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Increased atmospheric evaporative demand has important implications for humans and ecosystems in water-scarce lands. While temperature plays a significant role in driving evaporative demand and its trend, other climate variables are also influential and their contributions to recent trends in evaporative demand are unknown. We address this gap with an assessment of recent (1980–2020) trends in annual reference evapotranspiration (ETo) and its drivers across the continental United States based on five gridded datasets. In doing so, we characterize the structural uncertainty of ETo trends and decompose the relative influences of temperature, wind speed, solar radiation, and humidity. Results highlight large and robust changes in ETo across much of the western United States, centered on the Rio Grande region where ETo increased 135–235 mm during 1980–2020. The largest uncertainties in ETo trends are in the central and eastern United States and surrounding the Upper Colorado River. Trend decomposition highlights the strong and widespread influence of temperature, which contributes to 57% of observed ETo trends, on average. ETo increases are mitigated by increases in specific humidity in non-water-limited regions, while small decreases in specific humidity and increases in wind speed and solar radiation magnify ETo increases across the West. Our results show increases in ETo across the West that are already emerging outside the range of variability observed 20–40 years ago. Our results suggest that twenty-first-century land and water managers need to plan for an already increasing influence of evaporative demand on water availability and wildfire risks.Significance StatementIncreased atmospheric thirst due to climate warming has the potential to decrease water availability and increase wildfire risks in water-scarce regions. Here, we identified how much atmospheric thirst has changed across the continental United States over the past 40 years, what climate variables are driving the change, and how consistent these changes are among five data sources. We found that atmospheric thirst is consistently emerging outside the range experienced in the late twentieth century in some western regions with 57% of the change driven by temperature. Importantly, we demonstrate that increased atmospheric thirst has already become a persistent forcing of western landscapes and water supplies toward drought and will be an essential consideration for land and water management planning going forward.

HighlightsThe FAO-56 dual crop coefficient procedure was applied over the entire agricultural areas of Idaho and Nevada to determine evapotranspiration (ET) and net irrigation water requirements (IWR).Basal crop coefficients were expressed as functions of normalized cumulative growing degree days.ET during dormant seasons was included in the estimates.The procedure was applied to a U.S. West-wide study of climate change effects on ET and IWR.Abstract. The FAO-56 dual crop coefficient procedure was used to determine evapotranspiration (ET) and net irrigation water requirements for all agricultural areas of the states of Idaho and Nevada and in a western U.S. study on effects of climate change on future irrigation water requirements. The products of the applications are for use by state governments for water rights management, irrigation system planning and design, wastewater application system design and review, hydrologic water balances, and groundwater modeling. The products have been used by the U.S. federal government for assessing impacts of current and future climate change on irrigation water demands. The procedure was applied to data from more than 200 weather station locations across the state of Idaho, 200 weather station locations across the state of Nevada, and eight major river basins in the western U.S. for available periods of weather records. Estimates were made over daily, monthly, and annual time intervals. Methods from FAO-56 were employed for calculating reference ET and crop coefficients (Kc), with ET calculations performed for all times of the calendar year including winter. Expressing Kc as a function of thermal-time units allowed application across a wide range of local climates and elevations. The ET estimates covered a wide range of agricultural crops grown in the western U.S. plus a number of native plant systems, including wetlands, rangeland, and riparian trees. Evaporation was estimated for three types of open-water surfaces ranging from deep reservoirs to small farm ponds. Keywords: Consumptive use, Dual crop coefficient, Evapotranspiration, FAO-56, Irrigation water requirements.

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat. © 2019 The United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) are gratefully acknowledged for support and encouragement of the 2012–2017 Landsat Science Team ( https://landsat.usgs.gov/landsat-science-teams ). The Editor and Reviewers are thanked for the valuable insights and constructive suggestions made to improve this manuscript.

Dryland riparian areas are under increasing stress due to expanding human water demands and a warming climate. Quantifying responses of dryland riparian vegetation to these pressures is complicated by high climatic variability, which can create strong, transient changes in vegetation vigor that could mask other disturbance events. In this study, we utilize a 34-year archive of Landsat satellite data to (1) quantify the strength and timescales of vegetation responses to interannual variability in drought status and (2) isolate and remove this influence to assess resultant trends in vegetation vigor for riparian areas across the state of Nevada, the driest state in the USA. Correlations between annual late-summer Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation–Evapotranspiration Index (SPEI) were calculated across a range of time periods (varying timing and durations) for all riparian pixels within each of the 45 ecoregions, and the variability of these values across the study area is shown. We then applied a novel drought adjustment method that used the strongest SPEI–NDVI timescale relationships for each ecoregion to remove the influence of interannual drought status. Our key result is a 30 m resolution map of drought-adjusted riparian NDVI trends (1985–2018). We highlight and describe locations where impacts of invasive species biocontrol, mine water management, agriculture, changing water levels, and fire are readily visualized with our results. We found more negatively trending riparian areas in association with wide valley bottoms, low-intensity agricultural land uses, and private land ownerships and more positive trends in association with narrow drainages, public lands, and surrounding perennial water bodies (an indication of declining water levels allowing increased vegetative cover). The drought-adjusted NDVI improved the statistical significance of trend estimates, thereby improving the ability to detect such changes. Results from this study provide insight into the strength and timescales of riparian vegetation responses to drought and can provide important information for managing riparian areas within the study area. The novel approach to drought adjustment is readily transferrable to other regions.

Riparian vegetation along streams provides a suite of ecosystem services in rangelands and thus is the target of restoration when degraded by over-grazing, erosion, incision, or other disturbances. Assessments of restoration effectiveness depend on defensible monitoring data, which can be both expensive and difficult to collect. We present a method and case study to evaluate the effectiveness of restoration of riparian vegetation using a web-based cloud-computing and visualization tool ( ClimateEngine.org ) to access and process remote sensing and climate data. Restoration efforts on an Eastern Oregon ranch were assessed by analyzing the riparian areas of four creeks that had in-stream restoration structures constructed between 2008 and 2011. Within each study area, we retrieved spatially and temporally aggregated values of summer (June, July, August) normalized difference vegetation index (NDVI) and total precipitation for each water year (October-September) from 1984 to 2017. We established a pre-restoration (1984–2007) linear regression between total water year precipitation and summer NDVI for each study area, and then compared the post-restoration (2012–2017) data to this pre-restoration relationship. In each study area, the post-restoration NDVI-precipitation relationship was statistically distinct from the pre-restoration relationship, suggesting a change in the fundamental relationship between precipitation and NDVI resulting from stream restoration. We infer that the in-stream structures, which raised the water table in the adjacent riparian areas, provided additional water to the streamside vegetation that was not available before restoration and reduced the dependence of riparian vegetation on precipitation. This approach provides a cost-effective, quantitative method for assessing the effects of stream restoration projects on riparian vegetation.

One way to adapt to and mitigate current and future water scarcity is to manage and store water more efficiently. Reservoirs act as critical buffers to ensure agricultural and municipal water deliveries, mitigate flooding, and generate hydroelectric power, yet they often lose significant amounts of water through evaporation, especially in arid and semiarid regions. Despite this fact, reservoir evaporation has been an inconsistently and inaccurately estimated component of the water cycle within the water resource infrastructure of the arid and semiarid western United States. This paper highlights the increasing importance and challenges of correctly estimating and forecasting reservoir evaporation in the current and future climate, as well as the need to bring new ideas and state-of-the-art practices for the estimation of reservoir evaporation into operational use for modern water resource managers. New ideas and practices include i) improving the estimation of reservoir evaporation using up-to-date knowledge, state-of-the-art instrumentation and numerical models, and innovative experimental designs to diagnose processes and accurately forecast evaporation; ii) improving our understanding of spatial and temporal variations in evaporative water loss from existing reservoirs and transferring this knowledge when expanding reservoirs or siting new ones; and iii) implementing an adaptive management plan that incorporates new knowledge, observations, and forecasts of reservoir evaporation to improve water resource management.

Poor condition of many streams and concerns about future droughts in the arid and semi-arid western USA have motivated novel restoration strategies aimed at accelerating recovery and increasing water resources. Translocation of beavers into formerly occupied habitats, restoration activities encouraging beaver recolonization, and instream structures mimicking the effects of beaver dams are restoration alternatives that have recently gained popularity because of their potential socioeconomic and ecological benefits. However, beaver dams and dam-like structures also harbor a history of social conflict. Hence, we identified a need to assess the use of beaver-related restoration projects in western rangelands to increase awareness and accountability, and identify gaps in scientific knowledge. We inventoried 97 projects implemented by 32 organizations, most in the last 10 years. We found that beaver-related stream restoration projects undertaken mostly involved the relocation of nuisance beavers. The most common goal was to store water, either with beaver dams or artificial structures. Beavers were often moved without regard to genetics, disease, or potential conflicts with nearby landowners. Few projects included post-implementation monitoring or planned for longer term issues, such as what happens when beavers abandon a site or when beaver dams or structures breach. Human dimensions were rarely considered and water rights and other issues were mostly unresolved or addressed through ad-hoc agreements. We conclude that the practice and implementation of beaver-related restoration has outpaced research on its efficacy and best practices. Further scientific research is necessary, especially research that informs the establishment of clear guidelines for best practices.

The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ET0) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by FEWS NET to support food insecurity early warning. The ET0 reforecasts span the 1982–2009 period and are calculated following the American Society for Civil Engineers formulation of the Penman–Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from the National Centers for Environmental Prediction CFSv2 model and the National Aeronautics and Space Administration GEOS-5 model. The skill evaluation, using deterministic and probabilistic scores, focuses on the December–February (DJF), March–May (MAM), June–August (JJA), and September–November seasons. The results indicate that ET0 forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation > 0.35 at leads of 2 months) include the western United States, northern parts of South America, parts of the Sahel region, and southern Africa. The FEWS NET regions where forecasts are most skillful (correlation > 0.35 at lead 3) include northern sub-Saharan Africa (DJF; dry season), Central America (DJF; dry season), parts of East Africa (JJA; wet season), southern Africa (JJA; dry season), and central Asia (MAM; wet season). A case study over parts of East Africa for the JJA season shows that ET0 forecasts in combination with the precipitation forecasts would have provided early warning of recent severe drought events (e.g., in 2002, 2004, 2009) that contributed to substantial food insecurity in the region.

The extent and heterogeneity of rangelands in the state of Nevada (United States) pose a challenging situation for land managers when determining stocking levels for livestock grazing. Overutilization can cause lasting environmental damage, while underutilization can create unnecessary economic hardship for livestock operators. An improved ability to forecast vegetation stress later in the growing season would allow resource managers to better manage the tradeoffs between ecological and economic concerns. This research maps how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year by analyzing 15 yr of enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectroradiometer sensor, cumulative monthly precipitation, and the Palmer drought severity index. Land cover classes within the grazing allotments that are not relevant to grazing were removed from the analysis, as well as areas that showed > 50% change in EVI since these likely represented transitions or disturbances that were not related to interannual climate variability. The datasets were gridded at spatial resolutions from 4 to 72 km, and the correspondence between image and meteorological datasets was found to improve as measurements were averaged over larger areas. A 16-km sampling grid was judged to provide the best balance between predictive ability and spatial precision. The average R2 of regressions between the vegetation index and meteorological variables within each of the 16-km grid cells was 0.69. For most of Nevada, the ability to predict vegetation conditions for the entire growing season (February–September) generally peaks by the end of May. However, results vary by region, with the northeast particularly benefiting from late-season data. Regressions were performed with and without very wet years, and the ability to make early predictions is better when including wet years than in dry to typical conditions.

The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

The impact of groundwater withdrawal on surface water is a concern of water users and water managers, particularly in the arid western United States. Capture maps are useful tools to spatially assess the impact of groundwater pumping on water sources (e.g., streamflow depletion) and are being used more frequently for conjunctive management of surface water and groundwater. Capture maps have been derived using linear groundwater flow models and rely on the principle of superposition to demonstrate the effects of pumping in various locations on resources of interest. However, nonlinear models are often necessary to simulate head-dependent boundary conditions and unconfined aquifers. Capture maps developed using nonlinear models with the principle of superposition may over- or underestimate capture magnitude and spatial extent. This paper presents new methods for generating capture difference maps, which assess spatial effects of model nonlinearity on capture fraction sensitivity to pumping rate, and for calculating the bias associated with capture maps. The sensitivity of capture map bias to selected parameters related to model design and conceptualization for the arid western United States is explored. This study finds that the simulation of stream continuity, pumping rates, stream incision, well proximity to capture sources, aquifer hydraulic conductivity, and groundwater evapotranspiration extinction depth substantially affect capture map bias. Capture difference maps demonstrate that regions with large capture fraction differences are indicative of greater potential capture map bias. Understanding both spatial and temporal bias in capture maps derived from nonlinear groundwater flow models improves their utility and defensibility as conjunctive-use management tools.

Options for increasing reservoir storage in developed regions are limited and prohibitively expensive. Projected increases in demand call for new long-term water storage to help sustain agriculture, municipalities, industry, and ecological services. Managed aquifer recharge (MAR) is becoming an integral component of water resources around the world. However, MAR faces challenges, including infrastructure costs, difficulty in enhancing recharge, water quality issues, and lack of available water supplies. Here we examine, through simulation modeling of a hypothetical agricultural subbasin in the western US, the potential of agricultural managed aquifer recharge (Ag-MAR) via canal seepage and off-season field irrigation. Weather phenomenon in many regions around the world exhibit decadal and other multi-year cycles of extreme precipitation. An on-going challenge is to develop approaches to store greater amounts of water during these events. Simulations presented herein incorporate Ag-MAR programs and demonstrate that there is potential to enhance regional recharge by 7-13%, increase crop consumptive use by 9-12%, and increase natural vegetation consumption by 20-30%, where larger relative increases occur for lower aquifer hydraulic conductivity and higher specific yield values. Annual Increases in groundwater levels were 7 m, and sustained levels following several years of drought were greater than 2 m. Results demonstrate that Ag-MAR has great potential to enhance long-term sustainability of water resources in agricultural basins.

Meteorological data are often collected in dry, desert regions where the local environments exhibit effects of aridity caused by the lack of evapotranspiration (ET). In contrast, the standard reference ET (ETo) calculations of FAO56 and ASCE assume that the surfaces underlying collected weather data are well-watered so that near-surface meteorological measurements reflect the cooling and humidifying effects of an evaporating surface. In this study, we develop a weather data conditioning process and algorithms to adjust for biases in meteorological data that exhibit aridity effects. The conditioning process is intended to adjust the weather data to better exhibit characteristics of data collected over a well-watered vegetated surface prior to use of the data to estimate reference ET. The procedure involves the extrapolation of air temperature, vapor pressure, and wind speed profiles to and from a regional blending height using standard surface energy balance equations and flux-profile relationships and employing ET estimated for the ambient, dry conditions of an arid weather station and reference ET that should co-exist with weather measurements used to calculation reference ET. Example applications are given with 24-hour weather data and hourly weather data in Idaho and Nevada. Results indicate that reference ETo can be overstated by as much as 25% in southern Idaho and 8% in eastern Nevada. The methodology is intended to be transferrable to other regions and climates and is self-aware of the need for conditioning of weather data according to differences in ambient ET and the reference ET estimates.

Recent research into the complementary relationship (CR) between actual and apparent potential evaporation has resulted in numerous alternative forms for the CR. Inspired by Brutsaert [2015], who derived a general CR in the form y=function(x), where x is the ratio of potential evaporation to apparent potential evaporation and y is the ratio of actual to apparent potential evaporation, an equation is proposed to calculate the value of x at which y goes to zero, denoted xmin. The value of xmin varies even at an individual observation site, but can be calculated using only the data required for the Penman (1948) equation as expressed here, so no calibration of xmin is required. It is shown that the scatter in x-y plots using experimental data is reduced when x is replaced by X=(x-xmin)/(1-xmin). This rescaling results in data falling along the line y=X, which is proposed as a new version of the CR. While a reinterpretation of the fundamental boundary conditions proposed by Brutsaert [2015] is required, the physical constraints behind them are still met. An alternative formulation relating y to X is also discussed. This article is protected by copyright. All rights reserved.

Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~ 30 years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM +, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM + and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.

Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E0, as in sustained droughts, and 2) parallel ET and E0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in Part II.

Reliable evapotranspiration (ET) estimation is a key factor for water resources planning‎, attaining sustainable water resources use, irrigation water management, and water regulation. During the past few decades, researchers have developed a variety of remote sensing techniques to estimate ET. The Earth Engine Evapotranspiration Flux (EEFlux) application uses Landsat imagery archives on the Google Earth Engine platform to calculate the daily evapotranspiration at the local field scale (30 m). Automatically calibrated for each Landsat image, the EEFlux application design is based on the widely vetted Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model and produces ET estimation maps for any Landsat 5, 7 or 8 scene in a matter of seconds. In this research we evaluate the consistency and accuracy of EEFlux products that are produced when standard US and global assets are used. Processed METRIC products for 58 scenes distributed around the western and central United States were used as the baseline for comparison. The goal of this paper is to compare the results from EEFlux with the ‎standard METRIC applications to illustrate the utility of the EEFlux products as ‎they currently stand. Given that EEFlux is derived from METRIC, differences are expected to occur due to differing ‎calibration methods (automatic versus manual) and differing input datasets. The products compared include the fraction of reference ET (ETrF), actual ET (ETa), and surface energy balance components net radiation (Rn), ground heat flux (G), and sensible heat flux (H), as well as Ts, albedo and NDVI. The product comparisons show that the intermediate products of Ts, Albedo, and NDVI, and also Rn have similar values and behavior for both EEFlux and METRIC. Larger differences were found for H and G. Despite the more significant differences in H and G, results show that EEFlux is able to calculate ETrF and ETa values comparable to the values from trained expert METRIC users for agricultural areas. For non-agricultural areas such as semi-arid rangeland and forests, the automated EEFlux calibration algorithm needs to be improved in order to be able to reproduce ETrF and ETa that is similar to the manually calibrated METRIC products.

A novel contiguous United States (CONUS) wide evaluation of reference evapotranspiration (ET0; a formulation of evaporative demand) anomalies is performed using the Climate Forecast System version 2 (CFSv2) reforecast data for 1982–2009. This evaluation was motivated by recent research showing ET0 anomalies can accurately represent drought through exploitation of the complementary relationship between actual evapotranspiration and ET0. Moderate forecast skill of ET0 was found up to leads of 5 months and was consistently better than precipitation skill over most of CONUS. Forecasts of ET0 during drought events revealed high categorical skill for notable warm-season droughts of 1988 and 1999 in the central and northeast CONUS, with precipitation skill being much lower or absent. Increased ET0 skill was found in several climate regions when CFSv2 forecasts were initialized during moderate-to-strong El Nino–Southern Oscillation events. Our findings suggest that ET0 anomaly forecasts can improve and complement existing seasonal drought forecasts.

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ETg) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ETg. An iterative approach is provided that calibrates GWM to ETg derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11-year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ETg to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM-grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first-order second moment analysis shows ETg to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ETg despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ETg appears to bias ETg during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness. (KEY TERMS: groundwater models; groundwater evapotranspiration; remote sensing; depth to groundwater; Enhanced Vegetation Index; model calibration.) Carroll, Rosemary W.H., Greg M. Pohll, Charles G. Morton, and Justin L. Huntington, 2015. Calibrating a Basin-Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration. Journal of the American Water Resources Association (JAWRA) 1-14. DOI: 10.1111/jawr.12285

Diorhabda carinulata (northern tamarisk beetle) has been released in the western United States as a biological control agent for the invasive plant Tamarix spp. (tamarisk). A few studies have been conducted analysing the effects of beetle defoliation on tamarisk water consumption, but predefoliation and post-defoliation comparison based on field data is scarce. The question of whether beetles substantially alter tamarisk water consumption is still open for discussion. In this study, an eddy covariance station and groundwater monitoring well were installed in a tamarisk stand along the Virgin River near Mesquite, NV, in 2010 to observe the impacts of tamarisk defoliation on evapotranspiration (ET) and groundwater levels. ET was calculated using the eddy covariance method and the White method and supported by Landsat remote sensing estimates. Data collected in 2010 established a baseline since the beetles arrived at the site in late 2010 and started feeding on foliage causing tamarisk trees to turn to brown during 2011. Repeated defoliations in 2011 and 2012 show that post-defoliation ET values and magnitude of diurnal groundwater-level fluctuations decreased compared with the predefoliation values. Estimated annual ET and annual average daily groundwater fluctuations were reduced by 18% and 35%, respectively. However, the magnitude of effects of defoliation was dependent on the growth stage of tamarisk at the time of defoliation. Also, ET recovered within a month as tamarisk established new leaves. Results from this study suggest that long-term changes in ET are highly dependent on repeated defoliation occurrences over several years. Copyright © 2015 John Wiley & Sons, Ltd.

Predicting sharp hydroclimatic gradients in the complex terrain of the Great Basin can prove to be challenging because of the lack of climate observations that are gradient focused. Furthermore, evaluating gridded data products (GDPs) of climate in such environments for use in local hydroclimatic assessments is also challenging and typically ignored because of the lack of observations. In this study, independent Nevada Climate-Ecohydrological Assessment Network (NevCAN) observations of temperature, relative humidity, and precipitation collected along large altitudinal gradients of the Snake and Sheep mountain ranges from water-year 2012 (October–September) are utilized to evaluate four GDPs of different spatial resolutions: Parameter–Elevation Regressions on Independent Slopes Model (PRISM) 4 km, PRISM 800 m, Daymet 1 km, and a North American Land Data Assimilation System (NLDAS)–PRISM hybrid 4-km product. Inconsistencies and biases in precipitation measurements due to station siting and gauge type proved to be problematic with respect to comparisons to GDPs. This study highlights a weakness of GDPs in complex terrain: an underestimation of inversion strength and resulting minimum temperature in foothill regions, where cold air regularly drains into neighboring valleys. Results also clearly indicate that for semiarid regions, the assumption that daily average dewpoint temperature Tdew equals daily minimum temperature does not hold true and should not be used to interpolate Tdew spatially. Comparison statistics of GDPs to observations varied depending on the climate variable and grid spatial resolution, highlighting the importance of conducting local evaluations for hydroclimatic assessments.

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin-wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first-order ET estimates that are similar to time intensive manual efforts with less time investment.

A remaining challenge to applying satellite-based energy-balance algorithms for operational estimation of evapotranspiration (ET) is the calibration of the energy-balance model. Customized calibration for each image date is generally required to overcome biases associated with radiometric accuracy of the image, uncertainties in aerodynamic features of the landscape, background thermal conditions, and model assumptions. The CIMEC process (calibration using inverse modeling at extreme conditions) is an endpoint calibration procedure where near extreme conditions in the image are identified where the ET can be estimated and assigned. In the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC™) energy-balance model, two endpoints represent the dry and wet ends of the ET spectrum. Generally, user-intervention is required to select locations in the image to produce best accuracy. To bring the METRIC and similar processes into the domain of less experienced operators, a consistent, reproducible, and dependable statistics-based procedure is introduced where relationships between vegetation amount and surface temperature are used to identify a subpopulation of locations (pixels) in an image that may best represent the calibration endpoints. This article describes the background and logic for the statistical approach, how the statistics were developed, area of interest requirements and assumptions, adjustment for dry conditions in desert climates, and implementation in a common image processing environment (ERDAS Imagine).

Escalating concerns about water supplies in the Great Basin have prompted numerous water budget studies focused on groundwater recharge and discharge. For many hydrographic areas (HAs) in the Great Basin, most of the recharge is discharged by bare soil evaporation and evapotranspiration (ET) from phreatophyte vegetation. Estimating recharge from precipitation in a given HA is difficult and often has significant uncertainty, therefore it is often quantified by estimating the natural discharge. As such, remote sensing applications for spatially distributing flux tower estimates of ET and groundwater ET (ETg) across phreatophyte areas are becoming more common. We build on previous studies and develop a transferable empirical relationship with uncertainty bounds between flux tower estimates of ET and a remotely sensed vegetation index, Enhanced Vegetation Index (EVI). Energy balance-corrected ET measured from 40 flux tower site-year combinations in the Great Basin was statistically correlated with EVI derived from Landsat imagery (r2 = 0.97). Application of the relationship to estimate mean-annual ETg from four HAs in western and eastern Nevada is highlighted and results are compared with previous estimates. Uncertainty bounds about the estimated mean ETg allow investigators to evaluate if independent groundwater discharge estimates are “believable” and will ultimately assist local, state, and federal agencies to evaluate expert witness reports of ETg, along with providing new first-order estimates of ETg.

Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ETp) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection–aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ETp, and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day−1.

Nevada and eastern California are home to some of the driest and warmest climates, most mountainous regions, and fastest growing metropolitan areas of the United States. Throughout Nevada and eastern California, snow-dominated watersheds provide most of the water supply for both human and environmental demands. Increasing demands on finite water supplies have resulted in the need to better monitor drought and its associated hydrologic and agricultural impacts. Two multiscalar drought indices, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI), are evaluated over Nevada and eastern California regions of the Great Basin using standardized streamflow, lake, and reservoir water surface stages to quantify wet and dry periods. Results show that both metrics are significantly correlated to surface water availability, with SPEI showing slightly higher correlations over SPI, suggesting that the inclusion of a simple term for atmospheric demand in SPEI is useful for characterizing hydrologic drought in arid regions. These results also highlight the utility of multiscalar drought indices as a proxy for summer groundwater discharge and baseflow periods.

This paper implements an individual station-based downscaled approach based on a quantil–quantile bias correction mapping to further downscale regional gridded simulated output into individual station locations. We describe and propose a framework to optimize the usefulness of this downscaling approach over small watersheds in mountain regions, where downscale gridded data (∼10km) is still too coarse for use in sub-regional hydrologic simulation studies. Two regional downscaled data sets were downscaled to individual station locations: the Desert Research Institute-Regional Climate Model based on the Weather Research and Forecasting model (DRI-RCM; 12 km resolution); and the US Bureau of Reclamation Bias-Corrected Spatial Disaggregation (BCSD; 12 km resolution). We applied the station-based downscaled data in the integrated hydrologic model, GSFLOW, as adapted to the Incline Creek watersheds in the northeastern Lake Tahoe basin. Hydrologic model results showed that the proposed downscaled approach provides reasonable downscaled climate data that can be used to evaluate sub-regional scale hydrologic processes.

Ma and Zhang [2017] note a concern they have with our rescaled Complementary Relationship (CR) for land surface evaporation [Crago et al., 2016] when daily wind speeds are very low. We discuss conditions and specific formulations that lead to this concern, but ultimately argue that under these conditions, a key assumption behind the CR itself may not be satisfied at the daily time scale. Thus, careful consideration of the reliability of the CR is needed when wind speeds are very low.

Summary Owens Lake, California, a saline terminal lake desiccated after diversion of its water source, was formerly the single largest anthropogenic source of fugitive dust in North America. Over 100 billion m−3 yr−1 of fresh water are projected to be used for mandated dust control in over 100 km2 of constructed basins required to be wetted to curtail emissions. An extensive evaporite deposit is located at the lake’s topographic low and adjacent to the dust control basins. Because this deposit is non-dust-emissive, it was investigated as a potential replacement for the fresh water used in dust control. The deposit consists of precipitated layers of sodium carbonate and sulfate bathed by, and covered with brine dominated by sodium chloride perennially covered with floating salt crust. Evaporation (E) rates through this crust were measured using a static chamber during the period of highest evaporative demand, late June and early July, 2009. Annualized total E from these measurements was significantly below average annual precipitation, thus ensuring that such salt deposits naturally remain wet throughout the year, despite the arid climate. Because it remains wetted, the evaporite deposit may therefore have the potential to replace fresh water to achieve dust control at near zero water use.

Groundwater models provide an efficient means to evaluate the sustainability of new groundwater production wells. An important component of any groundwater modeling analysis is the quantification of the prediction uncertainty. This analysis incorporates uncertainty analysis techniques within the groundwater modeling environment to predict regions within a groundwater basin that have a low probability of excessive drawdown and groundwater contamination. The analysis focuses on the quantification of a basin-wide water-balance, generation of a spatial distribution of lithology, locating a nearby total dissolved solids (TDS) plume, and construction of an accurate groundwater flow model. Thousands of Monte Carlo realizations are simulated to determine the most probable parameter sets based on an objective function that minimizes the error between simulated and observed hydraulic head. Only those simulations with small error are used in a subsequent analysis to investigate 77 potential well locations near the city of Fernley, in northern Nevada. The final result is a map depicting the probability that a production well has either excessive drawdown and/or encroachment of the TDSs plume.