Impact of land cover on groundwater quality in the Upper Floridan Aquifer in Florida, United States

Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales

Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

A machine learning approach to investigate the impact of land use land cover (LULC) changes on groundwater quality, health risks and ecological risks through GIS and response surface methodology (RSM)

Groundwater resources are enormously affected by land use land cover (LULC) dynamics caused by increasing urbanisation, agricultural and household discharge as a result of global population growth. This study investigates the impact of decadal LULC changes in groundwater quality, human and ecological health from 2009 to 2021 in a diverse landscape, West Bengal, India. Using groundwater quality data from 479 wells in 2009 and 734 well in 2021, a recently proposed Water Pollution Index (WPI) was computed, and its geospatial distribution by a machine learning-based 'Empirical Bayesian Kriging' (EBK) tool manifested a decline in water quality since the number of excellent water category decreased from 30.5% to 28% and polluted water increased from 44% to 45%. ANOVA and Friedman tests revealed statistically significant differences (p < 0.0001) in year-wise water quality parameters as well as group comparisons for both years. Landsat 7 and 8 satellite images were used to classify the LULC types applying machine learning tools for both years, and were coupled with response surface methodology (RSM) for the first time, which revealed that the alteration of groundwater quality were attributed to LULC changes, e.g. WPI showed a positive correlation with built-up areas, village-vegetation cover, agricultural lands, and a negative correlation with surface water, barren lands, and forest cover. Expansion in built-up areas by 0.7%, and village-vegetation orchards by 2.3%, accompanied by a reduction in surface water coverage by 0.6%, and 2.4% in croplands caused a 1.5% drop in excellent water and 1% increase in polluted water category. However, ecological risks through the ecological risk index (ERI) exhibited a lower risk in 2021 attributed to reduced high-risk potential zones. This study highlights the potentiality in linking LULC and water quality changes using some advanced statistical tools like GIS and RSM for better management of water quality and landscape ecology.

Predicting Redox Conditions in Groundwater at a National Scale Using Random Forest Classification

Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.

Impact of land use and cover on shallow groundwater quality in Songyuan city, China: A multivariate statistical analysis

The utilization and development of land resources is an important process in which human activities affect groundwater quality. However, the impact of land use on groundwater chemical composition has complex multiple relationships, and is affected by the scale of the buffer zone. Based on these problems, this study used correlation analysis (CA) and principal component analysis (PCA) to discuss the mechanism of the effect of land use/land cover (LULC) on the hydrochemical composition of groundwater in Songyuan City. Samples were divided into two groups, i.e., quaternary unconfined aquifer (0-30 m) and quaternary confined aquifer (30-100 m). By comparing the variation trends of the correlation coefficient and cumulative variance interpretation rate of PCA in different buffer ranges, it was found that the optimal buffer range was 3000 m. Cropland had the greatest impact on groundwater hydrochemistry in the city. The transformation of natural landscapes (such as saline‒alkaline alkali land and grassland) to cropland inhibited salt accumulation in groundwater. This finding is noteworthy since few studies have involved areas where saline‒alkaline land is widely distributed. Compared with CA results, PCA results emphasized the deterioration of groundwater quality by agricultural pollution. Moreover, agricultural pollutants such as NO₃^- and K⁺ were accumulated in areas where cropland transitioned to natural landscapes such as grassland and water bodies. Considering that wide lakes and rivers provide the drainage area for irrigation water in the study area, the groundwater quality in the surrounding area was affected by the contaminated surface water. The multiple interaction relationship between LULC and hydrochemistry was further confirmed by the combination of principal component scores.

What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective

Good surface water quality is critical to human health and ecology. Land use determines the surface water heat and material balance, which cause climate change and affect water quality. There are many factors affecting water quality degradation, and the process of influence is complex. As rivers, lakes, and other water bodies are used as environmental receiving carriers, evaluating and quantifying how impacts occur between land use types and surface water quality is extremely important. Based on the summary of published studies, we can see that (1) land use for agricultural and construction has a negative impact on surface water quality, while woodland use has a certain degree of improvement on surface water quality; (2) statistical methods used in relevant research mainly include correlation analysis, regression analysis, redundancy analysis, etc. Different methods have their own advantages and limitations; (3) in recent years, remote sensing monitoring technology has developed rapidly, and has developed into an effective tool for comprehensive water quality assessment and management. However, the increase in spatial resolution of remote sensing data has been accompanied by a surge in data volume, which has caused difficulties in information interpretation and other aspects.

Assessing the future climate change, land use change, and abstraction impacts on groundwater resources in the Tak Special Economic Zone, Thailand

Groundwater is an important source of water supply in the Tak Special Economic Zone of Thailand. However, groundwater is under stress from climate change, land use change, and an increase in abstraction, affecting the groundwater level and its sustainability. Therefore, this study analyses the impact of these combined stresses on groundwater resources in the near, mid, and far future. Three Global Climate Models are used to project the future climate under SSP2-4.5 and SSP5-8.5 scenarios. According to the results, both maximum and minimum temperatures are likely to show similar increasing trends for both scenarios, with a rise of approximately 1 (1.5), 2 (3), and 3 (5) °C expected for SSP2-4.5 (SSP5-8.5) in each consecutive period. Annual rainfall is expected to continually increase in the future, with around 1500-1600 mm in rainfall (11ꟷ5.43% higher). Land use change is predicted for two scenarios: business as usual (BU) and rapid urbanisation (RU). The forest area is expected to increase to 30% (35%) coverage in 2090 for BU (RU) while agriculture is likely to reduce to 60% (50%) with the urban area increasing to 2.4% (7%). Water demand is predicted to increase in all future scenarios. The SWAT model is used to project recharge, which is likely to increase by 10-20% over time. The highest increase is predicted in the far future under SSP2 and RU scenarios. MODFLOW was used to project future groundwater resources, but due to the lack of consistent data, the time scale is reduced to yearly simulation. The results reveal that the groundwater level is expected to increase in the central part (urban area) of the study area and decrease along the boundary (agricultural area) of the aquifer. This research can aid policymakers and decision-makers in understanding the impact of multiple stressors and formulating adaptation strategies to manage groundwater resources in special economic zones.

Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest

Groundwater quality in plains and basins of arid and semi-arid regions with increased agriculture and urbanization development faces severe nitrate pollution, which is affected by both climate and anthropogenic activities. Here, shallow groundwater nitrate concentrations in the Yinchuan Region in central Yinchuan Plain were modeled during 2000, 2005, 2010, and 2015 using random forest. Multiple spatial environment factors were taken as predictor variables. The relative importance of these factors was also calculated using the constructed model. Remote sensing and GIS methods were used to compile various environmental factors to generate training and test sets for training and validation of the random forest model. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) between the observed and predicted groundwater nitrate concentrations were used to measure the model performance. As indicated by these metrics, the random forest model for groundwater nitrate prediction was performed well. The relative importance of the predictor variables computed by the model indicated groundwater nitrate was mainly affected by the distance to the Yellow River, meteorological elements (precipitation, evaporation, and mean air temperature), and water level elevation. Additionally, urban and arable land were the two land use/land cover types that mainly influenced groundwater nitrate concentration in the Yinchuan Region, of which urban land was more influential than arable land as a result of intense expansion of urban land from 2000 to 2015. Overall, the current study provides an approach to integrate multiple environmental factors for groundwater quality study and is also significant for sustainable groundwater management in the Yinchuan Region.

Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone

Nitrate-nitrogen (NO₃-N) contamination in groundwater is a major problem of drinking and domestic waters in rural areas. This study revealed the influence of land use type on shallow alluvial groundwaters in a typical rural area in South Korea by applying a self-organizing map (SOM), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The uncertainty of spatial information on land use was improved by using a buffer zone of the average influence radius of 32.65 m surrounding wells. Two major land-use types, forests (44.9%) and rice fields (28.8%), occupied a total of 73.7% of the rural area. The higher concentrations of NO₃-N in public facilities and livestock areas were demonstrated to directly recharge groundwater pollutants. NO₃-N contamination in rice paddies, which also contained chlorine (Cl) and sulfate (SO₄), was assessed according to the nutrients and residual salt in the soil. In addition, different NO₃-N concentrations for the same land use indicate various biochemical reactions and NO₃-N recharge types into the groundwater system. The shallow groundwaters in the study area were classified into three clusters according to their chemical constituents and land-use properties, especially NO₃-N concentration, including pH, Cl, and SO₄, using a SOM, PCA, and HCA. Unlike existing studies, we applied a buffer zone based on the Cooper-Jacob equation to obtain an improved SOM model prediction accuracy approximately 10% greater than that using the original dataset.

Inconsistent seasonal variation of antibiotics between surface water and groundwater in the Jianghan Plain: Risks and linkage to land uses

Antibiotics are widely used in humans and animals, but their transformation from surface water to groundwater and the impact of land uses on them remain unclear. In this study, 14 antibiotics were systematically surveyed in a complex agricultural area in Central China. Results indicated that the selected antibiotic concentrations in surface waters were higher in winter (average: 32.7 ng/L) than in summer (average: 17.9 ng/L), while the seasonal variation in groundwaters showed an opposite trend (2.2 ng/L in dry winter vs. 8.0 ng/L in summer). Macrolides were the predominant antibiotics in this area, with a detected frequency of over 90%. A significant correlation between surface water and groundwater antibiotics was only observed in winter (R² = 0.58). This study further confirmed the impact of land uses on these contaminants, with optimal buffer radii of 2500 m in winter and 500 m in summer. Risk assessment indicated that clarithromycin posed high risks in this area. Overall, this study identified the spatiotemporal variability of antibiotics in a typical agricultural area in Central China and revealed the impact of land uses on antibiotic pollution in aquatic environments.

Catchment versus Riparian Buffers: Which Land Use Spatial Scales Have the Greatest Ability to Explain Water Quality Changes in a Typical Temperate Watershed?

Identifying the multi-scale spatial relationship between land use and water quality is critical for determining the priorities and key areas of river management. To more accurately identify the scale effect of land-use patterns on water quality and quantitatively distinguish the difference in the impacts of land-use composition and configuration on water quality, we used 94 sites to extract the upstream catchment and riparian buffer zone with different widths. The results showed that the ability of land use variables with different buffer widths to explain water quality differed slightly from the ability of these variables at the catchment scale, and the joint explanatory ability of land use composition and configuration was greater than that of each individually. The patch density and landscape shape index of cultivated land, shrubland, and built-up land in the buffer area close to the water bodies were the main factors for the increase in the concentration of total nitrogen, nitrate nitrogen, total phosphorus, and suspended solids. As the width of the buffer increased, the role of the percent of land use increased. Our research indicates that water quality management needs to adopt a multi-scale perspective and focus on key local areas while coordinating at a broader scale.

Examining the Characteristics of the Cropland Data Layer in the Context of Estimating Land Cover Change

The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) provides spatially explicit information about crop production area and has served as a prevalent data source for characterizing cropland change in the U.S. in the last decade. Understanding the accuracy of the CDL is paramount because of the reliance on it for management and policy making. This study examined the characteristics of the CDL from 2007 to 2017 using comparisons to other USDA datasets. The results showed when examining the cropland area for the same year, the CDL produced comparable trends with other datasets (R2 > 0.95), but absolute area differed. The estimated area of cropland changes from 2007 to 2012, 2008 to 2012 and 2012 to 2017 varied from weak to moderate correlation between the CDL and the tabular data (R2 = 0.005~0.63). Differences in area of cropland change varied widely between data sources with the CDL estimating much larger change area. A series of image processing techniques designed to improve the confidence in cropland change estimated using the CDL reduced the area of estimated cropland change. The techniques also, unexpectedly, lowered the correlation in change estimated between the CDL and the tabular datasets. Estimated land cover change area varied widely based on analyses applied and could reverse from increasing to declining area in cropland. Further analyses showed unlikely change scenarios when comparing different year combinations. The authors recommend the CDL only be used for land cover change analysis if the error can be estimated and is within change estimates.

Land-use change caused by anthropogenic activities increase fluoride and arsenic pollution in groundwater and human health risk

Groundwater pollution is becoming a more serious issue because of various anthropogenic activities. A large proportion of the population living in the urbanized and industrialized world is exposed daily to hazardous materials. However, despite the knowledge that protecting groundwater is necessary, little is known about the role of land-use change for human health risks. In this study, we analyzed the temporal and spatial variation of groundwater fluoride (F) and arsenic (As) during 2010-2018 in Shanxi Province of Northern China. Distribution areas of high F and As increased from 2010 to 2018 and spread over time. We assessed human health risk by calculating carcinogenic risk and non-carcinogenic risk. The results showed that F exposure, frequency of high concentration, and risk from 2016 to 2018 were higher than that in 2010-2015, and similar results were obtained for As exposure. Further, land-use change caused by anthropogenic activities increased F and As pollution in groundwater and placed humans at a higher health risk. Our study sheds light on anthropogenic activities that could increase human health risks caused by groundwater F and As via changing land-use. The study provides supports and suggestions for policy-makers to reduce groundwater pollution and prevent adverse health risks to residents.

Impact of land use on shallow groundwater quality characteristics associated with human health risks in a typical agricultural area in Central China

Groundwater pollution seriously threatens water resource safety due to high-intensity land use throughout the world. However, the relationship between groundwater pollution characteristics and land use in alluvial plains is still unclear. In this study, the effects of land use on shallow groundwater quality and human health risk were investigated via two sampling campaigns in a typical alluvial plain, namely, Jianghan Plain, China. Results show that the shallow groundwater in this area was polluted by nitrogen (with average concentrations of 5.12 mg/L in the dry season and 4.46 mg/L in the rainy season) and phosphorus (0.29 and 0.13 mg/L in the two seasons, respectively). The nutrient concentrations during the dry season were significantly higher than those during the rainy season (p < 0.05). Correlation analysis indicated that the concentration of nutrients was significantly positively correlated with cultivated land and negatively correlated with water and residence, suggesting that land use patterns can affect the groundwater quality. The best buffer where land use patterns affect the total N concentration was about 1000 m for cultivated land and water, while the optimal ranges for ammonium N were about 1000 and 2500 m for the areas, respectively. For the total phosphorus, a radius of 2000 m leads to the best fitting effect on both areas. Human health risk assessment showed that the total health risk indexes in about 75% of the samples were higher than 1, indicating the potential risk of the shallow groundwater in this area to human health. The results indicate that land use patterns will greatly affect the shallow groundwater quality. Thus, adjusting the land use pattern can improve the water quality and reduce health risks. Identification and selection of appropriate management solutions for the groundwater protection should be based on not only water quality problems but also surface land use patterns.

Impacts of Land Use and Land Cover on Water Quality at Multiple Buffer-Zone Scales in a Lakeside City

Understanding the effect of land use/land cover (LULC) on water quality is essential for environmental improvement, especially in urban areas. This study examined the relationship between LULC at buffer-zone scales and water quality in a lakeside city near Poyang Lake, which is the largest freshwater lake in China. Representative indicators were selected by factor analysis to characterize the water quality in the study area, and then the association between LULC and water quality over space and time was quantified by redundancy analysis. The results indicated that the influence of LULC on water quality is scale-dependent. In general, the LULC could explain from 56.9% to 31.6% of the variation in water quality at six buffer zones (from 500 m to 1800 m). Forest land had a positive effect on water quality among most buffer zones, while construction land and bare land affected the representative water quality indicators negatively within the 1200 m and 1500 m buffer zones, respectively. There was also a seasonal variation in the relationship between LULC and water quality. The closest connection between them appeared at the 1000 m buffer zone in the dry season, whereas there was no significant difference among the buffer zones in the wet season. The results suggest the importance of considering buffer-zone scales in assessing the impacts of LULC on water quality in urban lakeshore areas.