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APEX (A gricultural P olicy/ E nvironmental e X tender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled internally via subroutines. The APEX-MODFLOW model is applied to three watersheds in the United States for testing code accuracy and hydrologic state variables and fluxes: the Animas River Watershed, Colorado and New Mexico (3543 km2); the Price River Watershed, Utah (4886 km2); and the Middle Bosque River Watershed, Texas (470 km2). Whereas the hydrology of the Animas River and Price River watersheds is driven by snowmelt and spring runoff, the hydrology of the Middle Bosque River Watershed is driven by summer thunderstorms. The model can be used for scenario analysis in groundwater-driven watersheds. • New integrated hydrologic model links APEX model to MODFLOW model. • Model passes recharge, water table elevation, and gw-sw flux rates internally. • APEX-MODFLOW applied to three watersheds in Utah, Colorado, and Texas, USA. • Model results show importance of including physically based groundwater flow. [ABSTRACT FROM AUTHOR]

An integrated space–time artificial neural network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Model-independent entropy measures and random forest (RF)-based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, five-fold cross-validation, and an adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30 691 km 2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 38 independent wells during the 1956–2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009–2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provide groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process. Incorporation of spatial variability was more critical than capturing temporal persistence. The use of the standardized precipitation–evapotranspiration index (SPEI) as a surrogate for pumping was generally adequate but was unable to capture the heterogeneous groundwater extraction preferences of farmers under extreme climate conditions. [ABSTRACT FROM AUTHOR]

This paper presents a version of SWAT + that uses MODFLOW to simulate groundwater flow and groundwater-surface interactions within a watershed system. The modeling code is applied to the 470 km2 Middle Bosque River Watershed (Texas, USA) to demonstrate accuracy and differences with SWAT+. The model is tested against field-measured stream discharge, groundwater levels and fluctuation patterns, runoff, baseflow, recharge, watershed boundary groundwater flow, groundwater-surface water interactions, and evapotranspiration. The model captures the dominant baseflow patterns throughout the watershed and the key patterns of water table dynamics and relation to nearby streams. Whereas annual rates of runoff, lateral flow, and annual percolation are unchanged from a stand-alone SWAT + simulation, annual groundwater discharge rates increase by 50% (37 mm–54 mm). In particular, temporal patterns of groundwater discharge are governed by the physically-based rise and fall of groundwater near streams, rather than storm-induced recharge pulses as in the SWAT + simulation. • We present a revised version of SWAT + that includes MODFLOW routines. • SWAT+(MODFLOW) simulates spatially-distributed groundwater flow. • SWAT+(MODFLOW) is tested in a 470 km2 watershed in central Texas, USA. [ABSTRACT FROM AUTHOR]

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Hydraulic gradient is a fundamental aquifer characteristic required to estimate groundwater flow and quantify advective fluxes of pollutants. Graphical and local estimation schemes using potentiometric head information from three or four wells are used to compute hydraulic gradients but suffer from imprecision and subjectivity. The use of linear regression is recommended when hydraulic head data from a groundwater monitoring network consisting of several wells are available. In such cases, statistical influence analysis can be carried out to evaluate how each well within the network impacts the gradient estimate. A suite of five metrics, namely-(1) the hat-values, (2) studentized residuals, (3) Cook's distance, (4) DFBETAs and (5) Covariance ratio (COVRATIO) are used in this study to identify influential wells within a regional groundwater monitoring network in Kleberg County, TX. The hat-values indicated that the groundwater network was reasonably well balanced and no well exerted an undue influence on the regression. The studentized residuals and Cook's distance indicated the wells with the highest influence on the regression predictions were those that were close to high groundwater production centers or affected by coastal-aquifer interactions. However, the wells in the proximity of the production centers had the highest impact on the estimated gradient values as ascertained using DFBETAs. The covariance ratio which indicates the sensitivity of a monitoring well on the estimated standard error of regression was noted to be significant at most wells within the network. Therefore, networks seeking to address changes in groundwater gradients due to climate and anthropogenic influences need to be denser than those used to ascertain synoptic gradient estimates alone. The magnitude of the groundwater velocity was greatly underestimated when the influential wells were excluded from the network. The direction of flow was affected to a lesser extent, but a complete gradient reversal was noted when the network consisted of only four peripheral wells. The influence analysis therefore provides a valuable tool to assess the importance of individual wells within a monitoring network and can therefore be useful when existing networks are to be pruned due to fiscal constraints. [ABSTRACT FROM AUTHOR]

This study used optical brighteners (OB) released from septic systems to show that groundwater flow direction is largely controlled by the structural framework in a faulted karst groundwater system. Effective protection of groundwater resources requires that groundwater systems are adequately characterized and source water protection areas (SWPA) are developed for drinking water wells. Karst aquifers are among the most sensitive to contamination due to high recharge rates, and among the most difficult aquifers to characterize due to heterogeneity, and anisotropy. Because septic systems may be used to treat wastewater within SWPAs for karst aquifers there is a need to characterize these groundwater systems using tracers. The objective of this study was to characterize groundwater flow in a faulted portion of the Edwards aquifer in Bexar County, Texas using OB that are released as incidental tracers from septic systems. This study included measurement of water levels, sampling of groundwater and surface water, analysis for OB, and spatial analysis in a GIS. Results show that OB intensities were highest to the southwest of the septic area, a direction that is sub-parallel to the fault and fracture orientation and nearly perpendicular to the hydraulic gradient. This indicates that movement of OB, solutes, or non-aqueous liquids/solids in a faulted karst system can be largely controlled by fault/fracture orientation and structural relay ramps. [ABSTRACT FROM AUTHOR]

Growth faults are widely distributed in the Greater Houston (GH) region of Texas, USA, and the existence of faulting could interrupt groundwater flow and aggravate local deformation. Faulting-induced property damages have become more pronounced over the last few years, necessitating further investigation of these faults. Interferometric synthetic aperture radar (InSAR) has been proved to be an effective way for mapping deformations along and/or across fault traces. However, extracting short-wavelength small-amplitude creep signal (about 10–20 mm/yr) from long time span interferograms is extremely difficult, especially in agricultural or vegetated areas. This study aims to position, map and monitor the rate, extent, and temporal evolution of faulting over GH at the highest spatial density using Multi-temporal InSAR (MTI) technique. The MTI method, which maximizes usable signal and correlation, has the ability to identify and monitor faulting and provide accurate and detailed depiction of active faults. Two neighboring L-band Advanced Land Observing (ALOS) tracks (2007–2011) are utilized in this research. Numerous areas of sharp phase discontinuities have been discerned from MTI-derived velocity map. InSAR measurements allow us to position both previously known faults traces as well as nucleation of new fractures not previously revealed by other ground/space techniques. Faulting damages and surface scarps were evident at most InSAR-mapped fault locations through our site investigations. The newly discovered fault activation appears to be related to excessive groundwater exploitation from the Jasper aquifer in Montgomery County. The continuous mining of groundwater from the Jasper aquifer formed new water-level decline cones over Montgomery County, corroborating the intensity of new fractures. Finally, we elaborate the localized fault activities and evaluate the characteristics of faulting (locking depth and slip rate) through modeling MTI-derived deformation maps. The SW–NE-oriented faults pertain to normal faulting with an average slip rate of 7–13 mm/yr at a shallow locking depth of less than 4 km. Identifying and characterizing active faults through MTI and deformation modeling can provide insights into faulting, its causal mechanism and potential damages to infrastructure over the GH. [ABSTRACT FROM AUTHOR]