Groundwater vulnerability to climate change: A review of the assessment methodology

Aslam, Rana Ammar; Shrestha, Sangam; Pandey, Vishnu Prasad

doi:10.1016/j.scitotenv.2017.08.237

Cited by 87 publications

(22 citation statements)

References 39 publications

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“…Rainfall is a climate factor and can be assumed as the aquifer inputs that impact on groundwater contamination through water budget [ 42 ]. The rainfall flows to groundwater recharge, which engenders the leaching of soil NO 3 - [ 43 ]. The rainfall map of the constituency was prepared from the statistics of seven synoptic stations around the constituency with a statistical period of 27 years and based on the inverse distance weighted interpolation (IDW) interpolation method.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…Physical models are used worldwide as the trustworthy systems to study the environmental phenomena, climate behavior, atmospheric and hydrological systems, and further study of the natural hazards, extreme events, and ecosystems [8][9][10][11]. Statistical models, including the time series analysis form another major popular group of modeling techniques widely used by scientists for studying the earth systems and deliver insight on climate change and hydrological related events [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Ardabili¹,

Mosavi

Dehghani

et al. 2019

Preprint

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Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

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“…Much research on the impact of CC in large water bodies has been performed, at both the oceanic level [3][4][5][6][7] and for inland surface waters (lakes, lagoons, estuaries, wetlands, etc.) [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], groundwaters [23][24][25][26][27][28], etc. Of particular interest from the human point of view are rivers, due to their typical extensions and their importance in socio-economics.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic-Based Numerical Assessment of Flood Risk of Tamuín City, Mexico, by Tampaón River: A Forecast Considering Climate Change

Rodríguez-Cuevas

Rocha-Escalante

Couder-Castañeda

et al. 2019

Water

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Climate change has unchained several natural extreme phenomena, including a major frequency and intensity of flooding episodes. From these, the ones of greatest importance are those which endanger human settlements as well as socioeconomic activities. This is the case of Tamuín city, settled in the shore of Tampaón River, in Mexico. In this work, we performed a detailed numerical modelling of the hydrodynamics of the zone, considering in situ topographic and bathymetric data as well as hydrodynamic parameters. Severe rainfall scenarios were simulated in order to determine the zones which are prone to flooding, as well as the potential periods of time between the beginning of the rainfall up to the flooding, considering the potential effects of climate change in the precipitation rate. The outcome of this research will help local governments undertake preventive actions to reinforce the identified risky zones, thus providing an adequate protection of rural and urban zones, as well as their inhabitants and their economical activities from current and future floods, considering potential climate change effects.

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Groundwater vulnerability to climate change: A review of the assessment methodology

Cited by 87 publications

References 39 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Hydrodynamic-Based Numerical Assessment of Flood Risk of Tamuín City, Mexico, by Tampaón River: A Forecast Considering Climate Change

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