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In this paper we present a hydro-chemical model that can be used to generate plausible karstic conduit networks that honor what is known about geology, hydrology and topography of a karst system. To make the model applicable to a range of natural karst systems, we introduce a flexible and physically realistic flow boundary condition along the land surface. Moreover, whereas comparable existing speleogenesis models use an explicit reactive-transport scheme, we propose an implicit reactive-transport scheme to permit a coarser spatial discretization of the conduit cells. An application to a real karst system illustrates that the model can generate a realistic karstic network that reproduces observed hydrologic behavior in terms of current spring flow rates, regional hydraulic head field as well as average groundwater residence times. Our model provides a useful tool to generate ensembles of possible karstic conduit networks that may be used within a stochastic framework to analyze flow and transport prediction uncertainty associated with a lack of knowledge about network geometry.

General circulation models (GCMs) have been widely used to simulate current and future climate at the global scale. However, the development of frameworks to apply GCMs to assess potential climate change impacts on regional hydrologic systems and compliance with water resource regulations is more recent. It is important to predict potential impacts of future climate change on streamflows and groundwater levels to reduce risks and increase resilience in water resources management and planning. This study evaluated future streamflows and groundwater levels in the Tampa Bay region in west-central Florida using an ensemble of different GCMs, reference evapotranspiration (ET0) methods, and water use scenarios to drive an integrated hydrologic model (IHM). Eight GCMs were bias-corrected and downscaled using the Bias Correction and Stochastic Analog (BCSA) downscaling method and then used, together with three ET0 methods, to drive the IHM for eight different human water use scenarios. Results showed that changes in projected streamflow were most sensitive to GCM selection, however, projections of groundwater level change were sensitive to both GCM and water use scenario. Projected changes in streamflow and groundwater level were relatively insensitive to the ET0 methods evaluated in this study. Six of eight GCMs projected a decrease in streamflow and groundwater level in the future regardless of water use scenario or ET method. These results indicate a high probability of a reduction in future water supply in the Tampa Bay region if environmental regulations intended to protect current aquatic ecosystems do not adapt to the changing climate.

Simultaneous effects of future climate and irrigation intensification on surface and groundwater systems are not well understood. Efforts are needed to understand the future groundwater availability and associated surface flows under business-as-usual management to formulate policy changes to improve water sustainability. We combine measurements with integrated modeling (MIKE SHE/MIKE11) to evaluate the effects of future climate (2040–2069), with and without irrigation expansion, on water levels and flows in an agricultural watershed in low-storage crystalline aquifer region of south India. Demand and supply management changes, including improved efficiency of irrigation water as well as energy uses, were evaluated. Increased future rainfall (7–43%, from 5 Global Climate Models) with no further expansion of irrigation wells increased the groundwater recharge (10–55%); however, most of the recharge moved out of watershed as increased baseflow (17–154%) with a small increase in net recharge (+0.2 mm/year). When increased rainfall was considered with projected increase in irrigation withdrawals, both hydrologic extremes of well drying and flooding were predicted. A 100-year flow event was predicted to be a 5-year event in the future. If irrigation expansion follows the historical trends, earlier and more frequent well drying, a source of farmers' distress in India, was predicted to worsen in the future despite the recharge gains from increased rainfall. Storage and use of excess flows, improved irrigation efficiency with flood to drip conversion in 25% of irrigated area, and reduced energy subsidy (free electricity for 3.5 h compared to 7 h/day; $1 billion savings) provided sufficient water savings to support future expansion in irrigated areas while mitigating well drying as well as flooding. Reductions in energy subsidy to fund the implementation of economically desirable (high benefit-cost ratio) demand (drip irrigation) and supply (water capture and storage) management was recommended to achieve a sustainable food-water-energy nexus in semi-arid regions.

Summary This study demonstrated the first application of a GSA technique to a transient ISSHM–LSM application developed for a large-scale river basin. The Morris method was used to identify the spatially and temporally variable sensitivity amongst a large number of model parameters to provide insights on hydrologic processes dominating behavior in the basin and to identify a small subset of parameters that should be evaluated in subsequent, more computationally intensive quantitative GSA and parameter estimation techniques. Results showed that in the upper region of the basin, evapotranspiration (ET), total streamflow and peak streamflow were less sensitive to surficial aquifer system characteristics, but highly sensitive to the hydraulic conductivity of the confining unit separating the surficial aquifer and the regional aquifer system and leaf area index of near stream vegetation. In the lower region of the basin, hydraulic conductivity of the regional aquifer system was found to have a significant effect on ET, total stream flow, and groundwater contributions to streamflow while surface–groundwater dynamics during storm events was most sensitive to storage properties of the regional aquifer system. Peak streamflow in the lower basin was most sensitive to the hydraulic conductivity of the confining unit in the upper basin, and the Manning’s coefficient of upper basin streams, indicating that all peak storm flows originate in the upper basin. Throughout the basin ET was sensitive to soil/geologic properties and vegetation properties, with unsaturated zone processes and relevant parameters gaining importance in moisture limited conditions existing in the lower regions of the basin.

Groundwater flow in karst includes exchange of water between large fractures, conduits, and the surrounding porous matrix, which impacts both water quality and quantity. Electrical resistivity tomography combined with end-member mixing analysis (EMMA) and numerical flow and transport modeling was used to study mixing of karst conduit and matrix waters to understand spatial and temporal patterns of mixing during high flow and base flow conditions. To our knowledge, this is the first time EMMA and synthetic geophysical simulations have been combined. Here we interpret an 8 week time-lapse electrical resistivity data set to assess groundwater-surface mixing. We simulate flow between the karst conduits and the porous matrix to determine fractions of water recharged to conduits that has mixed with groundwater stored in the pore space of the matrix using a flow and transport model in a synthetic time-lapse resistivity inversion. Comparing the field and synthetic inversions, our results enable us to estimate exchange dynamics, spatial mixing, and flow conditions. Results showed that mixing occurred at a volumetric flux of 56 m3/d with a dispersivity around 1.69 m during the geophysical experiment. For these conditions, it was determined that conduit water composition ranged from 75% groundwater during base flow conditions to less than 50% groundwater in high flow conditions. Though subject to some uncertainties, the time-lapse inversion process provides a means to predict changing hydrologic conditions, leading to mixing of surface water and ground water and thus changes to water quantity and quality, as well as potential for water-rock reactions, in a semiconfined, sink-rise system.

Pastures in beef cattle ranches are identified as one of the major land uses that contribute phosphorus (P) and nitrogen (N) loadings to Lake Okeechobee (LO), Florida. A study was conducted to quantify flow, P, and N contributions from groundwater to a 170 m ditch section within a beef cattle ranch located in the LO basin from June 2005 to May 2009. Groundwater depths and total N (TN) and total P (TP) concentrations measured in five wells, installed on each side (bank) of the ditch section, were combined with the ditch stage and N and P concentrations to quantify groundwater flow and nutrient loads. The Dupuit equation was used to estimate the groundwater flow to and from the ditch. The average net groundwater flow to the ditch section, estimated from a range (low and high) of saturated hydraulic conductivity (Ksat) values, ranged from 765 to 1,729 m3 per year. Annual groundwater flow contributions were positive (to the ditch) for all four years, while the groundwater nutrient contributions were positive for three years and negative (from the ditch) for one year (drought year). Average net annual TP loads to the ditch section ranged from 0.1 (low) to 0.2 (high) kg, while TN loads ranged from 2.1 (low) to 4.5 (high) kg. Extrapolating the results from this study to the LO basin indicated that groundwater was a major contributor of TP loads to the lake.

Flow of water through stream networks directly impacts flooding and transport of sediments and pollutants in watershed systems. Hence, knowledge of streamflow is critical for water management and mitigation of flooding and drought events. Unfortunately, spatially dense networks of in situ streamflow measurements are generally unavailable and would be prohibitively expensive to deploy and maintain. Thus, a data fusion framework is needed that utilizes available data to predict streamflow. Observed data in spatial (e.g., topography and land cover), temporal (e.g., streamflow and groundwater levels), and spatiotemporal domains (e.g., rainfall) impact streamflow. Some of these quantities can be obtained from remote sensing imagery; however, combining such disparate data types using traditional data fusion methods is problematic. Physically based hydrologic models have been used to predict streamflow but often with significant uncertainty because numerous assumptions are made for many unmeasured input and parameter values. Traditional Bayesian inference approaches suffer from superlinear increases in computational complexity as the number of data sets to be fused grows. In this paper, a scalable spatiotemporal approach based on Bayesian networks (BNs) is presented for estimating streamflow. An information-theoretic methodology based on conditional entropy is employed to quantify the impact of adding nodes in the BN in terms of information gained. The framework offers the flexibility of embedding knowledge from hydrologic models calibrated for the study area by introducing them as additional nodes in the network, thereby improving prediction accuracy. Posterior probabilities of estimates and the associated entropy provide valuable information on the quality of predictions and also offer directions for future watershed instrumentation.

Contamination of groundwater by nitrate is an important environmental concern in some parts of the citrus growing region in Florida. Although some of the test wells with high levels of nitrate in the surficial aquifer are in close proximity to citrus groves, there is no evidence to substantiate that the nitrate contamination of groundwater with the help of an alternate tracer i.e., “Bromide”;. A field experiment was conducted in two large blocks of a citrus grove of “Valencia”; orange trees [Citrus sinensis (L.) Obs.] on rough lemon rootstock [Citrus sinensis (L.)] planted in an Astatula fine sand (Hyperthermic, uncoated Typic Quartzipsamments) to determine the transport of Bromide (Br‐) in the vadose zone and its lateral movement in the surficial aquifer. Time required for Bromide (112 kg Br‐1 ha‐1 on treated area basis) applied on the soil surface to be detected in the surficial aquifer was evaluated by analysis of groundwater samples collected from monitoring wells (MWS) installed within the a...

A research project was conducted to evaluate the impact of alternative citrus nitrogen and water management practices on groundwater nitrate concentrations beneath the vulnerable sandy soils in the ridge citrus region of Central Florida. Fifteen months of baseline data indicated that groundwater nitrate-nitrogen concentrations were above the Environmental Protection Agency’s Maximum Contaminant Level (MCL) beneath mature groves on the Central Florida Ridge. Data from beneath a flatwoods grove off the ridge showed groundwater nitrate-nitrogen levels well below the MCL, and data from beneath a native vegetation site on the Central Florida ridge showed virtually no detectable nitratenitrogen in groundwater. After the baseline monitoring period the following site-specific best management practices (BMPs) were implemented: (1) application of a combination of slow release and dry soluble fertilizer at a rate of 180 kg N/ha/yr split into three applications; (2) application of 18 doses of liquid fertilizer at a rate of 180 kg N/ha/yr applied through a fertigation system; (3) application of 18 doses of liquid fertilizer at a rate of 168 kg N/ha/yr applied through a fertigation system; (4) application of a combination of 18 doses of liquid fertilizer at a rate of 78 N kg/ha/yr through a fertigation system and three applications of foliar spray fertilizer at a rate of 64 kg N/ha/yr (total 142 kg N/ha/yr); and (5) use of irrigation scheduling based on tensiometer measurements to minimize excess leaching. Analysis of 52 months of post-BMP monitoring data indicated that all of these BMPs produced statistically significant downward trends in nitratenitrogen concentration, and all have the potential to meet the EPA MCL for groundwater. The average downward trends ranged from –0.4 to –4.6 mg NO3-N/L-yr, and were greatest for the fertigation/foliar spray BMP, which represented the largest reduction in total N applied.

The difficult task of locating and quantifying nonaqueous phase liquids (NAPLs) present in the vadose and saturated zones has prompted the development of innovative, nondestructive characterization techniques. The use of the interwell partitioning tracer`s (IWPT) test, in which tracers that partition into the NAPL phase are displaced through the aquifer, is an attractive alternative to traditional coring and analysis. The first field test of IWPT was conducted in a hydraulically isolated test cell to quantify the total amount of a complex NAPL (a mixture of JP-4 jet fuel and chlorinated solvents) trapped within a 1.5-m smear zone in a shallow, unconfined sand and gravel aquifer at Hill Air Force Base (AFB), Utah. Tracer breakthrough curves (BTCs) were measured in three extraction wells (EWs) following a tracer pulse introduction through four injection wells (IWs). The measured retardation of the partitioning tracer (2,2-dimethyl-3-pentanol) relative to the nonreactive tracer (bromide) was used to quantify the NAPL present. The EW data were used to estimate an average NAPL saturation of 4.6--5.4% within the test cell. NAPL saturations estimated by using measured concentrations in soil cores of two significant compounds present in the NAPL were 3.0 and 4.6%.

[1] In the karstic upper Floridan aquifer, surface water flows into conduits of the groundwater system and may exchange with water in the aquifer matrix. This exchange has been hypothesized to occur based on differences in discharge at the Santa Fe River Sink-Rise system, north central Florida, but has yet to be visualized using any geophysical techniques. Using electrical resistivity tomography, we conducted a time-lapse study at two locations with mapped conduits connecting the Santa Fe River Sink to the Santa Fe River Rise to study changes of electrical conductivity during times of varying discharge over a six-week period. Our results show conductivity differences between matrix, conduit changes in resistivity occurring through time at the locations of mapped karst conduits, and changes in electrical conductivity during rainfall infiltration. These observations provide insight into time scales and matrix conduit conductivity differences, illustrating how surface water flow recharged to conduits may flow in a groundwater system in a karst aquifer.

It is reported that phosphorus level showed increasing trends in groundwater, river, spring water, and private drinking water wells in the Suwannee River Basin of Florida. Quantification and characterization of nutrients in groundwater at a dairy farm are necessary steps to provide fundamental data and knowledge for nutrient management. This paper was to investigate long-term temporal and spatial distribution of soluble reactive phosphorus (SRP) in groundwater at a dairy farm under actual production conditions. Twenty one monitoring wells were installed at different dairy farm components for monthly groundwater sampling from 2000 to 2007. Rotational grazing was conducted since 2004. The results indicated that large variability existed in temporal and spatial distribution of SRP in groundwater at different land uses. Yearly average SRP concentrations in the groundwater were high to 8 mg/L and 13 mg/L near lagoon and at drainage area, respectively. In contrast, average SRP concentrations in the whole research period were less than 1 mg/L in the groundwater at intensive area, pasture, and sprayfield areas of the dairy farm. In addition, data indicated that possibly, there was direct seepage of SRP from the unlined lagoon of dairy wastes to the groundwater. Therefore, it may be necessary to set up a lined lagoon to reduce potential SRP contamination to groundwater at a dairy farm on well drained sandy soils. Also, runoff should be collected in the lagoon rather than flowing into nearby surface water or infiltrating into the soil profile and leaching down to the underneath groundwater.

A Kalman filtering algorithm is developed to forecast groundwater levels in the Upper Floridan aquifer throughout the St. Johns River Water Management District (SJRWMD) in Florida. The algorithm processes historic and currently available head measurements to make optimal predictions of future head levels over a grid of 554 wells spanning the SJRWMD. Measurements are obtained monthly from a subset of 20 wells and semiannually from the remaining wells. The Kalman filter incorporates an empirical spatiotemporal model of regional groundwater fluctuations derived from long-term historical data records at the 20 monthly measured wells. The algorithm (1) extrapolates the measurements provided by the 20 monthly measured wells to estimate monthly head levels at all 554 wells in the grid and (2) predicts future head levels at each well in the absence of measurements. The performance of the Kalman filtering algorithm is assessed by examining its ability to forecast piezometric head behavior at the 534 well locations where historic data were not used to estimate either the system model or the spatiotemporal correlation structure of the model residuals.

Seasonal autoregressive integrated moving average (SARIMA) univariate models and single input-single output transfer function (SARIMA with externalities or SARIMAX) models of groundwater head fluctuations are developed for 21 Upper Floridan aquifer observation wells in northeast Florida. These models incorporate empirical relationships between rainfall input and head response based on historical correlations and cross correlations between these two time series. The magnitude of the forecast error terms indicates that the SARIMA and SARIMAX models explain an average of 84–87% of the variation observed in the monthly piezometric head levels for 1-month lead forecasts. Thus the models account for the dominant processes which affect temporal groundwater fluctuations. Both the SARIMA and SARIMAX models provide unbiased forecasts of piezometric head levels; however, the SARIMAX models produce more accurate forecasts (i.e., smaller forecast probability limits) than the SARIMA models, particularly as lead time increases. Modeling efforts reveal consistent model structures over the study region, with local hydrologic and geologic conditions causing site-specific variability in the time series model parameters.

The ultimate goal of this research was to develop Best Management Practices (BMP) to reduce nutrient loadings to ground water from vegetable farms in the Suwannee River Basin. To achieve this objective nitrogen and water movement through the vadose zone was monitored and modeled under one 56.7 ha center pivot at a 2020 ha vegetable farm just west of O’Brien, FL. This farm lies just a few miles from the Suwannee River in the upstream direction of groundwater flow. The sandy soils on this farm are extremely susceptible to leaching nitrogen through the soil and then into the Floridan Aquifer. Monitoring wells, soil samples, and plant samples were used to obtain onsite nitrogen, plant and soil moisture information over time to track nitrogen movement and calibrate mathematical models. Soil analysis, crop management and weather information provided inputs necessary for existing vadose zone and crop models. The DSSAT35 crop model and the HYDRUS2D vadose zone model were used to produce an estimate of the load and concentration of nitrogen and the quantity of water leaching through the vadose zone to the Floridan Aquifer. The DSSAT model was also used to predict crop growth, nitrogen uptake and crop yield. The DSSAT model was calibrated using the spring 2002 potato crop. The results from DSSAT were used to predict nitrogen leaching and crop yield to aid in the development of the BMP. Results from the 2002 calibration illustrate the importance of incorporating multi-dimensional water and nutrient transport and the need to replace or improve upon the current methods implemented by the DSSAT plant model.

This research project investigated the coupled effects of uncertain contaminant source morphology (organic immiscible liquids distribution and composition) and aquifer properties on predicting solute transport in saturated groundwater systems contaminated with residual Organic Immiscible Liquids (OIL's). Research efforts focused on the development of multiple stochastic inverse models for characterizing contaminant fluxes in the near field and for characterizing NAPL distributions within source. In addition, work was conducted on the forward stochastic modeling, that is, to evaluate individual and collective impacts of uncertain spatially and temporally variable source morphological characteristics on subsurface solute transport predictions. The study also involved 1-, 2-, and 3-dimensional experimental efforts to study bench-scale and intermediate-scale uncertainties associate with OIL sources. These studies focused on describing dissolution from multi-component OIL's at varying residual fluid saturations and distributions, and the characterization of OIL sources using partitioning and interfacial tracers. As a final component of the project, the various stochastic models developed were evaluated using data from the pilot-scale field experiments conducted at Hill AFB, Columbus AFB, and Base Borden, Ontario, Canada. Data derived from partitioning/interfacial tracers and far-field dissolved OIL component distributions were used with the stochastic models to characterize OIL sources at each site.