Geology topics

Miguel L. Villarreal

Publications and source records attributed to Miguel L. Villarreal.

At least 19 recordsLinked to original sources

USGS2026

Landscape connectivity and wildlife access to water across an international border: Barriers and opportunities for facilitating transboundary movement

Rapid global acceleration in the construction of physical barriers along international borders has greatly influenced biodiversity and animal movement. Physical barriers can fragment landscapes, hinder access to essential resources, impact long-distance migrations, and inhibit dispersal and gene flow. The effects of physical barriers on animal movement and landscape connectivity can be exacerbated in dryland environments where access to water is a limiting factor. In recent decades, the construction of border barrier infrastructure has accelerated along the international boundary between the United States and Mexico. Here, we used a landscape connectivity model to investigate the effects of barriers on wildlife access to the river in the Lower Rio Grande Valley. We used a modified omnidirectional connectivity model to compare access to the river for three large, terrestrial mammal species across three border barrier scenarios: (1) a landscape without border barriers; (2) a landscape with the existing barrier system; and (3) a potential future landscape with a continuous barrier system. The existing barrier system includes many discrete sections of barrier within tracts of the Lower Rio Grande Valley National Wildlife Refuge or on lands associated with the region's flood control system. Our results indicate that the existing border barriers can impede connectivity and wildlife access to the river in some areas, while some existing gaps between border barrier sections can serve as conduits for wildlife movement and river access. Our future scenario results show how a potential continuous border barrier system could further impede wildlife access to the river. We discuss management and landscape conservation options for enhancing wildlife access to water and riverine habitats. Collectively, our results illustrate the potential effects of border barriers on wildlife movement and access to water, providing information that can be used to better anticipate and lessen the ecological impacts of transboundary barriers.

Tamaulipas, Texas↗

USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗

USGS2026

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico↗

USGS2026

Multi-temporal mapping and analyses of post-wildfire surface soil moisture modeled from Landsat Thermal-IR and Sentinel-1 SAR

Wildfires alter surface soil moisture by consuming vegetation and exposing the soil to increased solar radiation, increased precipitation throughfall, and reduced infiltration from soil hydrophobicity. This can affect the water balance within the watershed and, in turn, influence post-fire nutrient cycling and ecosystem succession. Modeling surface soil moisture over large areas using satellite remote sensing can aid monitoring of soil moisture patterns and dynamics. We trained a random forest regression model using an in situ soil moisture network and examined pre- and post-fire soil moisture dynamics for three wildfires that burned in 2016 and 2017 in coastal California, U.S.A. Model inputs included Sentinel-1A synthetic aperture radar (SAR) and Landsat thermal infrared imagery (TIR), which produced a high resolution (30 m) soil moisture map time series with 18–20 estimates across 2 years. An unbiased root mean square error of 0.065 and an r 2 of 0.624 indicates a good fit between the model and reference data. We found a significant interaction between treatment (wildfire or unburned) and period (before or after wildfire) by examining the modeled surface soil moisture in high-severity burned areas. Before-after control-impact metrics showed that modeled surface soil moisture was lower after each fire within wildfire areas compared to unburned controls, and overall changes in soil moisture were greater in the wildfire perimeters than unburned areas. These results demonstrate the potential of Sentinel-1 SAR and Landsat TIR data for the development of high-resolution soil moisture time series products to support monitoring and assessment of post-fire soil hydrologic conditions.

California↗

USGS2026

A little-known piñon from the Mogollon Highlands: Natural history of Pinus x fallax in the Prescott National Forest, Arizona, USA

The Mogollon Highlands is an area of rich biodiversity in central Arizona and western New Mexico. The piñon-juniper/chaparral in the Mogollon Highlands is unique from other piñon-juniper woodlands in both taxonomy and structure. The hybridization of the common piñon there, Pinus × fallax (Mogollon Highlands piñon, MHP), is hypothesized to render it more tolerant to heat and aridity than one of the parent plants, Pinus edulis (Colorado piñon). Yet as Climate Water Deficit continues to increase, so does concern for the future of these woodlands and the MHP. Using demographic data on MHP from the last four decades, we evaluate fecundity and mortality patterns in woodlands where MHP is co-dominant with three Juniperus species and numerous perennial shrubs. Our data indicate that MHP stands continue to reproduce and that mortality is low relative to the more northern P. edulis stands. We also present a simple calculation of historical fire rotation in Prescott National Forest woodlands, estimating a fire rotation of two centuries, far shorter than other piñon-juniper associations. We also explore fire patterns since 1985 across the ecoregion; abundant charcoal indicates small patchy fires were common. In spite of widespread fires, most Mogollon Highlands fires have occurred in Pinus ponderosa (ponderosa pine) forests, leaving piñon-juniper/chaparral intact. Together these trends indicate that MHP and associated perennial co-dominants are tolerant of current climates. We suggest that hybrid genetic combinations, the complex perennial understory (e.g., actinorhizal shrubs, rich diversity of nurse microsites), and fire patterns are responsible for this resiliency.

Arizona↗

USGS2025

UAS and high-resolution satellite imagery improve the accuracy of cheatgrass detection across an invaded Yellowstone landscape

Context Cheatgrass ( Bromus tectorum L.) is a problem across the western United States, where it outcompetes and replaces native grass species, alters habitats, and increases the risk of wildfires. Cheatgrass greens up earlier in the growing season compared to native grasses, making it classifiable with multi-temporal and multi-spectral remote sensing. Objectives We mapped cheatgrass at different scales in the Greater Yellowstone Ecosystem using 10-m Sentinel-2 imagery, 3-m PlanetScope, and 10-cm Uncrewed Aerial Systems (UAS) imagery. We compared these maps to field-collected data to address 1) variation in seasonal phenological signals of native and cheatgrass patches, 2) the influence of scale on detectability and map accuracy across our study area. Results Model accuracy to predict cheatgrass presence increased with imagery resolution and ranged from 83% using 10-m Sentinel-2 to 94% with the integration of PlanetScope and UAS imagery. While there was spatial agreement across models, the fusion of UAS data with satellite sources allowed the detection of small cheatgrass with more precision. Our novel use of NExR and dNExR (a redness and differenced redness index) data in the classification of cheatgrass capitalizes on the senescence of cheatgrass during peak summer periods where cloud free imagery is more prevalent. Conclusions Our satellite and UAS-based models of cheatgrass prediction compare the fusion of very high resolution imagery and phenological time differencing to identify infested areas. Tradeoffs between accuracy and expense lead to important questions for management applications.

Montana↗

USGS2025

Mapping ecological states in the upper Colorado River basin: Implications for fire management

Spatially explicit information on ecosystem dynamics that offers a mechanistic understanding of ecological processes can benefit environmental management. Broad-scale maps based on state-and-transition models provide valuable insight into transitions among ecological states resulting from specific drivers within areas sharing similar climatic and edaphic characteristics ecological sites (ES). We aimed to quantify ecological dynamics of two ES groups in the Upper Colorado River Basin from 1986 to 2022 through annual maps of ecological states and assess potential drivers of observed state change. This region comprises important sagebrush shrublands and pinyon-juniper woodlands affected by non-native annual grass invasion, wildfires, and drought-induced tree mortality. Using field-based and remote sensing data, we modeled vegetation states using random forest models and mapped the states annually from 1986 to 2022. To demonstrate the utility of the state maps for monitoring and management, we used this time series of maps to investigate the influences of fire and drought on state occurrence. Our findings revealed a statistically significant increase in states invaded by non-native annual species (Invaded state), which replaced Grassland and Shrubland states, while Shrubland states decreased significantly, transitioning to invaded and Woodland states. Invaded states had the highest likelihood of burning, followed by Woodlands. Drought was associated with increased area of Grassland and Bare states, but with decreased area of invaded and Shrubland states. These results indicate an accelerating fire cycle is potentially leading to ongoing regional environmental degradation. Despite increasing drought conditions during the study period, the invaded states continued to increase in area, indicating additional underlying mechanisms. Our reproducible, broad-scale, ecologically-driven state mapping process enhances understanding of how drought, fire, and invasion by non-native plants can transform semiarid landscapes of the western USA.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2025

UAS-based geomorphic change detection of incised montane meadow stream channels with low-tech process-based restoration treatments

Context Montane meadows play an important hydrologic role in headwater catchments, but past land use has largely degraded their condition. Low-tech restoration methods, such as beaver dam analogs (BDAs), are increasingly used to support recovery of incised streams by promoting key geomorphic processes. However, there remains a need for studies that leverage UAS for monitoring low-tech restoration treatments in incised meadow systems. Objectives This study maps and characterizes geomorphic changes in two incised meadow stream channels in Red Clover Valley, CA with installed beaver dam analog structures. We used UAS-based photogrammetric surveys to track changes over a three-year period (2021–2023). Methods Geomorphic change was assessed using DEM differencing with error thresholding, repeat geomorphic unit (GU) classification, and Shannon Diversity Index (SHDI) to measure spatial shifts in geomorphic complexity. Results Geomorphic responses varied by site and survey period. The subchannel B (SCB) site exhibited net deposition, while the lower Dixie Creek (LDC) site showed net erosion. BDAs appeared to enhance geomorphic activity, particularly in LDC, where near BDA areas showed greater sediment deposition and localized erosion compared to reference sites. SHDI values were positively correlated with erosion at both sites, suggesting that erosional processes may have promoted geomorphic diversity by creating or reorganizing GU in the incised channels. Conclusions UAS-SfM surveys captured erosion and deposition patterns and revealed the influence of BDAs and local channel characteristics on geomorphic change and unit diversity. These findings highlight the utility of UAS methods for monitoring restoration impacts in incised montane meadow streams.

California↗

USGS2025

The tortoise and the antilocaprid: Adapting GPS tracking and terrain data to model wildlife walking functions

Context The relationship between slope and terrestrial animal locomotion is key to landscape ecology but underexplored across species. This is partly due to a lack of scalable methodology that applies to a diversity of wildlife. Objectives This study investigates the slope-speed relationship for two species, Texas tortoise ( Gopherus berlandieri ) and pronghorn ( Antilocapra americana ), through the combined application of remote sensing, GPS tracking, behavior models, and parametric distribution. While using readily available Digital Elevation Models (DEM) for pronghorn, we explore the use of very high-resolution lidar Digital Terrain Models (DTM) from Unoccupied Aerial Systems (UAS) to characterize tortoise movements at micro-scales. Methods After classifying animal behavior with GPS tracking data and Hidden Markov Models (HMMs), we analyzed the relationship between the speed of the animals and the slope of the terrain using a 30-m DEM for pronghorn, and a fine-scale UAS DTM for Texas tortoise, and three nonlinear models: Laplace, Gauss, and Lorentz. Results High-resolution DTM, coupled with GPS tracking, accurately models the relationship of speed and slope at a micro-scale, while a DEM is suitable for a larger scale. Laplace models best predicted the speed of both the Texas tortoise and pronghorn. Models showed tortoises, which are not known for rapid and agile movement like the pronghorn, have a broader tolerance for varying slopes at a fine scale. Conclusions These findings enhance understanding of species-specific movement offering valuable insights for habitat management and conservation tailored to species’ behaviors and capabilities.

Colorado, Texas, Wyoming↗

USGS2025

Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities

Context Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.

Landscape Ecology↗

USGS2024

Soil cover heterogeneity associated with biocrusts predicts patch-level plant diversity patterns

Context Soil resource heterogeneity drives plant species diversity patterns at local and landscape scales. In drylands, biocrusts are patchily distributed and contribute to soil resource heterogeneity important for plant establishment and growth. Yet, we have a limited understanding of how such heterogeneity may relate to patterns of plant diversity and community structure. Objectives We explored relationships between biocrust-associated soil cover heterogeneity and plant diversity patterns in a cool desert ecosystem. We asked: (1) does biocrust-associated soil cover heterogeneity predict plant diversity and community composition? and (2) can we use high-resolution remote sensing data to calculate soil cover heterogeneity metrics that could be used to extrapolate these patterns across landscapes? Methods We tested associations among field-based measures of plant diversity and soil cover heterogeneity. We then used a Support Vector Machine classification to map soil, plant and biocrust cover from sub-centimeter resolution Unoccupied Aerial System (UAS) imagery and compared the mapped results to field-based measures. Results Field-based soil cover heterogeneity and biocrust cover were positively associated with plant diversity and predicted community composition. The accuracy of UAS-mapped soil cover classes varied across sites due to variation in timing and quality of image collections, but the overall results suggest that UAS are a promising data source for generating detailed, spatially explicit soil cover heterogeneity metrics. Conclusions Results improve understanding of relationships between biocrust-associated soil cover heterogeneity and plant diversity and highlight the promise of high-resolution UAS data to extrapolate these patterns over larger landscapes which could improve conservation planning and predictions of dryland responses to soil degradation under global change.

Utah↗

USGS2024

Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau

Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C- and N-related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi- and hyperspectral missions with targeted band placement, such as the proposed 26-band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.

Utah↗

USGS2024

An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape

Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.

Baja California Sur↗

USGS2024

Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA

Wind erosion and dust emission from drylands have large consequences for ecosystem function and human health. Wind erosion is naturally reduced by soil crusting and sheltering by non-erodible roughness elements such as plants. Land uses that reduce surface roughness and disturb the soil surface can dramatically increase dust emission. Extraction of oil and gas is a common and growing land use in the western United States (US) that removes vegetation and other roughness elements for construction of well pads and unpaved access roads, resulting in thousands of small (1–4 ha), discrete patches of unprotected soil. Here, we use a satellite albedo-based model to assess the effect of oil/gas activity on surface roughness in the Uinta-Piceance Basin, an area of the Upper Colorado River Basin (UCRB) with dense oil and natural gas development and modelled how the change in surface roughness could impact aeolian sediment flux and dust emission. We also investigated how regional drought influences the response of surface roughness to well pads and access roads. Oil/gas activity reduced surface roughness and increased modelled aeolian sediment flux at the landscape scale across much of the study region, resulting in a modest increase of 10 139 kg of dust per year, which is small relative to dust loads from a single regional dust event observed in the region, but downwind impact could be significant. The magnitude of surface roughness reductions by oil/gas activity was generally consistent among land cover types. However, in parts of the basin that had high cover of annual forbs and grasses, oil/gas activity was associated with larger surface roughness and smaller potential dust emission. Drought decreased surface roughness across disturbed and undisturbed sites, but there was no interactive effect of oil/gas activity and drought on surface roughness. These results suggest that oil/gas activity may increase sediment fluxes and likely contributes to dust emission from landscapes in the UCRB. Understanding how drought and land use change contribute to dust emissions will benefit mitigation of undesirable impacts of wind erosion and dust transport.

Colorado, Utah↗

USGS2024

An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R 2 = 0.79 and R 2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

California↗

USGS2024

Dryland soil recovery after disturbance across soil and climate gradients of the Colorado Plateau

Drylands impacted by energy development often require costly reclamation activities to reconstruct damaged soils and vegetation, yet little is known about the effectiveness of reclamation practices in promoting recovery of soil quality due to a lack of long-term and cross-site studies. Here, we examined paired on-pad and adjacent undisturbed off-pad soil properties over a 22-year chronosequence of 91 reclaimed oil or gas well pads across soil and climate gradients of the Colorado Plateau in the southwestern United States. Our goals were to estimate the time required for soil properties to reach undisturbed conditions, examine the multivariate nature of soil quality following reclamation, and identify environmental factors that affect reclamation outcomes. Soil samples, collected in 2020 and 2021, were analyzed for biogeochemical pools (total nitrogen, and total organic and inorganic carbon), chemical characteristics (salinity, sodicity, pH), and texture. Predicted time to recovery across all sites was 29 years for biogeochemical soil properties, 31 years for soil chemical properties, and 6 years for soil texture. Ordination of soil properties revealed differences between on- and off-pad soils, while site aridity explained variability in on-pad recovery. The predicted time to total soil recovery (distance between on- and off-pad in ordination space) was 96 years, which was longer than any individual soil property. No site reached total recovery, indicating that individual soil properties alone may not fully indicate recovery in soil quality as soil recovery does not equal the sum of its parts. Site aridity was the largest predictor of reclamation outcomes, but the effects differed depending on soil type. Taken together, results suggest the recovery of soil quality - which reflects soil fertility, carbon sequestration potential, and other ecosystem functions - was influenced primarily by site setting, with soil type and aridity major mediators of on-pad carbon, salinity, and total soil recovery following reclamation.

Colorado, Utah↗

USGS2023

Time, climate, and soil settings set the course for reclamation outcomes following dryland energy development

Soil attributes, climate, and time since reclamation have important implications for oil and gas reclamation success on drylands. It is uncertain if reclaimed well pads, on highly degraded drylands, can successfully regain ecological function or meet indicator benchmarks for reclamation. Here, our goals were to assess patterns in reclamation outcomes relative to (1) soil attributes, climate, and time since reclamation; and (2) plant and soil reference benchmarks. We collected a suite of plant, soil, and landscape characteristics from 134 reclaimed well pads on the Colorado Plateau that spanned climate and soil gradients and ranged from 2 to 20 years post-reclamation, then compared characteristics to those from 583 reference plots. On the reclaimed pads, less saline soils, more mesic climates, and longer time since reclamation were associated with favorable reclamation outcomes based on metrics of plant species richness and diversity, plant structure, and cover of important plant functional groups. However, most pads had major departures in 1/4 indicators, suggesting unsuccessful reclamation. Arid warm locations with shallow soils had the greatest percentage of major departures, and noxious plant richness had the greatest departures across soil and climate settings. Our assessment indicates reclamation is failing in key metrics for most pads, and the results highlight the role of environmental conditions in driving reclamation outcomes. These new insights are valuable for ascertaining possible reclamation outcomes and success rates following energy related disturbance, including orphaned wells and renewable energy development. Importantly, the approach used here has applicability for setting benchmarks and outcome standards.

Land Degradation and Development↗

USGS2023

Resilience of riparian vegetation productivity to early 21st century drought in northern California, USA

Drought and intensive land use can interact as stressors on riparian vegetation, especially along rivers flowing through seasonally dry landscapes. Knowledge of past riparian vegetation response to drought and land use change can provide land managers with a better understanding of changes induced by upstream management actions, climate change, and chronic stressors. To investigate the response of riparian vegetation productivity to drought and land use, we developed a 21-year time series (2000–2020) of growing season vegetation dynamics using near-infrared reflectance of vegetation (NIR V ) derived from satellite data across 30 watershed subbasins that drain into the San Francisco Bay Delta in central California, USA. We observed a strong response of riparian vegetation to drought, but rapid recovery and very few long-term declines in productivity. At a local level, vegetation communities' response to drought and post-drought productivity dynamics were highly variable across biophysical settings and land use gradients. Most of the riparian areas with long-term declines in NIR V were located in the lower elevation Coast Range on the western side of the study area where there is little to no water engineering or agricultural irrigation runoff to subsidize riparian vegetation. Riparian areas with the greatest long-term increase were along rivers draining the higher elevation Sierra Nevada range to the east. Our results suggest that river systems with a high proportion of water originating as snowmelt may be more buffered against long-term drought-driven declines in productivity than those dependent exclusively on winter rainfall. The long-term increase in NIR V in the vast majority of riparian areas within our study area may also have been driven in part by increasing atmospheric CO 2 concentrations, which have been shown to increase plant water use efficiency.

California↗