Geographic Information System Techniques Research Articles

Groundwater flow simulation for artificial recharge: a GIS, remote sensing, and MODFLOW integration

ABSTRACT Groundwater (GW) emerges as a sustainable resource to meet water demand, yet current GW reservoirs fall short of projected population needs. Artificial GW recharge, bolstered by Geographical Information System (GIS) and Remote Sensing (RS) technologies, stands out as a pivotal solution. Examining Aurangabad district in Maharashtra, India the study uncovers distinct challenges: Aurangabad faces escalating water demand due to population growth, leading to GW over exploitation. Leveraging hydrogeological parameters, rainfall analysis, and GW availability, the study proposes tailored GW recharge techniques. Utilizing MODFLOW simulation, the study explores diverse recharge rates and existing GW availability, showcasing promising results. Integration of stream augmentation, bladder pillow tank methods, and dug well recharge is examined via GIS and RS techniques. Simulation outcomes project significant GW level increases (11.55–23.70 m) with varying recharge rates, effectively meeting region-specific water demand projections. The findings underscore the imperative of sustainable water management practices and technological advancements in addressing water scarcity crises.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Spatial modeling of micro‐scale carbon dioxide sources and sinks in urban environments: A novel approach to quantify urban impacts on global warming

AbstractUrban environments play a significant role in global carbon emissions and sequestration, necessitating a comprehensive understanding of their spatial distribution. This study presents a micro‐scale spatial modeling framework to elucidate the complex interplay between CO2 sources and sinks within urban settings. Utilizing advanced geospatial analysis, remote sensing data, and geographically weighted regression (GWR) modeling techniques, we provide a detailed characterization of emission patterns and identify the spatial distribution of carbon dioxide sequestration. Employing the bottom‐up method and geographic information system techniques, we quantified carbon dioxide emissions in Isfahan City, Iran, attributing 81.68% to stationary combustion sources (residential, commercial, industrial, and power plant sectors) and 18.32% to mobile combustion sources (road‐rail transportation, and non‐road transportation [agricultural machinery]). To model carbon sequestration, we calculated tree biomass using allometric equations and estimated carbon sequestration per tree unit. Subsequently, we employed GWR to map the spatial distribution of carbon deposition across the city. The results revealed an annual carbon sequestration capacity of 7,704 tons, equivalent to storing 28,275 tons of CO2. Our findings highlight the substantial contribution of urban areas to greenhouse gas emissions and the potential of urban green spaces to mitigate these emissions. The spatial modeling framework developed in this study provides a valuable tool for urban planners to optimize carbon management strategies and promote sustainable urban development. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Science as exploration in a knowledge landscape: tracing hotspots or seeking opportunity?

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in Geographic Information System (GIS) techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists’ topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress.

Criticism of the Effect of Green Cover Change on Air Quality with i-Tree Canopy (Bursa-Osmangazi Region Sample)

Anything that reflects the ethnicity of a society that has aesthetic and visual textures transferred from past generations to the present day, that is universally accepted, that contains abstract and concrete concepts, and that is intended to be preserved and transferred to future generations is called "cultural heritage". Today's urbanism continues to develop around the settlements of ancient civilisations, which are accepted as cultural heritage. Today, migration from villages to cities has increased and population density in cities has shown a rapid upward trend. The destruction of green areas due to rapid urbanisation has increased visibly and has made smart cities with green areas obligatory. In smart urbanisation, practical methods are sought to quickly identify green areas and determine the benefits provided by these areas. The most widely used software for measuring tree canopy cover is i-Tree Canopy, which allows urban planners to quantify the ecosystem services and benefits of tree communities and forests at multiple scales, including pollution reduction, carbon sequestration and storage, and runoff reduction. Within the scope of the study, the changes on green areas such as urbanisation, industrialisation, increase in transportation networks brought about by population growth in the historical city centre of Bursa and the negative and positive effects of these changes were examined. In the study, the time-dependent changes of green areas in urban heritage and urban planning were determined with GIS (Geographical Information Systems) techniques, and suggestions were developed for existing and new green areas to be planned in the city.

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.

Exploring GIS Techniques in Sea Level Change Studies: A Comprehensive Review

Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Modeling on comprehensive evaluation of groundwater quality status using Geographic Information System (GIS) and Water Quality Index (WQI): a case study of Bahir Dar City, Amhara, Ethiopia

Abstract Groundwater is the most important natural resource, and many people throughout the world rely on it for drinking, particularly in rural areas. The present study was carried out to assess the status of groundwater quality and to check its suitability for domestic purposes in urban centres of Bahir Dar City, Ethiopia. Twelve shallow wells were selected for sampling. The sampled water was taken during the dry and summer seasons of the year 2019/2020. pH, turbidity, EC, TDS, chloride (Cl−), nitrate (NO3−), phosphate (PO4−3), total hardness, and Escherichia coli were measured for the suitability analysis. Comparison of measured results with those of WHO and Ethiopian drinking water quality standards was done. Moreover, Geographic Information System (GIS) and Water Quality Index (WQI) data analysis techniques were applied in order to investigate the groundwater quality. The spatial distribution map showed that the city's core area had the poorest groundwater quality status. The WQI result obtained from the analysis showed that 41.67, 33.33, and 25% of the sampled groundwater has low, extremely poor, and unsafe quality for drinking purposes, respectively. The present study revealed that anthropogenic activities have a great impact on the quality of groundwater in the area, necessitating immediate mitigating actions.

Geospatial quality assessment of locally available ice for heavy metals and metalloids and their potential risks for human health in Karachi, Pakistan

Extreme hot conditions during summers, high poverty rate and continuous electricity load shedding affect commercial manufacturing and sale of ice in many countries. The vendors prepared ice using untreated piped water, tanker water and ground water. These waters may contain hazardous pollutants and ice made from them will pose a potential human health risk. Thus, it is important to regularly monitor the chemical composition of water sources and the quality of the manufactured ice. A contemporary examination was carried out to evaluate the physico-chemical properties and heavy metals and metalloids in the ice sold in all the districts of Karachi, Pakistan. This pioneering study was an innovative effort to assess the ice quality in relation to potential pollutant hazards to human health; with concomitant geospatial information. The geospatial distribution of ice quality and major constituents were among the measured parameters; carefully associated with further geospatial information, determined using GIS (Geographic Information Systems) and PCA (Principal Component Analysis) techniques. Interestingly, the physico-chemical analyses revealed that the ice quality was marginally adequate and the total mean metal-metalloid contents were in the sequence of Pb > Ni > Zn > Fe > Cr > As. The concentrations of these metals were above the upper allowable limits with reference to the recommended WHO guidelines. We observed that 57.1% and 35.7% ice samples had good physico-chemical properties assessed using the Ice Quality Index (IQI). Conversely, the IQI for metals showed that the ice was unsafe for human consumption. In terms of health risk assessment, the overall mean CDI (Chronic Daily Intake) and HQ (Hazard Quotient) values were in the order of Pb () > Ni (3.2) > Zn (2.3) > Fe (2.1) > Cr (1.6) > As (0.5) and Pb (7.4) > As (1.7) > Cr (0.5) > Ni (0.4 > Zn (0.008) > Fe (0.003), respectively. This study highlighted that routine monitoring of the water supplies available for making ice is required to protect public health.

Flood hazard mapping using a GIS-based morphometric analysis approach in arid regions, a case study in the Red Sea Region, Egypt

Flash floods are a major threat to life and properties in arid regions. In recent decades, Egypt has experienced severe flash floods that have caused significant damage across the country, including the Red Sea region. The aim of this study is to map the flood hazards in flood-prone areas along the Red Sea region using a Geographic Information System (GIS)-based morphometric analysis approach. To evaluate the flood hazard degree, the adopted methodology considers various morphometric parameters such as basin area, slope, sinuosity index, shape factor, drainage intensity, circularity ratio, and curve number. GIS techniques were employed to delineate the watershed and the drainage network. The delineated watershed was used together with the digitized maps of soil and land use types to estimate the curve number and the morphometric parameters for each subbasin. The flood hazard degrees are calculated based on the considered morphometric parameters and distinguished based on a five-degree scale ranging from very low to very high. Results indicate that 47% of the study area has a very high flood hazard degree. Furthermore, morphometric analysis results align with the runoff results simulated by a hydrological model, where, for example, basins with a high to very high hazard degree exhibited high runoff. This suggests the influence of physical characteristics on the hydrological behavior of the watershed and further validates the morphometric analysis presented in this work. The results presented here can help policy planners and decision-makers develop appropriate measures to mitigate flash floods and achieve sustainable development in arid regions.

Application of the fuzzy analytic hierarchy process for water resources in the Wadi AlHasa catchment, Jordan

This article introduces a groundwater vulnerability assessment model that utilises the fuzzy analytic hierarchy process (FAHP) in the Wadi AlHasa catchment, Jordan. The assessment takes into account both geomorphological and hydrogeological variables, employing a comprehensive methodology that integrates various parameters. To evaluate the catchment, the study employs remote sensing and Geographic Information System (GIS) techniques. The analysis of the digital elevation model enables the creation of a map illustrating the diverse geomorphology of the catchment. This geomorphology significantly influences drainage density, direction, and the spatial distribution and intensity of flash flood events. Moreover, the study develops and maps a fuzzy FAHP DRASTIC vulnerability index, which proves to be a valuable tool for assessing the susceptibility of groundwater resources to contamination. The unique feature of the index is its ability to incorporate uncertain or subjective data, providing a means to evaluate the significance of various influencing factors. This information serves as critical support for decision-making and management efforts geared towards safeguarding and enhancing groundwater resources. Within the study area, the DRASTIC vulnerability index values span from 0.08325 to 0.28409, with 18% of the site exhibiting a high vulnerability rate. Additionally, the article implements a managed aquifer recharge model (MAR), with 31% of the area falling into MAR classes. Among these, 22.1% are classified as a high MAR class, while 0.7% belong to a very high MAR class. These findings underscore the feasibility of MAR projects in regions with limited water resources.

SELECTION OF SUITABLE LANDFILL SITE FOR WASTE DISPOSAL IN ILORIN METROPOLIS USING GEOGRAPHICAL INFORMATION SYSTEM

Rapid urbanization globally has aggravated and worsen the management of municipal solid waste (MSW) in cities, especially the developing countries. This article underscores the pressing need for effective waste management solutions from the unruly contamination of soil and groundwater, or improper waste disposal and focuses on the selection of suitable landfill sites using Geographic Information Systems (GIS) techniques. With a global increase in MSW generation, especially in Sub-Saharan Africa, the environmental and health consequences of open dumping is hazardous. The study employs Multi-Criteria Evaluation (MCE) and Analytical Hierarchy Process (AHP) within the GIS environment, considering criteria such as elevation, slope, aspect, soil properties, proximity to roads, water bodies, and settlements. The analysis integrates diverse data sources, including remote sensing and soil databases, offering a comprehensive evaluation of topographical factors, soil characteristics, and accessibility. Results indicated that approximately 29.73%, falls under the category of “Restricted Area,” totaling 18,887.18 hectares. High Suitable areas cover 15.85% of the region, comprising 10,070.28 hectares, while “Very High Suitable” areas, though minimal in coverage at 0.02%, represent nearly perfect sites totaling approximately 12.35 hectares. The results offer valuable insights into landfill suitability, providing a foundation for informed decision-making and sustainable waste management strategies.

Storm surge risk assessment and sensitivity analysis based on multiple criteria decision-making methods: a case study of Huizhou City

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment.

Groundwater sustainability in the face of urban expansion: A case study on Kolkata's ongoing challenge

This study investigates the impact of urbanization on groundwater sustainability in the megacity of Kolkata, India, amidst the global trend of increasing urban populations. Utilizing a site-specific approach and multidisciplinary framework, the research analyses three decades (1990–2020) of urban development, correlating the expansion of built-up areas with changes in groundwater levels. Remote sensing and Geographic Information System (GIS) techniques, specifically the Normalized Difference Built-up Index (NDBI), are employed to assess built-up area dynamics. Groundwater data from governmental sources, combined with spatial analysis using Geographically Weighted Regression (GWR), reveal a complex relationship. Descriptive analysis indicates a significant rise in average built-up areas over the decades, with a notable increase in 2020. Correlation coefficients shift from negative in 1990 to strongly positive in 2020, signifying a growing dependence of groundwater levels on built-up areas. Spatial autocorrelation showing a strong clustered pattern in case of groundwater scenario over the years. Geographical analysis highlights varying impacts across different wards, especially in the southern part of Kolkata. Time series analysis forecasts a continued increase in built-up areas, potentially leading to a decline in groundwater levels. The study emphasizes the urgent need for site-specific interventions to ensure the sustainability of groundwater resources in Kolkata, as urbanization continues unabated. The methodology and findings offer valuable insights for policymakers and city planners to address the challenges posed by urban development and safeguard groundwater resources in the pursuit of a sustainable future.

EFFECT OF GLOBAL WARMING ON SUSTAINABLE LIVESTOCK PRODUCTION IN THE SUB-SAHARAN AFRICA

The greenhouse gas emissions from Animal production related activities has turn out to be an important cause of global warming. This can be attributed to the continent-wide massive burdens arising from the switch in food consumption pattern. Anthropogenic activitiesrelated to livestock production contribute 37% of methane (CH4) emission, 9% of carbon dioxide (CO2) output and utilize 8% of the world water. An estimated aggregate of 7.1 billion tonnes of CO2 are generated by the feed and livestock industries. The effect of global warming is presently being experienced such as unstable rainfall pattern, drought, extended walk in search of pasture and water, coupled with significant impact on livestock productivity (rate of weight gainand milk production potentials).The potential of Sub-Saharan Africa to achieve sustainable animal production is being threatened by global warming, it is. therefore necessary to leverage on the use of livestock breed that combine the heat tolerance of the local stock with the high milk yield of the exotic breed (crossbreeding) to produce an improved breed of livestock. Ameliorative strategies such as strategic supplementation of diets, matching genetics to specific systems and management of manure with potential to improve productivity of livestock should be included into livestock production systems in the Sub-Sahara Africa. The use of Geographic Information System (GIS) techniques will also assist to acquire ideal results on susceptibility of smallholder farmers. Finally, there is the need for shift in policy to promote the livelihood of indigenous livestock farmers under favourable environmental conditions.

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (<20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

Integrating GEO-Spatial Techniques for Reflecting Micro-Nutrients and Sulfur Variations and Spatial Distribution to Support Farm Management Practices

To understand the spatial dependency of available sulphur and micronutrients in an experimental farm, 83 soil samples (surface and subsurface) were taken from Agricultural College, Killikulam to characterize the spatial variability of available Sulphur and micronutrients. The geostatistics and geographic information system (GIS) techniques were applied. With the help of geostatistical analyst of ArcGIS software kriged map of different soil parameters were prepared. Available S, Fe, Cu and Zn were fitted with a spherical model and that available Mn was fitted with an exponential model. Soil available Cu had strong spatial dependence with a range of 3.62 km. Available Fe, Zn and Mn had moderate spatial dependence with a range of 9.47 km, 5.61 km and 2.70 km, respectively. Available sulphur had weak spatial dependence with a range of 6.86 km. The spatial distribution of the available sulphur and micronutrients were significantly correlated to the soil formation factors. Agricultural practices such as application of fertilizers and pesticides also had significant effects on the spatial distributions of the available micro-nutrients and sulfur.

Locustamigratoria (L.) (Orthoptera) in a warming world: unravelling the ecological consequences of climate change using GIS.

The migratory locust, Locustamigratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L.migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L.migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.

Assessment of soil erosion in the Irga watershed on the eastern edge of the Chota Nagpur Plateau, India

Human activities to improve the quality of life have accelerated the natural rate of soil erosion. In turn, these natural disasters have taken a great impact on humans. Human activities, particularly the conversion of vegetated land into agricultural land and built-up area, stand out as primary contributors to soil erosion. The present study investigated the risk of soil erosion in the Irga watershed located on the eastern fringe of the Chota Nagpur Plateau in Jharkhand, India, which is dominated by sandy loam and sandy clay loam soil with low soil organic carbon (SOC) content. The study used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) technique to determine the rate of soil erosion. The five parameters (rainfall-runoff erosivity (R) factor, soil erodibility (K) factor, slope length and steepness (LS) factor, cover-management (C) factor, and support practice (P) factor) of the RUSLE were applied to present a more accurate distribution characteristic of soil erosion in the Irga watershed. The result shows that the R factor is positively correlated with rainfall and follows the same distribution pattern as the rainfall. The K factor values in the northern part of the study area are relatively low, while they are relatively high in the southern part. The mean value of the LS factor is 2.74, which is low due to the flat terrain of the Irga watershed. There is a negative linear correlation between Normalized Difference Vegetation Index (NDVI) and the C factor, and the high values of the C factor are observed in places with low NDVI. The mean value of the P factor is 0.210, with a range from 0.000 to 1.000. After calculating all parameters, we obtained the average soil erosion rate of 1.43 t/(hm2•a), with the highest rate reaching as high as 32.71 t/(hm2•a). Therefore, the study area faces a low risk of soil erosion. However, preventative measures are essential to avoid future damage to productive and constructive activities caused by soil erosion. This study also identifies the spatial distribution of soil erosion rate, which will help policy-makers to implement targeted soil erosion control measures.