Detailed Description of Analysis (1) Problem definition and causal chain analysis

The water resource problems that the Gaza Strip faces are immense. North Gaza‘s, as well as the whole population of the Gaza Strip, face many economical, environmental, and social problems such as desertification, salination of fresh water resources, untreated sewage issues, water-borne disease, soil degradation and most significantly, the depletion of groundwater resources (CIA, 2010).

Problems concerning agriculture such as soil salination, aquifer over-exploitation, and groundwater contamination with chemicals such as nitrate have arisen due to inadequate water supplies, absence of water re-use systems such as Managed Aquifer Recharge (MAR), and lack of proper water resources planning. Overall, the mentioned water resources problems are the cause for many of the Gaza Strip‘s environmental and economic woes. With the aim to analyse the existing water resources problems of the study area, causal chain analysis using the Driver (D), Pressure (P), State (S), Impact (I) and Response (R), in short DPSIR, methodology was used.

The DPSIR concept has been developed for describing interactions between society and the environment (Kristensen, 2004; OECD, 2003), starting from the assumption that there is a causal chain between the two. The strategies, developed by the European Commission for the implementation of the Water Framework Directive, have identified the DPSIR framework as being a convenient way to identify stress factors and their effects on groundwater (OECD, 1993; OECD,

2003). The water resources problems of North Gaza were analyzed, decomposed, and structured in this method in order to find the potential response of the problem. Figure 7.4 shows the DPSIR analysis for the Northern Gaza Strip.

In brief, the water resources system of North Gaza is affected by two main drivers: population and urbanization. These drivers cause certain pressures on groundwater exploitation, wastewater status, land use change, salinization, etc. Consequently, these pressures put impacts on groundwater resources, either by reducing the availability or by deteriorating the quality for further use. The total causal chain on surface water is negligible as there are no surface water resources in the area. Due to scarcity of conventional water resources and availability of effluent water, MAR using treated effluent is considered as the most potential response to the existing water supply problems of the area.

Figure 7.4: DPSIR framework for the Northern Gaza water resources problems (2) Constraint mapping

The study area is 108 sq km and has been divided into six zones for analysis and discussion, according to the groundwater flow. In order to screen out the non-feasible areas, constraint mapping

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was performed early on. Table 7.1 shows the list of criteria and their threshold values for screening the suitable places in north Gaza.

Table 7.1: List of constraint criteria together with threshold value Criteria name Threshold value Explanation

Slope < 3 % The areas which have less than 3% slope are desirable for MAR.

Groundwater flow zones

Zone 3,4 5, and 6

The groundwater flow zones are considered feasible for MAR as the water from these zones does not pass through the pollution source or does not go to the sea directly.

Land use Built up area, natural forest, and planted forest are considered

After defining threshold values for each criterion, the thematic map of each constraint criterion was converted to a constraint map using Boolean logic. All the converted thematic maps were overlaid (by conjunctive screening) to have a final constraint map (Figure 7.9). This constraint map was used later as a mask for suitability mapping. All thematic maps were obtained from the Palestine Hydrology Group (PHG).

(3) Suitability mapping

After analysing all available data and site characteristics, the nine sub criteria such as slope, infiltration, nitrate and chloride concentration, aquifer thickness, water table depth, groundwater flow zones, distance to the lake, and cost of effluent transfer, were selected. The sub criteria maps of North Gaza were obtained from PHG.

Figure 7.5: Criteria hierarchy and weights (local and global weights are in italic and bold, respectively) for suitability mapping.

The sub-criteria (in other words, the thematic layers) were than standardized. Two value functions, such as linear and step-wise linear functions were used for the approach (Figure 7.6). Each of the main criteria and sub-criteria was assigned a weight according to its importance. The weighted criteria

were then overlaid. Weighted Linear Combination (WLC) method was used in this study. Suitability mapping was done in two steps, such as scheme analysis and final suitability mapping.

Figure 7.6: The standardized functions, for different sub-criteria, used in this case study

3a.Scheme analysis

Five MAR schemes were prepared by varying the importance of the main criteria. The main objective of the scheme analysis was to investigate the influence of weighting on the site suitability. In general, in each scheme one of the main criteria was given prime interest and the rest were of equal importance. The weights of the subcriteria were maintained as being the same for all schemes. Table 7.2 shows the weights for each main criterion and underlying sub criterion.

3b.Criteria overlay

After checking all results of the schemes, a participative process was undertaken among the local stakeholders and experts. Based on the discussion, a new scheme (Scheme - 6) was developed and weights for the main criteria were calculated by pair wise comparison (Table 7.3 and Figure 7.5).

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Main Criteria Sub-Criteria Scheme 1 Scheme 2 Scheme 3 Scheme 4 Scheme 5 Surface storage capacity of the aquifer and the flow direction of the groundwater are the most relevant criteria for constructing aquifer recharge basins in the study area. The second most important criterion is the groundwater quality, because no already clean fresh water reservoirs should become contaminated by the infiltrated water. The surface characteristics were the third most important, due to small elevation differences; however, infiltration does play a role for the basins. Farther characteristics were ranked as being the least important.

Table 7.3: Pair wise comparison weighting for the resulting final scheme based on WLC.

Main criteria overlay. When α = 1, OWA creates the same overlay as WLC (Figure 7.9). Further, six suitability

maps were created based on the following selected value of fuzzy quantifiers; at least one (α = 0), at least a few (α = 0), a few (α = 0.5), most (α = 2), almost all (α = 10) and all (α = 1000) (Figure 7.10).

(5) Site Ranking (i) Selection of project

Five locations here referred to as MAR ‗Projects‘, with high suitability scores (72-78) were chosen (Figure 7.9) within North Gaza. In addition to the five projects, one project that has a relatively low suitability score (49-51) was also considered for evaluation in order to check the hydrogeological impact and compare with other projects. This comparison, comparing project that has high suitability score with a project that has low suitability score by hydrogeological impact analysis, will give an in-depth idea of the spatial analysis methods implementation for site selection. Six environmental criteria that mostly represent the hydrogeological condition of the north Gaza strip were selected for project comparison by MCA (Table 7.4).

(ii) Impact assessment Groundwater Model:

A transient groundwater flow model was developed using visual Modflow software (v.2009; SWS, 2009) and its integrated modules, were used to quantify the six environmental criteria in this study.

Visual Modflow uses the finite difference code of MODFLOW (Harburg & McDonald, 1988). The three integrated modules, namely MODFLOW (groundwater flow model), ZONE BUDGET (water budget within user defined Zones), MT3DMS (Solute Transport) were used in the study. Relevant data and GIS maps to develop the model were obtained from PHG. The model area was discretized into a grid of 100 by 100 m square cells enclosing an area of 191.28 km² in the northern part of the Gaza Strip. The model domain was made larger than the area of interest to minimize the effects of model boundaries on the simulation result (Figure 7.7). The aquifer of North Gaza is unconfined and phreatic. One aquifer system was used in the study.

Aquifer properties such as hydraulic and vertical conductivity, specific yield, and storage coefficient were defined initially from the report EMCC, (2006). K_xy was set with a value of 50 m/day in the proximity of the proposed infiltration site and 30 m/day in the rest of the model domain. K_z was set for one tenth of K_xy. S_y was considered to be 0.2. Effective porosity (n_e) and total porosity were set to be 0.25 and 0.35, respectively. Little adjustment for the above mentioned parameters were made during the calibration of the transient model. Initial conditions in terms of groundwater hydraulic heads were specified for the model. Existing hydraulic heads of the monitoring wells were used to generate an initial condition contour map. Initial groundwater level varied 2.66 m to –3.41 m. The boundary conditions for the model are as follows: North and South – no flow boundary; West -

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constant boundary (0 m above sea level (ASL)), and East - constant head boundary, varying from 10 m in the south to 19 m ASL in the north. The bottom and upper boundaries are no flow boundaries.

For the model simulation, the water requirement and abstraction data from the years 2000 to 2003 were used. There are 1,061 abstraction wells within the model domain; out of these, 45 are domestic wells. The abstraction data were obtained from PHG.

Figure 7.7: Model boundary and the North Gaza area showing the recharge zones used in the flow model.

As usual, rain contributes the major portion of natural recharge in North Gaza. Besides rain, irrigation return flow (25% of the agricultural use) and recharge from domestic use (30% of the domestic use) were also considered. The simulation period data were taken from the years 2000 to 2003. The model was calibrated against the observed groundwater level data at five monitoring wells. Figure 7.8 shows two example of calibration plots.

Figure 7.8: Two exemplary calibration plots.