This study examines 11 years of lightning-initiated wildfires (LIWs) that burned 10001 acres over the conterminous United States spanning 2010-20 to identify the associated rainfall and land surface characteristics. The U.S. Forest Service database is used to examine LIWs along with Multi-Radar Multi-Sensor 1-km resolution hourly rainfall, a NASA Land Information System observations-driven simulation on a 3-km grid for historical soil moisture, 4-km resolution satellite-derived green vegetation fraction, and 5-km resolution evaporative stress index (ESI) of live vegetation health to examine land surface conditions surrounding LIWs. The study objectives are to quantify antecedent land surface conditions, compare how conditions vary between wildfires reported the same day (immediate) versus smoldering LIWs reported one or more days after ignition (holdover wildfires), and determine rainfall and land surface characteristics that favor LIWs. Composite results indicate that a steady decline in antecedent soil moisture occurred for the 3 months leading up to LIWs, with similar but not as pronounced declines in evaporative demand given by ratios of evapotranspiration (ET) to potential ET (PET) and ET/PET anomalies (ESI). Rain rates were considerably lower at flash locations igniting wildfires compared to noninitiating flash locations (p 5 100%). Additionally, rainfall tended to be highly associated with holdover LIWs than in immediate LIWs. Antecedent soil moisture and ET/PET were incrementally higher preceding holdover wildfires. Shallow soil moisture was modestly drier (wetter) for lightning flashes igniting wildfires (holdover wildfires), deep soil moisture was slightly wetter in LIWs, but few statistically significant differences were found in the ET/PET and ESI results. SIGNIFICANCE STATEMENT: This study examines the importance of antecedent land surface properties in conjunction with rain rates associated with lightning-initiated wildfires in the United States. Steadily declining soil moisture and evaporative stress of live vegetation preceded lightning-initiated wildfires. Rain rates were the most important factors in determining whether lightning will ignite wildfires; however, drier surface soil moisture and slightly wetter deep soil moisture occurred at lightning flash locations that initiated wildfires. Rain rates and soil moisture were notably higher for holdover wildfires that were identified at least a day or more after lightning ignition, whereas vegetation was less stressed on average in holdover wildfires.

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for the accurate simulation of snowpack dynamics. This becomes increasingly difficult at spatial resolutions smaller than common gridded meteorological forcing datasets due to the sparsity of long-term temperature measurements and the influence of local factors like cool air pooling and inversions. Near-surface air temperatures (Ta) are often assumed to decrease with elevation at a constant rate of 6.5oC km-1, which could lead to large model errors in snow evolution and other processes key to snow hydrology, water resource management, and other applications. This study evaluates the impact of local dynamical adjustments to downscaled Ta on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization (NoahMP) land surface model. Forcings are derived from remote sensing and reanalysis precipitation products and the (Modern-Era Retrospective Analysis for Research and Applications, version 2) MERRA-2 atmospheric products (including Ta) at the downscaled 1-km resolution. Hourly lapse rates at each grid cell are calculated by applying linear regression to Ta and elevation from surrounding grid cells (within one grid lengths in the x or y direction) at the Ta native MERRA-2 resolution and applied to the downscaled 1-km Ta product. We will present the impact on simulated snow water equivalent, snow cover, and snow depth across simulations forced with the downscaled Ta (1) without lapse rate correction, (2) corrected with a constant lapse rate (6.5oC km-1), and (3) corrected with the dynamic hourly lapse rate. Results will be compared against remote sensing-based products.

OpenET is a fully automated system for field-scale (30 m), satellite- based mapping of evapotranspiration (ET) at daily, monthly, and annual timesteps. OpenET currently provides data across the 23 westernmost states in the United States and includes low latency, near real -time information, and multi-year timeseries of ET data from October 1999 to present. The OpenET team has implemented an ensemble of six satellite-based ET models on Google Earth Engine, which provides a shared computing platform for collaboration on processing of data from Landsat and other satellites, land cover, and meteorological inputs, leading to increased consistency and accuracy across the ensemble of models. Earth Engine also facilitates hosting and distribution of data via open data collections and an application programming interface (API). OpenET was developed in close partnership with agricultural producers and water resource managers who provided guidance and defined user requirements. Here, the development of the OpenET framework and the most recent accuracy assessment is summarized with discussion of how co-production with the user community and open science has facilitated successful applications of OpenET data for a wide range of water resource management activities. Current applications of OpenET demonstrate that broad acceptance and ease of access to ET data contribute to the rapid adoption of ET data in support of innovative water management practices and facilitate meaningful progress towards more sustainable management of water supplies.

Abstract Addressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Twenty-six years of lightning data were paired with over 68 000 lightning-initiated wildfire (LIW) reports to understand lightning flash characteristics responsible for ignition in between 1995 and 2020. Results indicate that 92% of LIW were started by negative cloud-to-ground (CG) lightning flashes and 57% were single stroke flashes. Moreover, 62% of LIW reports did not have a positive CG within 10 km of the start location, contrary to the science literature's suggestion that positive CG flashes are a dominant fire-starting mechanism. Nearly 1=3 of wildfire events were holdovers, meaning 1 or more days elapsed between lightning occurrence and fire report. However, fires that were reported less than a day after lightning occurrence statistically burned more acreage. Peak current was not found to be a statistically significant delineator between fire starters and non-fire starters for negative CGs but was for positive CGs. Results highlighted the need for reassessing the role of positive CG lightning and subsequently long-continuing current in wildfire ignition started by lightning. One potential outcome of this study's results is the development of real-time tools to identify ignition potential during lightning events to aid in fire mitigation efforts.

Modern climate change presents unprecedented challenges, posing critical crises that threaten sustainable development, human well-being, and planetary health. A significant concern is the potential for global warming to cause irreversible disruptions to the water-carbon cycle, a topic that remains underexplored. This study seeks to address a crucial knowledge gap by examining how increasing wet extremes impact ecosystem productivity. The research agenda focuses on three primary questions: 1) How do the intensity and duration of various wet extremes affect evapotranspiration across different watersheds and terrestrial biomes? 2) How do immediate and lagged responses to wet extremes vary across different biomes, and what insights do these temporal patterns provide about the causal and predictive relationships between wet extreme and evapotranspiration? 3) To what extent do watershed characteristics (such as soil properties, hydrological conditions, and vegetation factors) modulate the relationship between wet extremes and ecosystem productivity? As climate change alters precipitation patterns, understanding these complex ecosystem responses becomes crucial for developing adaptive strategies and improving food and water resource management.

The effect of machine learning and other enhancements on statistical-dynamical forecasts of soil moisture (0-10 and 0-100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15-28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S fl ash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying i.e., prediction of conditions moving away from drought rather than into drought especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored.

This study uses the flash drought intensity index (FDII) to develop a multivariate flash drought climatology for the contiguous U.S. using data from 2001 to 2021. The FDII method uses the rate of intensification (FD-INT) and subsequent drought severity (DRO-SEV) to determine when a flash drought occurred and the strength of the event. Overall, the results showed that flash drought occurrence and severity varied with season and region and were sensitive to the drought indicator used to compute the FDII. Precipitation-based indicators identified more flash droughts across the western U.S. whereas soil moisture (SM) and evapotranspiration indicators identified more flash droughts across the central and eastern U.S. When assessed over the entire U.S., the most flash droughts were found when using an evaporative demand indicator. Though FD-INT was larger than DRO-SEV across the U.S. for most indicators, regional patterns were also evident in their relative importance. For example, a distinct east-west gradient was present in the SM and evapotranspiration FD-INT, with relatively large values in the central and eastern U.S. A combined data set synthesizing information from multiple indicators showed that the strongest flash droughts from a multivariate perspective were located in the central and southeastern U.S. A seasonal analysis revealed a distinct seasonal cycle in flash drought onset across the western and central U.S. Together, the results illustrate the need to use a multivariate framework to identify and characterize the occurrence and severity of flash droughts.

Evapotranspiration has long been understood to vary with soil moisture in drier regions and tobe relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability.

A steady rise in fires in the Western United States, coincident with intensifying droughts, imparts substantial modifications to the underlying vegetation, hydrology and overall ecosystem. Drought can compound the ecosystem disturbance caused by fire, although how these compound effects on hydrologic and ecosystem recovery vary among ecosystems is poorly understood. Here we use remote sensing-derived high-resolution evapotranspiration (ET) estimates from before and after 1,514 fires to show that ecoregions dominated by grasslands and shrublands are more susceptible to drought, which amplifies fire-induced ET decline and, subsequently, shifts water flux partitioning. In contrast, severely burned forests recover from fire slowly or incompletely, but are less sensitive to dry extremes. We conclude that moisture limitation caused by droughts influences the dynamics of water balance recovery in post-fire years. This finding explains why moderate to extreme droughts aggravate impacts on the water balance in non-forested vegetation, while moisture accessed by deeper roots in forests helps meet evaporative demands unless severe burns disrupt internal tree structure and deplete fuel load availability. Our results highlight the dominant control of drought on altering the resilience of vegetation to fires, with critical implications for terrestrial ecosystem stability in the face of anthropogenic climate change in the West.

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r 2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Vegetation health indices can be used to infer productivity, which is useful for yield estimation and land surface modeling. The Evaporative Stress Index (ESI) is an estimation of vegetation water stress. ESI is the standardized anomaly of evapotranspiration (ET) relative to potential ET (fPET), and is calculated operationally over the continental United States (CONUS) by NOAA and globally by NASA to support drought and crop monitoring on two, four, eight, and twelve week products. ET is estimated by the Atmosphere-Land Ex-change Inverse (ALEXI) model using thermal infrared observations of Earth's surface from the Geostationary Operational Environmental Satellites (GOES), meteorological data from the Climate Forecast System Reanalysis, and leaf area index estimation from MODIS/VIIRS. Negative ESI is indicative of reduced ET, higher surface temperature, and water stress, while positive ESI generally indicates well-watered and healthy vegetation conditions. Currently, ESI is computed considering climatological anomalies in ET based on day of year; however, over highly managed agricultural landscapes, small shifts in planting and emergence date can introduce false anomalies due to differences in phenology rather than moisture status. This study aims to retime these climatologies and anomalies based on vegetation development rather than calendar date. Vegetation emergence (also known as greenup) is estimated by the Terra and Aqua fusion MODIS land cover dynamics yearly product, which is available from 2001 to 2022. Initial testing is conducted over sites in Iowa, USA, with contrasting characteristic green-up dates and annual fPET curve widths. Vegetation emergence is often around 50 days later in cropland than in forests, as farmers may wait on soil to warm and dry before planting. There is also considerable variability in greenup date between years in one location, which can be around 20 to 30 days. When timed to greenup, ET rises at a more consistent part of the year relative to timing by day of year. When annual fPET curves are aligned on greenup date, variability in ESI is reduced, and correlations U.S. Drought Monitor increase. Impacts of modifying the time unit post-greenup from calendar day to degree day is also evaluated. These findings will be used to improve the information content in the operational national and global ESI products, better capturing actual stress signals over managed landscapes. We have found that retiming indices to phenology improves agreement in highly agricultural areas, while retiming to phenology does not significantly affect agreement between vegetative indices in a mixed cropland - forested area. Phenological ESI can be computed across CONUS, and there is the potential for this product to be posted operationally. In croplands, National Agricultural Statistics Services (NASS) crop condition reports are compared to ESI, shedding light on the condition of individual crops within a county. This study also aims to focus on ESI in grazinglands, as changes in management may result in grass recovery and productivity. ALEXI can also be run with microwave inputs for land surface temperature, offering another flavor of ESI.

We introduce a physics-informed machine learning technique to predict near real-time (NRT) Land Surface Temperature (LST) using real-time Landsat 8 data, aiming to overcome the latency constraints of the Landsat 8 USGS LST product in the OpenET system on Google Earth Engine (GEE). This approach utilizes stratified sampling of clear images across various land cover types, incorporating the physics-based Level 2 LST product from USGS, atmospheric parameters from North American Land Data Assimilation System (NLDAS) and land cover classifications from National Land Cover Database (NLCD) for model training. The predicted LST data, further integrated into the DisALEXI model, facilitates the calculation of ET. Evaluations against USGS LST and original OpenET ET data over randomly selected regions, with a focus on agricultural areas, demonstrate good model performance. The developed method enhances NRT applications in agricultural, forest, and water resource management by delivering timely and precise water use information. Furthermore, the developed method shows potential for adaptation to other thermal observation missions for timely LST estimation.

Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales.In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches.

Microwave satellite remote sensing has enabled observations of soil moisture (SM) at the global scale, and multiple SM data products have been developed in the past decades. However, single-sensor-based measurements are insufficient for continuous spatiotemporal coverage. In the context of its climate program, the Climate Change Initiative, the European Space Agency (ESA) has developed robust, long term, global scale, multi instrument satellite derived time series of climate data record for key component of the climate system, including soil moisture (CCI), while the Soil Moisture Operational Product System (SMOPS) was specifically developed by National Oceanic and Atmospheric Administration (NOAA) to offer the real time blended SM datasets through merging all available individual products. Before combining, all individual SM data ingested into both SMOPS and CCI blended products are scaled to Global Land Data Assimilation System (GLDAS) 0-10 cm SM climatology. Benefiting from land surface model evolution and the availability of high-quality forcing data, GLDAS has become more comprehensive to track SM changes and dynamic trends. The development of GLDAS and the scaling procedure in CCI and SMOPS leave an open scientific and operational question: do the blended satellite SM data products have added value comparing to the GLDAS product? This study clearly reveals that both CCI and SMOPS can provide the reliable SM observations with independent information, although their climatology matches well with GLDAS. Relative to assimilation of GLDAS 0-10 cm SM data, Noah-MP model can be further improved by assimilating the blended satellite SM observations with respect to the quality-controlled in situ measurements. The strong consistency of results presented in this paper proves that the blended satellite SM data products are more useful than the GLDAS product in terms of improving Noah-MP model performance.

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were performed where climatological or coarse-resolution surface datasets were replaced by high-resolution, real-time datasets depicting the lake surface temperatures (SSTs), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of two baseline simulations employing different parameterization schemes (referred to as AP-XM and YNT, respectively) showed that the AP-XM simulation produced more accurate analyses on the outermost 12 km resolution domain but that the YNT simulation was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12 km grid but differed greatly on the 1.3 km grid where the AP-XM simulation had a much smaller sensible heat flux during the daytime and a physically unrealistic ground heat flux. Switching to the YNT configuration led to more accurate 2 m temperature and 2 m water vapor mixing ratio analyses on the 1.3 km grid. Additional improvements occurred when satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses but had degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km a.g.l. (above ground level). These results demonstrate the value of using high-resolution satellite-derived surface datasets in model simulations.

This study aims at understanding the changes in the water balance in the Thaya river basin over the past 40 years. The Thaya River is one of the tributaries to the Danube basin with a catchment area of more than 13 000 km2. A number of hydroclimatic variables related to runoff were examined by a trend analysis based on Theil-Sen regression and Mann-Kendall tests for the two periods 1981–2020 and 2001–2020. The latter period was selected because it allows analysis of several relevant variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). These variables ecompass snow cover, leaf area index and land surface temperature based actual evapotranspiration. With our analyses we confirm previously found increasing trends in air temperature, ETo, and no trends in precipitation. We also found a consistent increase of ET during spring months and indication of summer decrease (not statistically significant). This change was associated with a significant increase of spring vegetation development followed by summer stagnation. We identified a significant trend decline in runoff, mainly in the upland sourcing areas. The correlation analysis reveals a different behavior along the elevation gradient, with evapotranspiration in the uplands being limited by energy and in the lowlands by water, especially in spring. During summer, however, the entire basin is often water-limited, with a more pronounced limitation in the lowlands. According to attribution analysis for the past 20 years, the significantly decreasing runoff is driven primarily by non-significantly decreasing precipitation, significantly increasing air temperature and vapor pressure deficit. Global radiation and wind speed affect the runoff only to a very limited extent. We conclude that complex adaptation measures reflecting the site specificity and elevation gradient are needed to sustain the water dependent sectors operating in the region facing increasing aridity.

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.