Domestic Water Demand in the West Bank Project Proposal Stephanie Galaitsis

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Oct 15, 2013 (3 years and 11 months ago)

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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 e
t al., 2008)
and environmental factors play an
important role
. These factors include precipitation (Maidment and
Miaou, 1986
,
Agthe and Billings,
1997,
Martinez
-
Espine
i
ra, 2002), evapotranspiration (Espey et al., 1997) a variet
y of temperature
measurement
s (
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)
.


T
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
e
region of interest during the periods under examination.


However, in the case of the West Bank, the 35 communities surveyed

for the current project

l
ie in
disparate environments

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
above
, th
is study employs GIS to examine previously generated rasters and satellite imagery to

develop
values for subse
quent

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 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
and modeling.

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
rst one
appear
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.


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, 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
s
ectors. The origin
s

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 app
roach.
41
(
1
)
,

71
-
82
.

T
his article
uses GIS to

characterize
s

agro
-
physical parameters
to suggest more efficient
agriculture in order to reduce stress on water resources while
protecting farmer

profit
s
.

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 subsequ
ent
ly 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.


Wo
lf
, N., Ho
f
f, 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.

G
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:


Che
val, 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 G
IS
-
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, prec
ipitation 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 m
ethods 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 avail
able.


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

Data

Source

30m Digital Elevation Model

Aster, June 2009

Global Evapotranspiration

MODIS, NASA, 2011

Precipitation

GIS raster, Water Systems Analysis Group, 2004
1

Temperature

Still looking for a source

NDVI, NDWI, NHI

Landsat,
earthexplorer.usgs.gov

West Bank Governorates

M Drive

Palestinian Communities in West
Bank

M Drive


Israeli Communities in the West
Bank

M Drive

The Israeli wall

M Drive


Data
Processing

1)

Make a layer of the 35 surveyed communities

2)

C
reate buffers for examination around the 35 communities

a.

If it is

a variable affected by the urbanization or irrigation

(evapotranspiration,
temper
ature, NDVI, NDWI, NHI), erase the parts

of the buffer
s

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 wa
ll was built, and thus
the primary concern becomes land use
alone.

b.

Alt
ernative, 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 varia
bles (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 determ
i
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.


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 Cor
rection,
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 resid
ential 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 pr
o
portions of users per block.
Environmental and Resource Economics,
26, 5
-
23.