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DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract 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. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗

NASA NTRS2010

Life Support with Failures and Variable Supply

The life support system for long duration missions will recycle oxygen and water to reduce the material resupply mass from Earth. The impact of life support failures was investigated by dynamic simulation of a lunar outpost habitat life support model. The model was modified to simulate resupply delays, power failures, recycling system failures, and storage failures. Many failures impact the lunar outpost water supply directly or indirectly, depending on the water balance and water storage. Failure effects on the water supply are reduced if Extra Vehicular Activity (EVA) water use is low and the water supply is ample. Additional oxygen can be supplied by scavenging unused propellant or by production from regolith, but the amounts obtained can vary significantly. The requirements for oxygen and water can also vary significantly, especially for EVA. Providing storage buffers can improve efficiency and reliability, and minimize the chance of supply failing to meet demand. Life support failures and supply variations can be survivable if effective solutions are provided by the system design

Jones, Harry↗

DOE OSTI2021

Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling

Abstract. Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Improvements in lake volume predictions using Landsat data

A cumulative error in the water balance budget for Lake Okeechobee produces a one million acre-foot discrepancy in the predicted water volume over a 4-year period. The major source of error appears to be complex shoreline marshes that comprise 20 percent of the lake surface. The water balance budget model presently treats these marshes as open water. Using Landsat data, the vegetation in the lake's littoral zone was classified multispectrally to provide a data base for determining water budget information. First, the acreage of a given plant species in the littoral zone was obtained with satellite data. Second, the surface area occupied by plants (which therefore could not be considered open water) was used to adjust the vegetation acreage giving an effective water surface. Based on this information, more detailed representations of evapotranspiration and total water surface (and hence total lake volume) could be provided to the water balance budget computation.

Gervin, J. C.↗

NASA NTRS1993

Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

A land surface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of land surface-atmosphere interactions.

Famiglietti, J. S.↗

DOE OSTI2022

Understanding the E balance for water management in hydroxide exchange membrane fuel cells

As a key challenge for hydroxide exchange membrane fuel cells (HEMFCs), understanding water management deeply is critical for performance and durability improvement. Here, in this work, we show that the balance between water generation and phase-change-induced (PCI) flow is the main factor controlling cell voltage over a wide range of conditions where the cells are vulnerable to severe flooding. The regulation of flooding by heat generation can be reflected by a stable voltage with increase of current density in the polarization curve. This PCI balance voltage (E balance ), which determines the shape of polarization curve, is modeled as a function of cell operating conditions and gas diffusion layer (GDL) properties. Temperature, pressure, and thermal conductivity of GDL were found important for tuning the level of E balance , both in HEMFCs and proton exchange membrane fuel cells. The understanding of E balance will help to generate strategies for resolving flooding in HEMFCs, and it offers a new perspective for polarization curve interpretation.

25 ENERGY STORAGE↗

NASA NTRS1994

Development of a multicomponent force and moment balance for water tunnel applications, volume 1

The principal objective of this research effort was to develop a multicomponent strain gauge balance to measure forces and moments on models tested in flow visualization water tunnels. An internal balance was designed that allows measuring normal and side forces, and pitching, yawing and rolling moments (no axial force). The five-components to applied loads, low interactions between the sections and no hysteresis. Static experiments (which are discussed in this Volume) were conducted in the Eidetics water tunnel with delta wings and a model of the F/A-18. Experiments with the F/A-18 model included a thorough baseline study and investigations of the effect of control surface deflections and of several Forebody Vortex Control (FVC) techniques. Results were compared to wind tunnel data and, in general, the agreement is very satisfactory. The results of the static tests provide confidence that loads can be measured accurately in the water tunnel with a relatively simple multicomponent internal balance. Dynamic experiments were also performed using the balance, and the results are discussed in detail in Volume 2 of this report.

Suarez, Carlos J.↗

DOE OSTI2019

pnnl/femp-water-balance

Analysis and reporting for water and irrigation usage PNNL developed the Water Balance Tool, which provides a method to determine potable water consumption, by end-use, of a campus. A water balance compares the total water supply to the volume of water consumed by water-using equipment and applications. The output of the tool provides the estimated potable water consumption by major end-use and compares the sum of the end-uses to the total potable water supplied to the campus.

Kobold, Mark A.↗

NASA NTRS1980

Fluid shifts and endocrine responses during chair rest and water immersion in man

The effects of external water pressure on intercompartmental fluid volume shifts and endocrine responses in man are investigated. Extracellular fluid volumes and plasma and urine electrolyte and endocrine responses of four male subjects were measured during eight hours of head-out water immersion and 16 hours of recovery bed rest and compared to responses obtained during eight hours of chair rest and 16 hours of bed rest without external hydrostatic pressure obtained in the same subjects five months later. Immersion is found to result in a substantial diuresis with respect to chair rest, accounted for by decreases in extracellular volume. A negative water balance during immersion and a positive water balance during chair rest were observed to be accompanied by a shift of extracellular volume to the intracellular compartment, as well as the suppression of plasma arginine vasopressin and renin activities in both regimes. The vasopressin and renin activity decreases are attributed to the increased central blood volume, and half of the plasma loss in immersed subjects is attributed to the effects of external water pressure.

Greenleaf, J. E.↗

NASA NTRS1992

Effect of leg exercise training on vascular volumes during 30 days of 6 degrees head-down bed rest

Plasma and red cell volumes, body density, and water balance were measured in 19 men (32-42 yr) confined to bed rest (BR). One group (n = 5) had no exercise training (NOE), another near-maximal variable-intensity isotonic exercise for 60 min/day (ITE; n = 7), and the third near-maximal intermittent isokinetic exercise for 60 min/day (IKE; n = 7). Caloric intake was 2,678-2,840 kcal/day; mean body weight (n = 19) decreased by 0.58 +/- 0.35 (SE) kg during BR due to a negative fluid balance (diuresis) on day 1. Mean energy costs for the NOE, and IKE, and ITE regimens were 83 (3.6 +/- 0.2 ml O2.min-1.kg-1), 214 (8.9 +/- 0.5 ml.min-1.kg-1), and 446 kcal/h (18.8 +/- 1.6 ml.min-1.kg-1), respectively. Body densities within groups and mean urine volumes (1,752-1,846 ml/day) between groups were unchanged during BR. Resting changes in plasma volume (ml/kg) after BR were -1.5 +/- 2.3% (NS) in ITE, -14.7 +/- 2.8% (P less than 0.05) in NOE, and -16.8 +/- 2.9% (P less than 0.05) in IKE, and mean water balances during BR were +295, -106, and +169 ml/24 h, respectively. Changes in red cell volume followed changes in plasma volume. The significant chronic decreases in plasma volume in the IKE and NOE groups and its maintenance in the ITE group could not be accounted for by water balance or by responses of the plasma osmotic, protein, vasopressin, or aldosterone concentrations or plasma renin activity. There was close coupling between resting plasma volume and plasma protein and osmotic content.(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Discipline Regulatory Physiology↗

NASA NTRS1975

Microwave remote sensing of soil water content

Microwave remote sensing of soils to determine water content was considered. A layered water balance model was developed for determining soil water content in the upper zone (top 30 cm), while soil moisture at greater depths and near the surface during the diurnal cycle was studied using experimental measurements. Soil temperature was investigated by means of a simulation model. Based on both models, moisture and temperature profiles of a hypothetical soil were generated and used to compute microwave soil parameters for a clear summer day. The results suggest that, (1) soil moisture in the upper zone can be predicted on a daily basis for 1 cm depth increments, (2) soil temperature presents no problem if surface temperature can be measured with infrared radiometers, and (3) the microwave response of a bare soil is determined primarily by the moisture at and near the surface. An algorithm is proposed for monitoring large areas which combines the water balance and microwave methods.

Cihlar, J.↗

NASA NTRS1995

Exercise Training During Bed Rest Attenuates Deconditioning

A 30-day 6 deg. head-down bed rest study was conducted to evaluate high-intensity, short-duration, alternating isotonic cycle ergometer exercise (ITE) training and high-intensity intermittent isokinetic exercise (IKE) training regiments designed to maintain peak VO2 and muscle mass, strength, and endurance at ambulatory control levels throughout prolonged bed rest. Other elements of the deconditioning (acclimation) syndrome, such as proprioception, psychological performance, hypovolemia, water balance, body composition, and orthostatic tolerance, were also measured. Compared with response during bed rest of the no exercise (NOE) control group: the ITE training regimen (a) maintained work capacity (peak VO2), (b) maintained plasma and red cell volume, (c) induced positive body water balance, (d) decreased quality of sleep and mental concentration, and (e) had no effect on the decrease in orthostatic tolerance; the IKE training regimen (a) attenuated the decrease in peak VO2 by 50%, (b) attenuated loss of red cell volume by 40%, but had no effect on loss of plasma volume, (c) induced positive body water balance, (d) had no adverse effect on quality of sleep or concentration, and (e) had no effect on the decrease in orthostatic tolerance. These findings suggest that various elements of the deconditioning syndrome can be manipulated by duration and intensity of ITE or IKE training regiments, and that several different training protocols will be required to maintain or restore physiological and psychological performance of individuals confined to prolonged bed rest.

Greenleaf, J. E.↗

DOE OSTI2026

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗

NASA NTRS1997

Intensive Exercise Training During Bed Rest Attenuates Deconditioning

Intensive exercise training during bed rest attenuates deconditioning. Med. Sci. Sports Exerc., Vol. 29, No. 2, pp. 207-215, 1997. A 30-d 6 deg head-down bed rest project was conducted to evaluate variable high-intensity, short-duration, isotonic cycle ergometer exercise (ITE) training and high-intensity intermittent resistive isokinetic exercise (IKE) training regimens designed to maintain peak VO2 and muscle mass, strength, and endurance at ambulatory control levels throughout prolonged bed rest. Other elements of the deconditioning (adaptive) syndrome, such as proprioception, psychological performance, hypovolemia, water balance, body composition, and orthostatic tolerance, were also measured. Major findings are summarized in this paper. Compared with response during bed rest of the no exercise (NOE) control group: the ITE training regimen (a) maintained work capacity (peak VO2), (b) maintained plasma and red cell volumes, (c) induced positive body water balance, (d) decreased quality of sleep and mental concentration, and (e) had no effect on the decrease in orthostatic tolerance; the IKE training regimen (f) attenuated the decrease in peak VO2 by 50%, (g) attenuated loss of red cell volume by 40% but had no effect on loss of plasma volume, (b) induced positive body water balance, (i) had no adverse effect on quality of sleep or concentration, and 0) had no effect on the decrease in orthostatic tolerance. These findings suggest that various elements of the deconditioning syndrome can be manipulated by duration and intensity of ITE or IKE training regimens and that several different training protocols will be required to maintain or restore physiological and psychological performance of individuals confined to prolonged bed rest.

Greenleaf, John E.↗

DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗

NASA NTRS2003

Fresh Water Content Variability in the Arctic Ocean

Arctic Ocean model simulations have revealed that the Arctic Ocean has a basin wide oscillation with cyclonic and anticyclonic circulation anomalies (Arctic Ocean Oscillation; AOO) which has a prominent decadal variability. This study explores how the simulated AOO affects the Arctic Ocean stratification and its relationship to the sea ice cover variations. The simulation uses the Princeton Ocean Model coupled to sea ice. The surface forcing is based on NCEP-NCAR Reanalysis and its climatology, of which the latter is used to force the model spin-up phase. Our focus is to investigate the competition between ocean dynamics and ice formation/melt on the Arctic basin-wide fresh water balance. We find that changes in the Atlantic water inflow can explain almost all of the simulated fresh water anomalies in the main Arctic basin. The Atlantic water inflow anomalies are an essential part of AOO, which is the wind driven barotropic response to the Arctic Oscillation (AO). The baroclinic response to AO, such as Ekman pumping in the Beaufort Gyre, and ice meldfreeze anomalies in response to AO are less significant considering the whole Arctic fresh water balance.

Hakkinen, Sirpa↗

NASA NTRS1994

Development of a multicomponent force and moment balance for water tunnel applications, volume 2

The principal objective of this research effort was to develop a multicomponent strain gauge balance to measure forces and moments on models tested in flow visualization water tunnels. Static experiments (which are discussed in Volume 1 of this report) were conducted, and the results showed good agreement with wind tunnel data on similar configurations. Dynamic experiments, which are the main topic of this Volume, were also performed using the balance. Delta wing models and two F/A-18 models were utilized in a variety of dynamic tests. This investigation showed that, as expected, the values of the inertial tares are very small due to the low rotating rates required in a low-speed water tunnel and can, therefore, be ignored. Oscillations in pitch, yaw and roll showed hysteresis loops that compared favorably to data from dynamic wind tunnel experiments. Pitch-up and hold maneuvers revealed the long persistence, or time-lags, of some of the force components in response to the motion. Rotary-balance experiments were also successfully performed. The good results obtained in these dynamic experiments bring a whole new dimension to water tunnel testing and emphasize the importance of having the capability to perform simultaneous flow visualization and force/moment measurements during dynamic situations.

Suarez, Carlos J.↗

DOE OSTI2026

More concentrated precipitation decreases terrestrial water storage

Terrestrial water availability is a key determinant of human and ecosystem well-being. Apart from mean precipitation and evaporation changes, it is unknown how daily-scale precipitation concentration into fewer, heavier events affects hydrologic partitioning and the land water balance. Here we show observationally that more concentrated precipitation decreases land water availability across all climates globally, a drying effect as strong in magnitude as the wetting effect of increased total precipitation. Simple and complex land-surface models recover the observed effect, whereas idealized simulations show that it arises from enhanced evaporation caused by hydrologic partitioning changes at the land surface. Projected terrestrial water storage impacts of warming-driven precipitation concentration at about 2 °C of warming shift the land surface to abnormally dry conditions (≥0.5 standard deviation) for 27% of the global population, independent of any total precipitation or irrigation changes. Our results show new key determinants of the land water balance, highlighting its sensitivity to the temporal distribution of precipitation, with broad implications for future water availability.

climate change↗