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.     

Evaluating Renewable Groundwater Stress with GRACE Data in Greece

Groundwater is a resilient water source and its importance is even greater in periods of drought. Areas such as the Mediterranean where adverse climate change effects are expected are bell‐weather locations for groundwater depletion and are of considerable interest. The present study evaluates renewable groundwater stress (RGS) as the ratio of groundwater use to groundwater availability, quantifying use as the trend in gravity recovery and climate experiment‐derived (GRACE) subsurface anomalies (ΔGW_trend) and renewable groundwater availability as mean annual recharge. Estimates for mean annual recharge for the various regions in Greece have been derived using numerical models. Our results highlight two RGS regimes in Greece (variable stress and unstressed) of the four characteristic stress regimes, that is, overstressed, variable stress, human‐dominated stress, and unstressed, defined as a function of the sign of use and the sign of groundwater availability (positive or negative). Variable stress areas are found in Central Greece (Thessaly region), where intensive agriculture results in negative ΔGW_trend values combined with positive mean annual recharge rates. RGS values range from ?0.05 to 0, indicating a low impact area. Within this region, adverse effects of groundwater overexploitation are already evident based on the negative GRACE anomalies; however, recharge is still positive, mitigating the effects of over‐pumping. The rest of Greek aquifers fall within the unstressed category, with RGS values from 0.02 to 0.05, indicating that the rate of use is less than the natural recharge rate.     

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.     

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.     

Assessing Groundwater Depletion and Dynamics Using GRACE and InSAR: Potential and Limitations

In the last decade, remote sensing of the temporal variation of ground level and gravity has improved our understanding of groundwater dynamics and storage. Mass changes are measured by GRACE (Gravity Recovery and Climate Experiment) satellites, whereas ground deformation is measured by processing synthetic aperture radar satellites data using the InSAR (Interferometry of Synthetic Aperture Radar) techniques. Both methods are complementary and offer different sensitivities to aquifer system processes. GRACE is sensitive to mass changes over large spatial scales (more than 100,000 km²). As such, it fails in providing groundwater storage change estimates at local or regional scales relevant to most aquifer systems, and at which most groundwater management schemes are applied. However, InSAR measures ground displacement due to aquifer response to fluid‐pressure changes. InSAR applications to groundwater depletion assessments are limited to aquifer systems susceptible to measurable deformation. Furthermore, the inversion of InSAR‐derived displacement maps into volume of depleted groundwater storage (both reversible and largely irreversible) is confounded by vertical and horizontal variability of sediment compressibility. During the last decade, both techniques have shown increasing interest in the scientific community to complement available in situ observations where they are insufficient. In this review, we present the theoretical and conceptual bases of each method, and present idealized scenarios to highlight the potential benefits and challenges of combining these techniques to remotely assess groundwater storage changes and other aspects of the dynamics of aquifer systems.     

The Trend of Groundwater Level Using Threshold-Based Wavelet De-Noising Approach

Water Resources - The aims of this study was to identify the groundwater level (GWL) trend and dominant periodic component of Ardabil plain (North-west of Iran) using three variations of the...     

Groundwater Recharge Rate and Zone Structure Estimation Using PSOLVER Algorithm

The quantification of groundwater recharge is an important but challenging task in groundwater flow modeling because recharge varies spatially and temporally. The goal of this study is to present an innovative methodology to estimate groundwater recharge rates and zone structures for regional groundwater flow models. Here, the unknown recharge field is partitioned into a number of zones using Voronoi Tessellation (VT). The identified zone structure with the recharge rates is associated through a simulation‐optimization model that couples MODFLOW‐2000 and the hybrid PSOLVER optimization algorithm. Applicability of this procedure is tested on a previously developed groundwater flow model of the Tahtal? Watershed. Successive zone structure solutions are obtained in an additive manner and penalty functions are used in the procedure to obtain realistic and plausible solutions. One of these functions constrains the optimization by forcing the sum of recharge rates for the grid cells that coincide with the Tahtal? Watershed area to be equal to the areal recharge rate determined in the previous modeling by a separate precipitation‐runoff model. As a result, a six‐zone structure is selected as the best zone structure that represents the areal recharge distribution. Comparison to results of a previous model for the same study area reveals that the proposed procedure significantly improves model performance with respect to calibration statistics. The proposed identification procedure can be thought of as an effective way to determine the recharge zone structure for groundwater flow models, in particular for situations where tangible information about groundwater recharge distribution does not exist.     

Groundwater Storage Change in the Jinsha River Basin from GRACE,Hydrologic Models,and In Situ Data

Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76 ± 0.10 cm/year during the period 2003 to 2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change, GWSC, and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.     

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.     

Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data

Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse‐gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse‐gas emissions scenario employed.     

Estimation of Submarine Groundwater Discharge

Leading methods for the evaluation of submarine groundwater discharge are presented, and their possible application under different hydrogeological conditions is discussed.     

Assessing and Improving Land Surface Model Outputs Over Africa Using GRACE,Field, and Remote Sensing Data

The Gravity Recovery and Climate Experiment (GRACE), along with other relevant field and remote sensing datasets, was used to assess the performance of two land surface models (LSMs: CLM4.5-SP and GLDAS-Noah) over the African continent and improve the outputs of the CLM4.5-SP model. Spatial and temporal analysis of monthly (January 2003–December 2010) Terrestrial Water Storage (TWS) estimates extracted from GRACE (TWS_GRACE), CLM4.5-SP (TWS_CLM4.5), and GLDAS-Noah (TWS_GLDAS) indicates the following: (1) compared to GRACE, LSMs overestimate TWS in winter months and underestimate them in summer months; (2) the amplitude of annual cycle (AAC) of TWS_GRACE is higher than that of TWS_LSM (AAC: TWS_GRACE > TWS_GLDAS > TWS_CLM4.5); (3) higher, and statistically significant correlations were observed between TWS_GRACE and TWS_GLDAS compared to those between TWS_GRACE and TWS_CLM4.5; (4) differences in forcing precipitation and temperature datasets for GLDAS-Noah and CLM4.5-SP models are unlikely to be the main cause for the observed discrepancies between TWS_GRACE and TWS_LSM; and (5) the CLM4.5-SP model overestimates evapotranspiration (ET) values in summer months and underestimates them in winter months compared to ET estimates extracted from field-based (FLUXNET-MTE) and satellite-based (MOD16 and GLEAM) ET measurements. A first-order correction was developed and applied to correct the CLM4.5-derived ET, soil moisture, groundwater, and TWS. The corrections improved the correspondence (i.e., higher correlation and comparable AAC) between TWS_CLM4.5 and TWS_GRACE over various climatic settings. Our findings suggest that similar straightforward correction approaches could potentially be developed and used to assess and improve the performance of a wide range of LSMs.     

基于GRACE和GLDAS水文模型反演安徽省地下水储量变化

地下水储量的有效监测是实现区域水资源管理的重要依据,传统监测方法存在各自的局限性导致其实现较为困难.本文提供一种使用GRACE卫星重力数据与GLDAS水文模型数据反演得到安徽省区域地下水储量变化的方法.利用2002年4月-2017年6月不同机构GRACE卫星重力数据的综合解,经过DDK去相关光滑滤波与退卷积法分别消除或削弱南北条带误差、改正信号泄露反演得到安徽省陆地水储量变化,扣除由GLDAS水文模型数据获取的地表水储量变化,得到安徽省地下水储量变化时间序列,并结合国家统计局官方发布的安徽省地下水资源量进行初步验证分析.研究结果表明:安徽省地下水储量变化长期表现为波动下降趋势,其年变化率约为-5.35 mm·a-1,且呈现出自东南方向至西北方向逐次递减的显著空间差异;地下水储量表现出明显的季节性变化,夏季和冬季地下水储量呈现回升趋势,春季和秋季呈现出下降趋势.除去反演过程存在较大干扰因素的情况,反演结果与国家统计局官方数据的相关系数达到89.62％,因此本文反演得到的地下水储量变化的结果是相对可靠的.     

基于广义高斯模型的SAR幅度图像震害检测

本文以2008年5月12日汶川地震前后的星载ALOS 合成孔径雷达图像作为数据源, 利用广义高斯分布模型作为差异图像中变化类和未变化类的分布模型, 在假设变化和未变化像元服从广义高斯分布的条件下, 估计变化和未变化像元的概率密度参数, 采用KI算法计算最佳分割阈值并提取变化区域。 以都江堰地区为例, 自动检测这次地震导致的地表变化, 快速圈定一些变化差异较大的区域。 研究表明, 该变化检测算法能较准确地描述差异影像的分布, 能够在地震灾害提取中发挥作用。     

小波极大值法在长波辐射地震异常信息提取中的应用

小波方法作为近年来兴起的一种数据挖掘方法已经应用于地震科学的研究领域.本文介绍了如何利用小波极大值法提取长波辐射值中的地震异常信息,即利用连续小波变换方法计算小波极大值,依据长波辐射在时间、空间上的连续性得出研究区的小波极大值分布,通过分析图中小波极大值的分布形态来识别地震异常,并以汶川地震为例介绍了如何利用小波极大值法提取长波辐射中的地震异常.     

Steric Sea-level Variations Inferred from Combined Topex/Poseidon Altimetry and GRACE Gravimetry

In this study, we propose to estimate the steric sea-level variations over a < 2-year period (April 2002 through December 2003) by combining global mean sea level (GMSL) based on Topex/Poseidon (T/P) altimetry with time-variable geoid averaged over the oceans, as observed by the GRACE (Gravity Recovery and Climate Experiment) satellite. In effect, altimetry-derived GMSL changes results from two contributions: Steric (thermal plus salinity) effects due to sea water density change and ocean mass change due to water exchange with atmosphere and continents. On the other hand, GRACE data over the oceans provide the ocean mass change component only. The paper first discusses the corrections to apply to the GRACE data. Then the steric contribution to the GMSL is estimated using GRACE and T/P data. Comparison with available thermal expansion based on in situ hydrographic data is performed. G. García: On leave from Space Geodesy Laboratory, Applied Mathematics Department, EPS, University of Alicante, Alicante, Spain.     

Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results

We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at \(5^\circ\) resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period.