Groundwater Level Series Research Articles

Multiparametric stations for real-time monitoring and long-term assessment of natural hazards

The present work would like to illustrate a new concept of multiparametric stations to characterize the crustal fluids-tectonic interaction in specific geological contexts. The dynamics of crustal fluids in relation to tectonics is a complex and sometimes intricate issue. Several factors act and mutually influence themselves, so that in each tectonic and geological context they follow a specific behavior, and a comprehensive cause-effect rule is hard to find. Changes in water chemistry and levels and in soil flux regimes (e.g., CO2, CH4, radon) are just a few examples well documented in the literature as being pre-, co- and post-seismic modifications as well as being markers of the local tectonic stress acting in the crust. A regional study combined with a long-lasting multiparametric monitoring is needed to prepare to a seismic sequence in a given place. The field infrastructure was set up starting from the end of 2021, and multiparametric stations have been installed in correspondence of active seismogenic sources initially located in Northern Italy. Data are transmitted in real-time and archived in an ad hoc developed relational database. Monitoring is mainly focused on groundwater parameters (water level, temperature, and electrical conductivity) of aquifers showing distinct degrees of confinement and lithologies. Sites are also equipped of meteorological sensors (pressure, temperature, rain, humidity, wind speed and direction), radon sensors and surface and borehole seismic stations providing accelerometric and velocimetric data. A mud volcano field is also monitored and holds the installation of a permanent CO2 soil flux station. A statistical analysis working flow is also proposed for a preliminary evaluation of the acquired time-series. In particular, a couple of tools to detect, and thus filter, anthropogenic and meteorological effects on a groundwater level series is described. We wish to provide a model of approach to analogous study cases in other potentially seismic areas.

A parsimonious methodological framework for short-term forecasting of groundwater levels

Groundwater plays a significant role as a strategic resource in reducing the impact of droughts. In spite of its importance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distributed mathematical models to forecast future potential levels. The main aim of this study is to propose and evaluate a novel parsimonious integrated method for the short-term forecasting of groundwater levels. It has low requirements in term of data, and it is operational and relatively easy to apply. It uses geostatistics, optimal meteorological exogenous variables and artificial neural networks. We have illustrated our method in the aquifer “Campo de Montiel” (Spain). The analysis of optimal exogenous variables revealed that, in general, the wells with stronger correlations with precipitation are located closer to the central part of the aquifer. NAR, which does not consider secondary information, is the best approach for 25.5 % of the cases and is associated with well locations with lower R2 between groundwater levels and precipitation. Amongst the approaches with exogenous variables, the ones that use effective precipitation have been selected more times as the best experiments. NARX and Elman using effective precipitation had the best approaches with 21.6 % and 29.4 % of the cases respectively. For the selected approaches, we obtained a mean RMSE of 1.14 m in the test and 0.76, 0.92, 0.92, 0.87, 0.90, and 1.05 m for the forecasting test for months 1 to 6 respectively for the 51 wells, but the accuracy of the results can vary depending on the well. The interquartile range of the RMSE is around 2 m for the test and forecasting test. The uncertainty of the forecasting is also considered by generating multiple groundwater level series.

TREND ANALYSIS OF GROUND-WATER LEVEL TO ASSESS VULNERABILITY OF GROUNDWATER IN CHITTAR SUB BASIN OF TAMIRABHARANI RIVER BASIN IN TAMIL NADU STATE

In Chittar Sub basin, agricultural, domestic and industrial water use is highly dependent on groundwater extractions. For meeting the increasing demand of groundwater resources, it is highly important to realize about interrelation between agriculture, climate change impacts, groundwater levels and other water resources for agricultural purposes. Keeping this point, the study was under taken in Chittar sub basin of Tamirabharani river basin, Tamil Nadu for investigating the temporal trends in groundwater level using the Mann–Kendall test and the Sen’s slope estimator in the area during 1986–2020. The results of the tests indicated a mix of negative and positive trends in the groundwater level series. The positive trends were found to be higher than the negative trends. The statistical tests reported a significant increasing trend in 25 percent of the wells. Moreover, the highest number of wells with significant positive trends occurred in zone 2 and zone 3.

Temporal correction of irregular observed intervals of groundwater level series using interpolation techniques

Temporal correction of irregular observed intervals of groundwater level series using interpolation techniques

Drought Index as Indicator of Salinization of the Salento Aquifer (Southern Italy)

Salento peninsula (Southern Italy) hosts a coastal carbonate and karst aquifer. The semi-arid climate is favourable to human settlement and the development of tourism and agricultural activities, which involve high water demand and groundwater exploitation rates, in turn causing groundwater depletion and salinization. In the last decades these issues worsened because of the increased frequency of droughts, which emerges from the analysis of Standardized Precipitation Index (SPI), calculated during 1949–2011 on the base of monthly precipitation. Groundwater level series and chloride concentrations, collected over the extreme drought period 1989–1990, allow a qualitative assessment of groundwater behaviour, highlighting the concurrent groundwater drought and salinization.

Spatial analysis of ground water level and fluctuation at regional level in North-East of Haryana: Reference to Karnal District, India

The study was conducted to found the ground water potentials in Karnal district of Haryana with an aerial extent of 2520 sq. km. The study has analysed the spatial-temporal changes in groundwater depth in Karnal district to understand the hydrological behaviour and status of the area. Various thematic maps were prepared such as (Groundwater and its fluctuation of pre-monsoon and monsoon in two decades (1996-2017). The main objective of the study was to investigate the temporal trends in annual and monthly groundwater level with the help of central ground water board data. The result indicate a mix of negative and positive trends in the ground water level series.

The effect of hydrogeological conditions on variability and dynamic of groundwater recharge in a carbonate aquifer at local scale

Summary Groundwater recharge in fractured karstic aquifers is particularly difficult to quantify due to the rock mass’s heterogeneity and complexity that include preferential flow paths along karst conduits. The present study’s major goals were to assess how the changes in lithology, as well as the fractured karst systems, influence the flow mechanism in the unsaturated zone, and to define the spatial variation of the groundwater recharge at local scale. The study area is located within the fractured carbonate Western Mountain aquifer (Yarkon-Taninim), west of the city of Jerusalem at the Ein Karem (EK) production well field. Field monitoring included groundwater level observations in nine locations in the study area during years 1990–2014. The measured groundwater level series were analyzed with the aid of one-dimensional, dual permeability numerical model of water flow in variably saturated fractured-porous media, which was calibrated and used to estimate groundwater recharge at nine locations. The recharge values exhibit significant spatial and temporal variation with mean and standard deviation values of 216 and 113 mm/year, respectively. Based on simulations, relationships were established between precipitation and groundwater recharge in each of the nine studied sites and compared with similar ones obtained in earlier regional studies. Simulations show that fast and slow flow paths conditions also influence annual cumulative groundwater recharge dynamic. In areas where fast flow paths exist, most of the groundwater recharge occurs during the rainy season (60–80% from the total recharge for the tested years), while in locations with slow flow path conditions the recharge rate stays relatively constant with a close to linear pattern and continues during summer.

The characteristics of probability distribution of groundwater model output based on sensitivity analysis

The probability distribution of groundwater model output is the direct product of modeling uncertainty. In this work, we aim to analyze the probability distribution of groundwater model outputs (groundwater level series and budget terms) based on sensitivity analysis. In addition, two sources of uncertainties are considered in this study: (1) the probability distribution of model's input parameters; (2) the spatial position of observation point. Based on a synthetical groundwater model, the probability distributions of model outputs are identified by frequency analysis. The sensitivity of output's distribution is analyzed by stepwise regression analysis, mutual entropy analysis, and classification tree analysis methods. Moreover, the key uncertainty variables influencing the mean, variance, and the category of probability distributions of groundwater outputs are identified and compared. Results show that mutual entropy analysis is more general for identifying multiple influencing factors which have a similar correlation structure with output variable than a stepwise regression method. Classification tree analysis is an effective method for analyzing the key driving factors in a classification output system.

Prediction and simulation of monthly groundwater levels by genetic programming

Groundwater level is an effective parameter in the determination of accuracy in groundwater modeling. Thus, application of simple tools to predict future groundwater levels and fill-in gaps in data sets are important issues in groundwater hydrology. Prediction and simulation are two approaches that use previous and previous-current data sets to complete time series. Artificial intelligence is a computing method that is capable to predict and simulate different system states without using complex relations. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two artificial intelligence tools to predict and simulate groundwater levels in three observation wells in the Karaj plain of Iran. Precipitation and evaporation from a surface water body and water levels in observation wells penetrating an aquifer system are used to fill-in gaps in data sets and estimate monthly groundwater level series. Results show that GP decreases the average value of root mean squared error (RMSE) as the error criterion for the observation wells in the training and testing data sets 8.35 and 11.33 percent, respectively, compared to the average of RMSE by ANFIS in prediction. Similarly, the average value of RMSE for different observation wells used in simulation improves the accuracy of prediction 9.89 and 8.40 percent in the training and testing data sets, respectively. These results indicate that the proposed prediction and simulation approach, based on GP, is an effective tool in determining groundwater levels.

Analysis of channel transmission losses in a dryland river reach in north‐eastern Brazil using streamflow series, groundwater level series and multi‐temporal satellite data

AbstractScarcity of hydrological data, especially streamflow discharge and groundwater level series, restricts the understanding of channel transmission losses (TL) in drylands. Furthermore, the lack of information on spatial river dynamics encompasses high uncertainty on TL analysis in large rivers. The objective of this study was to combine the information from streamflow and groundwater level series with multi‐temporal satellite data to derive a hydrological concept of TL for a reach of the Middle Jaguaribe River (MJR) in semi‐arid north‐eastern Brazil. Based on this analysis, we proposed strategies for its modelling and simulation. TL take place in an alluvium, where river and groundwater can be considered to be hydraulically connected. Most losses certainly infiltrated only through streambed and levees and not through the flood plains, as could be shown by satellite image analysis. TL events whose input river flows were smaller than a threshold did not reach the outlet of the MJR. TL events whose input flows were higher than this threshold reached the outlet losing on average 30% of their input. During the dry seasons (DS) and at the beginning of rainy seasons (DS/BRS), no river flow is expected for pre‐events, and events have vertical infiltration into the alluvium. At the middle and the end of the rainy seasons (MRS/ERS), river flow sustained by base flow occurs before/after events, and lateral infiltration into the alluvium plays a major role. Thus, the MJR shifts from being a losing river at DS/BRS to become a losing/gaining (mostly losing) river at MRS/ERS. A model of this system has to include the coupling of river and groundwater flow processes linked by a leakage approach. Copyright © 2012 John Wiley & Sons, Ltd.

Sensitivity analysis of the probability distribution of groundwater level series based on information entropy

Information entropy is an effective method to analyze uncertainty in various processes. The principle of maximum entropy (POME) provides a guide line for the parameter estimation of probability density function (PDF). Mutual entropy analysis is well qualified for delineating the nonlinear and complex multivariable relationship. The probability distribution of model output is the element of model uncertainty analysis. In this paper, a synthetic groundwater flow field is build to produce groundwater level series (GLS). The probability distribution of GLS is obtained by the frequency analysis method based on POME and Chi-Squared test. The important uncertainty factors that affect the parameters of PDF of GLS are assessed by the sensitivity analysis methods, which include stepwise regression analysis and mutual entropy analysis. Results of this analysis indicate that most of the GLS follow normal distribution (or log-normal distribution), while a few obey others. The mean and variance of normal GLS are affected differently by the input variables of groundwater model. Mutual entropy analysis is more competitive and appropriate for delineating the nonlinear and nonmonotonic multivariable relationship than stepwise regression analysis.

Investigation of groundwater level fluctuations in the north of Iran

Groundwater is the main source of water supply for drinking and agriculture uses in Mazandaran province. In recent years, the rapid growth of population and the increased need for water and food has put its land and water resources under severe stress. The main objective of this study was to investigate the temporal trends in annual, seasonal and monthly groundwater level using the Mann–Kendall test and the Sen’s slope estimator in the area during 1985–2007. The results indicated a mix of negative and positive trends in the groundwater level series. However, the positive trends were much more than negative ones. The statistical tests detected a significant increasing trend in more than 28% of the wells. The stronger increasing trends were identified in the series in summer and spring compared with those in autumn and winter. Moreover, the highest numbers of wells with significant positive trends occurred in August and July, respectively. The results of spatial analysis showed that the significant positive trends were concentrated in the central parts of Mazandaran province where paddy fields are the major water demanders. Analysis of climatic parameters revealed that decreasing trend of relative humidity and increasing trends of minimum and maximum air temperature can be attributed to groundwater level fluctuations in the study region. The research will be helpful for planners and policy makers to allocate groundwater resources in different sectors including agriculture, drinking and industry.

Groundwater recharge trends in CanadaGeological Survey of Canada Contribution 20090009.

Groundwater plays a major role in social and economic development and in human and ecosystem health. However, little is known about the potential impacts of climate change on this resource in Canada, namely if groundwater recharge is increasing or decreasing over time. This paper focuses on trend statistical analysis of historical series of baseflow and groundwater levels and their field significance as indicators of recharge. Monitoring wells are mainly located in the southern half of western Canada, where few gauging stations either are available or provide significant trends. Both data sets are thus complementary. Results show that most available groundwater level series have significant trends (80%), whereas most available baseflow series have not (3%–33%). However, groundwater level series usually show smaller slope magnitudes than baseflow series. Mixed trends are often observed across Canada for a given variable, period, or series length, although some regions can have marked trends. For instance, values below the 55°N latitude, and especially values in Atlantic Canada, show mostly downward trends (decreasing recharge). Values north of the 55° parallel often show upward trends. All groundwater level results are field significant at the 10% level, versus only 35% for baseflow results, but they show mixed results. Baseflow values show a majority of downward trends for annual values and the summer period for 40- and 50-year series, thus showing field significance, whereas mixed results are observed for 30-year series and the spring, fall, and winter seasons.