Groundwater Level Changes Research Articles

A new modelling framework to assess changes in groundwater level

Study regionThe Southern African region consists of ten countries including, Angola, Zambia, Malawi, Namibia, Botswana, Zimbabwe, Mozambique, South Africa, Lesotho, and Swaziland. Frequent drought episodes in the arid and semi-arid parts of the region suggest the impact of climate change and the need for a sustainable framework for groundwater level assessments. Study focusIn this study, we developed a machine learning modelling framework based on the deep belief network (DBN) to predict the changes in monthly groundwater levels (CGWLs) at 1–5 month time scales for 27 groundwater wells over the southern Africa region. With a predictor dataset constituted by hydrological parameters, groundwater level estimates, and global climate indices, we ascertain the possibility of forecasting changes in groundwater levels (CGWL) up to 5-month lead times at most locations in the study region. Using the quantile regression technique, the strength of the DBN network at 90% and 95 % confidence levels were tested, and this helped to determine the accuracy of our forecasts at five months lead times over the four representative wells. New hydrological insights for the regionDeep learning offers new capabilities in evaluating non-linear hydrological systems, including groundwater analysis. The re-injection procedure of the DBN network, which allows the prior CGWL estimates to serve as inputs for the next estimate was key in maintaining the forecast accuracy in the 4th and 5th month lead times. This was evidenced in the accuracy of the four representative wells (r = 0.91, 0.87, 0.81, 0.75), which formed part of the test samples in CGWL analysis. We also observed that the DBN is highly susceptible to local climate variables and global climate indices, which are important drivers of climate change, and can have strong impact on groundwater level fluctuations. Therefore, the DBN proved to be a robust algorithm in our general assessments of groundwater level fluctuations.

Synthetic modeling and field GPR survey to understand the near-surface deformation induced by coseismic groundwater dynamics following the 2019 Mirpur earthquake in Pakistan

The Mirpur earthquake of Mw 5.8 on 24 September 2019 severely damaged villages in the epicentral area. The earthquake-triggered ground dynamics produced widespread liquefaction features on the surface (e.g., fractured road structure and sand boils) and in the shallow subsurface (e.g., elevated groundwater table, fractures, and sand dikes). Due to complexity of liquefaction-induced deformation and timing of field surveys, the structural and hydrogeologic features that develop in the near surface during seismic events are often poorly resolved on ground-penetrating-radar (GPR) profiles. To track and delineate the internal architecture and coseismic changes in groundwater levels and/or pressures due to liquefaction, a GPR survey was carried out soon after the Mirpur earthquake. The sudden pore water pressure changes in the shallow aquifer system caused a rise in the water table via upward groundwater movement and fracturing followed by rapid infiltration. This cascade phenomenon has adverse effects on soil, water resources, civil infrastructure, and public health and safety. To better understand induced changes in the physical properties of road infrastructure and the shallow subsurface, we performed synthetic modeling of electromagnetic wave propagation by employing the finite-difference time-domain method. Based on the available information at multiple scales (borehole and geophysical), subsurface physical models representing the most common and important coseismic features were considered for synthetic modeling. Likewise, synthetic tests of paved road were conducted to simulate the radar response of structural discontinuities (fractures) developed at different angles. To validate synthetic results by identifying a similar dynamic response of the induced hydrogeologic changes in the form of coseismic liquefaction features, a field GPR survey was conducted. Our results clearly demonstrate that the GPR technique is a potential candidate for hydrogeophysical detection and characterization of features of interest in the case of paved roads as well as agricultural and residential areas. Additionally, the successful application of GPR proves it to be a cost-effective, high-resolution, and speedy tool for better assessment of hydrogeologic hazard, socioeconomic consequences, and mitigation in the Mirpur area. The proposed approach may find applications in other seismically active regions around the world.

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2months, but the models provided unsatisfactory results for a 3-month leading time.

Application and comparison of different statistical methods for the analysis of groundwater levels over time: Response to rainfall and resource evolution in the Piedmont Plain (NW Italy)

Monitoring and analysis of groundwater level (GWL) in space and time is one of the tools used to evaluate the quantitative status of groundwater (GW) resources and identify possible alterations and critical cases due to climate change and variability, anthropogenic influences and other driving factors.In this study, four statistical methodologies (trend, change-point, percentile and non-standardized anomaly analyses) were applied for GWL and rainfall (R) analysis in the Piedmont Plain (western Po Plain, NW Italy). To detect the interannual variations in the GW maximum annual amplitude, the coefficient of variation was also used.The aims of the study were 1) to compare the results of different statistical methods, highlighting their applicability and differences to evaluate the quantitative evolution of GW, 2) to identify the relationship between GWL and R, 3) to investigate the spatiotemporal variation in the GWL of shallow aquifers in the Piedmont Plain, and 4) to describe critical situations of GW depletion.The study highlights that the application of a single method for assessing the shallow GW resource status does not always guarantee a reliable evaluation. For this reason, it is advisable to apply different analysis methods at the same time. Completeness of data and medium to long time series are prerequisites for meaningful analyses. The use of the same time interval is always necessary for comparisons between different monitoring wells and between the results of different statistical analyses. Last, by spatializing the results, it was possible to identify areas characterized by similar GWL behaviour due to hydrological structure, climate variability, land use and the evolution of anthropogenic activities over time. These factors influence vary locally in the Piedmont plain and require local assessments to determine the impact of changes in GWL.

When climate variability partly compensates for groundwater depletion: An analysis of the GRACE signal in Morocco

Study regionMorocco, Northwestern Africa. Study focusSince April 2002, the Gravity Recovery and Climate Experiment (GRACE) mission have opened new pathways for hydrologists to monitor the changes in terrestrial total water storage (TWS). Here, the Center for Space Research (CSR), Goddard Space Flight Center (GSFC), Jet Propulsion Laboratory (JPL), and the average (AVG) GRACE mascon solutions were used to examine the changes in TWS and groundwater storages (GWS) in Morocco, with an emphasis on natural replenishment events and their link to snow cover area (SCA) and rainfall variability. New hydrological insights for the regionThe results showed that GRACE TWS from AVG (TWSAVG) and GSFC (TWSGSFC) can fairly describe the temporal patterns of the groundwater level (GWL). Moreover, during 2002–2020, the TWS underwent a strong depletion relatively masked by natural recharge events. This was revealed as we identified two intermittent depletion episodes with statistically significant rates (−1.03 ± 0.11 to −0.31 ± 0.1 cm yr−1) higher than those obtained for the long-term trend lines (−0.28 ± 0.11 to −0.15 ± 0.07 cm yr−1). The TWS appeared to be strongly linked with the SCA metrics and rainfall indices with 1–3 months of lag. Our findings suggest that the rainfall distribution can be more insightful about changes in groundwater levels compared to the rainfall monthly totals.

Combining river replenishment and restrictions on groundwater pumping to achieve groundwater balance in the Juma River Plain, North China Plain

In recent years, to alleviate the decline in groundwater levels, extensive restrictions on groundwater pumping have been implemented in the North China Plain (NCP). In September 2018, a large-scale ecological water replenishment project was executed involving 22 rivers and lakes. How to adjust the layout of reduction on groundwater pumping within the context of ecological water replenishment is a key issue to be addressed in the study of groundwater level recovery in the NCP. This study adopted the Juma River Plain in Baoding city as a case study, established a numerical model of river replenishment of groundwater, predicted groundwater level changes over the next 15 years (2021–2035) and quantitatively calculated the impact of river replenishment on groundwater levels. To achieve the goal of an overall groundwater balance by 2035, a suitable groundwater pumping restriction scenario was defined based on the impact of river replenishment on groundwater levels. The results indicated that by 2035, the relative rise in groundwater levels attributed to river replenishment and restrictions on groundwater pumping could reach 3.51 and 2.28 m, respectively. River replenishment significantly impacts groundwater levels, especially those near the river. Under the current groundwater exploitation conditions, river replenishment could ensure groundwater level recovery near the river, which accounts for 15% of the total study area. The goal of an overall groundwater balance by 2035 could be achieved if restrictions on groundwater pumping were superimposed, with an average annual reduction of 56 million m3. This study provides valuable insights into groundwater management across the NCP. The proposed methods are useful for the management of other depleted aquifers recharged via ecological water replenishment.

A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa

Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer. Using data from two boreholes, Ngabu (sample size = 96) and Nsanje (sample size = 45), we model two predictive scenarios: (I) predicting the change in the current month’s groundwater level, and (II) predicting the change in the following month’s groundwater level. For the Ngabu borehole, GBDT achieved R2 scores of 0.19 and 0.14, while LSTM achieved R2 scores of 0.30 and 0.30, in experiments I and II, respectively. For the Nsanje borehole, GBDT achieved R2 of −0.04 and −0.21, while LSTM achieved R2 scores of 0.03 and −0.15, in experiments I and II, respectively. The results illustrate that LSTM performs better than the GBDT model, especially regarding slightly greater time series and extreme GWL changes. However, closer inspection reveals that where datasets are relatively small (e.g., Nsanje), the GBDT model may be more efficient, considering the cost required to tune, train, and test the LSTM model. Assessing the full spectrum of results, we concluded that these small sample sizes might not be sufficient to develop generalised and reliable machine learning models.

The starting mechanism study on rainfall-induced sloop loose source in strong earthquake area

The 5.12 Wenchuan earthquake in 2008 led to a large number of loose deposits on the slope on both sides of the gully in the formation area. Under the triggering action of heavy rainfall, the previously unrecognized slope loose source in formation areas easily failed and started earlier than the overall start of the gully debris flow, which has practical significance for realizing the early warning of the gully debris flow starting. The special loose source conditions and postearthquake geological environment change the hydraulic mechanism of traditional debris flow start-up, which brings great difficulties to the monitoring and early warning of postearthquake debris flows. Therefore, based on hydraulics, on the premise of building a hydraulic model of groundwater level change of loose slope accumulation body, the characteristics and rules of groundwater level change are analyzed with the aid of hydraulic seepage theories, and the action characteristics of hydrodynamic pressure and hydrostatic pressure on the accumulation body are quantitatively studied. Based on field exploration data and average slope gradient, we divided the accumulation body in a reasonable manner; completed the sliding force, anti-sliding force and residual-sliding force mechanical expression and establishment; and performed a slope stability analysis. The research results show that the slope starting mode is divided into the thrust-type landslide and the retrogressive landslide mode of the subsection disintegration. The hydraulic deposit mechanism analytical results can also reverse the critical rainfall condition of slope failure. Finally, taking the Yindongzi gully in Dujiangyan as an example, the hydraulic mechanism of typical slope deposits in the formation area was analyzed and combined with the historical debris flow events in the study area to verify the physical simulation test, which was consistent with the field investigation results after the disaster.

Spatiotemporal Distribution and Statistical Analysis of Abnormal Groundwater Level Rising in Poyang Lake Basin

Studies on groundwater have traditionally been based on declining groundwater levels and associated ecological, environmental, and geological problems. However, due to extreme hydrometeorological events and human activities, rising groundwater levels have been observed in many areas. The daily groundwater levels from 2018 to 2020 for the Poyang Lake Basin (PLB) in Jiangxi Province were recorded. The statistical characteristics of abnormal groundwater level rising (AGLR) events and the factors influencing the dynamic changes in groundwater level were analyzed using geostatistical methods and outlier identification methods. The groundwater level in the lower terrain of the PLB has increased significantly in recent years. AGLR events identified by the median absolute deviation and interquartile range methods showed that AGLR events mainly occurred in the spring and summer and were mainly distributed near the surface water bodies. Correlation analysis of the factors influencing the groundwater level revealed that the correlation between precipitation and groundwater level was related to topography. In contrast, the correlation between river stage and groundwater level was related to runoff volume.

Effects of Hybrid-Type Artificial Groundwater Recharge and Underground Barrier in a Small Basin

Climate change is exacerbating water shortages in upstream basins in the Korean peninsula that lack agricultural water supply systems. The basin investigated in this study requires an extra 208 m3·d−1 of agricultural water during May (the busiest month for agriculture). The purpose of this study was to assess a hybrid-artificial recharge and circulation system, which was composed of a hybrid-recharge source and re-infiltration of pumped water in the field, and to estimate yield capacity by a field injection test and a numerical model. Injecting pretreated stream water for 42 d increased groundwater levels in the recharge basin. Water budget analysis in MODFLOW simulations revealed that injecting water increased groundwater levels as well as stream discharge due to the terrain’s gentle slope. To prevent downstream discharge and maintain groundwater levels after injection, we assumed the installation of an underground barrier at the basin outlet in the model, following which changes in groundwater levels and water balance were simulated. Water level was persistently maintained after a ~31-cm water level rise, and 590 m3·d−1 of water could be supplied from the collector well, which can ease water shortages. Therefore, it is necessary to develop structures to prevent recharged water escape when artificially recharging groundwater in small upstream basins. In upstream areas where reservoirs or water supply conduits are unfeasible, artificial recharge systems could solve water shortages.

Characterizing Spatiotemporal Patterns of Land Deformation in the Santa Ana Basin, Los Angeles, from InSAR Time Series and Independent Component Analysis

The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to infrastructure and human lives. However, the most recent research on land surface deformation in the area was conducted using the traditional parameter estimation method, resulting in little understanding of the regional spatiotemporal characteristics. The parametric method consists of a least square linear inversion, using the pre-defined mathematical geometric or geophysical theoretical models to describe groundwater deformation, and it requires precise external environmental variables and accurate geophysical parameters, which are more difficult to implement. In this study, multitemporal InSAR-derived deformation time series are analyzed by using 69 descending C-band Sentinel-1A SAR scenes acquired from 2015 to 2018. A method based on independent component analysis (ICA) is applied to characterize the spatial pattern and temporal evolution of land subsidence in the Santa Ana basin. The results reveal two different spatial and temporal deformation patterns in the basin. First, a widespread seasonal deformation is identified by the first component, related to annual seasonal groundwater level changes, and the overall deformation shows a concentrated spatial pattern. The second component captures a long-term signal with a large-scale spatial pattern. For quantitative assessment, the obtained deformation time series are compared with the GNSS data, validating an accuracy of millimeters. We further calculate the cross-correlation coefficient and the elastic skeletal storage coefficient from the ICA-derived seasonal deformation and groundwater level, which reveals that the deformation responds quickly (i.e., a lag of 8 days) to the change in groundwater and the Santa Ana aquifer retains almost the same elasticity for at least 15 years. Quantifying the spatiotemporal characteristics of the deformation in the Santa Ana basin can provide a reference for the monitoring and managing of groundwater.

Characteristics of soil carbon emissions and bacterial community composition in peatlands at different stages of vegetation succession

Microorganisms are important components of soil ecosystems and play an important role in material cycles. Northern peatlands are important ecosystems in middle-high latitude regions. In peatlands, different vegetation successions occur with changes in groundwater levels. The overall carbon emission of peat bogs is related to the carbon stability of the surrounding environment. Unraveling the assembly and distribution of bacterial communities at different succession stages in peatland is essential to understanding the soil nutrient cycle. In this study, we investigated the characteristics of soil carbon emissions and the composition of subsurface microorganisms under six different succession stages. The highest carbon emission was observed in mossy peatlands, and their soil enzyme activity was closely related to the aboveground vegetation cover type. The succession pattern of ground vegetation was the main driver of soil microorganisms. The abundance of the dominant Proteobacteria decreased with increasing soil depth, while the opposite trend was observed for Chloroflexi. Furthermore, the community structure of microorganisms became progressively simpler and looser as soil water content decreased. The bacterial alpha diversity was driven by soil dissolved organic carbon and Fe, and the beta diversity was driven mainly by soil water content. The bacteria presented a random distribution in a nutrient-rich soil environment and shifted to deterministic distribution with decreasing water and nutrient contents. The balance between taxonomic diversity and dispersal limitation mediates species coexistence in the soil microbiome. This study provides new insights into the soil environment at different stages of succession in peatlands.

Beijing Land Subsidence Revealed Using PS-InSAR with Long Time Series TerraSAR-X SAR Data

Beijing is a major city suffering from land subsidence due to long-term over-exploitation of groundwater. The South-to-North Water Diversion Project (SNWDP), however, has had a significant impact on the structure of water consumption since the end of 2014, and it is changing the status of land subsidence in Beijing. In this study, we employed Persistent Scatterers Synthetic Aperture Radar Interferometric (PS-InSAR) to investigate the decadal evolution of land subsidence in Beijing with 100 TerraSAR-X stripmap images collected from April 2010 to December 2019. The water resources, historic climate and urban construction data were compiled for the years of 2010 to 2019 to analyze changes in groundwater level, human activity, surface geology, active faults and land subsidence patterns. The results show that the changes in the water supply structure are correlated to a rise in groundwater level after 2015. These changes include an increase in the water supply from the SNWDP, a reduction in groundwater exploitation, the optimization of water consumption, replacing recycled water for environmental water and a reduction in the use of water for agriculture. Land subsidence in the study area was concentrated in the eastern regions, trending towards a decreasing velocity starting about two years after the commencement of SNWDP in 2015. Uneven subsidence in the land subsidence area was related to excavations of underground soil, and the construction of Line 6 and Line 7 led to rapid nonlinear subsidence. Our results have scientific significance for reducing subsidence hazards in the context of SNWDP and urban expansion.

Spatial and temporal trends in California coastal cliff retreat

Airborne LiDAR datasets were used to measure erosion and retreat along 866 km of California coastal cliffs between 2009–2011 and 2016. Erosion exceeding the level of detection was observed at 55% of cliffs. State-averaged cliff face and top retreat rates were both 0.06 m yr−1, and varied alongshore, with more erosion in northern California compared to southern and central California. Retreat rates were higher for unarmored cliffs and cliffs fronted by sandy beaches. Mean cliff top retreat rates were lower than in previous studies conducted between the 1920s–1930s and 1998/2002, and between 1998 and 2009–2011. However, the average cliff face retreat rates and statistical distributions were similar (r2 = 0.99) for the 1998 to 2009–2011 and 2009–2011 to 2016 time periods. No significant correlation was found between cliff erosion rates and environmental factors such as rock hardness, rainfall, groundwater, waves, and relative sea level change. However, the highest cliff face retreat rates (>1 m yr−1) occurred at locations with weak rocks.

Projected changes of groundwater levels in northeastern Romania according to climate scenarios for 2020–2100

Study regionA plain region covering 8000 km2 in extreme north-eastern Romania in which two groundwater levels are identified, near the surface (1.5 m) and in-depth (4.5 m) respectively. Study focusThe statistical relationship between climate conditions and underground water resources was assessed by means of correlation analysis and autoregressive neural networks (ANNs). Based on this, the study explores the quantitative changes in groundwater levels towards the end of this century according to most relevant climate scenarios regarding the evolution of air temperature and precipitation amount. New hydrological insights for the regionThe results of the ANNs model show that air temperature together with precipitation amount explain between 50% and 70% of the groundwater levels variability for 1983–2017 interval. For local conditions instead, analysed at the level of main regional weather stations, the explained variability by the climate conditions decreases between 40% and 60%. This overall good statistical performance enables us to assimilate into the model the climate scenarios data for the region. The groundwater projections indicate that the decline in groundwater levels is expected to be general in both climate scenarios analysed. Thus, the impact of climate change on groundwater supply is expected to be very high over the study region, adding to the onset effect of diminishing water quality, raising a great risk on water availability for natural and socio-economic environment.

Exploring recent groundwater level changes using Innovative Trend Analysis (ITA) technique over three districts of Jharkhand, India

Groundwater is the largest store of available freshwater in the world. With the increasing impact of climate change and human activities, assessment of variations in groundwater levels is essential. This study presents the trend of groundwater levels in three districts (Purbi Singhbhum, Ranchi and Saraikela) of Jharkhand State, India. The depth to groundwater level (DGWL) data from 24 wells over the three districts for 1996–2018 is collected for both pre-monsoon and post-monsoon seasons. The conventional non-parametric trend analysis techniques (e.g., Mann-Kendall, Sen's Slope, Spearman's Rho tests) have several restrictive assumptions, such as Gaussian distributed time series and absence of serial correlation. Innovative Trend Analysis (ITA) technique does not have these restrictive assumptions and therefore, can be effectively used to detect trends in a time series of shorter length. Moreover, ITA is a slope-based technique that provides visual interpretations as well as the statistical significance of trends in a time series, thereby comprehensively acquiring the benefits of multiple conventional methods. Therefore, ITA is employed in this study to investigate the trends in groundwater levels. The results reveal a significantly increasing trend of DGWL for 17 sites in pre-monsoon and 14 sites in post-monsoon seasons. On the other hand, only four and six stations showed negative trends during pre- and post-monsoon seasons, respectively. The increasing DGWL trends at a majority of the sites imply a serious decline in the groundwater levels over the years. This study will be helpful to water resources managers to identify the causes of the declining groundwater levels, take necessary actions to control them, and efficiently manage groundwater at local levels.

Groundwater Extraction Reduction within an Irrigation District by Enhancing the Surface Water Distribution

Today, in developing countries, the low surface water distribution efficiency and the lack of supplying water needs of farmers by surface water resources are compensated by excessive aquifer water withdrawal. This mismanagement has caused a sharp drop in the groundwater level in many countries. On the other hand, climate change and drought have intensified the pressure on water resources. This study aims to evaluate novel strategies for developing surface water distribution systems for stress reduction of the Najafabad aquifer in Isfahan, central plateau of Iran. The performance of several strategies for agricultural water distribution and delivery, such as hydro-mechanical operating system, manual-based operating system, and centralized automatic operating system, was evaluated in this study. In the first step, two indices, i.e., water distribution adequacy and dependability, were obtained using a flow hydraulic simulation model. Then, the water distribution adequacy map and amount of reduction in the water withdrawal of existing wells were determined for each strategy. Finally, using the MODFLOW groundwater simulation model, the changes in groundwater levels due to the normal and drought scenarios (15 and 30%) were extracted during five years for each strategy. The findings for the normal scenario showed that the centralized automatic operating system strategy had the most significant impact on agricultural water management in the surface water distribution system with a 30% increase in agricultural water distribution adequacy index compared to the current situation. This strategy increased the groundwater level by 11.6 m and closed 35% of the groundwater wells. In this scenario, the hydro-mechanical operating system strategy had the weakest performance by increasing the aquifer level by only 1.31 m. In the 15% and 30% drought scenarios, the centralized automatic operating system strategy exerted the best performance among other strategies by increasing the aquifer water level by 10.18 and 9.4 m, respectively, compared to the current situation. Finally, the results showed that the spatial segmentation of the aquifer exerted better efficiency and better monitoring in the more susceptible regions.

Interpretation of the Spatiotemporal Evolution Characteristics of Land Deformation in Beijing during 2003–2020 Using Sentinel, ENVISAT, and Landsat Data

Since the 1930s, due to the rapid development of the city and the increase of population, the demand from Beijing residents for water resources has gradually increased. Land deformation in the Beijing Plain is a serious issue. In order to warn of, and mitigate, disasters, it is urgently necessary to obtain the latest rate, extent, and temporal evolution of land subsidence in Beijing. Firstly, the temporal and spatial distribution characteristics of land deformation in Beijing during 2003–2020 were unveiled using the time-series interferometric synthetic aperture radar (InSAR) technique and two different satellite datasets, sentinel-1a/1b and ENVISAT ASAR. By means of combining calibration of InSAR results with the global positioning system (GPS), we studied the evolutionary process of long-term land subsidence in Beijing. The precision of our InSAR annual subsidence results is less than 10 mm. Land subsidence in Beijing is unevenly distributed, and so five main land subsidence zones were monitored. The time-series results showed that the rate of land subsidence rate continued to increase from 2003 to 2015, but has gradually shown a slowing trend from 2015 to 2020. Further, we used the quadratic polynomial fitting method to interpolate the time-series deformation results from 2010 to 2015, and compared these with GPS. The results demonstrated that although the InSAR observation method is not strictly registered with GPS in time, its deformation trend is consistent. In addition, the calibrated long time-series was consistent with the three deformation stages of land subsidence evolution in Beijing. Finally, we analyzed the deformation information obtained by InSAR technology in combination with land use type data, precipitation and groundwater data. The results demonstrated that the central area is mostly stable, and land deformation in the northeast is obvious and uneven. In addition, land use type and precipitation have little influence on land subsidence. Changes in land subsidence were closely related to changes in groundwater level, and seasonal variations in deformation correlated with precipitation.

Predicting groundwater level changes within the irrigation network range using the tree algorithm (case study: Alborz plain)

Predicting groundwater level changes within the irrigation network range using the tree algorithm (case study: Alborz plain)

Quantification of climate change sensitivity of shallow and deep groundwater in Denmark

Study regionThe study investigates six different geological regions covering 42,111 km or 98% of Denmark. Study focusClimate change impacts on spatial changes and uncertainties of water balance and groundwater levels are still not well understood, especially, the effect from hydrogeology and geomorphological setting on impact response. In this study, the aim is to quantify the climate change sensitivity of shallow and deep groundwater levels across multiple types of geologies and aquifers for the six Danish regions. 21 climate models under RCP4.5 and RCP8.5 were used to force the National Water Resources Model for 1971–2100. New hydrological insightsDenmark may expect future precipitation increases ranging from + 12% to + 20%, resulting in substantial hydrological changes e.g., actual evapotranspiration (+14–17%), recharge (+11–27%), discharge (+11–33%), drainflow (+16–32%), mean phreatic (+0–18 cm) and deep groundwater levels changes (+2–24 cm). Considerable differences in responses and sensitivity to climate change were found between the regions, and large precipitation changes were not always reflected in substantial groundwater level changes, not even if it led to a large increase in recharge. Climate sensitivities for the groundwater were shown to be low in regions with a shallow phreatic surface linked with restricting drainage networks or heightened actual evapotranspiration. Reversely, climate sensitivities were found to be high in areas with a deep phreatic zone and in high conductive deep chalk aquifers.