Water quantity planning and management require understanding of spatial variations of water catchment availability. Several environmental indicators are associated with water quantity such as flood occurrence, drought severity, seasonal supply and groundwater stress. Analyzing water stress at national geographic scale is crucial to detect and explore geographic shortage of water resources at national scale. In this study, Geographical Information Systems (GIS) techniques were employed to analyze the spatial variations of water scarcity across Sultanate of Oman provinces. For this main objective, various spatial and attribute datasets were prepared. Many variables were selected based on their importance and correlation with water quantity. GIS overlay function then was used to produce maps for each water indicator. This was followed by employing raster zonal statistics to aggregate the values of each catchment area within each province. The findings of this analysis indicated that significant spatial variation was found among Omani provinces in terms of water quantity stress and its determinants. The most important factors affecting the water quantity stress were drought severity and flood occurrence. Furthermore, physical risk of water quantity was higher in Mascut and Dhofar provinces while it was moderate in Al-Batinah, A’Dakhiliyah and Al-Wusta. Lower risk of water quantity was observed in A’Sharqiyah, Masandam, and A’Dhahriah provinces. Thus, in order to mitigate the impacts of water scarcity on agriculture, cultivation and domestic usages, policy makers in water sector should include spatial strategies for water resource maintain and allocation.
Scarcity of water quantity is one of the most environmental hazards that impact several countries particularly in arid and semiarid regions. Water quantity stress globally is much related to various physical and environmental factors such as rainfall, surface upstream system, groundwater, floods and drought. Such these factors are important determinants in characterizing water scarcity map in any country [
It was estimated that 60% of global population may challenge water scarcity by 2025 and most of scare water countries are located in arid and semi-arid zones especially in the Middle East and North Africa [
Integration of GIS and hydrological modelling in analyzing watershed-based resources management is a successful approach especially in allowing for various measures to be included in the analysis [
GIS and spatial analysis techniques fundamentally have potential roles in contributing to the instruments of water stress applications in different disciplines such as hydrology, climatology, agriculture, geography and water resources. For instances, a conceptual GIS data model was created and used to assess river basin water allocation through connecting GIS capabilities to water management policies [
Evidences on the scarcity of groundwater in the Middle East Countries were reported [
Although much of the research up to now has been carried out in water resources using GIS and remote sensing techniques, little known about analyzing variations of water quantity at national scale within one country. This paper provides a clear GIS approach to explore, assess and examine the spatial variation of water quantity and exposure to changes of environmental water supplies (e.g. rainfall, floods) among Omani provinces. The study also aims to identify provinces with the higher and lower rates of water withdrawal and availability that significantly determine water stress levels. The overall structure of the study is as follows: following the current section that serves as introduction, highlights the related works and laying out the theoretical dimensions of the research, next Section 2 represents the datasets focusing on study area, sources of spatial and attribute data. Section 3 describes the employed GIS and statistical methods. The findings and discussion are depicted in Section 4 followed by Section 5 which presents the conclusion and recommendations of the study.
The Sultanate of Oman is located on the south eastern corner of the Arabian Peninsula, along the Gulf of Oman and the Arabian Sea, from the Strait of Hormuz in the North to the borders of Yemen in the South. The Sultanate is bordered by the Republic of Yemen in the south, the United Arab Emirates (UAE) in the north and west and the Kingdom of Saudi Arabia in the west (
Oman lies between latitudes 16˚N and 28˚N, and longitudes 52˚E and 60˚E. A vast gravel desert plain covers most of central Oman, with mountain ranges along the north (Al Hajar Mountains) and southeast coast, where the country’s main cities are also located: the capital city Muscat, Sohar and Sur in the north, and Salalah in the south.
The climate of Oman is hot and dry in the interior regions while it is humid along the coastal areas. Overall, the country receives little rainfall where the annual rainfall in Muscat averages 100 mm, falling mostly in January. The mountain areas receive much higher rainfall especially in Al Hajar Mountains. While Dhofar Mountains receives seasonal rainfall (from late June to mid-September) as a result of the monsoon winds from the Indian Ocean, saturated with cool moisture and heavy fog. The higher parts of the Jabal Akhdar receive more rainfall compared with other parts of the country and probably exceed 400 mm (15.7 in).
The local scale analysis presented in this paper relies on using attribute data from Aqueduct Water Stress Projections which were conducted by World Resource Institute (WRI) in Washington DC, USA. The dataset includes several indicators of change in water quantity and quality such as water supply, water demand, water stress, and seasonal variability. The spatial data are in the form of ESRI shapefile covering catchment areas of Oman. The catchment area polygon layer contains a total of 106 features. The attribute variables that were joined to the spatial layer including water quantity indicators and they are summarized in
To assess the spatial variation of water quantity stress across Omani Provinces and apply geostatistical calcula-
Indicator | Definition | Calculation | |
---|---|---|---|
1 | Available blue water (m3) BA | The total amount of water available for a catchment area before any are satisfied. | Recording the available amount of water in cubic meter. |
2 | Total withdrawal (m3) | The total amount of water removed from fresh water sources for human use. | Recording the total amount of water in cubic meter. |
3 | Seasonal variability (SV) | Measures variation of water supply among months of the year. | Standard deviation divided by mean of total supply calculated using the monthly mean. |
4 | Flood occurrence (FO) | The number of recorded floods from 1985 to 2011. | Recording number of floods 1985-2011. |
5 | Drought severity (DS) | Measures the average length of drought times from 1901 to 2008. | Mean length × dryness. |
6 | Baseline water stress (BWS) | The total annual water withdrawal expressed as the percentage of the total annual available water. | Withdrawals divided by available flow (higher score indicate higher competition among user). |
7 | Weight physical risk quantity | The physical risk related to water quantity including indicator of availability and variability ( e.g. drought and floods). | Setting a specific weight for each quantity indicator based on its important and relevance. |
tion, GIS methods were used within ArcGIS 10.2 to aggregate the values of each cell in catchment area into the polygon (province) to which fall within. The method procedures were applied as follows:
・ A spatial layer of Omani Provinces overlaid with the catchment areas to explore the spatial distribution of water catchments across Omani administrative boundaries.
・ Converting the catchment area polygons to a raster layer using the tool feature to raster where the input field type is the indicator values of the input feature attribute table.
・ Using the output of catchment areas in the form of raster layer as an input for zonal statistics method which calculates statistics on values of a raster layer (catchment raster) within zones of another dataset (provinces).
・ To generate attribute data for each province from the raster layer, zonal statistics as table was used. The output then is a statistical summary for each province.
The advantage of using the above method is particularly in using the output attributes that can be joined to the administrative zones as input for various geo-statistical calculation and GIS modeling. For instances, using global or local models to predicate the structure of spatial pattern of water quantity stress as dependent variable and the relationship between this variable and other determinants.
To evaluate the relationship between water quantity risk and two environmental measures (drought severity and seasonal variation of water supply) as explanatory variables, we implemented a multiple regression model. The multiple regression analysis is mainly applied to produce an equation which predicts a dependent variable using two or more independent variables. This equation has the form as follows:
where y is the dependent variable which is the estimated values of water quantity risk at province j.
b0 to bn are the regression model coefficient determined by the analysis.
x1 to xn are the explanatory variables (drought severity and seasonal variation of water supply) that predict the independent variable.
It is not sufficient to explore and investigate issues of water quantity, supply and demand based on global trend. Local decisions on water use are important to manage effectively water accessibility and availability in different domains within every country. We begin with an analysis of water quantity availability and withdrawal. The amount of water in both indicators was converted from cubic meter to million cubic meter for ease detection of geographic variations. Water quantity can be defined as the total volume of all water types including both surface and underground water that are used in various domains such domestic, agricultural and industrial sectors. This indicator describes the water quantity available in each catchment area (
The water withdrawal indicator refers to the sum of the reported total annual of used water in different sectors (e.g., domestic, agricultural, industrial and others). This indicator is potentially associated with human activities and water usage and consumptions. The Spatial pattern of water withdrawal across Sultanate of Oman varies by catchment area and province (
eastern areas particularly in A’Dhahriah, Al-Batinah and Musandam provinces. A’Dakhiliyah, Dhofar and A’Sharqiyah show moderate level of water withdrawal. Interestingly, Muscat and Al-Wusta present the lowest water withdrawal values compared to other provinces and this can be investigated as the consumed water volume used mainly in the domestic sector which consumes lower quantity in comparison with irrigation and agriculture sectors. Thus a decrease in water withdrawal can be found where agriculture activities are limited.
The balanced between water consumption and availability can be evaluated using these two indicators.
Seasonal and annual variation of climatic variables (e.g., temperature, rainfall) is the most important factors influencing surface water supply. Understanding the impacts of climate variability on water quantity is essential for measuring geographic variations of water supply. Therefore, water quantity assessment requires adequate investigation of variables of seasonal climate conditions. The term seasonal variability refers to the estimated variation of water supply during year months specifically to measure differences in natural water surfaces. The spatial seasonal variability varies at subnational level (
country (e.g., in Al-Batinah and Masandam). Similarly, some of catchment basins that are located in Al-Wusta province illustrated higher variations. This spatial pattern of distribution could be explained by the fact that most of Omani regions experience arid and semi-arid climate conditions that affect seasonal variability of water supply.
The spatial pattern of drought risk is strongly associated with water quantity and availability. Drought severity mainly measures the mean of the reported drought events in a particular period of time. Areas with drought severity always experience higher degree of soil dray and multi-variation of precipitation deficit. The spatial pattern of drought severity across catchment basin within Omani provinces showed great heterogeneous distribution (
The geographic distribution of flood vulnerability levels could be an important indicator for surface water availability. Analyzing flood occurrence provides valuable information for water management and assessment of supply and demand. Evidently, flood events have a direct impact on various environmental domains in particular water quantity management. Flood occurrence measures the number of reported floods during a specific period of time.
Baseline water stress is the total water withdrawals to water availability ratio within a specific area. The indicator measures the total annual of water withdrawal for different usage such as domestic, agricultural and industrial. Higher scores indicate more competition among users while lower scores refer to a certain balance between water supply and demand.
The conceptual understanding of water risk assessment requires investigating the stress conditions that might have strong and significant effects on water quantity. Water quantity risk is defined as the exposure to change in water quantity. This index can be used to estimate the situation of water resources particularly supply, availability, reliability and demand.
were found in several catchments in Al-Wusta and Dhofar provinces. On the other hand, water quantity risk decreased in A’Sharqiyah and A’Dhahriah provinces where these two governorates show the lowest risk values.
Among different combination of independent variable, the best fit model consists of these two variables as predictors for water quantity risk in the Omani provinces. The result of this regression model (
The findings indicated that water quantity risk was a function of various environmental and climatic variables in particular seasonal variation of water supply, flood occurrence and more importantly drought severity. The variation of water supply was higher in Al-Batinah, Muscat and Musandam while lower variation was identified in A’Dakhiliyah, A’Sharqiyah and A’Dhahriah provinces. The reported events of flood occurrence during 26 years indicated that Dhofar, A’Dhahriah, Al-Batinah and Al-Wusta exhibited larger number of flood events while A’Dakhiliyah and Muscat provinces showed lower number of floods. It is obvious that better understanding of drought severity distribution could help essentially in detecting risks associated with water availability. The finding of drought distribution also revealed that there was a distinguished pattern along the Omani coasts
Variables | Coefficient | t-test | p-value |
---|---|---|---|
Drought severity | 0.1793033 | 5.26 | 0.003* |
Seasonal variation | 0.2745371 | 2.17 | 0.082** |
Constant | 3.290887 | 19.16 | 0.000* |
Prob > F = 0.0091. R-squared = 0.84. Adj R-squared = 0.7864.
* = significant at 0.05 level; ** = significant at 01 level.
where the catchments basins exhibited lower values of drought. In contrast to this pattern, internal places that are located in dray areas, further away from the coasts, illustrated higher values of drought specifically in Al-Wusta, Dhofar and A’Dakhiliyah.
Reviewing the literature, it was obvious that research on spatial variation of water quantity at national or subnational scale was very rare. In this study, an initial objective was to identify to what extent the influences of environmental factors on water quantity vary in Omani provinces. Clearly, an emphasis was placed on using GIS methods and statistical techniques to examine the spatial pattern of water quantity determinants. A multiple linear regression was fitted to estimate the degree of influence of each factor on water quantity risk in the Omani provinces. The larger and more significant predictors of water quantity risk were drought severity and seasonal variation of water supply. Initially, both variables explained approximately 79 percent of the spatial variation of water quantity risk. However, drought severity had direct impacts on water quantity risk and was significantly a strong predictor than seasonal variation of water supply.
Our study offers new insights into water stress assessment especially with regard to scope of managing water resources. Ultimately, this study contributes to understanding water scarcity problem in Sultanate of Oman and the findings can be an essential guide toward addressing the issues of the environmental effects on water resource management in the country. Considering climate change conditions and global water deficits, the geographic assessment at province level can lead to more effective utilization of all annual water supplies and abstraction in each province. In addition, it gives a broad spatial framework for reducing environmental risks to water quantity. Evaluating water resources at national and subnational scale could provide decision makers with better understanding of how to address the issues of water scarcity in each local administrative unit. Since the study was limited to aggregate data of water resources at province level, it was not possible to add more variables to the modeling analysis of environmental factors on water stress. However, in order to obtain a complete picture of water resources in the Omani provinces, other variables than environmental predictors have to be taken in account.
Further research on water quantity assessment in Sultanate of Oman using GIS and spatial analysis techniques is needed to develop effective water management strategies. Moreover, GIS analysis using additional ancillary data on water resources in each province would give a great opportunity to investigate spatial variations of water quantity. The valuable implications of such analysis are useful not only for managing water supplies but also in estimating future scenarios of water quantity required to be available for water usage sectors. To halt the negative trend of water scarcity in Sultanate of Oman, the government policies and plans for more sustainable water management in each Omani province should be based on incorporating integral spatial approaches into addressing availability and demand of water resources.
TalalAl-Awadhi,ShawkyMansour, (2015) Spatial Assessment of Water Quantity Stress in Sultanate of Oman Provinces: A GIS Based Analysis of Water Resources Variability. Journal of Geographic Information System,07,565-578. doi: 10.4236/jgis.2015.76045