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.

Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA\'s ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

The main objective of this research is to assess the impacts land use and land cover changes (LULC) on hydrological components using novel spatial models at sub-basin scales. The Soil and Water Assessment Tool (SWAT) was employed to analyze the long-term effect of LULC on hydrological components. The results of the calibrated and validated SWAT model demonstrated that run-off and actual evapotranspiration (ET) are expected to experience the largest increase, more than 130% and 90% in autumn, whereas the largest decrease is anticipated to occur in the summer and winter for potential evapotranspiration (PET) (-59%) and ET (-80%) by the projected time. The impacts of hydrological components, elevation, LULC, and an indicator of urbanization and land-use intensity (La) on water yield (WYLD) at sub-basin levels were then considered by four novel spatial models due to the problem of multicollinearity which is prevalent in traditional models. In particular, the Moran eigenvector spatially varying coefficients (MESVC) showed that the soil class out of LULC categories and lateral flow among hydrological properties are expected to have a statistically significant effect on spatial fluctuation of WYLD at the sub-basin scale. The results of spatially filtered unconditional quantile regression (SF-UQR) confirm the findings of the MESVC model and further implied that the lateral flow remains as a statistically significant contributor to WYLD only in lower quantiles (e.g., for quantiles lower than 0.25). The impacts of LULCs on WYLD were statistically lower than the effects caused by the hydrological components.

For the last three decades, Islamabad - a planned city, and Rawalpindi - an unplanned city, have experienced massive land use and land cover changes. The main objective of this study was a comparative assessment and quantification of LULC changes in relation to population growth and urbanization from 1990 to 2021 with the help of satellite imagery and population data in planned and unplanned cities. For classification four land-use land cover classes: built-up, vegetation, bare land, and water were selected. Maximum likelihood algorithm and confusion matrix were employed for classification and accuracy assessment. Results revealed that built-up increased from 5.7% (52 km2) to 25.7% (233 km2) and 3.7% (60 km2) to 14.1% (228 km2) from 1990 to 2021 for Islamabad and Rawalpindi, respectively. Wherein the bare land decreased from 42.2% (382 km2) to 18.1% (164 km2) in Islamabad and 65.5% (1058 km2) to 32.1% (518 km2) in Rawalpindi. Vegetation showed an increment of 4.7% for Islamabad and 24.5% for Rawalpindi. Surface water bodies decreased in both study areas. Population growth showed a strong positive correlation with the built-up class and a strong negative correlation with the bare land class for both cities. The outcomes of this study may be helpful in policymaking for better planning and management of land use land cover and urban sprawl in the context of sustainable development goals.

Global population growth and scarce resources increase the competition for land use. Despite the fact that the impacts of climate change have been recognized, the conversion of LULC is still often neglected and threatens catchment hydrology. This is mostly seen in the developing world, where agriculture is the crucial source of their food security. The conversion of LULC has been jeopardizing water balance components and damaging ecosystem. This study demonstrates the application of SWAT+ in quantifying the impacts of LULC changes on the Guder catchment water balance. The impacts were quantified between 2003 and 2021, and the watershed experienced an increase in agriculture and settlement while forest, shrubland, and wetlands declined. The time-series-based performance of the SWAT + model shows the model is more restructured and capable of simulating streamflow compared to observed during calibration and validation. In this long-term evaluation, the model simulates changes in runoff of 56.5%, water yield of 65.2%, lateral flow of 21.6%, percolation of 46.2%, return flow of 76.4%, and ET of 0.2% between 2003 and 2013. Moreover, some attributes of the water balance have increased from 2013 to 2021, with runoff of 34.3%, water yield of 2.3%, ET of 4.5%, and lateral flow of 72.6%. However, as a result of increasing settlement, which reduces infiltration through interceptions and converts rainfall to runoff, percolation and return flow were decreased by 45.6% and 86.7%, respectively. Water yield and runoff show a linear relationship with changes in LULC, and the most sensitive land use changes that affect them are agriculture, forest, and settlements. The simulation results show a water balance deficit under the impacts of LULC changes in the third simulation. Furthermore, the increased surface of runoff has been limiting the amount of groundwater recharge into the soil and reducing return flow and percolation in the second simulation.

Wildfires are behaving differently now compared to other time in history in relation to frequency, intensity and affected ecosystems. In Brazil, unprecedented fires are being experienced in the last decade. Thus, to prevent and minimize similar disasters, we must better understand the natural and human drivers of such extreme events. The Brazilian Pantanal is the largest contiguous wetland in the world and a complex environmental system. In 2020, Pantanal experienced catastrophic wildfires due to the synergy between climate, inadequate fire management strategies and weak environmental regulations. In this study, we analyzed recent patterns and changes in fire behavior across the Pantanal based on land use and cover (LULC) classes. The inter-annual variability of the fire and land cover changes between 2000 and 2021 was assessed using BA from MCD64A1 V.6 product and LULC data from Landsat satellite. Our work reveals that fires in the Pantanal over the last two decades tended to occur more frequently in grassland than in others land cover types, but the 2020 fires have preferentially burned forest regions. Large fire patches are more frequent in forest and grasslands; in contrast, croplands exhibit small patches. The results highlight that a broad scale analysis does not reflect distinct localized patterns, thus stratified and refined studies are required. Our work contributes as a first step to disentangling the role of anthropogenic-related drivers, namely LULC changes, in shaping the fire regime in the Pantanal biome. This is crucial not only to predict future fire activity but also to guide appropriated fire management in the region.

Anthropogenic activities responsible for modifying climatic regimes and land use and land cover (LULC) have been altering fire behavior even in regions with natural occurrences, such as the Pantanal. This biome was highlighted in 2020 due to the record number of fire foci and burned areas registered. Thus, this study aimed to understand how changes in LULC and climate affect the spatial, temporal and magnitude dynamics of fire foci. The Earth Trends Modeler (ETM) was used to identify trends in spatiotemporal bases of environmental and climatic variables. No trend was identified in the historical series of precipitation data. However, an increasing trend was observed for evapotranspiration, normalized difference vegetation index (NDVI) and temperature. For soil moisture, a decreasing trend was observed. The comparison between the mean of the historical series and the year 2020 showed that the variables precipitation, temperature, soil moisture and evapotranspiration had atypical behavior. Such behavior may have contributed to creating a drier environment with available combustible material, leading to a record number of burned areas, about three million hectares (248%) higher than the historical average. The 2020 fire foci data were used in two types of spatial statistical analyses: Grouping, showing that 76% of the registered fire foci were at high risk of fire and; Hot and Cold Spots, indicating high concentrations of Hot Spots in the northern region of the Pantanal, close to Cerrado and Amazon biomes agricultural frontier. The results of the Land Change Modeler (LCM) tool evidenced a strong transition potential from the natural vegetation to agriculture and pasture in the eastern region of the Pantanal, indicating that this could be, in the future, a region of high concentration of fire foci and possibly high risk of fire. This tool also allowed the prediction of a scenario for 2030 that showed that if measures for environmental protection and combating fires are not adopted, in this year, 20% of the Pantanal areas will be for agricultural and pasture use. Finally, the results suggest that the advance of agriculture in the Pantanal and changes in climatic and environmental variables boosted the increase in fire foci and burned areas in the year 2020.

Evaluation of carbon sequestration in various land cover types is a valuable tool for environmental policies targeting towards minimization of CO2 emissions and climate change impacts. For the past few decades, remotely sensed information on land cover has been used as useful alternative to ground observations and has proved to be a robust tool for studying land use / land cover (LULC) changes. The present work deals with the assessment of land-cover changes in a Mediterranean country - Greece, where expected climate change impacts and desertification risk are stated to be severe. This work focused on the CORINE land cover inventory at a spatial resolution of 100 m from 1990 to 2018 and selected Landsat images at 30 m spatial resolution for 1990, 2000 and 2018. Results indicated that the dominant land-cover changes in Greece over the predefined 29-year period, are related to land transformation from Non-irrigated arable land to Irrigated areas, implying an intensification of agricultural practices. Natural grasslands lose a substantial part of their areas transforming into Sclerophyllus vegetation and Sparsely vegetated areas. Forests gain areas from Transitional woodland-shrub and Olive groves increase their extent indicating an overall transition to woody vegetation. Estimation of Vegetation Carbon Stocks indicated a moderate decrease in the 1990 decade followed by a significant increase up to 2012 and a slight decrease thereafter. Forests of all types are by far the most important carbon sinks. Possible implications of country\'s recent economic crisis were examined and results indicated that economic welfare of the country seems to favor certain land cover types such as Mixed Forests and Permanently Irrigated land, but also preservation of the Vegetation Carbon Stocks.

The climate and Land Use/Land Cover (LULC) changes evince the considerable impact on water balance components by altering the hydrological processes. So, the present work focuses on the evaluation of the combined impact of both the climate and LULC changes along with and without water storage structures on water balance components of the Krishna river basin, India under present and future scenarios with the help of Soil Water and Assessment Tool (SWAT). Sequential Uncertainty Fitting algorithm (SUFI-2) was used for the model calibration and validation, which were carried out at the Vijayawada gauge station. The coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) values obtained during the calibration period were 0.63 and 0.61, respectively, whereas, in validation, these values were found to be 0.61 and 0.56, indicates satisfactory results. The results showed that the model simulations and performance were significantly influenced by the presence of water storage structures, whereas the LULC changes were effective at the sub-watershed level. Future LULC maps of 2025, 2055, and 2085 were simulated from the Cellular Automata (CA) Markov Chain model, and they were used along with future climate projections to investigate its impact on water balance components. The climate model projects an increase of water balance components specifically, surface runoff, streamflow, and water yield, except for evapotranspiration in the future. Whereas, the future LULC changes may influence in offsetting the streamflow 20 to 30% reference to the observed flow. Thus, LULC changes were significantly influenced the model simulations; therefore, it is essential to consider the LULC changes along with climate scenarios in climate change studies. Overall, the surface runoff, water yield, and streamflow may increase by 50% under Representative Concentration Pathway (RCP) 4.5, and they may double under the RCP 8.5 scenario by the end of the century.