Reactivity of southern Quebec aquifers to meteorological and hydrological conditions

2021 ◽  
Author(s):  
Trong Ahn Vu ◽  
Marie Larocque ◽  
Sylvain Gagné ◽  
Marc-André Bourgault
Keyword(s):  
Land Use ◽  
Groundwater Level ◽  
River Flow ◽  
Correlation Coefficients ◽  
Flow Rates ◽  
Groundwater Levels ◽  
Hydrological Conditions ◽  
Observation Network ◽  
Observation Wells

<p>Groundwater represents an important source of drinking water for 25% of the population in the province of Quebec (Canada) and for 80% of its rural population. The deployment of the Quebec Groundwater Observation Network (Réseau de suivi des eaux souterraines du Québec – RSESQ) since the start of the millennia provides important data on the dynamics of piezometric heads throughout southern Quebec. This study aims to use the wealth of available groundwater data available to better understand the resilience of groundwater resources to changes in meteorological and hydrological conditions. The study area is located between the St. Lawrence River and the Canada-USA border, and between the Quebec-Ontario border and Quebec City (36,000 km²). Available data consist of groundwater level time series from 81 observation wells (2000-2018; 43 in confined aquifers, 15 in semi-confined aquifers and 23 in unconfined aquifers), total flow rates from 179 hydrometric stations (1960-2017), and meteorological data from a spatially interpolated 10 km x 10 km grid (1960-2017). Statistical analyses (Mann Kendall and Sen’s slope) were used to understand if groundwater levels and flow rates are declining or rising, what is their short-, medium- and long-term memory and what are the geomorphological, land use, and climate controls of this reactivity. The results show that groundwater levels since 2007 exhibit statistically significant negative annual trends for most observation wells. Since 1960, river flow rates, total precipitation and air temperature all show significant increases. Trends calculated on five-year sliding windows confirm that groundwater levels and river flow rates are significantly correlated to the climate indices Southern Oscillation index (SOI), NINO-3 and Pacific Decadal Oscillation index (PDO). Autocorrelations of flow rates and groundwater level data indicate that rivers and aquifers have a short hydrological memory rarely extending beyond the hydrological year. Cross-correlations of flow rates and groundwater levels with temperature show high correlation coefficients with a lag of up to 60 days, indicating a season-long effect of temperature changes. As expected, cross-correlation analysis of the two data sets with precipitation shows smaller correlation coefficients and a shorter reaction time (10 days). Standard deviations of daily groundwater levels are significantly higher in shallower wells and in wells where groundwater levels are closer to the ground. This confirms the presence of highly dynamic shallow aquifers reacting rapidly to surface processes.  Analyses are under way to test if spatially distributed parameters (e.g., geological setting, slope, land use) and well-related parameters (e.g.: depth, confined or unconfined) are explaining factors of trends and variations in groundwater levels and flow rates. One key observation from this study is that the RSESQ is highly valuable to understand groundwater dynamics and should be maintained on a long-term horizon. This detailed analysis has allowed to identify external influences (e.g., pumping) on some observation wells that do not reflect natural conditions and could be removed from the observation network. Recommendations also include the need for new observation wells in specific locations to improve the representativity of groundwater flow conditions in the study area.</p>

Integrative hydrogeo-ecological assessment of the quantitative and qualitative response of groundwater to hydrological extremes

2021 ◽  
Author(s):  
Steffen Birk ◽  
Johannes Haas ◽  
Alice Retter ◽  
Raoul Collenteur ◽  
Heike Brielmann ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Ecological Status ◽  
Urban Land Use ◽  
In Situ Monitoring ◽  
Drought Indices ◽  
Groundwater Levels ◽  
Hydrological Conditions ◽  
Hydrological Extremes ◽  
Observation Wells

<p>An integrative interdisciplinary approach is currently developed to investigate groundwater systems in alpine and prealpine environments and how they respond to hydrological extremes such as droughts, heavy rain and floods in terms of water quantity, hydrochemical quality, and ecological status. The new approach is aimed at improving the understanding of the interaction between physical, chemical, and biological processes in groundwater responses to extreme events as well as developing indicators suitable for an integrative monitoring and management of the aquifers. For this purpose, observation wells of the existing state hydrographic monitoring net have been selected within the Austrian part of the Mur river basin, stretching from the alpine origin to the national border in the foreland. The investigation area thus comprises diverse hydrogeological settings and land-use types. The selected observation wells have long-term records of groundwater levels and are used for sampling campaigns under different hydrological conditions. Groundwater level fluctuations are evaluated using drought indices and statistical approaches, such as auto-correlation and cross-correlation with precipitation and stream stages. Our hydrochemical analyses of groundwater and surface waters also consider compounds indicative of agricultural sources (e.g., nitrate), wastewater-borne micro-pollutants, and stable isotopes of water. These indicators are used to identify different drivers controlling water origin and quality. The ecological status is characterized using microbiological measures, such as total number of bacteria and microbial activity, groundwater fauna, and the qualitative composition of dissolved organic matter (DOM). First results demonstrate a deterioration of water quality from groundwater to surface water and from the alpine region towards the foreland, corresponding to the more intense agricultural and urban land use in the foreland. Linkages between water quality and hydrological conditions are currently being evaluated and will be further examined using UV-Vis spectrometry for high-resolution in-situ monitoring of water quality changes (DOM and nitrate) at selected observation wells.</p>

scholarly journals Turbidity in the fluvial Gironde Estuary (S–W France) based on 10 year continuous monitoring: sensitivity to hydrological conditions

2015 ◽  
Vol 12 (3) ◽  
pp. 2843-2883 ◽  
Author(s):  
I. Jalón-Rojas ◽  
S. Schmidt ◽  
A. Sottolichio
Keyword(s):  
Continuous Monitoring ◽  
River Flow ◽  
Hysteresis Loops ◽  
Annual Runoff ◽  
Transitional Period ◽  
Hydrological Conditions ◽  
Frequency Monitoring ◽  
Turbidity Level ◽  
River Floods

Abstract. Climate change and human activities impact the volume and timing of freshwater input to estuaries. These modifications in fluvial discharges are expected to influence estuarine suspended sediment dynamics, and in particular the turbidity maximum zone (TMZ). Located in the southwest France, the Gironde fluvial-estuarine systems has an ideal context to address this issue. It is characterized by a very pronounced TMZ, a decrease in mean annual runoff in the last decade, and it is quite unique in having a long-term and high-frequency monitoring of turbidity. The effect of tide and river flow on turbidity in the fluvial estuary is detailed, focusing on dynamics related to changes in hydrological conditions (river floods, periods of low-water, inter-annual changes). Turbidity shows hysteresis loops at different time scales: during river floods and over the transitional period between the installation and expulsion of the TMZ. These hysteresis patterns, that reveal the origin of sediment, locally resuspended or transported from the watershed, may be a tool to evaluate the presence of remained mud. Statistics on turbidity data bound the range of river flow that promotes the TMZ installation in the fluvial stations. Hydrological indicators of the persistence and turbidity level of the TMZ are also defined. The long-term evolution of these indicators confirms the influence of discharge decrease on the intensification of the TMZ in tidal rivers, and provides a tool to evaluate future scenarios.

scholarly journals Many-year Fluctuations of River Runoff in South-taiga Subzone of the Western Siberia

2013 ◽  
pp. 34-45
Author(s):  
Keyword(s):  
Western Siberia ◽  
River Flow ◽  
Atmospheric Precipitation ◽  
Southern Taiga ◽  
Low Flow ◽  
Groundwater Levels ◽  
Level Regime ◽  
Southern Taiga Subzone ◽  
Bog Water

A detailed analysis of river flow long-term changes in the Southern taiga subzone of Western Siberia has been carried out with the Chaya River basin as an example. Causal statistical analysis of changes in groundwater levels, bog water level, air temperature and atmospheric precipitation has been performed. The conducted studies revealed a statistically significant trend in the increase of surface runoff in the winter low flow of the Chaya River and its large tributaries (the Iksa and the Parbig), as well as the underground runoff component for virtually the entire year. An ambiguous regularity has been observed in the change of the level regime of rivers. The main reason for the observed changes in the water regime of the said territory is the redistribution of atmospheric moisture and shifting of the boundaries of hydrological seasons.

scholarly journals Modelling groundwater level fluctuation in an Indian coastal aquifer

Water SA ◽  
2020 ◽  
Vol 46 (4 October) ◽  
Author(s):  
Safieh Javadinejad ◽  
Rebwar Dara ◽  
Forough Jafary
Keyword(s):  
Groundwater Level ◽  
Coastal Aquifer ◽  
M5 Model Tree ◽  
Groundwater Levels ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Input Variables ◽  
Lag Times ◽  
Observation Wells

Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an effect on groundwater), and historical GWL observations throughout the period 1996–2006. The efficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.

scholarly journals Self-optimizer data-mining method for aquifer level prediction

2019 ◽  
Vol 20 (2) ◽  
pp. 724-736
Author(s):  
Omid Bozorg-Haddad ◽  
Mohammad Delpasand ◽  
Hugo A. Loáiciga
Keyword(s):  
Groundwater Level ◽  
Hybrid Algorithm ◽  
Water Source ◽  
Support Vector ◽  
Mining Method ◽  
Parameter Calibration ◽  
Groundwater Levels ◽  
Municipal Water Supply ◽  
Predictive Skill ◽  
Observation Wells

Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.

Time series study for groundwater level evaluation in monitoring well

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Rubens Oliveira da Cunha Júnior ◽  
João Victor Mariano da Silva
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Sedimentary Basin ◽  
Precipitation Data ◽  
Environmental Aspects ◽  
Aquifer System ◽  
Groundwater Levels ◽  
Monitoring Well ◽  
Series Study

Climate and hydrogeological conditions of the Brazilian semi-arid demand sustainable and efficient water solutions. Groundwater monitoring programs are tools to subsidize the decision-making in this sense. In Ceará state, the monitoring of Araripe sedimentary basin aquifers is important for the development of the region. In this scenario, the present work aimed to study the groundwater level through an exploratory analysis of time series. The study area covered the eastern portion of the Araripe sedimentary basin, in the municipality of Milagres, in Ceará state. As the object of this study, it was obtained the time series of monthly average groundwater levels in a monitoring well of RIMAS/CPRM and installed in the Middle Aquifer System. Graphical and numerical methods were applied for the identification and description of time series main characteristics. Precipitation data in the study area were used to evaluate the system recharge. Results were discussed according to the environmental aspects of the study area. As a result, it was possible the identification and description of time series patterns such as trend and seasonality through the applied methods. It is also highlighted the sharp drawdown of groundwater levels in long term in the time series, reflecting the quantitative state of the aquifer system, as well as the groundwater recharge during the rainy season of the region, evidenced by the study of time series seasonality together with the precipitation data..

scholarly journals The dynamics of cultivation and floods in arable lands of Central Argentina

2009 ◽  
Vol 13 (4) ◽  
pp. 491-502 ◽  
Author(s):  
E. F. Viglizzo ◽  
E. G. Jobbágy ◽  
L. Carreño ◽  
F. C. Frank ◽  
R. Aragón ◽  
...  
Keyword(s):  
Land Use ◽  
Groundwater Level ◽  
Time Lag ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Central Argentina ◽  
Cyclic Mechanism ◽  
Subsurface Lateral Flow ◽  
Flooded Area ◽  
One Year

Abstract. Although floods in watersheds have been associated with land-use change since ancient times, the dynamics of flooding is still incompletely understood. In this paper we explored the relations between rainfall, groundwater level, and cultivation to explain the dynamics of floods in the extremely flat and valuable arable lands of the Quinto river watershed, in central Argentina. The analysis involved an area of 12.4 million hectare during a 26-year period (1978–2003), which comprised two extensive flooding episodes in 1983–1988 and 1996–2003. Supported by information from surveys as well as field and remote sensing measurements, we explored the correlation among precipitation, groundwater levels, flooded area and land use. Flood extension was associated to the dynamics of groundwater level. While no correlation with rainfall was recorded in lowlands, a significant correlation (P<0.01) between groundwater and rainfall in highlands was found when estimations comprise a time lag of one year. Correlations between groundwater level and flood extension were positive in all cases, but while highly significant relations (P<0.01) were found in highlands, non significant relations (P>0.05) predominate in lowlands. Our analysis supports the existence of a cyclic mechanism driven by the reciprocal influence between cultivation and groundwater in highlands. This cycle would involve the following stages: (a) cultivation boosts the elevation of groundwater levels through decreased evapotranspiration; (b) as groundwater level rises, floods spread causing a decline of land cultivation; (c) flooding propitiates higher evapotranspiration favouring its own retraction; (d) cultivation expands again following the retreat of floods. Thus, cultivation would trigger a destabilizing feedback self affecting future cultivation in the highlands. It is unlikely that such sequence can work in lowlands. The results suggest that rather than responding directly and solely to the same mechanism, floods in lowlands may be the combined result of various factors like local rainfall, groundwater level fluctuations, surface and subsurface lateral flow, and water-body interlinking. Although the hypothetical mechanisms proposed here require additional understanding efforts, they suggest a promising avenue of environmental management in which cultivation could be steered in the region to smooth the undesirable impacts of floods.

scholarly journals A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

2020 ◽  
Author(s):  
Chong Chen ◽  
Han Zhou ◽  
Hui Zhang ◽  
Lulu Chen ◽  
Zhu Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Groundwater Level ◽  
Learning Algorithm ◽  
Attention Mechanism ◽  
Short Term ◽  
Groundwater Levels ◽  
Deep Learning Algorithm ◽  
Term Prediction ◽  
Groundwater Level Prediction

Abstract Groundwater resources play a vital role in production, human life and economic development. Effective prediction of groundwater levels would support better water resources management. Although machine learning algorithms have been studied and applied in many domains with good enough results, the researches in hydrologic domains are not adequate. This paper proposes a novel deep learning algorithm for groundwater level prediction based on spatiotemporal attention mechanism. Short-term (one month ahead) and long-term (twelve months ahead) prediction of groundwater level are conducted with observed groundwater levels collected from several boreholes in the middle reaches of the Heihe River Basin in northwestern China. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the proposed algorithm and several baseline models (i.e., SVR, Support Vector Regression; FNN, Feedforward Neural Networks; LSTM, Long Short-Term Memory neural network). The results show that the proposed model can effectively improve the prediction accuracy compared to the baseline models with MAE of 0.0754, RMSE of 0.0952 for short-term prediction and MAE of 0.0983, RMSE of 0.1215 for long-term prediction. This study provides a feasible and accurate approach for groundwater prediction which may facilitate decision making for water management.

scholarly journals Future Flows Hydrology: an ensemble of daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain

2013 ◽  
Vol 5 (1) ◽  
pp. 101-107 ◽  
Author(s):  
C. Prudhomme ◽  
T. Haxton ◽  
S. Crooks ◽  
C. Jackson ◽  
A. Barkwith ◽  
...  
Keyword(s):  
Climate Change ◽  
Great Britain ◽  
Groundwater Level ◽  
River Flow ◽  
Hydrological Modelling ◽  
Time Slice ◽  
Practical Recommendation ◽  
Hydrological Models ◽  
Groundwater Levels ◽  
Modelling Uncertainty

Abstract. The dataset Future Flows Hydrology was developed as part of the project "Future Flows and Groundwater Levels'' to provide a consistent set of transient daily river flow and monthly groundwater level projections across England, Wales and Scotland to enable the investigation of the role of climate variability on river flow and groundwater levels nationally and how this may change in the future. Future Flows Hydrology is derived from Future Flows Climate, a national ensemble projection derived from the Hadley Centre's ensemble projection HadRM3-PPE to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications. Three hydrological models and one groundwater level model were used to derive Future Flows Hydrology, with 30 river sites simulated by two hydrological models to enable assessment of hydrological modelling uncertainty in studying the impact of climate change on the hydrology. Future Flows Hydrology contains an 11-member ensemble of transient projections from January 1951 to December 2098, each associated with a single realisation from a different variant of HadRM3 and a single hydrological model. Daily river flows are provided for 281 river catchments and monthly groundwater levels at 24 boreholes as .csv files containing all 11 ensemble members. When separate simulations are done with two hydrological models, two separate .csv files are provided. Because of potential biases in the climate–hydrology modelling chain, catchment fact sheets are associated with each ensemble. These contain information on the uncertainty associated with the hydrological modelling when driven using observed climate and Future Flows Climate for a period representative of the reference time slice 1961–1990 as described by key hydrological statistics. Graphs of projected changes for selected hydrological indicators are also provided for the 2050s time slice. Limitations associated with the dataset are provided, along with practical recommendation of use. Future Flows Hydrology is freely available for non-commercial use under certain licensing conditions. For each study site, catchment averages of daily precipitation and monthly potential evapotranspiration, used to drive the hydrological models, are made available, so that hydrological modelling uncertainty under climate change conditions can be explored further. doi:10.5285/f3723162-4fed-4d9d-92c6-dd17412fa37b

Modelling and reconstruction of the River Kennet palaeohydrology and hydrogeology: Silbury Hill and Avebury in 4,400 BP

2012 ◽  
Vol 43 (5) ◽  
pp. 551-559 ◽  
Author(s):  
P. G. Whitehead ◽  
W. M. Edmunds
Keyword(s):  
Environmental Conditions ◽  
Flow Rates ◽  
Groundwater Levels ◽  
Hydrological Conditions ◽  
Global Circulation ◽  
Local Populations ◽  
Global Circulation Models ◽  
The People ◽  
Modelling Techniques ◽  
Hydrogeological Mapping

With the availability of Global Circulation Models (GCMs) it is now possible to apply hydrological and hydrogeological models and knowledge to assess environmental conditions in past climates. In the upper Kennet there is considerable interest in the development of the construction of the man-made hill at Silbury. Silbury was built in several stages over a period of time and the question arises as to the availability of water for the people who built Silbury. The current Kennet flows at Silbury are low and the current stream tends to be dry for on average 5 months of the year. The aim of the research has been to assess the palaeohydrology of the Silbury Hill and Avebury area and determine the flow rates, groundwater levels and hydrological conditions in 4,400 BP. This has been undertaken using hydrogeological mapping and modelling techniques, making use of outputs from a GCM to recreate past flows and groundwater levels in the upper Kennet at Avebury and Silbury. The modelling results indicate a past wetter climate in the area, with higher river flows and higher groundwater levels, which would have sustained the local populations through dryer summer months.

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