Groundwater Monitoring Stations Research Articles

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU

ABSTRACT Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. This study applies the DRASTIC-LU framework, nitrate distribution data, and three machine learning models (RF, XGB, SVM) to classify nitrate levels (exceeding 10 mg/L as nitrogen) in Chongqing, China. Model evaluation uses accuracy and F1 score metrics, with RF achieving the highest accuracy (92.9%), kappa (0.857), and AUC (0.948) on test dataset. Furthermore, the SHAP interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt of using machine learning models for groundwater vulnerability assessment in the Chongqing region. It provides theoretical support for the construction layout of groundwater monitoring stations and the prevention and control of groundwater pollution in the future.

Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Spatiotemporal patterns of groundwater over South Korea

Understanding the large-scale spatiotemporal pattern of multi-depth groundwater levels is critical to develop water management plans and policies for sustainable ecological and social prosperity, which are still lacking. Here, we investigate three major spatiotemporal modes of groundwater levels from ∼200 groundwater monitoring stations over the southern Korean Peninsula (2009–2020), using the Cyclostationary empirical orthogonal function analysis. The first two major modes are associated with the seasonality of recharge and discharge and groundwater use during the 2016/17 drought, which explained half of the total variance. The third major mode indicated a decreasing trend of deep groundwater levels over the western Korean Peninsula, where key administrative and authority offices have been relocated via balanced national land development policies. Furthermore, at least three million Koreans over this region likely experience groundwater depletion by the 2080s. Observational evidence of emerging groundwater depletion suggests a window of opportunity for pre-emptive groundwater management plans.

Selection and validation of release sites for conservation translocations of temperate-zone snakes

Poor habitat quality is one of the most important reasons for reintroduction failure of reptiles; therefore, release site suitability ought to be evaluated prior to conducting conservation translocations. In temperate zone snakes, translocations have failed due to high overwinter mortality, so practitioners have recommended that release sites be located near suitable hibernacula. The presence of a Life Zone (LZ), the underground space above the groundwater table and below the frost line, may indicate the presence of suitable hibernation habitat. Identification and validation of sites with LZ, however, is challenged by the dynamic nature of groundwater and frost levels, coupled with the secretive nature of hibernating snakes. In this study, our goals were to 1) identify potential reintroduction sites for the globally imperiled Eastern Massasauga (Sistrurus catenatus) in southern Canada based on the presence of a LZ, 2) to validate the suitability of those reintroduction sites by hibernating a surrogate species (Eastern Gartersnake, Thamnophis sirtalis) in constructed hibernacula, and, 3) to determine if two separate measures of LZ were associated with the survival of individual gartersnakes to the end of hibernation. Four 1-ha study grids, each consisting of multiple groundwater and frost monitoring stations, were surveyed from 2015 to 19. Release sites were identified in each grid where a LZ of ≥ 10 cm was observed consistently for at least 2 full winters, and gartersnakes were successfully hibernated therein and at 2 reference sites over 3 winters. Overall, survival of subadult/adult snakes was very high (100 %; n = 20), regardless of site, whereas juvenile survival was lower (78 %; n = 93). A Kaplan-Meier test indicated that juvenile survival differed significantly among sites, and ranged from 60 % to 100 %. GLMM analysis indicated that mass at ingress had a significant positive effect on snake survival during hibernation. The temporary loss of a LZ did not impact snake survival. Contrary to our expectations, snake survival in hibernacula was negatively associated with both minimum LZ size and LZ frequency (i.e., the % of sampling occasions when LZ size ≥ 10 cm). Hibernating snakes can tolerate periods with a very small or non-existent LZ, but a large LZ may become unsuitable to juveniles, possibly due to a lack of moisture. Our results will guide the selection of release sites for Massasauga reintroductions, and for the development of a rigorous release site selection process for temperate zone snakes.

Land use dynamics and its influences on groundwater depth levels in South region of National Capital Territory (NCT) of Delhi, India.

The present study envisions the influences of land use dynamics on the spatial trend of groundwater depth levels over a period of two decades in the south region of NCT Delhi, India. The findings have inferred that among five major LULC categories, built-up has shown a sprawling trend (+2.17 km2/year) from the north, northeastern, and central portions to the confined patches observed in the south and southwest fringes of South Delhi from 2001 to 2021. Likewise, vegetation class has also witnessed significant increment (+1.91 km2/year) to the peripheral boundary, i.e., southern, southwest/eastern fringes, and central portions under recent initiatives of city forests, plantation drives, and urban green spaces programs. On the contrary, a negative rate of change has been observed in fallow land (-2.78 km2/year), agriculture (-1.22 km2/year), and water bodies (-0.07 km2/year). LULC transition matrix has also showed prominent conversion of fallow land into vegetation and built-up class, and change of vegetation into built-up, and fallow land category. Subsequently, corresponding LULC maps have been superimposed with long-term trends of groundwater depth levels (as spatial contours). For entire South Delhi region, depth to water levels ranged from 2.02 to 66.45 meters below ground level (mbgl) where shallower depths followed a negative trend and remain persistent throughout the time period in north and northeastern fringes. Higher fluctuations in groundwater depletion with positive trends directly get influenced with allied land use transitions such as a steady increase in built-up area and steeper depth levels (> 40 mbgl) as observed in central, southwestern, and southern parts. Moreover, buffer peripheries in the proximity of groundwater monitoring stations viz., Hauz Khas, Pushp Vihar, Jamali, Gadaipur, and Bhatti Kalan have observed deeper groundwater levels allied to built-up expansion. Thus, groundwater depletion trends can be ascribed to the incessant conversion of recharging areas into impervious zones along with uneven distribution of groundwater usage and supply. Conversely, expanding vegetative land has also shown improved groundwater depth levels. Therefore, land use influences must be managed in the long run for ensuring sustainable management of groundwater resources.

Impact of Urbanization on Groundwater and Surface Temperature Changes: A Case Study of Lahore City

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain

In recent years, the groundwater level (GWL) and its dynamic changes in the Hebei Plain have gained increasing interest. The GWL serves as a crucial indicator of the health of groundwater resources, and accurately predicting the GWL is vital to prevent its overexploitation and the loss of water quality and land subsidence. Here, we utilized data-driven models, such as the support vector machine, long-short term memory, multi-layer perceptron, and gated recurrent unit models, to predict GWL. Additionally, data from six GWL monitoring stations from 2018 to 2020, covering dynamical fluctuations, increases, and decreases in GWL, were used. Further, the first 70% and remaining 30% of the time-series data were used to train and test the model, respectively. Each model was quantitatively evaluated using the root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE), and they were qualitatively evaluated using time-series line plots, scatter plots, and Taylor diagrams. A comparison of the models revealed that the RMSE, R2, and NSE of the GRU model in the training and testing periods were better than those of the other models at most groundwater monitoring stations. In conclusion, the GRU model performed best and could support dynamic predictions of GWL in the Hebei Plain.

Development of a surrogate model to improve the efficiency of groundwater level fluctuation pattern-based hydrologic properties evaluation

In this study, a method of aquifer hydrologic property estimation incorporating the deep learning method was developed to improve the estimation efficiency of a process-based model based on groundwater level fluctuation (GLF) patterns. As a reference study, a data-driven method suggested by Jeong et al. (2020) was considered; the uncertainty of the GLF patterns resulting from different yearly patterns of precipitation, which were considered as noise in the previous study, was effectively discarded using the newly proposed method of applying the conditional variational autoencoder (CVAE). The CVAE was used to acquire the specific GLF patterns under certain identical precipitation patterns for all the monitoring stations. The data-driven hydrologic property estimation model was developed to predict two hydrologic parameters (ρ and k) of the process-based model using the generated GLF patterns from the CVAE network as the input variables. The actual GLF and precipitation data that were acquired from nationwide groundwater monitoring stations in South Korea were applied to validate the developed method. It was found that the estimated and target hydrologic properties were highly correlated (correlation coefficients [CC]: 0.9833 and 0.9589 for ρ and k, respectively), which significantly improved the results when compared to the previous study (CC: 0.7207 and 0.8663 for α/n and k, respectively). Consequently, the developed model can contribute to a more accurate hydrologic property estimation of aquifers. Additionally, it can facilitate efficient groundwater development planning since the manual fitting of the process-based model by an expert is not required.

Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data

• A data-driven ensemble time-series data estimation model is developed. • Statistical variability and the exogenous factor are considered in the estimation. • The range of normal data is calculated based on the ensemble estimation result. • Contextual and collective outliers in time-series data were effectively identified. In this study, a method to estimate the normal range of groundwater level time-series data was developed to identify outliers in terms of the global, contextual, and collective sense. To evaluate the normal range of groundwater level time-series data, the statistical characteristics of the data and the patterns of the precipitation time-series data were incorporated into the LSTM (Long Short-Term Memory)-based ensemble regressor (i.e., the LER model). Based on the LER model, multiple possible trends of the groundwater level were generated, and the general rules of outlier identification methods (i.e., σ and Tukey’s fences (TF) rules) were applied to the LER ensemble estimation result to finally define the range of the normal data. For outlier identification performance validation, the actual groundwater level acquired from three groundwater monitoring stations in South Korea (i.e., the Pohang–Gibuk (PG), Namwon–Dotong (ND), and Jeju–Sangyae (JS) monitoring wells) and the corresponding precipitation data acquired from the nearest weather stations were applied to the study. As the reference method for comparative performance validation, simple applications of the σ and TF rules were used. For the monitoring data, the developed LER-based outlier identification method evaluates the range of the data that might be explained by the modelled influences of the interest (i.e., normal data range). The developed method showed an outlier identification performance of >70% in general while the performance of the σ and TF rules was mostly <50%. In particular, as the method effectively estimated the seasonal trend and the variability of the groundwater level with consideration of the precipitation patterns and statistics on the groundwater level variation, it is superior for identifying the contextual or collective outliers compared to the simple σ and TF rules. Through in-depth analysis, it can be concluded that the developed LER-based outlier identification method is effective for discriminating the abnormal data by considering the intrinsic statistical characteristics of the original data trend and the exogenous factors. In the aspect of the practical applicability, as the result can be automatically acquired based on real-time monitoring data, the developed method is expected to apply for more efficient maintenance of the monitoring devices by embedding the model as the management software into the monitoring network system.

Investigating the effects of groundwater level changes on GNSS observations in the Konya Closed Basin

The Konya Closed Basin (KCB) that inhabited by approximately 4 million people has a semi-arid climate and has been an important agricultural and economic production area with its alluvial fertile soils. In recent years, the use of groundwater as a main source in the intense need for irrigation in the basin due to climate change, drought and wrong agricultural policies triggered land subsidence in the basin by causing the aquifer system compaction. In this study, the effects of groundwater level changes obtained from groundwater monitoring stations on the positions of continuously operating GNSS stations located in KCB were investigated by time series analysis. As a result of trend component analyses of time series processed based on the ITRF08 fixed frame, it was determined that the horizontal positions of stations in this region were moving in the northeast direction (18.88 mm/year). For the up coordinates, while stations ANRK, KLUU, and NIGD had movements (0.56 mm/year) in the up direction, the other stations moved in the down direction. The vertical movement of KNY1 station based on 560-day data was in the down (-) direction, with an annual movement of 70.96 mm, and this was the maximum movement or velocity compared to the other stations in this region. The trend analysis of 19 wells revealed linear groundwater level changes in the down direction with a mean value of -39.22 cm/year. The decrease in the water level of the wells in the Konya Closed Basin was statistically significant for the majority of the wells and at meter level in some wells. It was determined that the linear decrease was generally caused by the reduced level of groundwater in this region. The periodic component analysis showed that the movements of the GNSS stations changed from a day to a year due to several factors, such as atmospheric and hydrological loading and climatic effects.

Groundwater trends during 1985 to 2019 in a critical groundwater area of northeastern Arkansas

The Mississippi River Valley alluvial aquifer provides over 90% of irrigation water for agriculture in the Lower Mississippi River Basin. Overexploitation of the alluvial aquifer in the Cache River Critical Groundwater Area (CRCGA) in Arkansas started impacting crop production in the 2000s with previous historical data identifying sustained groundwater declines. Groundwater depletion resulted in many producers investing in on-farm reservoir-tailwater recovery systems to provide a more secure irrigation source. More focused and consistent water level measurements were necessary to identify any groundwater level changes. Groundwater trends focused on the CRCGA cone of depression were compared from two-time groupings: 1985 to 2012 and 2012 to 2019. On-farm reservoirs in the CRCGA were inventoried in 2015, and 90% of those inventoried were constructed by 2012. More frequent groundwater level data collection in the CRCGA started in 2012 as well. Groundwater levels during 1985 to 2012 had consistent decline rates of –0.17 to –0.44 m y^–1. Groundwater levels during 2012 to 2019 varied with decline rates of ≤–0.10 to ≤–0.50 m y^–1, but also areas of no significant change and recovery at rates of ≤0 to ≤0.25 m y^–1. Trend differences between the two periods showed improved water level trends for most of the study area, with rates ranging ≤–0.30 to ≤0.50 m y^–1. The measured improvements were attributed to less pumping due to greater precipitation and the use of surface water irrigation from 60 on-farm reservoir-tailwater recovery systems. Detection of these improvements is crucial to understanding the response and status of the alluvial aquifer within the agricultural area of the CRCGA. It is recommended that adding continuous groundwater monitoring stations in west Craighead and Poinsett counties would be beneficial to inform the status of the cone of depression, considering the importance of protecting the main agricultural irrigation supply for the state. If warmer temperatures occur in the future, higher temporal and spatial groundwater level data in the CRCGA will allow policymakers and water resource managers to make timely groundwater management decisions to ensure its supply for future generations.

Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation

Significant subsidence is susceptible to groundwater level variations in aquifer systems. The relation between groundwater level change and global positioning system (GPS) estimated subsidence is spatially variable. Time-dependent spatial regression can be used for the estimation of groundwater level changes using GPS based deformation data. Furthermore, the model can be validated using observed hydraulic head data from available monitoring stations. This study uses GPS station data to estimate the monthly groundwater levels in the west-central Taiwan for the period: 2016–17.Time-dependent spatial regression provides a more realistic estimation of groundwater level changes in response to highly heterogeneous aquifer properties than other methods. The high correlation (r = 0.95) between observed and estimated groundwater levels shows that GPS estimated deformations represent an alternative approach for estimating seasonal groundwater changes. Due to availability of spatially broad/low cost GPS data (compared to the sparse availability groundwater monitoring stations), the use of GPS data represents a powerful solution for future monitoring of estimated seasonal groundwater level changes in areas where only few groundwater observations are available.

Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey.

Konya, which is located within the Konya Closed Basin, is the most important agricultural production region in Turkey. The future of agriculture is threatened in this region due to the decline in groundwater levels and the intensive agricultural activities that require high water consumption and are not suitable to the climate conditions of the region. In addition to these parameters, the geological structure of Konya also poses various environmental problems such as land subsidence and sinkhole formation. This study aimed to investigate the causes of the land subsidence problem in Konya and its surroundings with the help of the interferometric synthetic aperture radar (InSAR) technique and auxiliary data, namely optic, Coordination of Information on the Environment (CORINE), and groundwater monitoring station data. In order to investigate the land subsidence in the study area, 58 Sentinel-1A images acquired between 2014 and 2018 were processed by using the small baseline subset (SBAS) technique. In addition, the time series derived from the SAR data was validated by a global navigation satellite system (GNSS) station located in the study area. The results revealed that severe land subsidence, some of which reached 75 mm/year, occurred in certain areas of the study area over a period of three and a half years. High consistency was found between the land subsidence and the groundwater level change observed in the region, with a cross-correlation of over 95%. Moreover, the temporal and spatial patterns of the cultivated area and urbanization, which are the main reasons for the consumption of groundwater in the region, were revealed using the optic data.

A novel LUMNAqSoP approach for prioritising groundwater monitoring stations for implementation of the Nitrates Directive

BackgroundThe Nitrates Directive (ND) is an EU anti-pollution legislative that, for almost 30 years, has controlled and protected hydro-resources against excessive levels of nitrates originating from agroecosystems, striving to prevent the further exacerbation of the nitrates in aquatic environments. ND sets several principal goals that member states must accomplish, such as performing spatiotemporal nitrate analyses in ground/surface water networks to achieve national water quality monitoring programme.ResultsIn this study, using the novel LUMNAqSoP approach, the prioritisation of 151 candidate groundwater stations for ND monitoring in Croatia was performed. The LUMNAqSoP integratively evaluates: (i) the most dominant loads from the agroecosystem (land use and management, net nitrogen application) and (ii) environmental (aquifer and soil) vulnerabilities and sensitivities of groundwater resources to nitrate pollution. By comparing stations which scored the most agro-environmental loads vs. the data from water stations containing elevated mean nitrate concentrations (from existing monitoring programmes) a very good agreement was confirmed. Moreover, deviations close to large urban zones were detected, suggesting that elevated nitrates in groundwaters in those areas likely originated from municipal/industrial rather than agricultural sources; however, further studies are needed for elucidation.ConclusionsThe presented approach can serve as a useful tool to policy makers and regulators for: (i) more efficient and reliable establishment of water monitoring programme pursuant to ND, as well as (ii) better management and shaping (designating/derogating) of nitrate-vulnerable zones, especially in diverse environmental conditions and dynamic agroecosystems as have been confirmed in recent decades across the EU member states.

WITHDRAWN: Application of GIS software in analyzing hydrodynamics of sub surface water demeanor zones in Gondwana sedimentary terrain of eastern India

Abstract Gondwana study terrain of Orissa, India has diverse geological, geophysical, hydrological, hydrogeological and meteorological information which have been integrated for extensive study. The study aims at acknowledging the geology and hydrogeology of the region backing up with the type and existence of aquifers around the region. To get the detailed features 225 observation wells, 40 National Hydrograph Network Stations (NHNS), 40 groundwater monitoring (GWM) stations and 83 exploratory wells were collected from different sources to analyse the groundwater occurrence in the study area. The data obtained were integrated and initiated for lithological distribution, groundwater flow directions, depth to water levels in pre- and post-monsoon. Limited aquifer thickness of unconfined aquifer and poor yield from deeper aquifers has been studied intricately. Groundwater occurrence has provided a paradigm to in-depth study in association with detailed lateral and vertical demarcation and behaviour of aquifers. Taking into consideration about lithology, the area can be hydrogeologically divided into consolidated, semiconsolidated and unconsolidated zones. The semi and unconsolidated zones are the groundwater potential zones where the aquifer remains under saturated condition. Though semiconsolidated zones bear abundant volume of water but hydrological parameters illustrate that they do have the potentiality for shallow water wells whereas the fissured and fractured zones in consolidated formations yield perennial source of groundwater illustrating deep borings.

Assessment and analysis of groundwater overexploitation in China

As an important water resource, groundwater has been unreasonably developed for a long time in our country, causing a lot of problems. This paper combines the data from the national groundwater monitoring stations and the groundwater depth data collected locally to statistics and analysis of groundwater overexploitation across the country. Especially in key plains, through the water level variation method. The research results are compared and verified with national authoritative data such as Groundwater Dynamics Monthly Report and predecessors’ records in the literature, revealing the current key areas of groundwater overexploitation, and clarifying the importance and urgency of groundwater governance in the future. This study also put forward some suggestions of groundwater overexploitation.

Factors Controlling the Spatial Distribution and Temporal Trend of Nationwide Groundwater Quality in Korea

Factors controlling the spatial distribution and temporal trend of groundwater quality at a national scale are important to investigate for sustaining livelihood and ecological balance. This study evaluated groundwater quality data for 12 parameters (n = 6405 for each parameter), collected from 97 groundwater monitoring stations (=289 monitoring wells) for ten years. Spatial distribution of groundwater quality parameters varied through the regional scale. Six parameters: T, EC, Ca2+, Mg2+, HCO3−, and Cl− were having dominant increasing trend, remaining pH, Eh, Na+, K+, SO42−, and NO3− showed a dominant decreasing trend over time. Among land use types, the upland fields had the highest mean of groundwater NO3− (22.2 mg/L), confirming plenty of application of fertilizers (5–10 kg/a more than standard) to upland fields. Means of groundwater Cl− and Na+ (705.3 and 298.4 mg/L, respectively) in the residential areas are greater than those in other land use types by 408–685.9, 154.3–274.2 mg/L, respectively. Agricultural activities were the main controlling factor of groundwater NO3− contamination in rural areas, domestic activities were responsible for groundwater Cl− and Na+ in urban areas, and seawater intrusion was controlling groundwater Cl− in coastal areas (within 10 km from sea). Groundwater hydrochemistry was controlled by the mechanism of geogenic rock and evaporation dominance. The rock dominance mechanism indicated that groundwater was interacting with rocks and resulted in groundwater chemistry. The findings of this study showed that groundwater was mainly contaminated by anthropogenic factors in some rural and residential areas. Effective measures by government authorities are needed to improve the groundwater quality.

Analysis of Meteorological Drought Resilience and Risk Assessment of Groundwater Using Signal Analysis Method

Groundwater plays an important role in mitigating drought. It is necessary to analyze the spatiotemporal variation characteristics of groundwater and establish an appropriate assessment for the risk of drought for disaster prevention. This study developed a novel approach for the drought risk assessment system based on the Hilbert Huang Transform (HHT) method to understand the spatial distribution of the risk of drought in terms of groundwater. We analyzed drought vulnerability applying the HHT to analyze the variation characteristics of groundwater level spatiotemporally and the physical mechanism of groundwater factors to quantify groundwater sensitivity to the environment, and present the resilience of each region. Furthermore, the drought hazard was determined using the standardized precipitation index and the dynamic drought intensity gave the durability characteristics of each region. The drought exposure was also investigated, which quantifies the water demand to satisfy people’s livelihoods for a certain population density. Based on the framework proposed in this study, an overall risk map of droughts in the Pingtung Plain was obtained. This study effectively classified the main time–frequency characteristics, the availability of groundwater, and the risk of drought in each region. A total of 11 of the 45 groundwater monitoring stations are located at the highest risk of level 5, most of which are located in the coastal area of Gaoping River and Linbian River. Over-pumping should be avoided in these areas. On the contrary, the alluvial fan area showed the lowest groundwater risk of drought. The results can be adopted for water management, drought resilience, sustainable development of groundwater resources, and decision making in determining drought risk.

Groundwater Vulnerability in the Piedmont Region under Climate Change

Groundwater vulnerability has increased in recent decades due to several factors, but mainly due to climate change. In this study, we applied a complex approach to determine groundwater vulnerability at a regional scale in the Piedmont region including high-resolution climate models. Aquifers, terrain model, climate data, land cover, and groundwater monitoring stations data of nitrate (NO3) concentration were the main layers used for the vulnerability and risk mapping. Validation of the groundwater vulnerability map consisted of pixel pair comparison (PPC) using the quality status of a monitored groundwater station, dating from 2005 to 2012. The groundwater vulnerability maps that related to the 1990s and 2020s periods indicated very high and high vulnerability of groundwater in the central and eastern parts of the region, especially in the porous aquifers of Po Plain. The areas with very low and low vulnerability extend into the north and west, mainly in the non-aquiferous media and fissured aquifers. The future scenarios of groundwater vulnerability indicate a consistent increase of the very high vulnerability class, from the 1990s to the 2050s periods, in all scenarios, and mainly in the Po Plain.