Groundwater Changes Research Articles

3D Modelling of Geoelectrical Resistivity Data to Determine the Direction of Landslides in Kastoba Lake, Bawean Island, Indonesia

Landslides are one of the factors caused by subsurface structures that are not compact, and have low density or resistivity values due to changes in groundwater. Rainwater that infiltrates into the ground will reach to an impermeable layer which is identified as a slip plane. This research was conducted in the Kastoba Lake area, Bawean Island, Indonesia. The purpose of this study is to determine the slip plane and the direction of the landslide in the studied area. Data acquisition was carried out based on a preliminary study of residual anomaly contour analysis from gravity data, then geoelectric data acquisition was carried out in the studied area. The results of lithological analysis on 3D geoelectric resistivity modeling in this study obtained three layers of subsurface structures, namely topsoil, clay, and lavas. The slip plane of the study area is above the clay layer (7.25 meters depth). The results of the 3D interpretation indicated that the direction of the landslide was towards the north, as evidenced by the existence of public information that the area studied had landslides with the avalanche direction towards the north. For further analysis, a factor of safety analysis is needed based on the analysis of the bore hole drilling method in the studied area

Climate resilience and the human-water dynamics. The case of tomato production in Morocco

In Morocco, droughts are an increasing threat affecting water availability, agricultural production and producers' livelihoods. Moreover, water demand for irrigation has led to overexploitation of the groundwater table causing significant natural resource management challenges. The combination of groundwater changes and increasing drought risk raises concerns about the ability of agricultural producers to be resilient against drought. In this study, we describe the interactions of environmental and socioeconomic processes which influence farmers' livelihoods involved in tomato production in Morocco. Building on system dynamics modelling tools, we aim to improve the understanding of the long-term dynamic behavior of water management and to explore plausible policy scenarios necessary for sustainable and resilient water resource management and agricultural development. Our results show that tomato production is not yet severely impacted by droughts. However, droughts are accelerating the process of groundwater depletion, impacting farmers' livelihoods, by decreasing crop productivity and reducing farmer's revenue over a longer time period, especially since tomatoes are a high-value crop. Therefore, integrated and effective policies are presented as a set of measures for a systemic enhancement of resilience. We conclude that a more radical approach toward water resource conservation and upholding the most vulnerable producers has to be adopted in order to enhance a sustainable and inclusive resilience of the tomato production in Morocco.

Impact of differential surface water mixing on seasonal arsenic mobilization in shallow aquifers of Nadia district; western Bengal Basin, India

Arsenic (As) mobilization in groundwater is linked to the dissimilatory reductive dissolution of As(V) coated Fe(III)-oxy-hydroxides from the aquifer sediment coupled with the degradation of available dissolved organic carbon (DOC) due to the presence of anaerobic microbes under anoxic conditions. Understanding the seasonal pattern of As mobilization in the shallow groundwater of the Bengal Basin remains a challenging task due to the heterogeneous character of the shallow aquifers and the involvement of multiple factors. To resolve this issue, in the present study, we showcased a comprehensive effort to advance understanding of the seasonal As mobilization process in the shallow groundwater, utilizing multiple geochemical tracers (i.e., the abundance of dissolved total As, Fe, Mn, NO3–, SO42-, DOC, Cl-, and δ18O, δ2H, δ13C-DOC isotopic tracers) between post-monsoon and pre-monsoon periods over multiple years from Nadia district, West Bengal, India. We quantified and explained the seasonal variation of dissolved total As concentrations in the groundwaters with the nature of the aquifer lithology (i.e., grey sand aquifer or ‘GSA’ and brown sand aquifer or ‘BSA’). The present study reported elevated dissolved total As concentrations in the shallow groundwater samples during dry pre-monsoon (‘PRM’) periods compared to post-monsoon (‘POM’) time. However, the magnitude of such seasonal changes in groundwater As concentrations (denoted as ΔAs = AsPRM-AsPOM) varied between years depending on the extent of rainfall, surface water infiltration, mixing, and groundwater withdrawal. Our current findings are different from the past studies that reported elevated As concentrations in the shallow groundwater during the monsoon and post-monsoon periods compared to the dry pre-monsoon periods. However, the limitations of the past findings are that most of the previous studies were carried out between seasonal intervals over a single annual cycle without reinvestigating the seasonal trends over different years under variable hydrological conditions. We proposed that the excess groundwater withdrawal during the dry pre-monsoon period drive draw-down and, therefore, trigger infiltration of surface-derived deep pond water into the shallow aquifer. Such surface water infiltration introduces fresh labile organic matter into the shallow aquifer, promoting high As mobilization during the dry pre-monsoon period. A viable alternative approach of ‘squeezing’ of aquifer intercalated clay-peat sedimentary lenses and mixing of organic-rich pore water in the adjoining groundwater can also enhance high As mobilization during the dry time, as examined in this study. This process is triggered by the excessive groundwater withdrawal practices and drawdown encountered during dry time, driving the aquifer intercalated clay-pockets compaction.

Investigation of quantitative and qualitative changes in groundwater of Ardebil plain using ensemble artificial intelligence-based modeling

Abstract Groundwater is an essential source to supply water for various sectors. This paper aimed to predict the quantitative and qualitative changes in groundwater over time and to evaluate the efficiency of different modeling methods. This study is based on three steps. In the first step, quantitative and qualitative piezometers were clustered by the Growing Neural Gas Network (GNG) method, and the central piezometer of each cluster was used on behalf of each cluster. In the second step, four different Artificial Intelligence (AI) models were applied, namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Emotional Artificial Neural Network (EANN). As a post-processing approach three different ensemble methods were used: simple average ensemble (SAE), weighted average ensemble (WAE), and nonlinear neural network ensemble (NNE). In the third step, the outputs of single AI models were used to enhance the evaluation results. Therefore, the results demonstrate that the NNE led to reach the better performance for three GWL, TDS, and TH parameters up to 37, 29, and 23% on average, respectively. Study results will lead to the improvement of AI applications in groundwater research and will benefit groundwater development plans.

On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes

Abstract. Hillslope similarity is an active topic in hydrology because of its importance in improving our understanding of hydrologic processes and enabling comparisons and paired studies. In this study, we propose a holistic bottom-up hillslope clustering based on a region's integrative hydrodynamic response quantified by the seasonal changes in groundwater levels ΔP. The main advantage of the ΔP clustering is its ability to capture recharge and discharge processes. We test the performance of the ΔP clustering by comparing it to seven other common hillslope clustering approaches. These include clustering approaches based on the aridity index, topographic wetness index, elevation, land cover, and machine-learning that jointly integrate multiple data. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed – the East River – located in the headwaters of the Upper Colorado River Basin. The ΔP clustering performs very well in identifying hillslopes with six out of the nine characteristics studied. The variability among clusters as quantified by the coefficient of variation (0.2) is less in the ΔP and the machine learning approaches than in the others (> 0.3 for TWI, elevation, and land cover). We further demonstrate the robustness of the ΔP clustering by testing its ability to predict hillslope responses to wet and dry hydrologic conditions, of which it performs well when based on average conditions.

Monitoring Mass Variations in Iraq Using Time-Variable Gravity Data

Iraq is facing a water shortage due to water scarcity and anthropogenic activities. The recent advance in technologies in geophysical methods has made groundwater monitoring possible. Time-variable gravity data and outputs of the climatic model, as well as rainfall data, are integrated to investigate the spatio-temporal mass variations caused by groundwater changes over Iraq. The findings are: (1) For the entire study period (04/2002–12/2020), Period I (04/2002–12/2006), Period II (01/2007–12/2017), and Period III (01/2018–12/2020), the study region had an average annual precipitation rate of 223.4, 252.5, 194.2, and 311.6 mm/y, respectively. (2) The average Terrestrial Water Storage variations (ΔTWSs) varied from −5.79 ± 0.70 to −5.11 ± 0.70 mm/y based on the three different gravity solutions with a mean of −5.51 ± 0.68 mm/y for the entire investigated period. (3) For Periods I, II, and III, the average ΔTWS fluctuation was calculated to be +6.82 ± 1.92, −6.20 ± 1.17, and +28.58 ± 12.78 mm/y, respectively. (4) During the entire period, Periods I, II, and II, the groundwater fluctuation was averaged at −4.86 ± 0.68, +2.47 ± 2.20, −3.79 ± 1.20, and −4.63 ± 12.99 mm/y, respectively, after subtracting the non-groundwater components. (5) At the beginning of the 2007 drought during Period II, a decline in rainfall rate, and significant groundwater withdrawal during Period III all appear to have contributed to groundwater depletion. The Euphrates and Tigris Rivers, as well as the Mesopotamian plain, receive water from the running streams created by the ground relief. The area of the Mesopotamian plain, which has a thicker sedimentary sequence that can reach 9000 m, is found to have a positive TWS signal, indicating that its groundwater potential is higher. The integrated approach is informative and cost-effective.

Response of groundwater to different water resource allocation patterns in the Sanjiang Plain, Northeast China

Study regionThe Sanjiang Plain, located at the confluence of Heilong, Songhua, and Wusuli Rivers, Northeast China. Study focusDue to the low and flat terrain, wetlands and swamps were widely distributed in plain areas in history. After decades of reclamation, farmland has replaced wetland as the dominant landscape. The competition of water resources between wetland ecological health and food security is very fierce, which is mainly reflected in the serious overexploitation of groundwater. This study used a new coupled surface water-groundwater model with an innovative function of irrigation and drainage simulation to research the changes of groundwater in the Sanjiang Plain under different water resource allocation schemes and paddy development scale in the future, which may provide a scientific basis for the planning of water resource allocation projects related to the restoration of groundwater and wetlands in this region. New hydrological insights for the regionWe established four prediction models for different water resource allocation schemes and paddy areas by replacing the water supply data and planting structure of the baseline model in the Sanjiang Plain. By comparing the groundwater balance and level of various schemes simulated by prediction models, it was found that the complete water diversion scheme covering the plain areas would greatly reduce groundwater exploitation, increase groundwater recharge, and turn groundwater storage from deficit to surplus, which would increase by 853 million m3/a. It would gradually compensate for the historical debts due to groundwater overexploitation while achieving a balance between groundwater recharge and discharge; thus, the groundwater depth in most part of the plain areas would be restored to less than 5 m. However, the scheme also had a defect, that is, it may cause soil secondary salinization in shallow groundwater areas. Therefore, the scheme needs to be further optimized to make full use of the low-cost groundwater.

Construction and Application of Groundwater Impact Analysis Model Based on Random Matrix Model

Traditional groundwater models do not consider the influence of random factors, so their results of simulation and prediction cannot reflect the actual dynamics of ongoing groundwater changes. By comparing the statistical properties of random multidimensional time series, the random matrix theory can reflect the degree of deviation from randomness in actual data and reveal the behavioral characteristics of the overall correlation in actual data. On the basis of summarizing and analyzing the previous literature, this study expounded the research status and significance of groundwater impact analysis models, elaborated on the development background, current status, and future challenges of the random matrix model, introduced the methods and principles of the finite isometric properties and observation feature dimension, conducted the spectral analysis of the random matrix of groundwater impact analysis data, analyzed the eigenvalue and the eigenvector distribution of the random matrix of the groundwater impact analysis data, constructed a groundwater impact analysis model based on the random matrix model, designed the hydrodynamic model for the groundwater impact analysis, discussed the parameter variation mechanism of the groundwater impact analysis, and finally carried out a case application and its result analysis. The study results show that in the membership calculation formula of the random matrix model, it is found that there is an inverse relationship between the expected value of the target index and its membership value. According to the initial fuzzy membership function value, the random matrix model uses the normalization formula and evaluation criteria of the catastrophe model to carry out a comprehensive quantitative recursive operation and finally determines the concept, characteristics, influencing factors, evaluation content, purpose, and scope of the groundwater impact analysis model. The random matrix model can not only reflect the spatial and temporal differences of groundwater-influencing factors but also reflect the dynamic mechanism of aquifer or groundwater system or the inherent law of groundwater movement, so it is more applicable and convenient to evaluate groundwater resources.

Hydrogeochemical characteristics and gradual changes of groundwater in the Baiquan karst spring region, northern China

Hydrogeochemical characteristics and gradual changes of groundwater in the Baiquan karst spring region, northern China

Ecological Impact Prediction of Groundwater Change in Phreatic Aquifer under Multi-Mining Conditions

In aeolian sandy grass shoal catchment areas that rely heavily on groundwater, mining-induced geological deformation and aquifer drainage are likely to cause irreversible damage to natural groundwater systems and affect the original circulation of groundwater, thus threatening the ecological environment. This study aimed to predict the impact of groundwater level decline on vegetation growth in the Hailiutu River Basin (HRB), which is a coal-field area. Based on remote-sensing data, the land use/cover change was interpreted and analyzed, and the central areas of greensward land in the basin were determined. Subsequently, the correlation between groundwater depth and grassland distribution was analyzed. Then, the groundwater system under natural conditions was modeled using MODFLOW, and the groundwater flow field in 2029 was predicted by loading the generalized treatment of coal mine drainage water to the model. The change in groundwater depth caused by coal mining and its influence on the grassland were obtained. The results show that coal mining will decrease the groundwater depth, which would induce degradation risks in 4 of the original 34 aggregation centers of greensward land that originally depended on groundwater for growth in HRB because they exceeded the groundwater threshold. The prediction results show that the maximum settlement of groundwater level can reach 5 m in the northern (Yinpanhao), 6 m in the eastern (Dahaize), and 10 m in the southern (Balasu) region of HRB. Attention should be paid to vegetation degradation in areas where groundwater depth exceeds the minimum threshold for plant growth.

Study of changes in groundwater quality indicators during operation of water intake well

Groundwater is one of the main sources of providing people with drinking water. However, during the period of operation of water wells, the quantitative and qualitative characteristics of groundwater change significantly. These changes depend on many factors of natural and man-made origin, in particular, depletion of reserves, conditions of operation and construction of wells, anthropogenic impact on the environment and so on. To assess the water quality of the underground source intended for water supply of one of the facilities in Kyiv, a number of studies of the quality of water extracted from a well 67 m deep were compared. Experiments conducted after the well in 2010 and 2021 were compared. The analysis of the obtained results showed changes in the indicators of groundwater quality during the well operation, which relate to water turbidity, color, total hardness, dry residue, total iron and manganese content.
 In the current operating conditions of the water intake, the groundwater raised to the surface does not meet the current regulatory quality requirements, and therefore it needs to be purified before it is delivered to consumers. For this case and the need to provide water to a small facility, it is recommended to use the technology of deironing water by biological oxidation. The use of a bioreactor with an insoluble carrier and a contact-clarifying filter with expanded polystyrene loading is proposed. The technology has shown high efficiency in both laboratory and production conditions for local water mains.

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.

Estimation of annual groundwater changes from InSAR‐derived land subsidence

AbstractUnderstanding the extent and quantity of groundwater drawdown is critical for developing a mitigation strategy for water management. This study illustrates that the data‐driven model can be used for the spatial estimation of groundwater drawdown using interferometric synthetic aperture radar (InSAR)‐based deformation data. Here, InSAR derived from Sentinel‐1 imagery is used to estimate surface deformations in the Choshui river alluvial fan, Taiwan, between 2016 and 2018. Spatial regression (SR) is applied to estimate the annual groundwater drawdown with a calculated R‐square of 0.96, which is shown to be superior to a nonspatial model. This study demonstrates the potential of the satellite‐based groundwater drawdown map prediction using InSAR‐derived land deformation. In predication, the SR model can reliably catch the patterns of annual predicted drawdown without requiring detailed groundwater observations.

Diaphragm wall deformation and ground settlement caused by dewatering before excavation in strata with leaky aquifers

In deep excavations, pre-excavation dewatering (PED) usually causes notable diaphragm wall (D-wall) deformation and ground settlement. However, this problem has not attracted sufficient attention. In this paper, the mechanisms and control methods of settlement caused by PED in a deep excavation in Tianjin, China are analysed. A hydraulic connection between the soil inside and outside the excavation area due to aquifer leakage was observed during PED. Therefore, although the first level of struts had been installed before PED, the deformation of the D-wall and ground settlement reached 6·9 mm and 12·5 mm, approximately 29·0% and 73·5% of the maximum allowable values, respectively. The monitoring results demonstrated that leaky aquifers could reduce the deformation of the D-wall but increased the magnitude and extent of ground settlement. Based on the numerical simulations of D-wall deformation and groundwater change, in this case, the ground settlement within the area extending two times the depth of unloading (two times the depth of dewatering, 2Hd) from the excavation was found to be caused by both the deformation of the D-wall and the drawdown of the groundwater, whereas the noticeable settlement in the area beyond 2Hd was caused by only the drawdown of the groundwater. When hydraulic connections exist between the soil inside and outside the excavation area, artificial recharge should be used to control the noticeable settlement induced by groundwater drawdown.

Groundwater Potential Zone Assessment using GIS and Remote Sensing

Over-exploitation of groundwater and marked changes in climate over the years have imposed immense pressure on the global groundwater resources. As demand of water increases across the globe for human consumption, agriculture and industrial uses, the need to evaluate the groundwater potential and productivity of aquifers also increases. This paper aimed to delineate the groundwater potential zones using GIS and remote sensing. Overlay analysis technique is used to develop the groundwater potential prospect zones by integrating different groundwater contributing thematic layers. The thematic layers of land use and land cover, drainage density, lineament density, soil, geology, slope and rainfall were prepared and used for groundwater potential map development by assigning weights to each thematic layer and features. The weights of each thematic layer were assigned based on their importance and relationship with groundwater recharge. Finally, the thematic maps were integrated by Raster calculator tool to develop groundwater potential zones.

Analysis of groundwater changes (2003–2020) in the North China Plain using geodetic measurements

Study regionNorth China Plain (NCP), China. Study focusThis paper investigates the spatiotemporal characteristics of groundwater storage (GWS) changes in the NCP during 2003–2020 using Gravity Recovery and Climate Experiment (GRACE) and Global Navigation Satellite System (GNSS) measurements with a focus on the subperiods separated by the implementation (December 2014) of the South-to-North Water Diversion Middle Route Project (MRP). New hydrological insights for the regionGRACE-derived results demonstrate that GWS depleted at rates of − 1.66 ± 0.17 cm/yr and − 2.76 ± 0.55 cm/yr for subperiods of 2003–2014 and 2015–2020, respectively, suggesting that GWS continued decreasing after the MRP. However, the spatial distribution of average monthly GWS shows an evident southwest-northeast band distribution, and the GWS depletion in the northwest has been alleviated compared with the southeast. The GNSS stations in the Beijing area show accelerated increasing rates of vertical displacements, while those in the plain areas of Hebei and Tianjin show continuous declining rates, implying that GWS in the Beijing area recovered faster after the MRP, while the GWS depletion in plain areas of Hebei and Tianjin continued. In addition, precipitation is the main driving factor to GWS variability during the whole study period, with grey correlation coefficients of 0.66–0.75. This strong correlation explains the significant reduction in GWS caused by the large precipitation deficit in 2019, which leads to a larger GWS depletion rate for the 2015–2020 period.

Simulation of regional groundwater levels in arid regions using interpretable machine learning models

Regional groundwater level forecasting is critical to water resource management, especially for arid regions which require effective management of groundwater resources to meet human and ecosystem needs. In this study Machine Learning and Deep Learning approaches - Support Vector Machine, Generalized Regression Neural Network, Decision Tree, Random Forest (RF), Convolutional Neural Network, Long Short Term Memory and Gated Recurrent Network- have been used to simulate the groundwater levels in the lower Tarim River basin (LTRB) which is an extreme dryland. The results showed that models developed here with easily available input data such as relative humidity, flow volume and distance to the riverbank can fully utilize spatiotemporally inconsistent groundwater monitoring data to predict the spatiotemporal variation of the groundwater system in arid regions where exist intermittent flow. The shapely additive explanations method was used to interpret the RF model and discover the effect of meteorological, hydrological and environmental variables on the regional groundwater. These explanations showed that the flow volume, the distance to the river channel and reservoir have a critical impact on groundwater changes. Within 300 m distance to the riverbank, groundwater would mainly be influenced by the flow volume and the distance to the reservoir. While far from the riverbank, these effects decreased gradually further away from the river course. The RF prediction results showed that in the next three years (2021−2023), the groundwater level on average may decline to −6.4 m, and the suitable areas for natural vegetation growth would be limited to 39% if no water conveyance in the LTRB. To guarantee the stability of ecosystems in the LTRB, it is necessary to convey the water annually. These results can support spatiotemporal predictions of groundwater levels predominantly recharged by intermittent flow, and form a scientific basis for sustainable water resources management in arid regions.

Disproportionate and chronic sediment delivery from a fluvially controlled, deep‐seated landslide in Aotearoa New Zealand

AbstractPast research has highlighted the importance of sediment delivery from multiple‐occurrence regional landslide events triggered by storms or earthquakes. Herein, we examine delivery from a more persistent source of sediment, that of a large, slow‐moving landslide in the soft‐rock hill country of Aotearoa New Zealand. We map and monitor the 80‐ha Rangitikei Landslide from 2015 to 2019 using time‐lapse photography, ground surveys, photogrammetry, and piezometers. We show that the landslide can be divided into several zones with distinctive movement patterns, but all zones respond to river erosion. The fastest zone moves more than 10 m per year in a flow‐like fashion, while other zones move 0.01 m per year via slow sliding. Movement occurs all year round, but is two to three times faster in winter and spring. While rainfall and associated groundwater change are commonly attributed to landslide movement patterns, our data show that river flow correlates closely with the weekly to seasonal variability in movement of the landslide toe. This suggests that fluvial erosion can play an important role in the movement dynamics of highly coupled landslides. We estimate an annual sediment yield to the Rangitikei River of at least 40 000 tonnes, in this first quantification of sediment delivery from an active soft‐rock landslide in Aotearoa. This volume implies 7% of the total catchment suspended sediment yield is derived from 0.03% of the contributing catchment area, demonstrating the disproportionate effect of this (and likely other) deep‐seated landslide(s) as a source of sediment in the Rangitikei catchment. Sediment delivery is more continuous than the episodic supply of multiple‐occurrence regional landslide triggering events, and by delivering mostly fine‐grained sediment, it has a potentially large impact on water quality.

A sharp contrasting occurrence of iron-rich groundwater in the Pearl River Delta during the past dozen years (2006–2018): The genesis and mitigation effect

Fe-rich (>0.3 mg/L) groundwater is generally present in areas where organic matter-rich fluvial, lacustrine, or marine sedimentary environments occur. The Pearl River Delta (PRD) that marine sediments is common, where a large scale of Fe-rich groundwater was distributed but disappearing in recent decade. This study aims to investigate the change of Fe-rich groundwater in the PRD, and to discuss the genesis controlling Fe-rich groundwater in the PRD during the past dozen years. A total of 399 and 155 groundwater samples were collected and analyzed at 2006 and 2018, respectively. Results showed that Fe-rich groundwater of the PRD was from 19.3% at 2006 dropped to 1.3% at 2018. Fe-rich groundwater in coastal-alluvial aquifers was more than 2 times that in other aquifers at 2006. Both of anthropogenic and geogenic sources were contributed to the widely distribution of Fe-rich groundwater in the PRD at 2006. The infiltration of industrial wastewater and the irrigation of Fe-rich surface water were the major anthropogenic driving forces for the occurrence of Fe-rich groundwater in the PRD at 2006. The reductive dissolution of Fe minerals in aquifer sediments, associated with the degradation of organic matter in marine sediments and the sewage infiltration, was the main driving force for the enrichment of groundwater Fe in coastal-alluvial aquifers at 2006. The intrusion of sewage triggering the reductive dissolution of Fe minerals in terrestrial sediments and the reductive dissolution of Fe minerals in carbon-rich rocks induced by sewage leakages were the major driving forces for the occurrence of Fe-rich groundwater in alluvial-proluvial and fissured aquifers at 2006. All these driving forces were weaker or even not work at 2018 because of the large decrease of untreated wastewater discharge in the PRD during 2006–2018. Therefore, limiting untreated wastewater discharge is the first choice to improve the groundwater quality in urbanized areas.

Seasonal changes in thermal process based on thermal response test of borehole heat exchanger

The ground-source heat pump (GSHP) system is one of the most energy-efficient cooling and heating systems popularly used worldwide. The subsurface thermal properties, especially the apparent thermal conductivity, are applied in designing an optimal system. A thermal response test (TRT) is usually conducted once to estimate the apparent thermal conductivity. However, seasonal changes in the apparent thermal conductivity obtained from the TRT have not been sufficiently discussed. Subsurface conditions such as groundwater flow and temperature distribution can vary seasonally because of natural or artificial causes. Therefore, to optimally design a GSHP system, an evaluation of the variation of apparent thermal conductivity caused by seasonal changes in subsurface conditions such as groundwater flow and temperature distribution is imperative. We performed TRT with temperature monitoring three times in January, May, and October, and analyzed the results using numerical simulation. The apparent thermal conductivity in January (winter season) was estimated to be approximately 10%–15% greater than that in May and October owing to the rapid groundwater flow in the partial aquifer, assumed to be because of pumping well operation for snow melting. To evaluate the rate of increase of the apparent thermal conductivity caused by the partial groundwater flow, numerical simulations were performed using a three-dimensional single borehole heat exchanger model. It was found that the parameter of dispersivity plays an important role in the numerical simulation of the thermal process with groundwater flow, and the rate of increase of thermal conductivity has a linear relationship with the mass flow rate, regardless of the partial aquifer thickness and dispersivity. This finding may aid in the estimation of apparent thermal conductivity when considering seasonal changes in groundwater.