Risk-Based Decision-Support Groundwater Modeling for the Lower San Antonio River Basin,Texas, USA

A numerical surface-water/groundwater model was developed for the lower San Antonio River Basin to evaluate the responses of low base flows and groundwater levels within the basin under conditions of reduced recharge and increased groundwater withdrawals. Batch data assimilation through history matching used a simulation of historical conditions (2006-2013); this process included history-matching to groundwater levels and base-flow estimates at several gages, and was completed in a high-dimensional (highly parameterized) framework. The model was developed in an uncertainty framework such that parameters, observations, and scenarios of interest are envisioned stochastically as distributions of potential values. Results indicate that groundwater contributions to surface water during periods of low flow may be reduced from 6% to 25% with a corresponding 25% reduction in recharge and a 25% increase in groundwater pumping over an 8-year planning period. Furthermore, results indicate groundwater-level reductions in some hydrostratigraphic units are more likely than in other hydrostratigraphic units over an 8-year period under drought conditions with the higher groundwater withdrawal scenario.     

A Model-Averaging Method for Assessing Groundwater Conceptual Model Uncertainty

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model-averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.     

Effects of variations of river stage and hydraulic conductivity on temporal scaling of groundwater levels: numerical simulations

It was found in previous studies that groundwater levels may fluctuate as a temporal fractal. In this study numerical simulations of groundwater level fluctuations in an unconfined aquifer near a river were conducted to investigate the effects of aquifer heterogeneity and river stage variations on the fractal behavior of the water levels, h(t). Groundwater recharge was taken to be a white-noise process. The aquifer heterogeneity was simulated with a second-order stationary field of hydraulic conductivity (K) with an exponential variogram model. The results showed that groundwater levels fluctuate as a temporal fractal in both homogeneous and heterogeneous aquifers as long as K is less than 10 m/d. Most aquifers may indeed act as a fractal filter which takes a random non-fractal recharge inputs and produces a fractal responses of groundwater level fluctuations. A crossover in temporal scaling of h(t) may appear in more permeable aquifers. Fluctuations of the groundwater level in a homogeneous aquifer are dominated by the recharge process when the river stage is constant or by the river stage variations when the river stage varies in highly permeable aquifers. Heterogeneity plays an important role in the temporal scaling of h(t) in more permeable aquifers: the stronger the heterogeneity, the stronger the temporal scaling of h(t).     

Event-based estimation of water budget components using a network of multi-sensor capacitance probes

Abstract

A new approach was developed for estimating vertical soil water fluxes using soil water content time series data. Instead of a traditional fixed time interval, this approach utilizes the time interval between two sequential minima of the soil water storage time series to identify groundwater recharge events and calculate components of the soil water budget. We calculated water budget components: surface-water excess (Sw), infiltration less evapotranspiration (I – ET) and groundwater recharge (R) from May 2001 to January 2003 at eight locations at the USDA Agricultural Research Center, Beltsville, Maryland, USA. High uncertainty was observed for all budget components. This uncertainty was attributed to spatial and temporal variation in Sw, I – ET and R, and was caused by nonuniform rainfall distributions during recharge events, variability in the profile water content, and spatial variability in soil hydraulic properties. The proposed event-based approach allows estimating water budget components when profile water content monitoring data are available.

Citation Guber, A., Gish, T., Pachepsky, Y., McKee, L., Nicholson, T. & Cady, R. (2011) Event-based estimation of water budget components using a network of multi-sensor capacitance probes. Hydrol. Sci. J. 56(7), 1227–1241.     

Evaluation of the Glugla method for estimating evapotranspiration and groundwater recharge

Abstract

Estimates of groundwater recharge are often needed for a variety of groundwater resource evaluation purposes. A method for estimating long-term groundwater recharge and actual evapotranspiration not known in the English literature is presented. The method uses long-term average annual precipitation, runoff, potential evaporation, and crop-yield information, and uses empirical parameter curves that depend on soil and crop types to determine long-term average annual groundwater recharge (GWR). The method is tested using historic lysimeter records from 10 lysimeters at Coshocton, Ohio, USA. Considering the coarse information required, the method provides good estimates of groundwater recharge and actual evapotranspiration, and is sensitive to a range of cropping and land-use conditions. Problems with practical application of the technique are mentioned, including the need for further testing using given parameter curves, and for incorporating parameters that describe current farming practices and other land uses. The method can be used for urban conditions, and can be incorporated into a GIS framework for rapid, large-area, spatially-distributed estimations of GWR. An example application of the method is given.     

Backward probabilistic model of groundwater contamination in non-uniform and transient flow

Backward location and travel time probabilities, which provide information about the former location of contamination in an aquifer, can be used to identify unknown contamination sources. Backward location probability describes the possible upgradient positions of contamination at a known time in the past, and backward travel time probability describes the time required for contamination to travel from a known upgradient location to an observation point. These probabilities are related to adjoint states of resident concentration, and their governing equation is the adjoint of a forward contaminant transport model. Using adjoint theory to obtain the appropriate governing equation, we extend the backward probability model for conservative solutes to more general non-uniform and transient flow fields. In particular, we address three important extensions, spatially-varying porosity, transient flow and temporally-varying porosity, and internal distributed sources and sinks of solute and water. For the first time we learn that forward and backward location and travel time probabilities are not necessarily equivalent to adjoint states, but are related to them. The extensions are illustrated using a vertically-integrated groundwater model, creating transient flow by a step change in pumping and using areal recharge as an internal distributed source. Both the movement and spread of probabilities are affected. With internal sources of water, there are two interpretations of backward probability, depending on whether or not the source of water is also a source of solute. The results demonstrate how the backward probability model can be applied to other, perhaps more important, non-uniform and transient flow conditions, with time- and space-varying water storage, such as time-varying pumping or unsaturated (or saturated–unsaturated) flow and transport with spatially- and temporally-varying moisture content.     

Evaluating Groundwater Interbasin Flow Using Multiple Models and Multiple Types of Data

The groundwater interbasin flow, Q_y, from the north of Yucca Flat into Yucca Flat simulated using the Death Valley Regional Flow System (DVRFS) model greatly exceeds assessments obtained using other approaches. This study aimed to understand the reasons for the overestimation and to examine whether the Q_y estimate can be reduced. The two problems were tackled from the angle of model uncertainty by considering six models revised from the DVRFS model with different recharge components and hydrogeological frameworks. The two problems were also tackled from the angle of parametric uncertainty for each model by first conducting Morris sensitivity analysis to identify important parameters and then conducting Monte Carlo simulations for the important parameters. The uncertainty analysis is general and suitable for tackling similar problems; the Morris sensitivity analysis has been utilized to date in only a limited number of regional groundwater modeling. The simulated Q_y values were evaluated by using three kinds of calibration data (i.e., hydraulic head observations, discharge estimates, and constant‐head boundary flow estimates). The evaluation results indicate that, within the current DVRFS modeling framework, the Q_y estimate can only be reduced to about half of the original estimate without severely deteriorating the goodness‐of‐fit to the calibration data. The evaluation results also indicate that it is necessary to develop a new hydrogeological framework to produce new flow patterns in the DVRFS model. The issues of hydrogeology and boundary flow are being addressed in a new version of the DVRFS model planned for release by the U.S. Geological Survey.     

Impacts of model initialization on an integrated surface water–groundwater model

Integrated hydrologic models characterize catchment responses by coupling the subsurface flow with land surface processes. One of the major areas of uncertainty in such models is the specification of the initial condition and its influence on subsequent simulations. A key challenge in model initialization is that it requires spatially distributed information on model states, groundwater levels and soil moisture, even when such data are not routinely available. Here, the impact of uncertainty in initial condition was explored across a 208 km² catchment in Denmark using the ParFlow.CLM model. The initialization impact was assessed under two meteorological conditions (wet vs dry) using five depth to water table and soil moisture distributions obtained from various equilibrium states (thermal, root zone, discharge, saturated and unsaturated zone equilibrium) during the model spin‐up. Each of these equilibrium states correspond to varying computation times to achieve stability in a particular aspect of the system state. Results identified particular sensitivity in modelled recharge and stream flow to the different initializations, but reduced sensitivity in modelled energy fluxes. Analysis also suggests that to simulate a year that is wetter than the spin‐up period, an initialization based on discharge equilibrium is adequate to capture the direction and magnitude of surface water–groundwater exchanges. For a drier or hydrologically similar year to the spin‐up period, an initialization based on groundwater equilibrium is required. Variability of monthly subsurface storage changes and discharge bias at the scale of a hydrological event show that the initialization impacts do not diminish as the simulations progress, highlighting the importance of robust and accurate initialization in capturing surface water–groundwater dynamics. Copyright © 2015 John Wiley & Sons, Ltd.     

Insights From a Multi-Method Recharge Estimation Comparison Study

Although most recharge estimation studies apply multiple methods to identify the possible range in recharge values, many do not distinguish clearly enough between inherent uncertainty of the methods and other factors affecting the results. We investigated the additional value that can be gained from multi-method recharge studies through insights into hydrogeological understanding, in addition to characterizing uncertainty. Nine separate groundwater recharge estimation methods, with a total of 17 variations, were applied at a shallow aquifer in northwest Ethiopia in the context of the potential for shallow groundwater resource development. These gave a wide range of recharge values from 45 to 814 mm/a. Critical assessment indicated that the results depended on what the recharge represents (actual, potential, minimum recharge or change in aquifer storage), and spatial and temporal scales, as well as uncertainties from application of each method. Important insights into the hydrogeological system were gained from this detailed analysis, which also confirmed that the range of values for actual recharge was reduced to around 280-430 mm/a. This study demonstrates that even when assumptions behind methods are violated, as they often are to some degree especially when data are limited, valuable insights into the hydrogeological system can be gained from application of multiple methods.     

Joint Estimation of Gross Recharge,Groundwater Usage,and Hydraulic Properties within HydroSight

Groundwater management decisions are often founded upon estimates of aquifer hydraulic properties, recharge and the rate of groundwater usage. Too often hydraulic properties are unavailable, recharge estimates are very uncertain, and usage is unmetered or infrequently metered over only recent years or estimated using numerical groundwater models decoupled from the drivers of drawdown. This paper extends the HydroSight groundwater time-series package ( http://peterson-tim-j.github.io/HydroSight/ ) to allow the joint estimation of gross recharge, transmissivity, storativity, and daily usage at multiple production bores. A genetic evolutionary scheme was extended from estimating time-series model parameters to also estimating time series of usage that honor metered volumes at each production bore and produces (1) the best fit with the observed hydrograph and (2) plausible estimates of actual evapotranspiration and hence recharge. The reliability of the approach was rigorously tested. Repeated calibration of models for four bores produced estimates of transmissivity, storativity, and mean recharge that varied by a factor of 0.22-0.32, 0.13-0.2, and 0.03-0.48, respectively, when recharge boundary effects were low and the error in monthly, quarterly, and biannual metered usage was generally <10%. Application to the 30 observation bores within the Warrion groundwater management area (Australia), produced a coefficient of efficiency of ≥0.80 at 22 bores and ≥0.90 at 12 bores. The aquifer transmissivity and storativity were reasonably estimated, and were consistent with independent estimates, while mean gross recharge may be slightly overestimated. Overall, the approach allows greater insights from the available data and provides opportunity for the exploration of usage and climatic scenarios.     

Definition of catchment area in karst: case of the rivers Krčić and Krka,Croatia

Abstract

The study area consists of the spring zones of the Kr?i?, Krka and Cetina river catchments located in the Dinaric karst, Croatia. Classical hydrological approaches and some newer time and frequency domain methods are used in order to validate the existing hypotheses both qualitatively and quantitatively, and these contribute to factual information about the hydrological behaviour of the catchments. The groundwater recharge rates are calculated by a mathematical model based on Palmer's soil-moisture balance method. The values of parameters of the groundwater recharge model are estimated by the spectral method. The calculated monthly and annual groundwater recharge rates form the basis for estimating the hydrological catchment areas of the spring zones and also for the determina-tion of quantitative relationships between the catchments.     

Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model

Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to the scarcity of WTD observations, physically-based groundwater models are growing in their ability to map WTD at large scales; however, they are still challenged to represent simulated WTD compared to well observations. In this study, we develop a purely data-driven approach to estimating WTD at continental scale. We apply a random forest (RF) model to estimate WTD over most of the contiguous United States (CONUS) based on available WTD observations. The estimated WTD are in good agreement with well observations, with a Pearson correlation coefficient (r) of 0.96 (0.81 during testing), a Nash-Sutcliffe efficiency (NSE) of 0.93 (0.65 during testing), and a root mean square error (RMSE) of 6.87 m (15.31 m during testing). The location of each grid cell is rated as the most important feature in estimating WTD over most of the CONUS, which might be a surrogate for spatial information. In addition, the uncertainty of the RF model is quantified using quantile regression forests. High uncertainties are generally associated with locations having a shallow WTD. Our study demonstrates that the RF model can produce reasonable WTD estimates over most of the CONUS, providing an alternative to physics-based modeling for modeling large-scale freshwater resources. Since the CONUS covers many different hydrologic regimes, the RF model trained for the CONUS may be transferrable to other regions with a similar hydrologic regime and limited observations.     

Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty

Radar estimates of rainfall are being increasingly applied to flood forecasting applications. Errors are inherent both in the process of estimating rainfall from radar and in the modelling of the rainfall–runoff transformation. The study aims at building a framework for the assessment of uncertainty that is consistent with the limitations of the model and data available and that allows a direct quantitative comparison between model predictions obtained by using radar and raingauge rainfall inputs. The study uses radar data from a mountainous region in northern Italy where complex topography amplifies radar errors due to radar beam occlusion and variability of precipitation with height. These errors, together with other error sources, are adjusted by applying a radar rainfall estimation algorithm. Radar rainfall estimates, adjusted and not, are used as an input to TOPMODEL for flood simulation over the Posina catchment (116 km²). Hydrological model parameter uncertainty is explicitly accounted for by use of the GLUE (Generalized Likelihood Uncertainty Estimation). Statistics are proposed to evaluate both the wideness of the uncertainty limits and the percentage of observations which fall within the uncertainty bounds. Results show the critical importance of proper adjustment of radar estimates and the use of radar estimates as close to ground as possible. Uncertainties affecting runoff predictions from adjusted radar data are close to those obtained by using a dense raingauge network, at least for the lowest radar observations available. Copyright © 2004 John Wiley & Sons, Ltd.     

Isotopic variation in groundwater across the conterminous United States – Insight into hydrologic processes

Groundwater supplies a significant proportion of water use in the United States and is critical to the maintenance of healthy ecosystems and environmental processes, thus characterizing aquifer hydrology is important to managing and preserving these resources. While groundwater isotopes provide insight into hydrologic and ecologic processes, their application is limited to where measurements exist. To help overcome this limitation, we used the random forest algorithm to develop a predictive model for shallow groundwater isotopes in the conterminous United States. Our model uses environmental variables (e.g. temperature, elevation, precipitation isotopes) as predictors. We used our model to develop the first shallow groundwater isoscape of δ²H and δ¹⁸O for the conterminous United States. We describe the patterns in groundwater isotopes using both observations and our modelled isoscape. We find that throughout much of the Eastern United States, groundwater isotopes are close to annual amount weighted precipitation, while groundwater isotopes are significantly depleted relative precipitation across much of the High Plains and Western United States. Furthermore, we compare the observations compiled for this study to isotopes of precipitation, which allows us to determine the relative recharge efficiency (i.e. ratio of groundwater recharge to precipitation) between seasons and the proportion of annual recharge that occurs in a given season. Our findings suggest that winter recharge is generally more efficient than summer recharge; however, the dominant recharge season is more varied as it is the product of both seasonal recharge efficiency and the seasonal timing of precipitation. Parts of the central United States have summer dominant recharge, which is likely the result of heavy summer precipitation/nocturnal summer precipitation. Interestingly, parts of coastal California appear to have summer dominant recharge, which we suggest could be due to recharge from fog-drip. Our results summarize spatial patterns in groundwater isotopes across the conterminous United States, provide insight into the hydrologic processes affecting shallow groundwater, and are valuable information for future ecologic and hydrologic studies.     

Robust Data Worth Analysis with Surrogate Models

Highly detailed physically based groundwater models are often applied to make predictions of system states under unknown forcing. The required analysis of uncertainty is often unfeasible due to the high computational demand. We combine two possible solution strategies: (1) the use of faster surrogate models; and (2) a robust data worth analysis combining quick first-order second-moment uncertainty quantification with null-space Monte Carlo techniques to account for parametric uncertainty. A structurally and parametrically simplified model and a proper orthogonal decomposition (POD) surrogate are investigated. Data worth estimations by both surrogates are compared against estimates by a complex MODFLOW benchmark model of an aquifer in New Zealand. Data worth is defined as the change in post-calibration predictive uncertainty of groundwater head, river-groundwater exchange flux, and drain flux data, compared to the calibrated model. It incorporates existing observations, potential new measurements of system states (“additional” data) as well as knowledge of model parameters (“parametric” data). The data worth analysis is extended to account for non-uniqueness of model parameters by null-space Monte Carlo sampling. Data worth estimates of the surrogates and the benchmark suggest good agreement for both surrogates in estimating worth of existing data. The structural simplification surrogate only partially reproduces the worth of “additional” data and is unable to estimate “parametric” data, while the POD model is in agreement with the complex benchmark for both “additional” and “parametric” data. The variance of the POD data worth estimates suggests the need to account for parameter non-uniqueness, like presented here, for robust results.     

Automating Recession Curve Displacement Recharge Estimation

Recharge estimation is an important and challenging element of groundwater management and resource sustainability. Many recharge estimation methods have been developed with varying data requirements, applicable to different spatial and temporal scales. The variability and inherent uncertainty in recharge estimation motivates the recommended use of multiple methods to estimate and bound regional recharge estimates. Despite the inherent limitations of using daily gauged streamflow, recession curve displacement methods provide a convenient first‐order estimate as part of a multimethod hierarchical approach to estimate watershed‐scale annual recharge. The implementation of recession curve displacement recharge estimation in the United States Geologic Survey (USGS) RORA program relies on the subjective, operator‐specific selection of baseflow recession events to estimate a gauge‐specific recession index. This paper presents a parametric algorithm that objectively automates this tedious, subjective process, parameterizing and automating the implementation of recession curve displacement. Results using the algorithm reproduce regional estimates of groundwater recharge from the USGS Appalachian Valley and Piedmont Regional Aquifer‐System Analysis, with an average absolute error of less than 2%. The algorithm facilitates consistent, completely automated estimation of annual recharge that complements more rigorous data‐intensive techniques for recharge estimation.     

Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation

Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators; however, the former consumes far more memory and time than the latter.     

Significance of stemflow in groundwater recharge. 2: A cylindrical infiltration model for evaluating the stemflow contribution to groundwater recharge

A primary model for evaluating the effect of stemflow on groundwater recharge has been developed. The model, a cylindrical infiltration model (CI model), is based on the infiltration area of stemflow-induced water instead of canopy projected area for determining the stemflow inputs to the soil surface. The estimated ratio of recharge rate by stemflow to the total recharge rate determined with this model agrees closely with values obtained from the mass balance of chloride in subsurface waters. This primary model is considered to be useful for estimating the effect of stemflow on groundwater recharge.