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Dr Amir Saffari, Dr Ramin Hatamifard, Dr Mansor Parvin,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Karst Geomorphology effects on the environmental hazard intrinsic vulnerability of groundwater resources (Case study: the Aleshtar and Nourabad basins)
Introduction
Karst is the result of the dissolution (physical and chemical) in carbonate (limestone and dolomite) and evaporate rocks. Karst developing is affected by climatological and geological factors. In the other words Karst landscapes and karst aquifers are formed by the dissolution of carbonate rocks by water rich in carbon dioxide waters. Karst aquifers include valuable freshwater resources, but are sometimes difficult to exploit and are almost always vulnerable to contamination, due to their specific hydrogeologic properties, therefore, karst aquifers require increased protection and application of specific hydrogeologic methods for their investigation. The groundwater protection in karst aquifer has a special importance, because the transit time for unsaturated and saturated zone is so quickly that the attenuation of the pollutant. Karst groundwater vulnerability mapping should form the basis for protection zoning and land use planning. A conceptual framework was devised for vulnerability mapping based on this European approach.
Social and economic life of cities such as Nourabad, Alashtar, and numerous rural societies is connected to the Gareen anticline springs. In this paper we used PaPRIKa method for vulnerability assessment in the Aleshtar and Nourabad basins.
Material and Methods
The Gareen anticline in the Zagros Mountain range is located in the active deforming Zagros fold-thrust belt and Sanandaj-Sirjan zon. Alashtar and Nourabad karst aquifers are located in the north of Lorestan province. There are several thrust faults with northwest–southeast strike such as Gareen-Gamasiab and Gareen-Kahman Faults. Nourabad unit is composed mainly by gray limestone rocks, embedded marl limestone, recrystallized limestone and pyroclastic rocks. One of the most important features of the structural geology of the Alashtar unit, is abundance of the sedimentary rocks and scarcity of igneous rocks in this area. In other words In the Study basins the main geological formations incloud: Bakhtiarian conglomerate, carbonates of Sormeh, Taleh Zang, Pabdeh and Kashkan Formations.
The groundwater vulnerability assessment methods (PaPRIKa) applied at the test sites were designed specifically for karst aquifers. They are based on various types of information concerning the physical characteristics of the unsaturated and saturated zones, the aquifer structure and its hydrological behavior.
The PaPRIKa method takes into consideration criteria for both structure and functioning of the aquifer. Based on EPIK and RISK resource methods, PaPRIKa method was developed as a resource and source vulnerability mapping method, allowing assessing vulnerability with four criteria: Protection, Rock type, Infiltration and Karstification. The P map (Protection) considers the protection provided to the aquifers by layers above the aquifers: the S (soil texture, structure and thickness), Ca (permeability formations), the Uz (thickness, lithology and fracture degree of unsaturated zone) and E (Epikarst aquifer). Moreover, including the catchments of water losses where the vulnerability is higher. R map (Rock type) considers the lithology and the degree of fracturing of the sutured zone. I map (Infiltration) distinguishes concentrated from diffuse infiltration. Ka map (Karstification development) assesses the drainage capacity and the organization of the karst conduits network.
To calculate the vulnerability index, the four mentioned maps(P. R. I. Ka) have been combined using the following equation coefficients (eq.1):
PaPRIKa Index= 0.2 P + 0.2 R + 0.4 I + 0.2 Ka (1) eq
Due to the fact that karst geomorphology has a great impact on the quantitative and qualitative characteristics of water resources and the vulnerability assessment of these resources, fuzzy logic has been used to zonation of the Karst development in the Aleshtar and Nourabad basins. In the fuzzy method used a gamma operator (eq.2):
µ Combination= ((Fuzzy Algebraic Sum) (Fuzzy Algebraic Product)) 1-γ (2) eq
The vulnerability map for aquifers was prepared using the software Arc GIS10.4.
Discussion and Results
In the Gareen Antarctic region, due to the availability of suitable Karstification, includes: Lithology, Active Tectonics, Mediterranean climate (with average rainfall of between 454-448 mm and average temperature of 13 C˚) features are formed by various forms of karst such as closed pits (Doline, Swallow Hole, Aven, Polyeh (Peljee), several types of Karrens, dissolution Cavities, small and large Caves and Springs. The most important karst features in this area including Dolines (Solutional, Collapse, Subsidence and Dropout) which are known the Karst Nival. Based on the Karst development zoning map by using the fuzzy logic, 15% of the study area has been developed. Due to the vulnerability based on PaPRIKa method, the Aleshtar and Nourabad basins divided into 5 categories. Resuls show that the vulnerability of the study area is mainly classified as High or Very High, due to the highly developed Epikarst, which minimizes the protective function of the unsaturated zone. There are many karst landforms such as dolines and Swallow Holes that are highly vulnerable.
Conclusions
The final evaluation of the vulnerability ground waters in the Aleshtar and Nourabad basins using the PaPRIKa method shows that the study area is divided into five vulnerable (very high, high, moderate, low and very low). So that areas with a very low, low and moderate vulnerability are 27.3%, 22.3% and 20.6% of the basin area respectively. Also that areas with a high and very high vulnerability are 17% and 12.8% of the study area cover, respectively. Due to the lack of soil and plant cover, heavy snowfall and the formation of Karst-Nival (including Dolines) highlands of the Gareen Anticline have a very high vulnerability potential. Validation of the results of the karstic aquifers vulnerability to Electrical Conductivity (EC) data and monthly discharge of springs shows that the Zaz and Ahangaran springs are in a high vulnerability zone. In the aquifer of this springs, Rapid reductions in EC are detected after each recharge period. Also in contrast Rapid increases in EC with reductions in recharge. This situation shows the High developed of this aquifers, as a result, the potential for vulnerability in these aquifers is high.
But in the springs of Niaz and Abdolhosseini in the Nourabad basin, the EC chart has not changed much compared to recharge. Therefore, the aquifer of these springs is less undeveloped or low developed and also less vulnerable.
Key Words: Gareen Anticline, Geomorphology, Karst, Lorestan, Pa
Fateme Emadoddin, Dr Amir Safari,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
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.
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