Zoning of Karst Evolution and Vulnerability, Using Multiple Linear Regression Model in the Shaho Area
Abstract
Extended Abstract
Introduction
Large areas of the ice-free continental area of the Earth are underlain by karst developed on carbonate rocks and roughly 20–25% of the global population depends largely or entirely on groundwaters obtained from them (Ford & Williams, 2007, 1). So that karst system has a critical role on the development of human communities all around the world. karst evolution is a complicated process and relates to numerous factors such as lithology, hydrology, hydrogeology and etc. Karst resources have widespread scientific, cultural and economic values. In developing countries, human activities threat these valuable and non renewable resources seriously. Nowadays decision makers in their plans for karstic areas in order to protect these valuable resources, regarding the intrinsic vulnerability of karst systems, attempt to propose methods and models for determine the degree of karstification and its sensitivities. Regional assessment of karst system vulnerability especially in the watersheds is a useful tool for management and protection of karstic areas and helps local authorities and decision makers to choose best policies to use karst recourses. In this paper a well known karstic area, called Shaho in the NW Zagros, using a multiple linear regression model has been studied for evaluating its vulnerability and design vulnerability maps.
Methodology
In this research, firstly, we designed the karst geomorphology map of the area using topographic maps (1:50000), aerial photographs (1:55000), IRS panchromatic image at 5 meter resolution. The position of sinkholes and shafts were mapped mainly in the filed works. Then for our purpose using GIS tools, Slope, aspect and elevation maps were derived from topographic maps. Lithology and liniment maps were prepared in the same way from geology maps (1:100000). For designing and overlaying maps, we used Ilwis 3.3 and Arc View 3.1softwares. Statistical analysis was done by Spss 11.5.
Then GIS-based raster maps including lithology, distance from faults, slope, aspects and elevation as independent variables and the sinkhole and shaft's map(geomorphology) as independent factor (predictive factors) were entered in the multiple linear regression model.
Results and Discussion
GIS-based maps and data sets related to variables (distance from faults, slope, aspects, and elevation) all were converted to raster maps. Lithology and geomorphology maps as quality maps by a different process were converted to raster maps. Lithology map by applying weighs (1-5) in which 1 belonged to less effective formation and 5 belonged to most effective formation, was converted to a raster map. The geomorphology map was rasterized by converting it to 0, 1 map which 0 was related to the non karstic parts and 1 was related to karstic parts. The number of records was taking into account in the linear regression equation for all maps equally were 12783 records. Results obtained from regression equation or in the other phrase, multiplying the impact factor of regression coefficients by the data matrixes resulted in a range of positive and negative figures. Positive figures relate to the parts of the area that are well karstified or are capable for karstification and negative figures are vice versa. In fact, overlaying kart geomorphology map of the area with the derived map from regression model show that the results of the model are reliable. Because the pattern of positive figures mostly cover the distribution of sinkhole and shafts. The derived map from linear regression model (karst capability map), that shows the karstic parts and predict the capable parts for karst development was classified into five classes (very high, high, moderate, low, very low) which very high refers to the parts of the area that have well developed surface karsts or are very capable for karst development and very low refers to the non karstic parts or parts of very low capability for karstification. In the vulnerability point of view areas with high rates of karsification are very sensitive for contaminant infiltrations and human interference. Finally the vulnerability map was derived from primary map (capability map) and classified into three high, moderate and low classes and symbolized with colors. High class with blue color refers to the very sensitive parts and low class with yellow color refers to the parts with the low sensitivities.
Conclusion
The results obtained from linear regression model including tables and maps especially vulnerability map, are highly correspond to the real conditions of Shaho karstlands and express the more reliability of multiple linear regression model. According to our vulnerability classifications (high, moderate and low) and assessment of the human impacts, karst system in this area is seriously under threat. About 23% of the area has been located at the high sensitivity class, 26.34% at moderate sensitivity class and 50.65% at low sensitivity class. Obviously any unplanned activities including quarrying, construction of roads and buildings, irregular livestock grazing and recreational activities inside the high and moderate classes can make damages and seriously affect the karst system. In this area mostly the catchments of karstic springs that have a well developed surface karst, correspond to the high sensitivity class and high permeability as well. Thus in these parts disposal of waste and contaminants resulting from human activities can put groundwater resources at risk. In order to reduce the human impacts and protect the karst sources in this area local authorities and decision maker should pay proper attention to them.
Introduction
Large areas of the ice-free continental area of the Earth are underlain by karst developed on carbonate rocks and roughly 20–25% of the global population depends largely or entirely on groundwaters obtained from them (Ford & Williams, 2007, 1). So that karst system has a critical role on the development of human communities all around the world. karst evolution is a complicated process and relates to numerous factors such as lithology, hydrology, hydrogeology and etc. Karst resources have widespread scientific, cultural and economic values. In developing countries, human activities threat these valuable and non renewable resources seriously. Nowadays decision makers in their plans for karstic areas in order to protect these valuable resources, regarding the intrinsic vulnerability of karst systems, attempt to propose methods and models for determine the degree of karstification and its sensitivities. Regional assessment of karst system vulnerability especially in the watersheds is a useful tool for management and protection of karstic areas and helps local authorities and decision makers to choose best policies to use karst recourses. In this paper a well known karstic area, called Shaho in the NW Zagros, using a multiple linear regression model has been studied for evaluating its vulnerability and design vulnerability maps.
Methodology
In this research, firstly, we designed the karst geomorphology map of the area using topographic maps (1:50000), aerial photographs (1:55000), IRS panchromatic image at 5 meter resolution. The position of sinkholes and shafts were mapped mainly in the filed works. Then for our purpose using GIS tools, Slope, aspect and elevation maps were derived from topographic maps. Lithology and liniment maps were prepared in the same way from geology maps (1:100000). For designing and overlaying maps, we used Ilwis 3.3 and Arc View 3.1softwares. Statistical analysis was done by Spss 11.5.
Then GIS-based raster maps including lithology, distance from faults, slope, aspects and elevation as independent variables and the sinkhole and shaft's map(geomorphology) as independent factor (predictive factors) were entered in the multiple linear regression model.
Results and Discussion
GIS-based maps and data sets related to variables (distance from faults, slope, aspects, and elevation) all were converted to raster maps. Lithology and geomorphology maps as quality maps by a different process were converted to raster maps. Lithology map by applying weighs (1-5) in which 1 belonged to less effective formation and 5 belonged to most effective formation, was converted to a raster map. The geomorphology map was rasterized by converting it to 0, 1 map which 0 was related to the non karstic parts and 1 was related to karstic parts. The number of records was taking into account in the linear regression equation for all maps equally were 12783 records. Results obtained from regression equation or in the other phrase, multiplying the impact factor of regression coefficients by the data matrixes resulted in a range of positive and negative figures. Positive figures relate to the parts of the area that are well karstified or are capable for karstification and negative figures are vice versa. In fact, overlaying kart geomorphology map of the area with the derived map from regression model show that the results of the model are reliable. Because the pattern of positive figures mostly cover the distribution of sinkhole and shafts. The derived map from linear regression model (karst capability map), that shows the karstic parts and predict the capable parts for karst development was classified into five classes (very high, high, moderate, low, very low) which very high refers to the parts of the area that have well developed surface karsts or are very capable for karst development and very low refers to the non karstic parts or parts of very low capability for karstification. In the vulnerability point of view areas with high rates of karsification are very sensitive for contaminant infiltrations and human interference. Finally the vulnerability map was derived from primary map (capability map) and classified into three high, moderate and low classes and symbolized with colors. High class with blue color refers to the very sensitive parts and low class with yellow color refers to the parts with the low sensitivities.
Conclusion
The results obtained from linear regression model including tables and maps especially vulnerability map, are highly correspond to the real conditions of Shaho karstlands and express the more reliability of multiple linear regression model. According to our vulnerability classifications (high, moderate and low) and assessment of the human impacts, karst system in this area is seriously under threat. About 23% of the area has been located at the high sensitivity class, 26.34% at moderate sensitivity class and 50.65% at low sensitivity class. Obviously any unplanned activities including quarrying, construction of roads and buildings, irregular livestock grazing and recreational activities inside the high and moderate classes can make damages and seriously affect the karst system. In this area mostly the catchments of karstic springs that have a well developed surface karst, correspond to the high sensitivity class and high permeability as well. Thus in these parts disposal of waste and contaminants resulting from human activities can put groundwater resources at risk. In order to reduce the human impacts and protect the karst sources in this area local authorities and decision maker should pay proper attention to them.
Keywords
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