Vulnerability mapping of karst springs and its application for the delineation of protection zones (Mecsek Karst, Hungary)

Over the past decade or two, vulnerability mapping become a useful tool to determine the sensitivity of karst aquifers and allows the analysis of karstic aquifers affected by human activities. The Tettye Catchment, one of the eight catchments of the Mecsek Karst aquifer (SW Hungary), supplies drinking water for Pécs, the fifth most populous city in Hungary. However, due to its partly urbanized character and heterogeneous karstic features, this catchment is highly sensitive to anthropogenic impacts. In this study we aimed to generate resource vulnerability maps and risk maps to assess the role of physical and anthropogenic factors on groundwater vulnerability in the Mecsek Karst. Two formerly validated methods were used, the COP (Concentration, Overlaying layers and Precipitation) and SA (Slovene Approach) methods. The resource vulnerability maps, validated by former tracer tests, were combined with the hazard map obtained from the COST action 620 and EU Water Directive to generate risk maps. Tracer-based transit times were commonly less than 20 days in the majority of the areas of extreme vulnerability. During the current study, a new protocol has been elaborated for the delineation of the protection zones of karstic aquifers. Comparing the two methods, the SA performed better in terms of intrinsic vulnerability mapping, as it had a higher spatial resolution and was more detailed than the COP map and had a more sophisticated vulnerability indexing. In addition, high spatial correlation was revealed between the transit time maps and the SA map. Reassessed risk zonation, with appropriate legal consequences, likely minimizes undesired human activities within the zone of protection, hence maintaining water quality that complies with the protection acts

Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

AHP and GIS-based Risk Zonation of COVID-19 in North East India

On 31st December 2019, a novel virus was reported from Wuhan City of Hubei Province of China, and later it was recognized as SARS-COV-2 (COVID-19). As the virus is highly human to human contagious, it has spread worldwide within a very short time. Since 24th March 2020, after the first reported case in North East India, the total confirmed cases reached up to 4,633 on 11th June 2020. In this work, an attempt has been made to delineate risk zones of COVID-19 in North East India using the Analytic Hierarchy Process (AHP) and overlay analysis in Geographical Information System (GIS). The evaluation is based on 14 criteria that were classified into promoting and controlling factors. The promoting factors include population size, population density, urban population, elderly population, population below the national poverty line, and percentage of marginal workers. In contrast, the controlling factors include available doctors, other health workers, public health facilities, available beds, governance index (composite and health), and testing laboratories. The results were classified into very high, high, moderate, low, and very low risk zones. Most densely populated states with massive pressure on health facilities are likely to have a higher risk of COVID-19. Assam, Tripura, Meghalaya, and Nagaland show a high COVID-19 risk, which constitutes almost 76.93% of the North East India population, covering 48.80% of surface area. The states under a moderate risk zone include 6.92% of the population over 8.52% of the area. Lastly, 16.15% of the people living over 42.69% of the total area belong to the states with a lower risk zone.

Wildfire Risk Zonation of Sudurpaschim Province, Nepal

Wildfire is one of the major destructive hazards which have significant effect on environment, society, and economy. However, limited studies have been carried out on spatial and temporal distribution of wildfire, especially in developing countries like Nepal. The objective of this study was to assess wildfire risk zonation of Sudurpaschim province of Nepal by applying Remote Sensing and GIS. Sudurpaschim province has been divided into four fire risk zones i.e., high, moderate, low and no risk zone. In Sudurpaschim province, about 30.84% area falls under high fire risk zone followed by moderate risk (58.30%), low risk (10.13%) and no risk (0.72%). Among five physiographic regions, Siwalik region is more susceptible to fire due to various factors, such as deciduous forest, topography, terrain, etc. From 2012 to 2019, about 44,342 fire incidences were reported in this province. Approximately 88% wildfire was recorded in spring, the season being dry. Overall, geographically Siwalik region and temporarily spring season should be in high priority for developing and implementing wildfire management activities in Sudurpaschim province.

EFFECT OF INDIAN OCEAN CYCLONE ON COASTAL REGION USING REMOTE SENSING AND GIS

The effect of four different cyclones making land fall on four different coastal regions is studied viz., Nisha (2008, Tamil Nadu), Laila (2010, Andhra Pradesh), Sidr (2007, Bangladesh) and land depression BOB 03 (2008, Orissa). Remote sensing and Geographic Information System (GIS) technique are used to detect change in Land use and Land cover (LU/LC). Change in vegetation cover by Normalized Vegetation Index (NDVI) is also investigated. Further, preparation of slope map, processing of buffer zoning map is exercised. These parameters are analyzed to find the impression of cyclones after hitting the coastal boundaries by considering the images before and after the cyclone has passed. Change detection assessment of LU/LC features provides information for monitoring the trend of change in an area. In almost every considered region, it is found that dense vegetation is changed to sparse vegetation. Also, decrease in the irrigated cropland due to heavy rainfall caused by cyclone is noted. Risk zone is created by buffer ring of cyclone track to spot the area under risk zone. The area calculation suggests the effect of cyclone at the distance of 20–50[Formula: see text]km from the cyclone path which is validated from the slope effect on LU/LC, also. Some of the common features such as dense vegetation, show decrease in the area by 71%, 17%, 67% and 60%, or settlement area also shows decrease by 38%, 15%, 57% and 17% due to Laila, BOB 03, Nisha and Sidr cyclones, respectively. Increase in shrubland mix with rangeland by 18%, 113% and 98% is also seen due to Laila, Nisha and Sidr cyclones. Other LU/LC shows changes such as, water bodies increasing by 6%, 189% due to BOB 03 and Nisha cyclones. Changes are also seen in sparsed vegetation, which is decreased in Orissa and Tamil Nadu and increased in Andhra Pradesh and Bangladesh. It is demonstrated that by preparing risk zonation map, risk assessment can be done.

GIS based Landslides Susceptibility Mapping using Probabilistic Certainty Factor for Hazard Zonation in Mandakini Valley

Landslides are highly threatening a phenomenon which is very common in hilly region and mountainous regions. These landslides trigger major risks leading to heavy losses in terms of life and property. Many studies were conducted globally to determine Landslide vulnerability of different locations. In order to assess vulnerability, there were few studies around Landslides Susceptibility mapping also whose main objective is to identify high-risk vulnerable areas, there by applying measure to reduce the damage caused, if it were to happen in near future. In literature, there are many methods available for predictive susceptibility mapping of landslides. However, identification of any of the prevalent method for a specific area require utmost care and prudence because land sliding is a result of complex geo-environmental spatial factors. Mandakini valley is highly ruggedized terrain with intensive rains during monsoon season. As a result, Landslides are very common in the Mandakini River valley and its catchment area. These landslides cause severe damage to human settlements and infrastructure present in this area. In this study, we have used certainty factor method in order to generate landslide susceptibility map for the catchment area of Mandakini river. Certainty factor approach is a bi-variate probabilistic method which uses Geo-environmental parameters like elevation, slope, aspect, rainfall distance away from river, soil characteristics etc. to generate landslide susceptibility map. A Script was developed in ArcPy - a python package to design tools for generating susceptibility map. These tools can run both at desktop level and at server level and generate results in an integrated way. Esri ArcMap 10.7 is used in order to generate required data layers and thematic maps. Overall, this paper leverages GIS technology and its tools to performs Landslide Susceptibility Mapping using Probabilistic Certainty Factor and generate Hazard Zonation of Mandakini Valley using an automated script for generating Landslide Susceptibility Mapping and Hazard Risk Zonation. It was found that out of 696, total 136 villages are under high risk of landsides, total 329 villages are under moderate risks and around 231 villages are under low risk zonation impacting lives of approx. 216166 people. Also, it is worth mentioning that a GIS based script was developed to automate generation of Landslide Susceptibility Maps which can be used where the same geological and topographical feature prevails.