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The estimation of spatiotemporal variability of vegetation health and associated climatic variables is a prerequisite for efficient management of mountain ecosystems. The present study aimed to quantify the changing effect of different climatic parameters on vegetation health in Khyber Pakhtunkhwa, Pakistan, from 2012 to 2021. The association between NDVI and three explanatory variables, i.e., temperature, precipitation, and wind speed, was analyzed using geostatistical model optimization and stationarity index analysis. Results showed that local model GWR presented optimal results owing to its ability to deal with spatial non-stationarity and stochasticity inherent in regression relationships. The most dominant climatic factor influencing vegetation dynamics in KPK was temperature, followed by wind speed and precipitation. However, there was a gradual decline in the influence of temperature in controlling vegetation dynamics after 2018, and precipitation emerged to be the major determinant. Moreover, the stationarity index was calculated at multi-scales to determine the operational scale range for each climatic variable. The scale-dependency of NDVI-T relationship exhibited highest spatial heterogeneity, up to a distance of 600 km. Overall, geostatistical modeling using earth observation datasets enabled insightful understanding of spatiotemporal variations in vegetation response to climate change, necessary for sustainable development and responsive climate action. [ABSTRACT FROM AUTHOR]

Assessment of changing groundwater storage is an important factor that needs to be assessed over both time and space to understand the regional scenarios. This study has employed Geographical Temporal Weighted Regression (GTWR) along with Geographic Weighted Regression and Ordinary Least Squares to find the impact of various variables on Groundwater Storage Anomaly (GWSA). The study has made use of satellite data of gravity change, extracted using fishnet point observation to reduce processing complexity. All three methods have been compared using correlation coefficient, Akaike information criterion, and root mean squared error. Results show that GTWR, with highest R-square of 65.3 and lowest root mean square error of 0.18, is the more comprehensive option for quantifying the effect of controlling factors among its counterparts as it incorporates both spatial and temporal heterogeneity. Runoff, population density, and soil moisture are the dominant factors controlling groundwater changes with interquartile ranges of 2.35, 0.62 and 1.58 respectively, much bigger than twice the standard error. This indicates a significant effect of anthropogenic activities including rapid urbanization and increase in extraction for irrigation. Additionally, the use of GTWR led the analysis to highlight factors that influence neighboring regions. Instead of climate change and poor management of water, the alteration to the natural course of rivers has been highlighted as the biggest cause of water table decline in the region. [ABSTRACT FROM AUTHOR]

Combustion of coal in thermal power plants poses significant threat to surrounding ecology, by the release of harmful effluents. This study assesses the bio-thermal impact of emissions from Catalagzı Thermal Power Plant Region (CTPPR). A geospatial analytical framework has been modeled, utilizing satellite-based bio and thermal indicators. Average hazardous bio and thermal influence zones around CTPPR have been found to be 1465 m and 1380 m, respectively. Bio-influence zone extent depends upon a combination of meteorological factors, whereas, the bio-hazard severity depends mainly on atmospheric temperature. Thermal influence extent is inversely related to seasonal temperature, whereas corresponding severity depends upon local heat sinks as well. Collectively, hazardous bio-thermal influence in the surroundings of CTPPR shows spatial constancy over long term. The study concludes usefulness of satellite-based economical alternatives for monitoring area under stress around CTPPR for sustainable environmental management of ecological landscapes, needed to improve the human-environment nexus. [ABSTRACT FROM AUTHOR]

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS. [ABSTRACT FROM AUTHOR]