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.     

Earthquake Forecasting Using Hidden Markov Models

This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock within 1, 5, and 10 days in the entire study region or in specific subregions and are based on the observations available at the forecast time, namely the interevent times and locations of the previous mainshocks and the elapsed time since the most recent one. Hidden Markov models have been applied to many problems, including earthquake classification; this is the first application to earthquake forecasting.     

Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input

Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi-arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross-correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance.     

Parameter Estimation for Groundwater Models under Uncertain Irrigation Data

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression‐based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least‐squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least‐squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least‐squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.     

Multi-Constrained Catchment Scale Optimization of Groundwater Abstraction Using Linear Programming

Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over-abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases. The reduction in baseflow and alteration of the streamflow regime can potentially have an adverse effect on groundwater-dependent ecosystems. A spatially distributed, coupled groundwater–surface water model can simulate the impacts of groundwater abstraction on aquatic ecosystems. A constrained optimization algorithm and a simulation model in combination can provide an objective tool for the water practitioner to evaluate the interplay between economic benefits of groundwater abstractions and requirements to environmental flow. In this study, a holistic catchment-scale groundwater abstraction optimization framework has been developed that allows for a spatially explicit optimization of groundwater abstraction, while fulfilling a predefined maximum allowed reduction of streamflow (baseflow [Q95] or median flow [Q50]) as constraint criteria for 1484 stream locations across the catchment. A balanced K-Means clustering method was implemented to reduce the computational burden of the optimization. The model parameters and observation uncertainties calculated based on Bayesian linear theory allow for a risk assessment on the optimized groundwater abstraction values. The results from different optimization scenarios indicated that using the linear programming optimization algorithm in conjunction with integrated models provides valuable information for guiding the water practitioners in designing an effective groundwater abstraction plan with the consideration of environmental flow criteria important for the ecological status of the entire system.     

A Simple Relation to Constrain Groundwater Models Using Surface Deformation

A simple relation between pore pressure change and one-dimensional surface deformation is presented. The relation is for pore pressure change in a confined aquifer that causes surface deformation. It can be applied to groundwater models of any discretization and is computationally efficient. The estimated surface deformation from model results can be compared to observed surface deformation through geodetic techniques such as Differential Interferometric Synthetic Aperture Radar. Model parameters then are constrained using the observed surface deformation. The validity of this relation is shown through constraint of model parameters for surface uplift due to pore pressure increase caused by wastewater disposal injection.     

Multimodel Validity Assessment of Groundwater Flow Simulation Models Using Area Metric Approach

We demonstrate the application of the Area Metric developed by Ferson et al. (2008) for multimodel validity assessment. The Area Metric quantified the degree of models' replicative validity: the degree of agreement between the observed data and the corresponding simulated outputs represented as their empirical cumulative distribution functions (ECDFs). This approach was used to rank multiple representations of a case study groundwater flow model of a landfill by their Area Metric scores. A multimodel approach allows to account for uncertainties that may either be epistemic (from lack of knowledge) or aleatory (from variability inherent in the system). The Area Metric approach enables explicit incorporation of model uncertainties, epistemic as well as aleatory, into validation assessment. The proposed approach informs understanding of the collected data and that of the model domain. It avoids model overfitting to a particular system state, and in fact is a blind assessment of the models' validity: models are not adjusted, or updated, to achieve a better numerical fit. This approach assesses the degree of models' validity, in place of the typical binary model validation/invalidation process. Collectively, this increases confidence in the model's representativeness that, in turn, reduces risk to model users.     

Improvement of Global Hydrological Models Using GRACE Data

After about 6 years of GRACE (Gravity Recovery and Climate Experiment) satellite mission operation, an unprecedented global data set on the spatio-temporal variations of the Earth’s water storage is available. The data allow for a better understanding of the water cycle at the global scale and for large river basins. This review summarizes the experiences that have been made when comparing GRACE data with simulation results of global hydrological models and it points out the prerequisites and perspectives for model improvements by combination with GRACE data. When evaluated qualitatively at the global scale, water storage variations on the continents from GRACE agreed reasonably well with model predictions in terms of their general seasonal dynamics and continental-scale spatial patterns. Differences in amplitudes and phases of water storage dynamics revealed in more detailed analyses were mainly attributed to deficiencies in the meteorological model forcing data, to missing water storage compartments in the model, but also to limitations and errors of the GRACE data. Studies that transformed previously identified model deficiencies into adequate modifications of the model structure or parameters are still rare. Prerequisites for a comprehensive improvement of large-scale hydrological models are in particular the consistency of GRACE observation and model variables in terms of filtering, reliable error estimates, and a full assessment of the water balance. Using improvements in GRACE processing techniques, complementary observation data, multi-model evaluations and advanced methods of multi-objective calibration and data assimilation, considerable progress in large-scale hydrological modelling by integration of GRACE data can be expected.     

Statistical Modeling of Hydrogeologic Data - Part I: Regression and ANOVA Models

Statistical modeling can be a powerful tool for analyzing complex hydrogeologic and water quality data. The objectives of this paper are to discuss the nature of statistical models; to describe two types of statistical models, regression models, and analysis of variance (ANOVA) models; and to give examples of how the models have been and could be applied to solve hydrogeologic problems.     

利用逐步回归分析红山地磁台观测数据质量

选用逐步回归分析方法,利用红山地磁台2009年观测数据的基线值数据,计算台站地磁观测系统的温度系数与滑动系数以及年剩余均方差,通过这3个指标的结果,给出观测系统受这3个参数影响的程度,为控制红山台地磁观测数据的质量提供依据.同时,对台站合理的选用基线值提供有力帮助.     

On Integrating Groundwater Transport Models with Wireless Sensor Networks

The emerging technology of wireless sensor networks (WSNs) is an integrated, distributed, wireless network of sensing devices. It has the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by providing environmental information at greater spatial and temporal resolutions. Furthermore, due to continuing high-performance computing development, these data may be introduced into increasingly robust and complex numerical models; for instance, the parameters of subsurface transport simulators may be automatically updated. Early field deployments and laboratory experiments conducted using in situ sensor technology and WSNs indicated significant fundamental issues concerning sensor and network hardware reliability—suggesting that investigations should first be conducted in controlled environments before field deployment. A first step in this validation process involves evaluating the predictive capability of a computational advection-dispersion transport model when incorporating concentration data from a WSN simulation. Data quality is a major concern, especially when sensor readings are automatically fed into data assimilation procedures. The appropriate employment of an independent WSN fault detection service can ensure that erroneous data (e.g., missing or anomalous values) do not mislead the model. Parameter estimation regularization techniques may then deal with remaining data noise. The primary purpose of this study is to determine the suitability of WSNs (and other in situ data delivery technologies) for use in contaminant transport modeling applications by conducting research in a realistic simulative environment.     

Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data

In this study, a scheme is presented to estimate groundwater storage variations in Iran. The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (GRACE) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (GLDAS) model including soil moisture, snow water equivalent, and total canopy water storage. To do so, the sums of GLDAS outputs are subtracted from terrestrial water storage variations determined by GRACE observations. Because of stripping errors in the GRACE data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the GRACE data. It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations. To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.     

Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model

The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively.     

Groundwater Quality Modeling with a Small Data Set

Seventeen groundwater quality variables collected during an 8‐year period (2006 to 2013) in Andimeshk, Iran, were used to implement an artificial neural network (NN) with the purpose of constructing a water quality index (WQI). The method leading to the WQI avoids instabilities and overparameterization, two problems common when working with relatively small data sets. The groundwater quality variables used to construct the WQI were selected based on principal component analysis (PCA) by which the number of variables were decreased to six. To fulfill the goals of this study, the performance of three methods (1) bootstrap aggregation with early stopping; (2) noise injection; and (3) ensemble averaging with early stopping was compared. The criteria used for performance analysis was based on mean squared error (MSE) and coefficient of determination (R²) of the test data set and the correlation coefficients between WQI targets and NN predictions. This study confirmed the importance of PCA for variable selection and dimensionality reduction to reduce the risk of overfitting. Ensemble averaging with early stopping proved to be the best performed method. Owing to its high coefficient of determination (R² = 0.80) and correlation coefficient (r=0.91), we recommended ensemble averaging with early stopping as an accurate NN modeling procedure for water quality prediction in similar studies.     

Groundwater Monitoring Using GRACE and GLDAS Data after Downscaling Within Basaltic Aquifer System

Gravity Recovery and Climate Experiment (GRACE) satellite mission is ground-breaking information hotspot for the evaluation of groundwater storage. The present study aims at validating the sensitivity of GRACE data to groundwater storage variation within a basaltic aquifer system after its statistical downscaling on a regional scale. The basaltic aquifer system which covers 82.06% area of Maharashtra state in India, is selected as the study area. Five types of basaltic aquifer systems with varying groundwater storage capacities, based on hydrologic characteristics, have been identified within the study area. The spatial and seasonal trend analysis of observed in situ groundwater storage anomalies (ΔGWSano) computed from groundwater level data of 983 wells from the year 2002 to 2016, has been performed to analyze the variation in groundwater storages in the different basaltic aquifer system. The groundwater storage anomalies (ΔGWSDano) have been derived from GRACE Release 05 (RL05) after removing the soil moisture anomaly (ΔSMano) and canopy water storage anomaly (ΔCNOano) obtained from Global Land Data Assimilation System (GLDAS) land surface models (NOAH, MOSAIC, CLM and VIC). The artificial neural network technique has been used to downscale the GRACE and GLDAS data at a finer spatial resolution of 0.125°. The study shows that downscaled GRACE and GLDAS data at a finer spatial resolution is sensitive to seasonal groundwater storage variability in different basaltic aquifer systems and the regression coefficient R has been found satisfactory in the range of 0.696 to 0.818.     

Air Surface Temperature Correlation with Greenhouse Gases by Using Airs Data Over Peninsular Malaysia

The main objective of this study is to develop algorithms for calculating the air surface temperature (AST). This study also aims to analyze and investigate the effects of greenhouse gases (GHGs) on the AST value in Peninsular Malaysia. Multiple linear regression is used to achieve the objectives of the study. Peninsular Malaysia has been selected as the research area because it is among the regions of tropical Southeast Asia with the greatest humidity, pockets of heavy pollution, rapid economic growth, and industrialization. The predicted AST was highly correlated (R = 0.783) with GHGs for the 6-year data (2003–2008). Comparisons of five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the wet season (within 1.3 K). The in situ data ranged from 1 to 2 K. Validation results showed that AST (R = 0.776–0.878) has values nearly the same as the observed AST from AIRS. We found that O₃ during the wet season was indicated by a strongly positive beta coefficient (0.264–0.992) with AST. The CO₂ yields a reasonable relationship with temperature with low to moderate beta coefficient (?0.065 to 0.238). The O₃, CO₂, and environmental variables experienced different seasonal fluctuations that depend on weather conditions and topography. The concentration of gases and pollution were the highest over industrial zones and overcrowded cities, and the dry season was more polluted compared with the wet season. These results indicate the advantage of using the satellite AIRS data and a correlation analysis to investigate the effect of atmospheric GHGs on AST over Peninsular Malaysia. An algorithm that is capable of retrieving Peninsular Malaysian AST in all weather conditions with total uncertainties ranging from 1 to 2 K was developed.     

On Solving Groundwater Flow and Transport Models with Algebraic Multigrid Preconditioning

Iterative solvers preconditioned with algebraic multigrid have been devised as an optimal technology to speed up the response of large sparse linear systems. In this work, this technique was implemented in the framework of the dual delineation approach. This involves a single groundwater flow linear solution and a pure advective transport solution with different right-hand sides. The new solver was compared with other preconditioned iterative methods, the MODFLOW's GMG solver, and direct sparse solvers. Test problems include two- and three-dimensional benchmarks spanning homogeneous and highly heterogeneous and anisotropic formations. For the groundwater flow problems, using the algebraic multigrid preconditioning speeds up the numerical solution by one to two orders of magnitude. The algebraic multigrid preconditioner efficiency was preserved for the three dimensional heterogeneous and anisotropic problem unlike for the MODFLOW's GMG solver. Contrarily, a sparse direct solver was the most efficient for the pure advective transport processes such as the forward travel time simulations. Hence, the best sparse solver for the more general advection-dispersion transport equation is likely to be Péclet number dependent. When equipped with the best solvers, processing multimillion grid blocks by the dual delineation approach is a matter of seconds. This paves the way for its routine application to large geological models. The paper gives practical hints on the strategies and conditions under which algebraic multigrid preconditioning would remain competitive for the class of nonlinear and/or transient problems.