Geology topics

Eric K. Waller

Publications and source records attributed to Eric K. Waller.

7 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↗

USGS2025

Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing

California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

California↗

USGS2023

Biophysical factors control invasive annual grass hot spots in the Mojave Desert

Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp . a nd Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots , across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots , while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.

California↗

USGS2022

Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019

Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant ( p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

Water Resources Research↗

USGS2018

Differential changes in the onset of spring across US National Wildlife Refuges and North American migratory bird flyways

Warming temperatures associated with climate change can have indirect effects on migratory birds that rely on seasonally available food resources and habitats that vary across spatial and temporal scales. We used two heat-based indices of spring onset, the First Leaf Index (FLI) and the First Bloom Index (FBI), as proxies of habitat change for the period 1901 to 2012 at three spatial scales: the US National Wildlife Refuge System; the four major bird migratory flyways in North America; and the seasonal ranges (i.e., breeding and non-breeding grounds) of two migratory bird species, Blue-winged Warbler ( Vermivora cyanoptera ) and Whooping Crane ( Grus americana ). Our results show that relative to the historical range of variability, the onset of spring is now earlier in 76% of all wildlife refuges and extremely early (i.e., exceeding 95% of historical conditions) in 49% of refuges. In all flyways but the Pacific, the rate of spring advance is generally greater at higher latitudes than at lower latitudes. This differential rate of advance in spring onset is most pronounced in the Atlantic flyway, presumably because of a “warming hole” in the southeastern US. Both FLI and FBI have advanced markedly in the breeding ranges–but not the non-breeding ranges–of the two selected bird species, albeit with considerable intra-range variation. Differences among species in terms of migratory patterns and the location and extent of seasonal habitats, as well as shifts in habitat conditions over time, may complicate predictions of the vulnerability of migratory birds to climate change effects. This study provides insight into how differential shifts in the phenology of disparate but linked habitats could inform local- to landscape-scale management strategies for the conservation of migratory bird populations.

PLoS ONE↗

USGS2018

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

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.

International Journal of Applied Earth Observation↗

USGS2018

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Colorado Plateau↗