Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India

Climatic driving mechanisms of the propagation from meteorological drought to agricultural and ecological droughts

Droughts significantly impact terrestrial vegetation ecosystems. Understanding the mechanisms by which drought affects ecosystems under different hydrogeological conditions is crucial for ecosystem protection. The aim of this study was to investigate the characteristics and mechanisms of propagation from meteorological drought (MD) to agricultural drought (AD) and ecological drought (ED) in the Jinsha River Basin from 2000 to 2014. The monthly standardized precipitation evapotranspiration index (SPEI), soil moisture index (SSMI), normalized difference vegetation index (SNDVI), and solar-induced chlorophyll fluorescence (SSIF) data were used to investigate the responses of AD and ED to MD. On the basis of the maximum correlation coefficients (MCCs), the differences in the drought propagation times of MD to AD and ED were explored in positively and negatively correlated areas. A random forest algorithm was used to identify the impacts of climatic factors driving drought propagation. The results revealed that AD was mainly positively correlated with MD, whereas the correlation coefficients between ED and MD ranged from negative to positive. The propagation time from MD to AD was relatively short in summer and autumn. In positively correlated areas, the propagation time from MD to ecological drought indicated by NDVI (EDndvi) was longer than that indicated by SIF (EDsif), and the opposite was true in negatively correlated areas. The random forest algorithm results indicated that temperature (T), solar radiation (S) and precipitation (P) were key factors influencing ED in positively correlated areas and that T was an important factor in controlling the occurrence of ED in negatively correlated areas. Solar-induced chlorophyll fluorescence (SIF) was more sensitive to MD and had a shorter response time in positively correlated areas, suggesting its potential for monitoring vegetation growth responses to drought. We found that MD was not the main factor influencing vegetation growth in negatively correlated areas. The findings of this study had significant implications for understanding the mechanisms of the response of vegetation growth to MD and offered scientific guidance for maintaining terrestrial ecosystem health.

Spatio-temporal trends and resilience of forests in central India: insights from vegetation, temperature, and rainfall dynamics (2001-2023)

Natural Vegetation is a key component of the ecological system and is involved in regulating various biogeochemical cycles. Analysis of spatio-temporal trends in vegetation dynamics is crucial to ensure the sustainability of the environment and the safety of biodiversity of the region. Studying forest trends helps us understand the impact of changing climate on forests. The study aims to (a) analyse spatio-temporal dynamics and trends in vegetation vigour, temperature, and rainfall dynamics in Madhya Pradesh (MP) and Chhattisgarh (CG) states in Central India, (b) identify significant change breakpoints within the study region, (c) compare trend results from EVI and NDVI, and (d) analyse the relationship between vegetation trends with temperature and rainfall. Several normalized indices and reanalysis products were utilized to study vegetation trends from 2001 to 2023, and Theil Sen Median Trend test, Man Kendall test, and Pettitt change point detection methods were used. Findings reveal that the majority of the forest area in MP exhibits a positive trend (~ 94%in EVI and ~ 95.5% in NDVI) while a negative trend is minimal (~ 6% and 4.5% in EVI and NDVI respectively). Specifically, the northern part of MP demonstrates a pronounced positive trend. In Chhattisgarh, ~ 92% of the forest area indicates a positive trend, with the remaining ~ 8% displaying a negative trend. The validation of results using Google Earth showed that EVI captures the trend more significantly than NDVI, where vegetation is sparse. Pettitt Change Point detection suggests that the most significant positive changes in EVI occurred between 2015 and 2020 in MP, while in CG, the maximum increase in greenery transpired between 2011 and 2015. The outcomes underscore the resilience of forests to evolving climatic conditions and provide valuable insights into the sustainability of their ecological and biological integrity.

Exploring the LULC dynamics and its relation with land surface temperature variation using split window algorithm: A study of Barasat subdivision, West Bengal, India

Rapid urban growth in an unplanned way, decreased forest cover and consequential changes in land use in urban and rural areas have become pivotal obstacles for policymakers and urban planners to a great extent. Specifically, in a developing country like India, the repercussions of these changes have generated a substantial alteration in climate in the form of increased land surface temperature (LST), heat islands and associated human health risks. This research was initiated to assess the interrelationship between LST and LULC dynamics using spatial techniques over the Barasat Sub-Division in West Bengal (India). This study encompasses the extraction of LST using satellite images, seasonal variations of LST, the relationship of different land indices (i.e. NDVI, NDBI, NDMI and NDWI) with LST, and hotspot analysis of the landscape to get a comprehensive understanding of LST changes from 1988 to 2023, seasonal patterns, and relationships with LULC dynamics in the study area. The results of this research signify a substantial percentage increase in built-up spaces (51%), a decline in fallow areas (- 73%), relatively higher LST changes (3.41 °C) in high-density built-up areas (e.g. Madhyamgram), a positive relationship (e.g. r2 = 0.59) between NDBI and LST, a transition from a cold spot to a hot spot share of 20% and a persisting hot spot of 42% throughout the summer season (1988 to 2023) in the selected landscape. The findings of this study will improve our present knowledge of seasonal fluctuations in land surface temperature (LST) and the variables that influence them. These findings will also draw attention to the growing worries about the fast variations in LST and the climatic and health hazards they pose. It is advised to establish planned urban expansion, efficient land use and land cover (LULC) management laws and encourage green areas that support sustainable development in India's urban surroundings in order to reduce these dangers.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22 years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Evaluation of soil texture classification from orthodox interpolation and machine learning techniques

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.

Rainwater harvesting for agriculture development using multi-influence factor and fuzzy overlay techniques

Rainwater harvesting (RWH) is an essential technique for enhancing agricultural development, particularly in regions facing water scarcity or unreliable rainfall patterns. Water shortage, however, is one of the key causes of low crop production especially in mountainous regions like the Khyber Pakhtunkhwa province where most rainwater is lost by runoff. Therefore, rainwater harvesting could be a suitable to make better use of runoff and increase crop production. The study focuses on selecting suitable rainwater harvesting sites in District Karak to enhance agriculture by utilizing multi-influence factor (MIF) and fuzzy overlay techniques. We considered seven factors, i.e., land use land cover (LULC), slope, geology, soil, rainfall, lineament, drainage density, to create a ranking system to understand its application in site selection analysis. The results were combined into one overlay process to produce a rainwater harvesting suitability map. The weighted overlay analysis of the MIF model results reveals that 167.96 km2 area has a very high potential for rainwater harvesting, 874.17 km2 has a high potential, 1182.92 km2 has a moderate and 354.50 km2 has a poor potential for rainwater harvesting. The fuzzy overlay analysis revealed that 257.53 km2 has a very high potential for rainwater harvesting, 896.56 km2 area is classified as high, 1018.30 km2 moderate, and 407.7 km2 has poor potential for rainwater harvesting. The findings of this research work will help the policymakers and decision-makers construct various rainwater harvesting structures in the study area to overcome the water shortage problems.

Water Quality Index Assessment of River Ganga at Haridwar Stretch Using Multivariate Statistical Technique

The Ganges (Ganga) river contributes significant water resources for the ecology and economy, but it frequently encounters severe deterioration due to cumulative impact from upstream natural and anthropogenic variables. Knowledge and understanding of the dynamic behavior of such networks remain a significant challenge, particularly in the context of rising environmental pressures, such as climate change and industrialization, as well as constraints in both process and data understanding across geographies. An interdisciplinary approach is required to be developed to investigate the hydrogeochemical dynamics and anthropogenic sources influencing water quality in major river systems. The present study has been carried out to evaluate the characterization of river water quality in terms of the physico-chemical & bacteriological parameters. Also, the development of a water quality index (WQI) for Domestic (drinking) and Spiritual (bathing) usage is a part of the study. The water quality index has been developed using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI). The river's water quality index score in the present study lies in the range of 38.32 to 79.82, indicating the quality of water from fair to poor for drinking purposes. The highest water quality index value of 79.82 has been observed at Guru Kashnik Ghat, while the lowest WQI value of 38.32 has been observed at Har ki Pauri for drinking purposes. However, the water quality score for bathing purposes ranged from 71.04 to 91.22 thus signifying the quality of the water from fair to good for bathing purposes. The highest water quality index value of 91.22 has been assessed at Guru Kashnik Ghat, while the lowest WQI value of 71.04 has been assessed at Bhimgoda Barrage. The developed water indices assessment in the present study will be beneficial for society to provide a benchmark for the control of water pollution in River Ganga. These findings will support policymakers and stakeholders in addressing water quality issues in a more efficient and effective manner. The study also emphasizes the requirement for ongoing water quality monitoring and evaluation in order to guarantee the long-term well-being of the river and its ecosystems.