Sreeram Singaraju

A successive steady-state model for simulating freshwater discharges and saltwater wedge profiles at Baffin Bay, Texas

Submarine groundwater discharges (SGD) are an important source of freshwater to coastal bays and estuaries in arid and semi-arid regions. Understanding groundwater flows to these ecologically sensitive bodies is important for coastal environmental sustainability. A management-oriented mathematical model capable of simulating the flow of groundwater into a coastal bay (i.e., submarine groundwater discharge) is developed here using the principles of quasi-steady-state flow and the existence of a sharp interface between the freshwater and the saltwater portions of the aquifer. The model is applied to the Baffin Bay in South Texas, a hypersaline coastal body with no major river discharges. Two global sensitivity approaches (the one-at-a time design; OAT) and the grid-based Monte Carlo sensitivity index are used to identify critical model inputs. The sensitivity of the model inputs to the Nash–Sutcliffe Efficiency (NSE) criterion is calculated making use of synoptic observed SGD measurements made over a period of one tidal cycle. The results of the study indicate that global sensitivity analysis methods are particularly sensitive to the number of model realizations. The ability of these techniques to screen out insensitive model inputs increased with increasing number of realizations. The variability in the identified inputs was more prominent with the OAT sensitivity methods than Monte Carlo-based techniques. In general, the aquifer properties (hydraulic conductivity and aquifer thickness) as well as fluid properties (seawater and fresh water densities) along with the antecedent SGD was noted to be the most sensitive parameters. This result indicates that the implementation of sharp-front coastal–aquifer models can be improved through better hydrogeologic characterization and measuring temperature and salinity data to improve density estimation. The global sensitivity methods also help identify reasonable values for model inputs which can serve as a starting point for advanced calibrations. The results, however, indicated that the model is likely over-parameterized with different input sets yielding similar NSE estimates. Based on these initial parameter estimates, the model was able to capture the general trend in the observed SGD but could not capture the dynamic associated with high water levels in the bay. Pre-calibration global sensitivity analysis is recommended in similar applications as it not only provides insights into future data collection efforts but can also help assess the likely success of model calibration. However, given the variability among the techniques, it is suggested that multiple global sensitivity methods be utilized.

Identifying influencing wells for gradient estimation in the confined portion of the Gulf Coast aquifer near Kingsville, TX

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.

Combined optimization of a wind farm and a well field for wind-enabled groundwater production

Abstract Energy requirements constitute a significant cost in groundwater production and can also add to a large carbon footprint when fossil fuels are used for power. Wind-enabled water production is advantageous as it minimizes air pollution impacts associated with groundwater production and relies on a renewable resource. Also, as groundwater extraction represents a deferrable load (i.e., it can be carried out when energy demands within an area are low), it provides a convenient way to overcome the intermittency issue associated with wind power production. Multiple turbine wind farms are needed to generate large quantities of power needed for large-scale groundwater production. Turbines must be optimally located in these farms to ensure proper propagation of kinetic energy throughout the system. By the same token, well placement must reconcile the competing objectives of minimizing interferences between production wells while ensuring the drawdowns at the property boundary are within acceptable limits. A combined simulation–optimization based model is developed in this study to optimize the combined wind energy and water production systems. The wind farm layout optimization model is solved using a re-sampling strategy, while the well field configuration is obtained using the simulated annealing technique. The utility of the developed model is to study wind-enabled water production in San Patricio County, TX. Sensitivity analysis indicated that identifying optimal placement of turbines is vital to extract maximum wind power. The variability of the wind speeds has a critical impact on the amount of water that can be produced. Innovative technologies such as variable flow pumping devices and aquifer storage recovery must be used to smooth out wind variability. While total groundwater extraction is less sensitive to uncertainty in hydrogeological parameters, improper estimation of aquifer transmissivity and storage characteristics can affect the feasibility of wind-driven groundwater production.

Prioritizing Groundwater Monitoring in Data Sparse Regions using Atanassov Intuitionistic Fuzzy Sets (A-IFS)

Water quality index (WQI) is a single measure that is commonly used to prioritize water wells and manage groundwater resources. WQI is pragmatic as it combines several water quality parameters into a single index. However, the process of aggregation is imprecise and suffers from uncertainties in measurements and subjective specification of weights. The goal of this study is to demonstrate how Atanassov’s Intuitionistic Fuzzy Sets (A-IFS) can be used to aggregate water quality parameters into a composite index to rank and prioritize groundwater wells. The A-IFS weighted geometric mean (A-IFS-WGM) method and the A-IFS based Technique for Order of Preference by Similarity to Ideal Solution (A-IFS-TOPSIS) using Euclidean (A-IFS-TOPSIS-E) and Hamming (A-IFS-TOPSIS-H) are introduced and illustrated to prioritize and rank water supply wells in a fast growing yet poorly studied area in Guntur, Andhra Pradesh, India. The concept of A-IFS entropy is also presented to directly ascertain weights from the data. This objective selection of weights from the data eliminates the subjectivity and difficulties associated with assigning relative importance to different water quality parameters. The results of the study indicate that the weights obtained using the entropy methods are consistent with the geochemical characteristics of the regional aquifer. The A-IFS-WGM method is more sensitive to weights compared to the A-IFS-TOPSIS methods which are influenced to a larger extent by the membership and non-membership values (ratings). Special consideration must be placed on ascribing the hesitation margin of the decision maker and identifying the membership values for non-preference as the methods exhibit greater sensitivity to these factors. The developed methods provide pragmatic data-driven approaches to prioritize and rank groundwater wells within a monitoring network.

Detecting seasonal and cyclical trends in agricultural runoff water quality—hypothesis tests and block bootstrap power analysis

Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen—TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.