Determination of Mean Areal Rainfall in the Gaza Strip Using ...

Determination of Mean Areal Rainfall in the Gaza Strip Using Geographic Information System (GIS) Technique Akram Hassan Al-Hallaq(1), Basheer Sofyan Abu Elaish(2) (1) Al-Aqsa University, Geography Department, Gaza, Palestine (2) Environment Quality Authority, Gaza, Palestine

ABSTRACT

Three practical methods or models, Thiessen polygons, Isohyets and Inverse Distance Weight (IDW), were applied for computing individual station weights and Mean Areal Rainfall (MAR) over the entire the Gaza Strip area. This study offers three graphic models for estimating MAR over a period of 25 years recorded in 10 observation stations in the Gaza Strip. GIS is used to interpolate annual rainfall totals between gauging station to produce regional estimates of MAR. Both areas and mean values over a given area (Gaza Strip) are standard GIS computations. The MAR was computed by applying each model separately to find out the effect of the method applied as well as the regional and geographic coordinates. The results show that the MAR in the Gaza Strip are between 335.7 mm, 338.3 mm and 345.4 mm according to IDW method, Thiessen polygons and Isohyetal method respectively. The accuracy of these methods has a little variation (less than 10 mm). Based on MAR, the annual volume of the rainfall over the area of the Gaza Strip will be 121, 122, and 124 million cubic meters according to the three methods. The Isohyetal method is the most precise method in simulating monthly and yearly rainfall. Therefore, these results must be considered in the climatic and water studies such as application of GIS for map preparation. Keywords: Mean areal rainfall, Thiessen polygons, Isohyets, Inverse Distance Weight (IDW)

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May 2008

University of Sharjah Journal of Pure & Applied Sciences Volume 5, No. 2

105

Determination of Mean Areal Rainfall in the Gaza Strip Using Geographic Information System (GIS) Technique

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1. INTRODUCTION: Rainfall over an area is usually estimated from a network of rain gauges stations. Rainfall data shows a considerable spatial variation over any region as explained by Jackson (1972) and Summer (1988). Wilson and Atwater suggested that this variation is due to differences in the type and scale of precipitation producing processes which are strongly influenced by local or regional factors such as topography and wind direction (Wilson, J. W. and Atawater, M. A., 1972). Areal average rainfall is most conveniently determined from a well-sited network of rain gauges which show the local variations of precipitation (Sen, Z. and El Jadid, A. G. 2000). In the past, many efforts have been put into the question of estimating the mean areal rainfall (MAR) from point data. Mean However, MAR does not handle spatial variability. On contrary, when estimating MAR from point data, spatially distributed data, are lumped together into one average value. Station weights are used to transform point rainfall observed at rainfall gauging stations into associated mean rainfall over an area that the station data are assumed to represent. A linear combination of these transforms is used to compute MAR over areas. Stations weights are used primarily to determine MAR (Fiedler, F. R., 2003). Several methods are available and routinely used to compute MAR and station weights from an assumption of areal (i.e., spatial) distribution using rainfall from a gauge network. The most common and useful methods are: Thiessen Polygon, Isohytes, and Inverse distance weight. Fortunately, the emergence of powerful GIS in the two most recent decades has made it easier to handle spatial data and treat spatial variability explicitly. One of

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Akram Hassan Al-Hallaq, Basheer Sofyan Abu Elaish ( 105- 126)

the many features of GIS is spatial interpolation. In this study, GIS is used to interpolate annual rainfall totals between gauging stations to produce estimates of MAR and station weight. 2. AREA OF STUDY The Gaza Strip is located in the Middle East (at 31o25'N 34o20'E). It has 11 km border with Egypt, near the city of Rafah, and 51 km border with the occupied Palestine. The Gaza Strip is a narrow coastal strip of land along the Mediterranean Sea with a 40 km coastline. The Gaza Strip area is about 360 km² and the land use is predominantly agricultural. The terrain is flat or rolling, with dunes near the coast (Fig. 1). The highest point is Abu Awdah, at 105 metres above sea level. The Gaza Strip has a temperate climate, with mild winter, and dry hot summer subject to drought. The annual average of rainfall ranges between 240 in the south of the Strip and 440 millimeter (mm) in the north. Rainfall in the Gaza Strip is linked with direct and inverse relations with other climatic variables such as air temperature, relative humidity, speed and direction of winds, insolation, evaporation and evaportranspiration. Some studies have related topographic characteristics to rainfall (Schermerhorn, V. P., 1967). Groundwater is one of the major water resources in the Gaza Strip. The Strip entirely depends on groundwater for domestic, industrial and agricultural purposes. The Gaza Strip is still suffering from water deficit due to over pumping of the aquifer. The water balance in the Gaza Strip concluded that the rate of water depletion would be 45 MCM (Al Hallaq, A., 2002). Rainfall is a main component for charging and renewal of groundwater resources in the Gaza Strip. Estimation of rainfall data is necessary in many natural resources and agricultural studies (Chegini, E. H. and others, 2001). Many studies illustrated the direct linkage between rainfall and groundwater. Studies have also been conducted to estimate the rates of replenishing groundwater basins in the Gaza Strip from direct rainfall. These studies depend in its estimations on the arithmetic average of the annual rainfall. So, it is imperative to find out the MAR in the Gaza Strip as the basic variable for determining the long-range underground water policy.

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Figure (1) Topography of the Gaza Strip Source: Ministry of Planning and International cooperation, (1997), The Technique Atlas: Gaza Governorates, Part 1, Gaza, Palestine.

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In computations of water balance, the estimations of rainfall form a maximum significance, whereas these estimations are one of the main components of water resources (Qurani, E. A., 1988). Therefore, they require special considerations (Sokolov, A. A., 1974) including application of accurate models to obtain a precise estimation of the average rates of long-term by using GIS technique. 3. OBJECTIVES The main objectives are to: 1. Determine station weights and the actual rainfall distribution pattern and obtain an accurate estimation of MAR in the Gaza Strip. 2. Determine the total volume of rainfall in the Gaza Strip. 3. Evaluate the applicability of different interpolation methods in estimating annual MAR in the Gaza Strip. 4. DATA, METHODOLOGY, AND DATA INTERPOLATION The rainfall data of 10 climatic stations with 25 years records (1980-2005) were used for analysis in this study (Fig. 2). The data were converted from MS excel files to MS Access as DBF files, then the rain stations are drawn on the map, using the ArcView 3.2 within the X and Y Axis coordinates. The methodology adopted to estimate the MAR and rainfall volume is through using the GIS techniques. The rainfall estimations and the spatial distribution of rainfall are very accurate by using GIS tools. The GIS methods can be used to analyze spatial rainfall distributions. Standard and commonly used methods of deriving the areal rainfall over a given area from rain gauge measurements at the rainfall stations are arithmetic means, Thiessen Polygon, isohyetal, and the Inverse Distance Weight. These methods yield good estimates in flat terrain, like the Gaza Strip, if the gauges are uniformly distributed and the individual gauge catches do not vary widely from the mean (Pokhrel, N., 2003). Tabios and Salas compared several areal average rainfall methods and concluded that a geostatistical method with spatial correlation structure is superior to Thiessen polygons and inverse-distance weighting (Tabios, G. O. and Salas, J. D., 1985).

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Figure (2) Location of rain gauging stations within the Gaza Strip Source: By researchers.

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One of the many features of GIS is spatial interpolation (Skop, Eli. and Acquarone, Mario., 1998). To interpolate rainfall data for all Gaza strip stations, we have created a surface grid for spatial analysis using GIS. Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data such as rainfall. There are many methods used for data interpolation to create a raster data (Environmental Systems Research Institute (ESRI), 1999) 5. RESULTS AND DISCUSSION 5.1- Data Processing Rainfall data in the Gaza Strip were collected from the sources which were already mentioned above. These data were compiled and tabulated in the form of annual rates for a period of 25 uninterrupted years from 1980/1981 to 2004/2005 and registered in 10 sites for monitoring (Table 1). The overall average was calculated at each site, as well as the value of the standard deviation of rainfall. Since the overall average rate of rainfall varies in its value during the 25 years from location to another from about 239 mm/year in Rafah to more than 443 mm/year in Beit Lahia, the standard deviation varies in its value from about 94 mm to about 182 mm, which emphasizes the impact of semi-dry climate, where rates of rain differ from one place to another and from one year to another along the Gaza Strip. Rainfall averages in the Gaza Strip are characterized with high spatial and temporal variance. Rainfall has expansive and scattered distribution during a long period which extends from October to April, while the number of raining days in this period does not exceed 50 days. 5.2 Statistical Relationships Coefficient of Variation (CV) of rainfall was calculated by the formula below in each of the sites deployed in the Gaza Strip. In probability theory and statistics, the CV is a measure of dispersion of a

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probability distribution. It is defined as the ratio of the standard deviation (S) to the mean (X¯) (Shehada, N., 1997). _ CV=(S/X) x 100 Figure (3) illustrates the relationship between this ratio and the average amount of rainfall. It shows that the coefficient of variation of rainfall varies in its degree between 37.6% and 43.3%. These rates are not too high, and the difference between them is insignificant (less than 6%) especially in the semi-arid areas and temperate, while these rates may reach up to 50% in the areas of dry desert climate (Qurani, E. A., 1988). It is noticed also that the coefficient of variation increases with decreasing of rainfall rates under such climatic conditions. In other words, the variation in south (Khanyounis) and middle (Moghragah and Nussirate) of the Gaza Strip is more than in the north (Beit Hanon and Beit Lahia).

Annual average of rainfall (mm)

500

400

300

200

100

0 0

10

20

30

40

50

60

70

80

90

100

Cofficient of Variation (%)

Figure (3) Relation between cofficient of variation and the annual average of rainfall in the Gaza Strip Source: By researchers.

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Table (1): Annual amounts and averages of rainfall (mm) in Gaza Strip Beit Hanon

Beit Lahia

Shati

Gaza city Remal

Gaza City (Meteo)

Mogh-ragah

Nussirate

Der-Elbalah

Khan Younis

Rafah

1980/1981

276.5

288.1

208.1

172.0

233.6

172.0

245.4

207.5

218.7

166.4

1981/1982

358.0

318.5

356.6

304.0

341.3

304.0

300.9

314.0

295.7

190.0

1982/1983

667.5

637.5

563.9

514.5

606.7

514.5

521.2

459.0

471.6

362.0

1983/1984

275.0

250.0

274.5

199.3

212.1

196.8

173.4

188.0

114.7

127.0

1984/1985

263.5

245.7

236.4

214.0

231.1

214.0

159.4

172.4

145.2

193.5

1985/1986

215.5

258.0

232.0

228.5

205.5

229.5

204.5

240.0

301.2

150.2

1986/1987

666.1

676.2

648.4

588.7

628.2

587.7

595.3

617.1

465.1

262.8

1987/1988

390.8

486.5

435.6

303.2

535.5

426.7

282.8

275.0

263.7

173.7

1988/1989

425.3

474.0

407.3

318.0

408.7

263.5

357.1

289.5

339.4

263.2

1989/1990

510.6

537.5

576.6

421.0

583.9

415.0

349.2

245.6

259.2

275.0

1990/1991

442.0

435.5

446.8

365.6

434.7

377.6

370.7

324.6

348.6

241.5

1991/1992

652.6

839.2

669.8

636.2

906.8

737.0

540.5

470.2

541.0

350.0

1992/1993

506.2

529.5

501.5

461.5

574.1

460.0

457.5

318.0

419.2

293.5

1993/1994

306.3

330.0

196.4

193.2

198.6

212.7

236.5

229.1

140.5

113.0

1994/1995

618.1

679.5

580.9

601.3

578.7

581.3

628.0

596.5

536.5

487.3

1995/1996

455.0

460.2

480.6

433.7

453.3

434.5

412.5

371.1

331.0

249.2

1996/1997

294.7

333.5

228.1

296.7

298.5

318.7

304.0

314.6

340.4

313.1

1997/1998

303.5

277.0

212.5

250.9

344.8

327.0

242.0

216.5

174.2

193.9

1998/1999

161.5

164.8

133.7

157.5

164.7

183.5

25.9

132.5

88.6

61.5

1999/2000

406.4

390.5

425.1

334.8

349.8

363.6

278.5

256.7

191.8

198.5

2000/2001

497.5

490.4

478.9

511.9

488.3

554.1

558.3

550.5

381.0

308.0

2001/2002

548.4

542.0

522.1

544.4

548.3

660.5

545.5

390.6

311.7

241.7

2002/2003

801.5

724.0

627.0

599.0

623.3

790.7

446.2

372.6

298.0

220.8

2003/2004

349.4

393.1

324.5

378.2

361.5

502.6

316.5

316.4

207.0

173.5

2004/2005 Annual Average

358.7

320.6

296.6

310.7

289.2

323.6

405.0

345.5

373.0

360.2

430.0

443.3

402.6

373.6

424.0

406.0

358.3

328.5

302.3

238.8

St. deviation Coff. Of Variation(%)

161.8

172.7

160.9

149.4

181.9

175.7

152.2

128.1

125.4

93.5

37.6

39.0

40.0

40.0

42.9

43.3

42.5

39.0

41.5

39.1

Year

Source: Ministry of Agriculture, Gaza, (2005), Unpublished Data. * Annual averages (mm), St. deviation (mm), and Coefficient of variation (%) are computed by researcher.

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6. APPLICATION OF GRAPHIC METHODS (MODELS) FOR DETERMINATION OF MAR Single point precipitation measurement is quite often not representative of the volume of precipitation falling over a given catchment area. A dense network of point measurements can provide a better representation of the true volume over a given area. A network of precipitation measurements can be converted to areal estimates using any of a number of techniques which include the following: 6.1 Thiessen Polygons For the region studied, there are several gagging stations within each area in the Gaza Strip for collecting rainfall data. Traditionally the annul rainfall is taken as the average of the rainfall data collected. However, the rainfall may vary from one station to another. Therefore, the average based annul rainfall value might be less accurate. Thiessen polygons are graphical technique which calculates station weights based on the relative areas of each measurement station in the Thiessen polygon network. Thiessen polygons are often used to assign real weights of the various points in a point theme (e.g. representing rainfall stations) to each polygon in a polygon theme (often representing runoff catchments). The weights are often used to calculate an area average rainfall for runoff catchments (Chen, T., 2004). Often we need to determine the average amount of rainfall over an area, and it is not always clear how to get the most representative value. The advent of geographic information systems (GIS) has greatly streamlined the problem of determining spatial statistics. The Thiessen polygon approach is probably the most common method used for determining average rainfall over an area when there is more than one measurement. The basic concept is to divide the Gaza Strip area into several polygons, each one is around a measurement point, and then take a weighted average of the measurements based on the size of each one’s polygon. The individual weights are multiplied by the station observation and the values are summed to obtain the MAR. For creation of Thiessen polygon, it used a specialized ArcView 3.2 extension that called the Areal-

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Rain extension (Department of Water Affairs and Forestry, 2001).The general formula to calculate area weighted averages is: n

P A + P2 A2 + P3 A3 + ... + Pn An MAR = 1 1 = A1 + A2 + A3 + ... + An

∑PA i =1 n

i

i

∑A i =1

i

Where MAR is the mean areal rainfall (or the weighted average), P’s are rainfall points of the stations located at the centroid of the polygons, and A’s are areas of each polygon. The Thiessen weights are the ratio of the gauge's polygon area divided by the area of the entire Gaza Strip, as indicated in figure (4). The Gaza Strip average depths are computed as shown in table (2), in which the storm rainfall of figure (2) is used. Figure (4) shows the Thiessen method for a rain gauge network. As computational results (Table 2), the area of the Gaza Strip is enclosed by 10 Thiessen polygons. The total area is, of course, constant (360.76 km2). For a given storm rainfall, the gages recorded the rainfall amounts. Using the above weighted averaging formula, the Thiessen MAR estimate for the entire Gaza Strip is: 338.32 mm. In contrast, the simple arithmetic average of rainfall is 370.74 mm. Differences between arithmetic and Thiessen averages increase for non-uniform storm rainfall when Thiessen areas differ widely. According to the Thiessen average rainfall (338.32 mm), the total volume of rainfall in the Gaza Strip is about 122 million cubic meters. 6.2 Isohyetal lines This technique involves drawing estimated lines of equal rainfall over a map of the area based on point measurements. The magnitude and extent of the resultant rainfall areas of coverage are then considered versus the area in question in order to estimate the areal rainfall value. Isohyetal maps are often used, with the network of any configuration, to get area averages or for studies of rainfall distributions. Isohyetal maps typically define a more

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accurate spatial distribution of rainfall than Thiessen do polygons, and can be used to compute the total of the studied area (Fiedler, F. R., 2003), areas between adjacent isohyets (zones), and then the MAR.

Figure (4) Thiessen polygons model for estimating the MAR in the Gaza Strip Source: By researchers.

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Table (2): The basic data used to compute the MAR in the Gaza Strip by Thiessen method (1980-2005) Shape

Average of Rainfall (mm) (Pi)

Area of Polygon (km2) (Ai)

% of Total Area for Polygon

Area Weighted Rainfall (mm) (MAPi)

Polygon 1 Polygon 2 Polygon 3 Polygon 4 Polygon 5 Polygon 6 Polygon 7 Polygon 8 Polygon 9 Polygon 10 Total

430.0 443.4 402.6 373.6 424.0 406.0 358.3 328.5 302.3 238.8 370.74 *

35.55 20.89 8.52 28.19 1.08 37.28 28.51 38.14 100.46 62.15 360.76

9.9 5.8 2.4 7.8 0.3 10.3 7.9 10.6 27.8 17.2 100.0

42.37 25.67 9.51 29.19 1.26 41.95 28.31 34.73 84.18 41.14 338.32

Source: By researchers.

* Arithmetic average of rainfall.

The Isohyetal Method uses the area of the watershed enclosed between adjacent isohyets. Isohyetal lines are drawn on a watershed in the same manner as topographic lines are drawn on a topographical map, using precipitation depth rather than elevation of the controlling variable. This method computes the MAR as a weighted average of nearby gauges. The equation used is: MARi = ∑ Ti Pi i

Where: MARi= the mean areal rainfall; Ti = the Isoheytal weight, computed as: Ti=Ai / AT where Ai = area defined by the two enclosed Isohyetal lines, and AT = total area of the Gaza Strip; and Pi = the i average rainfall in each sub-area between any two Isohyets. With the Isohyetal Method, Pi in the equation is the average rainfall depth associated with the Isohytal weight Ti and is generally taken as the average of the two enclosed Isohyetal lines. As we mentioned before, the accuracy of this method is greater than the Thiessen polygons and Arithmetic methods but the amount of work is significantly greater.

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ArcView spatial Analyst v2.0 extension is used to create contour lines of equal rainfall on the Gaza Strip map which express the Isohyetal lines. Inverse Distance weight (IDW) was applied as a method of interpolation with the help of ArcView system to draw a contour map of rainfall. The AutoCad and ArCad softwares were used to calculate the area between any two isohyets after conversion to *.dxf files from ArcView, and then we converted the data to shape file by using GIS. The ArCad Software can clean and build topology, which means creating data base. The average rainfall in each sub-area between any two Isohyets is calculated as the average. The result was 27 sub-areas with 22 different rainfall areas. The mean rainfall varies from 230 to 440 mm. Figure (5) shows the rainfall contour map of the studied area for the period 1980-2005. Based on the isohyetal method and with the help of the rainfall contour map (Figure 5), the computations were implemented and are summarized in Table (3). Table (3): The basic data used to compute the MAR in the Gaza Strip by Isohyetal method (1980-2005) Area Weighted Rainfall (mm)

% of Total Area (Ti)

Area (km2) (Ai)

Average (m)

Average (mm) (Pi)

118

Contour (mm)

Sub-area 1 2 3 4 5 6 7 8 9 10 11 12

230 – 240 240 – 250

235 245

0.235 0.245

1.66 9.57

0.46 2.65

1.08 6.50

250 – 260 260 – 270

255 265

0.255 0.265

10.38 12.68

2.88 3.51

7.34 9.31

270 – 280 280 – 290 290 – 300 300 – 310

275 285 295 305

0.275 0.285 0.295 0.305

10.39 12.78 17.18 43.74

2.88 3.54 4.76 12.12

7.92 10.10 14.05 36.98

310 – 320 320 – 330

315 325

0.315 0.325

26.51 18.04

7.35 5.00

23.15 16.25

320 – 330 330 – 340

325 335

0.325 0.335

1.52 28.81

0.42 7.99

1.37 26.76


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Area Weighted Rainfall (mm)


Area (km2) (Ai)

Average (m)

Average (mm) (Pi)

Contour (mm)

Sub-area 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Total

340 – 350 350 – 360 360 – 370 370 – 380 380 – 390 400 – 410 370 – 380 380 -390 390 -400 420 – 430 400 – 410 410 – 420

345 355 365 375 385 405 375 385 395 425 405 415

0.345 0.355 0.365 0.375 0.385 0.405 0.375 0.385 0.395 0.425 0.405 0.415

7.82 12.54 9.68 5.89 6.07 8.00 0.53 2.39 40.58 0.12 18.60 14.97

2.17 3.48 2.68 1.63 1.68 2.22 0.15 0.66 11.25 0.03 5.16 4.15

7.48 12.34 9.79 6.13 6.48 8.99 0.55 2.55 44.43 0.14 20.88 17.22

> 440 430 – 440

445 435

0.445 0.435

1.03 4.51

0.29 1.25

1.27 5.43

425

0.425

34.76

9.64

40.95

360.76

100.00

345.44

420 – 430

347.96 *



Table (3) shows that the average depth of the rainfall over the Gaza Strip during the period 1980-2005 is 345.4 mm/year. Taking into consideration that average during the period 1980-2005, is 345.4 mm and that the Gaza Strip has an area of 360.76 km2, the annual volume of the rainfall over the area will be 124.6 million cubic meters. 6.3 Inverse Distance Weight (IDW) This is another station weighting technique. A grid of point estimates is made based on a distance weighting scheme. Each observed point value is given a unique weight for each grid point based on the distance from the grid point in question. The grid point precipitation value is calculated based on the sum of the individual station weight multiplied by observed station value. Once the grid points have all been

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estimated they are summed and the sum is divided by the number of grid points to obtain the areal average rainfall.

Figure (5) The rainfall contour map of the Gaza Strip, showing the sub-area on which the calculation of the MAR was based. Source: By researchers.

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The Inverse Distance Weighted (IDW) interpolator assumes that each input point has a local influence that diminishes with distance. It weights the points closer to the processing cell greater than those farther away. A specified number of points, or optionally all points within a specified radius, can be used to determine the output value for each location. The interpolation is performed using the following equation: (Steduto, p.2000 and Donald, S., 1968) n

Z=

∑zW i

i =1 n

i

∑W i =1

i

Where Z is the interpolated grid-cell value and Wi is the weighting function. Such weighting function depends on the distance between the grid cell and the point-in-space observation, and is calculated as:

Wi =

1 m

di

Where d i is the distance between the grid cell and the point-inspace observation at the location ( xi , y i ) and m is a parameter that controls the significance of surrounding points upon the interpolated value. The outputs of the Grid extent are the same as the Gaza strip area, and the grid cell size was 30X30 m. All gauging stations within the default of nearest neighbors from the interpolated point were used, and there aren’t barriers. As a result, nine different grids were created, and then we converted the grid areas to a vector data to calculate the area in each polygon. IDW method with the help of ArcView spatial Analyst v2.0 extension are used for interpolation between the rain stations measurements. So, the rain estimations depend on the previous concept in each method for calculating the MAR and rainfall volume. The result was 16 sub-areas with 12 different rainfall areas. The spatial distribution of calculated rainfall for 1980-2005 is shown in

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figure (6). The mean rainfall varies from 238 to 424 mm. The IDW map of rainfall shows a continuous and smooth variation. Based on the IDW method and with the help of the rainfall IDW map (figure 6), the computations were implemented and summarized in table (4).

Figure (6) IDW model for estimating the MAR in the Gaza Strip Source: By researchers.

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Table (4): The basic data used to compute the MAR in the Gaza Strip by IDW method (1980-2005) Sub-area 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Total

Average of Rainfall (mm) (Pi)

Average (m)

Area (km2) (Ai)


Area Weighted Rainfall (mm)

424 424 406 406 368 406 387 368 313 350 331 313 294 275

0.424 0.424 0.406 0.406 0.368 0.406 0.387 0.368 0.313 0.35 0.331 0.313 0.294 0.275

10.868 12.241 0.699 0.001 1.966 37.963 63.546 10.923 3.697 21.257 32.096 37.546 63.408 24.239

3.01 3.39 0.19 0.0002 0.55 10.52 17.62 3.03 1.02 5.89 8.90 10.41 17.58 6.72

12.77 14.39 0.79 0.0010 2.01 42.72 68.17 11.14 3.21 20.62 29.45 32.58 51.68 18.48

238 257

0.238 0.257

18.636 21.663

5.17 6.01

12.29 15.43

360.75

100.00

335.73

348 *



Using the IDW weights (Table 4), the MAR is computed to be 335.73 mm. According to the IDW average rainfall (335.73 mm), the total volume of rainfall in the Gaza Strip is about 121 million cubic meters. It is clear from the previous that the MAR in the Gaza Strip ranges between 335.7 mm, 338.3 mm and 345.44 mm according to IDW method, Thiessen polygons and Isohyetal method respectively. The difference of MAR among the three methods is little due to the few variance of accuracy of each method. However, in figures (4), (5) and (6), the spatial distributions of annual rainfall for 1980-2005 are shown. The isohyetal method is considered more accurate because the Isohyetal method allows the use of judgment and experience in GIS to structure the contour map. The accuracy is largely dependent on the skill

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of the person performing the analysis and the number of gauges. If simple linear interpolation between stations is used for drawing the contours, the results, approximately, will be essentially the same as those obtained by the other two methods. 7. CONCLUSIONS AND RECOMINDATIONS

Several interpolation methods were evaluated for estimating monthly and yearly MAR in the Gaza Strip. Based on these results, the following conclusions can be extracted from this research: • The coefficient of variation of rainfall varies in its degree between 37.6% and 43.3%. These rates are not too high, and the difference between them is insignificant (less than 6%) especially in the semi-arid areas and temperate. From coefficient of variation, it is concluded that the variation in south and middle of the Gaza Strip is more than in the north. • Based on Thiessen polygons, Isohyetal and IDW methods, the averages depth of the rainfall over the Gaza Strip during the period 1980-2005 are 338.3, 345.4, 335.7 mm/year respectively. Taking into consideration that averages of MAR, and that the Gaza Strip has an area of 360.76 km2, the annual volumes of the rainfall over the area will be 122, 124, and 121 million cubic meters. The accuracy of these methods has a little variation (less than 10 mm) ranged between 335.7 mm for IDW and 345.4 mm for the method of Isohyetal. • The distribution pattern seems to be a general tendency for rainfall to increase from south to north. On the other hand, a local maximum of rainfall is found in the North of the Gaza Strip, while a local minimum is found in the South. Maximum and minimum values coincide with the lowest and highest rainfall values from the rain gauging stations since the applied interpolator preserve measured values. • The Isohytal method is the most precise method in simulating monthly and yearly rainfall. Therefore, these results must be considered

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in the climatic and water studies such as application of GIS for map preparation. • When the concentration of data in a region is low, by dividing of region into homogenous sub regions, the precision of estimation can be improved considerably. • It is clear that the application of three graphic models for obtaining the MAR of any of hydrological variables can be considered as one of the precise scientific methods which achieve a high degree of safety particularly when it concerns to the water budget of groundwater resources, both in the Gaza Strip or in other areas, which are mainly located within the moderate or semi arid range. REFERENCES [1] Al Hallaq, Akram H., (2002), Groundwater Resources Depletion in Gaza Strip: Causes and Effects, (In Arabic) Unpublished Ph.D. dissertation, Ain Shams University, Cairo, Egypt, pp. 149-151 [2] Chegini, E. H. and others, (2001), "Survey on application of geostatistical methods for estimation of rainfall in arid and semiarid Regions in South West of Iran", Soil Conservation Watershed Management Research Center, Tehran, Iran, pp. 1-12. [3] Chen, T., (2004), "Evaluating Spatial Variability of Precipitation and Event Mean Concentrations (EMC) Impact on Pollutant Loading Estimation Using GIS", GIS Application in Water Resources, pp. 1-10. [4] Department of Water Affairs and Forestry, (2001), The ArealRain Extension instruction, Pretoria, South Africa. [5] Environmental Systems Research Institute (ESRI), (1999), “ArcView Spatial Analyst“, USA, ESRI. [6] Fiedler, F. R., (2003), "Simple, Practical Method for Determining Station Weights Using Thiessen Polygons and Isohyetal Maps", Journal of Hydrologic Engineering, ASCE, vol. 8, No. 4, pp. 219-221. [7] Jackson, I. J., (1972), "Mean daily rainfall intensity and number of rainy days over Tanzania," Geography Annals, A.54, pp. 369-375. [8] Ministry of Planning and International cooperation, (1997), The Technique Atlas: Gaza Governorates, Part 1, Gaza, Palestine.

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