Potential evapotranspiration (PET), defined as the evapotranspirative flux from a region under fully saturated conditions, is a critical variable in hydrologic modeling, water stress assessment, and understanding ecosystem responses to climate. The widely used Priestley-Taylor method provides a simple, low-data requirement approach for estimating PET. However, it uses a fixed coefficient ( alpha PT = 1.26) in most applications, but this oversimplification neglects biome-specific variability, limiting its accuracy across diverse environments. Although numerous studies have attempted to derive dynamic characterizations of alpha PT, most estimates are developed for either obtaining reference evapotranspiration (ET0) or actual evapotranspiration (ET). Consequently, these approaches do not provide alpha PT that accounts for ecosystem-specific aerodynamic and plant conductance constraints required to fully represent true ecosystem-level PET. In this study, we utilized 3128 site-years of eddy covariance data from 246 FluxNet sites worldwide to optimize alpha PT values across a broad range of biomes. Results showed significant spatial and seasonal variability in alpha PT, with higher values in forests and winter months and lower values in savannas' summer. Temperature and radiation emerged as key drivers of this variability. Using the influencing variables, we next derived functional equations to estimate alpha PT based on key bio-environmental variables. These equations yielded demonstrable improvements in PET estimates, and can be directly incorporated into land surface and hydrologic models, and generation of remote sensing products. Furthermore, PET derived using our simplified functional equations of alpha PT, when applied to obtain the evaporative stress index and ET, yielded improved estimates of both ET and plant stress. Overall, these findings offer a more ecologically representative approach to PET estimation using the Priestley-Taylor method, with broad implications for hydrologic modeling and drought assessment.

Accurate estimates of transpiration (T) remain particularly challenging in mixed C3-C4 ecosystems, where the relative dominance of species and their physiological characteristics shifts dynamically throughout the growing season. Some methods for evapotranspiration (ET) partitioning, such as Flux Variance Similarity (FVS), typically require an explicit specification of either C3 or C4 photosynthetic pathways to parameterize intercellular carbon dioxide concentrations (ci). Other methods, such as Modified Relaxed Eddy Accumulation (MREA), Conditional Eddy Covariance (CEC), Conditional Eddy Accumulation (CEA), and the FVS optimum approach, do not require prior ci approximations or designation of C3 and C4 vegetation. This study evaluated and inter-compared five ET partitioning methods: FVS, MREA, CEA, CEC, and a water-use efficiency-integrated modification of CEC (CECw) within two differently managed (grazing and hay harvest) C3-C4 mixed tallgrass prairies in Central Oklahoma, USA. Specifically, we investigated how partitioning outputs varied when using a static, year-round C4 parameterization compared to a seasonally dynamic C3-C4 framework. Results indicated that T:ET ratios from FVS methods using constant ci values or ci/ca ratios were similar across seasonal and annual scales, regardless of whether a year-round C4 or temporally dynamic C3-C4 parameterization was applied. During peak growth, MREA and CEC produced the highest T:ET ratios (0.93–0.95), whereas FVS and CEA yielded more moderate values (<0.75), and CECw provided the lowest estimates (0.56–0.62). Consequently, MREA and CEC estimated T 10–20% higher than the biophysically reasonable estimates from FVS and CEA. Methodological discrepancies were most pronounced during periods when either stomatal or non-stomatal fluxes dominated, with closer agreement occurring when these exchanges were nearly equivalent. Validation against lysimeter measurements showed that FVS and CECw optimum approaches performed best (r = 0.84–0.85; RMSE = 0.23–0.25 mm d−1), while other methods deviated substantially. As the first intercomparison of five partitioning methods in mixed C3-C4 tallgrass prairies, this study demonstrates that the choice of method significantly influences the resulting ecosystem water budget.

Abstract Evapotranspiration (ET) plays a critical role in water and energy budgets over the land surface. Eddy Covariance (EC) is the most widely used technique to measure ET at ecosystem scale, providing insights into land–atmosphere interactions and serving as a benchmark for Earth System Models (ESMs). However, ET measurements at EC flux sites suffer from a fundamental limitation: the persistent issue of surface energy budget non‐closure. It is essential to correct EC‐measured ET fluxes for energy imbalance, both for improved system understanding and diagnostic benchmarking. Here, we introduce PULSE, a new correction approach based on the concept of potential underlying water use efficiency. We implement the method at >250 flux sites globally, and evaluate its performance using a data‐driven framework and an ecosystem conductance model. We also benchmark PULSE against existing energy closure correction methods, including the Bowen ratio‐based flux correction method (OFC) and Available Energy‐based correction (AEC) method. Our results demonstrate that PULSE not only identifies and corrects ET fluxes for energy imbalance but also produces more physically consistent estimates. PULSE is simple, robust, avoids arbitrary assumptions such as Bowen ratio constancy, and is broadly applicable across diverse EC sites.

Soil moisture (SM) is a key regulator of ecosystem biogeophysics, influencing plant water relations and land-atmosphere energy exchanges. We evaluate the representation of SM in 16 Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using the International Land Model Benchmarking (ILAMB) framework, focusing on surface (0-5, 0-10 cm) and rootzone (0-100 cm) depths, as well as key ecohydrological variables like gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET), and their coupling. Models are benchmarked against multiple observational and assimilated datasets to assess both state variables and cross-variable relationships. Surface SM is generally well represented (r > 0.87), while rootzone SM variability is systematically overestimated (normalized standard deviation > 1). ET shows strong agreement with observations (r > 0.9), whereas GPP and LAI exhibit larger inter-model spread. Skill in individual variables does not guarantee realistic SM-ecohydrology coupling, which varies strongly across models and depends on the reference dataset. K & ouml;ppen-based regional analyses reveal strong regime dependence, with several models performing well in Tropical and Temperate regions but degrading in Continental (high-latitude) zones. Across both global and regional benchmarks, models cluster by land surface framework, indicating that structural choices in soil hydrology and soil-plant coupling exert a first-order control on performance. These results provide process-relevant benchmarks and suggest that improving the representation of vertical soil structure, rooting depth distributions, and soil-plant hydraulic coupling will be central to advancing soil moisture realism in next-generation Earth system models.

Evaporative Stress Index (ESI) is a commonly used indicator for assessing ecosystem-level water stress, particularly for monitoring agricultural and forest droughts. This study assesses performance of two remote sensing ESI products: ECO4ESIPTJPL and ECO4ESIALEXI from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), against in-situ data from 59 AmeriFlux sites across the continental United States. Results indicate that ECOSTRESS ESI, overall, underestimates site-based estimates, suggesting a tendency to indicate higher evaporative stress. When aggregated across all sites and time steps, ECO4ESIPTJPL underestimates ESI 55.1% of the time, while ECO4ESIALEXI underestimates 62.42%. However, under high evaporative stress conditions, both the products, particularly ECO4ESIPTJPL, overestimate ESI, meaning they indicate the stress is lower than it actually is. Further analysis reveals that ECOSTRESS-derived evapotranspiration (ET), rather than potential ET, is the primary source of bias in the ECOSTRESS ESI products. These findings emphasize the need for refining ECOSTRESS ESI products, particularly improving ECOSTRESS ET, to enhance accuracy of assessing evaporative stress across diverse landscapes and climatic conditions.

Evapotranspiration (ET) plays a crucial role in determining water and energy partitioning of precipitation between land and atmosphere. A recognized uncertainty in modeling of ET is a proper representation of leaf phenology dynamics. Many existing land surface models often employ a “static scheme” of leaf phenology while performing ET simulations, i.e., they assume identical intra-annual variation in leaf phenology, often quantified as the variation in leaf area index (LAI), from year to year. There is a need to assess the influence of this “static” scheme on ET prediction errors. Furthermore, it is important to determine where improvements in predictions of leaf phenology dynamic models will yield the largest improvements in ET predictions. Utilizing site-specific artificial neural network models for the estimation of ET, this study investigates the influence of static versus dynamic representation of leaf phenology dynamics on ET estimation errors across 30 flux network (FLUXNET) sites. Results indicate that neglecting the interannual LAI dynamics in the ET prediction model led to an additional loss of accuracy of up to 29% (0.18) with the average loss being about 1.7% (0.01) based on the percentage bias (coefficient of determination) metric. Our findings also show that interannual leaf phenology dynamics have a larger impact on ET estimates in warmer and drier regions. Phenological traits such as green-up onset anomaly and maximum annual LAI anomaly are identified as key contributors to model error in static phenology scenarios.

Evapotranspiration (ET) is a critical process influencing energy, water, and carbon cycles. Numerous methods have been developed to estimate ET accurately and robustly across diverse scales. Many of these methods are constrained by reliance on remote sensing data, which is prone to gaps, or by the need for model calibration and training. This study evaluates the performance of the calibration‐free surface flux equilibrium theory (SFET) for ET estimation at 33 Ameriflux sites in the continental USA. SFET‐derived ET estimates are intercompared with widely used continental remote sensing products, including ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, Moderate Resolution Imaging Spectroradiometer, and SSEBop. Results indicate that SFET consistently outperforms these ET products. SFET's performance is found to be better under wet conditions and clear skies, with reduced accuracy under arid and high evaporative stress conditions. Overall, SFET exhibits significant potential for providing accurate, continuous, long‐term ET estimates, paving the way for operational application in uninstrumented regions over large scales.

Evaporative Stress Index (ESI), also sometimes referred as Evaporative Stress Ratio (ESR), has been widely used as an indicator of vegetation evaporative stress, and is often used to track forest and agriculture droughts. Lower the stress, higher is the value of ESI or ESR. The goal of this study is to assess the suitability of these indices for tracking vegetation evaporative stress. As the dynamics of water loss from vegetation through transpiration (T) can be different than that of evapotranspiration (ET) from the ecosystem, it is hypothesized that ESI or ESR may not be sufficiently representative of the vegetation evaporative stress. Using eddy covariance flux tower data of 518 site years, distributed across 49-sites and 9 land covers globally, our findings reveal underestimation of vegetation evaporative stress by ESI during periods of high vapor pressure deficit (VPD) and overestimation during dry, low-VPD periods. The results highlight the need to improve representativeness of ESI for monitoring vegetation evaporative stress. Notably, this may entail accurate estimation of ecosystem T in systems lacking in-situ data, a challenge that warrants further attention.

Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (gsc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimating gsc using a variety of models, ranging from relatively simple empirical models to more complex and data-intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models of gsc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these gsc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture gsc dynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements in gsc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate. Current stomatal conductance models underutilize the site-specific hydrometeorological data Structural constraints in empirical models are more restrictive compared to plant hydraulic models Enhancements are needed for the simplified depiction of the water potential gradient across the root-xylem-leaf continuum in plant hydraulic models

Irrigation expansion is often posed as a promising option to enhance food security. Here, we assess the influence of expansion of irrigation, primarily in rural areas of the contiguous United States (CONUS), on the intensification and spatial proliferation of surface freshwater scarcity. Our study shows that the rainfed to irrigation-fed (RFtoIF) transition of water-scarce croplands can impact scarcity in both transitioned and non-transitioned regions, with the magnitude of impact being dependent on multiple factors including local water demand, abstractions in the river upstream, and the buffering capacity of ancillary water sources to cities. Overall, RFtoIF transition will result in an additional 169.6 million hectares or 22% of the total CONUS land area facing moderate or severe water scarcity. Analysis of just the 53 large urban clusters with 146 million residents shows that the transition will result in 97 million urban population facing water scarcity for at least one month per year on average versus 82 million before the irrigation expansion. While these reported figures are subject to simulation uncertainties despite efforts to exercise due diligence, the study unambiguously underscores the need for strategies aimed at boosting crop productivity to incorporate the effects on water availability throughout the entire extent of the flow networks, instead of solely focusing on the local level. The results further highlight that if irrigation expansion is poorly managed, it may increase urban water scarcity, thus also possibly increasing the likelihood of water conflict between urban and rural areas.

Irrigation expansion is often posed as a promising option to enhance food security. Here, we assess the influence of expansion of irrigation, primarily in rural areas of the contiguous United States (CONUS), on the intensification and spatial proliferation of freshwater scarcity. Results show rain-fed to irrigation-fed (RFtoIF) transition will result in an additional 169.6 million hectares or 22% of the total CONUS land area facing moderate or severe water scarcity. Analysis of just the 53 large urban clusters with 146 million residents shows that the transition will result in 97 million urban population facing water scarcity for at least one month per year on average versus 82 million before the irrigation expansion. Notably, none of the six large urban regions facing an increase in scarcity with RFtoIF transition are located in arid regions in part because the magnitude of impact is dependent on multiple factors including local water demand, abstractions in the river upstream, and the buffering capacity of ancillary water sources to cities. For these reasons, areas with higher population and industrialization also generally experience a relatively smaller change in scarcity than regions with lower water demand. While the exact magnitude of impacts are subject to simulation uncertainties despite efforts to exercise due diligence, the study unambiguously underscores the need for strategies aimed at boosting crop productivity to incorporate the effects on water availability throughout the entire extent of the flow networks, instead of solely focusing on the local level. The results further highlight that if irrigation expansion is poorly managed, it may increase urban water scarcity, thus also possibly increasing the likelihood of water conflict between urban and rural areas.

Despite the high sensitivity of water use efficiency (WUE) estimates to intracellular carbon dioxide concentrations (ci) in the Flux Variance Similarity (FVS)-based partitioning method, a systematic analysis of the sensitivity of WUE to ci parameterizations has largely been lacking. Using high-frequency (10 Hz) eddy covariance data for two crop sites: wheat (Triticum aestivum L.) and canola (Brassica napus L.), we performed a sensitivity analysis of four ci parameterizations (constant ci value, constant ci/ca ratio, and ci/ca as square root and linear functions of vapor pressure deficit) and compared them with the optimized WUE approach with no adjustable parameter. The results illustrated the role of ci parameterizations on the evapotranspiration (ET) partitioning results (i.e., transpiration (T) to ET ratios). Notably, constant ci value and constant ci/ca ratio parameterizations for the largest considered ci values (commonly used default values in most previous studies) showed comparable T:ET with the optimized WUE approach. Additionally, all these three models produced reduced T:ET in wet periods and increased T:ET in dry periods. In contrast, square root and linear models were unable to accurately capture expected trends of T:ET for wet and dry periods, and also showed large discrepancies when compared with the optimal WUE approach. The results suggest that optimal parameterizations of ci should be derived in constant ci value and constant ci/ca ratio methods to accurately capture temporal variations of WUE and T:ET. The results also indicate the potential of the optimum model for inter-model comparison, especially in sensitivity analysis, for FVS partitioning in C3 species. This study provides novel insights into the implications of the choice of parameterization on the WUE estimations and partitioning outcomes.

In this study, the hydrometeorological impacts of climate change in the Upper Godavari River basin in India are quantified for historical and future periods using a well-calibrated hydrological model H08. The study provides a quantitative assessment of various hydrological fluxes projected for the future that are useful in water resource planning and management. The results revealed that under RCP4.5 (RCP8.5) climate scenarios, the rainfall in the entire basin was projected to increase by 24.4

Partitioning evapotranspiration (ET) into its primary components, that is, evaporation ( E ) and plant transpiration ( T ), is needed in a range of hydrometeorological applications. Using vegetation index (VI) to obtain spatially resolved T:ET ratio over large areas has emerged as a promising approach in this regard. Here, we assess the effectiveness of this approach in differently managed wheat systems. Results show a weak relation between T:ET and VI in disturbed (i.e., grazed) systems. Furthermore, flux partition based on a canonical T:ET versus VI relation or the relation derived in a neighboring undisturbed wheat system introduce large errors in disturbed systems, thus underscoring the limits on the transferability of the VI‐based ET partitioning approach. The effectiveness of the VI‐based approach is found to be related to the strength of correlation between VI and vapor pressure deficit and/or radiation. This correlation metric can help identify settings where the approach is likely to be effective.

Root zone soil moisture (RZSM) is a dominant control on crop productivity, land-atmosphere feedbacks, and the hydrologic response of watersheds. Despite its importance, obtaining gap-free daily moisture data remains challenging. For example, remote sensing-based soil moisture products often have gaps arising from limits posed by the presence of clouds and satellite revisit period. Here, we retrieve a proxy of daily RZSM using the actual evapotranspiration (ETa) estimates from Surface Flux Equilibrium Theory (SFET). Our method is calibration-less, parsimonious, and only needs widely available meteorological data and standard land-surface parameters. Evaluation of the retrievals at Oklahoma Mesonet sites shows that our method, overall, matches or outperforms widely available RZSM estimates from three markedly different approaches, viz. remote sensing data based Atmosphere-Land EXchange Inversion (ALEXI) model, the Variable Infiltration Capacity (VIC) model, and the Soil Moisture Active Passive (SMAP) mission RZSM data product. When compared with in-situ observations, unbiased root mean square difference of retrieved RZSM were 0.03 (m 3 m −3 ), 0.06 (m 3 m −3 ), and 0.05 (m 3 m −3 ) for our method, the ALEXI model, and the VIC model, respectively. Better performance of our method is attributed to the use of both SFET for the estimation of ETa and non-parametric kernel-based method used to relate the RZSM with ETa. RZSM from our method may serve as a more accurate and temporally-complete alternative for a variety of applications including mapping of agricultural droughts, assimilation of RZSM for hydrometeorological forecasting, and design of optimal irrigation schedules.

Forecast of the Indian summer monsoon on an extended range (beyond the conventional one-week lead time) is critical for an agronomic economy like India. Although dynamic models have been quite successful in capturing and representing monsoon circulation, they fail to sustain this skill beyond the standard weather time scale (7–10 days). As such, the present study is directed at developing a computationally feasible, yet reliable method of statistical downscaling that further improves the present skill of global dynamic models for extended range forecasting. We quantitatively demonstrate the feasibility of this post-processing statistical module for improving the predictability of the dynamic Extended Range Prediction System (ERPAS), which is developed by Indian Institute of Tropical Meteorology, Pune, India and now operational in the country. It first develops climate cluster(s) (rainfall states in the present case), then builds a statistical relationship between these clusters and a set of appropriate climate variables using a robust and advanced classification technique known as Extreme Gradient Boosting (XGboost), and eventually delivers the real-valued precipitation at individual grid cells via a non-parametric regression. The module is able to skilfully capture the active and break phases of the Indian summer monsoon, and also subsequently project them for the ensuing regression component of the module. This approach shows to significantly boost the prediction skill over the Core Monsoon Zone of Indian mainland up to a lead time of 4 weeks. Our statistical downscaled model is comparable in the week 1 lead time but outperforms global models for 2nd, 3rd, and 4th weeks lead time. Nonetheless, this performance is retained only in the northern and central region, and not ubiquitous over the rest of India.