Domestic Water Demand in the West Bank
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 e
t al., 2008)
and environmental factors play an
. These factors include precipitation (Maidment and
Agthe and Billings,
ra, 2002), evapotranspiration (Espey et al., 1997) a variet
y of temperature
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
o date there
are no papers that explicitly sour
ce their environmental information from GIS for use in
domestic water demand estimations
This may be because m
any of the studies have a greater temporal
spread than spatial,
enabling environmental data collection to rely on generalized spatial data for th
region of interest during the periods under examination.
However, in the case of the West Bank, the 35 communities surveyed
for the current project
and data sourcing becomes difficult due to spars
e governmental data collection
(see Comair et al., 2012).
Therefore, to obtain indicators for environmental factors
such as those listed
is study employs GIS to examine previously generated rasters and satellite imagery to
values for subse
use in the
statistical regression model.
examination include rasters
taken from satellite imagery such as normalized difference vegetation index
(NDVI) and, the normalized difference water index (NDWI) and normalize
d humidity index (NHI).
Ultimately, a step function performed within the regression analysis should reveal which of these
quantitative variables renders water demand stat
istically predictable, and thereby enable future planning
Through the a
nalysis, 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 deman
d estimation. The fi
s 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.
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
M. Melesse. Pages 359
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, rainf
all 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
ectors. The origin
of the GIS data layers is not stated, nor is the GIS component explicitly
Choudhury, B. U.; Sood, Anil; Ray, S. S.; et al. (2013)
. Agricultural area diversification and crop water
demand analysis: A remote sensing and GIS app
uses GIS to
to suggest more efficient
agriculture in order to reduce stress on water resources while
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
In this article GIS is used to measures environmental factors in different GIS
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
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.
, N., Ho
f, A. (2012). Integrating machine learning techniques and high
resolution imagery to
ready information for urban water consumption studies.
Earth Resources and
Remote Sensing/GIS Applications III:
Edited by D. L. Civco , M. Ehlers, S. Habib, et al.
Proceedings of SPIE: 8538, article number 85280H.
olf 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:
val, S., Baciu, M, Breza, T. (2003). An investigation into the precipitation conditions in Romania
using a GIS
Theoretical & Applied Climatology,
76, ½, 77
Precipitation data is used from fourteen Romanian weather stations to demonstrate G
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
ipitation and evapotranspiration.
Water Resources Management,
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 m
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,
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 avail
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 calc
ulated for within each
province/administrative territory of Pakistan.
GIS Data Layers
30m Digital Elevation Model
Aster, June 2009
MODIS, NASA, 2011
GIS raster, Water Systems Analysis Group, 2004
Still looking for a source
NDVI, NDWI, NHI
West Bank Governorates
Palestinian Communities in West
Israeli Communities in the West
The Israeli wall
Make a layer of the 35 surveyed communities
reate buffers for examination around the 35 communities
If it is
a variable affected by the urbanization or irrigation
ature, NDVI, NDWI, NHI), erase the parts
of the buffer
on the Israeli side of the
, 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
before the wa
ll was built, and thus
the primary concern becomes land use
ernative, for areas affected by excessive urbanization and irrigation,
imagery to find a nearby area that appears to represent the “natural environment”,
meaning an area without irrigation.
bles (precipitation, elevation, temperature if I can get it
), keep the
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.
layer files from various sources and make sure they are in the correct projection.
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.
Using the results of the regression analysis, use raster calculation to represent the areas of high
water demand and low water demand, as determ
ned 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 als
o show the results of
the statistical analysis.
I have not been able to locate a temperature layer. I am still working with this.
If I do
buffers around the communities, I end up with different sized areas with different amounts
This should not affect my results, but it bears some consideration.
I would also like to look for windspeed
not sure if that exists.
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,
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.
Journal of Physical Sciences,
Quanibet, M. H., Johnston, R. S. (1985). Municipal demand for water in Kuwait: methodological
issues and empirical results.
Water Resources Research,
Arbues, F., Garcia
Valinas, M. A., Martinez
Espineira, R. (2003). Estimation of residential water
demand; a state
Journal of Socio
Bell, D. R. and Griffin, R. C. (2011). Urban Water Demand with Periodic Error Cor
Dandy, G., Nguyen, T., Davies, C. (1997) “Estimating residential water demand in the presence of free
Espey, M. Espey, J. Shaw, W.D. (1997). Price elasticity of resid
ential demand for water: A meta
Water Resources Research,
Gaudin, S. (2006). Effect of price information on residential water demand.
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
44 (1), 192
Maidment, D. R., Maiou, S. P. (1986). Daily water use in nine cities.
Water Resources Research,
Mazzanti, M. Montini, A. (2006). The determinants of residential water demand: Empirical evidence for a
panel of Italian municipalities. Appl.
Econ. Lett., 13, 107
Espineira, R. (2003). Estimating water demand under increasing block
tariffs using aggregate
data and pr
portions of users per block.
Environmental and Resource Economics,