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USGS2025

40Ar/39Ar geochronology supporting mineral resources research at USGS Denver

The 40 Ar/ 39 Ar geochronology method is used to date potassium-bearing rocks and minerals, based on the decay of 40 K to 40 Ar, which provides important temporal constraints for geological events. The USGS Denver Argon Geochronology Laboratory dates samples from a variety of projects, mainly in the USGS Mineral Resource Program and the National Cooperative Geologic Mapping Program, facilitating in-depth research into the timing of geological events and processes. Recent applications of this method include geochronological studies in the Yellow Pine district, revealing mineralization ages that range from 51 to 70 Ma, and investigations into the Bear Lodge alkaline complex, providing insights into the timing of REE carbonatite formation. Additionally, studies at Alunite Ridge highlight the method's potential for understanding concealed mineral deposits. Ongoing projects continue to leverage 40 Ar/ 39 Ar data to enhance understanding of geologic frameworks and mineral resources, underpinning its importance in modern geochronology and mineral resource assessment.

Conference Paper↗

USGS2025

Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery

The U.S. Geological Survey and the U.S. Fish and Wildlife Service have developed a three-tiered strategy for monitoring eelgrass ( Zostera marina ) beds at Izembek Lagoon, Alaska, that targets different spatial and temporal scales. The broadest-scale monitoring (tier-1) uses satellite imagery about every 5 years to delineate the spatial extent of eelgrass beds throughout the lagoon. This report describes the most recent (mid-2020s) tier-1 eelgrass monitoring at Izembek Lagoon. The monitoring effort began by canvasing all satellite imagery collected during summer, under clear daytime skies and at low-tide, since the last tier-1 effort in 2006. Two eelgrass maps of Izembek Lagoon were generated by first creating maps of spectrally unique classes from two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing those spectral classes with information about eelgrass conditions based on field data. Specifically, maps depicting various eelgrass metrics, such as percentage of cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons of the 2016 and 2020 Sentinel-2 maps showing eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined during 2006–20 were most prevalent in the central part of Izembek Lagoon. More recently, during 2016-20, areas of possible biomass decline were more prevalent in the southern part of the lagoon. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data allows conditions to be compared over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.

Alaska↗

USGS1999

Effects of snowmobile use on snowpack chemistry in Yellowstone National Park, 1998

Snowmobile use in Yellowstone National Park has increased substantially in the past three decades. In areas of greatest snowmobile use, elevated levels of by-products of gasoline combustion such as ammonium and benzene have been detected in snowpack samples. Annual snowpacks and snow-covered roadways trap deposition from local and regional atmospheric emissions. Snowpack samples representing most of the winter precipitation were collected at about the time of maximum annual snow accumulation at a variety of locations in the park to observe effects of a range of snowmobile traffic levels. Concentrations of organic and inorganic compounds in snow samples from pairs of sites located directly in and off snow-packed roadways used by snowmobiles were compared to concentrations in samples collected at nearby off-road sites. Concentrations of ammonium were 2 to 5 times higher for the in-road snow compared to off-road snow for each pair of sites. Thus, concentrations decreased rapidly with distance from roadways. In addition, concentrations of ammonium, nitrate, sulfate, benzene, and toluene in snow were positively correlated with snowmobile use.

Wyoming↗

USGS2026

Distinguishing natural from mining-related metal sources by including streambank groundwater data in a stream mass loading study

Distinguishing stream metal loading caused by mine features from that caused by natural background sources remains challenging, yet this distinction is essential for making effective remedial decisions at many legacy mine sites. We combine a stream tracer injection and synoptic sampling study with data from shallow near-stream groundwater wells to estimate left-bank versus right-bank metal loading contributions at the 100-m spatial scale. The study was performed in the East Mancos River, a mountain headwater stream in Colorado, USA. The dominant source of elevated stream metal concentrations could be either groundwater infiltration through right-bank Doyle Mine waste piles or natural acid rock drainage from hydrothermally altered bedrock located mainly on the left bank. For the five metals of concern (Cu, Al, Zn, Cd, and Mn), we find that 15 % of the load contributed by diffuse groundwater inputs in the section potentially influenced by Doyle mine waste originates from the right bank. This right-bank potential mine contribution equates to only 3 % of the total watershed instream load for these metals. Furthermore, apparent 3 H/ 3 He groundwater ages in segments contributing most of the right-bank metal loading are sufficiently old (9–12 yr) to suggest that infiltration through the waste piles, located only 140–180 m from the stream, is unlikely. Estimated potential Doyle mine loading contributions can therefore be considered maximum values. Study results thus indicate that Doyle mine waste piles are a minor source of metal loading under low-flow conditions, and streambank groundwater data can provide valuable additional information in stream mass loading studies.

Colorado↗

USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗

USGS2025

Sources and risk factors for nitrate, pathogens, and fecal contamination of private wells in rural southwestern Wisconsin, USA

Household well water can be degraded by contaminants from the land's surface, but private well owners lack means to protect the source water from neighboring disturbances. Rural residents of southwestern Wisconsin, USA, rely on private well water, and the combination of land use and fractured carbonate bedrock makes groundwater vulnerable to contamination. To identify the extent, sources, and risk factors of private well contamination, randomly selected wells sampled during two-day periods in fall (n = 301) and spring (n = 529) were analyzed for nitrate and indicator bacteria, and a subset (n = 138) was sampled across four seasonal events for analysis of pathogens and microbial source tracking markers by quantitative polymerase chain reaction. Risk factors representing land use, hydrology, geology, and well construction were analyzed for associations with contamination in multivariable models. The importance of risk factors varied by contaminant, illustrating the multifaceted nature of rural groundwater quality. Nitrate contamination was associated with agricultural land use, and wells with casings that extended below a shale aquitard accessed less contaminated water than those drawing water from above it. Human fecal microbes were detected in 64 wells (46%), and rainfall was the key risk factor for contamination, indicating that wastewater from septic systems was available to contaminate wells when transport conditions were favorable. Manure microbes from cattle/ruminants and pigs were detected in 33 and 13 wells, respectively, and concentrations increased with the hectarage of cultivated land near wells. Pathogen genes for viruses, bacteria, and protozoa were detected in 66 wells (48%), including more detections of zoonotic than human-specific pathogens, and human Bacteroides , an indicator of wastewater, was an equivocal predictor of pathogen presence in private wells. Characterizing important elements of the setting, like geology, and identifying sources and risk factors for contaminants can inform landscape-level policies to protect groundwater quality.

Wisconsin↗

USGS2025

Rainfall thresholds for postfire debris-flow initiation vary with short-duration rainfall climatology

The size, frequency, and geographic scope of severe wildfires are expanding across the globe, including in the Western United States. Recently burned steeplands have an increased likelihood of debris flows, which pose hazards to downstream communities. The conditions for postfire debris-flow initiation are commonly expressed as rainfall intensity-duration thresholds, which can be estimated given sufficient observational history. However, the spread of wildfire across diverse climates poses a challenge for accurate threshold prediction in areas with limited observations. Studies of mass-movement processes in unburned areas indicate that thresholds vary with local climate, such that higher rainfall rates are required for initiation in climates characterized by frequent intense rainfall. Here, we use three independent methods to test whether initiation of postfire runoff-generated debris flows across the Western United States varies similarly with climate. Through the compilation of observed thresholds at various fires, analysis of the spatial density of observed debris flows, and quantification of feature importance at different spatial scales, we show that postfire debris-flow initiation thresholds vary systematically with short-duration rainfall-intensity climatology. The predictive power of climatological data sets that are readily available before a fire occurs offers a much-needed tool for hazard management in regions that are facing increased wildfire activity, have sparse observational history, and/or have limited resources for field-based hazard assessment. Furthermore, if the observed variation in thresholds reflects long-term adjustment of the landscape to local climate, rapid shifts in rainfall intensity related to climate change will likely induce spatially variable shifts in postfire debris-flow likelihood.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗

USGS1983

Technique for estimating depth of floods in Tennessee

Estimates of flood depths are needed for design of roadways across flood plains and for other types of construction along streams. Equations for estimating flood depths in Tennessee were derived using data for 150 gaging stations. The equations are based on drainage basin size and can be used to estimate depths of the 10-year and 100-year floods for four hydrologic areas. A method also was developed for estimating depth of floods having recurrence intervals between 10 and 100 years. Standard errors range from 22 to 30 percent for the 10-year depth equations and from 23 to 30 percent for the 100-year depth equations.

Tennessee↗

USGS2025

Arctic speleothems reveal nearly permafrost-free Northern Hemisphere in the Late Miocene

Arctic warming is happening at nearly four times the global average rate. Long-term trends of permafrost dynamics cannot be estimated directly from monitoring of present-day thaw processes, requiring paleoclimate-proxy information. Here we use cave carbonates (speleothems) from a northern Siberian cave to determine when the Northern Hemisphere was mostly permafrost-free. At present, thick continuous permafrost in this region prevents speleothem growth. In a series of partially eroded caves, speleothems grew during the late Tortonian stage (8.68 ± 0.09 Ma), a time when the geographic position of this site was already similar to today. Paleotemperatures reconstructed from speleothems show that mean annual air temperatures (MAAT) in the region were + 6.6°C to + 11.1°C, when contemporary global MAAT were ~ 4.5 °C higher than modern. Our findings provide direct evidence that warming to Tortonian-like temperatures would leave most of the Northern Hemisphere permafrost-free. This may release up to ~ 130 petagrams of carbon, enhancing further warming.

Siberia↗

USGS2025

Assessing nonpoint-source uranium pollution in an irrigated stream-aquifer system

Uranium (U) in rocks and soils of arid and semi-arid environments can be mobilized by irrigation and fertilization, posing environmental and health risks. Elevated U, along with selenium (Se) and nitrate (NO 3 ) co-constituents, necessitates careful monitoring and management. We developed a distributed-parameter numerical model to assess U pollution in an irrigated stream-aquifer system, applying it to a 552 km 2 region in Colorado's Lower Arkansas River Valley (LARV) over 14 years. A MODFLOW model, describing groundwater and stream flow, was coupled with an RT3D-OTIS model to portray reactive U transport. Calibration using the PESTPP-iES iterative ensemble smoother (iES) software indicated good agreement with observed U concentrations. The model revealed substantial and variable U levels across the LARV, highlighting potential hotspots and possible contributing factors, such as geological composition of the bedrock and near-surface shale and aquifer sediments derived from them, irrigation practices, and riparian landscape. U levels exceed the chronic standard (85th percentile = 30 μg/L, set by the US Environmental Protection Agency), which is the permissible regulatory threshold, in groundwater across 44 % of the region and along the river by an average factor of 2.9. Simulated average U concentrations in the non-riparian aquifer and river are 124 μg/L and 60 μg/L, respectively, compared with 112 μg/L and 62 μg/L for measured values. The average 85th percentile U concentration is 222 μg/L in the aquifer and 82 μg/L in the river. Average simulated U mass loading to the river is 0.17 kg/day per km, compared to an estimated 0.23 kg/day per km. Findings provide a baseline for comparing future simulated outcomes of alternative best management practices (BMPs) for U pollution mitigation and offer a methodology applicable to other irrigated regions.

Colorado↗

USGS2024

Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions

Study region The Mississippi Alluvial Plain (MAP) in the United States (US). Study focus Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km 3 /year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R 2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.

Journal of Hydrology Regional Studies↗

USGS2024

Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin

Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. This work is part of that reexamination. The stationarity assumption is a concern because flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done to assess potential nonstationarity in peak flows in the north-central United States. This report summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.

Scientific Investigations Report↗

USGS2026

Diverging mineral chemistry of iron and nickel throughout Earth’s changing redox conditions reveals foundation for their evolution as protein cofactors

Iron (Fe) and nickel (Ni) were both foundational to early metabolism, yet their biological trajectories diverged as Earth’s surface redox state changed. Here, we integrate mineral chemistry network analysis, protein metal-site coordination-sphere analysis, and curated redox comparisons to test how geochemistry and metalloprotein architecture co-evolved. Mineral network analyses show broader electronegativity variation and network diversity for Fe-bearing minerals through time relative to Ni-bearing minerals. In structural analyses of protein metal centers in a combined Fe/Ni protein structure set, it is shown that Fe- and Ni-associated environments differ in amino-acid composition, hydropathy structure, and cysteine representation. The greater chemical diversity and electronegativity variation in Fe minerals mirror the higher redox and structural versatility of Fe-binding proteins. The presence of Fe in a broader range of mineral and protein environments demonstrates the chemical adaptability of the metal, from the anoxic Archean to oxidative Earth surface conditions following the Great Oxidation Event. Iron, with its broad redox potential range in Fe-oxidoreductases, has a central role in both anaerobic and aerobic metabolisms. Nickel, by contrast, is less widespread in biology. Today, Ni is predominantly employed in deeply branching anaerobic pathways and by proteins with narrower redox potential ranges. Our results show that evolutionary processes, constrained by metal chemistry, habitually utilize Fe as a redox generalist while retaining Ni in specialized roles. The divergent paths of Ni and Fe, from rocks to proteins, demonstrate the intimate relationship between planetary geochemistry and metabolic origins on Earth and suggest that Fe/Ni geochemistry may inform habitability assessments in extraterrestrial environments when interpreted within specific planetary environmental contexts.

Life↗

USGS2025

From hydrated silica to quartz: Potential hydrothermal precipitates found in Jezero crater, Mars

On Earth, silica-rich phases from opal to quartz are important indicators and tracers of geological processes. Hydrated silica, such as opal, is a particularly good matrix for the preservation of molecular and macroscopic biosignatures. Cherts, a type of silica-dominated rocks, provide a unique archive of ancient terrestrial life while quartz is the emblematic mineral of the Earth's continental crust. On Mars, hydrated silica has been detected in several locations based on remote sensing and rover-based studies. In the present article we report on the detection of cobbles made of hydrated silica (opal or chalcedony), as well as well-crystallized quartz. These detections were made with the SuperCam instrument onboard Perseverance (Mars 2020 mission), using a combination of LIBS, infrared and Raman spectroscopy. Quartz-dominated stones are detected unambiguously for the first time on the Martian surface, and based on grain size and crystallinity are proposed to be of hydrothermal origin. Although these rocks were all found as float, we propose that these detections are part of a common hydrothermal system, and represent different depths / temperatures of precipitation. This attests that hydrothermal processes were active in and around Jezero crater, possibly triggered by the Jezero crater-forming impact. These silica-rich rocks, in particular opaline silica, are very promising targets for sampling and return to Earth given their high biosignature preservation potential.

Earth and Planetary Science Letters↗

USGS2025

SURF: An automated method for building nonplanar 3D fault models from earthquake hypocenters

Accurately characterizing 3D fault geometry is vital for improving our understanding of earthquake behavior and informing the development of seismic hazard models. Despite their importance, subsurface fault structures tend to be poorly constrained because of limitations in observational data. Improvements to the seismic networks and earthquake detection algorithms have increased the precision and volume of earthquake catalogs, which help illuminate detailed subsurface fault structure and provide the most direct information available about fault geometries at depth. We present a Python package to automate generating 3D fault geometries directly from hypocentral seismicity patterns. This method begins with clustering events based on their spatial density, identifying coherent patterns. Nearby clusters are then merged based on the similarity of their orientations. We fit nonplanar surfaces using support vector regression to balance surface accuracy with minimal deviations from planarity. The fault models are output as quadrilateral meshes at user‐defined resolution. In the process of generating the 3D fault surfaces, we compute the spatial density of seismicity around the surface and the planarity as quantitative metrics of the model outputs. As a proof of concept, we apply this approach to the San Andreas–Calaveras fault junction region and the 2019 Ridgecrest earthquake sequence, both in California, which contain complex subparallel faults well defined at the Earth’s surface and abundant microseismicity. These case studies demonstrate the method’s ability to model complex fault structures, including long continuous fault surfaces, crossing faults, variably dipping segments, and subparallel faults. We test the method on both standard network catalogs and double‐difference relocated catalogs. We find that our seismicity‐based fault model results align with published 3D models that incorporate additional constraints and interpretations ( Plesch et al. , 2020 ; Aagaard and Hirakawa, 2021 ). This workflow provides a low‐user‐input solution for estimating fault geometries at depth from earthquake catalogs.

California↗

USGS2025

U.S. Geological Survey Groundwater Climate Response Network, 2024

As of October 2024, the U.S. Geological Survey (USGS) operated 588 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells selected to monitor the effects of climate variability, such as droughts, on groundwater levels nationwide. The CRN includes nearly 500 locations with real-time data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗

USGS2025

Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches

Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗