Groundwater Level Monitoring Research Articles

The development of a hydrogeological conceptual model of groundwater and surface water flows in the Silala River Basin

AbstractFrom 1999 a dispute between Bolivia and Chile concerning the status and use of the Silala River developed. In 2016 it was taken before the International Court of Justice. Over time, a key disagreement between the parties emerged, concerning artificial channels in the headwater wetlands of the Silala River in Bolivia and their impact on the river and groundwater discharge from Bolivia to Chile. To quantify this, a numerical groundwater model was developed by Chile, requiring a hydrogeological conceptual model of the Silala River Basin, reviewed here. This focussed on understanding the groundwater system and its interactions with the Silala River. The data and information used to gain this understanding included geological mapping, drilling, satellite imagery interpretation, geophysical surveys, hydrogeochemical analyses, groundwater level monitoring, surface flow monitoring, and groundwater pumping tests. These were focussed along the course of the river and wetlands. A three‐dimensional geological model of the Silala Basin was based on these investigations. The groundwater model area covers the topographic catchment, plus three adjacent closed (endorheic) topographical catchments to the northeast, which together form the groundwater catchment of the Silala River. The main deep regional aquifer is found in ignimbrite pyroclastic rocks and an interbedded alluvial deposit, but secondary perched aquifers on the flanks of the Silala River ravine contribute to the complexity. Although uncertainty remains, the hydrogeological conceptual model describes the main observed features of the system in a scientifically sound and coherent manner, with appropriate detail to support a credible numerical model.This article is categorized under: Science of Water > Hydrological Processes Engineering Water > Sustainable Engineering of Water

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

This paper presents an alternative method for monitoring groundwater levels and estimating specific yields of an unconfined aquifer under different seasonal conditions. The approach employs the Time-Lapse Electrical Resistivity Imaging (TL-ERI) method and machine learning-based time series clustering. A TL-ERI survey was conducted at ten sites (WS01-WS10 sites) throughout the dry and wet seasons, with five-time measurements collected for each site, in the Taichung-Nantou Basin along the Wu River, Central Taiwan. The obtained resistivity raw data was inverted and converted into normalized water content values using Archie’s law, followed by applying the Van Genuchten (VG) model for the Soil Water Characteristic Curve to estimate the Groundwater Level (GWL), and estimated the theoretical specific yield (Sy) by computing the difference between the saturated and residual water contents of the fitted VG model. In addition, the specific yield capacity (Sc), representing the nature of the storage capacity in the aquifer, was also calculated. The results showed that this approach was able to estimate those hydrogeological parameters. The spatial distribution of the GWL reveals that during the dry-wet seasons from February to July, there was a high GWL that extended from southeast to northwest. Conversely, during the wet-dry seasons from July to October, the high GWL shrank, which can be attributed to recharge variations from rainfall events. The determined spatial distribution of Sy and Sc fall within the range of 0.03–0.24 and 0.14–0.25, respectively. To quantitatively establish areas of similar groundwater level changes along with the VG model parameter variations during the study period, a Time series Clustering analysis (TSC) was performed by utilizing Hierarchical Agglomerative Clustering (HAC). The findings suggest that the WS03 site is a promising area for further investigation due to its highest Sc value with a slight change in groundwater levels during the dry and wet seasons. This study brings an advanced development of the geoelectrical method to estimate regional hydrogeological parameters in an area with limited available groundwater observation wells, in different seasonal conditions for groundwater management purposes.

The Environment Agency Chalk groundwater level monitoring network in England

Abstract This paper reviews the Environment Agency chalk groundwater level monitoring network. The network has evolved over many years to enable management of the resource and to assess the impact of abstractions on the environment. The paper considers the strengths and weaknesses of the network, the use and accessibility of the data and how the network supports, and works with, the Environment Agency's regional groundwater models. It concludes with the suggestion that the network is suffering from a degree of lack of maintenance and that there is a disparity between the ambitions of the modelling programme with its Modflow6-driven shift to multi-layer conceptualization and a largely open-hole, single-layer monitoring installation.

Changes in widespread aquifer properties caused by a magnitude 6-class earthquake evaluated using InSAR analyses

Correlations between surface displacements and groundwater level changes have been widely used to understand aquifer properties and their site characteristics; however, the underlying mechanisms of various correlation types and the influence of earthquakes have not been fully investigated. In this study, we examine correlations between surface displacements from interferometric synthetic aperture radar analyses and groundwater level monitoring data in Osaka and Kyoto, Japan, over 4 years, a period including the 18 June 2018 Mw 5.6 northern Osaka earthquake. Both positive and negative correlations between the seasonal groundwater level changes and the seasonal surface displacements are identified. Based on the observations of the effects of the earthquake, a new conceptual aquifer dynamical model driving the relationship between the surface displacements and the groundwater level changes is proposed. We further reveal that sites with negative correlations increased after the earthquake, suggesting that the earthquake increased the groundwater recharge rate as a result of increases in aquifer transportation properties such as permeability and porosity.

Response mechanism of groundwater dynamics to Freeze–thaw process in seasonally frozen soil areas: A comprehensive analysis from site to regional scale

Studying the dynamic mechanism of water-level variations during the freeze–thaw period in seasonally frozen soil regions is an important premise and foundation for winter agricultural irrigation and groundwater resource assessment. Freezing and thawing-induced groundwater-level variations have been observed in regions with a shallow water table depth. However, whether the groundwater level is affected by the freeze–thaw process and the extent of this effect should be verified and discussed on a wider spatiotemporal scale. To this end, this study selected the plain area of Jilin Province, China as an example, and based on a comprehensive analysis of dynamic groundwater-level monitoring data from 114 monitoring wells, the distribution of the meteorological data and soil types and two typical experimental monitoring sites in the area, the dynamic characteristics and influencing factors of groundwater-level variations during the freeze–thaw period were identified. Combined with a multi-factor statistical analysis of the monitoring data at the regional scale of the plain area of Jilin Province, China, the response mechanism of the groundwater level to the freeze–thaw process in the seasonally frozen plain area was summarized. The results showed that: (1) The dynamic phenomenon where the groundwater level falls in the freezing period and rises in the thawing period due to the freeze–thaw process was widespread in seasonally frozen soil regions; (2) The depth threshold of the groundwater level affected by the freeze–thaw process was the sum of the maximum frozen soil depth and the maximum capillary rise height; (3) The main factors controlling the groundwater-level variation during the freeze–thaw period were the initial water-level depth and the maximum snow cover thickness; based on a random forest model, the groundwater-level variation during the freeze–thaw process could be accurately calculated with these main controlling factors; (4) The groundwater-level dynamics during the freeze–thaw process was mainly controlled by the exchange of water between the groundwater and the vadose zone system, and the rise in the water level was only partially replenished by the snowmelt water. The research results have an important guiding significance for winter agricultural irrigation and groundwater recharge resource evaluation.

DETERMINATION OF PARAMETERS OF WATER EXCHANGE AND MODELLING OF GROUNDWATER FLOW PROCESS FOR URANIUM MILL TAILINGS “ZAHIDNE” OF THE PRYDNIPROVSKY CHEMICAL PLANT

Results are presented of field hydrogeological studies aimed at determining the parameters of water exchange and modelling predictions of groundwater flow processes in the zone of influence of the “Zahidne” uranium mill tailing situated at the Prydniprovsky Chemical Plant, Kamianske. Hydrogeological observations allowed to estimate the infiltration recharge rate to groundwater at the study site within the first terrace (200 mm/year) and within the second terrace (150 mm/year) of the Dnipro River. According to tracer test the real groundwater flow velocity in the unconfined aquifer in sandy alluvial deposits in the zone of influence of the tailing is estimated at 0.26–0.52 m/day. Determined water exchange parameters and groundwater level monitoring data were used to develop using Visual Modflow software and calibrate the groundwater flow model for the area between the “Zahidne” uranium mill tailing and Konoplyanka River. Groundwater travel time in the unconfined aquifer from uranium mill tailing to Konoplyanka River estimated using calibrated model is 13 years, which is significantly less than the previous estimates on regional groundwater flow model (20 years). Several remediation scenarios were modelled, which allowed to develop recommendations on improving the protection of groundwater and surface water from radioactive contamination

Design of preliminary groundwater monitoring networks for the coastal Tra Vinh province in Mekong Delta, Vietnam

Study RegionThe study region is the coastal Tra Vinh province located in Mekong Delta, Vietnam Study FocusThe objective of the study is to design preliminary monitoring networks for groundwater level and salinity monitoring in the three heavily abstracted aquifers in the Tra Vinh province. Since data from the existing monitoring were insufficient to explore variogram analysis and subsequent Kriging interpolation for the network design, groundwater flow and saltwater transport models were constructed to create groundwater level and salinity distributions. An iterative procedure was adopted to select monitoring well locations based on the contour maps of groundwater levels and total dissolved solids (TDS) constructed from the groundwater models. The standard deviation of Kriging interpolation error was used as the criteria to assess the quality of the designed monitoring networks. Priorities for the locating groundwater level monitoring wells were targeted in areas with cones of depression and boundaries, while priorities for salinity monitoring were given to coastal zone and inland fresh/saltwater interfaces. New Hydrological Insights for the RegionGroundwater overexploitation has resulted in storage depletion and saltwater intrusion in the Tra Vinh province. Simulations from the numerical models determined the cones of the depression and predicted intrusion from inland saltwater trapped in clays from past marine transgressions and from seawater in the coastal zone. A limited number of monitoring wells is operated and is insufficient to delineate the cone of depression and fresh/salt water interfaces. This study combined the numerical model simulation results and Kriging interpolation method and designed preliminary groundwater level and salinity monitoring networks for the Tra Vinh province. Once the designed networks are implemented, observed data from the networks will provide valuable information for sustainable groundwater resources management in Tra Vinh province. Data availabilityAll data generated or analyzed during this study are available upon request from the corresponding author.

The Spatio-Temporal Dynamic Patterns of Shallow Groundwater Level and Salinity: The Yellow River Delta, China

Shallow groundwater in coastal aquifers is a highly dynamic and complex system with a high risk of seawater intrusion. Analyzing the spatio-temporal dynamic patterns of groundwater can help to manage the groundwater resource and prevent it from degradation. Based on the groundwater level (GWL) and electrical conductivity (EC) monitoring data of 18 observation wells in the Yellow River Delta (YRD) from 2004 to 2010, this research analyses the groundwater dynamics using a robust seasonal trend decomposition technique (STL) and spatial interpolation method to detect the groundwater spatio-temporal dynamic patterns of groundwater level and salinity. Combined with hydro-climatic data, the Pearson correlation method and the Mann-Kendall (MK) trend analysis were used to further reveal the impacts that induce their trends and seasonal variations. Our analyses show that the risk of seawater intrusion into local shallow aquifers in this region is high, with the mean groundwater level over 42% of the region lower than the local sea level, and the mean groundwater EC over 96% of the region met the standards for seawater intrusion. In addition, the trends of groundwater level generally declined by 0.01~0.45 m/a and salinity increased by 1.153~25.608 μs/cm.a, which are consistent with the trend of precipitation decline. The seasonal dynamics of groundwater level and salinity are highly correlated with the seasonal components of rainfall and evaporation. It can be concluded that the extent of seawater intrusion will increase in the future with sea level rise. The approaches used in this study proved to be effective and can certainly serve as an example for the analysis of the spatio-temporal dynamics of groundwater in other coastal regions.

Groundwater level monitoring of Danish chalk and limestone aquifers to survey trends in quantitative status and groundwater resources

Abstract Chalk and limestone aquifers contribute one-third of the drinking water supply in Denmark, and one-sixth of that national groundwater resource is assessed as having a ‘Poor’ status in terms of the quantitative Water Framework Directive due to intensive abstraction. This paper describes the national groundwater level monitoring network with regard to the following three applications: (1) when used for the annual surveying and reporting of groundwater resources and impacts from climate and groundwater abstraction; (2) as part of real-time monitoring and modelling for daily and seasonal forecasting; and (3) for tracking long-term climate change impacts on groundwater levels. Groundwater level monitoring provides a particularly important indicator of abstraction pressure and sustainable balance compared with recharge. Many larger chalk and limestone groundwater bodies in Denmark are only monitored by local water companies and not represented in the national groundwater level network. This raises the concern that current national groundwater level monitoring does not fully support integrated modelling and assessment purposes for chalk and limestone groundwater bodies. This also implies that, for tracking long-term climate change and anthropogenic impacts on groundwater levels, the national groundwater monitoring network especially lacks long-term records with complete 30-year time-series for many intensively exploited large chalk and limestone aquifers.

Estimation of shallow groundwater abstraction for irrigation and its impact on groundwater availability in the Lake Tana sub-basin, Ethiopia

Study regionLake Tana sub-basin of the Upper Blue Nile River Basin, Ethiopia Study focusGroundwater use for small-scale irrigation is increasing in the Lake Tana sub-basin. However, the abstraction amount and its impact are not well understood. In this study, a new methodological approach was utilized to estimate the irrigation water abstraction amount, which is based on groundwater level monitoring before, during, and at the end of the irrigation season (2021/2022). The monitoring was conducted on 361 hand-dug wells distributed throughout the sub-basin, which is subdivided into East, Southwest, and North zones. New hydrological insights for the regionGroundwater abstraction for irrigation and associated groundwater level decline estimates are 10.6 × 106 m3 and 2.43 m in the East, 4.2 × 106 m3 and 3.23 m in the Southwest, and 0.6 × 106 m3 and 1.32 m in the North. These abstractions account for 103%, 97%, and 62% of the mean annual groundwater recharge in the East, Southwest, and North zones, respectively. Groundwater is overexploited in the East and Southwest zones although, at the sub-basin scale, the amount of groundwater used for irrigation is small compared to the renewable groundwater resource. However, if groundwater-based irrigation continues to expand especially in the East and Southwest zones, groundwater scarcity at the local scales will worsen. Adaptive management strategies are required to minimize the potential adverse effects on groundwater resources.

Hydrogeochemical Characteristics and Groundwater Quality Assessment of a Relatively- Pristine Agricultural Basin (Palas Basin)

This study investigates the spatial and temporal changes in groundwater levels and quality in the Palas Basin, a relatively-pristine semi-arid agricultural basin in T . Although groundwater is solely used as irrigation and drinking potable water resource throughout the basin, measurements regarding the dynamics and quality of groundwater were quite rare. The analyses were based on data collected from 12 water quality monitoring wells and 15 groundwater level monitoring wells during June 2019-May 2020. Water samples were analyzed for pH, electrical conductivity, temperature, nitrate, nitrite, ammonium, total nitrogen, total hardness, alkalinity, chloride,sulfate, phosphate, total organic carbon, calcium, magnesium, sodium, potassium, and arsenic. Data analyses included hydrogeochemical analysis and multivariate statistical analysis such as principal component analysis and cluster analyses. Results showed that water quality in the basin is mostly controlled by natural factors, however, anthropogenic impacts from agriculture activities were apparent in some regions. The basin shows significant changes in water levels throughout the year due to irrigation activities. The groundwater quality was classified as either Ca-Mg-HCO3and Ca-Mg-SO4 type. In the majority of the basin, water quality was suitable for irrigation and drinking water uses, however, a few sampling sites had very high electrical conductivity, sulfate, nitrate, and arsenic levels. The high levels of nitrates were detected in areas, where agriculture is intense, indicating that agricultural activities might be affecting water quality. High sulfate, electrical conductivity, and arsenic levels could be related to the hydrogeological setting of the basin. This study showed that agricultural activities and natural factors were effective on the hydrogeochemical characteristics of the Palas Basin.

Influence of groundwater level change on deep foundation pit and its control technology

During the construction of deep foundation pits with rich water straight grooves, instability of deep foundation pits due to groundwater frequently occurs, posing a huge challenge to construction safety and economy. Based on the deep foundation pit of the intercity connecting line of Beijing New Airport, the technical method of combining theoretical analysis and on-site monitoring is adopted to analyze the influencing factors of groundwater on the retaining structure of deep foundation pit, and control and analyze the influencing factors. The deformation control technology for deep foundation pit is proposed. The following conclusions are obtained: (1) Based on the on-site geological conditions and the mechanism of groundwater level change, the deformation and failure forms and precipitation forms of deep foundation pits are obtained through theoretical analysis. (2) Based on the Deformation monitoring technology of deep foundation pit, the change of groundwater level and Deformation monitoring of retaining structure are carried out. (3) Combining on-site monitoring with theoretical calculation, conducting comparative analysis of data, summarizing the influencing factors of deep foundation pit damage, obtaining targeted deformation control technology for deep foundation pit, guiding the construction of practical projects, and providing reference for similar projects.

Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas

Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models.

Spatial Variability in Land Subsidence and Its Relation to Groundwater Withdrawals in the Choshui Delta

In this article, the characterization of land subsidence with the spatial variability of soil formation and groundwater withdrawals in Choshui delta, Taiwan, is presented. Levelling surveys, borehole logging, multi-layer compaction monitoring network, multi-layer groundwater level monitoring network, and the electricity consumption of wells in the study area are adopted. Various factors, including the percentage of coarse-grained soil, percentage of fine-grained soil, length of average maximum drainage path, percentage of agricultural land use, electricity consumption of wells, and accumulated depth of land subsidence, are applied. Thematic maps based on these factors are established using geographic information system spatial analysis. A principal component analysis (PCA) is then employed to obtain the dominant factors for land subsidence. The results indicate that the largest subsidence rate is observed in the region that has both a high electricity consumption of wells and a large percentage of fine-grained soil. The PCA results reveal that the electricity consumption of wells is highly correlated with the accumulated depth of land subsidence. The first principal component is the acquired factor causing land subsidence, such as the excessive withdrawal of groundwater. The second principal component is the congenital factor causing land subsidence, which corresponds to fine sand, silty and clayey soils.

Middle Risle River Critical Zone Observatory: monitoring karst-process evolution and its impact on surface water and on aquatic ecosystems

Abstract Rivers in karstic environments are known to be greatly influenced by surface water–groundwater interactions, with significant localized inflows during floods from springs, or with losses that can dry up rivers. The Middle Risle River is frequently affected by the development of sinkholes in a chalk karst area (Normandy, France). In the 2010s, two new major sinkholes in the riverbed caused a complete loss of water into the underlying phreatic aquifer, causing the river to dry up over several kilometres. The resulting changes in hydrogeological processes and surface water–groundwater interaction greatly affected water quality, water use and water-dependent ecosystems, causing a political crisis in this river-dependent touristic valley. To understand these phenomena and improve crisis management, the Middle Risle Critical Zone Observatory was set up to enhance monitoring, surveying and/or modelling of groundwater and river levels, river and spring flow, water temperature and conductivity, and ecosystem characteristics (fish, macro-invertebrates and vegetation). The results showed notable impacts on fish, macro-invertebrates and vegetation, some plants proving to be reliable indicators of surface-water–groundwater interaction. The dynamics of local hydrogeological processes were assessed and linked to the measured effects on ecosystems and water supply. Inverse modelling based on an analytical solution of the diffusive wave equation assessed lateral flow during floods, quantifying the spatial–temporal variability of surface-water and groundwater exchanges. It also highlighted the important role of karst zones in both storage and flood-peak attenuation processes, thereby protecting downstream villages against floods.

Influencing factors, deformation mechanism and failure process prediction for reservoir rock landslides: Tanjiahe landslide, three gorges reservoir area

Compared with terrestrial rock landslides, reservoir rock landslides are also affected by the rise and fall of the reservoir water level, and these landslides are more threatening. High-speed debris flows may form once they lose stability, and once they enter the water a surge is formed. This endangers the safe operation of reservoirs. This study explored the deformation characteristics and influencing factors of the Tanjiahe reservoir rock landslide in the Three Gorges Reservoir using field investigations, GPS surface displacement monitoring, and groundwater level monitoring. The discrete element system MatDEM was used to simulate failure motion, and predict the hazard area affected by the Tanjiahe landslide. The results show that within the reservoir water variation section (145–175 m), the Tanjiahe landslide mass was composed of surface soil (156–175 m) with low permeability and deep cataclastic rock (145–156 m) with high permeability. Due to the difference in permeability between the deep and surface layers, the response of landslide deformation to water level rise is not obvious. The high-level (175 m) operation of the reservoir and the decline in the reservoir water level (175–145 m) are key factors affecting the landslide deformation. Rainfall had a positive effect on landslide deformation. Under their combined action, the stability of the front gentle anti-sliding section of the landslide decreases, and the displacement of the middle and rear steeper sliding section increases under the driving force, which may lead to slope failure. The simulation results show that the upper part of the Tanjiahe landslide slides first and pushes the lower part to move, which is a typical of thrust load-caused failure. The speed of the sliding mass has three stages: rapid rise, rapid decline, and slow decline. The higher the slope angle, the higher the acceleration of the sliding mass in the direction parallel to the slope surface, the higher the speed peak value and the faster the sliding mass speed reaches the peak value. During the failure process, energy is transferred between sliding mass through collisions. Landslides can easily lead to debris flow. The maximum height of the first wave generated when the debris flow entered the water is 5.95 m, and the wave height that propagated to the opposite bank is 3.09 m. The landslide-induced waves propagated along the reservoir area for 30 km.

Optimizing groundwater level monitoring networks with hydrogeological complexity and grid-based mapping methods

Optimizing groundwater level monitoring networks with hydrogeological complexity and grid-based mapping methods

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

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

Meteorological and hydrological data from the Alder Creek watershed, SW Ontario

Abstract. Data for small to mid-sized watersheds are seldom publicly available, but may be representative of diverse types of hydrological contexts when assessing patterns. These types of data may also prove valuable for informing numerical experimentation and practical modelling. This paper presents data collected in the Alder Creek watershed, located within the Grand River basin in Ontario, Canada. The Alder Creek watershed provides source water from the aquifers of the Waterloo Moraine for multiple well fields that supply the cities of Kitchener and Waterloo. Recharge rates and human impacts on streamflow are important topics for the watershed, and many numerical models of the area have been constructed. In order to support these types of analyses, field equipment was deployed within the watershed between 2013 and 2018 to monitor groundwater levels, stream stage, soil moisture, soil temperature, rainfall, and other weather parameters. The available data are described, complementary information is presented, and examples of possible analyses are cited and illustrated. The data presented and described in this paper are available at https://doi.org/10.20383/101.0178 (Wiebe et al., 2019).

Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area

The monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.