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. Despite advancements in the performance of machine learning (ML) based hydrologic models, some institutions are hesitant to pursue ML as a replacement for existing conceptual or process-based hydrologic models in many applications. In several of these circumstances, traditional hydrologic models continue to be favored due to their familiarity, reliability, interpretability, established performance benchmarks under varied settings, availability of detailed training modules and a trained workforce, as well as close integration with data, processing, and decision-making pipelines. Recognizing these advantages, this perspective argues for two pragmatic and institutionally compatible paths forward for integration of ML within applied models: (1) reconciling ML as a complementary option in applied hydrologic modeling workflows; and (2) revamping or upskilling hydrologic modeling workflows using ML. To support this perspective, we highlight key opportunities where ML can be used as a tool to enhance results across various stages of the model implementation and operational workflow including data pre-processing, parameter calibration, parameter transferability, data assimilation, solver enhancement, accelerating scenario simulations and post-processing. Each of these two integration strategies can be implemented into current applied model frameworks, thereby combining the strengths of both physical modeling and ML. These strategies can help overcome current bottlenecks and address institutional needs of continuity and compatibility, while also offering the potential to improve model performance with ML.

The rapid adoption of Electric Vehicles (EVs) necessitates the strategic placement of Electric Vehicles Charging Stations (EVCSs) to support sustainable urban mobility. This research employs Geographic Information Systems (GIS) and Remote Sensing (RS) to determine optimal EVCSs sites. This research uses Multi-Criteria Decision Analysis (MCDA) methodologies i.e. Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) to evaluate the importance of various criteria such as proximity to amenities, Fuel Stations, population density, Major road networks, and existing infrastructure that are used to determine the optimal location of EVCSs. The developed methodology involved spatial data collection, criteria weighting, data standardization, overlay analysis, and site validation. The results reveal key insights for policymakers and urban planners, emphasizing the importance of considering multiple criteria for effective EVCSs deployment. This research also recommends future research directions, including the application of AI and machine learning for enhanced decision-making. This research provides valuable guidance for government officials, administrators, and stakeholders in making informed decisions to develop efficient EV charging infrastructure, promoting sustainable transportation and reducing carbon emissions.

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 temperature is inextricably linked with hydrological and biogeochemical processes. This study analyzes the long-term minimum soil temperature (TSMIN) trends at 526 stations located in snow-affected regions of CONUS. Long short-term memory (LSTM) are implemented to extend TSMIN beyond the observational period, producing long-term (1981-2020) continuous records for robust analysis. Trends show annual TSMIN is rising at nearly all stations, however, 60% and 29% of sites show winter and spring declines, respectively, despite air temperature increasing at 98.7% and 72% of them. Explanatory analysis links these seasonal contrasts to mean air and soil temperature, and trends of snow-free days and snowpack depth. Findings show seasonal soil temperature trends can diverge from air temperature trends, especially in winter and spring. They also underscore the value of LSTM-extended data sets for revealing soil temperature responses with implications for freeze-thaw dynamics, infiltration, and ecosystem function under changing snowpack.

Forecasting precipitation accurately at local scale especially in mountainous regions like North-west Himalayas (NWH) remains a major challenge. Although Machine Learning (ML) techniques offers potential advantages over traditional Numerical Weather Prediction (NWP) models but with a condition that the available input meteorological variables can effectively differentiate between various weather and precipitation events. This study utilizes near surface meteorological variables (air temperature, wind speed, water vapor mixing ratio and sea level pressure) of the High Asia Refined Analysis, version 2 (HARv2) data for the past 41 years (1980–2020) at 13 locations in the Western Himalaya (WH) and Central Himalaya (CH), India. The results are statistically analyzed with the help of Spearman rank correlation and the Mann–Whitney U-test along with the comparison of means, variances, and daily tendencies of input meteorological variables across binary (precipitation/no precipitation) and percentile-based precipitation categories. Results confirm the availability of significant discriminatory capability. The findings of this study support the use of ML models for real-time, local-scale weather forecasting in Himalayan regions by utilizing local meteorological observations for improved predictive accuracy.

The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, we assess the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on our selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment.

Long length homogeneous meteorological observations are required for various hydro-meteorological applications, develop forecasting models for mitigation of hydro-meteorological hazards, climatic change and its impact studies, planning for sustainable developments and climate change adaption etc. Such observations for the high-altitude mountainous areas such as the North-West Himalaya (NWH) (or elsewhere) are not available due to the inhospitable climatic conditions and complex topography. The reanalysis (or refined analysis) data provide estimates of the meteorological variables. However, it has been reported that the reanalysis (or refined analysis) data exhibit errors and biases for meteorological variable(s). The errors and biases in the reanalysis (refined) analysis meteorological variable(s) can be reduced and long length data can be generated efficiently via statistical downscaling. In this study, three statistical downscaling methods; altitude correction (ALTC), regression (SR), quantile–quantile mapping (SQ), are developed and employed to statistically downscale daily mean air temperature of the High Asia Refined Analysis version 2.0 (HARv2) at 10 stations in the NWH, India and their performances are evaluated and compared. The daily mean air temperature of the HARv2 is found to exhibit statistically significant positive correlation (CC) with the observed daily mean air temperature (OB) at each station. This suggests suitability of the ALTC, SR and SQ for statistical downscaling of daily mean air temperature of the HARv2.The root mean square error (RMS) for the estimation of the observed daily mean air temperature of the HARv2 data is found to fall in the range 5.7–11.6 (6.3–11.9) ℃ and it is found in the range 5.6–9.1(6.3–8.6) ℃, 4.2–7.8 (3.9–6.4) ℃, 4.2–7.8 (3.9–6.4) ℃, 4.2–7.8 (3.9–6.4) ℃, and 4.2–7.8 (3.9–6.4) ℃ for the training (test) data sets at 10 stations in the NWH. These results show that the statistical downscaling improves estimation of the observed daily mean air temperature in the NWH and it is fairly possible to develop long length data on daily mean air temperature utilizingHARv2 at a specific location in the NWH. Developed long length data on daily mean air temperature can be useful for many applications such as hydro-meteorological applications and forecasting, study of climatic change and its variability, disaster mitigation, planning for sustainable development in the NWH.

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.

Abstract Coastal cities are increasingly threatened by hurricane‐induced compound flooding. However, the relative contributions of rainfall‐surge interactions and other flood drivers, such as runoff from surrounding rural and local impervious areas, in shaping urban flooding remain unclear. Here, we use a multi‐scale Earth system modeling framework to disentangle the contributions of diverse flooding drivers. The results show that urban flooding is primarily affected by topographic factors (low elevation, flat slopes, and localized flow constrictions), rather than increased runoff from their impervious surfaces. Importantly, runoff from rural areas contributes more to severely flooded urban areas with inundation depth exceeding 1.7 m than local runoff. Although rainfall–surge interactions and sea‐level rise exacerbate inundation along the coastline, over 90% of the compounding effects are absorbed by wetlands. By resolving flooding processes across scales and components, this study emphasizes the role of runoff from surrounding rural areas in shaping zones of severe urban flooding.

An essential component of life and the ecology is groundwater. This Precious resource is under extreme strain in India due to man-made and natural caused factors. Determining groundwater potential zones (GWPZs) is becoming a more crucial undertaking due to the world’s increasing demand for freshwater. In the current study, the groundwater potential zones of the Mahoba district are identified using geospatial and Analytical Hierarchy Process (AHP) technique. Mahoba district falls in the state of Uttar Pradesh, India which is a very water scare region. The influencing factors that affects the potentiality of the research region i.e. lithology (LI), geomorphology (GM), rainfall, slope, land use and land cover (LULC), lineament density and drainage density taken under the consideration to evaluate the groundwater potential zones. The GIS software was used to create these influencing factors with the satellite images, which is acquired from many websites and organizations. The GWPZs maps were created by superimposing all of the thematic layers on top of one another after the appropriate weights were assigned using AHP technique. The delineated Groundwater potential zones (GWPZs) were divided into several categories, including very high, high, moderate, low, and very low GWPZs. The moderate form of GWPZ occupies the highest area within the research region. Well discharge data was used to validate the obtained GWPZs. The validation shows the 80

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.

Reanalysis datasets provide spatially and temporally complete climate fields and are widely used as observational surrogates. Temperature trends from these datasets underpin numerous geoscience studies, yet the alignment of their directional trends with observations at the continental scale remains unclear. This study evaluates the sign of annual mean daily maximum (TMAX) and minimum (TMIN) temperature trends from three reanalysis data, viz. ERA5, MERRA-2, and NLDAS-2 against long-term Global Historical Climatology Network daily (GHCN) observations at 7059 (TMAX) and 6,983 (TMIN) stations across the continental United States (CONUS). We observe substantial trend misalignment between GHCN and reanalysis data, with similar to 27%-31% of TMAX and similar to 21%-30% of TMIN locations exhibiting discrepancies, mainly driven by false positive trends. Misalignment persists even for the longer records (>= 10-30 years) and is concentrated at stations with strong negative observed trends. Aggregating TMAX and TMIN trend agreement across different regions, including snow and rain-affected regions, western and eastern snow-dominated areas, urban and non-urban locations, and elevation classes, demonstrates that stations with trend misalignment prevail irrespective of the aggregation approach. NLDAS-2 dataset demonstrates a distinct behavior, with a greater percentage of stations exhibiting trend misalignment in snow-affected regions relative to rain-affected areas. Moreover, within the snow-affected domain, NLDAS-2 displays a contrasting spatial pattern, showing elevated misalignment across the western United States compared to other datasets. These findings call for caution while using reanalysis datasets for regional temperature trend analyses and derived applications.

The Budyko framework is widely used to infer long-term water-energy partitioning. Its original uniform parameters limited accuracy, while locally calibrated formulations are difficult to apply in ungauged basins. This study develops a machine-learning (ML)-based parameterization linking Budyko parameters to catchment attributes, enabling annual estimates of evapotranspiration (ET) and streamflow in gauged and ungauged basins. Using 671 CAMELS catchments, we optimized parameters for six Budyko-type equations with the Shuffled Complex Evolution (SCE) algorithm, then adjusted them to catchment attributes using a ML model. The resulting attribute-based parameterization improved evaporative-index estimates relative to both SCE-based calibration and a calibrated SAC-SMA model. Applied to test catchments, it achieved a mean KGE of 0.67 for annual streamflow, outperforming calibrated SAC-SMA (0.48) and site-specific Budyko predictions (0.61). Results highlight a scalable approach for annual water-budget estimation in data-limited regions.

Rivers play a crucial role in maintaining ecological balance and providing essential resources; however, many are facing significant degradation, particularly in India, where urbanization and population growth exacerbate water scarcity. This study focuses on the Kalyani River, located in Barabanki district, Uttar Pradesh (India), which spans approximately 69.65 km and is vital for local communities. We employed an integrated approach combining geospatial technology, the HEC-RAS (Hydrologic Engineering Centers-River Analysis System) model, and field verification to assess river conditions and identify restoration needs. Our analysis revealed critical challenges affecting the Kalyani River, including siltation and blockages that hinder flow and contribute to flooding. The results indicate a recommended excavation length of approximately 22.37 km in the Nindora block, along with a total cleaning length of around 47.28 km for both Nindora and Fatehpur blocks. These findings underscore the necessity for immediate eco-restoration efforts to rejuvenate the river ecosystem and mitigate the impacts of human-induced changes. Furthermore, we mapped critical zones requiring intervention, emphasizing the need for community engagement in conservation initiatives. This study highlights the importance of not only addressing the physical restoration of the river but also fostering long-term ecological health through sustainable management practices. By comparing our findings with established river restoration projects, we contextualize the significance of our approach to enhance the resilience of the Kalyani River. Ultimately, this research offers valuable insights and actionable recommendations that can aid local authorities and stakeholders in implementing effective river management strategies, contributing to improved water security and ecological sustainability in the region.

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.

Robust large-domain predictions of water availability and threats require models that work well across different basins in the model domain. It is currently common to express a model's accuracy through aggregated efficiency scores such as the Nash–Sutcliffe efficiency and Kling–Gupta efficiency (KGE), and these scores often form the basis to select among competing models. However, recent work has shown that such scores are subject to considerable sampling uncertainty: the exact selection of time steps used to calculate the scores can have large impacts on the scores obtained. Here we explicitly account for this sampling uncertainty to determine the number of models that are needed to simulate hydrologic processes across large spatial domains. Using a selection of 36 conceptual models and 559 basins, our results show that model equifinality, the fact that very different models can produce simulations with very similar accuracy, makes it very difficult to unambiguously select one model over another. If models were selected based on their validation KGE scores alone, almost every model would be selected as the best model in at least some basins. When sampling uncertainty is accounted for, this number drops to 4 models being needed to cover 95 % of investigated basins and 10 models being needed to cover all basins. We obtain similar conclusions for an objective function focused on low flows. These results suggest that, under the conditions typical of many current modelling studies, there is limited evidence that using a wide variety of different models leads to appreciable differences in simulation accuracy compared to using a smaller number of carefully chosen models.