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Results for “Hydrology Journal”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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257 recordsLinked to original sources
USGS2025

Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model

Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.

California↗
USGS2026

Assessing future hydrologic extremes using an integrated hydrology and river operations model in the Russian River watershed

Study region The Russian River watershed, situated in coastal, northern California, experiences hydrologic extremes, including periodic droughts and flooding. Water managers are working to maintain sustainable water supplies and environmental flows, while mitigating flood risks. Study focus This paper introduces an integrated hydrology and river operations model for the Russian River watershed. This model is distinct from models in previous studies because it represents surface-groundwater interactions and uses climate forcings to estimate dynamic water use demands that are superimposed onto both reservoir operations and water supply constraints. The model was used to examine three historical (1990–2015) and eight future (2016–2099) water use and climate change scenarios. New hydrological insights for the region The direct connection between streams and aquifers facilitated both annual aquifer replenishment by high winter streamflows and streamflow depletion by groundwater wells (19 % of pumped groundwater in alluvial aquifers from stream leakage) during critical low flow periods. Simulated streamflow changes included 59 % longer and 54 % more severe streamflow droughts, 26 % lower seasonal low streamflows, and up to 125 % higher peak streamflows, averaged over future climate and water use scenarios, suggesting increased future flood and water availability risks. Results showed the importance of reservoir operations for mitigating the impacts of increased hydroclimatic volatility, despite a decrease in reservoir reliability at Lake Mendocino, suggesting that reservoir management may be used to decrease future risks.

California↗
USGS2025

Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA

The ecological importance of springs in semiarid regions is far greater than their small size and sparse distribution, yet little is known about the hydrologic functioning of these systems. During 2016–22, 261 springs were visited in the volcanic terrane of eastern Oregon and northern Nevada. When conditions were suitable, measurements of discharge, water temperature, and specific conductance were made, and samples for the analysis of carbon-14, tritium, and water stable isotopes (WSI) were collected. A subset of 60 springs was revisited during different seasons in the same year and during the dry season in multiple years to evaluate variability in discharge, chemistry, and groundwater age. Specific conductance and WSI varied considerably among springs across the study area but were unexpectedly stable across seasons and years at individual springs. Seasonal and interannual variability in spring discharge was related to the residence time of the discharging groundwater. Springs discharging older groundwater (10 3 –10 4 years) had significantly less variability in their discharge compared to springs discharging younger groundwater (10 0 –10 1 years). Variability among springs discharging younger groundwater included cessation of late-summer discharge at 18 % of the repeat-visit springs. A logistic regression model predicted the age of discharging spring water with 89 % accuracy using only the spring latitude, longitude, elevation, and δ 2 H value. This study framework provides a simple, inexpensive, and robust method to provisionally assess the hydrologic behavior of springs having little or no prior information in understudied, semiarid regions across the globe.

Nevada, Oregon↗
USGS2026

Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models

Characterizing changes to water availability for domestic, industrial, agricultural, and other uses is essential to support water management. To better quantify these changes, the U.S. Geological Survey and National Science Foundation National Center for Atmospheric Research produced two hydrologic models simulating water budget components from 1980 to 2021 over the contiguous United States (CONUS). Both hydrologic models were driven by a common atmospheric forcing dataset and aggregated to common spatial and temporal scales, which enables a novel evaluation of congruency between the models. We present annual and seasonal trends in six water budget components (precipitation, evapotranspiration, streamflow, groundwater recharge, soil saturation, and snow water equivalent) based on the Mann–Kendall test for monotonic trend and Theil-Sen slope estimate for the water year 1983–2021 period for ~86,000 catchments in CONUS. Additional components and metrics from our analysis pipeline are available in an associated published dataset, which contains more than 46 million trend results. The water budget trends showed broad agreement with prior observational and modeling studies that indicate increasing trends in the northeast and decreasing trends in southwestern CONUS. We found the seasonal variability in water budget trends was greatest in the southern, central, and northwest CONUS. These findings support integrated trend assessments when coupled with trends in water quality and use.

Contiguous United States↗
USGS2025

Hydrologic response of groundwater and streamflow to natural and anthropogenic drivers of change in headwaters of the upper Colorado River basin during recent wet (1982–1999) and drought (2000–2022) conditions

Study region: Headwaters of the upper Colorado River basin (UCOL), USA Study focus: Surface-water and groundwater numerical models incorporating water-use information were used to investigate changes in climate, water use, and simulated hydrologic responses of snow processes, evapotranspiration, groundwater, and streamflow during recent wet (1982–1999) and drought (2000–2022) periods in the headwater subregions of the upper Colorado River basin. New hydrologic insights for the region: Decreases in average streamflow between wet and drought periods ranged from 20 % in the Colorado River headwaters subregion to 23 % in the Gunnison River headwaters subregion. Like streamflow, average surface runoff was statistically less during the drought than the wet period, with decreases from 24–31 % in the headwaters. On a volume basis, runoff decreases were greater than streamflow decreases in both the Colorado River and Gunnison River headwaters. Although the amount of water-year groundwater discharge to streams remained nearly the same between the wet and drought periods, groundwater as a percentage of streamflow increased between the wet and drought periods, highlighting the importance of groundwater in sustaining streamflow during drought conditions. Multiple linear regression analyses revealed that snowmelt-only models were better than the best precipitation and temperature models at explaining streamflow variability from all headwater subregions for both the wet and drought periods.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2026

Post-fire soil hydrologic response and recovery in northern California (USA)

Background Wildfires abruptly change landscapes by altering soil properties and vegetation cover. These changes are thought to reduce soil infiltration capacity, making landscapes susceptible to runoff and erosion. However, post-fire soil response is complex and likely varies across locations and time. Aims Here, we aim to understand regional post-fire soil response and recovery by tracking changes across different northern California (USA) lithology and vegetation types. Methods We conducted repeat in situ soil infiltration tests for 3 years post-fire at 31 burned and 10 unburned sites spanning the 2021 Dixie, 2020 LNU Lightning Complex, 2020 Walbridge and 2020 Glass fires. Key results Our two main findings are: (1) burned chaparral soils have increased hydraulic conductivity compared with unburned sites, and (2) infiltration rates return to pre-fire conditions within 3 years across most lithologies and vegetations. Conclusions Recovery might be generalizable by vegetation and lithology but differ regionally, making it important to identify meaningful hydrologic response units (HRUs). Multi-year studies with paired burned and unburned measurements can constrain the recovery timeline and provide information missed by observations solely of burned soils. Implications Understanding where, and for how long, soil remains susceptible to runoff and erosion can help prioritize areas and time periods most in need of mitigation.

California↗
USGS2025

Systematic approach to prioritize wells for effective groundwater monitoring and management in the Arkansas Headwaters Basin, Colorado, USA

Study region The Arkansas Headwaters Basin, an intermountain basin in the Southern Rocky Mountains of North America. Study focus Our specific focus is choosing a set of wells to support a possible future regional groundwater-surface water model that would support water management. We present a three-step process using multiple criteria to score, predict, and choose prioritized wells that capture the full distribution of data including extremes. The three-step process provides accessible visualizations, fiscally efficient well prioritization, and screening useful for subsequent groundwater modeling. The novelty of the proposed methodology is the systematic approach integrating a scoring and a predictive approach to support a selection path. The systematic approach may be broadly adapted for other basins. New hydrological insights for the region Understanding regional hydrology hinges on efficient collection of hydrologic data that captures the relevant dynamics including extremes. The present study, a case study for a particular basin in the Southern Rocky Mountains, is the first use of a scripted (R software) strategy to select an economical and representative set of monitoring wells. Our findings suggest caution when using proximity as a proxy for correlation, because proximal wells in the same geologic formation and similar depths are not always correlated. In the Arkansas Headwaters Basin, subsurface geology may be less influential on groundwater elevations than broader hydrologic influences, such as regional drought.

Colorado↗
USGS2026

Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances

Identifying effective solutions for locating groundwater resources and ensuring the quality of drinking water is increasingly urgent, given the challenges posed by climate change and population growth. This review investigates electromagnetic geophysical imaging techniques, in both time- and frequency-domain, that can provide valuable insights for groundwater assessment. We explore computational electromagnetic methods used to evaluate electromagnetic data and several recent hydrogeophysical case studies. As open-source frameworks for modeling electromagnetic geophysical problems become available, a broader range of researchers can interpret their data with computationally advanced software. We provide an overview of documented open-source codes for evaluating electromagnetic data and analyze various hydrological targets in relation to their electromagnetic surveying technique and the computational method applied. Furthermore, we evaluate the potential of advanced computational techniques, including three-dimensional modeling, non-deterministic inversion and machine learning, to couple geophysical with numerical groundwater modeling and apply it in groundwater system studies. Despite obstacles such as complexity and resource demands, our findings indicate that the quantification and integration of predictive uncertainties from both electromagnetic and hydrological data and simulations would significantly improve the reliability of hydrogeophysical models. This can lead to a deeper understanding of groundwater systems and improved management practices.

Journal of Hydrology↗
USGS2026

Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements

This study examines the spatial variability of shallow groundwater on Dhigelaabadhoo Island using electromagnetic induction surveys, groundwater monitoring, and sediment analyses. The research reveals how variations in island morphology—such as differences in elevation, reef flat width, and sediment composition—affect the spatial distribution of groundwater lenses and the overall aquifer dynamics. Saltwater intrusion is especially pronounced in low elevated areas, with narrow reef flat plate and areas where higher hydraulic conductivity—driven by the presence of coarser sediments—is observed, whereas regions characterized by finer sediments, higher elevation, and wider reef flat plates tend to support more symmetric and less saline groundwater lenses. The geophysical investigations reveal that tidal oscillations alter groundwater movement by markedly changing water levels and conductivity, thereby underscoring the critical need to account for temporal dynamics in atoll coastal aquifer systems and the importance of integrating tidal dynamics into the aquifer zone. The findings highlight the significant role of intrinsic morphological and external hydrodynamic factors in shaping groundwater distribution on atoll islands, offering critical insights for sustainable freshwater resource management.

Dhigelaabadhoo Island↗
USGS2025

Linking stream-reach nitrogen loads and groundwater “reachsheds” to inform wastewater-nitrogen management actions, Cape Cod, Massachusetts

Study Region Cape Cod, Massachusetts, U.S.A. Study Focus Anthropogenic nitrogen (N) is a key factor in degrading groundwater and surface-water quality, particularly in coastal New England where onsite wastewater systems are prevalent. This study evaluated whether direct N-load measurements in streams on Cape Cod, Massachusetts, coupled with flow-path information from groundwater-flow models, can effectively identify potential land areas where nitrogen mitigation could substantially reduce loads to receiving waters. Nitrogen fluxes were measured along 63 stream reaches during winter and summer and paired with simulated groundwater recharge areas to identify and rank potential areas for reduction of nonpoint-source N inputs. New Hydrologic Insights for the Region Reach-scale nitrate-N loads ranged from −39.1–1182 kg-N/yr per 100 m of stream, indicating spatially variable groundwater inputs across seasons. “Reachsheds” — areas contributing groundwater recharge to specific stream reaches — were delineated using a regional groundwater-flow model. Strong correlations were found between observed N loads and land-use characteristics, especially the number of septic systems and total N inputs from the sum of considered sources. Observed N loads were moderately correlated with recharge area size and wastewater flow estimates. Correlating reach-specific groundwater N loads with land use and parcel-scale nitrogen-yield data identified reachsheds with the highest potential for N load reduction. This approach enables targeted implementation of restoration efforts to optimize nutrient management and support regional load reduction.

Massachusetts↗
USGS2025

Borehole geophysical time-series logging to monitor passive ISCO treatment of residual chlorinated-ethenes in a confining bed, NAS Pensacola, Florida

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness.

Florida↗
USGS2025

Per- and polyfluoroalkyl substances (PFAS) mass flux and mass balance at an aqueous film-forming foam release site in semiarid eastern New Mexico, USA

Passive flux meters (PFMs) directly measure groundwater chemistry mass flux and Darcy flux, providing insight into contaminant source-zone architecture and transport properties. This study uses PFMs to characterize PFAS flux in groundwater at a semiarid site with a thick (greater than 90-m) unsaturated zone where groundwater has been contaminated with per - and polyfluoroalkyl substances (PFAS) related to the use of aqueous film-forming foam (AFFF) for fire training and fire suppression. PFAS mass discharge (PFAS mass flux integrated over a control plane) in groundwater downgradient from several PFAS release areas is calculated using PFM results. In groundwater downgradient from fire-training areas, total PFAS mass discharge (summed across 14 compounds) was estimated to be between 6.0 and 31 g per day in 2020 and between 5.9 and 23 g per day in 2021. Site-specific documentation, generic information on AFFF properties, and literature values of PFAS concentration in AFFF are used to estimate site-specific PFAS-application rates at fire-training areas. These PFAS-application rates are compared to groundwater PFAS-discharge rates. Results suggest that transformation processes (exact pathways unknown) have led to increased discharge of measured PFAS in groundwater relative to initial AFFF formulations. The mass balance approach has broad applicability as a high-level approach that can provide insight into PFAS transport at AFFF sites.

New Mexico↗
USGS2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Colorado, Utah↗
USGS2025

Ecohydrological response of a forested headwater catchment to a flash drought in the Southeastern U.S.

Flash droughts differ from traditionally defined droughts in their rapidity of intensification and often associated high vapor-pressure deficit. These droughts can lead to declines in streamflow and water table depth and induce water stress to vegetation at a greater rate than droughts that manifest over longer periods. However, little is known regarding the response of forested environments to flash drought because most studies of impacts have been conducted in agricultural settings. In this study we investigated water-use patterns of riparian trees using sap flow methods and examined the role of groundwater as a source of moisture over three periods that were delimited by antecedent soil moisture conditions. For a longer-term perspective we also examine monthly streamflow over the 35-year record. We observed that trees at only one monitoring plot showed a decrease in water use relative to evaporative demand during a flash drought. Total reverse sap flow (flow toward the roots rather than the canopy) greatly increased during the flash drought period, suggesting the likely occurrence of hydraulic redistribution to the excessively dry soils. Over the drought period groundwater became a more dominant source of moisture for sustaining forest water use. Monthly mean streamflow during the flash drought approached levels observed in past multiyear droughts. This is the first study, to our knowledge, to specifically investigate the response of multiple water budget components to flash drought in a humid forest. As more studies are conducted, a better understanding of the range of expected responses are likely to emerge.

Georgia↗
USGS2025

Multi-lithofacies alluvial characterization via airborne electromagnetic-borehole fusion using ordinary interval kriging and geologic constraints

Airborne electromagnetic (AEM) survey provides extensive spatial coverage and detailed resolution at the near surface and can be used to develop hydrogeological models. However, utilization of AEM data is not straightforward because AEM resistivity is an indirect measurement for inferring sediment types. This study develops an ordinary interval kriging (OIK) algorithm and a resistivity-to-multi-lithofacies (R2ML) data fusion workflow for multi-lithofacies alluvial characterization. OIK utilizes irregular interval data to construct three-dimensional (3D) resistivity fields from one-dimensional inverted AEM resistivity models. The R2ML workflow maps the resistivity field generated from OIK into a multi-facies lithological model, incorporating geologic constraints derived from well logs and geological observations. The numerical and real-world cases demonstrate that OIK is computationally efficient, accounts for 3D anisotropy, and minimizes the smoothing effect, thereby preserving resistivity contrasts and reducing interpolation uncertainty. The methodology is applied to lithologic characterization of the Mississippi River Valley alluvial aquifer (MRVA) in the Shellmound area, Mississippi, U.S. A frequency-domain AEM survey was conducted to support groundwater studies for the managed aquifer recharge (MAR) to the MRVA. The resulting lithological model, including four types of lithofacies—clay, very fine sand, fine-medium sands, and graveliferous sands, illustrates the geomorphological processes of the MRVA and implies potential MAR. The alignment between the lithological model and existing geological and hydrogeological investigations demonstrates that OIK and R2ML workflow effectively capture the subsurface architecture of the MRVA. The methods have broad applicability for characterizing alluvial aquifers through AEM-borehole data fusion, supporting sustainable groundwater management.

Mississippi↗
USGS2026

Distinguishing natural from mining-related metal sources by including streambank groundwater data in a stream mass loading study

Distinguishing stream metal loading caused by mine features from that caused by natural background sources remains challenging, yet this distinction is essential for making effective remedial decisions at many legacy mine sites. We combine a stream tracer injection and synoptic sampling study with data from shallow near-stream groundwater wells to estimate left-bank versus right-bank metal loading contributions at the 100-m spatial scale. The study was performed in the East Mancos River, a mountain headwater stream in Colorado, USA. The dominant source of elevated stream metal concentrations could be either groundwater infiltration through right-bank Doyle Mine waste piles or natural acid rock drainage from hydrothermally altered bedrock located mainly on the left bank. For the five metals of concern (Cu, Al, Zn, Cd, and Mn), we find that 15 % of the load contributed by diffuse groundwater inputs in the section potentially influenced by Doyle mine waste originates from the right bank. This right-bank potential mine contribution equates to only 3 % of the total watershed instream load for these metals. Furthermore, apparent 3 H/ 3 He groundwater ages in segments contributing most of the right-bank metal loading are sufficiently old (9–12 yr) to suggest that infiltration through the waste piles, located only 140–180 m from the stream, is unlikely. Estimated potential Doyle mine loading contributions can therefore be considered maximum values. Study results thus indicate that Doyle mine waste piles are a minor source of metal loading under low-flow conditions, and streambank groundwater data can provide valuable additional information in stream mass loading studies.

Colorado↗
USGS2025

A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed

Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water signal ratios as proxies for groundwater influence but were limited to specific sites and periods, without dynamic forecasting. This study is the first to forecast daily A r as a spatiotemporal signal using a Graph Convolutional Network–Long Short-Term Memory (GCN-LSTM) model. The model was trained using hydroclimate data (air temperature, precipitation, shortwave radiation, streamflow) and watershed physical features (e.g., sand content, slope). Results showed high predictive skill, achieving R 2 (NSE, RMSE) of 0.86 (0.73, 0.0004) for one-day-ahead to 0.52 (0.50, 0.0009) for seven-days ahead forecasts. Prior studies often have not explicitly incorporated spatial hydrogeologic drivers, but this model explicitly incorporates them to assess their impact on A r forecasting and stream-groundwater connectivity. Feature analysis identified mean sand, elevation, slope, clay, and TWI as key predictors of A r . Stronger groundwater signals appeared in hillslopes, elevations, and tributaries, highlighting watershed influence on streamflow. However, limitations include reliance on historical air–water temperature patterns for training and limited representation of extreme climate conditions. Despite these limitations, unlike previous studies relying on measured in-situ stream and air temperature, this study forecasts A r directly from climate and physiographic features after training, avoiding in-situ data requirements. Findings aiding predictions of stream ecosystem resilience.

New York↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗