Spatio-temporal effect of climate change on water balance and interactions between groundwater and surface water in plains

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review

Climate change is a major concern for a range of environmental issues including water resources especially groundwater. Recent studies have reported significant impact of various climatic factors such as change in temperature, precipitation, evapotranspiration, etc. on different groundwater variables. For this, a range of tools and techniques are widely used in the literature including advanced machine learning (ML) and artificial intelligence (AI) approaches. To the best of the authors' knowledge, this review is one of the novel studies that offers an in-depth exploration of ML/AI models for evaluating climate change impact on groundwater variables. The study primarily focuses on the efficacy of various ML/AI models in forecasting critical groundwater parameters such as levels, discharge, storage, and quality under various climatic pressures like temperature and precipitation that influence these variables. A total of 65 research papers were selected for review from the year 2017-2023, providing an up-to-date exploration of the advancements in ML/AI methods for assessing the impact of climate change on various groundwater variables. It should be noted that the ML/AI model performance depends on the data attributes like data types, geospatial resolution, temporal scale etc. Moreover, depending on the research aim and objectives of the different studies along with the data availability, various sets of historical/observation data have been used in the reviewed studies Therefore, the reviewed studies considered these attributes for evaluating different ML/AI models. The results of the study highlight the exceptional ability of neural networks, random forest (RF), decision tree (DT), support vector machines (SVM) to perform exceptionally accurate in predicting water resource changes and identifying key determinants of groundwater level fluctuations. Additionally, the review emphasizes on the enhanced accuracy achieved through hybrid and ensemble ML approaches. In terms of Irish context, the study reveals significant climate change risks posing threats to groundwater quantity and quality along with limited research conducted in this avenue. Therefore, the findings of this review can be helpful for understanding the interplay between climate change and groundwater variables along with the details of the various tools and techniques including ML/AI approaches for assessing the impacts of climate changes on groundwater.

Effects of water-table changes following rainfall events on arsenic fate and transport in groundwater-surface water mixing zones

This study investigated the effects of groundwater-surface water (GW-SW) interactions on the fate and transport of arsenic (As) following rainfall events and subsequent water-table changes in GW-SW mixing zones, comprising the riparian and hyporheic zones, near an abandoned gold mine. During the dry and wet periods, stream conditions changed from flow-through to gaining, respectively. Water-table changes caused by rainfall events controlled flow paths between riparian zones and the stream, affecting spatiotemporal variation in the redox and pH conditions of the aquatic environment. Subsequently, the fate and transport of As in GW-SW mixing zones was responsive to variations in redox and pH conditions. Through the oxidative dissolution of As-bearing sulfide minerals and the reductive dissolution of iron (Fe) oxides with adsorbed As, As was released into the groundwater in the riparian zones and transported to the stream and streambed along the baseflow discharge. However, As was also immobilized in the sediment through adsorption onto Fe-oxides and coprecipitation with calcium (Ca) and zinc (Zn), suggesting that the sediment acts as a sink-and-source of As in aquatic environments. Therefore, water-table changes and GW-SW interactions could play an important role in the fate and transport of As in aquatic environments, specifically groundwater-riparian-streambed-stream systems. The findings of this study will provide scientific insights into the mechanisms of As in aquatic environments, aiding in improved decision-making to ensure safe and sustainable water management in response to future climate change.

Combined effects of precipitation anomalies and dams on streamwater-groundwater interaction in the Fen River basin, China

Both water management measures like damming and changes in precipitation as a result of anthropogenic induced climate change have exerted profound effects on the dynamics of streamwater-groundwater interaction (SGI). However, their compound effects on SGI have not been investigated so far. Taking the Fen River of China as an example, this study aims to examine the synergistic impacts of damming and precipitation anomalies on SGI dynamics. The sampling considered the seasonal and interannual variability of precipitation (May and September in 2019 representing a dry year; May and August in 2021 representing a wet year), and long-term daily observational data, including water levels and water discharge, were combined to elucidate the compound effects. Precipitation anomalies and damming exert significant individual and combined influences on SGI. Separately, dams and reservoirs reversed the SGI dynamics, significantly increasing the contributions of streamwater to groundwater from 0 to 29 % to 78 % in the dam-affected areas. Further, the groundwater discharge ratios behind the dam (about 60 %) were three times higher than those in front of the dam. Precipitation anomalies significantly amplified interannual variability in SGI patterns, and groundwater discharge ratios increased by 47 % during the dry period (2019) compared to flood period (2021). The combined influence of precipitation anomalies and dam regulation remarkably changed the lateral, vertical, and longitudinal water exchange dynamics. Precipitation anomalies affected the SGI dynamics at the whole watershed scale, whereas dam regulation regimes exhibited a stronger control at the local scale. The compound effects of dam regulation and precipitation anomalies can result in different SGI patterns under various climate scenarios. More attention should be paid to the interrelated feedback mechanisms between damming, extreme precipitation events, and their impact on the watershed-scale hydrological cycle.

Impact of rainfed agriculture on spatio-temporal patterns of water balance and the interaction between groundwater and surface water in sub-humid plains

The expansion of rainfed agriculture, especially soybean cultivation in sub-humid plains, alters water balance and the exchange between groundwater-surface water (GW-SW). However, to date, there are no studies that analyze how these anthropic disturbances affect hydrological connectivity in these systems, especially the GW-SW interactions. The objective of this study is to analyze how the increase in rainfed agriculture affects the spatio-temporal patterns of the water balance and the GW-SW interaction. For this analysis, a coupled GW-SW flow model was implemented under land use and land cover (LULC) scenarios, to quantify the spatio-temporal dynamics for different components of water balance and GW-SW interactions for the upper creek basin of Del Azul. A simulation was carried out for a period of 13 years (2003-2015) on a daily scale and it was contrasted through three multitemporal LULC maps. The results point that substitution of natural pastures, the reduction of winter crops and the decrease of crop rotation, due to the increase of soybean monoculture in the basin under study, modifies the water balance, especially the annual rates of surface runoff and soil moisture which may increase between 3.5 and 9.4 % and between 1.4 and 4.4 % respectively, thus increasing the annual streamflows between 2.6 and 6.8 % and the groundwater heads between 0.2 and 0.6 m. This leads to changes in the hetereogeneity of the GW-SW interaction, a reduction between 0.3 and 3 % is observed in the discharge from the Pampeano aquifer to the Del Azul stream, while the recharge rates from the Del Azul stream to the Pampeano aquifer increase between 2 and 17.8 %. The application of the SWAT-MODFLOW model under LULC scenarios, improves the prediction of the regional hydrologic connectivity on sub-humid plains, because the hydrological processes occurring in the surface and non-saturated zone are governed by shallow groundwater dynamics.

Assessment of groundwater salinization impact in coastal aquifers based on the shared socioeconomic pathways: An integrated modeling approach

Seawater intrusion (SWI) has become a significant threat to human health and sustainable economic development in coastal areas with the rapid pace of climate change. Therefore, it is crucial to determine the response of SWI to climate change. However, most studies cannot reflect the direct impact of future climate change on groundwater salinity. This study first established the SWAT-MODFLOW coupled model after unifying both spatiotemporal computational units. Streamflow, groundwater level observation data, etc., were used to calibrate and validate the coupled model. And then SEAWAT model was loaded into the coupled model to form a new integrated model. Finally, precipitation of six Global Climate Models (GCMs) under two shared socioeconomic pathways (1-2.6 and 5-8.5 scenarios) was imported into the above calibrated integrated model separately to make SWI prediction from December 30, 2020, to December 30, 2030. The results show that this integrated model accurately reflected the study area's current flow and concentration field distribution. Precipitation under different ssps had little effect on future SWI, while the uncertainty of SWI prediction was mainly derived from different GCMs. This study provides important implications for exploring the occurrence and the prediction of SWI in the coastal aquifer. It has specific reference significance for the optimal management of water resources in coastal areas and the effective mitigation of SWI.

Impact of climate change on groundwater recharge in shallow young glacial aquifers in northern Poland

We investigated the influence of climate change in the period 1951-2020 on shallow aquifers in the Brda and Wda outwash plains (Pomeranian Region, Northern Poland). There was a significant temperature rise (0.3 °C/10 years), which accelerated after 1980 (0.66 °C/10 years). Precipitation became increasingly irregular - extremely rainy years occurred right after or before extremely dry years, and intensive rainfall events became more frequent after 2000. The groundwater level decreased over the last 20 years, even though the average annual precipitation was higher than in the previous 50 years. We carried out numerical simulations of water flow in representative soil profiles for the years 1970-2020 using the HYDRUS-1D model, developed and calibrated during our earlier work at an experimental site in the Brda outwash plain (Gumuła-Kawęcka et al., 2022). We used a relationship between the water head and flux at the bottom of the soil profiles (the third-type boundary condition) to reproduce groundwater table fluctuations caused by recharge variability in time. The calculated daily recharge showed a decreasing linear trend for the last 20 years (0.05-0.06 mm d^-1/10 years), and dropping trends in water table level and soil water content in the entire profile of vadose zone. Field tracer experiments were performed to estimate impact of extremely rain events on water flux in vadose zone. The results suggest that tracer travel times are strongly determined by water content in the unsaturated zone which is determined by precipitation amount in span of weeks, rather than extremely high precipitation events.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport

Identifying regional-scale surface water-groundwater interactions (SGI) is vital for predicting anthropogenic effects on surface water bodies and underlying aquifers. However, large-scale water and nutrient flux studies rely on surface water or groundwater-focused models. This study aims to model the effect of urbanization, which is usually accompanied by high groundwater abstraction and surface water pollution, particularly in the developing world, on a regional-scale SGI and nitrate loading. In the study area, the urban expansion increased by over 3% in the last decade. The integrated SWAT-MODFLOW model, Soil and Water Assessment Tool (SWAT) and Modular Finite-Difference Groundwater Flow (MODFLOW) coupling code, was used to assess SGI. By coupling SWAT-MODFLOW with Reactive Transport in 3-Dimensions, the nutrient loading to the river from point and non-point sources was also modeled. Basin average annual results show that groundwater discharge declined with increasing groundwater abstraction and increased with Land use/Land cover (LULC) changes. Groundwater recharge decreased significantly in the Belge season (February to May), and the river seepage and groundwater discharge decreased correspondingly. High spatiotemporal changes in SGI and nitrate loading were found under the combined LULC and groundwater abstraction scenarios. The water yield decreased by 15%. In a large part of the region, the nitrate loading increased by 17–250%. Seasonally controlled groundwater abstraction and water quality monitoring are essential in this region.

Modeling groundwater and surface water interaction: An overview of current status and future challenges

The interaction between surface water and groundwater constitutes a critical process to understand the quantitative and qualitative regime of dependent hydrosystems. A multi-scale approach combining cross-disciplinary techniques can considerably reduce uncertainties and provide an optimal understanding of groundwater and surface water exchanges. The simulation process constitutes the most effective tool for such analysis; however, its implementation requires a variety of data, a detailed analysis of the hydrosystem, and time to finalize a reliable solution. The results of the simulation process contribute to the raising of awareness for water protection and the application of better management strategies. Knowledge of models' parameters has great importance to ensure reliable results in the modeling process. In this study, a literature overview of modeling applications in groundwater - surface water interaction is provided. In this context, a comprehensive and holistic approach to groundwater and surface water simulation codes is here presented; results, case studies, and future challenges are also discussed. The main finding of the analysis highlights uncertainties and gaps in the modeling process due to the lack of high frequency and depth dependent field measurements. In many studies, authors underestimate the importance of the hydrogeological regime, and the discretization of hydraulic parameters is often lumped in a simplified manner. The modeling ethics in terms of data transparency and openness should be widely considered to improve the modeling results. The current study contributes to overcome common weaknesses of model applications, fulfils gaps in the existing literature, and highlights the importance of the modeling process in planning sustainable management of water resources.

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

Prediction of groundwater drawdown using artificial neural networks

Groundwater drawdown is typically measured using pumping tests and field experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on artificial neural network (ANN)s and field measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the influence radius of each well (1000 m) were identified and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown ​​(Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was fitted to available input data to map the annual drawdown ​​across the entire aquifer. The accuracy assessment of the final map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.

The State-of-the-Art Estimation of Groundwater Recharge and Water Balance with a Special Emphasis on India: A Critical Review

Groundwater recharge estimation is essential for sustainable water management and water supply schemes. In this paper, we review groundwater recharge estimation techniques and identify the appropriate methods by considering India’s hydrological and climatic conditions. Significant components of recharge, factors affecting groundwater recharge, aquifer systems of India, and historical groundwater recharge estimation practices are reviewed. Currently used recharge estimation methods are assessed based on case studies. The most popular estimation methods are studied and compared based on their application in various regions. It is observed that the accuracy of the recharge estimates is largely influenced by false assumptions, the possibility of erroneous measurements, a potential lack of reliable data, and a variety of problems associated with parameter estimation. The suitability of different methods for a region is found to depend on time and space considerations, the objective of the study, hydrogeological condition, and availability of data. In Indian conditions, it is suggested to use water table fluctuation and water balance methods for the recharge estimation, provided that accurate water level measurements are assured.

Appraising climate change impacts on future water resources and agricultural productivity in agro-urban river basins

Climate change can have an adverse effect on agricultural productivity and water availability in semi-arid regions, as changes in surface water availability lead to groundwater depletion and resultant losses in crop yield. These inter-relationships necessitate an integrated management approach for surface water, groundwater, and crop yield as a holistic system. This study quantifies the future availability of surface water and groundwater and associated crop production in a large semi-arid agro-urban river basin in which agricultural irrigation is a leader consumer of water. The region of study is the South Platte River Basin (72,000 km²), Colorado, USA. The coupled SWAT-MODFLOW modeling code is used as the hydrologic simulator and forced with five different CMIP5 climate models downscaled by Multivariate Adaptive Constructed Analogs (MACA), each for two climate scenarios, RCP4.5, and RCP8.5, for 1980-2100. The hydrologic model accounts for surface runoff, soil lateral flow, groundwater flow, groundwater-surface water interactions, irrigation from surface water and groundwater, and crop yield on a per-field basis. In all climate models and emission scenarios, an increase of 3 to 5 °C in annual average temperature is projected. Whereas, variation in the projected precipitation depends on topography and distances from mountains. Based on the results of this study, the worst-case climate model in the basin is IPSL-CM5A-MR-8.5. Under this climate scenario, for a 1 °C increase in temperature and the 1.3% reduction in annual precipitation, the basin will experience an 8.5% decrease in stream discharge, 2-5% decline in groundwater storage, and 11% reduction in crop yield. These results indicate the significant effect of climate change on water and food resources of a large river basin, pointing to the need for immediate implementation of conservation practices.

Water balance and influence mechanism analysis: a case study of Hongjiannao Lake, China

Hongjiannao groundwater exchange was the largest desert freshwater lake in China (57.25 km² in 1986). However, it shrank sharply over the past 34a (1986-2019), with the smallest lake area 31.41 km² in 2015. The objective of this study was to use the Landsat images, ASTER GDEM V2 data, and meteorology and statistics data, in combination with the water balance model to calculate the dynamics of water balance elements, quantify and characterize the interannual variations in lake-groundwater exchanges, and analyze its influencing factors by using the geographical detector. The results showed that in the stable stage (1986-1997), the average rate of the lake area, water level, and lake volume change was -0.26 km²/a, -0.0483 m/a, and -0.0009 km³/a, respectively. Precipitation, river inflow, and groundwater were 0.0203 km³, 0.0485 km³, and 0.0098 km³, which accounts for the whole input were 25.83%, 61.70%, and 12.47%, respectively; evaporation was 0.0786 km³. In the reduction stage (1998-2015), the average rate of the lake area, water level, and lake volume change was -1.21 km²/a, -0.2422 m/a, and -0.0101 km³/a, respectively. Before 2006, precipitation, river inflow, and groundwater were 0.0154 km³, 0.0475 km³, and -0.0025 km³, respectively; from 2006 to 2009, precipitation, river inflow, and groundwater were 0.0143 km³, 0.0334 km³, and 0.0058 km³, respectively; after 2009, precipitation, river inflow, and groundwater were 0.0139 km³, 0.0199 km³, and 0.0085 km³, respectively. Evaporation decreased from 0.0714 to 0.0480 km³ from 1998 to 2015. In the growth stage (2016-2019), the average rate of the lake area, water level, and lake volume change were 1.38 km²/a, 0.27 m/a, and 0.0088 km³/a, respectively. Precipitation, river inflow, and groundwater were 0.0209 km³, 0.0005 km³, and 0.0373 km³, which accounts for the whole input were 46.63%, 52.12%, and 1.25%, respectively; evaporation was 0.0187 km³. Compared with the stable stage, groundwater in the growth stage reduced from 12.47% (0.0098 km³) to only 1.25% (0.0005 km³). From 1998 to 2004, Hongjiannao Lake experienced continuous losing conditions (discharge from the lake to groundwater), with a variable exchange volume of up to -0.01582 km³ in 1999. Through geographical detector analysis, it was found that temperature was the dominant factor from 1988 to 1997, while human factors were the dominant factors from 1998 to 2015.