Volume 10, Issue 3 (12-2017)
ijhe 2017, 10(3): 317-328 |
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:
Gorgani S, Bafkar A, Fatemi S. Prediction of groundwater pollution potential using the DRASTIC index and annual time series analysis (case study: Plain Mahidasht Kermanshah). ijhe 2017; 10 (3) :317-328
URL: http://ijhe.tums.ac.ir/article-1-5962-en.html
URL: http://ijhe.tums.ac.ir/article-1-5962-en.html
1- Department of Water Engineering, College of Agriculture, Razi University, Kermanshah, Iran , shahram_gorgani@yahoo.com
2- Department of Water Engineering, College of Agriculture, Razi University, Kermanshah, Iran
2- Department of Water Engineering, College of Agriculture, Razi University, Kermanshah, Iran
Abstract: (4419 Views)
Background and Objective: Rainfall and groundwater level are important parameters of DRASTIC index, thus their time-series were examined using time series analysis for Mahidasht plain vulnerability in Kermanshah Province.
Materials and Methods: DRASTIC model is a quantitative model that seven parameters for transfer of pollution are considered including depth of water table, net recharge, aquifer, soil, topography, unsaturated environment and hydraulic conductivity. The data was prepared in seven-layer information in Arc GIS10 software. After integration, weighting and ranking, DRASTIC index for the region was estimated between 34 and 120. Precipitation is an uncertainty factor in water projects. Precipitation is the origin of other uncertainties such as surface runoff, recharge, and water balance. Underground water level and recharge are main factors in the DRASTIC model that are considered as component hydrological variables and time series, thus, they were analyzed and forecasted using stochastic methods on the horizon in 2032.
Results: Finally, selection of the data predicted in 2032 and the creation of dual new depth to the water table and recharge, as well as the weighting and ranking of the repeated placement in the DRASTIC model, another vulnerabilities map is prepared in which the index DRASTIC was 34 to 110 units.
Conclusion: Results showed that due to further decrease of water table and reduced rainfall, DRASTIC index will be less in the next 18 years (2014-2032).
Materials and Methods: DRASTIC model is a quantitative model that seven parameters for transfer of pollution are considered including depth of water table, net recharge, aquifer, soil, topography, unsaturated environment and hydraulic conductivity. The data was prepared in seven-layer information in Arc GIS10 software. After integration, weighting and ranking, DRASTIC index for the region was estimated between 34 and 120. Precipitation is an uncertainty factor in water projects. Precipitation is the origin of other uncertainties such as surface runoff, recharge, and water balance. Underground water level and recharge are main factors in the DRASTIC model that are considered as component hydrological variables and time series, thus, they were analyzed and forecasted using stochastic methods on the horizon in 2032.
Results: Finally, selection of the data predicted in 2032 and the creation of dual new depth to the water table and recharge, as well as the weighting and ranking of the repeated placement in the DRASTIC model, another vulnerabilities map is prepared in which the index DRASTIC was 34 to 110 units.
Conclusion: Results showed that due to further decrease of water table and reduced rainfall, DRASTIC index will be less in the next 18 years (2014-2032).
Type of Study: Research |
Subject:
WATER
Received: 2017/10/7 | Accepted: 2017/10/9 | Published: 2017/12/12
Received: 2017/10/7 | Accepted: 2017/10/9 | Published: 2017/12/12
Send email to the article author
| Rights and Permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
