Groundwater Level Monitoring Research Articles

Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.

Investigating the response of groundwater and streamflow to rainfall in a tropical catchment using non-parametric methods

The present study investigates the monthly and seasonal patterns of groundwater levels and streamflow in response to rainfall in a humid tropical catchment in central Kerala, India. The trends were investigated at a temporal scale using data from eight groundwater-level observation wells, four stream-gauging stations and rainfall records of three subcatchments, viz. Cheripad, Pala and Kidangoor. The exercise of trend detection was carried out using two widely adopted non-parametric tests, viz. the Mann–Kendall (MK) test, Spearman's Rho (SR) test and a relatively new visual graphical approach, innovative trend analysis (ITA). The results revealed predominantly rising trends (monthly and seasonal scale) within the catchment, across all three hydrological components examined. Notably, the ITA test identified specific changes in the trend direction within the time series (positive to negative or vice versa), which were not observable in the MK or SR tests. Data published in the Groundwater Book of Kerala for 2021–22 corroborated this increasing trend, showing, on average, a rising trend in well water levels throughout the watershed across pre-monsoon and post-monsoon seasons. This trend was further confirmed using ITA by pairing the observations from the groundwater-level monitoring station at KTM-OW-11 with rainfall data from the nearby gauging station at Erattupettah.

Response of dissolved organic carbon in streams draining peatbogs to extreme rainfall-runoff events: a case study from Šumava (Bohemian Forest) National Park, Czech Republic

The presented study investigates the dynamics of DOC concentrations in headwater peatbog areas with respect to the extreme rainfall-runoff (R-R) events hydrometeorological catchment preconditions (23 variables in total). The main data sources were automatic devices for monitoring of groundwater level, discharges and rainfalls providing data in 10 min steps installed in the Vydra River catchment and one automatic water sampler ISCO in sub-catchment of the Rokytka River basin in the Šumava (Bohemian Forest) National Park. The study period was 2018–2021, in which 18 R-R events were analysed. Data of DOC variability and catchment conditions were analysed using Spearman’s correlation coefficient, Principal Component Analysis and DOC/Q hysteresis loops. Changes in groundwater level and discharges had the greatest influence on DOC concentrations. Higher mean and maximum DOC were measured during events after a longer period without an extreme R-R event. The greater lag time of maximum DOC after peak flow and the higher mean DOC during the event were primarily due to hydrometeorological preconditions of the catchment. The highest DOC was in autumn after the previous summer period with low discharges and low groundwater levels. DOC was also positively correlated with air and water temperatures.

Use of groundwater level measurements to support hydrogeological studies in the Maputo aquifer, Mozambique

AbstractHigh-frequency dataloggers for groundwater level monitoring were used in combination with other tools to analyze tidal effects on groundwater levels (GWLs) in the Maputo aquifer, Mozambique. Power spectral analysis was used to ascertain the dominant periodic components in the tide and GWLs, and cross-spectral analysis was used to determine the lag time between them. Wavelet analysis was applied to investigate changes in periodic components over the measured period in the time-frequency domain. The estimated amplitudes and lag times were then used to estimate aquifer diffusivity and the water-table fluctuation (WTF) method was used to compute groundwater recharge. The results identified a 12.42 h dominant periodic component both in the tide and GWLs in the coastal area. However, GWLs lag behind the tide by 2–4 h, depending on the distance of the observation wells to the coastline. The wavelet analysis results showed no changes in the dominant periodic components over time. The estimated specific storage values for four piezometers were estimated to be 3.19 × 10–5, 5.04 × 10–5 and 1.02 × 10–4 1/m, respectively. Annual groundwater recharge for the young sand dune aquifer was estimated for one piezometer with a specific yield of 0.15–0.25 was within the range of 123–205, 185–309, 504–840 and 244–407 mm, for four hydrological years from 2018 to 2021. Estimated specific storage values and recharge rates are essential inputs to support the construction of transient groundwater models for the Maputo aquifer.

Field and model investigations of clay layer permeability in the area of Paks II NPP construction

Background. The construction of a nuclear power plant inevitably requires excavation of a deep pit. This task may be impeded by high groundwater levels. The groundwater inflow into the pit can be limited by erecting a cut-off wall. As a rule, the cut-off wall is deepened to the aquitard elevation; however, the presence of hydrogeological windows therein may reduce the efficiency of such a solution.Aim. To determine the nature of clay layer deformation along the regional dislocation area of the Paks II NPP construction site.Materials and methods. A comprehensive geological and hydrogeological study was carried out to identify the continuity of the clay layer. This included an analysis of core samples from more than 1000 engineering and geological boreholes, surface and borehole geophysical surveys, a multi-level borehole network for groundwater level monitoring, as well as numerical hydrogeological modelling.Results. It was found that extensive borehole data does not always guarantee the sufficiency of information for mapping the hydrogeological settings. The continuity of the clay layer with a vertical displacement amplitude of 100 meters was established through a probabilistic analysis using a numerical model and a set of hydrogeological surveys aimed at confirming or refuting the modeling results.Conclusion. Assessment of hydrogeological conditions when implementing high-risk projects, such as nuclear power plants, underground excavations, and open-pit quarries, determines their safety and economic feasibility. In the absence or inconsistency of geological structure knowledge, hydrogeological surveys can serve as an independent source of missing information.

The Sliding Surface Determination of A Deep-seated Landslide on Cisumdawu Highway, West Java, Based on The Electrical Resistivity Tomography

Sumedang is one of the regencies in West Java Province that usually experiences landslides due to its lithology, slopes, and water level conditions. Previously slow-moving landslides occurred between 2016 and February 2021, affected the new Cileunyi-Sumedang-Dawuan (Cisumdawu) Highway at Section 2, Station 21 in the North Sumedang District. This research aims to identify the causes of these landslides using a combination of geological field observation, subsurface geo-electric resistivity-based survey, and borehole drilling. A total of fourteen boreholes were drilled to collect geotechnical data from the subsurface of the researched area, including the soil material and N-SPT value. The soil hardness and resistivity were measured and compared to establish the relationship between resistivity and engineering properties. The result of the resistivity measurement showed that the percolating water zone in the permeable loose soil was located above the impermeable layer, estimated as a slip surface. The subsurface measurement and borehole data show that the lithology of the sliding surface is a layer of clay with a thickness of 5 - 12 m, the slope of the sliding surface is 20o, and the depth is between 24 - 26 m. The cover layer of the sliding surface is a layer of silty clay and gravelly clay with a thickness of 5 - 10 m. Thus, based on the data presented, installation of bore piles and groundwater level monitoring need to be done as mitigation efforts.

Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change

Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R2 ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R2. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

Estimating Groundwater Level in Peatlands by Using Submersible Sensor

Peatland fires pose a significant challenge in peatland management, with declining groundwater levels being a contributing factor. Real-time monitoring of groundwater levels (GWL) is essential to address this issue effectively. This study examines GWL data collected from submersible sensors and manual readings in peatlands of Tangkit Baru and Pematang Rahim villages, Jambi Province. Results reveal an increase in GWL in Tangkit Baru coinciding with rising precipitation, while Pematang Rahim experiences a contrasting decrease despite heavy rainfall. Statistical analysis, specifically t-tests, indicates no significant difference (p > 0.05) between the two measurement methods. However, slight discrepancies (0.1-1 cm) between submersible sensor and manual measurements underscore the importance of sensor maintenance for accurate GWL assessment. Keywords: Peatlands, sensors, groundwater level

Unraveling aquifer dynamics: Time series evaluation for informed groundwater management

Optimizing groundwater monitoring networks is essential for the sustainable management of this critical resource, which is vulnerable to both anthropogenic and natural changes. Focusing on the Hashtgerd aquifer in Iran, this research aims to enhance the efficiency of groundwater level monitoring systems by integrating statistical methods, artificial intelligence (AI), and hydrogeological studies. The study's core objective is to pinpoint and comprehend the factors that cause certain observation wells to become isolated from the main aquifer body, leading to parts of the aquifer being cut off from the central hydrogeological system. Employing clustering techniques on data from 29 observation wells, based on parameters like groundwater level (GWL), groundwater level change (GWLC), groundwater depth (GWD), air temperature (AT), distance (D), ground level (GL), and temperature (T) through Hierarchical Cluster Analysis (HCA), the research categorizes the groundwater information into four distinct groups. Significantly, Cluster 4 encompasses wells with similar characteristics. The analysis reveals that two wells, categorized in Clusters 1 and 2, exhibit unique behaviors, likely due to their connection to local karstic aquifers, as corroborated by geological evidence. Cluster 3's separation is determined through geophysical investigations, identifying confined conditions near specific wells, notably Wells 4 and 8. AI techniques further distinguish differences in meteorological influences and water discharges among the clusters, noting minimal weather impact on Clusters 1 and 2, considerable shallow groundwater influence on Cluster 3, and a pronounced effect of groundwater discharge on Cluster 4. This nuanced identification of aquifer segments disconnected from the main system underscores the need to refine monitoring networks for accurate assessment of groundwater resources. By leveraging a detailed analysis of aquifer complexities through state-of-the-art methodologies, the study guides towards informed, sustainable groundwater management strategies.

An Inversion Study of Reservoir Colluvial Landslide Permeability Coefficient by Combining Physical Model and Data-Driven Models

The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are significant deviations from actual observations when used in seepage field calculations for reservoir landslide analysis. This study proposes an intelligent inversion method that combines a physical model and a data-driven model for reservoir landslide ks based on actual groundwater level (GWL) monitoring data. This method combines Latin Hypercube Sampling (LHS), unsaturated flow finite element (FE) analysis, particle swarm optimization algorithm (PSO), and kernel extreme learning machine model (KELM). Taking the Hongyanzi landslide in Sichuan Province, China, as the research object, the GWL of the landslide under different ks was first obtained by LHS and transient seepage FE analysis. Then, a nonlinear functional relationship between ks and the landslide GWL was fitted based on the PSO-KELM model. Finally, the optimal landslide ks was obtained by minimizing the root-mean-squared error between the predicted and actual GWL using the PSO. A global sensitivity analysis was also conducted on the ks of different rock and soil layers to reveal their control rules on the calculation of landslide GWL. The research results demonstrate the feasibility of the proposed method and provide valuable information for similar landslides in practice.

Evaluating groundwater resources trends through multiple conceptual models and GRACE satellite data.

In this research, three numerical groundwater flow models, developed and calibrated from three equally plausible conceptual models over the Nasia Basin, have been used to assess groundwater resources variations over a transient period. The use of multiple numerical models reduces the effect of uncertainties in conceptual model formulation. All the three calibrated numerical models indicate an increasing trend of groundwater recharge and storage over the period of the groundwater level monitoring. This suggests that the prevailing erratic climatic conditions in the area are conducive for increasing groundwater recharge and storage in the terrain. The high-intensity, short duration rainfall patterns, attending climate change in the basin, enhance high levels of infiltration and percolation, leading to steadily increasing groundwater recharge. Groundwater recharge estimates from each of the models over the transient period appear to reflect the pattern of seasonal variations in rainfall in the region. Data from the models indicates a significant role of baseflow in sustaining perennial streamflow in the area. This presents a significant development in terms of groundwater-based adaptation projects, especially in agriculture. The trend of groundwater recharge in the Nasia Basin is in sync with regional groundwater storage variations estimated from the Gravity Recovery and Climate Experiment (GRACE) satellite data collected and processed over the Volta Basin. At the Volta Basin level, groundwater storage variations indicate a strong positive trend of increasing groundwater recharge from 2002 (beginning of the GRACE mission) to 2022 (end point of the data used for this research). Analysis of the GRACE data suggests that there is a cumulative increase in groundwater storage by 30cm, representing approximately 120 km3 of groundwater over the period in the basin. This translates into approximately 15mm/year of groundwater storage increase. Thus, at both the regional and local levels, groundwater appears to be responding positively to the impacts of erratic rainfall patterns observed in the area recently. The high-intensity, short duration rainfall patterns appear to favor significant groundwater recharge, resulting in a strong positive groundwater storage signal. The high positive groundwater storage signal suggests increasing groundwater resources potential in the area, indicating promising opportunities for groundwater-based climate change adaptation interventions.

Participatory approaches for water monitoring and harvesting: Case study from India

Abstract North Gujarat in India currently extracts three billion cubic meters of groundwater per year, which is up to 95% of the groundwater resources available in the region. This unsustainable abstraction has led to changes in groundwater levels and created water scarcity in many parts of the region. To address these issues, integrated groundwater resource management is required, which should be driven by good quality and quantity of groundwater data. However, current groundwater data are scarce; thus, new, affordable monitoring approaches are necessary. Participatory and community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring. This study demonstrates the feasibility of developing a large-scale groundwater level monitoring wells network by directly involving farmers in two agriculturally-dominated blocks in North Gujarat, India. First, long-term groundwater level data for government-monitored wells were analyzed, and the regions lacking monitoring were identified. Then a network of 43 farmers was established through the field survey, who were trained to provide groundwater level observations for their wells every month. The data collected through the field survey were then integrated with the data from the existing government monitoring programs to understand the groundwater dynamics in the region. Results for the post-monsoon season 2022 show that the groundwater levels in Unjha block (Mehsana district) have declined to more than 100 meters below ground level due to unsustainable pumping for irrigation. The evaluation of the participatory approach showed that concern for existing groundwater challenges, social inclusion and contribution to scientific knowledge were the top three reasons that motivated farmers to participate in this research. Of the total volunteering farmers, 71% have shown interest in providing long-term observations for up to 3 years, and 57% agreed to provide observations weekly. Additionally, 70% of the farmers agreed to engage fellow farmers in groundwater monitoring, and 50% agreed to train new farmers. Thus, this study shows that farmers can play an important role in improving the existing challenges of groundwater monitoring through participatory training, and the integration of primary and secondary data can lead to better decision-making regarding need for well construction, crop selection, recharge methods and pathways for sustainable groundwater management.

Monitoring and Analysis of Land Subsidence in Cangzhou Based on Small Baseline Subsets Interferometric Point Target Analysis Technology

Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables.

Study and Analysis on Groundwater Modelling in Watershed Area of the Wainganga River Gondia District Using GIS Techniques

Currently, Geographic information systems (GIS) are the most significant mapping and modelling tool for groundwater analysis.The determination of the groundwater level of In a few years, the availability of groundwater will be a problem in Kati Village, Gondia District, Maharashtra, India, a case study Wainganga River watershed area pre-monsoon and post-monsoon analysis.The monsoon rains in Gondia District are concentrated in the four months of June to September, with 90.81% rainfall, 1.86% post-monsoon, 4.83% pre-monsoon, and 2.48% winter. The annual rainfall distribution in Gondia is very irregular. The main river is Wainganga, and the tributaries are Bagh.The water has a neutral to alkaline pH range of 6.6 to 8.92 and a high TDS range of 140 to 2184 ppm. The suitability of the groundwater level must be established before using GIS interpolation model techniques to evaluate it. Groundwater level monitoring, hydrology, and other fields increasingly make use of geospatial techniques for Geographic information systems (GIS). Using this method of groundwater level monitoring to gather information on water level potential position conditions in the study area is crucial for effective analysis, prediction, and validation. The result is reached through statistically significant analysis of physicochemical parameters and groundwater modelling using GIS. This study's objective is to use GIS techniques to analyse groundwater modelling in the Wainganga River Gondia District.The information can be used in the future for studies on area management, resource conservation, and restoration.

Groundwater Modelling Using GIS Techniques: A Case Study Bagh River Watershed Area of Gondia District

Geographical information systems (GIS) are the most important mapping and modeling tool for groundwater level resources in the present day. There was a challenge to define the groundwater level of A case study Bagh River watershed area pre-monsoon and post-monsoon analysis of Birsola Village, Gondia District, Maharashtra, India, is confronted with the issue of groundwater availability within a few years. To evaluate the suitability of groundwater levels using GIS interpolation model techniques, one must determine the groundwater level's suitability. Geospatial techniques working for Geographical information systems (GIS) are increasingly utilized in water resources management, hydrology, and groundwater level monitoring. This is the greatest benefit of utilizing GIS interpolation techniques. This method of study of groundwater level monitoring to generate data on water level potential position conditions in the area under study is crucial for successful analysis, prediction, and validation. The result is determined by analysis of physicochemical parameters with statistical significance and using the GIS technique for groundwater modeling.

Modeling of daily groundwater level using deep learning neural networks

Groundwater is an essential water source, becoming more vital due to shortages in available surface water resources. Hence, monitoring groundwater levels can show the amount of water available to extract and use for various purposes. However, the groundwater system is naturally complex, and we need models to simulate it. Therefore, we employed a deep learning model called CNN-biLSTM neural networks for modeling groundwater, and the data was obtained from USGS. The data included daily groundwater levels from 2002 to 2021, and the data was divided into 95% for training and 5% for testing. Besides, three deep CNN-biLSTM models were employed using three different algorithms (SGDM, ADAM, and RMSprop(. Also, Bayesian optimization was used to optimize parameters such as the number of biLSTM layers and the number of biLSTM units. The model's performance was based on Spearman's Rank-Order Correlation (r), and the model with SGDM showed the best results compared to other models in this study. Finally, the CNN model with LSTM can simulate time series data effectively.

Groundwater level monitoring network design with machine learning methods

This research introduces a method combining groundwater models and machine learning (ML) algorithms to locate observation wells and design optimal Groundwater Level Monitoring Networks (GLMNs). Groundwater models and stochastic simulations are used to extract required hydrogeological datasets for ML algorithms. In addition to data generation, the stochastic simulations minimize the uncertainties in the aquifer characterization, leading to a precise design of GLMNs. In this research, K-means clustering and relevance vector machine (RVM) are the ML algorithms employed to determine the optimal configuration of observation wells in terms of number and location in a monitoring network. This study proposes three GLMNs (K-mean, RVM, modified RVM), compares them with the existing observation wells, and investigates their effects on the accuracy of groundwater modeling and running time. The groundwater model with a K-mean network runs faster than other configurations, while the model with a modified RVM network shows a significant decrease in errors.

Statistical Analysis of the Hydrological and Hydrometeorological Characteristics of the Upper Tisza Basin

Tisza is the largest water base in the eastern part of Szabolcs-Szatmár-Bereg County, in north-eastern Hungary, and its indirect use is mainly associated with agriculture. Our work investigates the hypothesis that decrease in water levels and water discharges of the Upper Tisza in recent decades, together with the incision of the riverbed and the precipitation falling in the area, have an impact on the groundwater level of the Szatmár-Bereg Plain. In our study, data from three water gauges located between the settlements of Tiszaújlak and Vásárosnamény and data from groundwater level monitoring wells (MW) and Hydrometeorological (HMS) stations on the Szatmár-Bereg Plain were compared using statistical methods in Past 4.11 software. The aim of the current study was to identify the strategic steps that need to be taken by the organisations responsible for water management in the Upper Tisza basin in the light of the changes in hydrological, hydrometeorological, and meteorological factors. To ensure that the region has sufficient water supplies for the next decade, appropriate water management and agricultural strategies could be the solution.

Hydrochemical and isotopic characteristics of the Lodwar Alluvial Aquifer System (LAAS) in Northwestern Kenya and implications for sustainable groundwater use in dryland urban areas

Groundwater is a crucial resource for dryland regions such as this, where surface water resources are limited and unreliable. This paper presents a study of the Lodwar Alluvial Aquifer System (LAAS) in northwestern Kenya and its hydrochemical and isotopic characteristics, with the goal of understanding how to sustainably manage the groundwater system. As a result, the paper focuses on elucidating the hydrogeochemical and river-groundwater interaction (using environmental isotopes and major ion chemistry) of an aquifer that is located in the north-western dryland of Kenya. The study utilised environmental isotopes of oxygen-18 (18O), deuterium (2H), and tritium (3H) as tracers for establishing recharge sources and origin of groundwater. A sampling campaign involving 112 water samples was conducted to establish isotopic compositions of rain, spring, surface water (rivers, scoop holes, dams and lake) and groundwater at the peak of the wet season in May 2018. The tritium values in the study area ranged from 1.1 to 2.4 TU. Considering the median values of δ18O and δ2H in surface water and groundwater samples, four clusters emerge based on the degree of enrichment; Cluster 1 comprises the lake water (δ18O = +6.01, δ2H = +41.9); Cluster 2 is the Turkwel and Kawalase river water with slightly positive relative to VSMOW and with different δ2H values (+7.6‰ versus −9.8‰). The third cluster is the groundwater of the Shallow Alluvial Aquifer (SAA) and the Deep Aquifer (DA) (δ18O = −0.96‰ and −0.70‰; and δ2H = +0.4‰ and +0.6‰, respectively). The last cluster comprises the most isotope-depleted waters of water pans, scoop holes, Intermediate Aquifer (IA) and Turkana Grit Shall Aquifer (TGSA) with median values ranging from −2.87‰ to −2.48‰ for δ18O and −8.6‰ and −16.4‰ for δ2H. While the SAA is mainly recharge by the Turkwel River, a relationship is observed between the values deuterium in the Kawalase (−9.6‰ VSMOW) and IA (−8.6‰ VSMOW). Understanding recharge sources and aquifer vulnerability of similar strategic aquifers helps scientists appropriately advise policymakers and the water community who develop sustainable water use, aquifer protection and conservation strategies. In addition, the study contributes scientific evidence of isotopic compositions of groundwater in the Horn of Africa. Furthermore, the evidence of surface water-groundwater interaction presents a case of a fragile dryland ecosystem. Future work will involve the installation of piezometers in strategic aquifer zones to monitor groundwater levels in relation to river gauging data to quantify the amount of recharge and establish the impacts of rainfall variability in the upstream catchment.

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Groundwater level monitoring is critical to the protection and management of groundwater resources. Properly designed and executed instrumentation can play an important role in increasing the quality and reliability of collected data and reducing total monitoring costs. The efficiency of the instrumentation depends mainly on the accuracy and reliability of the installed sensors. This study presents the testing and application of a cost-effective pressure sensor (0–689 kPa range) for water level monitoring based on microelectromechanical system (MEMS) technology and the internet of things concept. The sensor performance, in terms of accuracy, precision, repeatability, and temperature, was investigated in laboratory columns (with constant water level, increasing and decreasing water levels at various rates) and in situ conditions in an observation bore (with natural groundwater level fluctuations). The results show that the MEMS sensor is capable of providing a reliable and adequate monitoring scheme with an accuracy of 0.31% full scale (FS) (2.13 kPa).