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The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT Key Points: Artificial neural networks can accurately forecast semiseasonal groundwater level changesMuMoC improved the groundwater well‐level forecasting skill for 1‐ to 4‐month lead times with respect to a single ANN model by 25% in NSEThe implementation of MuMoC is recommended in case of densely gauged areas [ABSTRACT FROM AUTHOR]

The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscapeMultiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United StatesMultiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping [ABSTRACT FROM AUTHOR]

Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low‐flow prediction compared to snow metrics at a rain‐dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep‐storage‐dominated systems. Overall, including RLGW improved low‐flow prediction across diverse watersheds. Plain Language Summary: Water managers across the western United States depend on accurate streamflow prediction models for water planning and allocation during summer months. Historically, these models use snow metrics to predict summer flows, but increasing temperatures across the western US are decreasing snow input and accumulation and increasing early snow melt rates leading to changes in streamflow generation mechanisms. Here, we seek to understand how stream flows will respond under warmer climates in three watersheds with distinct climate and underlying bedrock in the western US. We expanded upon commonly used low‐flow model snow metrics to include snow and streamflow metrics from the previous year as well as groundwater dynamics during the annual recession curve. We found that including variables outside of commonly used snow parameters in the models produced more accurate predictions of summer low flows. We provide a framework for future analysis of streamflow response to warming across the western US. Key Points: Recession limb groundwater proportion is a strong predictor of summer low flowsRecession limb dynamics are especially important for improving low‐flow prediction at rain‐dominated sitesRecession limb groundwater may be a better predictor of low flow at sites where dynamic storage exceeds deep storage [ABSTRACT FROM AUTHOR]

Specific yield (Sy) is one of the most important aquifer parameters in groundwater models with large storage changes. However, it has received limited attention from the modeling community and there are few, if any, approaches that can be widely applied for determination of Sy at the scale required for regional aquifer studies. Despite the limitations of parameter estimation methods and of direct measurements of aquifer storage properties in the field, the large uncertainty in model Sy values is typically ignored and Sy is incorrectly assumed to be a known quantity. These practices can introduce errors into model predictions of aquifer budget components, such as recharge and storage depletion. In turn, aquifer management and planning can be greatly impacted, as estimates of aquifer responses to pumping changes are highly dependent on those simulated budget components. In this work, we use a groundwater model of a portion of the High Plains aquifer in the central United States to illustrate the impacts of inappropriately large Sy values on model predictions and the related water management ramifications. Building on our recent work, we propose a data‐driven approach for determining specific yield for regional groundwater models. Results demonstrate that this new approach can improve model reliability and lead to more robust predictions of aquifer responses to management scenarios. Key Points: Specific yield values are uncertain in many models of unconsolidated aquifersModels with inappropriate specific yield values can produce unreliable aquifer budgets and responses under future management scenariosA new approach is proposed for estimating the spatial distribution of specific yield for regional groundwater models [ABSTRACT FROM AUTHOR]

Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as <65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated United States is about 6,000 km3, or about 0.5% of globally stored young groundwater. Plain Language Summary: Groundwater is an important source of water for people and ecosystems. The quality of groundwater depends partly on how long it has resided underground, but this is difficult to know. There are several methods to determine groundwater residence time, but they mostly do not apply over large regional aquifers. In this study, we used a novel method coupling statistical learning and numerical modeling to calculate groundwater residence time throughout the glaciated areas of the United States. The results reveal that most of the groundwater in the area is less than 65 years old and usually travels distances ranging from less than 1 km to about 30 km before discharging to streams, rivers, and lakes. Key Points: A novel method coupling statistical learning and numerical models is presentedThree‐dimensional continuous digital data sets of groundwater age metrics are presentedTwo novel data sets, Multi‐Order Hydrologic Position and hydrogeologic terrane, contain information about groundwater residence time [ABSTRACT FROM AUTHOR]

Key Points: Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutionsVarious multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawalsThe results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary: Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water‐food‐energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues. [ABSTRACT FROM AUTHOR]

Groundwater discharge zones connect aquifers to surface water, generating baseflow and serving as ecosystem control points across aquatic ecosystems. The influence of groundwater discharge on surface flow connectivity, fate and transport of contaminants and nutrients, and thermal habitat depends strongly on hydrologic characteristics such as the spatial distribution, age, and depth of source groundwater flow paths. Groundwater models have the potential to predict spatial discharge characteristics within river networks, but models are often not evaluated against these critical characteristics and model equifinality with respect to discharge processes is a known challenge. We quantify discharge characteristics across a suite of groundwater models with commonly used frameworks and calibration data. We developed a base model (MODFLOW‐NWT) for a 1,570‐km2 watershed in the northeastern United States and varied the calibration data, control of river‐aquifer exchange directionality, and resolution. Most models (n = 11 of 12) fit similarly to calibration metrics, but patterns in discharge location, flow path depth, and subsurface travel time varied substantially. We found (1) a 15% difference in the percent of discharge going to first‐order streams, (2) threefold variations in flow path depth, and (3) sevenfold variations in the subsurface travel times among the models. We recalibrated three models using a synthetic discharge location data set. Calibration with discharge location data reduced differences in simulated discharge characteristics, suggesting an approach to improved equifinality based on widespread field‐based mapping of discharge zones. Our work quantifying variation across common modeling approaches is an important step toward characterizing and improving predictions of groundwater discharge characteristics. Key Points: Spatial patterns of simulated groundwater discharge dynamics along river networks are largely unevaluated in most groundwater flow modelsDischarge zone source flow path depth, travel time, and network position varied greatly among models with similar calibration metricsEquifinality was reduced when knowledge of groundwater discharge patterns was included in model calibration [ABSTRACT FROM AUTHOR]

While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental‐scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base‐flow discharge, but is not well‐characterized at large regional to continental scales. We used a process‐based inversion of CONUS‐extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base‐flow, and (d) long‐term average water content in the unsaturated zone. CONUS‐extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long‐term average transmissivity for each subdomain model calibrated against water‐levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first‐order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long‐term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways. Plain Language Summary: We developed a simulation of the shallow groundwater system for the contiguous United States (CONUS) and compared it to large data sets of water table measurements in order to generate a map of surficial transmissivity, which is a property that controls the flow of water between its entrance into the groundwater system and its discharge to streams and rivers. Better maps of this property are one ingredient to improved understanding of how the landscape stores and releases water at large spatial scales. Key Points: Calibrated simulations of the shallow groundwater system were used to estimate the CONUS‐extent spatial distribution of transmissivityCorrelations were explored between the estimated transmissivities and recharge, elevation, and topographic slopeSimulated depth to water and groundwater discharge were used to derive unsaturated zone properties and long‐term average base‐flow on the NHDPlusV2 stream network [ABSTRACT FROM AUTHOR]

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible. Key Points: Remote sensing and geological data sets are integrated to estimate land subsidence due to groundwater extractionThe estimates are integrated using a machine learning method called random forestsUsing this method we estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to land subsidence in the western United States [ABSTRACT FROM AUTHOR]

Incentive‐based policies, such as the cap‐and‐trade system, have been shown to be useful in the context of groundwater management. This study compares the performance of a groundwater market with water quotas when assumptions of perfect information are violated due to climate change and hydrogeologic heterogeneity and explores how changes in future climate affect market performance. A subbasin of the Republican River Basin, overlying the Ogallala aquifer in the High Plains of the United States, is used as a case study. Building on a previously developed model, a multiagent system model simulating a groundwater market is developed where self‐interested agents can trade water use permits to maximize individual benefits subject to irrigation and land constraints. This economic model is coupled with a calibrated physically based groundwater model for the study region that allows for an evaluation of streamflow depletion impacts, which has been the focus of management efforts in the basin. Results show that trading of permits between farmers results in increased economic benefits and, in some cases, reduced environmental violations. However, the benefits of a groundwater market are distributed unequally resulting in "winners" and "losers" across the system. Future changes in climate are shown to significantly influence farmers willingness to pay for groundwater and thus increase the variation in groundwater price and pumping. These findings emphasize the importance of addressing hydroclimatologic variability and change in the design of groundwater markets. Key Points: Heterogeneous economic and environmental benefits of groundwater trading are quantified and spatially illustratedClimate change increases the price elasticity of groundwater and leads to increased variation in groundwater price and pumpingGroundwater overdraft is sensitive to changes in both precipitation and temperature, with a higher sensitivity to changes in precipitation [ABSTRACT FROM AUTHOR]

We examine the relative contributions of root zone soil moisture (SM) and groundwater (GW) storage to trends and interannual variability in total water storage (TWS) from essentially the entire Gravity Recovery and Climate Experiment (GRACE) record (2003–2016). Our study region is the Eastern United States where GW generally is shallow, and we use observations and simulations from a suite of land surface models (LSMs) where observations are not available. We first consider Illinois, which has a long‐term network of SM sensors and observation wells. We assessed the uncertainties in GRACE TWS in comparison with the observation‐based TWS using observed SM, well‐derived GW, and model‐reconstructed snow water equivalent. We evaluated LSM SM compared with observations over Illinois; generally good agreement encouraged us to use the LSM‐ensemble average SM, well‐derived GW, and Snow Data Assimilation System snow water equivalent over the entire Eastern United States to examine the relative contribution of storage components to GRACE TWS over this larger domain. Taken together, the three components explained 2%–85% of interannual variability in GRACE TWS across the 2‐digit Hydrologic Unit Code regions. Root zone SM was the dominant contributor to the interannual variability (but not trends) of GRACE TWS over much of the Eastern United States. We also compared three independent approaches to estimating GW: well data, baseflow observations, and GRACE. Despite much smaller magnitudes of variation, baseflow‐derived GW correlated more highly with well‐derived GW over the eastern‐most and Upper Mississippi regions, while GRACE‐derived GW matched better with well‐derived GW over the remaining regions. Key Points: Over the Eastern United States, soil moisture accounts for most of the interannual variability (but not trends) of terrestrial water storageModels that do not represent groundwater underestimate interannual variations in terrestrial water storage by about one third [ABSTRACT FROM AUTHOR]

The Southeastern United States experiences recurring hydrological droughts, which can reduce water availability and result in water deficits. Long-term monitoring data from Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, GA, was used to quantify interactions of climatic variability with shallow and deep storage and evapotranspiration. Watershed storage (WS) and actual evapotranspiration (AET) were estimated monthly from 1985 through 2015 using a relationship with baseflow and a water-budget approach, respectively. A watershed shallow storage (SS) water budget, based on soil moisture profile data, indicated a transition from recharge to surplus as dormant season SS increased to field capacity, and a transition from utilization of stored water to water deficits as growing season SS declined toward its wilting point. Deeper storage appeared unavailable to AET during dry conditions. The majority of deeper storage recharge occurred during the dormant season and required SS to be near field capacity. WS was an effective drought indicator. Growing season droughts typically occurred when WS was below normal at the end of the dormant season and growing season precipitation (P) was below or near normal. A hydrologic persistence analysis showed that monthly month-standardized WS was significantly correlated (p-value<0.05) with past monthly month-standardized WS for the previous 19 months and with past monthly P for the previous 11 months, indicating the importance of past hydrologic conditions on WS. These findings allowed assessment of expected climatic changes on the water cycle, which included effects on future recharge, water deficits, and drought occurrence. [ABSTRACT FROM AUTHOR]

The landscape of the coastal plain of the southeastern United States is rapidly changing due to urbanization and climate-change-related impacts. In addition to the forecasted population increase, this region could experience significant changes in precipitation patterns making watershed management very challenging. In order to establish baseline data, storm-event flow pathways were studied in three lower coastal plain (LCP) forested lowland watersheds of the southeastern United States between 2010 and 2011. Two of the watersheds had clay loam subsoils while the third had sandy soils throughout the profile. Stream flow and water samples from water-table wells, piezometers, lysimeters, and rain gauges were analyzed for ion concentrations; ion trends were assessed using principal components analysis; and chemical hydrograph separation was performed for nine storm events using end-member mixing analysis. End-members consisted of lower concentration rainwater; a near-stream source (riparian or streambed groundwater); and a distant or deep groundwater source. Storm-event stream water on the clayey sites was composed primarily of rainwater (45-67% by volume) and shallow groundwater (21-55%), with small inputs from deep (below the clay-rich soil horizon) groundwater (0.2-21%). At the sandy site, a greater proportion was groundwater (56-61%), with smaller inputs from rainwater (28-33%) and soil water (6-16%). Dry antecedent soil moisture (ASM) conditions and larger storms resulted in greater rainwater contribution at the clayey sites. Shallow groundwater was an important contributor even in dry ASM conditions, perhaps due to the high specific retention of the soils. The results from this study will inform researchers about stormwater routing in forested, shallow water table watersheds and provide land managers with baseline data as they plan stormwater mitigation practices. [ABSTRACT FROM AUTHOR]

In the western U.S., the mismatch between public water demands and natural water availability necessitates large interbasin transfers of water as well as groundwater mining of fossil aquifers. Here we identify probable situations of nonlocal water use in both space and time based on isotopic comparisons between tap waters and potential water resources within hydrologic basins. Our approach, which considers evaporative enrichment of heavy isotopes during storage and distribution, is used to determine the likelihood of local origin for 612 tap water samples collected from across the western U.S. We find that 64% of samples are isotopically distinct from precipitation falling within the local hydrologic basin, a proxy for groundwater with modern recharge, and 31% of samples are isotopically distinct from estimated surface water found within the local basin. Those samples inconsistent with local water sources, which we suggest are likely derived from water imported from other basins or extracted from fossil aquifers, are primarily clustered in southern California, the San Francisco Bay area, and central Arizona. Our isotope-based estimates of nonlocal water use are correlated with both hydrogeomorphic and socioeconomic properties of basins, suggesting that these factors exert a predictable influence on the likelihood that nonlocal waters are used to supply tap water. We use these basin properties to develop a regional model of nonlocal water resource use that predicts ( r2 = 0.64) isotopically inferred patterns and allows assessment of total interbasin transfer and/or fossil aquifer extraction volumes across the western U.S. [ABSTRACT FROM AUTHOR]

Recent U.S. Supreme Court rulings have limited federal protections for geographically isolated wetlands (GIWs) except where a 'significant nexus' to a navigable water body is demonstrated. Geographic isolation does not imply GIWs are hydrologically disconnected; indeed, wetland-groundwater interactions may yield important controls on regional hydrology. Differences in specific yield (Sy) between uplands and inundated GIWs drive differences in water level responses to precipitation and evapotranspiration, leading to frequent reversals in hydraulic gradients that cause GIWs to act as both groundwater sinks and sources. These reversals are predicted to buffer surficial aquifer dynamics and thus base flow delivery, a process we refer to as landscape hydrologic capacitance. To test this hypothesis, we connected models of soil moisture, upland water table, and wetland stage to simulate hydrology of a low-relief landscape with GIWs, and explored the influences of total wetland area, individual wetland size, climate, and soil texture on water table and base flow variation. Increasing total wetland area and decreasing individual wetland size substantially decreased water table and base flow variation (e.g., reducing base flow standard deviation by as much as 50%). GIWs also decreased the frequency of extremely high and low water tables and base flow deliveries. For the same total wetland area, landscapes with fewer (i.e., larger) wetlands exhibited markedly lower hydrologic capacitance than those with more (i.e., smaller) wetlands, highlighting the importance of small GIWs to regional hydrology. Our results suggest that GIWs buffer dynamics of the surficial aquifer and stream base flow, providing an indirect but significant nexus to the regional hydrologic system. [ABSTRACT FROM AUTHOR]