Domestic Water Demand in the West Bank
Project Proposal
Stephanie Galaitsis
Policy makers in the West Bank need accurate estimations of domestic water demand in order to meet
those demands in the future. Researchers have long recognized the multitude of factors known to affect
domestic water demand (Arbues et al.,2003 and Kenney et al., 2008) and environmental factors play an
important role . These factors include precipitation (Maidment and Miaou, 1986, Agthe and Billings,
1997, Martinez-Espineira, 2002), evapotranspiration (Espey et al., 1997) a variety of temperature
measurements (Al-Quanibet and Johnston (1985), Almutaz et al (2012), Bell and Griffin (2011) Gaudin
(2006), Dandy et al., (1997)), windspeed (Al-Quanibet and Johnston, 1985) and elevation (Mazzanti and
Montini, 2006).
To date there are no papers that explicitly source their environmental information from GIS for use in
domestic water demand estimations. This may be because many of the studies have a greater temporal
spread than spatial, enabling environmental data collection to rely on generalized spatial data for the
region of interest during the periods under examination.
However, in the case of the West Bank, the 35 communities surveyed for the current project lie in
disparate environments and data sourcing becomes difficult due to sparse governmental data collection
(see Comair et al., 2012). Therefore, to obtain indicators for environmental factors such as those listed
above, this study employs GIS to examine previously generated rasters and satellite imagery to develop
values for subsequent use in the demand estimation statistical regression model. Quantities under
examination include rasters taken from satellite imagery such as normalized difference vegetation index
(NDVI) and, the normalized difference water index (NDWI) and normalized humidity index (NHI).
Ultimately, a step function performed within the regression analysis should reveal which of these
quantitative variables renders water demand statistically predictable, and thereby enable future planning
and modeling. Through the analysis, one or more of the variables will be selected as the best method for
characterizing the relationship between the environment and domestic water demand.
The following four citations combine GIS mapping software with water demand estimation. The first one
appears to be attempting a version of this project, but is not detailed enough, and others are estimating
different types of water demand using different types of indicators. These sources serve to demonstrate
that GIS mapping has not been used to estimate domestic water demand.
Sources
Hoffman, C., Melesse, A. M. Mcclain, M. E. (2011) Geospatial Mapping and Analysis of Water Demand,
and Use Within the Mara River Basin, in Nile River Basin: Hydrology, Climate and Water Use ed. Assefa
M. Melesse. Pages 359-382.
This study combines hydrologic records, site interviews, population census data and spatial
datasets generated from GIS to determine water demand, including but not limited to domestic
water demand. Temperature, rainfall and evaporation within the basin are reported from historical
data. GIS was used around the defined study area to give spatial attributes to water demand
factors using topological modeling, overlay and data extraction for each of the six water demand
sectors. The origins of the GIS data layers is not stated, nor is the GIS component explicitly
detailed
Choudhury, B. U.; Sood, Anil; Ray, S. S.; et al. (2013). Agricultural area diversification and crop water
demand analysis: A remote sensing and GIS approach.41(1), 71-82.
This article uses GIS to characterizes agro-physical parameters to suggest more efficient
agriculture in order to reduce stress on water resources while protecting farmer profits. It does not
predict water demand.
Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., Marsouka, F. A. (2012). Mapping urban water
demands using multi-criteria analysis and GIS. “Water Resources Management, 26 (5), 1347-1363.
In this article GIS is used to measures environmental factors in different GIS layers, including
topographic slope and land use and land cover. However, the goal of the article is not to
determine future water demand by environmental factors, but to predict future population growth
based predominantly on political demands and subsequently estimate water demand. Thus, the
GIS layers also include road network, distance to city center, distance from coastline, different
zoning areas, current population density and existing water and sewer infrastructure. These are
helpful for predicting demographic growth, but not for modeling water demand.
Wolf, N., Hoff, A. (2012). Integrating machine learning techniques and high-resolution imagery to
generate GIS-ready information for urban water consumption studies. Earth Resources and
Environmental Remote Sensing/GIS Applications III: Edited by D. L. Civco , M. Ehlers, S. Habib, et al.
Proceedings of SPIE: 8538, article number 85280H.
Golf courses, ornamental gardens, swimming pools all contribute to water demand in urban
landscapes and, using satellite imagery, these objects can create a spectral signature with
implications for urban water demand. Additionally, a Random Forest classifier was selected to
deliver classified input data for the estimation of evaporative water loss the subsequent net
landscape irrigation requirements.
Instead of looking for citations addressing this project’s specific aims, it is better to look for literature
about the methodology that will be used:
Cheval, S., Baciu, M, Breza, T. (2003). An investigation into the precipitation conditions in Romania
using a GIS-based model. Theoretical & Applied Climatology, 76, ½, 77-88.
Precipitation data is used from fourteen Romanian weather stations to demonstrate GIS-based
methods for data visualization and the identification and qualitative assessments of relationships
among climatological variables.
Comair, G. F., McKinney, D. C., Siegel, D. (2012). Hydrology of the Jordan River Basin: Watershed
delineation, precipitation and evapotranspiration. Water Resources Management, 26(14), 4281-4293.
Using GIS layers and satellite imagery, data for environmental realities in the Jordan River Basin
are compared to data available through other sources. Includes calculation methods for
evapotranspiration and listings of available raster layers for the region.
Dragan, M. Sahsuvaroglu, T., Gitas, I., Feoli, E. (2005). Application and validation of a desertification
risk index using data for Lebanon. Management of Environmental Quality: An International Journal,
16(4), 309-326.
This article specifies how temperature and precipitation maps for GIS were created using spatial
interpolation of obtained data. This may mean temperature GIS maps are not available.
Ali, H., Qamer, F. M., Ahmed, M.S., Khan, U., Habib, A. H., Chaudry, A. A., Ashraf, S. Khan, B. N.
(2012). Ecological ranking of districts of Pakistan: A geospatial approach.
Using overlay techniques, values for various ecological dynamics were calculated for within each
province/administrative territory of Pakistan.
GIS Data Layers
Data
30m Digital Elevation Model
Global Evapotranspiration
Precipitation
Temperature
NDVI, NDWI, NHI
West Bank Governorates
Palestinian Communities in West
Bank
Israeli Communities in the West
Bank
The Israeli wall
Source
Aster, June 2009
MODIS, NASA, 2011
GIS raster, Water Systems Analysis Group, 20041
Still looking for a source
Landsat, earthexplorer.usgs.gov
M Drive
M Drive
M Drive
M Drive
Data Processing
1) Make a layer of the 35 surveyed communities
2) Create buffers for examination around the 35 communities
a. If it is a variable affected by the urbanization or irrigation (evapotranspiration,
temperature, NDVI, NDWI, NHI), erase the parts of the buffers on the Israeli side of the
wall, and all urbanized land within the buffer (using Palestinian and Israeli layrers).
However, thus far the only available satellite data with sufficiently detailed rasters are
from 2001 – before the wall was built, and thus the primary concern becomes land use
alone.
b. Alternative, for areas affected by excessive urbanization and irrigation, use satellite
imagery to find a nearby area that appears to represent the “natural environment”,
meaning an area without irrigation.
c. For unaffected variables (precipitation, elevation, temperature if I can get it), keep the
entire buffer.
1
Unfortunately, however, I have been unable to locate the data on this website. I am now trying to source it from
the authors of the article that described it.
3) Upload data layer files from various sources and make sure they are in the correct projection.
4) Use zonal statistics to find the raster values for all the layer files within the confines of the
polygons. Record for use in the statistical demand model.
5) Using the results of the regression analysis, use raster calculation to represent the areas of high
water demand and low water demand, as determined by the environment factors alone.
Project Products for the Poster
I would like to create raster maps showing the variation of my different variables, and in a separate map
show the location of the communities and the community buffers. Maybe I can also show the results of
the statistical analysis.
Current Problems
1) I have not been able to locate a temperature layer. I am still working with this.
2) If I do buffers around the communities, I end up with different sized areas with different amounts
of data. This should not affect my results, but it bears some consideration.
3) I would also like to look for windspeed – not sure if that exists.
Bibliography
Agthe, D. E., Billings, R. B. (1997). Equity, price elasticity and household income under increasing block
rates for water. American Journal of Economics and Sociology, 46(3), 273-286.
Almutaz, I. Ajbar, A.H., Ali, E. (2012). Determinants of residential water demand in an arid and oil rich
country: A case study of Riyadh city in Saudi Arabia. International Journal of Physical Sciences,
7(43), 5787-5796.
Al-Quanibet, M. H., Johnston, R. S. (1985). Municipal demand for water in Kuwait: methodological
issues and empirical results. Water Resources Research, 2(4), 433-438.
Arbues, F., Garcia-Valinas, M. A., Martinez-Espineira, R. (2003). Estimation of residential water
demand; a state-of-the-art review. Journal of Socio-Economics 32, 81-102.
Bell, D. R. and Griffin, R. C. (2011). Urban Water Demand with Periodic Error Correction, Land
Economics, 87(3), 528-544.
Dandy, G., Nguyen, T., Davies, C. (1997) “Estimating residential water demand in the presence of free
allowanced. Land Economics, 73(1), 125-139.
Espey, M. Espey, J. Shaw, W.D. (1997). Price elasticity of residential demand for water: A meta-analysis.
Water Resources Research, 33, 1369-1374.
Gaudin, S. (2006). Effect of price information on residential water demand. Applied Economics, 38(4),
383-393.
Kenney, D.S., Goemans, C. Keien, R., Lowrey, J. Reidy, K. (2008). Residential water demand
management: lessons from Aurora, Colorado. Journal of the American Water Resources
Association, 44 (1), 192-207.
Maidment, D. R., Maiou, S. P. (1986). Daily water use in nine cities. Water Resources Research, 22(6),
845-885.
Mazzanti, M. Montini, A. (2006). The determinants of residential water demand: Empirical evidence for a
panel of Italian municipalities. Appl. Econ. Lett., 13, 107-111.
Martinez-Espineira, R. (2003). Estimating water demand under increasing block-tariffs using aggregate
data and proportions of users per block. Environmental and Resource Economics, 26, 5-23.