Estimating Water Budgets and Vertical Leakages for Karst Lakes in North‐Central Florida (United States) Via Hydrological Modeling1

Lin, Zhulu, 2011. Estimating Water Budgets and Vertical Leakages for Karst Lakes in North‐Central Florida (United States) Via Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐16. DOI: 10.1111/j.1752‐1688.2010.00513.x Abstract: Newnans, Lochloosa, and Orange Lakes are closely hydrologically connected karst lakes located in north‐central Florida, United States. The complex karst hydrology in this region poses a great challenge to the hydrological modeling that is essential to the development of Total Maximum Daily Loads for these lakes. We used a Hydrological Simulation Program – Fortran model coupled with the parallel Parameter ESTimation model calibration and uncertainty analysis software to estimate effectively the hydrological interactions between the lakes and the underlying upper Floridan aquifer and the water budgets for these three lakes. The net results of the lake‐groundwater interactions in Newnans and Orange Lakes are that both lakes recharge the underlying upper Floridan aquifer, with the recharge rate of the latter one magnitude greater than that of the former. However, for Lochloosa Lake, the net lake‐groundwater interaction is that the lake gains water from groundwater in a significant amount, approximately 40% of its total terrestrial water input. The annual average vertical leakages estimated for Newnans, Lochloosa, and Orange Lakes are 6.0 × 10⁶, ?8.9 × 10⁶, and 44.4 × 10⁶ m³, respectively. The average vertical hydraulic conductance (K_v/b) of the units between a lake bottom and the underlying upper Floridan aquifer in this region are also estimated to be from 1.26 × 10^?4 to 1.01 × 10^?3 day^?1.     

Forecasting the Probability of Future Groundwater Levels Declining Below Specified Low Thresholds in the Conterminous U.S.

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater‐level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness‐of‐fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month‐to‐month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low‐threshold events. We identified challenges in deriving probabilistic‐forecasting models and possible approaches for addressing those challenges.     

Model Development and Calibration of a Saltwater Intrusion Model in Southern California1

Abstract: A nine‐layered confined‐unconfined flow and transport model is developed for the Alamitos saltwater intrusion barrier in Southern California. The conceptual model is based on the geological structure of the coastal aquifer system. The key parameters in the flow and transport models are calibrated using a two‐phase procedure which matches the types of data available for calibration. Because of the abundance of point measurements of hydraulic conductivity, the heterogeneous and random hydraulic conductivity field for each of the five aquifers is estimated by the geostatiscal method of natural‐neighbor‐kriging in Phase 1. In Phase 2, the longitudinal and transverse dispersivities in the transport model are estimated by a traditional inverse procedure that minimizes the least‐squares error for concentration (LSE‐CON). The minimum LSE‐CON is achieved near 15.2 and 1.52 m for the longitudinal and transverse dispersivities, respectively. Additional simulations with increasing transport parameter complexity did not yield significant improvements in LSE‐CON. Also, tracking least‐squares error for head while parametrically varying the transport parameters revealed there is a negligible interaction between predicted head and transport parameters.     

Assessment of Groundwater Depletion and Implications for Management in the Denver Basin Aquifer System

The Denver Basin Aquifer System (DBAS) is a critical groundwater resource along the Colorado Front Range. Groundwater depletion has been documented over the past few decades due to the increased water use among users, presenting long‐term sustainability challenges. A spatiotemporal geostatistical analysis is used to estimate potentiometric surfaces and evaluate groundwater storage changes between 1990 and 2016 in each of the four DBAS aquifers. Several key depletion patterns and spatial water‐level changes emerge in this work. Hydraulic head changes are the largest in the west‐central side of the DBAS and have decreased in some areas by up to 180 m since 1990, while areas to the northwest show increases in hydraulic head by over 30.5 m. The Denver and Arapahoe aquifers show the largest groundwater storage losses, with the highest rates occurring in the 2000s. The results highlight uncertainty in the volumetric predictions under various storage coefficient calculations and emphasize the importance of representative aquifer characterization. The observed groundwater storage depletions are due to a combination of factors, which include population growth increasing the demand for water, variable precipitation, and drought influencing recharge, and increased groundwater pumping. The methods applied in this study are transferable to other groundwater systems and provide a framework that can help assess groundwater depletion and inform management decisions at other locations.     

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed1

Herr, Joel W., Krish Vijayaraghavan, and Eladio Knipping, 2010. Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed. Journal of the American Water Resources Association (JAWRA) 46(6):1255–1263. DOI: 10.1111/j.1752-1688.2010.00489.x Abstract: Accurate simulation of time-varying flow in a river system depends on the quality of meteorology inputs. The density of meteorology measurement stations can be insufficient to capture spatial heterogeneity of precipitation, especially in mountainous areas. The Watershed Analysis Risk Management Framework (WARMF) model was applied to the Catawba River watershed of North and South Carolina to simulate flow and water quality in rivers and a series of 11 reservoirs. WARMF was linked with the AMSTERDAM air model to analyze the water quality benefit from reduced atmospheric emissions. The linkage requires accurate simulation of meteorology for all seasons and for all types of precipitation events. WARMF was driven by the mesoscale meteorology model MM5 processed by the Meteorology Chemistry Interface Processor, which provides greater spatial density but less accuracy than meteorology stations. WARMF was also run with measured data from the National Climatic Data Center (NCDC) to compare the performance of the watershed model using measured data vs. modeled meteorology as input. A one year simulation using MM5 modeled meteorology performed better overall than the simulation using NCDC data for the volumetric water balance measure used for calibration, but MM5 represented precipitation from a dissipated hurricane poorly, which propagated into errors of simulated flow.     

Groundwater Level Trends and Drivers in Two Northern New England Glacial Aquifers

We evaluated long‐term trends and predictors of groundwater levels by month from two well‐studied northern New England forested headwater glacial aquifers: Sleepers River, Vermont, 44 wells, 1992‐2013; and Hubbard Brook, New Hampshire, 15 wells, 1979‐2004. Based on Kendall Tau tests with Sen slope determination, a surprising number of well‐month combinations had negative trends (decreasing water levels) over the respective periods. Sleepers River had slightly more positive than negative trends overall, but among the significant trends (p < 0.1), negative trends dominated 67 to 40. At Hubbard Brook, negative trends outnumbered positive trends by a nearly 2:1 margin and all seven of the significant trends were negative. The negative trends occurred despite generally increasing trends in monthly and annual precipitation. This counterintuitive pattern may be a result of increased precipitation intensity causing higher runoff at the expense of recharge, such that evapotranspiration demand draws down groundwater storage. We evaluated predictors of month‐end water levels by multiple regression of 18 variables related to climate, streamflow, snowpack, and prior month water level. Monthly flow and prior month water level were the two strongest predictors for most months at both sites. The predictive power and ready availability of streamflow data can be exploited as a proxy to extend limited groundwater level records over longer time periods.     

Modeling Long‐Term Trends of Chlorinated Ethene Contamination at a Public Supply Well

A mass‐balance solute‐transport modeling approach was used to investigate the effects of dense nonaqueous phase liquid (DNAPL) volume, composition, and generation of daughter products on simulated and measured long‐term trends of chlorinated ethene (CE) concentrations at a public supply well. The model was built by telescoping a calibrated regional three‐dimensional MODFLOW model to the capture zone of a public supply well that has a history of CE contamination. The local model was then used to simulate the interactions between naturally occurring organic carbon that acts as an electron donor, and dissolved oxygen (DO), CEs, ferric iron, and sulfate that act as electron acceptors using the Sequential Electron Acceptor Model in three dimensions (SEAM3D) code. The modeling results indicate that asymmetry between rapidly rising and more gradual falling concentration trends over time suggests a DNAPL rather than a dissolved source of CEs. Peak concentrations of CEs are proportional to the volume and composition of the DNAPL source. The persistence of contamination, which can vary from a few years to centuries, is proportional to DNAPL volume, but is unaffected by DNAPL composition. These results show that monitoring CE concentrations in raw water produced by impacted public supply wells over time can provide useful information concerning the nature of contaminant sources and the likely future persistence of contamination.     

PAST AND FUTURE OF ANALYSIS OF WATER RESOURCES TIME SERIES1

ABSTRACT: water resources supply and demand time series consist of several or all of the four basic characteristics: tendency, intermittency, periodicity and stochasticity. Their importance changes from one type of variables to another. Historic developments of analysis of time series in hydrology have varied significantly over the past, from the stress on search for periodicities and persistence in annual series to the emphasis on the series stochastic properties. Supply and demand series are often highly interrelated, which fact is most often neglected in planning water resources systems in general, and water storage capacities in particular. The future of series analysis in water resources will likely be by a joint use of physically-based structural analysis and the use of advanced methods of treating data by stochastic processes, statistical estimation and inference techniques. The most intriguing challenge of the future of this analysis may be the treatment of nonnormal, nonlinear and in general nonstationary hydrologic and water use time series. The proper treatment of complex multivariate processes will also challenge the specialists, especially for the purposes of transfer of information between data on variables at given points, or between data at several points of a given variable, or both.     

Historical Groundwater Trends in Northern New England and Relations with Streamflow and Climatic Variables

Water‐level trends spanning 20, 30, 40, and 50 years were tested using month‐end groundwater levels in 26, 12, 10, and 3 wells in northern New England (Maine, New Hampshire, and Vermont), respectively. Groundwater levels for 77 wells were used in interannual correlations with meteorological and hydrologic variables related to groundwater. Trends in the contemporary groundwater record (20 and 30 years) indicate increases (rises) or no substantial change in groundwater levels in all months for most wells throughout northern New England. The highest percentage of increasing 20‐year trends was in February through March, May through August, and October through November. Forty‐year trend results were mixed, whereas 50‐year trends indicated increasing groundwater levels. Whereas most monthly groundwater levels correlate strongly with the previous month's level, monthly levels also correlate strongly with monthly streamflows in the same month; correlations of levels with monthly precipitation are less frequent and weaker than those with streamflow. Groundwater levels in May through August correlate strongly with annual (water year) streamflow. Correlations of groundwater levels with streamflow data and the relative richness of 50‐ to 100‐year historical streamflow data suggest useful proxies for quantifying historical groundwater levels in light of the relatively short and fragmented groundwater data records presently available.     

The Role of Climate and Human Influences in the Dry‐Up of the Jinci Springs,China1

Abstract: One of the largest karst springs in North China, the Jinci Springs, dried up and has remained dry since 1994. We develop a correlation analysis with time‐lag and a regression analysis with time‐lag to study the relation between spring flow and precipitation. This allows us to obtain a better understanding of karst hydrological processes by differentiating the contribution of variation in precipitation from anthropogenic impacts on the dry‐up of Jinci Springs. We divided the karstic hydrological processes into two phases: pre‐1961 and post‐1961. In the first phase (i.e., 1954‐1960) the groundwater recharge was affected by precipitation alone, and in the second phase (i.e., 1961‐1994) the groundwater recharge was influenced by both precipitation and human activities. Using precipitation and groundwater recharge data in the first phase, we set up a groundwater recharge model with time‐lags. By running the time‐lags model, we acquired the groundwater recharge likely to occur under the sole effect of precipitation in the second phase. Using a water‐balance calculation, we conclude that the groundwater recharge exhibited statistical stationarity, and the Jinci Springs dry‐up was the result of anthropogenic activities. At least three specific types of anthropogenic activities contributed to the drying‐up of Jinci Springs: (1) groundwater pumping accounts for 51%, (2) the dewatering from coal mining accounts for 33%, (3) and dam‐building 14%. The drying‐up of Jinci Springs meant that the groundwater drained from the aquifer’s fractures, and subsequently changed the structure of the karst aquifer. Although groundwater exploitation has been reduced, the flow at Jinci Springs has not reoccurred.     

TIME SERIES ANALYSIS IN PERSPECTIVE1

ABSTRACT: By employing a set of criteria for classifying the capabilities of time series models, recent developments in time series analysis are assessed and put into proper perspective. In particular, the inherent attributes of a wide variety of time series models and modeling procedures presented by the authors of the 18 papers contained in this volume are clearly pointed out. Additionally, it is explained how these models can address many of the time series problems encountered when modeling hydrologic, water quality and other kinds of time series. For instance, families of time series models are now available for modeling series which may contain nonlinearities or may follow nonGaussian distributions. Based upon a sound physical understanding of a problem and results from exploratory data analyses, the most appropriate model to fit to a data set can be found during confirmatory data analyses by following the identification, estimation and diagnostic check stages of model construction. Promising future research projects for developing flexible classes of time series models for use in water resources applications are suggested.     

Relation Between Flow and Temporal Variations of Nitrate and Pesticides in Two Karst Springs in Northern Alabama1

Abstract: Two karst springs in the Mississippian Carbonate Aquifer of northern Alabama were sampled between March 1999 and March 2001 to characterize the variability in concentration of nitrate, pesticides, selected pesticide degradates, water temperature, and inorganic constituents. Water temperature and inorganic ion data for McGeehee Spring indicate that this spring represents a shallow flow system with a relatively short average ground‐water residence time. Water issuing from the larger of the two springs, Meridianville Spring, maintained a constant temperature, and inorganic ion data indicate that this water represents a deeper flow system having a longer average ground‐water residence time than McGeehee Spring. Although water‐quality data indicate differing short‐term responses to rainfall at the two springs, the seasonal variation of nitrate and pesticide concentrations generally is similar for the two springs. With the exception of pesticides detected at low concentrations, the coefficient of variation for most constituent concentrations was less than that of flow at both springs, with greater variability in concentration at McGeehee Spring. Degradates of the herbicides atrazine and fluometuron were detected at concentrations comparable to or greater than the parent pesticides. Decreases in concentration of the principal degradate of fluometuron from about July to November indicate that the degradation rate may decrease as fluometuron (demethylfluometuron) moves deeper into the soil after application. Data collected during the study show that from about November to March when recharge rates increase, nitrate and residual pesticides in the soil, unsaturated zone, and storage within the aquifer are transported to the spring discharges. Because of the increase in recharge, fluometuron loads discharged from the springs during the winter were comparable to loads discharged at the springs during the growing season.     

ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

FORECASTING QUARTER-MONTHLY RIVERFLOW1

Recent developments with respect to transfer function-noise models are reviewed and used to model and forecast quarter-monthly (i.e., near-weekly) natural inflows to the Lac St-Jean reservoir in the Province of Quebec, Canada. The covariate series are rainfall and snowmelt, the latter being a novel derivation from daily rainfall, snowfall and temperature series. It is clearly demonstrated using the residual variance and the Akaike information criterion that modeling is improved as one starts with a deseasonalized ARMA model of the inflow series and successively adds transfer functions for the rainfall and snowmelt series. It is further demonstrated that the transfer function-noise model is better than a periodic autoregressive model of the inflow series. A split-sample experiment is used to compare one-step-ahead forecasts from this transfer function-noise model with forecasts from other stochastic models as well as with forecasts from a so-called conceptual hydrological model (i.e., a model which attempts to mathematically simulate the physical processes involved in the hydrological cycle). It is concluded that the transfer function-noise model is the preferred model for forecasting the quarter-monthly Lac St-Jean inflow series.     

EVALUATION OF A STREAM AQUIFER ANALYSIS TEST USING ANALYTICAL SOLUTIONS AND FIELD DATA1

ABSTRACT: Considerable advancements have been made in the development of analytical solutions for predicting the effects of pumping wells on adjacent streams and rivers. However, these solutions have not been sufficiently evaluated against field data. The objective of this research is to evaluate the predictive performance of recently proposed analytical solutions for unsteady stream depletion using field data collected during a stream/aquifer analysis test at the Tamarack State Wildlife Area in eastern Colorado. Two primary stream/aquifer interactions exist at the Tamarack site: (1) between the South Platte River and the alluvial aquifer and (2) between a backwater stream and the alluvial aquifer. A pumping test is performed next to the backwater stream channel. Drawdown measured in observation wells is matched to predictions by recently proposed analytical solutions to derive estimates of aquifer and streambed parameters. These estimates are compared to documented aquifer properties and field measured streambed conductivity. The analytical solutions are capable of estimating reasonable values of both aquifer and streambed parameters with one solution capable of simultaneously estimating delayed aquifer yield and stream flow recharge. However, for long term water management, it is reasonable to use simplified analytical solutions not concerned with early‐time delayed yield effects. For this site, changes in the water level in the stream during the test and a varying water level profile at the beginning of the pumping test influence the application of the analytical solutions.     

Estimating Lag to Peak between Rainfall and Peak Streamflow with a Mixed‐Effects Model

We test the use of a mixed‐effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km²). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed‐effects model allows for multiple observations at sites without violating model assumptions inherent in traditional ordinary least squares models, which assume each observation is independent. The mixed model includes basin drainage area and maximum 15‐min rainfall depth for individual storms as explanatory features. Based on a remove‐one‐site cross‐validation analysis, the prediction errors of this model ranged from ?42% to +73%. The mixed model substantially outperformed three published models for lag to peak and one published model for centroid lag for estimating lag to peak for small basins in Maine. Lag to peak estimates are a key input to rainfall–runoff models used to design hydraulic infrastructure. The improved accuracy and consistency with model assumptions indicates that mixed models may provide increased data utilization that could enhance models and estimates of lag to peak in other regions.     

INTERPRETATION OF THE DUAL PROGRAM FOR OPTIMAL GROUNDWATER HYDRAULIC CONTROL1

ABSTRACT: Optimization formulations for hydraulic control that take the form of linear programs possess a corresponding dual linear program. The economic and physical interpretations of the dual linear program are examined for formulations in which hydraulic head in groundwater systems is constiained. In each case it is shown that the dual linear program has a physically meaningful interpretation. For a hydraulic gradient control formulation used for remedial analysis it is shown that the dual variable can be interpreted as the remedial benefit due to each gradient control constraint. The dual linear program maximizes the remedial benefit. The value of the dual variable can be used to compute such useful properties as the total remedial benefit of pumping at a specific location. For a formulation that optimizes aquifer yield while constraining drawdown the dual variable can be used to measure the total cost of drawdown capacity consumption per unit of pumping at a specific location. The dual program minimizes the cost of drawdown capacity consumption. By examining the meaning of the dual linear program an alternate statement of the problem under study is revealed. Quantities arising from the dual program add to the value of the optimization approach. Significant new information can be derived from existing linear optimization formulations with minimal additional computational effort.     

STOCHASTIC ANALYSIS OF TIME-AGGREGATED HYDROLOGIC DATA1

Stochastic models fitted to hydrologic data of different time scales are interrelated because the higher time scale data (aggregated data) are derived from those of lower time scale. Relationships between the statistical properties and parameters of models of aggregated data and of original data are examined in this paper. It is also shown that the aggregated data can be more accurately predicted by using a valid model of the original data than by using a valid model of the aggregated data. This property is particularly important in forecasting annual values because only a few annual values are usually available and the resulting forecasts are relatively inaccurate if models based only on annual data are used. The relationships and forecasting equations are developed for general aggregation time and can be used for hourly and daily, daily and monthly or monthly and yearly data. The method is illustrated by using monthly and yearly streamflow data. The results indicate that various statistical characteristics and parameters of the model of annual data can be accurately estimated by using the monthly data and forecasts of annual data by using monthly models have smaller one step ahead mean square error than those obtained by using annual data models.     

MODELING CONCENTRATION VARIATIONS IN HIGH-CAPACITY WELLS: IMPLICATIONS FOR GROUNDWATER SAMPLING1

ABSTRACT: High-capacity wells are used as a convenient and economical means of sampling groundwater quality. Although the inherent limitations of using these wells are generally recognized, little has been done to investigate how these wells actually sample groundwater. A semi-analytical particle tracking model is used to illustrate the influence of variable vertical contaminant distributions and aquifer heterogeneity on the composition of water samples from these wells during short pumping periods. The hypothetical pumping well used in the simulations is located in an unconfined, alluvial aquifer with a shallow water table and concentration gradients of nitrate-nitrogen contamination. This is a typical setting for many irrigated areas in the United States. The main conclusions are: (1) high-capacity wells underestimate the average amount of contamination within an aquifer; (2) shapes of concentration-time curves for high-capacity wells appear to be governed by the distribution of the contaminant and travel times to the well; (3) variables such as well construction, pumping rate, and hydrogeologic properties contribute to the magnitude of the concentration-time curves at individual high-capacity wells; and (4) a sampling strategy using concentration-time curves based on the behavioral characteristics of the well rather than individual samples will provide a much better framework for interpreting spatial contaminant distributions.     

RECHARGE TO ALLUVIAL VALLEY AQUIFERS FROM OVERBANK FLOW AND EXCESS INFILTRATION1

ABSTRACT: Recharge is an important parameter for models that simulate water and contaminant transport in unconfined aquifers. Unfortunately, measurements of actual recharge are not usually available causing recharge to be estimated or possibly added to the calibration procedure. In this study, differences between observed water-table elevations and water-table elevations simulated with a model based on the one-dimensional Boussinesq equation were used to identify both the timing and quantity of recharge to an alluvial valley aquifer. Observed water table elevations and river stage data were recorded during a five-year period from 1991 to 1995 at the Ohio Management Systems Evaluation Area located in south-central Ohio. Direct recharge attributed to overbank flow during and shortly after flood conditions accounted for 65 percent of the total recharge computed during the five-year study period. Recharge of excess infiltration to the aquifer was intermittent and occurred soon after large rainfall events and high river stage. Specification of constant recharge with time values in ground-water simulation models seems inappropriate for stream-aquifer systems given the strong influence of the river on water table elevations in these systems.