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USGS2025

A spatiotemporal interrogation of hydrologic drought model performance for machine learning model interpretability

The predictive accuracy of regional hydrologic models often varies across both time and space. Interpreting relationships between watershed characteristics, hydrologic regimes, and model performance can reveal potential areas for model improvement. In this study, we use machine learning to assess model performance of a regional hydrologic model to forecast the occurrence of streamflow drought. We demonstrate our methodology using a regional long short-term memory (LSTM) deep learning model developed by the U.S. Geological Survey (USGS) and data from 384 streamgages across the Colorado River Basin region. Performance was assessed by clustering catchments using: (a) physical and climatological catchment attributes, and (b) streamflow drought signatures time series. We examined the association of USGS LSTM model error measures with clusters generated by both approaches to interpret meaningful spatial and temporal information about LSTM model performance. Clustering static catchment attributes identified elevation, degree of streamflow regulation, baseflow contribution, catchment aridity, and drainage area as the most influential attributes to model performance. Clustering gages by their drought signatures revealed that catchments with significant seasonal peak runoff between January and June generally exhibited better model performance. Additionally, a Random Forest classifier was trained to successfully predict LSTM model performance (F1 score of 0.72) based on physical and climatological catchment attributes. Low degree of flow regulation was identified as a key indicator of better LSTM model performance. These findings point to the opportunities for improving the USGS LSTM model performance in future hydrologic drought prediction efforts across regional and CONUS scales.

Arizona, California, Colorado, Idaho, Montana, Nev↗

USGS2025

Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration

Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model-data integration workflow to address uncertainty in groundwater response to beaver-induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning-based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down-valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down-valley underflow, from 2% during dry pond periods to 20% during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down-valley underflow, minimizes the effect of beaver-induced inundation on water quality. We emphasize the need for field-scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response.

Colorado↗

USGS2025

High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon

The distribution of river-bed grain sizes plays a foundational role in river morphology and ecology. River-bed grain size is a key driver of channel form and process, and has first order effects on aquatic macroinvertebrate assemblages, fish nesting, and biogeochemical processes. Despite this importance, tools to spatially quantify grain-size distributions, particularly submerged grain-size distributions, are lacking. Efforts to address this knowledge gap include developing optical and sonographic tools, however, these approaches have limitations, especially in shallow rivers and over large spatial extents. This study quantifies submerged grain size at high resolution (1 m 2 ) across 260 km of geomorphically diverse river corridors in the Santiam River Basin, Oregon, by pairing bathymetric Lidar point clouds with georeferenced pebble counts. Results suggest that derivatives of Lidar point clouds are able to accurately estimate measured median grain size across seven of the eight river reaches investigated, including reaches above and below high-head dams. Spatial analysis of predicted grain-sizes in the context of Chinook salmon spawning habitat suggests that suitable size sediment patches in the upper, unregulated reaches the study basin is typically small and unorganized. In contrast, the larger rivers downstream of high-head dams typically have larger areas of suitable spawning gravels. This method may be useful for quantification of fish and macroinvertebrates habitats, surface grain-size metrics for sediment transport models, and monitoring of natural and anthropogenic changes in river systems.

Oregon↗

USGS2025

Public supply water delivery analysis and estimation for the conterminous United States

Public supply water withdrawals represent 14% of all withdrawals in the conterminous United States (CONUS), supplying approximately 87% of the population with fresh water. Deliveries for public water supply are crucial for associating water use amounts with populations because they often differ from total withdrawals due to wholesales, transfers, losses, and other factors. Understanding these differences helps identify the drivers for each type of delivery. The goal of this study was to compile all available public water supply delivery data for the CONUS and develop a data-driven model to estimate deliveries for all water service areas within the CONUS. Annual deliveries were estimated between 2010 and 2020, encompassing total water deliveries; combined commercial, industrial, and institutional deliveries (CII); and domestic deliveries. Data were compiled for 2,744 water service areas to produce the most comprehensive public water supply delivery data set for the CONUS to date. Three ensemble modeling approaches were developed to estimate total, CII, and domestic per capita (DPC) deliveries using a gradient boosted regression tree modeling approach. Estimates of daily domestic and CII per capita deliveries were generated from these models for approximately 18,800 water service areas, covering most public water systems in the CONUS. Domestic delivery was found to be lowest in the midwestern region and higher in the southern and southwest regions of the United States. Results indicate that climate and land use can be associated with regional differences in DPC delivery. Population metrics and land use were identified as significant contributors to CII delivery estimates.

conterminous United States↗

USGS2025

Model‐based decomposition of spatially varying temporal shifts in seasonal streamflow across north temperate US rivers.

Anthropogenically forced climate shifts disrupt the seasonal behavior of climatic and hydrologic processes. The seasonality of streamflow has significant implications for the ecology of riverine ecosystems and for meeting societal demands for water resources. We develop a hierarchical Bayesian model of daily streamflow to quantify how the shape of annual hydrographs are changing and to evaluate temporal trends in model-based hydrologic indices related to flow timing and magnitude shifts. We apply this model to 1,112 gages across the Northern US over the years 1965–2022. We identify large-scale patterns in temporal changes to streamflow profiles that are consistent with regional changes in hydroclimate, including decreasing seasonal flow variability in the Pacific Northwest and increasing winter flows in the northeastern United States. Within these regions we also observe fine-scale heterogeneity in streamflow timing and magnitude shifts, both of which have potentially significant implications for riverine ecosystem function and the ecosystem services they provide.

northern United States↗

USGS2025

Efficacy of oblique bubble screen deterrents on redirecting live eggs and larvae of grass carp (Ctenopharyngodon idella) at different developmental stages

Invasive carp have severely damaged aquatic ecosystems in the USA, particularly in the Mississippi River Basin. Behavioral deterrents have been developed in the last few decades to control population expansion into new ecosystems. However, none of these deterrents are capable of controlling early-life stage carp, which have limited or no mobility during their drifting stage in rivers. Capturing eggs and larvae in large numbers warrants new methods due to their distinct physical and biological properties as well as their lack of behavioral response. We tested a novel method to redirect downstream drifting eggs and larvae for their efficient removal in streams using an oblique bubble screen (OBS) deterrent. We investigated the effects of mean water velocity and airflow rate on redirection of eggs, pre-gas bladder inflation (GBI) larvae, near-GBI larvae, and dead larvae. Although similar OBS configurations had shown high efficacy redirecting plastic spheres (egg surrogates) in previous studies, they underperform redirecting live eggs and larvae. However, distinct patterns were identified for eggs, pre-GBI, near-GBI, and dead larvae. A detailed hydrodynamic analysis showed that eggs closely follow the larger scales of motions created by the OBS, and that larvae can actively respond to turbulence cues. This study yielded new insights into the movement of early-life stage grass carp in a turbulent flow with strong recirculation, and provided important data to improve the design of bubble screen dispersal barriers for invasive carp management and population control.

Water Resources Research↗

USGS2026

Remotely sensed surface water storage shows distinct patterns from SWAT-simulated data

Quantifying and projecting the downstream benefits of water stored in lakes and wetlands (SW storage ) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled SW storage dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) SW storage data set (Sentinel-1 and Sentinel-2) for verifying simulated SW storage estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ∼440,000 km 2 ). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal SW storage dynamics in the UMRB. Mean SW storage as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS SW storage , where SW storage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT SW storage and RS SW storage were positively correlated in only 38.8% of subbasins. As RS SW storage is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS SW storage , the method and data set presented here are a promising option for improved parameterization and calibration of SW storage processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗

USGS2025

Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration

Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior-to-calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less-adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less-adequate model outputs receiving lower weight. The framework is demonstrated using a one-dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less-adequate models.

Water Resources Research↗

USGS2026

A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States↗

USGS2025

A streamflow permanence classification model for forested streams that explicitly accounts for uncertainty and extrapolation

Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging-derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub-watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5-m stream sub-reach across 426 sub-watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub-watersheds that contained training data and decreased to 0.67 for sub-watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.

Oregon↗

USGS2026

More water, more of the time: Spatial changes in flooding over 83 years in the upper Mississippi River floodplain and relationships with streamgage-derived proxies

The hydrologic regime of the upper Mississippi River (UMR) has become wetter, with greater discharges, longer-lasting high-flow conditions, and seasonal shifts in these patterns over the past several decades. How these changes are expressed spatially as floodplain inundation area, frequency, depth, duration, and timing is not well understood. It is also unclear to what degree spatial patterns of submergence are represented by examining discharge data alone. We assessed changes in floodplain inundation characteristics from 1940 to 2022 in navigation pools 3–10 of the UMR using a geospatial model to simulate daily inundation depths. Inundation characteristics shifted significantly across pools, but the direction and magnitude of change varied by pool and metric. Characteristics summarized at the pool scale correlated with streamgage-derived proxies but the strength of the relationship varied. Within pools, variability in inundation trends highlighted the importance of spatially explicit modeling. Our study demonstrates that changes in discharge over 83 years have manifested across the UMR floodplain in ways that may have consequences for ecological patterns and processes. By mapping hydrologically sensitive areas, we can anticipate which areas may be susceptible to additional shifts in river discharge in a climatically uncertain future.

Iowa, Minnesota, Wiscosnin↗

USGS2026

Modeling future groundwater depletion to evaluate sustainability goals set under the Sustainable Groundwater Management Act in the critically overdrafted basins of the Central Valley, California, USA (2020–2070)

In 2014, California's Sustainable Groundwater Management Act (SGMA) mandated local agencies to devise and implement groundwater sustainability plans to address critically overdrafted conditions throughout the state's aquifers. However, the feasibility of these agencies' sustainability goals has not previously been assessed through a regional-scale, integrative lens. Here, we develop and analyze a novel, basin-wide database of 936 sustainability indicator wells located within Central Valley subbasins designated as critically overdrafted, most of which lie in the San Joaquin Valley. Our database shows 2040 groundwater elevation goals vary widely from 60 m above to 80 m below 2020 levels, with variability within and between adjacent subbasins. To evaluate the feasibility of achieving these goals, we coupled the database with a regional hydrologic model (Central Valley Hydrologic Model version 2) and simulated multiple future pumping scenarios. Results show that under increased groundwater demand, 60%–70% of indicator wells may fail to meet their 2040 goals. Even a 50% reduction from 2020 demand levels leaves nearly 40% of wells failing to meet their sustainability thresholds by 2040. Baseline models show that by 2070, up to 70% of wells could fail to meet their goals due to large-scale, spatially connected regions of groundwater depletion. This integrated framework, linking the first region-wide compilation of SGMA indicator wells with a regional groundwater model, demonstrates that many local sustainability goals may be unattainable with substantial (up to 50%) reductions in pumping. Additional management interventions, such as expanded recharge or coordinated demand reductions, may help achieve sustainability goals.

California↗

USGS2026

Estimating discharge from undular hydraulic jumps: Feasibility assessment based on flume experiments

Rapids are common in steep rivers, often forming where flow transitions from supercritical (Froude number, Fr > 1) to subcritical ( Fr < 1) through a hydraulic jump. When upstream Fr is supercritical but close to 1, this transition may occur as an undular hydraulic jump, exhibiting a train of stationary waves downstream of the jump toe. Previous studies proposed a method to estimate discharge using only UHJ wave spacing and channel width combined with a wave dispersion equation for large water depths relative to the UHJ wavelength. This method is based on the hypotheses that, by their presence, UHJs indicate near-critical flow conditions ( Fr ≈ 1) and that wave celerity c is equal to and opposite the cross-sectionally averaged flow velocity U . However, these hypotheses have not been thoroughly tested. We used data from published UHJ flume experiments to test the hypotheses that Fr ≈ 1 and c = U , compare the deep-water and general wave dispersion equations, and evaluate the accuracy of discharge estimates. In these experiments, the stationary waves exhibited shallow depths relative to wavelength and flow was subcritical ( Fr < 1) when averaged across multiple wavelengths. Additionally, wave celerity more closely approximated the surface flow velocity than U . By using a Fr representative of actual conditions and applying a coefficient to correct for c ≠ U , the accuracy of the discharge estimates improved. This finding suggests that the critical flow-based method is robust and can produce reliable streamflow estimates if the remotely observed wave trains are correctly interpreted as UHJs, without requiring in situ measurements.

Water Resources Research↗

USGS2026

Modeling legacy nitrogen transport under instantaneous, steady-state, and transient groundwater flow conditions

In hydrologic settings where groundwater discharge contributes substantially to surface waters, legacy nitrogen in groundwater can confound surface water nitrogen loads estimated exclusively from current terrestrial sources. Additionally, legacy nitrogen in groundwater can contribute to lagged responses to nitrogen management efforts. Some methods of estimating groundwater contributions to surface water nitrogen loads account for legacy nitrogen, while others do not. The resulting differences are rarely quantified. We used a numerical modeling framework to compare three methods of estimating time-varying annual groundwater nitrogen loads to surface water receptors on eastern Long Island, New York. The instantaneous load method used steady-state contributing areas and includes no temporal groundwater lag. The second method used numerical simulations of nitrogen loads under steady-state flow, which captures groundwater transport lags but omits the annual variability in transient hydrologic stresses. The third method numerically simulated both transient groundwater flow and nitrogen transport to explicitly capture the effects of legacy nitrogen in groundwater. Depending on antecedent nitrogen and hydrologic conditions, historical nitrogen loads estimated from the numerical simulations were sometimes similar (<10% difference) and other times substantially different (±100%) from the instantaneous load estimates. Additionally, simulated future surface water nitrogen loads responded asymptotically over several decades following reductions in terrestrial nitrogen sources, further highlighting the effect of groundwater transport lag times. The comparison of the three methods, quantification of historical interannual variability, and prediction of lagged responses to nitrogen source reductions provide important context for decision makers using estimated groundwater nitrogen loads to help evaluate nitrogen management efficacy.

New York↗

USGS2026

A method to implement natural flow regimes for regulated rivers

Rivers throughout the world have been dammed for flood control, irrigation, hydropower, and water storage for centuries. Dams service the economic and development needs of societies, but degrade the ecology of rivers. To conserve diminishing aquatic species and their habitats, methods are needed to help managers implement flow releases with timescales and patterns that are relevant to aquatic species and physical processes. We present a method to synchronize flow releases from dams with local hydrology, while ensuring constraints of flood control and water allocations are met. We demonstrate an optimization technique that scales flows of a proximal unregulated river and results in discharges that are synchronized with natural hydrologic patterns. Importantly, our method preserves operational and regulatory requirements such as water allocation volumes and flood control thresholds. This technique can be used at various timescales, and can incorporate many system-specific constraints. We selected the Trinity River, California as an example, and scale flows according to a proximal gage on the Salmon River in California. We evaluate our technique using an array of flow regime metrics from the literature, and our method produced hydrographs with greater flow variation throughout a larger portion of the water year than current management strategies. These hydrographs yielded flow metrics more similar to those from unimpaired flow patterns, including those measuring within and among year flow variability. The Real Time Management framework is a new tool that can be implemented to improve the health of rivers and the aquatic species that depend upon them.

California↗

USGS2026

Evaluating approximations of river channel shape using a national cross section database

Many hydrologic applications require basic information on the size and shape of river channels, but measuring cross section (XS) geometry in the field or via remote sensing can be costly and often provides only partial coverage. Given these challenges, we capitalized upon an existing data set of 46,971 XS from gaging stations to evaluate various approximations of channel shape. After screening and pre-processing these data, we fit four model types to each XS, including a new approach that involves Stacking PDFs (probability density functions) to Approximate River Channel Shapes (SPARCS). This framework produced depth estimates that closely matched field measurements, with typical cross-sectional area errors <1 % and a median R 2 of 0.77 for comparison of observed and predicted depths. SPARCS model parameters can be interpreted in terms of channel characteristics: mean depth, asymmetry, bar convexity, and flatness of the bed. The model performed well for the XS included in the database, which was biased toward straight, uniform channels conducive to operational streamflow measurement. Neither model parameters nor accuracy were dependent on discharge. We also assessed the potential of SPARCS to fill in measurement gaps and found that although the model can help, the accuracy of inferred depths decreased as the observable fraction of the channel decreased. An important limitation of SPARCS is that mid-channel bars or multi-threaded morphologies cannot be produced. Graphical tools can help visualize how model parameters affect simulated river forms. SPARCS could facilitate satellite-based discharge estimation by providing prior information on channel shape.

Water Resources Research↗

USGS2026

Large streamflow differences between forested and urbanized watersheds in the energy-limited eastern United States: The role of evapotranspiration and impervious surfaces

Urban forests and other green infrastructures have been viewed as part of the “Nature-based Solutions” (NbS) to mitigate emerging urban environmental change. This study focuses on the role of evapotranspiration (ET) in regulating water balances of small watersheds in the eastern United States. We compared streamflow and ET patterns at daily, monthly and annual scales and linked these hydrological variables to the physical properties of 11 paired watersheds dominated by forests (FW) or urban (UW) land covers. The annual precipitation ranged from 1028 mm to 1683 mm and potential ET (PET) from 815 mm to 1450 mm. The mean annual flow/precipitation (Q/P) ratios were 0.26 ± 0.13 and 0.41 ± 0.1 for FW and UW, respectively. Overall, UW had lower annual ET (772 mm in UW vs. 947 mm in FW), but higher mean annual and (∼58% higher), monthly water yield (17%–186% higher), and peakflow rates (up to 100 times higher) than FW. The streamflow differences between FW and UW were most pronounced during the growing season and early winter (June-November). The mean Q/P ratios for 30 large hurricane events (2016–2021) were 0.12 ± 0.11 and 0.38 ± 0.23 for FW and UW, respectively. The flow rates in the dormant season (around December-May) in UW were similar or lower than FW. We developed conceptual models to explain the seasonal and storm event streamflow differences using background climate (PET), ET, and land surface characteristics. Urban NbS designs should factor in strategies that maximize ET while minimizing impervious surfaces enhancing watershed “sponge” and “pump” functions.

eastern United States↗

USGS2026

Towards affordable wetland evapotranspiration monitoring using the Variance-Bowen Ratio method: Insights from three contrasting wetlands

Accurate measurement of evapotranspiration (ET) is essential for sustainable water management. Standard methods such as eddy covariance (EC) are costly, while alternatives such as surface renewal are cheaper but require calibration and complex data processing. This study evaluates the utility of the Variance-Bowen Ratio (VBR) method for estimating ET across three California’s wetlands. Using data from 2023, half-hourly latent heat flux (λE) and daily/monthly ET from VBR were compared with EC at one non-tidal (site A) and two tidal (sites B and C) wetlands. λE VBR consistently underestimated λE EC , with root mean squared errors (RMSE) of 61.2 W m -2 at sites A, 106.1 W m -2 at site B, and 137.2 W m -2 at site C, largely due to storage fluxes across sites. Temporal integration improved VBR’s performance at tidal sites, where compensating water heat storage errors yielded low daily and monthly biases (site B: RMSE = 0.78 mm/d and 12 mm/month; r = 0.93; site C: RMSE = 0.90 mm/d and 13 mm/month; r = 0.93), with reduced major axis (RMA) regression slopes of 0.98 and ~0.91. In contrast, biomass heat storage at site A caused persistent biases (RMSEs = 0.97 mm/d and 23 mm/month; daily and monthly RMA slopes ~0.75; r = 0.85). These results highlight VBR’s limitations in environments with substantial storage fluxes. Despite this, VBR is cost-effective for estimating daily and monthly ET, with sensor costs at least tenfold lower than EC and simpler setup, making it suitable for ET monitoring in resource-limited and hard-to-access regions.

California↗