Project Deliverable: D07.1
Case Study Report: Abou Ali River Basin Lebanon
Programme name:
Sustainable Management of Scarce Resources in
the Coastal Zone
Program Areas:
A3, (d)
Project acronym:
SMART
Contract number:
ICA3-CT-2002-10006
Project Deliverable:
D07.1: Case Study Report: Abou Ali River Basin Lebanon
Related Work
Package:
WP 07 Regional Case Study: Lebanon
Type of Deliverable:
RE Technical Report
Dissemination level:
Public
Document Author:
NCRS
Edited by:
Reviewed by:
Document Version:
Final
Revision history:
First Availability:
09-2004
Final Due Date:
06-2005
Last Modification:
06-2005
Hardcopy delivered to:
Dr. Cornelia Nauen
European Commission, Research Directorate General,
SDME 1/02
B-1049 Brussels, Belgium
Table of Contents
1. Introduction .................................................................................................................... 2
2. Data basis ...................................................................................................................... 3
2.1. Water and the socio-economic profile ..................................................................... 3
2.2. GIS data ................................................................................................................. 5
2.3. Land use/cover maps (LUC) ................................................................................... 6
2.4. Climate/Water resources and Modeling ................................................................ 11
2.5 Water Resources Model (WRM) ............................................................................ 24
2.6 Coastal water modeling – TELEMAC..................................................................... 33
2.7 Scenario definition ................................................................................................. 42
2.8 Networks and user groups ..................................................................................... 95
3. Discussion and Recommendations .............................................................................. 97
1. Introduction
Tripoli, the second largest city in Lebanon along the eastern Mediterranean with a
population of around 400000, is the end journey of the Abou Ali river which drains about
482 km2 of watershed (Fig. 1). The city lies in a small plain at the foot of a plateau with
three tributaries of Abou Ali. The plateau, and the three tributaries, grade quickly up the
steep slopes into Mount Lebanon chain to heights exceeding 1500 m where their major
springs occur. Thus, within a short distance one is going from humid warm to dry and
temperate. This nature, with sudden changes in topography as well as climate, induces
socio-economic stresses on the community, which is scattered in lots of rural settlements,
related to land exploitation and resource management.
The main water supply is precipitation which falls only for 80 days varying from 900 mm (+)
annually at Tripoli city, to more than 1400 mm at the mountains, plus a considerable
amount of snow (not yet well gauged). These rejuvenate the water in the subsurface, but
also there is a very high rate of run-off as well as up to 46% evaporation plus
evapotranspiration. In spite of the available water, it is unfortunate that quality control in
the highly scattered settlements is lacking rendering that water of low quality. Thus,
although Abou Ali river discharges 207 Mm3 annually passing through Tripoli, the city
barely benefits from it. It depends on some springs in the surroundings yielding together
some 80000 m3, and on a series of water wells yielding a similar amount. The water
authorities run an old network which adds to the water quality and quantity problems
because of leakage and infiltration. This makes the problem worse as there are acute
shortages with a per capita demand of 220 m3. The annual water demand is 88 Mm3 which
is supplied from surface and subsurface sources, with a very small amount, about 15 Mm3,
is treated for re-use. The highest consumer is the agricultural sector at 65% - 70%,
followed by 25%, 6%, 3%, and 1% for the domestic, industrial, touristic and environmental
sectors, respectively.
Although the growth of Tripoli urban families at about 2% is lower than the inland rural,
about 3%, its population increased about 33% with density of 14000/km2 since the 1980’s,
while its urban built-up land increased about 200% since the 1960’s. Obviously, this meant
a huge increasing demand and stress on water, as well as devouring its once fertile and
productive land. Already the preceding identifies linkages between the socio-economic
activity and water uses. It is worthwhile to note that water usage is partially controlled as
subscribers pay a fixed amount annually, usually for 1 m3/day per household even though
this quantity is never achieved. This is why they depend on drilling their own water wells,
thus further stressing the available water. Most of those private wells, i.e. thousands, go
unchecked for pumping rate and water quality.
On the otherhand, Lebanon is famous to always has had a high rate of emigration. This
applies well in the study area standing at about 10% - 15%, notably due to the young
generation opting to go to African or other Arabian or American countries seeking jobs that
are short in Lebanon. In addition, the long internal strife (1975-1990) shook the country’s
socio-economic base severely. It induced huge waves of immigration, in and out of the city,
and large segments of the productive sectors were damaged for no return, i.e. industry,
agriculture and tourism. Unfortunately, there are no reliable data at regional or municipality
levels, but North Lebanon is overall less well developed than the rest of the country with a
per capita of about $ 6000-7500 compared to the national $ 8000. It is still highly
agricultural, which contributes less than 14% to GDP, and not well encouraged. Industry
has been traditionally weak, and tourism contributes about 22% to GDP with a very good
potential in the north, but not yet fully realized. The domestic sector depends for about
40% and 60% on surface and subsurface water, while it is almost the opposite in
2
agriculture and industry. It is interesting to note that tourism is becoming recently a factor
of increasing water demand.
Problems abound hindering the sufficient supply of water to meet growing and changing
demands of the community. Several basic changes overtook the area, and are taking
place that affect the linkages between socio-economics and water uses. We covered both
the demographic and land use above, which leaves technological and institutional change.
Notable in the former is the decision to privatize the water sector which implies upgrading
the drinking water network, the treatment plant, erecting a wastewater treatment plant (in
Tripoli and smaller others elsewhere) and modifying water prices now standing at 0.57 €
per liter for bottled and 0.4x10-3 € for domestic water. In agriculture, irrigation is increasing
in area, especially with green houses, and changing in methods towards more water
conservation. The social fabric of the productive human power is also changing since
family businesses are changing, e.g. less in agriculture, more in services and informatics.
On the institutional front, a reorganization in the water authority has started giving more
power to de-centralized agencies. This is associated with upgrading some water
legislation, regulations and policies including the code of the environment which allows
more quality control criteria to be practiced. Better management approaches due to
privatization would hopefully improve the overall situation. Still critical about water
legislation in Lebanon is its old age and thus lacking on many serious issues. Among
these are the land ownership and water rights, which render a spring in somebody’s land
his own. Another is the lack of focus on new methods of using water in a sustainable
manner, like open flow irrigation rather than drip. And certainly the need to upgrade
legislation on water quality and prices. Although the water Ministry is trying to update and
upgrade those legislations, still one can not find a global approach to watershed
investigations. Recently, the Japanese have given an aid package for Lebanon including a
comprehensive plan to rehabilitate the Ministry focusing on sustainable management.
Stakeholders concerned with a sustainable management of scarce water resources in the
area of study are many and varied. They include those in charge of water (North Water
Authority) who are responsible for the treatment and distribution plus pricing, which is now
undergoing privatization. Of course, there is the Tripoli municipality which is responsible
for the health and welfare services of the community. There are further several interest
groups, i.e. private sector like ordinary citizens worried about the lowering of the water
table and pollution in their wells; the order of engineers who extend lots of help to the
municipality, environmental NGOs concerned with the safety of people and sustainability
of natural resources; there are also academic researchers working on both hydrology
and/or health effects of the water.
Finally, an idea about some on-going projects in the area of study that may affect water
sustainability. The most important is the privatization of the water sector which will have an
improved impact on its management, pricing, allocation and quality control. Another is the
upgrading and expansion of the Tripoli wastewater treatment plant as well as creation of
few small ones in the rural area. Similarly is the improving of the solid waste management
plan. There are several touristic and recreational projects, whether in the coastal plain or
inland in the Abou Ali watershed, which implies further increasing demand and the need
for closer quality control.
2. Data basis
2.1. Water and the socio-economic profile
The resident population increased 33% since the 1980’s, although this figure is a best
estimate as the country passed in a difficult internal strife that affected the stability of
resident communities, especially in big cities including Tripoli. This puts the annual
increase at about 2.3% which is certainly a large number. The situation, where the city
attracted many inland people, was expected after the cease of the internal troubles as it
3
would have more job opportunities. But in addition, this is due to non-existing policies on
birth control. In spite of that and the fact that rural families still have large sizes, i.e. 5
children are common, the economic difficulties and increased cost of living, almost twice
since 1998, have forced urbanites to control their family off-springs. Thus, about 2% urban
population growth compared to 3% rural population growth still makes a difference.
The National water policies are undergoing reform as the old ones are lacking on many
fronts. There is a distinct difference in sectoral uses and consumption of water between
agriculture at 60% - 70% and 6% for industry, while for domestic and tourism it stands at
25% and only 1%. Of course, the distribution, the amounts of supply per user, the quality
of the original water and the used water are issues that still have to undergo updated
regulations, especially on allocation and pricing. Those policies also have to tackle the
source of the water, surface vs. underground, the property rights and quality standards.
With only 80 days of episodic rain and the rest are dry, while the need for water increases
in summer, the community is facing recurrent water shortages. The poor management and
old existing networks are not helping solve the problem. Although water resources are
available to meet the needs, mismanagement and lack of proper policies result in the
water shortages. This is impacting the community markedly forcing them to drill their own
wells. In the city this means a well for almost every building, i.e. thousands of unchecked,
uncontrolled wells. That is affecting the local groundwater reserves forcing authorities to
import the water from further localities, which implies higher costs on the citizen. In the
rural areas where irrigation consumes huge amounts of water, it becomes a problem for
the poor farmers.
The Tripoli community and surroundings are very much affected by water quality as waterrelated diseases recur almost on annual basis. Overall, most water sources, i.e. springs,
wells, rivers … etc. are polluted with a high amount of organics, bacteria and other
sewage-originating pollutants because of non-existence of treatment plants, no control on
flow of pollutants directly into a river or even in wells. The use of septic tanks is
supposedly widespread (no one surveyed how much septic they actually are), but they go
un-maintained so become self-defeating. In many areas, slopes are graded into steps and
greenhouses for protected agriculture are erected. They use lots of fertilizers, chemicals
and irrigation practices that are rendering the groundwater saline. In industrial areas,
effluents and wastewater are untreated in most cases, and go unchecked into valleys,
rivers or the sea. The pollution is affecting both surface and groundwater rendering them
difficult to use. This varies locally inducing people to depend more on groundwater,
although surface water may be available. But when one observes algae growing in a river,
he would abstain from using it. This is the case in Tripoli where Abou Ali river passes and,
unfortunately, serves as an outlet of sewage, effluents, solid wastes and debris.
The preceding reflects the poor level of awareness among the community, and makes
more difficult any water exploitation. This explains the low investment in water projects in
the private sector. On the management side, again it explains the emphasis on the supply
rather than the demand. This has been the position of the water authorities, i.e. drilling
more wells, withdrawing more water from distant sources, rather than controlling use
wastage, proper distribution and quality. Indeed, if demand management and relevant
awareness campaigns were implemented, it would have made a marked difference. Over
and above this situation is the lack of implementation of landuse planning, both in the city
and inland. Tripoli, which used to lie within large green stretches of gorgeous citrus, olives
and vegetable gardens, is losing that green belt quickly being replaced by bare lands (for
selling as real estate) or concrete buildings. The change is really drastic standing at 208%
increase in urban area since the 1960’s with an associated decrease of 35% in agricultural
lands. Analyzing current landuse resources, shows that only about 10% of prime land is
properly used while one third is misused.
Although tourism has a very high potential in the north, and that means higher demand on
water, its growth is rather slow compared to other areas in Lebanon. This applies to
constructing more hotels, more water parks, more beaches and more residential plus
4
services related to the water sector. The tourism industry is regaining its once glorious
past in Lebanon, and the north could claim about 10% of the one to one and a half million
tourists that come annually. No doubt, capacity building and institutional reform is a must
for an efficient running of the water resources in the area.
2.2. GIS data
Certainly, SMART being an approach using advanced technology especially dealing with
information, relies heavily on Geographic Information Systems (GIS). In fact, all
components in the project used an interdisciplinary interactive approach necessitating the
use of GIS. Be it in the socio-economic domain or in land use or indeed in the water
resource itself, all superposed on different layers of the natural environment in the study
area, GIS has proved valuable for that regard.
Few examples of the GIS data are given here extracted from the study area just as a
representative sample of the contribution of GIS. Since SMART focuses on water
resources management and optimization, the examples given in maps relate to the water
sector. The following Figures show the drainage, the digital terrain model, the geology,
and the hydrogeology of the study area.
5
2.3. Land use/cover maps (LUC)
The capacity for humans to change their environment and thus affect their own quality of
living is of high potential. Since land cover is related to land use, which are consumed by
the community, increasing the density of population will result in a decrease in the quality
and quantity of natural resources. The link of water to land cover/use comes at the top of
those resources. SMART focuses on this link in a way to optimize it.
The spatial distribution of land use/ land cover information and its changes is necessary
for any planning, management and monitoring programs at local, regional and national
levels. This information not only provides a better understanding of land utilization aspects
but also plays a vital role in the formulation of policies and programs required for proper
development. For ensuring sustainable development, it is necessary to monitor the
ongoing changes in land use/ land cover pattern over a period of time.
For the SMART area in Lebanon, three data sets of Land use/cover maps (Figs 2.3.a,
2.3.b, 2.3.c) were obtained from remotely sensed data and based on CORINE
(CoORdination des INformations sur l’Environnement) classification (level 3). The first
LUC (1988) was extracted from multispectral Landsat TM images of a resolution (30m).
Two series of spring and winter imageries were stacked and a supervised classification
was applied to obtain the required thematic map. A similar procedure was applied on a
couple of Spot data (April & September, 1994). The advantage of using SPOT data was
the availability of time series of this type of images at the CNRS and the better resolution
of the multispectral data (20m). The final LUC map was adopted using a visual
interpretation of the Indian IRS (5m) that was verified and updated in June 2002 with
intensive field work.
Based on CORINE classification “level three”, an average of twenty eight classes were
differentiated (Table 2.3.1). For easier interpretation we sum up the results in five main
6
categories: (1) Human practices, (2) Agricultural lands, (3) Natural vegetation, (4) Bare
lands, (5) Water bodies (Table 2.3.2).
1) Human practices are continuous and discontinuous urban areas, activity zones
(industrial areas, airport, ports) artificial green zones, roads/ highways, material
extraction (quarries), embankments, etc. It is obvious the great influence of the
urban expansion between the year 1988 (19 km2), that is (2.4%) of the study area
to almost 63 km2 (7.8 %) in the year 1994. This “chaotic” urban spreading came
mostly during the Lebanese civil war where construction took place in the absence
of urban legislation and rules.
LANDCOVER
Continuous Urban Fabric
Discontinuous Urban Fabric
Industry and Commercial Units
Road and Rail Network
Port Areas
Airports
Mineral Extraction Sites
Dumpsites
Construction Sites
Green Urban Areas
Sport and Leisure facilities
Vineyards
Fruit trees and berry plantations
olive grooves
Annual Crops Ass Perm. Crops
Complex cultivation Pattern
Mixed Fruits and Annual Crops
Broad Leaved forest
Mixed Forest
Natural Grassland
Sclerophyllous Vegetation
Transitional woodland/shrubs
Beach, dunes, and sand-plains
Bare Rock
Salt marches
Water courses
Water Bodies
Area 1988
4.8445
0.8319
5.4593
1.5823
0.4156
0.3160
2.9867
0.0436
2.6588
0.0063
0.0000
8.2082
90.4668
173.0549
4.6449
0.5415
70.5155
58.0685
65.3378
108.2271
196.8959
16.7902
0.3542
0.5435
0.0000
0.0000
0.0000
Area 1994
5.4987
40.7631
4.2496
1.6200
0.4244
0.3146
3.0489
0.0582
7.0526
0.0000
0.1848
4.1874
54.6364
145.3165
34.8229
0.2609
77.3869
84.7191
22.9462
12.1907
165.2161
107.2377
3.8714
35.1389
1.1937
0.2783
0.0620
Area 2002
7.0985
40.2356
4.5002
1.7254
0.4302
0.3130
4.5818
0.0582
5.5719
0.0000
0.2021
5.1370
42.2910
102.1784
36.0471
0.8538
125.8170
101.1264
21.5854
11.6503
164.0902
96.1845
5.6748
33.7648
1.3382
0.2449
0.0933
2) Agricultural lands: They include annual and permanent crops, Fruit trees and
berry plantations, vineyards, olive groves, complex cultivation patterns, etc. It is
clear the deterioration of the agricultural lands from 1988 (347 km2) to a sum of
(312 km2) which is about 38 % of the study area in 2002. This can be correlated to
three main factors: a) chaotic urban expansion, b) water shortage and reallocation
7
to social and industrial needs, and c) population increase, which is due to
immigration towards the big cities.
3) Natural vegetation: occupies the largest portion of the study area between 445
km2 in 1988 (55%) and 395 km2 in 2002 (48.5 %). It covers dense and different
types of forests (pinus species, juniperus sp, Cuperessus sp, quercus sp, etc.)
shrubs, natural grass lands, etc. In addition to the urban expansion, the absence of
policies for forest management and the desertification process (forest fire, over
grazing, cutting the woods, etc.) have led to the deterioration of the green areas in
the last few decades.
Table 2.3.2 Major classification categories of the three LUC data sets
LUC
Human practices
Agricultural lands
Natural vegetation
Bare lands
Water bodies
1988
(Km2)
%
area
occupied
1994
(Km2)
% area
occupied
2002
(Km2)
% area
occupied
19.145
347.432
445.320
0.898
-
2.4%
42.7%
54.8%
0.1
-
63.215
316.611
392.31
39.001
1.534
7.8%
39%
48.3%
4.72%
0.187%
64.796
312.324
394.64
39.44
1.676
8.1%
38.2%
48.5%
4.9%
0.296%
4) Bare lands are ascribed to the unexploited terrains that are dominated by rocks,
soils, beaches and dunes. The increase of occupation percentage of this category,
0.1 % in 1988 to almost 5 % in 2002, is the resultant of what was mentioned in the
previous three classes.
5) Water bodies include water surfaces such as rivers, reservoirs, marches, etc.
actually no changes occurred on this category except for the slight increase in the
salt marches between 1994 and 2002. Nevertheless absence of data in 1988 was
due to the resolution (30m) of the Landsat imageries.
Land Use Change Model
In the LUC land use change model established for SMART project the previous trends can
be stated in a set of transitional rules Table 1. The following rules concern urban
expansion and can be considered as a Business as Usual (BAU) status.
Table 1. Transitional rule for urban expansion (BAU)
Rule number one (red) means “If more than 50% of the immediate neighbors of cells are
continuous urban then the probability of transition to continuous urban increases by 50%”.
On the other hand rule three (green) means “If more than 95% of the neighbors around a
cell are of discontinuous urban then the probability of transition to continuous urban will
increase 95%. Running the model will give us the results shown in Figure 1
8
a)
b)
Figure 1. LUC predicted changes from 1990 (a) to 2014 (b) as proposed by
transitional rules stated in Table 1.
A pessimistic status of the area could be drawn by defining rules shown in Table 2.
Table 2. Transitional rule for urban expansion (Pessimistic scenario)
Tripoli city and surrounding coastal area could show a possible extreme urbanization as
shown in Figure 2. New additions to the city grow at the edges of existing city. This is
given by the two rules where if the more then half of the immediate neighbors of the cell
are of continuous urban then the probability of transition will absolutely increase by 50%
and the continuous urban areas will double. Same procedure will follow the discontinuous
urban area by a probability of a 90 % change from Discontinuous urban area to continuous
urban area if the neighbors in a 5x5 area (giving a 2 value in the rule) around a cell are
more than 50% of discontinuous urbanism Figure 2.
a)
b)
Figure 2. LUC predicted changes from 1990 (a) to 2014 (b) as proposed by
transitional rules stated in Table 2., all green turns red indicating urban expansion.
Nevertheless, optimistic rules can be drawn if the area of study undergoes master land
management plans and legislations to organize the land use and implement those plans. A
set of transitional rules are stated in Table 3 indicating an optimistic status.
9
Table 3. Transitional rule for urban expansion (optimistic scenario)
These rules could be explained as follows “if less than 50% is of a continuous urbanism
than the probability of transition in the neighboring cells to continuous urban will increase
10 % only. Additionally, if there are more than 80% of the neighbor cells surrounding the
continuous urban (which implies exhaustion, therefore the above plans would prohibit
further expansion) then the probability of transition to continuous urban will have no
increase. Same thing is true for the discontinuous urbanism”.
Actually, it is enough to define scenarios for urban expansion or change to have an
overview of the changes that may occur in the study area, since increase in urban areas
will have its influence on all other types of land cover/use. Nevertheless, some transitional
rules can be written, as an example, to define land degradation and green cover
deterioration Table 4.
Table 4. Transitional rule for green cover deterioration
1
2
3
4
5
6
7
The transitional rules stated in Table 4 can be explained as follows: In rule number 1, “if
more than 95% of the cells surrounding are already coniferous forest than the probability
of transition to Coniferous forest is absolute null (0)”, this is the same for rule 3 and 6.
Accordingly, if less than 50% of the neighbor cells are already coniferous then the
probability of transition into transitional woodland will increase 99% (rule 2). Rule 4, 5 and
7 reflects the transformation of Broad leave forest to transitional woodlands, from
transitional woodlands to scierophyllous vegetation, and from transitional woodlands to
burnt areas, respectively.
The importance of the previous sections is the help such as an analysis would secure to
the planning authorities. The scenarios are a possible outlook to the future. The change,
for our concern, is significant in terms of its implications on the water sector. What
stresses would the water resources be subject to, how would that affect the supplydemand picture. The LUC model is thus a good tool to project the future. As usual, the
better input one has as base information, the better (closer to reality) the model output
would be. A final comment relates to the waterware components, i.e. the WRM, the RRM
and the LUC. This is more obvious in the 16 Runs of the values of the different scenarios
10
with the annual mass budget summaries and sectoral demand summaries (see later in
section on WRM).
2.4. Climate/Water resources and Modeling
2.4.1. Climatic data
The study area lies at the foot of the highest peaked mountain in Lebanon enjoying a
typical Mediterranean climate. There are about 80 days of episodic torrential rain and the
rest is essentially dry. Since the SMART project is concerned with water issues, data on
climate parameters and water yield must be gathered preferably in time series. Both these
elements of the hydrologic cycle represent, in a broad sense, the water input
(precipitation) and output (rivers discharge) that constitute part of the water balance within
a specific hydrologic system, the Abou Ali river watershed. In addition, a number of
climatic and hydrologic elements are necessary to run the Waterware and Telemac
software, which are applied in this project (other sections).
Unfortunately, many gauging and climatic stations in Lebanon were destroyed during the
civil war. Thus, out of some 70 major meteorological stations, only 20 stations are
reactivated. These stations are divided into synoptic with time series of precipitation and
temperature, and pluviometric with precipitation values only. For the gauging stations on
rivers, only about 20 are working out of 70 that existed before.
For the SMART area of Lebanon eight meteorological stations are present. These are:
Tripoli, Abdeh, Batroun, Kafer Shakhna, Zgharta, Koura, Bechmizzine and Amioun. The
first three are coastal, while the other ones are inland. Though not all these stations give
the same climatic data and cover the same time interval, they could build an overall clear
idea about the climatic regime of the studied area. The following data could be gained
from these stations (Table 2.4.1.1).
From calculations, the average precipitation rate is about 1190mm, thereby, a total volume
of around 480 million m3 is precipitated over the Abou Ali River watershed. The average
maximum temperature is about 30 Cº, and the minimum is around 8 Cº, with a median of
19Cº.
The remaining stations on the issuing rivers in the study area, are only three. These are
from north to south: Al-Bared, Abou-Ali (the major river, Figure 1) and El-Jauz.
Measurements of discharge started since 1965, but there are several disconnected
intervals due to the damage in the stations (Table 2.4.1.2).
11
Table 2.4.1.1: Available climatic data for SMART area of Lebanon
Station
Given climatic data
Precipitation
(mm)
Duration
Timeseries
Temperature
Tripoli

19402003
daily

Abdeh

Jan,
1998Mar,
2003
May,
2000Oct,
2000
-
Ditto

Batroun

Ditto

Kafer
Shakhna
-
-

Zgharta
Koura
Bechmizine
Amioun




19481978
Monthly
Duration
Timeseries
Others
Duration
Timeseries
Jan,
2001Nov,200
3
Apr,
1998Jan,
2001
May,
2000Oct,
2000
Jan,
2002May,
2002
daily
-
-
-
Ditto
-
-
-
Ditto
-
-
-
Ditto
Wind
velocity
Dail
y
Monthl
y
-
Jan,
2002May,
2002
-
-
-
-
-
-
(Cº)




19481978
-
Table 2.4.1.2: Measuring intervals of rivers discharges in the SMART area
River
Duration
Time-series
Average annual discharge
(Million m3)
Al-Bared
1965-1973
Monthly
168
1991-2001
1965-1975
Ditto
Abou-Ali
1990-2001
369
1996-2001
Daily
El-Jauz
1965-1975
82
1979-1985
Monthly
1990-2001
2.4.2. Water Ware Modeling
It is a software system that can manipulate hydrologic information as an advanced tool for
water resources management. It can be applied in multi-criteria decision support for a
broad range of water resources problems. The system has the capability to integrate a
range of information resources for different optimized outputs.
Several data sets are required in applying Waterware models. They can be static and
dynamic data. The latter is set in a time series sequence for future scenarios. Data needed
for WaterWare are linked to on-line monitoring systems for environmental impact
assessment (EIA). It has the capability to integrate data base management for:
- Geographic data
River basin objects
Hydrological and meteorological objects
Water quality data
Economic data
- Geographic data
The case study area extends between Al-Bared and El-Jauz Rivers (Abou Ali River is
inside) and about 10km eastward. It has an area of around 430 km2 and lies between the
following longitude and latitude:
North: 34°13’ - 34°31’
12
East: 35°38’ - 35°39’
The watershed of Abou-Ali River is the largest one in the Lebanese coastal area and has
an area of about 482 km2.
- River basin objects (Abou Ali River)
This contains river nodes, here classified into five principal node types (Table 2.4.2.1).
These are:
- River nodes
- Spring nodes
- Water Tanks, Reservoirs and Wells Nodes
- Aquifer and Sub-catchment
- Demand
nod
13
Table 2.4.2.1: Water Nodes and Areas in Abou Ali River Basin, Tripoli-Batroun Area, Lebanon
A. River Nodes
Node
Definition
Gauging
stations
Control nodes
along the river
network used
for calibration
Major
confluences
Conjunction
between a
tributary and
the primary
water course
No.
According to
accompanied figure
Local Name
Description
G1
G2
G3
G4
G5
Abou Samara
Rachien-Zgharta
Darya Kfer Zghab
Kousba
Joueit-Marh
Hydrological station (Liminograph) with weekly
measures. Rehabilitated in 1998
Non-operating station (Liminograph)
Hydrological station (Liminograph) with weekly
measures. Rehabilitated in 1998
C1
Tahoun Ed-Dier
C2
El-Mikhda
C3
Haref Arde
C4
Mar Sarkis edDanni
C5
Mazra’at En-Naher
C6
Ain Stanboul
Connecting Abou Ali River with
streams of Aacha’ach,
Rachaaine and Joueit
Connecting streams of
Aacha’ach, Rachaaine and
Jouiet at El-Mikhda region in
Zgharta
Connecting streams of
Aacha’ach and Rachaaine
Connecting streams of Ouadi
El-Ajrame with Rachaaine
stream
Connecting streams of Kadisha
and Mar Sarkis
Connecting a dense drainage
network derived from the
mountainous, karstic region
These are mostly permanent streams
Intermittent streams
14
Major
diversions
Dams
(proposed)
Lakes
Split point
between
streams
Dm1
L1
D1
Tripoli
D2
Tripoli
D3
Rachaaine
D4
Zgharta
D5
Rachaaine
D6
Raskifa
D7
Kousba
D8
Electricity of
Blousa
D9
Deir Saidet
Qannoubine
Near Haret ech-Charfe, parallel to
Abou Ali course, (extends ~ 600m)
From Majdalaya to Haret Ech-Charfe
(~ 1km)
From Ain El-Mekadem to Tahounet
Ras En-Naher, (extends ~ 1km)
Two branches from Zgharta to
Rachaaine and from Zogharta to Kfer
Dlaqus. About 1.5km length for each
From Ain Makadam to Taouahine Ras
En-Naher
(~ 1.2km)
Extends from Die Mar Nahra to the
electrical station at Dier Hanntoura
(~2.5km)
From Kousba to the electrical station
at Dier Hanntoura (~ 1.5km)
Two branches from EL-Fradis (~ 2km)
and Aintounrine (~ 1.5km) to Blousa
electrical station
From Saidet Qannoubine to Saidet
Kannoubine then to El-Qadissa Barbra
(~ 3km)
Relative flow rate
(m3/sec)
Laal
This is a supposed dam
Estimated retaining water volume
(million m3/year)
Bchaani
A permanent lake of
250m diameter is
resulted from this dam
Estimated retaining water volume
(million m3/year)
15
B. Springs Nodes
S1
Major springs
S2
S3
Ain EchChrahtouni
Rachaaine
Nabaa Chouailit
S4
Ain El-Ghazal
S5
S6
Nabaa Ed-Delbe
Nabaa El-Fouar
S7
Nabaa Mar
Sarkis
Naba Kadisha
S8
Annual
average
discharge
(m3/sec)
Estimated at
0.5
1.4
Estimated at
0.25-0.5
Estimated at
0.25
1.4
Estimated at
0.5-0.7
0.5
Overflow
Fault
Overflow
Spring
type
Water wells
Fault
Karstic
Karstic
1.5
C. Water Tanks, Reservoirs and Wells Nodes
These are concrete reservoirs that stand on elevated spots. They usually have a
Water tanks
water capacity of about 300-350 m3
Reservoirs
Overflow
These represent ground reservoirs which are built of concrete or compacted
clayey materials. They are of different sizes, but usually have a volume of about
200-250 m3
Fault
They are constructed at top mountains
nearby the urban areas. See
accompanied figure
Mostly located in within the settlement
areas. See accompanied figure
Most water wells in the Abou ali River basin are private ones. However, data on these wells are
obscure and not complete.
Not defined
16
D. Aquifer and Sub-catchment
Symbol
Rock formation
Aq1
Sannine L.st.
(C4)
(Cenomanian)
Aquifers
Aq2
Subcatchments
Bikfaya and
Kesrouan L.st.
(J6 &J4)
(Up.PortlandianKimmeridjian )
Description
Transmissi
vity
(m3/sec)
Thin bedded to massive,
highly fractured, jointed,
chertified and well
karstified dolomitic
limestone and limestone
with some thin beds of
marly limestone
Thick bedded to
massive, highly fissured,
fractured, jointed and
karstified limestone and
dolomitic limestone
2.5x10-65.9x10-6
Storage
capacity
(m3/1hr/
m)
Infiltratio
n rate
(%)
Area
(km2)
216
35-40
1.6x10-6
18
2.3x10-63.2x10-6
Main flow
direction
of
groundwat
SW and
er
West
(seaward)
1.17x10-6
40-45
S-SW and
partially to
West
No. of
stream
orders
5
Relief
gradient
(m/km)
50-55
Width/length ratio
Surface
roughness
131
Drainage
density
(No. of
2
streams/25km
)
23-28
1:3.5
Partially rugged
Sc2
112
20-22
4
45-50
1:5.5
Sc3
239
25-30
6
25-30
1:9.5
Karstic with rugged
topography
Rugged
Symbol
Area
(km2)
Sc1
17
E. Demand Nodes
Cities
Names
Names
Names
Names
Wady al Njass
Beit jida
Bakhaoun
Azqey
Kfar Chillane
Mrah Es Sreij
Hilane
Beit Knaty
Miryata
Bousit
Beit Aabeid
Beit Aaoukar
Beit Barakat
Aalma
Haret Ej Jdide
Aardat
Et Talle
Asnoun
Karabach
Er Rmaile
El Qadriye
Arde
Kfar Zghab
Kfar Yachit
Sibaal
Jdeide
Laal
Qareit Sakhra
Harf Mizaira
Baho
Kahf El - Malloul
Aamar
Bchernata
Zghartighrine
Dairaiya
Karm El - Mohr
El Khaldiye
Kafar Zeina
Kfar Hada
Deir Nbouh
Beit Daoud
Ba'zakoune
Beit Hassane
Izal
Bayader Rach'ine
Kfar Dlaqous
Zgharta
Harfe Arde
Aachach
Kfar Habou
Beit Zoud
Rachaain
Bchannine
Kfar Qahel
Kfar Chakhna
Kfar Haoura
Bkeftine
Harsoun Ej Jdide
Terbol
Majdalaya
BcharrΘ
Ariz
Bqaa Kafra
Hadet Ej jebbe
Braissat
Dimane
Kfar Saroun
Hasroun
Bazaoun
Bqerqacha
Moghr El Ahoual
Rechdibbine
Kousba
Raskifa
Darayia
Aarjess
Bnichaai
Miziara
Hmais
Mazraat Et Toufah
Aslout
Toula
Bhaira (el)
Afqa
Hadchit
Wady Qannoubine
Blaouza
Qnaiouer
Beit Menzer
Hauqet en-Nahr
Bane
Selouane Sghab
Ain Tourine
Fradis
Mazraat al Nahr
Tourza
Seraal
Aarbet Qozhaiya
Ehden
Mar Semaane
Ejbeaa
Baslouqit
Aitou
Karm Sadde
Karbribe
Kfar Fou
Sibaal
Souaqi
Trablous Jardins
Tal
Noury
Rammanet
Mhatreh
Hadid
Jessrine
Tabbaneh
Zahrieh
Qoubbe
Trablous
18
- Hydrological data
On the SMART case study selected area, the average annual precipitation is about 830 mm.
While it is higher for the whole water basin of Abou-Ali River, and reaches to about 1190mm.
According to Thiessen method, the average volume of water will be about 285 Mm3/year for
the SMART area, and about 480 Mm3/year for the Abou-Ali River watershed. For the latter,
some 30% of this volume is derived from snowmelt.
The hydrological properties of the issuing rivers in the area can be summarized in Table
2.4.2.2.
Table 2.4.2.2: Major hydrological properties of the SMART area rivers
No
River
Average
annual
discharge
(Mm3/year)
Length
(curved)
(km)
Area
(km2)
Relief
gradient
(m/km)
Drainage
density D
(km/km2)
Major sources of
replenishment
1
2
Al-Bared
Abou-Ali
168
369
37
42
284
482
25
46
1.05
1.20
3
El-Jouz
82
33
196
42
0.92
Nabaa Essoukar and the melting
snow
Nabaa Qadisha,Nabaa Mar
Sarkis, Nabaa Rachaien and the
melting snow
Nabaa El-Jouz, Nabaa Dalle and
the melting snow: Tannourine,
Arrez and Niha regions
Water catchment areas were classified into three major types, according to the
hydrological properties they hold. Table 2.4.2.3 shows these types and the
estimated water that precipitated and evapotranspirated for each one.
Table 2.4.2.3: Volume of precipitated and evapotranspirated water from watersheds of
SMART region
Major watershed
No.
Intermediate watershed
Volume of
Volume of
precipitated
evapotranspirated
water
water
Million m3/year
No.
Volume of
Volume of
precipitated
evapotranspirated
water
water
Million m3/year
Minor watershed
No.
Volume of
Volume of
precipitated
evapotranspirated
water
water
Million m3/year
1
113
55
1
29
16.5
1
12
6.5
2
3
4
5
6
7
146
105
224
504
64
122
73
58
148
265
50
117
2
3
4
5
6
7
8
34
69
4
5
8.5
9
34.5
21
7
1.5
2
3.5
4
15
2
3
4
5
6
7
8
9
11.5
2
11.5
36
5.5
11
5
14
6
1.5
6
17
2.5
5
2.5
6.5
19
In addition to these rivers, some 75 issuing springs are available (Fig. 2.4.2.1),
but only 5 of them are major ones (i.e. yield more than 100 l/sec). The
measured discharges from these springs are not precise, as they have no
continuous measures. An estimated volume of about 235 Mm3/year is
obtained from them. Table 2.4.2.4 shows their known yield, but a reduction in
these values is likely in recent years.
Table 2.4.2.4: Major issuing springs in the SMART region
El-Jauz
Discharge
(m3/sec)
2.20
Dalle
1.93
Qadisha
1.50
Mar Sarkis
0.5
Essoukkar
0.3
Spring
Source rock formation
Cenomanian
(limestone)
Neocomian-Jurassic
(Sandstone, clay and dolomitic
limestone)
Cenomanian
(limestone)
Cenomanian
(limestone)
Albian-Cenomanian
(shale, marl and limestone)
Geologic origin
Karstic
Overflow
Karstic
Karstic
Overflow
Moreover, there are a number of submarine springs that discharge a considerable volume of
water. The estimated water volume from these springs is about 318 million m 3/year (Table
2.4.2.5). Although they produce a considerable amount of water, they are difficult to tap in
the sea.
Table 2.4.2.5: Major submarine springs along the coastal stretch of the SMART region
N°
Locality
1
Hai elMaqateh
2
Bahsas
3
Bahsas –
Abu Halqa
4
Chekka-1
5
Chekka-2
6
Chekka-3
7
Chekka-4
8
Fadou’s
9
Madfoun-1
Coordinates
Longitude &
latitude
35º 54′ 46″
34º 28′ 30″
35º 44′ 10″
34º 25′ 20″
35º 48′ 58″
34º 25′ 10″
35º 43′ 35″
34º 20′ 35″
35º 43′ 06″
34º 20′ 28″
35º 43′ 26″
34º 20′ 11″
35º 39′ 11″
34º 20′ 10″
35º 39′ 11″
34º 13′ 29″
35º 38′ 54″
34º 12′ 29″
Main flow Featuring
Ditto (like above)
Estimated
yield
(l/sec)
60
Water seeps within rocky islets
extending 100m into sea
Ditto, but extending 700m
200
Artesian spring 25m offshore,
5m diameter water cone
Ditto, 700m offshore, 60m
diameter
Ditto, 300m offshore, 15m
diameter
Ditto, 300m offshore, 10m
diameter
Water seeps either parallel or
protruding from rocky beach, 50300m
Irregular water leakage lateral &
linear 40-350m
500
600
6000*
1500
1000
60
120
20
10
Madfoun-2
35º 38′ 58″
34º 12′ 26″
More linear into the sea about
40-150m
60
21
- Water quality data
Periodical water quality analysis in the SMART area and Abou-Ali River basin is almost
lacking. Selective analysis of springs and wells available from literature are shown in Table
2.4.2.6.
Table 2.4.2.6. Selective water analysis of SMART area and Abou-Ali River
watershedPhysical and Chemical Analysis of Drinking Water in Abou Ali River Basin
(Average of 1420 Selected Samples)
Physical and Chemical
Properties
Wet
Temperature
season
Dry
Cº
season
Wet
Color
season
Dry
TCU
season
Wet
Conductivity
season
Dry
µmohs/cm
season
Wet
Alkalinity
season
mg/1 as Ca CO3
Dry
season
Wet
Hardness
season
Dry
mg/1 as Ca CO2
season
Wet
Chloride
season
Dry
mg/l
season
Wet
Nitrates
season
Dry
mg/1 as NO3-N
season
Wet
Sulfates
season
Dry
mg/l
season
Wet
Phosphates
season
Dry
mg/l
season
Wet
Iron
season
Dry
mg/l
season
Springs
Wells
Tanks
Networks
11.9
10.1
14.6
17.7
15.4
22.5
20.5
22.7
1.0
0
8.0
6.0
5.0
5.0
4.0
5.0
278
178
271
345
391
606
359
473
185
138
156
328
201
270
209
215
272
83
190
227
248
370
206
257
24
15
18
26
18
42
13
26
0.23
024
0.43
0.94
0.23
8
3.57
3
2.16
9
2.47
13
5
26
4
8
0.12
0.04
0.12
0.18
0.026
0.18
0.07
0.12
0
0.03
0.017
0.022
0.1
0
0
0
22
Chemical Quality of Selective Major Springs Located in Abou Ali River Basin
Spring
Total
Calcium Magnesium
Hardness Hardness Hardness
mg/ 1 as
mg/ 1 as
mg/ 1 as
CaCO3
CaCO3
Mg+ Co3
+
Mg Co3
Clmg/1
So4- mg/1
Co3- mg/1
HCo3mg/1
Na++k+
mg/1
Fe +++
Cu++
mg/1
mg/1
Si o2
mg/1
T.D.S
mg/1
Mar
Sarkis
Rachine
96
78
18
13
70
-
174.4
4.2
0.10
-
1.6
359.4
126.73
100
26.73
21.27
9.6
-
207.8
6.9
0.1
-
1.4
373.9
Kadicha
117.87
96
21.87
14.18
76.8
-
175.1
3.8
0.07
-
1.4
379.32
Concentration Ranges for Waters of Major Aquifers in Abou Ali River Basin
Aquifer
constituent
(p.p.m)
Callovian (J4)
&
Kimm erdgian
(J6)
Cenomanian
(C4)
Ca++
Mg++
Na++
K+
Fe+++
Cu++
Cl-
SO--4
CO—3
HCO-3
SiO2
T.D.S.
64-149
7-34
5-9
0.07-.02
0
13-20
0-39
0-6
134-248
0-1.6
271-478
38-96
8-12
2-5
0.07-0.1
0
2-14
3-56
0
122-177
1.3-1.6
179-400
23
- Economic data
Water price would reflect service anddistribution costs and environmental costs. Relevant
information are: what people actually pay, or what a liter of bottled water costs at the store
are useful.
The Cost of water can be summarized as follows:
- Water price for domestic use = 0.375 EU /m3
- Water price for agriculture use = 0.450 EU /m3
- Water price for industry = 0.450 EU /m3
- Water price for the tourism units = 0.375 EU /m3
- Water consumption per capita = 600 l/day
- Domestic water consumption per capita = 160-180 l/day
- Commercial water consumption per capita = 15 l/day
- Agricultural water consumption per capita = 400 l/day
- Industrial water consumption per capita = 35 l/day
-Total water consumption
2.5 Water Resources Model (WRM)
1. Case description
The case study of WRM in Lebanon was applied on the largest coastal water basin in
Lebanon (482 km2). The Abou Ali River flows from the surrounding mountains of Tripoli
(>2500m) and outlets water at the Tripoli city coast.
The selected case study for the WRM shows funnel-like shape catchment area. The
drainage system which is located within the watershed limits represents a dendritic stream
pattern. This almost expresses a regular flow regime of surface water.
The selected area is inhabited with some 400000 persons who are concentrated in the
coastal region. Accordingly, although water resources are apparently available in
considerable quantities, there is a shortage of water supply. Therefore, a water budget
evaluation is a must for proper management plans because that budget was not recorded
properly before. WRM can thus help re-estimate the previous water status for previous years
(example year 2000 which was used in this case), as well as can plot future scenarios under
different physical and anthropic changes.
2. Model components
The majority of the model components lie in the input/output equation. These are expressed
in terms of Water Nodes. The nodes occupy Supply and Demand water elements. In addition
to the latter, a number of generic nodes are located to represent measuring stations,
treatment stations, etc, which have no effect on water budget.
In Abou Ali case study there are 60 major water nodes (Fig.2). These nodes are:
- 10 start nodes
- 2 end nodes
- 1 reservoir node
- 10 demand nodes
- 9 diversion nodes
- 17 confluence nodes
- 9 geometry nodes
- 2 control nodes
Figure 2: Topological presentation of water nodes in Abou Ali watershed
Start node
Demand node
Reservoir node
End node
Diversion
Geometry node
The above nodes are connected with 67 reaches, totaling a length of 114805m. There are 34
reaches representing the main stream and the remaining 21 represent diversion reaches.
The topological presentation of Abou Ali River watershed is shown in Figure 2.
Specific codes according to their sources or geographic locations, e.g. Nabaa Rachaaine or
Kousba control station, etc. are shown (Table 7). In case there is no remarkable location,
they were coded and labeled according to the node, e.g. CONF. 3, DIV 1, etc. (Table 7).
The above nodes and reaches were plotted within a topological concept, regardless to scale,
distance or any dimensional element. Nevertheless, the joining direction between nodes, as
well as discrimination reaches of mainstream from those of diversions was necessary to pass
25
the model connection correctly. However, detailed information was set for each node or
reach. This can be seen in the scenario information, by pressing the digit of the concerned
node or reach.
Table 7: Used nodes in Abou Ali River scenario
Type
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
confluence
control
control
demand
demand
demand
demand
demand
demand
demand
demand
demand
demand
diversion, single
diversion, single
diversion, single
diversion, single
diversion, single
diversion, single
diversion, single
diversion, single
diversion, single
end
end
geometry
geometry
geometry
X
2
9
-16
-8
20
-4
17
12
2
14
9
-16
2
9
9
2
12
17
-4
17
17
7
5
11
-12
2
2
23
-20
-12
17
9
9
-16
12
2
20
14
12
9
9
-4
7
Y
0
140
115
115
55
115
75
140
115
55
340
30
30
250
205
85
290
102
-15
250
170
55
290
15
150
250
175
15
85
115
140
290
175
85
250
55
15
15
470
420
115
150
85
Node Name
CONF.3
CONF.6
CONF.10
CONF.8
CONF.11
CONF.14
CONF.1
CONF.7
CONF.5
CONF.2
CONF.18
CONF.9
CONF.4
CONF.15
CONF.12
CONF. 13
CONF.17
CONT.1-Kousba
CONT.3-Joueit-Mrah
DEM.9
DEM.4
DEM.3
DEM.10
DEM.2
DEM.6
DEM.8
DEM.7
DEM.1
DEM.5
DIV.2
DIV.12
DIV.24
DIV.21
DIV.15
DIV.23
DIV.8
DIV.1
DIV.4
Submarine-End-2
End-1
Geom.8
Geom.5
Geom.3
26
geometry
geometry
12
11
170
55
Geom.7
Geom.2
geometry
geometry
geometry
geometry
reservoir
start
start
start
start
start
start
start
start
start
start
17
2
23
-20
8
21
-16
8
14
-20
-4
-8
-4
20
2
290
205
55
115
-10
175
-50
-90
-30
-20
-55
-60
185
-30
-70
Geom.9
Geom.6
Geom.1
Geom.4
Bnchaani Reservoir
Groundwater-2
Nabaa Rachaaine
Nabaa Chouailit
Nabaa Mar Sarkis
Ain Ech-Chartouni
Nabaa Ed-Delbe
Nabaa El-Faouar
Groundwater-1
Nabaa Kadisha
Ain El-Ghazal
27
3.
Model data and results
After completing the correct connections and patterns of the different nodes and reaches,
all with detailed data and time-series, running the model can then be achieved. However,
some basic data resulting from the WRM are necessary to show different aspects of
information on the water budget.
The information expresses the water status for the year 2000, including the annual mass
budget summary (Table 8), annual groundwater mass budget (Table 9), annual sectoral
demand (Table 10), and detailed information about each node and reach. The
information is presented as follows:
a. Annual mass budget summary
Table 8: Annual mass budget resulting from the WRM
Annual mass budget summary (Mio. m3)
Inflow
197.06
26.81%
Direct rain
538.02
73.19%
Total inputs
735.08
100.00%
Consumptive use
14.41
1.96%
Evaporation
270.63
36.82%
Seepage
240.47
32.71%
Delta storage
-13.81
-1.88%
Outflow
223.73
30.44%
Mass Budget Error
-0.33
-0.05%
Supply/demand ratio
41.98%
Reliability
58.73%
Total shortfall
36.40
4.95%
Total unallocated
527.33
71.74%
Table 8 shows that there is a very minimal error ratio (i.e. -0.33%), which indicates the
correct data set for both input and output. The total input in the Abou-Ali case study is
735.08 million m3; which is derived from the sum of inflow (197.06 million m3) and direct
precipitation (538.02 million m3). Besides, the output expresses natural losses plus human
needs. For the natural, it tackles the evaporation, different seeps, delta storage and
discharge.
The ratio of supply/demand is 41.98%, whilst the total shortfall and unallocated are 36.40
million m3 and 527.33 million m3; respectively.
28
b. Annual groundwater mass budget
This deals directly with the groundwater balance, also in terms of input-output. The total input
to the aquifer (Sannine Limestone aquifer-C4) in the Abou-Ali case study is (247.47 million
m3), while the output is (209.97 million m3). This means that there is 37.03 million m3 positive
net amount.
Table 9: Annual groundwater mass budget resulting from the WRM
Annual summary (Million m³, Mm3)
Natural recharge
6.59
Pumped recharge
0.00
Seepage from reservoirs
0.00
Seepage from demand nodes
240.47
Total input
247.06
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
11.14
185.92
2.58
10.32
209.97
37.08
234.15
2.67%
0.00%
0.00%
97.33%
100.00%
4.51%
75.26%
1.05%
4.18%
84.99%
15.01%
94.77%
c. Annual sectoral demand
Different demand items in the Abou-Ali case study are those attributed to domestic,
agricultural, industrial plus different general services. For each use, several numerical values
were calculated via running the model (Table 10). There is a loss value that due to the
domestic sector (i.e. 161.86million m3), which if added to the other sectors gives a sum of
285.50 million m3. This shows a real problem in the study area.
For the comparative analysis, the items would change values if different time series are used.
This is shown later by scenario runs in order to extract future scenarios for the business as
usual BAU, optimistic and pessimistic conditions.
29
Table 10: Annual sectoral demand resulting from the WRM
Demand
(Mm3)
Net
Supply
(Mm3)
Consumpti
ve
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocat
ed (Mm3)
Supply/
Reliabili
deman
ty (%)
d (%)
Domestic
20.27
280.71
9.29
161.86
6.50
266.95
67.91
66.44
Agricultural
26.96
55.59
4.83
14.52
15.35
43.98
43.07
41.92
Industrial
1.76
217.36
0.29
109.12
0.80
216.40
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Total
62.74
553.67
14.41
285.50
36.40
527.33
41.98
54.60
54.62
63.56
Rainfall-Runoff Model (RRM)
1. Case description
The area of the case study of RRM in Lebanon includes, in addition to the watershed of Abou
Ali River, which was used for the WRM case study, the coastal area that lies between ElJauz River from south and Al-Bared River from north, and up the mountain to some 800m
altitude. It also includes Tripoli city (the second largest Lebanese city).
The running water in Abou Ali (Runoff) is an essential water node in the selected area,
notably for irrigation purposes. Therefore, hydrologic evaluation through RRM is of great
importance. It would be highly affected by any stress due to climatic and anthropic changes.
This exposes the urgent need to build a future scenario considering all probable changes.
Due to data availability for the year 1998, the model was applied on that data. Changing
variables would reveal future perspectives.
Model components
The rainfall-runoff model is a dynamic (daily) lumped water budget model for small to
medium-sized basins. Time-series data are required for the model. The majority of the model
components lie within three parameter groups. They are:
- Basin parameters
Describe the dimensional elements and drainage characteristics of the catchment. This is in
addition to Land use/Land cover classification.
Model parameters.
They primarily tackle the climatic conditions and major hydrologic elements. Thus, that
involves several hydrologic elements controlling water percolation from surface into
subsurface media. This is expressed by initial conditions and groundwater parameters.
30
- Basin specific constants
Include constants relating to basin and land use parameters. Main among them are root
zones, interception storage and infiltration rate.
Model data and results
RRM MODEL OUTPUT
Basin: Abou_Ali
Basin id: (null)
Scenario: 226
Catchment Data:
---------------------------------------Number of layers:
26
Number of days:
365
Catchment Size:
430.0000 km2
Landuse forest:
15.0000 %
Landuse agriculture:
45.0000 %
Landuse meadow:
10.0000 %
Landuse residual:
30.0000 %
min elevation:
100.0000 m
max elevation:
2555.0000 m
Basin length:
27.0000 km
Channel length:
21.8000 km
Channel width:
10.2000 m
Drainage length:
125.2200 km
Model Parameters:
---------------------------------------Initial Snowpack
0.0000 mm
Interception storage
0.1800 mm
Inital soil moisture
85.3800 mm
Inital groundwater
100.0000 mm
Precipitation factor
5.0000 %/100m
Adiabatic Lapse Rate
1.0000 deg/100m
Temperature Shift
0.0000 deg
Precipitation Scale
1.0000
Field capacity
234.4300 mm
Maximum percolation rate
10.5000
Groundwater response lag
280.0000
Groundwater response coefficient
0.0020
Groundwater response exponent
0.5000
Model Constants:
---------------------------------------average rootzone thickness agriculture
average rootzone thickness forest
average rootzone thickness pastures
deg day coefficient evaporation interception
deg day coefficient impervious areas
deg day coefficient pevt agriculture
deg day coefficient pevt forest
deg day coefficient pevt pastures
deg day coefficient snowmelt
heavy rain limit
interception storage capacity agriculture
interception storage capacity forest
interception storage capacity pastures
0.8000
0.5000
0.3000
0.1000
0.0000
0.1800
0.1000
0.1500
1.0000
10.0000
0.6000
0.7000
0.3000
31
maximum infiltration forest
maximum infiltration pastures
25.0000
maximum infiltration agriculture
maximum infiltration uncultivated land
pevt reduction factor for heavy rain
reduction factor for infiltration rate
reduction factor for percolation rate
temperature minimum for evt pevt
warm rain coefficient snowmelt
70.0000
0.4000
35.0000
0.1500
0.7500
0.2000
0.4000
0.1000
Model Input Summary:
---------------------------------------Average Flow
:
1.0 mm/day
Average Flow
:
0.2 m3/s
Total Precipitation:
577.6 mm
Average Temperature:
19.2 deg
Model Output: Water Budget:
---------------------------------------Corrected temperature:
19.2 deg
Corrected precipitation:
837.3 mm
Initial soil water:
110.1 mm
Initial ground water:
99.9 mm
INITIAL WATER BUDGET:
210.0 mm
Final soil water:
59.4 mm
Final ground water:
143.7 mm
Total precipitation:
837.3 mm
Total evapotranspiration: 503.3 mm
Total Runoff (in mm):
367.5 mm
FINAL WATER BUDGET:
203.1 mm
CHANGE in water content
-6.9 mm
---------------------------------------AVERAGE RUNOFF COEFFICIENT
43.9
AVERAGE FLOW (m3, modelled)
5.0 m3
----------------------------------------TOTAL MASS BUDGET
-26.7 mm
==========================================
****************************************** THE TABLE
MONTHLY MODEL OUTPUT AND BUDGET:
month temp prec
runoff evpt
perc
base
|----------|--------|---------|---------|---------|---------|---------|
mon
deg
mm
mm
mm
mm
mm
|----------|--------|---------|---------|---------|---------|---------|
September 8.9 13.92 7.86 28.61
0.00
0.59
October 11.5 20.87 8.03 25.82
0.00
0.62
November 12.9 109.15 14.00 38.21
0.00
0.57
December 22.0 276.72 136.55 72.86 60.75
0.64
January 24.6 138.58 61.54 83.97 15.88
0.76
February 29.8 75.96 21.20 104.38
4.70
0.71
March 29.6 154.23 66.81 68.27 17.22
0.76
April 32.8 47.83 24.98 74.15
3.56
0.78
May 25.9
0.00 6.83
6.27
0.00
0.79
June 18.4
0.00 6.52
0.62
0.00
0.75
32
July 7.3
0.00 6.64
0.08
0.00
0.76
August 8.1
0.00 6.53
0.05
0.00
0.75
|----------|--------|---------|---------|---------|---------|---------|
TOTAL: 19.2 837.25 367.50 503.30 102.11
8.48
SOIL WATER BUDGET:
-50.7 mm
GROUND WATER BUDGET:
43.8 mm
OVERALL WATER BUDGET
-6.9 mm
BASIN RUNOFF COEFFICIENT
43.9
****************************************** END TABLE
Runoff Coefficient:
Evapotranspiration:
60.1 mm
Surface flow
18.8 mm
Interflow
24.1 mm
Baseflow
1.0 mm
Total Runoff
43.9 mm
Content Change
-0.8 mm
2.6 Coastal water modeling – TELEMAC
Introduction
The European Commission-funded SMART project (INCO program) aims to use the
necessary methods and resources for defining a development and management strategy for
optimum water use in selected highly stressed (urban) coastal zones, and their catchment
areas around the Mediterranean.
SMART is based on an integrated, multidisciplinary approach involving scientific components
(numerical simulation models, environmental indicators, satellite pictures, etc.), socio –
economic components and political components. Project co-financed by the European
Commission in the context of the INCO-MED A3 program – contract ICA3-CT-2002-10006.
This part describes the results of applying water-modeling software (TELEMAC) that
contributes to better water management. It constitutes an important segment of
WorkPackage 03 of the project (Analytical tools)
Project Area – Lebanon
The Lebanese case study has few km of shoreline lying along the northern segment of the
eastern Mediterranean at the seafront of the city of Tripoli, the second largest in Lebanon,
with a population of about 400000 people. Like other coastal stretches, urbanization and
development projects are rapidly overtaking the area. This is occurring at the expense of the
coastline on one hand and agricultural plantations through which river Abou Ali passes (Fig.
2.5.2.1).
The coastline is affected first by the shadow of the Tripoli headland to its south, and where
the seaport jetty lies. That is where most of the winds and resultant currents blow, i.e. south
westerly and westerly, hence the wave energy regime is normally not high. The tidal cycle is
minimal not exceeding 30-40 cm, while long shore currents inclined or sub-parallel to the
shoreline are important. A second effect on the area is of course the river mouth itself with
the loads of sediments, solid and liquid pollution that it brings annually. That load is
redistributed by the waves building sandy beaches mixed with debris. The third effect is the
interference of the local authorities which, in the past, used the river mouth as an open solid
waste dump of the city. Recently, they modified the spot into a more controlled “dump-&-fill”
operation where soil is spread in layers over the waste and this is repeated with compaction.
This forced a modification in the natural beach line and its constituents with lots of reworking.
Due to the open nature of the beach, except for the seaport, the pollution is worked
outwardly from the river mouth, which itself lies in a localized small bay.
33
The Abou Ali river, which is the resultant confluence of three tributaries about eight to ten km
east of Tripoli inland, serves as the place of outfall of untreated multi-pollution sources. This
includes agro-chemicals, sewage, wastewater, some industries effluents and a mixture of
solid wastes, especially from the city, through which the river passes for less than two km.
The configuration of the river tributaries meeting in the open plateau a short distance east of
Tripoli, with annual flow of about 262 Mm3, and the bottle-neck situation it has just at the
entrance of the city, often leads to flooding. The authorities thus converted the passage
where the river passes in the city into concrete lining. A prominent flood event occurred a
couple of decades ago (before the concrete lining) with resultant huge damages to property,
loss of lives and a deposition of about 200000 m3 of sediments in one day creating a
delta.Due to current and wave erosion, plus decreased river flow, that delta is mostly gone
now.
Major problems
Lying at the city of Tripoli, the study area is affected by aspects or shortcomings,
environmental or otherwise, which leave an impact. For example, the socio-economic status
leads to the creation of slums (without any infrastructure) whose dense population living
close to the river, dump everything in it. This is in addition to the poorly implemented housing
policies, areas outside proper legislation and the lack of resources (human & financial) at the
Municipality. The public attitude towards participation is not encouraging, as it has not been
properly promoted or given tangible incentives.
With a population density of 15000/km2, it is obvious that Tripoli’s environmental living
conditions are not that positive. The absence, yet, of land-use planning and adequate
infrastructure, explains the environmental deterioration of natural resources including the
area covering the river mouth and adjacent beach. The exploitation of the beach without
proper implementation of existing laws is making it non-available to the public and
deteriorating at a high rate. Indeed, elsewhere in the surrounding, the pattern of change in
land use has been drastic with an increase in losses of prime productive land. So long as the
problems of waste water network and solid waste management system are not properly
treated, pollution and health problems – both form the river and sea, will remain.
The above explains the significance of applying TELEMAC2D in the Tripoli case.
TELEMAC2D
The software has many fields of application. In the maritime sphere, particular mention may
be made of the sizing of port structures, the study of the effects of building submersible dikes
or dredging, the impact of waste discharged from a coastal outfall or the study of thermal
plumes. In river applications, mention may also be made of studies relating to the impact of
construction works (bridges, weirs, groins), dam breaks, flooding or the transport of decaying
or non-decaying tracers. TELEMAC2D has also been used for a number of special
applications, such as the bursting of industrial reservoirs, avalanches falling into a reservoir,
etc.
The TELEMAC2D code solves depth-averaged free surface flow equations as derived first by
Barré de Saint-Venant in 1871. The main results at each node of the computational mesh are
the depth of water and the depth-averaged velocity components. The main application of
TELEMAC2D is in free-surface maritime or river hydraulics and the program is able to take
into account the following phenomena:
 Propagation of long waves, including non-linear effects
 Friction on the bed
 The effect of the Coriolis force
 The effects of meteorological phenomena such as atmospheric pressure and wind
 Turbulence
 Supercritical and sub critical flows
 Influence of horizontal temperature and salinity gradients on density
 Cartesian or spherical coordinates for large domains
34








Dry areas in the computational field: tidal flats and flood-plains
Entrainment and diffusion of a tracer by currents, including creation and decay terms
Particle tracking and computation of Lagrangian drifts
Treatment of singularities: weirs, dikes, culverts, etc.
Inclusion of the drag forces created by vertical structures
Inclusion of porosity phenomena
Inclusion of wave-induced currents (by link-ups with the ARTEMIS and
TOMAWAC modules).
Data description
Data used in modeling is obtained from various sources and is digitized and modified (Figure
1)
- Bathymetry (source: previous study – Dar al-Handasah)
- Coastline (source: topographic map, scale : 1:50000
- Wind Direction (source: Directorate General of Civil Aviation)
- Discharge flow of Abou Ali river (source : Litani River Authority)
Figure 1. Gulf of Tripoli and Abou Ali River
In Addition a cross section describing the river is shown in (Figure 2) where the following are
the characteristics of this cross section
- River estuary width = 12 m
- Average depth at the mid-point = 1m
- Cross-section area = 7.70 m2
- Depth at the SW side = 1.25 m
- Depth at the NE side = 0.75 m
35
SW
NE
Mid-depth =1.0m
Meters
1.5
Water body
0.5
12.0 m
Figure 2. Cross-section along the estuary of Abou-Ali River
Beside the estuary of Abou-Ali River the depth of water reaches 2.5 m at a distance of about
400m, the bathymetric gradient at the estuary of Abou-Ali River is around 9.25m/km, which is
relatively gentle. This would decrease at some distance into the sea and reaches around
6.25m/km.
Hydro Dynamic Model
In this model we consider the influence of wind on the water surface. The model domain is
limited by three types of boundary conditions (Figure 3).
Boundary condition solid limit
2222
Boundary condition 5444
Boundary condition imposed
flow rate 4555 condition
imposed
Figure 3. Boundary conditions
To build our model for the area of study, “Matisse”, a module that is a part of Telemac2D
software is used to construct our mesh (Figure 4).
36
Figure 4. Mesh created using Matisse
After constructing the mesh of the studied area we can notice that meshes nodes are denser
in the area of concern near the river, this is done intentionally to obtain more accuracy in
computation. The following are the properties of the mesh:
1. Minimum depth is 4 m and maximum depth is 2000 m
2. Number of nodes is 3059 and the number of elements is 5867
The following is the bathymetry map generated by “Rubens”, another module of Telemac2D
software (Figure 5).
37
Figure 5 Bathymetry map displayed using Rubens
Hydro Dynamic computation
1-the duration of the simulation in Telemac 2D is dt = 259200 seconds corresponding to 72
hours
2-Graphic printout is equal to 600 seconds
350
300
250
200
150
100
50
0
direction in degree
14
12
10
8
6
4
2
0
"Wind Intensity"
"Wind Direction"
36 0
10 00
8
21 00
6
32 00
4
43 00
2
54 00
0
64 00
8
75 00
6
86 00
4
97 00
10 200
8
11 000
8
12 800
9
14 600
0
15 400
1
16 200
2
25 000
92
00
intensity in m/s
Hydro Dynamic conditions: (wind imposing)
Wind direction and intensity at different timing is shown in the chart created by MS Excell
(Figure 6), To show how Telemac2D simulates our wind direction and intensity, we pick
some points at different times (1 day, 3 days) in the chart and display their graphs using
Telemac2D. At 6 hours run the wind is in the direction north-east , then becomes north-south
direction after one day of run, and then in the direction of north-east again after three days of
run as shown in the Figures 7 (a) & (b)
Time in seconds
Figure 6 Excel chart showing wind intensity and direction
38
(a)
(b)
Figure 7 different wind directions in different time periods
The direction and intensity of the wind create sea currents, and define currents direction and
intensity as shown in the chart created by Excel (Figure 8). Some points are selected, and
39
depicted and simulated by Telemac2D in Figures 9 (a) & (b) at two different periods. We can
notice the abrupt change in wind direction after one day of run and the consequences
caused by this change (the slow down of the current and the direction deviation), and the
sudden increase in current velocity towards the northeast after wind direction change
towards the northeast and the slight increase in wind speed.
14
360
12
Speed in m/s
3days
run
10
300
270
240
210
intensity
current intensity
180
direction
curreny direction
150
8
6
1day run
120
4
90
60
2
30
0
0
50000
100000
150000
200000
0
300000
250000
Time in seconds
Figure 8. Wind and currents relationship chart
40
Direction in degree
330
(a)
(b)
Figure 9. current intensity and direction for different periods
41
Maximum current intensity is also computed by Telemac2D. Figure 2.5.10 shows current
intensity due to wind stress.
Figure 10 maximum current intensity
2.7 Scenario definition
The important factors that have an influence on the hydrodynamics of the area are the wind
and the currents resulting from the wind action.
Our long-term goal is to understand the disposition of river pollutants along the Lebanese
northern coast. Ultimately, we want to be able to predict the fate of pollutants spilled into one
of the Lebanese rivers. Specifically, we wish to determine how much of the pollutants would
escape the waters near the mouth of the river and enter the sea. The main pollutants source
in Abou Ali river, that outlets in the sea near Tripoli city, are the untreated sewage, minor
industry effluents and the seasonal waste of the olive oil industry “jift”.
A strongly irregular coastline and non-uniform bottom relief, characterizes the coast region.
According to calculation of the waters in the region under consideration [1,2], the mean
current velocity vector is directed along the coast to the north-northeast. The mean current
velocities mv = U 2  V 2 (whereas the direction of the vector m
arctg(v/u)
u and v are the projections of the vector V on the meridian and parallel
respectively). According to ABS (self-contained buoy stations) (Figure 11) measurements
data [3] are given in the following Table:
ABS stations
Observation level, m
mv cm/s
D
25
3.0
A
25
3.3
T1
27
2.2
42
Figure 11. Current measurement stations
The reason for including Beirut and Jounieh in (Fig. 11) is due to the fact that it is the only
information available as a report study and the study itself is a typical representation of the
different current direction and intensity due to the influence of wind, rivers, and Jounieh bay.
The wind characteristics define the selected scenarios:
-Wind effect (December 1997 data vary between 15-40 km/h N-E; N-S) on the pollution
plume dispersion.
Comparing the measurement done with above stations (Figure 12) and Telemac2D results,
we can see slight differences but Telemac2D results keep increasing due to the increase in
Wind speed, but during station measurements, the wind was varying between 10km/s to 15
Km/s.
Comparison between ABS measurements and Telemac2D
results
comparison between ABS current measurment and
Telemac results
Current speed m/s
0.25
0.2
measured by
station
0.15
intensity
0.1
0.05
0
0
10000
20000
30000
Tim e in seconds
Figure 12 Chart showing the comparison between stations and Telemac2D
43
Scenario I:
Business as usual(BAU)
The average daily spill of sewage is about 0.2 m3 / s, the solid waste mass is 15 kg, and the
river discharge is about 12 m3/s, this means that the concentration in the river is 1.23 kg/m 3.
The water treatment plant in Tripoli treats about 40000 m3/day. Applying Subief2D, which
helps in studying the transport of the plume pollutant using the above parameters, the
following Subief2D results are shown in Figure 13.
N
Figure 13. Plume pollutant movement and concentration after 3 days for (BAU)
Scenario
Using the software “indicators”, which reflect on the quality of the water, using Subief2D
results with Mandatory and guide quality indicators values (these are lower and upper limits
given by the model requirements), between 0.1 and 0.01, we obtain the following results
(Figure 14) which indicates the quality of water. These indicators refer to pollutions mass.
44
N
Figure 14. Quality indicator for the Business As Usual (BAU) Scenario case
Scenario II:
Pessimistic
When the season of olive oil production is on, huge quantities of “jift” are thrown in the
running water, which causes malfunction of the treatment plant decreasing its capacity to
10000 m3/day or less which cause an increase of solid waste concentration from 1.25 g/l to
approximately 4.5 g/l. Applying the Subief2D, which helps in studying the transport of the
plume pollutant, the results are shown in Figure 15.
N
Figure 15. Plume pollutant movement and concentration after 3 days for
Pessimistic Scenario case
45
The quality indicator map for the Pessimistic case (Figure 16) shows that all the area around
the coast is in bad quality conditions.
Figure 16. Quality indicator for the Pessimistic Scenario case
46
Scenario III:
Optimistic
Decrease in sewage discharge and pollution would be due to improving the existing
treatment plant and increase in price of water supply plus improving service network. In
addition, olive oil, erecting a support one industry waste “jift” would be treated and recycled,
so the pollutant discharge dropped to half. Tripoli water treatment plants are under
maintenance contract and upgraded to larger capacity, would bring down the pollutant
concentration to less than 0.4 g/l. Applying Subief2D which helps study the transport of the
plume pollutant, the results are shown in Figure 17.
N
Figure 17. Plume pollutant movement and concentration after 3 days for Optimistic
Scenario case
Using the software “indicators” again with Mandatory and guide value between 0.1 and 0.01,
the following (Figure 18) is obtained indicating the quality of water.
47
Figure 18. Quality indicator for the Optimistic Scenario case
Conclusion
From the above, applying Telemac2D and Subief2D we can notice the vertical and horizontal
motion of seawater due to local currents caused by water discharge at river mouth. We
observe the currents caused by the wind stress in the direction N-NE comparable with
measured currents in the area. Although, the period is short, 3 days, the model created by
Subief2D would help us monitor the movement of plume of pollutants discharged from river
mouth. The different scenarios tried showed the concentration of pollutants is lowest in the
optimistic case and highest in the pessimistic. Telemac2D software could help us in the
future to monitor other sources of pollution along different river mouths at sea.
Scenarios – Driving Forces on Water
As revealed in the SMART documentation, the European framework on “DPSIR” (Driving
force – Pressure – Status – Impact – Response) is used depending on indicators that reflect
the forces that drive the pressures on the water system. To conform to the project
requirements, the case studies had to formulate this approach classifying the driving forces
indicators into 3 broad categories: population, economic development and the climatehydrological cycle. The pressure indicators, on the other hand, are classified into 4 main
categories, all related to water: supply, demand, abstraction and pollution. These had to help
define scenarios reflecting good or bad situations through identifying variables that lead to
the scenarios. They have to start with the actual situation which is revealed through both
“Baseline” data or status and “Business As Usual” (BAU). Then “Optimistic” and a
“Pessimistic” scenarios are given in view of expert analysis of the case study.
In Lebanon, the main issues within the above mentioned indicators categories are: A.
Management, social planning and policies, B. Land use on sectoral basis, C. Climate change,
D. Water supply-demand interaction, E. Technological change. Here, only A and D are given
as examples. For the socio-economic part, as shown in Table 3.1 the variables, or driving
forces, consider a variety of issues, e.g. on urban growth, environment policies, regulations
48
on industry and agricultural pollution, solid wastes, disasters … etc. For each, the different
scenarios are given starting with the “Baseline” and BAU plus the “optimistic” and
“pessimistic”. To arrive at similar output for the water supply-demand picture Fig. 4.1 and
Table 4.2 reveal the main elements of scenarios build-up.
Explaining some of the given figures in the two Tables will clarify more the scenarios
orientations and aspirations:
A. Management & social policies
 urban growth rate: The 1.5% is reached if serious efforts on population growth are
continued, while the pessimistic 3% if those efforts are relaxed
 on items related to “implementation” of regulations or new plans, the optimistic is self
explanatory while the pessimistic arises from the fact that several laws & regulations
are NOT implemented & can drag for a long time
 on solid waste: the pessimistic is due to presence of a plan (among others) relying on
incineration which is an option rejects by most environmentalists
 expenditure on research: The optimistic 5% is really “super ideal” because even the
pessimistic 1%, which is what regulations say now, did not come through – but in
case it comes through it would be too low
49
Variables/
Driving Forces
Urban
growth
rate
Integrated
National Master
Land
use
planning
Baseline
BAU
Optimistic
Pessimistic
80,000
1.97%
1.5%
3%
10% of the
country
On-going project of
comprehensive land use
planning (SDATL)
Full
implementation
of the SDATL
with a focus on
the coastal
zone and
control of water
and sea
pollution
Adopt a
national policy
for the
environment
Ratify &
implement fully
code of
environment
Partial
implementation
of SDATL
National
Exist in
environmental
established
policies
and codes only
programs
Regulations and Many are old
law enforcement,
& not
covering many
enforced
aspects
of
environmental
management
Regulations
to
Survey
control damages
covered
caused
by industries &
industry
causes
emissions
assigned
Regulations
to
control damages
caused
by
excessive use of
pesticides
and
fertilizers
Solid
waste
management
Human
economic
and
loss
Ineffective
control
Restricted attempts,
programs international
implemented to priorities
- low levels of regulatory
enforcement
- lack of precision in the
law
unclear
role
of
responsibilities
- master plan regulating
the polluting industries
- uncontrolled discharges
of industrial emissions
(liquid, solid and gaseous
without any form of
treatment)
Rational use of agrochemicals (pesticides
and fertilizers) are still
scant or localized
Highly
variable &
local mostly
non-effective
solutions;
minimal
separation,
composting
and
recycling
- landfill for solid waste
- uncontrolled disposal of
sewage in some areas
- raw sewage is
discharged into rivers
and sea
- partial modem
techniques
Mostly
unplanned
Restricted emergency
measures
Partial
implementation
Ratify &
implement
partially code of
environment
Effective and
enforceable
industrial
pollution control
regulations
Ineffective control
measures &
monitoring
Set standards
for the use of
pesticides,
fertilizers and
hormones
Ineffective control
measures &
monitoring
- rehabilitation
of disposal sites
- set a national
waste
management
plan that adopts
integrated
approach and
defines
appropriate
solutions for
each region
- adopt a
municipal waste
disposal
Comprehensive
Natural Disaster
Adopt
incineration &
land filling
Ineffective partial
plan
50
due to natural
disasters
solutions
Expenditure on
Research
and
Development as
percent of GDP
Environmental
protection
expenditures as
a percent of
GDP
Less than
mandated
0.0001% of GDP
Not even
mandated
Restricted calls for law
Reduction
Management
Plan
(CNDRMP)
5% of GDP
Full mandate in
accordance
with sectoral
need
1% of GDP
Partial sectoral
restrictions
D. Inefficient water supplies
 precipitation rate & losses to sea: the optimistic +10% is within the positive range of
cases where a “wet” year is observed in Lebanon, while the pessimistic -10% is the
opposite. Similarly for losses, the optimistic if they can be controlled (& they can)
while pessimistic is not
 agricultural lands & construction: the +5% is likely in a feasible plan within securing
water demand, while 15% would be too water stressing & may occur locally. The
same applies for construction
 polluted water & harvesting plus awareness: efforts are spent to face these issues.
The optimistic reflects full-fledge program with community participation, while the
pessimistic doesn’t
 losses, control & exploitation: again here government authorities are putting plans,
including privatization of water services, the optimistic-pessimistic outcome depend
on how much these plans are implemented
51
Insufficient water supply
Construction
development
Global climate
changes
Losses from
pipes & supply
implements
Non-maintained
sanitation supplies
Intensive rainfall
(torrents)
Technological
changes
Wastewater
seepages to aquifers
Increase in
agricultural lands
Regular increase
in population rate
Higher rate in
construction and
building
practices
Reduce in
precipitation rate
Immigration of
non-Lebanese
citizens
Losses into
the sea
Lack in policies
to arrange birth
rate
Solid and liquid
wastes dumping
into river and
spring courses
Lack of monitoring
(water analyses)
implements
Deterioration
in water
quality
Decline in
water quantity
Increase in
population
rate
Land use
change
Increasing the
imperviousness of
land surface
Subtle run-off to
the sea
Overexploitation of
coastal aquifers
Saltwater
intrusion
Lack
of of
suitable
Lack
suitable
water
forfor
water
domestic
uses
domestic
uses
Lack
Lackofofsuitable
suitable
waterwater
for different
for
uses
different
Higher demand
for water
purposes
Insufficient water supply
Higher request for
water for agriculture
plus water loss
Variables/deriving forces
Precipitation rate
Losses to the sea
Increase of agricultural lands
Increase in construction
Increase of polluted water
Water harvesting in lakes and
reservoirs
Water exploitation awareness
Losses from pipelines and
network
Governmental control on water
use (obligatory gauges)
Proper exploitation of surface
water sources
Baseline
900 mm/y
2200 million m3/y
169 km2
68 km2
Surface and
subsurface water
pollution
2000 reservoir
BAU
+0 % (steady state)
+0 % (steady state)
+2%
+5%
80% of surface water &
70% of subsurface polluted
water
On-going
Optimistic
+10%
-25%
+5%
+2%
30% of surface water &
20% of polluted
subsurface water
+20%
pessimistic
-10%
+25%
+15%
+10%
50% of surface water &
40% of polluted
subsurface water
-20%
Insufficient
awareness
Extensive losses
Insufficient awareness
5-50% losses
Comprehensive
awareness
0%
Continuity in insufficient
awareness
10-30%
Insufficient control
<50%
100%
<75%
Improper
exploitation
Increasing 5-10%
20-25%
<5%
53
Comparative analysis
Water mass budget is normally subjected to several changes with time. There are dynamic
factors controlling the resultant output of this budget. These are mainly due physical and
anthropic changes with time. The concentrate was mainly on the anthropic influences, while
the uncertainty in plotting prospects for climatic conditions put it at the second level. In order
to have different figures for future perspectives, it is necessary to apply several inputs to that
present scenario. Therefore, one would be enable to figure out the dimensions of the water
mass changes with respect to different dynamic parameters.
In the Abou-Ali River case study, the applied scenario was to the year 2000, as the set data
were attributed to that year. Thus, a couple of outputs were resulted as discussed in the
previous sections. Nevertheless, that was exposes the actual water status at that time. Of
course, dramatic changes are obviously noticed in the Abou-Ali River basin. It referred
mainly to the anthropic influences, weather these influence are positive or negative. For
example, in the obtained scenario, assumptions were made on population growth, (sectoral)
economic developments, etc; thereby, any increase in population growth is followed by
increase on water demand or a decrease in agriculture will produce less demand for
irrigation nodes and so on. Also economic growth means more investment, thus high
technology; therefore produces less losses. Moreover, the higher efficiency will reduce the
demand and the consumptive use through water saving technologies, while poor economy
will result decay in the infrastructure, which means higher losses.
The above discussion exhibits some involved examples of several parameters influencing
water mass budget either in ascending or descending trend. Therefore, fluctuations in these
parameters will result changes in the water budget with time.
In the WaterWare model, and after applying the Abou-Ali River scenario, a comparative
analysis was achieved. This comparison was obtained between the sustainable change
indicators and different responses. Therefore, sixteen runs were resulted (Table 1).
Sustainable indicators represent the elements of the economic, social and environmental
indicators, whilst, responses express the following:
- Current responses
- Water demand management
- Water supply management
- Water quality management
- All future responses
For each of these responses, three “Probable status” were proposed. These are:
Business As Usual (BAU), Optimistic (OPT) and Pessimistic (PESS).
For Abou-Ali scenario, in addition to the applied scenario of the year 2000, the current
responses were applied to the year 2005 (status-quo), and then the rest responses were to
the year 2010. The latter is attributed to the fact that: a correct figure can be applied to the
changes for the next five years. While for the scenario of all future responses, longer period
was assumed (i.e. to the year 2015) (Tables 1, 2, 3, 4, 5).
The economic indicators include the demand/supply ratio for domestic, agriculture, industrial
and tourist uses, which are expressed in percentage (%). Also, economic indicators include
the efficiency in the system, that calculated by currency (i.e. €). Besides, the social indicators
dealt with the number of days for domestic supply. The environmental indicators measured
the global quality, and were classified into five classes (according to FEEM) plus the
demand/supply ratio of environmental uses in percentage (Table 1).
Sustainable indicators was followed several items of economical, social and environmental
changes. Figure 1 exhibits a schematic presentation of these changes, which were plotted in
the formulated tables (Tables, 1-42).
Increase in population
Economic
indicators
Increase in demand for
Domestic, agricultural,
industrial and tourist
purposes
Social
indicators
Development of
investment implements
Figure 1. Schematic presentation for the change
Increase in the number of
supply days for domestic uses
Quality control
Environmental
indicators
Development of new
environmental plans
55
Table 1: Used forma for models running for the comparative analysis of Abou-Ali River basin
Model runnings
Sustainability indicators
Economic
Social
Environmental
D/S ration for
domestic*
D/S ratio for
agriculture*
D/S ratio for
industry*
D/S ratio for
tourism*
Economic
efficiency of
the system**
No. of days
with restricted
domestic
supply***
Global
quality****
D/S ratio for
environmental
uses*
*:%
** : €
***: days/year
****: Classes I-V
Current
respons
es
Baseline
Current
responses
BA
U
OP
T
PES
S
Water demand
management
BA
U
OP
T
PES
S
Water supply
management
BA
U
OP
T
PES
S
Water quality
management
BA
U
OP
T
PES
S
All future
responses
BA
U
OP
T
PES
S
Table 2: Models running for the comparative analysis of Abou-Ali River
Year 2005
(Current responses)
* Model Application
Sustainability
indicators
Current responses
D/S
ratio for
domestic
D/S
ratio for
agricultur
e
Increase in
irrigated
areas
(%)
90
85
90
75
Run
No.
Changing
elements
Increase in
population
(%)
1
2
3
4
Baseline
BAU
OPT
PESS
70
90
90
70
Economic
D/S
ratio for
industry
D/S
ratio for
tourism
Economic
efficiency of
the system
Increase in
industrial
tasks
(%)
85
75
95
60
Increase in
tourist
Sites
(%)
80
70
90
70
More
investment &
high
technology
0.45
0.45
0.30
0.60
Social
No. of days
with
restricted
domestic
supply
Increase in
No. of
supply days
(day/year)
100
144
150
80
Environmental
Global
D/S
quality
ratio for
environmental
uses
Quality
control
III
III
I
IV
Development in
environmental
plans
(%)
80
70
70
70
57
*Model Run Results
Run -1
Table 2-1: Mass budget summary for Baseline- 2005
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
Annual mass budget summary (Mio. m3)
229.91
538.02
767.93
11.59
296.74
270.26
-13.64
203.23
-0.25
39.00
499.54
29.94%
70.06%
100.00%
1.51%
38.64%
35.19%
-1.78%
26.47%
-0.03%
37.83%
57.16%
5.08%
65
Run -1
Table 2-2: Groundwater mass budget for Baseline- 2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.26
276.85
2.38%
0.00%
0.00%
97.62%
100.00%
11.14
218.77
2.58
10.33
242.83
34.02
263.93
4.02%
79.02%
0.93%
3.73%
87.71%
12.29%
95.33%
Run -1
Table 2-3: Annual sectoral demand for Baseline- 2005
Demand
(Mm3)
Net Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
20.27
239.17
7.43
125.94
6.44
225.35
68.22
65.48
Agricultural
26.96
37.36
3.88
11.94
18.00
28.40
33.24
35.48
Industrial
1.76
246.75
0.29
173.19
0.81
245.80
54.02
64.93
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
62.74
523.28
11.59
311.07
39.00
499.54
37.83
52.88
Total
Run -2
Table 2-4: Mass budget summary for Business As Usual (BAU)- 2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
13.56
296.21
269.24
-13.64
202.81
-0.25
44.44
494.96
29.94%
70.06%
100.00%
1.77%
38.57%
35.06%
-1.78%
26.41%
-0.03%
38.20%
56.14%
5.79%
64.45%
Run -2
Table 2-5: Groundwater mass budget for Business As Usual (BAU)- 2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
269.24
275.83
2.39%
0.00%
0.00%
97.61%
100.00%
11.14
218.77
2.58
10.33
242.83
33.00
262.91
4.04%
79.31%
0.94%
3.74%
88.04%
11.96%
95.32%
Run -2
Table 2-6: Annual sectoral demand for Business As Usual (BAU)- 2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
25.94
238.88
9.29
125.17
8.58
221.51
66.93
63.70
Agricultural
28.31
37.33
3.95
11.92
19.26
28.28
31.97
35.34
Industrial
1.99
246.22
0.32
172.80
0.93
245.17
53.28
64.66
Services
15.68
0.00
0.00
0.00
15.68
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
71.92
522.43
13.56
309.89
44.44
494.96
38.20
51.75
Total
Run -3
Table 2-7: Mass budget summary for Optimistic (OPT)- 2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
11.59
296.74
270.26
-13.64
203.23
-0.25
39.00
499.54
29.94%
70.06%
100.00%
1.51%
38.64%
35.19%
-1.78%
26.47%
-0.03%
37.83%
57.16%
5.08%
65.05%
Run -3
Table 2-8: Groundwater mass budget for Optimistic (OPT)- 2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.26
276.85
11.14
218.77
2.58
10.33
242.83
34.02
263.93
2.38%
0.00%
0.00%
97.62%
100.00%
4.02%
79.02%
0.93%
3.73%
87.71%
12.29%
95.33%
Run -3
Table 2-9: Annual sectoral demand for Optimistic (OPT)- 2005
Demand
(Mm3)
Net Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
20.27
239.17
7.43
125.94
6.44
225.35
68.22
65.48
Agricultural
26.96
37.36
3.88
11.94
18.00
28.40
33.24
35.48
Industrial
1.76
246.75
0.29
173.19
0.81
245.80
54.02
64.93
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
62.74
523.28
11.59
311.07
39.00
499.54
37.83
52.88
Total
Run -4
Table 2-10: Mass budget summary for Pessimistic-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
11.20
296.80
270.46
-13.64
203.37
-0.26
32.75
500.38
29.94%
70.06%
100.00%
1.46%
38.65%
35.22%
-1.78%
26.48%
-0.03%
41.35%
57.93%
4.26%
65.16%
Run-4
Table 2-11: Groundwater mass budget for Pessimistic-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.46
277.05
11.14
218.77
2.58
10.34
242.83
34.22
264.13
2.38%
0.00%
0.00%
97.62%
100.00%
4.02%
78.96%
0.93%
3.73%
87.65%
12.35%
95.34%
Run -4
Table 2-12: Annual sectoral demand for Pessimistic-2005O
Demand
(Mm3)
Net Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
20.27
239.23
7.43
125.98
6.44
225.40
68.22
65.48
Agricultural
22.38
37.36
3.57
11.99
13.79
28.77
38.38
39.59
Industrial
1.23
246.88
0.20
173.34
0.56
246.21
54.84
65.21
Services
11.97
0.00
0.00
0.00
11.97
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
55.84
523.47
11.20
311.31
32.75
500.38
41.35
53.73
Total
Table 3: Models running for the comparative analysis of Abou-Ali River
Year 2010
(Water demand management)
* Model Application
Sustainability
indicators
Water demand
management
D/S
ratio for
domestic
D/S
ratio for
agricultur
e
Increase in
irrigated
areas
(%)
70
80
60
Run
No.
Changing
elements
Increase in
population
(%)
5
6
7
BAU
OPT
PESS
60
75
50
Economic
D/S
ratio for
industry
D/S
ratio for
tourism
Economic
efficiency of
the system
Increase in
industrial
tasks
(%)
80
90
70
Increase in
tourist
Sites
(%)
90
100
80
More
investment &
high
technology
0.35
0.25
0.50
Social
No. of days
with
restricted
domestic
supply
Increase in
No. of
supply days
(day/year)
180
200
100
Environmental
Global
D/S
quality
ratio for
environmental
uses
Quality
control
II
I
IV
Development in
environmental
plans
(%)
50
60
70
*Model Run Results
66
Run -5
Table 3-1: Mass budget summary for BAU-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
10.12
297.10
271.01
-13.64
203.60
-0.26
27.02
502.76
29.94%
70.06%
100.00%
1.32%
38.69%
35.29%
-1.78%
26.51%
-0.03%
43.98%
58.98%
3.52%
65.47%
Run-5
Table 3-2: Groundwater mass budget for BAU-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
271.01
277.60
11.14
218.77
2.58
10.34
242.84
34.76
264.67
2.37%
0.00%
0.00%
97.63%
100.00%
4.01%
78.81%
0.93%
3.72%
87.48%
12.52%
95.34%
Run -5
Table 3-3: Annual sectoral demand for BAU-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
20.27
239.23
7.43
125.98
6.44
225.40
68.22
65.48
Agricultural
22.38
37.36
3.57
11.99
13.79
28.77
38.38
39.59
Industrial
1.23
246.88
0.20
173.34
0.56
246.21
54.84
65.21
Services
11.97
0.00
0.00
0.00
11.97
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
55.84
523.47
11.20
311.31
32.75
500.38
41.35
53.73
Total
Run -6
Table 3-4: Mass budget summary for OPT-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
11.86
296.65
270.12
-13.64
203.21
-0.26
33.15
498.79
29.94%
70.06%
100.00%
1.54%
38.63%
35.17%
-1.78%
26.46%
-0.03%
42.42%
57.46%
4.32%
64.95%
Run-6
Table 3-5: Groundwater mass budget for OPT-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.12
276.70
11.14
218.77
2.58
10.33
242.83
33.87
263.79
2.38%
0.00%
0.00%
97.62%
100.00%
4.03%
79.06%
0.93%
3.73%
87.76%
12.24%
95.33%
Run -6
Table 3-6: Annual sectoral demand for OPT-2005
Demand
(Mm3)
Net Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
21.69
239.14
7.90
125.78
6.97
224.42
67.88
64.98
Agricultural
23.72
37.35
3.67
11.97
15.02
28.64
36.71
38.63
Industrial
1.85
246.72
0.30
173.16
0.85
245.73
53.72
64.93
Services
10.32
0.00
0.00
0.00
10.32
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
57.57
523.21
11.86
310.91
33.15
498.79
42.42
53.21
Total
Run -7
Table 3-7: Mass budget summary for PESS-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
8.88
297.43
271.64
-13.64
203.88
-0.26
26.71
505.57
29.94%
70.06%
100.00%
1.16%
38.73%
35.37%
-1.78%
26.55%
-0.03%
41.51%
59.73%
3.48%
65.83%
Run-7
Table 3-8: Groundwater mass budget for PESS-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
271.64
278.23
11.14
218.77
2.58
10.34
242.84
35.39
265.30
2.37%
0.00%
0.00%
97.63%
100.00%
4.00%
78.63%
0.93%
3.72%
87.28%
12.72%
95.35%
Run -7
Table 3-9: Annual sectoral demand for PESS-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
14.39
239.59
5.42
126.84
4.36
229.56
69.73
67.81
Agricultural
17.79
37.38
3.22
12.05
9.67
29.26
45.67
42.47
Industrial
1.45
247.55
0.24
173.78
0.65
246.75
54.78
65.21
Services
12.03
0.00
0.00
0.00
12.03
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
45.67
524.52
8.88
312.67
26.71
505.57
41.51
55.70
Total
Table 4: Models running for the comparative analysis of Abou-Ali River
Year 2010
(Water supply management)
* Model Application
Sustainability
indicators
Water supply
management
D/S
ratio for
domestic
D/S
ratio for
agricultur
e
Increase in
irrigated
areas
(%)
60
80
50
Run
No.
Changing
elements
Increase in
population
(%)
8
9
10
BAU
OPT
PESS
70
80
50
Economic
D/S
ratio for
industry
D/S
ratio for
tourism
Economic
efficiency of
the system
Increase in
industrial
tasks
(%)
65
80
55
Increase in
tourist
Sites
(%)
70
80
60
More
investment &
high
technology
0.25
0.15
0.60
Social
No. of days
with
restricted
domestic
supply
Increase in
No. of
supply days
(day/year)
100
200
80
Environmental
Global
D/S
quality
ratio for
environmental
uses
Quality
control
II
I
IV
Development in
environmental
plans
(%)
80
70
80
*Model Run Results
73
Run-8
Table 4-1: Mass budget summary for BAU-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
8.88
297.43
271.64
-13.64
203.88
-0.26
26.71
505.57
29.94%
70.06%
100.00%
1.16%
38.73%
35.37%
-1.78%
26.55%
-0.03%
41.51%
59.73%
3.48%
65.83%
Run-8
Table 4-2: Groundwater mass budget for BAU-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.62
277.21
11.14
218.77
2.58
10.34
242.83
34.38
264.29
2.38%
0.00%
0.00%
97.62%
100.00%
4.02%
78.92%
0.93%
3.73%
87.60%
12.40%
95.34%
Run -8
Table 4-3: Annual sectoral demand for BAU-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
20.27
239.30
7.43
126.03
6.44
225.47
68.22
65.48
Agricultural
17.79
37.36
3.22
12.04
9.67
29.23
45.66
42.47
Industrial
1.34
247.04
0.22
173.43
0.60
246.30
54.75
65.21
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
53.15
523.69
10.87
311.51
30.47
501.01
42.67
54.30
Total
Run-9
Table 4-4: Mass budget summary for OPT-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
12.30
296.52
269.89
-13.64
203.12
-0.26
35.30
497.80
29.94%
70.06%
100.00%
1.60%
38.61%
35.14%
-1.78%
26.45%
-0.03%
41.67%
57.24%
4.60%
64.82%
Run-9
Table 4-5: Groundwater mass budget for OPT-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
269.89
276.48
11.14
218.77
2.58
10.33
242.83
33.65
263.56
2.38%
0.00%
0.00%
97.62%
100.00%
4.03%
79.13%
0.93%
3.74%
87.83%
12.17%
95.33%
Run -9
Table 4-6: Annual sectoral demand for OPT-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
23.11
239.07
8.36
125.58
7.50
223.46
67.55
64.57
Agricultural
23.72
37.34
3.67
11.97
15.02
28.64
36.70
38.63
Industrial
1.66
246.60
0.27
173.09
0.76
245.70
54.25
64.93
Services
12.03
0.00
0.00
0.00
12.03
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
60.52
523.01
12.30
310.65
35.31
497.80
41.66
52.96
Total
Run-10
Table 4-7: Mass budget summary for PESS-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
8.59
297.47
271.79
-13.64
203.99
-0.26
25.76
506.24
29.94%
70.06%
100.00%
1.12%
38.74%
35.39%
-1.78%
26.56%
-0.03%
41.72%
60.72%
3.35%
65.92%
Run-10
Table 4-8: Groundwater mass budget for PESS-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
271.79
278.38
11.14
218.77
2.58
10.34
242.84
35.54
265.45
2.37%
0.00%
0.00%
97.63%
100.00%
4.00%
78.59%
0.93%
3.72%
87.23%
12.77%
95.36%
Run -10
Table 4-9: Annual sectoral demand for PESS-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
14.47
239.64
5.45
126.87
4.38
229.55
69.71
67.81
Agricultural
14.83
37.38
2.95
12.09
7.11
29.66
52.07
47.95
Industrial
1.14
247.66
0.19
173.89
0.51
247.03
54.92
65.21
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
44.19
524.68
8.59
312.86
25.76
506.24
41.72
56.79
Total
Table 5: Models running for the comparative analysis of Abou-Ali River
Year 2010
(Water quality management)
* Model Application
Sustainability
indicators
Water quality
management
D/S
ratio for
domestic
D/S
ratio for
agricultur
e
Increase in
irrigated
areas
(%)
50
70
40
Run
No.
Changing
elements
Increase in
population
(%)
11
12
13
BAU
OPT
PESS
50
80
40
Economic
D/S
ratio for
industry
D/S
ratio for
tourism
Economic
efficiency of
the system
Increase in
industrial
tasks
(%)
50
80
30
Increase in
tourist
Sites
(%)
75
90
60
More
investment &
high
technology
0.05
0.02
0.07
Social
No. of days
with
restricted
domestic
supply
Increase in
No. of
supply days
(day/year)
100
245
75
Environmental
Global
D/S
quality
ratio for
environmental
uses
Quality
control
II
I
IV
Development in
environmental
plans
(%)
50
80
35
*Model Run Results
80
Run-11
Table 5-1: Mass budget summary for BAU-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
8.54
297.48
271.81
-13.64
204.00
-0.26
20.52
506.36
29.94%
70.06%
100.00%
1.11%
38.74%
35.40%
-1.78%
26.56%
-0.03%
47.16%
60.72%
2.67%
65.94%
Run-11
Table 5-2: Groundwater mass budget for BAU-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
271.81
278.40
11.14
218.77
2.58
10.34
242.84
35.56
265.47
2.37%
0.00%
0.00%
97.63%
100.00%
4.00%
78.58%
0.93%
3.71%
87.23%
12.77%
95.36%
Run -11
Table 5-3: Annual sectoral demand for BAU-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
14.39
239.64
5.42
126.88
4.36
229.61
69.73
67.81
Agricultural
14.83
37.38
2.95
12.09
7.11
29.66
52.07
47.95
Industrial
1.03
247.67
0.17
173.91
0.47
247.10
55.00
65.21
Services
8.60
0.00
0.00
0.00
8.60
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
38.85
524.69
8.54
312.88
20.52
506.36
47.17
56.79
Total
Run-12
Table 5-4: Mass budget summary for OPT-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
12.33
296.52
269.88
-13.64
203.11
-0.26
37.24
497.74
29.94%
70.06%
100.00%
1.61%
38.61%
35.14%
-1.78%
26.45%
-0.03%
40.41%
57.21%
4.85%
64.82%
Run-12
Table 5-5: Groundwater mass budget for OPT-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
269.88
276.46
11.14
218.77
2.58
10.33
242.83
33.64
263.55
2.38%
0.00%
0.00%
97.62%
100.00%
4.03%
79.13%
0.94%
3.74%
87.83%
12.17%
95.33
Run -12
Table 5-6: Annual sectoral demand for OPT-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
23.15
239.07
8.38
125.58
7.51
223.43
67.54
64.57
Agricultural
23.94
37.34
3.68
11.97
15.21
28.62
36.45
38.49
Industrial
1.66
246.59
0.27
173.08
0.76
245.69
54.25
64.93
Services
13.75
0.00
0.00
0.00
13.75
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
62.50
523.00
12.33
310.63
37.24
497.74
40.42
52.93
Total
Run-13
Table 5-7: Mass budget summary for PESS-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
7.05
297.90
272.54
-13.64
204.34
-0.26
14.88
510.16
29.94%
70.06%
100.00%
0.92%
38.79%
35.49%
-1.78%
26.61%
-0.03%
50.46%
63.74%
1.94%
66.43%
Run-13
Table 5-8: Groundwater mass budget for PESS-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
272.54
279.13
11.14
218.77
2.58
10.35
242.84
36.29
266.20
2.36%
0.00%
0.00%
97.64%
100.00%
3.99%
78.38%
0.93%
3.71%
87.00%
13.00%
95.37%
Run -13
Table 5-9: Annual sectoral demand for PESS-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
11.55
239.84
4.42
127.33
3.40
231.68
70.59
69.13
Agricultural
11.86
37.39
2.52
12.15
5.20
30.73
56.16
60.00
Industrial
0.62
248.09
0.10
174.25
0.27
247.75
55.71
66.30
Services
6.01
0.00
0.00
0.00
6.01
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
30.04
525.32
7.05
313.73
14.88
510.16
50.46
60.11
Total
Table 6: Models running for the comparative analysis of Abou-Ali River
Year 2015
(All future responses)
* Model Application
Sustainability
indicators
All future responses
D/S
ratio for
domestic
D/S
ratio for
agricultur
e
Increase in
irrigated
areas
(%)
65
80
40
Run
No.
Changing
elements
Increase in
population
(%)
14
15
16
BAU
OPT
PESS
65
90
50
Economic
D/S
ratio for
industry
D/S
ratio for
tourism
Economic
efficiency of
the system
Increase in
industrial
tasks
(%)
60
75
30
Increase in
tourist
Sites
(%)
75
85
55
More
investment &
high
technology
0.30
0.20
0.40
Social
No. of days
with
restricted
domestic
supply
Increase in
No. of
supply days
(day/year)
125
200
75
Environmental
Global
D/S
quality
ratio for
environmental
uses
Quality
control
III
I
IV
Development in
environmental
plans
(%)
75
85
50
*Model Run Results
87
Run-14
Table 6-1: Mass budget summary for BAU-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
10.44
296.99
270.85
-13.64
203.56
-0.26
30.31
502.07
29.94%
70.06%
100.00%
1.36%
38.67%
35.27%
-1.78%
26.51%
-0.03%
41.80%
58.63%
3.95%
65.38%
Run-14
Table 6-2: Groundwater mass budget for BAU-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
270.85
277.44
11.14
218.77
2.58
10.34
242.83
34.60
264.52
2.37%
0.00%
0.00%
97.63%
100.00%
4.02%
78.85%
0.93%
3.73%
87.53%
12.47%
95.34%
Run -14
Table 6-3: Annual sectoral demand for BAU-2005
Demand
(Mm3)
Net Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand (%)
Reliability
(%)
Domestic
18.65
239.35
6.88
126.23
5.85
226.56
68.62
66.21
Agricultural
19.41
37.36
3.35
12.03
11.11
29.06
42.75
41.23
Industrial
1.23
247.12
0.20
173.50
0.56
246.44
54.84
65.21
Services
12.79
0.00
0.00
0.00
12.79
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
52.08
523.84
10.44
311.76
30.31
502.07
41.80
54.49
Total
Run-15
Table 6-4: Mass budget summary for OPT-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
13.21
296.29
269.41
-13.64
202.93
-0.26
38.88
495.69
29.94%
70.06%
100.00%
1.72%
38.58%
35.08%
-1.78%
26.43%
-0.03%
40.91%
56.79%
5.06%
64.55
Run-15
Table 6-5: Groundwater mass budget for OPT-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
269.41
276.00
11.14
218.77
2.58
10.33
242.83
33.18
263.09
2.39%
0.00%
0.00%
97.61%
100.00%
4.04%
79.26%
0.94%
3.74%
87.98%
12.02%
95.32%
Run -15
Table 6-6: Annual sectoral demand for OPT-2005
Demand
(Mm3)
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
25.94
238.93
9.29
125.21
8.58
221.57
66.94
63.74
Agricultural
23.72
37.33
3.67
11.97
15.02
28.63
36.70
38.63
Industrial
1.55
246.35
0.25
172.93
0.70
245.50
54.53
64.93
Services
14.58
0.00
0.00
0.00
14.58
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
65.79
522.61
13.21
310.10
38.88
495.69
40.91
52.47
Total
Run-16
Table 6-7: Mass budget summary for PESS-2005
Annual mass budget summary (Mio. m3)
Inflow
Direct rain
Total inputs
Consumptive use
Evaporation
Seepage
Delta storage
Outflow
Mass Budget Error
Supply/demand ratio
Reliability
Total shortfall
Total unallocated
229.91
538.02
767.93
8.05
297.61
272.03
-13.64
204.15
-0.26
18.36
507.88
29.94%
70.06%
100.00%
1.05%
38.75%
35.42%
-1.78%
26.58%
-0.03%
48.14%
62.99%
2.39%
66.14%
Run-16
Table 6-8: Groundwater mass budget for PESS-2005
Annual summary (Million m³, Mm3)
Natural recharge
Pumped recharge
Seepage from reservoirs
Seepage from demand nodes
Total input
Extractions (pumped wells)
Natural springs
Evapotranspiration
Deep percolation
Total output
Groundwater Mass Budget
Sustainable yield
6.59
0.00
0.00
272.03
278.62
11.14
218.77
2.58
10.34
242.84
35.78
265.69
2.36%
0.00%
0.00%
97.64%
100.00%
4.00%
78.52%
0.93%
3.71%
87.16%
12.84%
95.36%
Run -16
Table 6-9: Annual sectoral demand for PESS-2005
Demand
(Mm3)
5.
Net
Supply
(Mm3)
Consumptive
use (Mm3)
Losses
(Mm3)
Shortfall
(Mm3)
Unallocated
(Mm3)
Supply/
demand
(%)
Reliability
(%)
Domestic
14.39
239.69
5.42
126.92
4.36
229.66
69.74
67.81
Agricultural
11.86
37.38
2.52
12.15
5.20
30.72
56.15
60.00
Industrial
0.62
247.84
0.10
174.07
0.27
247.50
55.53
66.03
Services
8.53
0.00
0.00
0.00
8.53
0.00
0.00
0.00
Generic
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
35.40
524.91
8.05
313.15
18.36
507.88
48.14
59.29
Total
MODELS RUNNING FOR THE COMPARATIVE ANALYSIS
Model runnings
Sustainability indicators
Economic
Social
D/S ratio for
agriculture*
D/S ratio for
industry*
D/S ratio for
tourism*
Economic
efficiency of the
system**
No. of days with
restricted
domestic
supply***
Global quality****
Current
response
s
Baseline
Current responses
Water demand
management
Water supply
management
Water quality
management
All future
responses
90
BA
U
85
OP
T
90
PES
S
75
BA
U
70
OP
T
80
PES
S
60
BA
U
60
OP
T
80
PES
S
50
BA
U
50
OP
T
80
PES
S
50
BA
U
65
OP
T
80
PES
S
50
85
75
95
60
80
90
70
65
80
55
50
80
40
65
75
45
80
70
90
70
90
100
80
70
80
60
75
90
70
75
85
65
0.45
0.45
0.30
0.60
0.35
0.25
0.50
0.25
0.15
0.60
0.05
0.02
0.07
0.30
0.20
0.40
100
144
150
80
180
200
100
100
200
100
100
245
100
125
200
125
III
III
I
IV
II
I
IV
II
I
IV
II
I
IV
III
I
IV
80
70
90
70
50
60
70
80
70
80
50
80
50
75
85
60
Environmental
D/S ratio for
environmental
uses*
*:%
** : €
***: days/year
****: Classes I-
2.8 Networks and user groups
The SMART case – Lebanon is contributing/ cooperating with a considerable number of
agencies or people supplying them with data for good use. We tend to group those
beneficiaries into two groups: 1st the professional at National level, 2nd the scientific
community at large which includes National, Regional and International levels. Obviously,
they are using the data for exploitation in their planning and situation analysis operations, or
to feed in their databases, or for some scientific research and, of course, for spreading of
knowledge. The following Table 5.1 shows how SMART data is disseminated and different
groups making use of it.
Table 5.1. Dissemination – exploitation of SMART data
Dissemination
1. At National Level
- contribute data to general scheme on land use planning, i.e. DTM,
Drainage, Soil, Land use/cover, Change detection, Hazards
Data are given to “Council for Development & Reconstruction” (Dr. W.
Charafeddine) through contribution to the Comprehensive Land use
Planning Project in Lebanon, subcontracted by “Dar Al-Handasa”
Consultants (Dr. S. Srour), & IAURIF (Dr. F. Awada) e-mail:
iaurif.fa@dargroup.com
2. At Municipal Level
- contribute all maps and attribute data
The GIS Center – Environmental Observatory of the Union of Municipalities
of the North (Mr. A. Abdulwahab, Head) e-mail: tripoli@tripoli.gov.lb
3. Environment
- supply Ministry of Environment, especially coastal area people, with
relevant data: several sectors of different interests, general coordinator
M. L. Chamas, e-mail: lchamas@moe.gov.lb
4. Water authority
- supply Regional water authorities with relevant data as available
Dr. J. Krayim, General Director, North Lebanon Water Authority, Fax
(961.6) 430075
2. Scientific
A PhD student is part of our staff (Mr. Basbous) carrying on his research
focusing on themes & approaches of SMART project
Mr. Basbous is carrying on work for his PhD (at Marne la Vallée University
– France), his e-mail is: basbous_mo@hotmail.com
- supply local researchers with needed data
Dr. J. Halwani: Lebanese University, e-mail: jhalwani@cyberia.net.lb
Mr. K. Nabbout, another student doing his PhD at Dresden University of
Technology (Germany), his e-mail: khalednabbout@hotmail.com
- NGO: Environment Protection Committee, Mr. Amer Haddad, President,
e-mail: pipoo98@hotmail.com
- supply international organizations i.e. UNESCO, ESCWA, ACSAD, MAP
with relevant data
ACSAD (Arab Center for Study of Arid & Desert lands): Dr. Abdallah Droubi
& Dr. W. Erian, e-mail: droubi@scs-net.org
ESCWA: (Economic – Social Commission for Western Asia): Dr. M. Abdul
Razzak, e-mail: abdulrazzam@un.org
MAP (Mediterranean Action Plan): Dr. Marko Prem (PAP/RAC Center), email: marko.prem@ppa.tel.hr
- contribute to thematic networks of relevance
The Euro-Mediterranean project MEDCOASTLAND net
Another similar network: MedWaterLand net
The Mediterranean network MERSI web
Exploitation
The
planning
authorities are using
that for analysis of
requirements
for
comprehensive
land
use plan
The authorities put data
in their GIS server, &
use them for service,
i.e. – other purposes
They will use them to
supply their coastal
programs
They are useful for
several administrative
&
water
balance
purposes
For scientific purpose
They use them for their
own research
They put them on their
databases &/or GIS
servers as part of their
Regional
information
system
Good
spread
of
knowledge, exchange
& networking
Scientific paper: “Environmental water management through clustering to
improve water availability in coastal Mediterranean areas; Tripoli –
Lebanon”. This is a paper being prepared to be sent to the journal “Water
Resources & Management”
Public seminar with the North Municipalities & water authorities plus others
(July, 2004)
Another public seminar to expose the results is planned for Sept. or Oct.
2005
- contribute to scientific workshops or conferences as they emerge (when
convenient) & we are aware of them:
- Remote sensing in studying stress increase of land use change for water
resource management – Lebanon.
UN/ESA Sudan Regional Workshop on the Use of Space Technology for
Natural Resource Management, Environmental Monitoring & Disaster
Management. Khartoum – Sudan, April 4-8, 2004
- Environmental water management through clustering to improve water
availability in coastal Mediterranean areas, Tripoli – Lebanon.
Paper sent to the Water Resource Management Journal.
- This research paper is going to be presented also at the WaterMed 2
Conference to be held in Morocco (Nov. 2005)
- Modeling Lebanese sea coast water quality using TELEMAC and GIS.
This research paper is going to be presented at the 6th Arab GIS
Conference - Cairo, 12-13 September 2005.
Knowledge community
spread & supply of
information
Spread of
knowledge
scientific
96
3. Discussion and Recommendations
The Lebanese case study focusing on the second largest city in Lebanon along the
Mediterranean coast, Tripoli, and the watershed of Abou Ali river that passes through it,
typifies the eastern part of the Mediterranean in terms of dwindling water resources and the
increasing stresses on the community because of that. It also typifies the underdeveloped
southern Mediterranean countries in terms of failure of the essential elements of proper
development: lack of full-fledge updated information, mismanagement of natural resources,
weakness of institutional structures, old or lacking relevant legislation, limited environmental
awareness and, therefore, poor implementation.
This is why SMART is important to the area as it contributes, directly or indirectly, to all the
above elements. SMART focuses on the sustainable management of water (a scarce source
in the Middle East area) and therefore lots of data related to water was obtained for the
project. This covered the sources of water, the supply, the demand, the sectoral uses
especially linking that to the socio-economic conditions and land use. Of significance here is
the land use change in terms of changing demands on water, implying both increasing
pollution of the water and its being stressed. This is one of the models that SMART
contributed in the project.
Furthermore, valuable data on the watershed was obtained to secure requirements of the
Water Ware model. This included time series hydro-meteorological variables used in the
simulation and an embedded GIS with watershed river network, nodes, reaches, confluences
and diversions … etc. The Water Ware covered both the River Runoff and the Water
Resource Management Models (RRM & WRM) where the latter stimulated the behavior of
the river in terms of land use-demand on water, i.e. settlements, irrigation areas, reservoirs,
municipality … etc.
On the otherhand, there is the Telemac, another modeling system used in SMART in the
field of free-surface flows. In Lebanon’s case it is applied on the marine environment
adjacent to Tripoli where Abou Ali river estuary exists. It allowed to use data on littoral depths,
waves and wind regime to produce models of the thermal and pollution distribution in the
maritime area.
The Lebanese case study immediately reveals the impacts of mismanagement as there is an
ample waste of water. The amount of water supply, from rainfall and snow, could well exceed
600 Mm3 annually. With around 60% natural losses, i.e. evapotranspiration and runoff to the
sea, there is still more than the required to secure demands. But, when one observes the
rate of pollution of both surface and subsurface waters, however, it explains why that water
can not be used … River Abou Ali is used extensively inland for irrigation, while in Tripoli it is
useless. Except, of course, serving as an outlet for garbage and sewage!
It is understandable that the population increase would result in the need to secure more
water. However, the additional supply does not have to come necessarily from new sources,
thus stressing more the system. Rather, the authorities should secure additional supply by
“arranging the in-house” sources, i.e. those that are being used with a high proportion of
waste … This applies to several aspects: replacing the old deteriorated network,
controlling/preventing the pollution affecting many springs in the area rendering them
unavailable, similarly affecting the ground water, notably controlling deleterious practices on
Abou Ali river and its tributaries which would have added at least another 100 Mm3. Applying
new techniques in water conservation in the agricultural, domestic and industrial sectors
would be extremely fruitful, as would tertiary treatment of wastewater for reusing it. Applying
the WaterWare optimization over the whole watershed would prove very beneficial in this
regard. It has the capacity to link all water sources and sinks in a GIS interpolation that would
yield an outcome that the water authorities would be happy with, both geographically and
sectorally.
We know there is a privatization process going on in handling the water sector in North
Lebanon. This would hopefully imply that things should improve … but at what price? If that
means at a higher price to be paid by the community … then we did not do much. There is
need for reviewing water pricing, allocation with selectivity for sectoral purposes. Of course, it
requires proper measurements, control and a gauging network. The management has to
97
focus on facing the stresses of demand rather than securing more water supplies. There has
to be institutional restructuring and capacity building, both top-down and bottom-up. Water
authorities personnel have to be trained and upgraded, at the managerial level and the
technical level. Linked with this institutional reform is the need, at the National level, of
updating/upgrading relevant water legislation especially on water rights, pricing and quality
standards, and to work to mobilize two important elements: the decision-maker and the
public. Water strategy must be very simply securing the needs of water to all the community
within the set international standards, at reasonable prices, selectively chosen to reflect
sectoral capacities and requirements. The water authorities managing this domain must
make sure that the legislators and decision-makers are aware of all policies to arrive at that
strategy the soonest possible.
It is unfortunate that the public is still missing from the formula of the water framework. It is
likely that this is contributing to the “laisser-faire” felt by the community, i.e. not really
concerned to put efforts towards protecting, saving on, and preserving the available water
resources that they have. Public participation should be institutionalized, incorporated within
the organizational structure of the water sector and enhanced to become a functional
element. This could take place in two main directives: 1. enhance the water committees of
the Municipalities so that they are an active stakeholder not an over-looker (not to say an
undertaker!) in the water services given to the community. Positive interactive dialogue
channels should be established between them and the North Water Authorities. 2. involve
the community at large, both organizations (syndicates, clubs, schools, universities,
cooperatives, NGOs, scouts … etc.) and individuals (experts, researchers, engineers,
lawyers, farmers, labor … etc.) in some kind of frequent surveys, or seminars, public
hearings … etc. asking their input and opinions on some hot issues relating to water. This
can also be partly geared to environmental public awareness campaigns focusing on the
requirements of sustainability of water resources, and the vital role that can be played by the
public in this regard.
Recommendations
In view of the above, reflecting the wide diversity of SMART’s involvements and interests,
and focusing above all on benefits to the community, we suggest the following simple priority
recommendations:
A. Socio-economic frame
- a better control on population growth
- creating economic incentives for rural communities to stay in their areas
- improve water services to the community through adopting a strategy of securing
needs and implementing policies for that purpose (see B)
- enhance geographical and sectoral water use linkages within a frame of re-using
treated water (from one into the other)
- find the proper balance in a new system of water pricing that is fair to supplier & user
- educate the public for incorporating demand-control measures in their daily routine
- educate the community on the impacts of land use change in further stressing the
water balance
B. Water legislation frame
- avail a simple water strategy: securing the needs of water to all the community within
the set international standards, at reasonable prices, selectively chosen to reflect
sectoral capacities, use and requirements within the coming 5 years
- avail all policies that allow implementing the above strategy at its best, i.e. in view of a.
determining needs and priorities geographically, b. assure quality control measures
(standards, networks, gauging, monitoring quality …), c. define the proper time
frames, d. evaluate the sources & resources, conventional & non-conventional, e.
assure sustainability through proper assessment of supply-demand balance, now &
for the coming future
98
-
-
-
upgrade and update all regulations on: a. water rights to fit more with modern social
needs, b. on water sectoral allocation & pricing system, c. on standards of water
quality
create new legislation to encourage and enhance the role of both the public and
municipalities in contributing to water authorities handling the communities’ water
affairs
speed up the ratification of the code of the environment
C. Management/institutional capacity building
- secure training for water authorities to undertake institutional reform and
modernization
- upgrade water management approaches focusing on demand management,
environmental policies and decision-support systems
- undertake capacity building for water authorities, especially in advanced modeling
techniques, such as Water Ware & Telemac, to apply such knowledge in their
operations to improve balancing, management of the water sector & make it a more
efficient system
- upgrade capacities on monitoring, quantitative measurements & quality control
- define sources, quantity and determine needs to face water pollution from direct and
indirect causes (agriculture, industry, solid wastes, wastewater … etc.), human &
natural
- train water staff to analyze & assess impacts of change detection, both natural
(climate change) & human (land use)
D. Awareness
- organize awareness campaigns to serve:
a. the public in rapid environmental assessment for water quality & raise its level of
involvement in self-restraint/control on deleterious practices (on pollution, drilling
private wells, & water conservation)
b. raise public’s conviction in water demand-control measures on the long run
c. decision-makers willingness & convictions on introducing & implementing
measures of demand measurements
d. the water authorities in getting to know such directives as the European Directive
on Water Framework, and such tools as the Water Ware & Telemac packages.
99