Volume 9, Issue 4 (3-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 9(4): 41-56 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Emadoddin F, Safari A. Vulnerability assessment of karst aquifer using COP and PI model (Case study: Bisotun and Paraw aquifers). Journal of Spatial Analysis Environmental Hazards 2023; 9 (4) :41-56
URL: http://jsaeh.khu.ac.ir/article-1-3347-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3347-en.html
1- Kharazmi University , saffari@khu.ac.ir
Abstract: (3658 Views)
Vulnerability assessment of karst aquifer using COP and PI model (Case study: Bisotun and Paraw aquifers)
Introduction
Drinking karst water resources, especially in arid and semi-arid regions, like Iran, are considered as valuable and strategic water resources. A sharp decrease in rainfall reduces the quality and quantity of karst water sources (Christensen et al., 2007). On the other hand, urban and industrial development, which is accompanied by the increase in population growth, increases the risk of underground water pollution caused by the dumping of chemicals, waste and change of use (McDonald et al., 2011). Protection of karst aquifer is one of the most important measures in the management of karst water resources due to its vulnerability and high sensitivity to pollution (Khoshakhlagh et al., 2014, Afrasiabian, 2007). Therefore, With the advancement of geographic information system technology, rapid progress was made in the ability to identify and model groundwater pollution, as well as the vulnerability of water sources from these pollutants (Babiker et al., 2004, Rahman, 2008). The pollution potential decreases from the center to the periphery (Saffari et al., 2021).
Materials and methods
In this study to evaluate the vulnerability of Bisotun and Paraw aquifer which is karstically developed and has, crack and fissure and various landforms; COP and PI vulnerability models have been used to identify areas at risk of contamination. The COP model includes three main factors including concentration of flow (C), overlaying layers (O) and precipitation (P). Factor C, which indicates surface features (Sf), slope and vegetation (Sv). It was obtained between 0.8-0.0 in 5 classes. From the overlap of the subfactores soil, layer index and lithology, the O factor map was prepared in three classes, including class 2 with low protection value, 2-4 with medium protection value and 4-8 with high protection value. The P factor, which is the temporal distribution of precipitation along with the intensity and duration of precipitation, can show the ability of precipitation to transfer pollutants from the surface to the underground water. P factor was 0.8 in 2 layers in the northwest of the study area and 0.8-0.9 with low protection value. Furthermore, top Soil, precipitation, net recharge, fracture density, bedrock and lithology maps were used for the protective cover factor (P) in the PI model. The zoning of the P factor showed 2 classes such as very low and low most of the study area is in the low class. The infiltration condition factor (I) using the characteristics of the soil, the slope layer, and the land use in four layers showed high, aamedium, low, very low, which due to the high slope of the area of the high layer has the highest dispersion, which causes the reduction of the protective cover.
Results and discussion
Consequently, COP vulnerability map in 5 classes with very high vulnerability (0-0.5) equal to 38774.74 hectares (41.4%) and very low vulnerability (4-9-4) with 57.86 hectares (0.06%) of the largest and smallest area respectively. Also, the PI vulnerability map of the combination of these two factors showed very high vulnerability with the largest area of about 68,783 hectares and 72.9% scattered throughout the study area and the high vulnerability class with an area of about 25,526 hectares and 27%.
Conclusion
The results of this research showed that the simulation performance of each COP and PI vulnerability model is closely related to the amount of pollution in the environment. It seems that the COP vulnerability model can better and more accurately showed the level of vulnerability in the karst aquifers of Bisotun and Paraw.
Keywords: karst aquifer, Bisotun and Paraw, COP model, PI model, vulnerability.
Introduction
Drinking karst water resources, especially in arid and semi-arid regions, like Iran, are considered as valuable and strategic water resources. A sharp decrease in rainfall reduces the quality and quantity of karst water sources (Christensen et al., 2007). On the other hand, urban and industrial development, which is accompanied by the increase in population growth, increases the risk of underground water pollution caused by the dumping of chemicals, waste and change of use (McDonald et al., 2011). Protection of karst aquifer is one of the most important measures in the management of karst water resources due to its vulnerability and high sensitivity to pollution (Khoshakhlagh et al., 2014, Afrasiabian, 2007). Therefore, With the advancement of geographic information system technology, rapid progress was made in the ability to identify and model groundwater pollution, as well as the vulnerability of water sources from these pollutants (Babiker et al., 2004, Rahman, 2008). The pollution potential decreases from the center to the periphery (Saffari et al., 2021).
Materials and methods
In this study to evaluate the vulnerability of Bisotun and Paraw aquifer which is karstically developed and has, crack and fissure and various landforms; COP and PI vulnerability models have been used to identify areas at risk of contamination. The COP model includes three main factors including concentration of flow (C), overlaying layers (O) and precipitation (P). Factor C, which indicates surface features (Sf), slope and vegetation (Sv). It was obtained between 0.8-0.0 in 5 classes. From the overlap of the subfactores soil, layer index and lithology, the O factor map was prepared in three classes, including class 2 with low protection value, 2-4 with medium protection value and 4-8 with high protection value. The P factor, which is the temporal distribution of precipitation along with the intensity and duration of precipitation, can show the ability of precipitation to transfer pollutants from the surface to the underground water. P factor was 0.8 in 2 layers in the northwest of the study area and 0.8-0.9 with low protection value. Furthermore, top Soil, precipitation, net recharge, fracture density, bedrock and lithology maps were used for the protective cover factor (P) in the PI model. The zoning of the P factor showed 2 classes such as very low and low most of the study area is in the low class. The infiltration condition factor (I) using the characteristics of the soil, the slope layer, and the land use in four layers showed high, aamedium, low, very low, which due to the high slope of the area of the high layer has the highest dispersion, which causes the reduction of the protective cover.
Results and discussion
Consequently, COP vulnerability map in 5 classes with very high vulnerability (0-0.5) equal to 38774.74 hectares (41.4%) and very low vulnerability (4-9-4) with 57.86 hectares (0.06%) of the largest and smallest area respectively. Also, the PI vulnerability map of the combination of these two factors showed very high vulnerability with the largest area of about 68,783 hectares and 72.9% scattered throughout the study area and the high vulnerability class with an area of about 25,526 hectares and 27%.
Conclusion
The results of this research showed that the simulation performance of each COP and PI vulnerability model is closely related to the amount of pollution in the environment. It seems that the COP vulnerability model can better and more accurately showed the level of vulnerability in the karst aquifers of Bisotun and Paraw.
Keywords: karst aquifer, Bisotun and Paraw, COP model, PI model, vulnerability.
Type of Study: Research |
Subject:
Special
Received: 2022/11/26 | Accepted: 2023/03/4 | Published: 2023/03/18
Received: 2022/11/26 | Accepted: 2023/03/4 | Published: 2023/03/18
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
