservation practices have been implemented to achieve their management goals of maintaining desired plant communities and watershed processes. To assess the effects of the conservation practices on long-term soil and water loss, RHEM was applied to the entire CCW using National Resources Inventory (NRI) data. Four analysis periods were selected based on climatic contrasts, conservation spending, and availability of NRI sample points. The CCW results showed that the simulations using RHEM parameters derived from NRI data could be used to demonstrate the impact of climate on vegetation condition and sediment yield. A subsection, dubbed “select treatment areas,” of the CCW, which received extensive treatments with brush removal, prescribed burns, and stock ponds, was assessed using remotely sensed data. The select treatment areas were modeled over three different periods, representing preconservation spending, postconservation spending, and later postconservation spending. Remote sensing was capable of detecting and estimating the changes in vegetation cover from brush removal and prescribed burns. Simulations of sediment yield using pre- and posttreatment data indicated a modest reduction in sediment yield. Simulations in the select treatment areas indicated mechanical brush treatments were typically more effective than prescribed burns for improving watershed condition as estimated by reductions in sediment yield, but were more costly to implement. Stock ponds had a larger impact on sediment yield than the land treatments. The assessment demonstrated the utility of remotely sensed estimates of plant growth form and cover for model inputs to estimate changes in runoff and sediment yield with changing cover conditions over large areas.

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

In August 2016, the National Weather Service Office of Water Prediction (NWS/OWP) of the National Oceanic and Atmospheric Administration (NOAA) implemented the operational National Water Model (NWM) to simulate and forecast streamflow, soil moisture, and other model states throughout the contiguous United States. Based on the architecture of the WRF-Hydro hydrologic model, the NWM does not currently resolve channel infiltration, an important component of the water balance of the semiarid western United States. Here, we demonstrate the benefit of implementing a conceptual channel infiltration function (from the KINEROS2 semidistributed hydrologic model) into the WRF-Hydro model architecture, configured as NWM v1.1. After calibration, the updated WRF-Hydro model exhibits reduced streamflow errors for the Walnut Gulch Experimental Watershed (WGEW) and the Babocomari River in southeast Arizona. Model calibration was performed using NLDAS-2 atmospheric forcing, available from the NOAA National Centers for Environmental Prediction (NCEP), paired with precipitation forcing from NLDAS-2, NCEP Stage IV, or local gauge precipitation. Including channel infiltration within WRF-Hydro results in a physically realistic hydrologic response in the WGEW, when the model is forced with high-resolution, gauge-based precipitation in lieu of a national product. The value of accounting for channel loss is also demonstrated in the Babocomari basin, where the drainage area is greater and the cumulative effect of channel infiltration is more important. Accounting for channel infiltration loss thus improves the streamflow behavior simulated by the calibrated model and reduces evapotranspiration bias when gauge precipitation is used as forcing. However, calibration also results in increased high soil moisture bias, which is likely due to underlying limitations of the NWM structure and calibration methodology.

The detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m-2 [1.1 t ha-1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > ~60 mm h-1) and high rainfall depth (>~200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

Agriculture in the United States must respond to escalating demands for productivity and efficiency, as well as pressures to improve its stewardship of natural resources. Growing global population and changing diets, combined with a greater societal awareness of agriculture's role in delivering ecosystem services beyond food, feed, fiber, and energy production, require a comprehensive perspective on where and how US agriculture can be sustainably intensified, that is, made more productive without exacerbating local and off-site environmental concerns. The USDA's Long-Term Agroecosystem Research (LTAR) network is composed of 18 locations distributed across the contiguous United States working together to integrate national and local agricultural priorities and advance the sustainable intensification of US agriculture. We explore here the concept of sustainable intensification as a framework for defining strategies to enhance production, environmental, and rural prosperity outcomes from agricultural systems. We also elucidate the diversity of factors that have shaped the past and present conditions of cropland, rangeland, and pastureland agroecosystems represented by the LTAR network and identify priorities for research in the areas of production, resource conservation and environmental quality, and rural prosperity. Ultimately, integrated long-term research on sustainable intensification at the national scale is critical to developing practices and programs that can anticipate and address challenges before they become crises.

An ecohydrological stream type classification was developed to improve decision making for ephemeral and intermittent streams at four military reservations in the southwestern U.S.: Fort Irwin, Yuma Proving Ground (YPG), Fort Huachuca, and Fort Bliss. Agglomerative hierarchical cluster analysis was used to classify stream reaches by ecohydrologic properties (vegetation, hydrologic, and geomorphic attributes derived using geographic information system analyses), and Classification and Regression Trees (CART) were used to determine thresholds for each variable for a predictive model. Final stream types were determined from statistical analyses, cluster validity tests, examination of mapped clusters, and site knowledge. Climate regime and geomorphology were most important for YPG and Fort Irwin where annual precipitation is low. Vegetation variables were important at Fort Bliss and hydrologic variables were important at Fort Huachuca where higher annual precipitation and a bimodal rainfall pattern occur. The classification results and input variables are spatially linked to specific stream reaches, allowing managers to identify locations with similar attributes to support management actions. These methods enable the development of a stream type classification in gauged or ungauged watersheds and for areas where intensive field data collection is not feasible.

The hydroclimatology, hydrometeorology, and hydrology of flash floods in the arid/semiarid southwestern United States are examined through empirical analyses of long-term, high-resolution rainfall and stream gauging observations, together with hydrological modeling analyses of the 19 August 2014 storm based on the Kinematic Runoff and Erosion Model (KINEROS2). The analyses presented here are centered on identifying the structure and evolution of flood-producing storms, as well as the interactions of space–time rainfall variability and basin characteristics in determining the upper-tail properties of rainfall and flood magnitudes over this region. This study focuses on four watersheds in Maricopa County, Arizona, with contrasting geomorphological properties. Flash floods over central Arizona are concentrated in both time and space, reflecting controls of the North American monsoon and complex terrain. Thunderstorm systems during the North American monsoon, as represented by the 19 August 2014 storm, are the dominant flood agents that determine the upper tail of flood frequency over central Arizona and that also shape the envelope curve of floods for watersheds smaller than 250 km2. Flood response for the 19 August 2014 storm is associated with storm elements of comparable spatial extent to the drainage area and slow movement for the three compact, headwater watersheds. Flood response for the elongated and relatively flat Skunk Creek highlights the importance of the spatial distribution of rainfall for transmission losses in arid/semiarid watersheds.

Bartlett et al. [2016] performed a re-interpretation and modification of the space-time lumped USDA NRCS (formerly SCS) Curve Number (CN) method to extend its applicability to forested watersheds. We believe that the well documented limitations of the CN method severely constrains the applicability of the modifications proposed by Bartlett et al. [2016]. This forward-looking comment urges the research communities in hydrologic science and engineering to consider the CN method as a stepping stone that has outlived its usefulness in research. The CN method fills a narrow niche in certain settings as a parsimonious method having utility as an empirical equation to estimate runoff from a given amount of rainfall, which originated as a static functional form that fits rainfall-runoff data sets. Sixty five years of use and multiple reinterpretations have not resulted in improved hydrological predictability using the method. We suggest that the research community should move forward by (1) identifying appropriate dynamic hydrological model formulations for different hydro-geographic settings, (2) specifying needed model capabilities for solving different classes of problems (e.g. flooding, erosion/sedimentation, nutrient transport, water management, etc.) in different hydro-geographic settings, and (3) expanding data collection and research programs to help ameliorate the so-called “over-parameterization” problem in contemporary modeling. Many decades of advances in geo-spatial data and processing, computation, and understanding are being squandered on continued focus on the static CN regression method. It is time to truly “move beyond” the Curve Number method.

Triple collocation has found widespread application in the hydrological sciences because it provides information about the errors in our measurements without requiring that we have any direct access to the true value of the variable being measured. Triple collocation derives variance-covariance relationships between three or more independent measurement sources and an indirectly observed truth variable in the case where the measurement operators are additive. We generalize that theory to arbitrary observation operators by deriving nonparametric analogues to the total error and total correlation statistics as integrations of divergences from conditional to marginal probability ratios. The nonparametric solution to the full measurement problem is under-determined, and we therefore retrieve conservative bounds on the theoretical total nonparametric error and correlation statistics. We examine the application of both linear and nonlinear triple collocation to synthetic examples and to a real-data test case related to evaluating space-borne soil moisture retrievals using sparse monitoring networks and dynamical process models.

Classic rainfall–runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, model parameters change temporally. To overcome this problem, normalized difference vegetation index (NDVI) derived from remotely sensed data was used in this study to investigate the effect of land cover variations on hydrological response of watersheds using a conceptual rainfall–runoff model. The study area consists of two sub-watersheds (Hervi and Lighvan) with varied land cover conditions. Obtained results show that the one-parameter model generates runoff forecasts with acceptable level of the considered criteria. Remote sensing data were employed to relate land cover properties of the watershed to the model parameter. While a power form of the regression equation could be best fitted to the parameter values using available images of Hervi sub-watershed, for the Lighvan sub-watershed the fitted equation shows somewhat lower correlation due to higher fluctuations of the model parameter. The average values of the Nash–Sutcliffe efficiency criterion of the model were obtained as 0.87 and 0.55, respectively, for Hervi and Lighvan sub-watersheds. Applying this methodology, the model's parameters might be determined using temporal NDVI values.

The Walnut Gulch Experimental Watershed is a semi-arid experimental watershed and long-term agro-ecosystem research (LTAR) site managed by the USDA-Agricultural Research Services (ARS) Sout...

We used a value of information approach to demonstrate the cost-effectiveness of using satellite imagery as part of the Burn Area Emergency Response (BAER), a US federal program that identifies imminent post-wildfire threats to human life and safety, property and critical natural or cultural resources. We compared the costs associated with producing a Burn Area Reflectance Classification map and implementing a BAER when imagery from satellites (either Landsat or a commercial satellite) was available to when the response team relied on information collected solely by aerial reconnaissance. The case study included two evaluations with and without Burn Area Reflectance Classification products: (a) savings of up to US$51000 for the Elk Complex wildfire incident request and (b) savings of a multi-incident map production program. Landsat is the most cost-effective way to input burn severity information into the BAER program, with savings of up to US$35 million over a 5-year period.

On The Ground•The utility of ecological site descriptions (ESD) in the management of rangelands hinges on their ability to characterize and predict plant community change, the associated ecological consequences, and ecosystem responsiveness to management.•We demonstrate how enhancement of ESDs with key ecohydrologic information can aid predictions of ecosystem response and targeting of conservation practices for sagebrush rangelands that are strongly regulated by ecohydrologic or ecogeomorphic feedbacks.•The primary point of this work is that ESD concepts are flexible and can be creatively augmented for improved assessment and management of rangelands.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

The KINematic runoff and EROSion model (version 2; KINEROS2) provides an urban component that is capable of modeling urban hydrology including green infrastructure (GI) practices at various scales. The KINEROS2 model provides 15 parameters that can represent a variety of GI practices. The goal of this study is to understand the sensitivity of the KINEROS2 model outputs (infiltration, peak flow, runoff volume, and outflow hydrograph) to these 15 parameters, across seven different precipitation events, and at the watershed as well as the parcel scale. A global sensitivity analysis (GSA) was performed using the Variogram Analysis of Response Surfaces (VARS) framework to produce sensitivity metrics. Results from the time-aggregate GSA indicate that parameter sensitivities follow trends based on precipitation intensity and duration. Time-varying GSA indicate sensitivity of parameters to hyetograph shape, especially the duration to maximum precipitation intensity. The variation in sensitivity is influenced by the peaks and valleys in the precipitation hyetograph.