This research is an attempt to develop a new GIS index to investigate the surface water susceptibility to pollution (SWSi). In this index, the Weighted Linear Combination techniques within GIS environment was used to calculate the overall surface water susceptibility to pollution scores based on using 6 factors. The model includes 3 natural factors: gradient slope, distance to surface water and soil. Also, it includes 3 man-made activities: urban, agriculture and roads. Each factor was given the appropriate weight and ratings and then the final index was calculated using GIS techniques. The final results showed that the study area (1235 km 2) could be classified into low susceptibility with an area of 250 km 2 (20.2%), moderate susceptibility with an area of 815.5 km 2 (66%), high susceptibility with an area of 166.2 km 2 (13.5%) and very high susceptibility with an area of 3.3 km2 (0.3%).
The assessment of surface water resources susceptibility to pollution is important for drawing pollution risk maps [
Jordan is currently one of the poorest countries in the world in terms of water resources. It is characterized by arid climate, with more than 90% of its area receiving less than 200 mm rainfall annually. In Jordan, surface water is considered as a major source for household and agricultural usages. It is the major supplier to the agricultural sector and it is the second largest source for household consumption. The annual supply of surface water in Jordan is 214.69 million m3. This precious source of water is not invulnerable to pollution. Surface water resources systems are subject to several man-made pollution impacts. Surface water in Jordan suffers from various sources of pollution. The polluted surface water resources are often those lying within or downstream of urbanized and industrialized areas, as well those surrounding irrigated lands (use and overuse of fertilizers, pesticides and insecticide) ( [
This research is an attempt to modify existing surface water susceptibility to pollution indices based on the available literature. The modified index will be tested on a study area in the Northern part of Jordan.
The selected study area for this research is located in the Northern part of Jordan (
The study area is mainly flat, where elevation varies between 642 m above sea level in the South to 1224 m above sea level near the Syrian border in the North (
Surface water in the area is mainly from rainfall that occurs between November and March annually. The area receives approximately 250 million cubic meter of rainfall on annual basis. Most of the rainfall is lost due to evaporation (approximately 90%), while only 5% of rainfall generate runoff. The generated runoff flows through the Wadis (streams) that runs mainly from the North towards the South, South East and the South West (
1. The use of fertilizers, pesticides, insecticides and herbicides by farmers in the area.
2. Runoff generated within urban areas which carryout garbage and other pollutant substances to the nearby Wadis.
3. Use of vehicles with oil spills, lead and corroded particles.
There are few mythologies developed in the USA to investigate surface water susceptibility to pollution. Among these methodologies are the followings:
A methodology by [
An overlay and index methodology developed by the USGS for rating the characteristics of a watershed [
A methodology developed by [
A methodology developed by the Laboratory for Spatial Analysis and the Geosciences at the University of Minnesota-Duluth, USA ( [
In this research, a modified methodology will be introduced by having the factors that might influence surface water susceptibility to pollution. The modified index (SWSi) has 6 factors; including gradient slope (%) (GS), distance to Wadis (streams) (DW), soil clay (%) (SC), distance to agricultural lands (DA), distance to urban areas (DU) and distance to roads (DR). In order to allocate the appropriate weight for each factor, 7 experts in the field of surface water quality from various Jordan Universities and organizations were invited to assign a weight for each factor. Experts were asked to give 6 for the factor that has the highest impact on surface water pollution and 1 for the lowest impact factor. A methodology adopted by [
The highest weight (6) was given to the gradient slope (GS) (%), while the lowest weight (1) was given to distance to roads (DR).
1. GS: Surface water runoff occurs whenever there is excess water on a slope that cannot be absorbed into the soil or is trapped on the surface. The steeper the slope of a field, the higher potential for runoff [
2. DS: It is an important factor in determining whether surface water is susceptible to pollution or not. In this research a modification to distances suggested by [
3. SC: High clay contents’ soils have several properties that might lead to the movement of pollutants from agricultural lands. Also, surface structure of soils can become degraded in high clay contents’ soils. This will lead to the formation of crust which restricts infiltration and increases runoff. Runoff increases when clay soils are wet, due to soil compactness. The runoff may contain pollutants and could affect the surface water quality [
Factors | Experts | Mean | Median | ||||||
---|---|---|---|---|---|---|---|---|---|
Ex1 | Ex2 | Ex3 | Ex4 | Ex5 | Ex6 | Ex7 | |||
GS | 6 | 6 | 5 | 5 | 6 | 5 | 6 | 5.57 | 6 |
DW | 5 | 5 | 6 | 6 | 5 | 4 | 5 | 5.14 | 5 |
SC | 3 | 4 | 4 | 3 | 4 | 6 | 4 | 4.00 | 4 |
DA | 4 | 3 | 3 | 4 | 3 | 3 | 3 | 3.29 | 3 |
DU | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 1.71 | 2 |
DR | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1.29 | 1 |
GS | DW | ||||||
---|---|---|---|---|---|---|---|
(%) | Weight | Ratings | W × R | Distance (m) | Weight | Ratings | W × R |
>20 | 6 | 5 | 30 | ≤50 | 5 | 5 | 25 |
>10 - ≤20 | 4 | 24 | >50 - ≤100 | 4 | 20 | ||
>5 - ≤10 | 3 | 18 | >100 - ≤200 | 3 | 15 | ||
>2 - ≤5 | 2 | 12 | >200 - ≤500 | 2 | 10 | ||
≤2 | 1 | 6 | >500 | 1 | 5 | ||
SC | DA | ||||||
Clay (%) | Weight | Ratings | W × R | Distance (m) | Weight | Ratings | W × R |
>30 | 4 | 5 | 20 | ≤500 | 3 | 5 | 15 |
>25 - ≤30 | 4 | 16 | >500 - ≤1000 | 4 | 12 | ||
>20 - ≤25 | 3 | 12 | >1000 - ≤2000 | 3 | 9 | ||
>15 - ≤20 | 2 | 8 | >2000 - ≤5000 | 2 | 6 | ||
≤15 | 1 | 4 | >5000 | 1 | 3 | ||
DU | DR | ||||||
Distance (m) | Weight | Ratings | W × R | Distance (m) | Weight | Ratings | W × R |
≤500 | 2 | 5 | 10 | ≤500 | 1 | 5 | 5 |
>500 - ≤1000 | 4 | 8 | >500 - ≤1000 | 4 | 4 | ||
>1000 - ≤2000 | 3 | 6 | >1000 - ≤2000 | 3 | 3 | ||
>2000 - ≤5000 | 2 | 4 | >2000 - ≤5000 | 2 | 2 | ||
>5000 | 1 | 2 | >5000 | 1 | 1 |
4. DA: The agricultural non-point source (NPS) pollution is the leading source of water quality impacts on rivers and lakes [
5. DU: Urban area is one of the most harmful factors affecting surface water health and a major challenge facing watershed managers. Urban runoff affects water chemistry by changing heavy metals and nutrients levels such as phosphorus and nitrogen [
6. DR: Highway run-off could be identified as a major source of diffuse pollution that might contaminate surface water [
The governing equation (Equation (1)) for the modified index (SWSi) is shown below:
where GS: Gradient Slope (%), DS: Distance to Surface Water, SC: Soil Clay (%), DA: Distance to Agricultural Lands, DU: Distance to Urban Areas, DR: Distance to Roads, w: Weight and r: Ratings.
The overall index calculation could be classified into 4 susceptibility classes as listed in
Dada required for this research were collected from several governmental agencies in Jordan and international agencies.
The adopted methodology for analysing the data in this research is based on the use of Weighted Linear Combination (WLC). WLC is a technique of Multi-Cri- teria Evaluation (MCE) which is based on overlaying layers based on factors weights, factors ratings and/or constraints to create a suitability map [
1. Giving the appropriate ratings for each layer (vector format),
2. Converting all layers into raster format,
3. Multiplying maps weights by their ratings,
4. Combining all layers in order to have the overall suitability scores, and
5. Classifying the outcome into the required classes.
WLC within GIS enviornmnet has been adopted in several applications related to envionmental issues ( [
1. The highest scores for the Slope factor (24 and 30) have a small area (5.7%) of the total study area, while the smallest scores (6 and 12) cover 66.6% of the study area. This could be attributed to the fact that most of the study area is flat with a slope of less than 5%.
Susceptibility Class | Low | Moderate | High | Very High |
---|---|---|---|---|
Susceptibility Range | 21 - 42 | 42 - 63 | 63 - 84 | 84 - 105 |
Map Type | Date | Scale/Resolution | Source |
---|---|---|---|
DEM | 2000 | 1 arc-second (30 meter) | The Shuttle Radar Topography Mission (SRTM), USGS |
Wadis | 1995 | 1:250,000 | Royal Jordanian Geographic Centre |
Soil | 1998 | 1:250,000 | Jordan Ministry of Agriculture |
Roads | 2010 | 1:250,000 | Royal Jordanian Geographic Centre |
Urban | 2016 | 1 m | Digitizing from Google Earth®/Digital Globe |
Agriculture | 2016 | 1 m | Digitizing from Google Earth®/Digital Globe |
GS Scores | 6 | 12 | 18 | 24 | 30 |
---|---|---|---|---|---|
Area (km2) | 254.5 | 568.2 | 340.9 | 65.8 | 5.5 |
% of area | 20.6 | 46 | 27.6 | 5.3 | 0.4 |
DW Scores | 5 | 10 | 15 | 20 | 25 |
Area (km2) | 630.5 | 344.5 | 128.8 | 65.4 | 66 |
% of area | 51 | 27.9 | 10.4 | 5.3 | 5.3 |
SC Scores | 4 | 8 | 12 | 16 | 20 |
Area (km2) | 0 | 19.7 | 119.4 | 987.7 | 108.2 |
% of area | 0 | 1.6 | 9.7 | 80 | 8.8 |
DA Scores | 3 | 6 | 9 | 12 | 15 |
Area (km2) | 811.9 | 160.7 | 98.8 | 66.9 | 96.7 |
% of area | 65.7 | 13 | 8 | 5.4 | 7.8 |
DU Scores | 2 | 4 | 6 | 8 | 10 |
Area (km2) | 481.9 | 385 | 161.5 | 85.6 | 121 |
% of area | 39 | 31.2 | 13.1 | 6.9 | 9.8 |
DR Scores | 1 | 2 | 3 | 4 | 5 |
Area (km2) | 0.9 | 209.5 | 326.9 | 285.8 | 412 |
% of area | 0.1 | 17 | 26.5 | 23.1 | 33.4 |
2. The highest scores for the Distance to Wadis (20 and 25) have a small area (10.6%) of the study area, while the smallest scores (5 and 10) cover (78.9%) of the study area.
3. The Soil (Clay%) factor highest scores (16 and 20) cover a large area (88.8%) of the study area, while the smallest scores (4 and 8) cover only 1.6% of the study area.
4. The distance to Agricultural Lands factor highest scores (12 and 15) cover (13.2%) of the study area, while the smallest scores (3 and 6) cover (78.7%) of the study area. This could be attributed by the fact that most of the agricultural activities within the study area are located in the Western part of the study area.
5. The Distance to Urban Areas factor highest scores (8 and 10) cover (16.7%) of the study area, while the smallest scores (2 and 4) cover (70.2%) of the study area. This could be explained by the fact that most of the urban areas are concentrated in the Western part of the study area.
6. The Distance to Roads highest scores (4 and 5) cover an area (56.5%) of the study area, while the smallest scores (1 and 2) cover an area (17.1%) of the study area.
Figures 6-12 illustrate the factors (weight × ratings) used in this research to calculate the SWSi.
Based on Equation (1), the six factors shown in Figures 6-11 were summed using the raster calculator in ArcGIS® and then classified based on
Class | Area (km2) | % of total area |
---|---|---|
Low | 250 | 20.2 |
Moderate | 815.5 | 66.0 |
High | 166.2 | 13.5 |
Very High | 3.3 | 0.3 |
Total | 1235 | 100 |
20.2% of the total study area. The areas with high and very high susceptibility have an area of 169.5 km2 which comprises 13.8% of the study area. The remaining areas have a moderate susceptibility with a total area of 815.5 km2 which comprises 66% of the total study area.
Based on the map removal sensitivity analysis test developed by [
where: S is the sensitivity index of the factor;
V is the intrinsic vulnerability index of the method;
N is the total number of factors used to calculate V;
Vxi represents the intrinsic vulnerability index obtained after removal of the factor X and
n: the number of factors after removing one factor.
Based on
In this research, 6 factors were used to estimate the Surface Water Susceptibility to pollution in a study area in Northern part of Jordan. These factors included slope, distance to Wadis, soil clay (%), distance to urban areas, distance to agri-
Factor Removed | Mean | Min | Max | SD |
---|---|---|---|---|
GS | 38.5 | 20 | 75 | 8.86 |
DW | 42.35 | 21 | 76 | 8.45 |
SC | 35.8 | 18 | 80 | 9.65 |
DA | 46.35 | 23 | 90 | 8.89 |
DU | 47.3 | 24 | 90 | 9.23 |
DR | 47.9 | 24 | 92 | 10.1 |
Factors | Sensitivity Index | |||
---|---|---|---|---|
S Minimum | S Average | S Maximum | Standard Deviation (SD) | |
GS | 0 | 1.03 | 5 | 0.83 |
DW | 0 | 0.72 | 4 | 0.77 |
SC | 0 | 1.43 | 3 | 0.58 |
DA | 0 | 0.83 | 3 | 0.55 |
DU | 0 | 0.89 | 3 | 0.58 |
DR | 0 | 0.95 | 3 | 0.56 |
cultural lands and distance to roads. Each factor was given a weight appropriate to its importance in calculating the SWSi. Also, each factor was given the appropriate ratings at a scale 1 to 5, where 5 refers to the most susceptible area and 1 for the least susceptible area. The weighted linear combination (WLC) technique within GIS environment was used to calculate the overall score of the SWSi which then was classified into 4 classes (Low, Moderate, High and Very High susceptibility). The results showed that the low susceptible areas have a total area of 250 km2 (20.2%). The very high and high susceptible areas have an area of 169.5 km2 (13.8%), while the moderate susceptibility areas have an area of 815.5 km2 (66%). The sensitivity analysis of the SWSi was carried out to determine the most significant factors. It was found that the most important factors in the SWSi were DR, DU, DA and DW. Also, the map removal sensitivity analysis was carried out to identify the sensitivity of each factor. It was found that SC, GS and DR factors have a strong influence on the SWSi map.
Based on these results, it is concluded that the major factors affecting surface water susceptibility to pollution are addressed in this index. Distances used for ratings urban, agriculture and roads are reasonable since these three factors are major contributors to surface water pollution. Contaminants flushed with rainfall when runoff is generated or passed one of these factors will degrade with distance. Based on that, it is recommended to use the SWSi to estimate surface water susceptibility to pollution. It is also recommended to look for other factors that might contribute to surface susceptibility to pollution. Furthermore, it’s recommended to conduct a filed study in order to collect surface water samples at various distances from urban areas, agricultural lands and roads to investigate surface water quality. This might show the pollution of the surface water in reality to verify the outcomes of the SWSi and lead to the modification of given distances at this research.
Al-Adamat, R. (2017) Modelling Surface Water Susceptibility to Pollution Using GIS. Journal of Geographic Information System, 9, 293-308. https://doi.org/10.4236/jgis.2017.93018