Travis W. Nauman

Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau

A new disturbance automated reference toolset (DART) was developed to monitor human land surface impacts using soil-type and ecological context. DART identifies reference areas with similar soils, topography, and geology; and compares the disturbance condition to the reference area condition using a quantile-based approach based on a satellite vegetation index. DART was able to represent 26-55% of variation of relative differences in bare ground and 26-41% of variation in total foliar cover when comparing sites with nearby ecological reference areas using the Soil Adjusted Total Vegetation Index (SATVI). Assessment of ecological recovery at oil and gas pads on the Colorado Plateau with DART revealed that more than half of well-pads were below the 25th percentile of reference areas. Machine learning trend analysis of poorly recovering well-pads (quantile<0.23) had out-of-bag error rates between 37 and 40% indicating moderate association with environmental and management variables hypothesized to influence recovery. Well-pads in grasslands (median quantile [MQ]=13%), blackbrush (Coleogyne ramosissima) shrublands (MQ=18%), arid canyon complexes (MQ=18%), warmer areas with more summer-dominated precipitation, and state administered areas (MQ=12%) had low recovery rates. Results showcase the usefulness of DART for assessing discrete surface land disturbances, and highlight the need for more targeted rehabilitation efforts at oil and gas well-pads in the arid southwest US.

Soil property and class maps of the conterminous United States at 100-meter spatial resolution

With growing concern for the depletion of soil resources, conventional soil data must be updated to support spatially explicit human-landscape models. Three US soil point datasetswere combined with a stack of over 200 environmental datasets to generate complete coverage gridded predictions at 100 m spatial resolution of soil properties (percent organic C, total N, bulk density, pH, and percent sand and clay) and US soil taxonomic classes (291 great groups and 78 modified particle size classes) for the conterminous US. Models were built using parallelized random forest and gradient boosting algorithms. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100 and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Model validation results indicate an out-of-bag classification accuracy of 60 percent for great groups, and 66 percent for modified particle size classes; for soil properties cross-validated R-square ranged from 62 percent for total N to 87 percent for pH. Nine independent validation datasets were used to assess prediction accuracies for soil class models and results ranged between 24-58 percent and 24-93 percent for great group and modified particle size class prediction accuracies, respectively. The hybrid SoilGrids+ modeling system that incorporates remote sensing data, local predictions of soil properties, conventional soil polygon maps, and machine learning opens the possibility for updating conventional soil survey data with machine learning technology to make soil information easier to integrate with spatially explicit models, compared to multi-component map units.

Generalizing Ecological Site Concepts of the Colorado Plateau for Landscape-Level Applications ☆

On the Ground Numerous ecological site descriptions in the southern Utah portion of the Colorado Plateau can be difficult to navigate, so we held a workshop aimed at adding value and functionality to the current ecological site system. We created new groups of ecological sites and drafted state-and-transition models for these new groups. We were able to distill the current large number of ecological sites in the study area (ca. 150) into eight ecological site groups that capture important variability in ecosystem dynamics. Several inventory and monitoring programs and landscape scale planning actions will likely benefit from more generalized ecological site group concepts.

Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Abstract Oil and natural gas development in the western United States has increased substantially in recent decades as technological advances like horizontal drilling and hydraulic fracturing have made extraction more commercially viable. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive. Understanding the rates, controls, and degree of recovery of these reclaimed well sites to a state similar to pre-development conditions is critical for energy development and land management decision processes. Here we use a multi-decadal time series of satellite imagery (Landsat 5, 1984–2011) to assess vegetation regrowth on 365 abandoned well pads located across the Colorado Plateau in Utah, Colorado, and New Mexico. We developed high-frequency time series of the Soil-Adjusted Total Vegetation Index (SATVI) for each well pad using the Google Earth Engine cloud computing platform. BFAST time-series models were used to fit temporal trends, identifying when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The time series metrics are used to calculate the relative fractional vegetation cover (RFVC) of each pad, a measure of post-abandonment vegetation cover relative to pre-drilling condition. Mean and median RFVC were 36% (s.d. 33%) and 26%, respectively, five years after abandonment, with one third of well pads having RFVC greater than 50%. Statistical analyses suggest that much of the high vegetation cover is associated with weedy invasive annual species such as cheatgrass (Bromus tectorum) and Russian thistle (Salsola spp.). Climate conditions and the year of abandonment also play a role, with increased cover in later years associated with a wetter period. Non-linear change at many pads suggests longer recovery times than would be estimated by linear extrapolation. New techniques implemented here address a complex response of cover change to soils, management, and climate over time, and can be extended to the operational monitoring of energy development across large areas.

Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity: Colorado Plateau aeolian transport

Dryland wind transport of sediment can accelerate soil erosion, degrade air quality, mobilize dunes, decrease water supply, and damage infrastructure. We measured aeolian sediment horizontal mass flux (q) at 100 cm height using passive aspirated sediment traps to better understand q variability on the Colorado Plateau. Measured q ‘hot spots’ rival the highest ever recorded including 7,460 g m 2 day 1 in an off-highway vehicle (OHV) area, but were more commonly 502,000 g m 2 day . Overall mean q on rangeland sites was 5.14 g m 2 day , considerably lower than areas with concentrated livestock use (9-19 g m 2 day ), OHV use (414 g m 2 day ), and downwind of unpaved roads (13.14 g m 2 day ), but were higher than areas with minimal soil disturbance (1.60 g m 2 day ). Rangeland q increased with increasing annual temperature, increased winds, and decreasing precipitation. Spatial modeling suggests that ~92-93% of regional q occurs in rangelands versus ~7-8% along unpaved roads. Four of the five largest road q values (n=33) measured were along roads used primarily for oil or gas wells. Our findings indicate that predicted future mega-droughts will increase q disproportionately in disturbed rangelands, and potentially further compromise air quality, hydrologic cycles, and other ecosystem services. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Automated Reference Toolset (ART) Data

These environmental raster covariate, geospatial vector data, and tabular data were compiled as input data for the Automated Reference Toolset (ART) algorithm. These data are a subset of all the environmetal raster covariate data used in the ART algorithm. Users are advised to read the mansuscript, associated with these data and identified as the larger work citation. It is recommended that data users contact the data set author if they have any questions about the development, processing, contribution, and use of these data in the Automated Reference Toolset. The purpose of these data are to validate the Automated Reference Toolset process. These data are associated with the journal manuscript: Nauman, T.W., and Duniway, M.C. 2016, The Automated Reference Toolset: A Soil-Geomorphic Ecological Potential Matching Algorithm, Soil Science Society of America Journal (online), http://dx.doi.org/10.2136/sssaj2016.05.0151.

An inventory and monitoring plan for a Sonoran Desert ecosystem; Barry M. Goldwater Range-West

........................................................................................................................................................................ 1 Purpose and Scope ....................................................................................................................................................... 2 1.0 Introduction and Background ................................................................................................................................... 3 1.