FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA

Inernaional Review of Business Research Papers Vol.4 No. 5 Ocober-November 8 Pp. 31-48 FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA Fahis F. Lawgali* This paper examines waer demand for all needs o deermine he fuure waer demand for agriculural, indusrial and domesic use; i uses annual daa on consumpion of waer demand by he year. The mehod of demand forecasing of waer is based on Box Jenkins mehod.by, as a whole, waer demands will increase o he double in Libya. So, Available waer in will be less han half of waer demands which means an increase of he shorage over ime. The fuure waer demand for agriculure purposes is expeced o increase. Also, i becomes he bigges consumer of waer, I represens abou 83%of he esimaed waer consumpion of. Field of Research: Economics of Waer Resource Managemen in Libya 1. Inroducion One of he serious problems ha many counries are facing oday is waer shorage, even hough here is over 7% of surface waer covered he earh. Waer shorage like oher economic resources, i is no differen from one counry and par of a counry ino anoher. In he las few years, domesic waer shorage has increased worldwide, increase waer demand as resul of, increase of he populaion, increase in he individual agriculural domesic and indusrial demand and rising of living sandard. Libya, like oher counries worldwide, is no differen in respec of he causes leading o he increase of waer shorage and I believe ha populaion growh and waer consumpion are among he areas ha should be addressed by any scienific sudy. Large increases in waer demand wih very lile recharge have srained Libya s groundwaer resources resuling in serious declines in waer levels and qualiy, especially along he Medierranean coas where mos of he agriculural, domesic and indusrial aciviies are concenraed. *Fahia.F. Lawgali is a PhD Suden a Dundee Business School, Universiy of Aberay Dundee, Scoland, U.K. Email:flawgali@yahoo.com f.lawgali@aberay.ac.uk

The fuure esimaions of waer consumpion for all possible purposes indicae o oal waer consumpion increasing from 693.89 million cubic meers in 6 o 1473. million cubic meers in wih an average of compound annual rae of 4.97%. In i is expeced, ha he increase would be 98% of he waer consumpion in 6. So, he aims and objecive of his sudy are o forecas he waer demand for agriculural, domesic and indusrial use in Libya using Box Jenkins mehod.. The Daa Sources This sudy requires collecing and analysing daa abou he Libyan waer for he period from 1975 o 5 his daa is annual daa because only annual daa available covering his period and informaion relaed o waer demand in Libya. All reference daa were colleced from Libyan Auhoriies: General Environmenal Auhoriy; General Waer Resources he Public Corporaion of Waer, he General People s Commiee of Planning, he General People s Commiee of Agriculure and he General Corporaion for Invesmen of he Grea Man- Made River Waers. 3. Lieraure Review Sudies and researches underaken in recen years show, ha one of he mos imporan economic problems facing many counries of he world nowadays is he shorage of ground waers. Waer is naural resource renewable in limied quaniies. Demand for waer increase wih ime populaion and sandard of living increase The purpose of his secion is o review he experience of researchers in he area of waer demands and waer resources. Modelling of waer demand consiss of he search for variables ha underlie or deermine waer demand and he deerminaion of heir relaionships o waer use in quaniaive erms. 3.1 Review of References The review of pas sudies of waer demand shows a significan number of sudies in various caegories of waer use. There is much variabiliy in he selecion of boh dependen and independen variables in waer use sudies, even wihin narrowly defined individual waer use secors. Few sudies were available for comparison using a single comparable se of variables. The resuls are ofen conradicory and he values of repored coefficiens frequenly have signs and values ha fail o conform o reason or heory. The availabiliy of sudies varies from secor o secor and he differences in daa and mehodologies used preclude comparisons of resuls across individual sudies. 3

3.1.1 Waer Demand Reviews of he empirical lieraure on waer demand show he dominance of residenial (urban) over ha of rural waer demand sudies. Single and sysem demand equaions wih differen funcional forms have been employed o esimae elasiciies of waer demand wih respec o price, income, populaion characerisics and composiion, among ohers. These sudies uilise ime series, cross-secional daa or panel daa. Arbués e al. (3) noes he absence of a general consensus regarding he mehodology o analyse waer demand and his has resuled in differen ranges in price-elasiciy esimaes of waer demand. Through mea-analyses of residenial waer demand sudies, Espey e al. (1997) as cied by Arbués e al. Aenion has laely shifed o a demand-oriened approach where he price of waer is used as he main insrumen o regulae demand. Significan facors ha explain household waer demand in 8 rural communiies in Madagascar. 4. Mehodology Economiss define he demand for waer as he relaionship beween waer use and price, when all oher facors are held consan. Demand is a negaive funcional relaionship represened by he demand curve. This curve describes he relaionship beween price and waer use for a single user. The demand imposed by each waer user can be represened by a similar demand curve, and all such curves are expeced o be negaively sloped (increased price resuls in decreased waer use). In general, waer use relaionships are in he form of mahemaical equaions which express waer use as a mahemaical funcion of one or more independen variables. The mahemaical form (i.e., linear, muliplicaive, exponenial) and he selecion of he righ hand side or independen (explanaory) variables depend on he ype and aggregaion of waer demand represened by he lef-hand side or dependen variable. The mehodology of he sudy is defined as an analyical mehod pracised o realize he sudy goal. The esimaes of waer consumpion for differen purposes are calculaed by using he comparaive equaions saed. The Box-Jenkins is used o forecas he waer demand for all purposes.in addiion, his sudy uses economeric ess for Uni Roos, Co inegraion o esimae his model: 4.1 The Forecasing Model 4.1.1 Waer Demand Equaions W = WA + WI + WD (1) Where W = oal waer demand, W A, WI, WD = waer demand for he purposes of, agriculural, indusrial and domesic use, respecively W = f P, Y, pop emp () ( ) A A, 33

Where P A, is he price of agriculure waer,y is he income, pop is he number of people and emp is he emperaure W = f P, Y, pop emp (3) ( ) D D, WhereY is income and P D is he price of domesic waer W = f p, Y, pop emp (4) ( ) I I, Where p I is he price of indusry waer Esimaion hese equaions (), (3), (4) by secor. Subsiuing equaions (), (3), (4) ino equaion (4) W = WA + WI + WD (5) W (). = WA ( PA, Y, pop, emp) + WI ( PI, Y, pop, emp) (6) + WD ( PD, Y, pop, emp) W = f ( PA, PI, PD, Y, pop, emp) (7) Esimaion equaion (5), Aggregae W = f P, P, P, Y, pop emp (8) ( ) A I D, Esimaion Equaions (), (3), (4) and (8) To esimae oal waer demand, ransforming equaions ino double log form (), (3), (4) and (8) I have an esimable model: lnw = α + β 1 ln emp + u A I I A 1 ln PA + θ1 ln pop + γ 1 lny + ψ lnw = α + β ln emp + u ln PI + θ ln pop + γ lny + ψ ln D α D + β 3 ln PD + θ 3 ln pop + γ 3 ln Y + ψ 3 W = ln emp + u A I D lnw = α + β1 ln PA + β ln PI + β 3 ln P + ψ ln emp + u D + θ ln pop + γ lny Where: α = Inercep coefficiens β 1, β, β 3, θ, γ, ψ = Slope coefficiens u = residual erm ln = Naural logarihm Linear waer demand funcions are ofen chosen because of heir ease of esimaion. These can be derived from a quadraic uiliy funcion, bu are mos ofen presened wih no formal derivaion (Al- Quanibe and Johnson, 1985). The linear regression funcional form is ofen criicized, because i implies ha he change in quaniy demanded in response o a price change is he same a every price level. (Billings and Day, 1989). 34

5. Major Findings 5.1 ARIMA Forecasing Models Lawgali This secion examines saionary and non saionary ime series by formally esing for he presence of uni roos. Various Box-Jenkins Auoregressive Inegraed Moving Average (ARIMA) models are esimaed over he period 1975-5 for oal waer demand and demand for waer for agriculure, domesic and indusry use. The ARIMA models provide a useful framework o undersand how he waer ime series is generaed. Unlike univariae smoohing models which are more commonly used, he ARIMA approach requires a waer ime series o be esed for nonsaionariy prior o underaking esimaion and forecasing. If a series is nonsaionary (ha is, he series has a mean and variance ha are no consan over ime), he series has o be differenced o ransform i o a saionary series, before generaing forecass. A saionary waer demand series ypically provides beer and more reliable forecass. The work of Box and Jenkins (197) shifed professional aenion in ime series modelling away from saionary processes o a class of nonsaionary processes and he relaed ideas of he order of inegraion necessary o obain saionary series. Furhermore, he Box-Jenkins mehod is popular because of is generaliy since i can handle any saionary or nonsaionary ime series. In he idenificaion phase, a general class of models applicable o a paricular siuaion is examined wih he aid of he sample correlograms, and auocorrelaion and parial auocorrelaion funcions. 5. Tesing for Saionariy 5..1 Graphs of Variables The firs mehod which can be used o check saionariy of he variables is o graph he series. The graphs of hese variables in logarihm form are shown in figure (1). Figure (1) Graphs of he variables (in level and in firs and second differences) 9. 8.5 8. 7.5 7. 6.5 6. 1975 198 1985 199 1995 5 LNW 35

.5.4.3..1. -.1 1975 198 1985 199 1995 5 DLNW 9. 8.5 8. 7.5 7. 6.5 6. 5.5 1975 198 1985 199 1995 5 LNWA.6.5.4.3..1. -.1 1975 198 1985 199 1995 5 DLNWA 36

6.8 6.4 6. 5.6 5. 4.8 4.4 1975 198 1985 199 1995 5 LNWD.5.4.3..1. -.1 1975 198 1985 199 1995 5 DLNWD 6 5 4 3 1-1 1975 198 1985 199 1995 5 LNWI.1. -.1 -. -.3 -.4 -.5 1975 198 1985 199 1995 5 DDLNWI The original ime series in logarihm form is checked for saionariy using he augmened Dickey- Fuller (ADF) es for uni roos. 5.. The ADF-Tes for Difference Versus Trend Saionariy The resriced model assumes he ime rend is zero and he series for all variables are difference saionary. As shown in ables (1), (), (3) and (4), hen he series are ransformed by aking appropriae differences o render he series saionary. A deailed explanaion of he es procedure is given in Gujarai (3). Table (1): ln W Wald Tes: Equaion: Uniled Null Hypohesis: C()= C(3)= F-saisic 3.4887 Probabiliy.48313 Chi-square 6.857743 Probabiliy.344 37

ΔY = α + λ + θy + δ ΔY 1 1 D ( ln W ) C Trend ln W (-1) D ( ln W (-1)) H : θ = λ = F = 3.43 < F = 7.4 c We can no rejec H,because he F-saisic is less han he 5% criical value,hen we can say ha we are 95% confiden ha he series ln W follows a difference saionary process. Table (): ln WA Wald Tes: Equaion: Uniled Null Hypohesis: C()= C(3)= F-saisic 4.717876 Probabiliy.1865 Chi-square 9.435753 Probabiliy.8934 ΔY = α + λ + θy + δ ΔY 1 1 D ( ln WA ) C Trend ln WA (-1) D ( ln WA (-1)) H : θ = λ = F = 4.7 < F = 7.4 c We can no rejec H,because he F-saisic is less han he 5% criical value,hen we can say ha we are 95% confiden ha he series ln WA follows a difference saionary process. Table (3): ln WD Wald Tes: Equaion: Uniled Null Hypohesis: C()= C(3)= F-saisic 1.43434 Probabiliy.58117 Chi-square.86869 Probabiliy.395 ΔY = α + λ + θy + δ ΔY 1 1 D ( ln WD ) C Trend ln WD (-1) D ( ln WD (-1)) H : θ = λ = F = 1.43 < F = 7.4 c We can no rejec H,because he F-saisic is less han he 5% criical value,hen we can say ha we are 95% confiden ha he series ln WD follows a difference saionary process. 38

Table (4): ln WI Wald Tes: Equaion: Uniled Null Hypohesis: C()= C(3)= F-saisic 5.331 Probabiliy. Chi-square 1.13 Probabiliy. ΔY = α + λ + θy + δ ΔY 1 1 D ( ln WI ) C Trend ln WI (-1) D ( ln WI (-1)) H : θ = λ = F = 5.3 < F = 7.4 c We can no rejec H,because he F-saisic is less han he 5% criical value,hen we can say ha we are 95% confiden ha he series ln WI follows a difference saionary process. 5..3 Uni Roo Tes Anoher mehod which can be used o check saionariy of he variables is he ADF ess which are performed sequenially show ha no included any lag of he differenced variable for oal waer demand and demand for waer for agriculure is significan, and he ADF es saisics is calculaed wih and wihou ime rend for waer demand for, indusrial, and domesic use respecively for lag lengh of one. Each of he calculaed saisics exceeds he criical value he value of his es saisics wih 5 per cen criical value, as abulaed in Mackinnon (1991), is included in able (5), so he null hypohesis of a uni roo is no rejeced, which implies ha each of he four waer demand series is non saionary in is level. Taking firs differences renders each series saionary excep demand for waer for indusry, wih he ADF saisics in all cases for oal waer demand and demand on waer for agriculural and domesic, respecively) while demand for waer for indusrial use, aking second difference renders i saionary being more negaive han he criical value. Table (5) indicae he saionariy of all he variables. Table (5): Uni Roo Tes Level Wih rend Wihou rend Firs difference Wihou rend Second difference Wihou rend Variable ADF ADF ADF ADF Conclusion ln W -.71 -.58-4.51 _ I(1) ln W -.78 -.84-3..77 _ I(1) A ln W -.19-1.74-3.5 _ I(1) D 39

ln W -.59 -.91-1.39-3.7 I() I 5% criical values. Wihou ime rend ADF-.97 Wih ime rend DF ADF-3.57 5.3. Esimaes of he ARIMA Model 5.3.1 Using he Bes Fiing Model during he Period 1975-5 The bes fiing ARIMA models are esimaed separaely for waer demand series from 1975 o and he ess indicae ha he ARIMA (3,1),(3,1),(1,1)and(1,,1) performs well. The coefficiens and all significan, and hey saisfy he saionariy and inveribiliy condiions. I has he highes adjused R and he lowes AIC and SIC values six candidae models. The correlogram and uni roo ess of he series before and, if necessary, afer differencing are examined for saionariy. Afer empirical examinaion, he mos appropriae models for oal waer demand and demand for waer for agriculural, domesic and indusrial use are deermined as ARIMA (3,1,1), ARIMA (3,1,1), ARIMA (1,1,1) and ARIMA (1,,1) respecively. Using he bes fiing model for oal waer demand, demand for waer for agriculural, domesic and indusrial use are calculaed in ables (6),(7),( 8) and (9). (Wih absolue -raios in parenheses): Δ lnw = α W + e β e ARIMA(3,1,1) + α1δ lnw 1 + α Δ lnw + α 3Δ ln 3 1 1 Δ lnwa = α + α1δ lnwa + α 1 Δ lnw + 3Δ ln + A α W A e 3 β1e 1 ARIMA(3,1,1) lnw = α + α Δ lnw + e β e ARIMA(1,1,1) Δ D 1 D 1 1 1 ΔΔ lnwi = + α1δδ lnwi + e β1e 1 α ARIMA (1,,1) 1 Table (6): d ln W (1975-5) R AIC SC SIG STAT INV ARIMA(3,1,1).63 -.3 -.8 All sign ARIMA(3,1,).45-1.87-1.58 insign ARIMA(5,1,1).6 -.18-1.84 4 insign ARIMA(4,1,1).51-1.95-1.66 insign ARIMA(,1,1).5-1.66-1.47 insign ARIMA(1,1,).3-1.58-1.59 All sign 4

Table (7) d ln (1975-5) WA R AIC SC SIG STAT INV ARIMA(3,1,1).4-1.5-1.3 All sign ARIMA(3,1,). -1.5-1.5 3 insign α < 1 ARIMA(4,1,1).64-1.95-1.66 3 insign ARIMA(1,1,).5-1. -1.13 one insign α < 1. ARIMA(1,1,1).9-1.3-1.9 insign ARIMA(,1,1).19-1.8-1.9 3 insign Table (8) d ln (1975-5) WD R AIC SC SIG STAT INV ARIMA(1,1,1).5 -.3 -.8 All sign ARIMA(1,1,).7 -.17-1.98 One insign ARIMA(,1,).16 -. -1.98 insign ARIMA(3,1,).1 -.11-1.8 3 insign ARIMA(5,1,1) -.8-1.8-1.49 4 insign ARIMA(4,1,) -.18-1.84-1.6 4 insign α < 1 41

Table (9) dd ln (1975-5) WI R AIC SC SIG STAT INV ARIMA(1,,1).48 -.83 -.69 One insign ARIMA(1,,) -. -.19 -.9 One insign α < 1. ARIMA(,,1) -. -.9-1.9 insign ARIMA(3,,1) -.4-1.99-1.75 3 insign ARIMA(4,,1) -.6-1.9-1.61 4 insign ARIMA(1,,).45 -.75 -.56 3 insign Where: R is Adjused R-squared, AIC is Akaike info crierion, SC is Schwarz crierion, SIG is Significan, STAT is Saionary i.e. α < 1, INV is Inveribiliy i.e. β < 1 and insign is insignifican Since he specific ARIMA models ha adequaely describe oal waer demand and demand for waer for agriculure, indusry and domesic are given above, he fied models used for forecasing waer demand for four caegories are given as follows: Toal waer demand (1975-5) Δ lnw =.11+ 1.1Δ lnw 1 +.55Δ lnw 83Δ lnw 3 + e + 1. 59e - values (.86) (6.9) (.6) (4.78) (4.) R =.63 SC=-.8 AIC=-.3 Demand for waer for agriculure (1975-5) Δ lnwa =.5 +.89Δ lnwa +.39Δ lnw.45δ ln A W A 3 + e +.99e - values (3.399) (4.71) (.44) (.59) (9.7) R =.4 SC=1.3 AIC=1.5 Demand for waer for domesic use (1975-5) Δ lnwd =.5 +.77Δ lnwd + e +.96 1 e 1 - values (5.65) (7.76) (6.54) R =.5 SC=-.8 AIC=-.3 Demand for waer for indusry (1975-5) ΔΔ lnwi =. +.55ΔΔ lnwi + e + 1.45 1 e - values (.9) (3.7) (6.6) R =.48 SC=-.69 AIC=-.83 1 1 Tess for whie noise residuals Having decided o use he ARIMA (3,1,1),(3,1,1),(1,1,1) and (1,,1) model for oal waer demand, demand for waer for agriculural, domesic and indusrial use, i is necessary o use five differen ess, o deermine if he residuals are whie noise heses ess are (residual line graph, check he size of he differences beween he fied and acual values, check he residual 1 1 4

correlogram for ARIMA (3,1,1) if whie noise,es for auocorrelaion in he residuals is he Serial Correlaion Lagrange Muliplier (LM), normaliy of he residuals and es if he series is saionary by using uni roo) on i. The key ess o deermine wheher he esimaed from he ARIMA (3, 1, 1), (3, 1, 1), (1, 1, 1) and (1,, 1) model are whie noise. 5.4 Magniude of Forecasing Errors (1-5) Wih he forecas observaions being demand for waer for five years (1-5), able (1) presens he Roo Mean Squared Error (RMSE) forecas accuracy measure of he ARIMA models for oal waer demand, demand for waer for agriculure domesic and indusry,he mean absolue percenage error(mape) of he ARIMA model lower in boh(saic and dynamic). However, he saic ARIMA model forecass were beer han he dynamic ARMA model forecass, hese resul sugges ha he ARIMA (3,1, 1), (3,1, 1), (1,1, 1) and (1,, 1) model performs beer in forecasing oal waer demand, demand for waer for agriculural, domesic and indusrial use. Table (1): Roo Mean Squared Error (RMSE) for Five years Ex pos Forecass of he Logarihm of Demand for Waer, 1-5 RMSE ARIMA Saic Dynamic ln W (3,1,1).1.4 (3,1,).3.6 ln W (3,1,1)..7 A (1,1,1).5.7 ln W D (1,1,1).4.6 (1,1,).18.5 ln W (1,,1)..1 I (1,,).. Table (1) shows he RMSE for he fied ARIMA (3,1, 1), (3,1, 1), (1,1, 1) and (1,, 1) models agains (3,1,),(1,1,1),(1,1,) and (1,,) models according o he forecasing. I suggess ha he models which we esed o forecasing are more accurae han ohers during he period 1-5 The fied values, which are inerpreed as he forecass for he nex five years, are sufficienly close o he acual values for oal waer demand, demand for waer for agriculural, domesic and indusrial use using he ARIMA models. 43

6. Discussion of he Resuls Lawgali Low RMSE for forecasing purposes is a desirable measure of forecasing fi. The RMSE for forecasing compued over he forecas range provides a measure of he abiliy of he model o forecas. For esimaion of fuure waer consumpion he equaions for oal waer demand and demand for agriculural, domesic and indusrial use have been applied. By viewing able (11) which shows he esimaions of fuure waer consumpion during he period 6, he following could be noiced: The fuure esimaions of waer consumpion for all possible purposes indicae o oal waer consumpion increasing from 693.89 million cubic meers in 6 o 1473. million cubic meers in wih an average of compound annual rae of 4.97%. In i is expeced, ha he increase would be 98% of he waer consumpion in 6. Figure (7.): Waer Demands in Libya, 6-1, Million cubic meer 1, 8, 6, 4,, 6 9 1 15 17 Agriculure use Domesic use Indusry use Daa source: able (11) Figure (3) Waer Consumpion in 15% % 83% Agriculure Domesic Indusry Daa source: able (11) 44

Agriculure will coninue o be he major waer consumer; i becomes he bigges consumer of waer as shown in able (11) and figure (3). I represens abou 83%of he esimaed waer consumpion of of he curren waer demand and despie he use of pressurized irrigaion echniques in pracically all farming areas, applicaion raes are sill among he highes in he world. Acually, his grea increase in waer consumpion for agriculural use will affec he waer reserve. Therefore, he way o guide waer consumpion in he agriculure secor has o be necessarily considered. This is mainly due o he unsuiable climaic and soil condiions. Differen scenarios can be presened for he esimaion of fuure waer demand by he agriculural secor. A reasonable one is ha shown in Table (11). Table (11) Waer Demands by Differen Users in Libya, 6- Forecass Year Waer Demand (Million Cubic Meer) Agriculural Domesic Indusrial Use Toal Demand Use Use 6 54.43 895.75 193.71 693.89 7 5384.9 958.96.3 6545.55 8 561.55 1.69 1.7 6834.94 9 5854.41 184.98 18.69 7158.8 1 6171.39 1147.69 7.4 7546.3 11 6194.89 14.19 33.8 763.16 1 683.48 175.93 41.8 83.49 13 717.95 134.17 47.79 876.91 14 7564.41 141.49 54.7 98.97 15 7975.77 1494.9 59.85 973.54 16 845.78 1555.31 65.1 16.1 17 8853.87 1631.83 69.8 1755.5 18 93. 1711.56 73.93 1135.71 19 985.61 1794.76 77.41 11877.78 1311.3 1881.66 8.4 1473. The fuure waer consumpion for domesic purposes will increase from 895.75 million cubic meers in 6 o 1881.66 million cubic meers in wih an average of compound annual rae of 5.4% in. Tha could be explained by he expeced increase of populaion and heir needs of waer. I is worh menioning here, ha he consumed waer quaniy in he norhern regions will depend, in addiion o he groundwaer, on waers obained from desalinaion plans. The waer consumpion of indusrial uses will increase. The waer quaniy o be consumed for indusrial purposes in is expeced o be abou % of he oal waer consumpion. Using waer for indusrial purposes will rely mainly on desalinaed waer. In spie of he posiive relaion beween indusrial expansion and waer demand, and he expeced increase of waer consumpion during he period 6, he consumed quaniy of indusrial purposes is considered small, if compared wih waer quaniies consumed for oher purposes. Indusry consumes he leas waer of all secors, wih a curren share of abou %. A large number of indusries depend on privae sources for waer supply, including desalinaion of seawaer, as in he case of chemical, perochemical, 45

seel, exile and oher indusries. Indusry uses % of he Libyan waer resource. Today he volume of waer used by indusries rises, bu an increase in demand, wih a rae of % is forecas, which increases waer demand for indusry o 8.4 million cubic meers in. 7. Conclusions and recommendaions This sudy has provided he Box-Jenkins approach o modelling ARMA processes. The use of such procedures, paricularly ess for uni roos, improves he validiy of using he ARIMA models for forecasing and allows he forecaser o make informed judgmens a each sep as he resuls are presened by he saisical packages. The dickey-fuller es was used es he saionariy of each individual variable. The es ADF saisic of all variables clearly no rejecs he null hypohesis; his is meaning we are 95% confiden ha he series for all follows a difference saionariy process. Overall, his sudy shows ha by comparing he roo mean squared errors, lower pos-sample forecas errors were obained when ime series mehods, such as he Box- Jenkins ARIMA models, was used. As we discussed in he previous secion he bigges user of waer in in Libya is agriculure (83 %) followed by domesic use (15 %) and indusrial use ( %). Large increases in waer demand during he period 6 o wih very lile recharge from precipiaion have srained Libya s groundwaer resources resuling in declines of groundwaer levels and is qualiy, especially on Medierranean coasal areas where mos of he agriculure, domesic and indusrial aciviies are concenraed Hirji and Ibrekk,( 1). The growh of he populaion has a marked impac on he waer resources of Libya as a resul of increasing demand for agriculural, domesic and indusry which suffered serious depleions and qualiy deerioraion By, he populaion of Libya is projeced o become 1.5 million In 6, he available renewable fresh waer per capia was 459 liers/day i is decreased by populaion growh in o 33 liers Ground waer is considered he main source for irrigaion and domesic uses followed by surface waer. Wih he curren rend in waer use, i is anicipaed ha wihin he nex decade, Libya will have uilised all he poenially available convenional waer resources. The fuure waer supply will srongly depend on desalinaion, reamen, and reuse and o a greaer degree on he improvemen of irrigaion pracices. The mos imporan Recommendaions as follows: Guiding he ciizens hrough he mass media in consumpion of waer in general and groundwaer in agriculural areas paricular. Addiionally, i should be aemped o find ou irrigaion mehods o limi unreasonable consumpion of waer for agriculural purposes. Researches and sudies have o be done as soon as possible in order o find ou he bes mehods, which could be applied for reducing he exhausion of waer for indusrial and agriculural purposes. Issuance of waer legislaions in order o limi exhausion of groundwaer and organize is exploiaion. 46

Mainenance of he waer neworks indoors as well as oudoors, and imposing he use of waer-meers in order o conrol he waer consumpion. I is necessary, ha he General Corporaion of Waer runs periodic forecasing abou available waer sources and waer quaniies expeced o be consumed, so ha boh he individuals as well as he esablishmens are well informed abou he waer siuaion. Paying aenion o he sources of waer desalinaion and rying o develop he echnology praciced in hem wih he aim o reduce he coss of waer desalinaion and enable he curren desalinaion plans o reach he level of designed capaciy, hen waer desalinaion is in fac he main everlasing source of waer. All he insiuions which work in waer shorage issues in order o avoid he in crease waer demand in decisions. Supporing he scienific insiuions and insiues in order o increase he role of researches, educaion, and raining. Monioring, analyzing and forecasing variaion is of prime imporance o Expansion of safely cropped areas by inroducing crops which are more resisan o exreme condiions and by improving mehods of culivaion and waer conservaion. Prioriy has o given o he qualiy of agriculure producions which has o be improved, insead of culivaing more marginal lands. Modern echnologies and approaches in agriculure and waer resources managemen are very imporan o mee increasing demands and o alleviae he effecs of climae change and deserificaion. Improved irrigaion mehods, mechanizaion, ferilizaion, plan proecion and he selecion of crops ha use waer more efficienly are imporan for facing waer shorage. References: AI-Quanibe, H. and Johanon, S. 1985. Municpal demand for waer in Kuwai: mehodological issues and empirical resuls. Waer Resources Research.():pp. 433-438. Arbués, F., Garcıa-Valiñas, M.Á, and Marınez-Espiñeira, R.3.Esimaion of residenial waer demand: a sae-of-he-ar review.journal of Socio- Economics. (3): pp. 81-1. Billings, B, and Day, M. 1989. Demand Managemen Facors in Residenial Waer Use: he Souhern Arizona Experience. Journal of he American Waerworks Associaion.81(3).pp. 58-64. Box, G and Jenkins, G.197.Time Series Analysis: Forecasing and Conrol. San Francisco. Holden-Day Burke, Thomas R. 197. A Municipal Waer Demand Model for The Conerminous Unied Saes, Waer Research Bullein, (4):pp.661-675 De Rooy, J. 1974. Price Responsiveness of he Indusrial Demand for Waer, Waer Resources Research, 1(3): pp.43-46. Dickey, D. and Fuller, W.1979. Disribuion of The Esimaors for Auoregressive Time Series a Uni Roo, Journal of he American 47

Saisical Associaion, 74(366):pp.47-431. EL- Tanawl, A. 1998b. Waer Balance in Libya, Presened in he Firs Arabic Conference on Waer and Deserificaion, Academy of Scienific Research, Cairo (in Arabic). Espey, J. and Shaw, W. 1997. Price Elasiciy of Residenial Demand for Waer: A Mea-Analysis. Waer Resources Research.33(6):pp. 1369-1374. Foser, Henry S. Jr. and Bruce R. Beaie. 1979. Urban Residenial Demand for Waer in he Unied Saes, Land Economics, 55 (1): pp.43-58. Franses, P.1998. Time Series Models for Business and Economic Forecasing. Cambridge Universiy Press.UK. Gujarai, D.N.3.Basic Economerics. 4 h Ediion. McGraw Hill. Fuller, W.A.1985.Inroducion o Saisical Time Series Analysis.New York: John Wiley & Sons. General Planning Council.6.Repor of Libyan Economic and Social Indicaors 196-,Tripoli, Libya. General Waer Auhoriy. 1999. Repor on Waer Siuaion in Libya [in Arabic]. Hirji, R. and Ibrekk, H. 1. Waer sressed and waer scarce counries 5 projecions, Environmenal and waer resource managemen sraegy series.(). Ocober 1. World Ban Environmen Deparmen available from: hp://www.waerinfogr/pages/daa.hm Johansen, S.1988. Saisical Analysis of Co-inegraion Vecors, Journal of Economic Dynamics and Conrol,(1):pp.31-54. Jones, Charles I. 1998.Inroducion o Economic Growh. New York Naief Al-Muairi and Mohammed El-sakka.. Deerminans of he Demand for Fresh Waer in Kuwai: an Economeric Sudy, The Journal of Energy and Developmen.7 (). Omer M. Salem.1998. Waer Shorage in Libya and he Needs for is Managemen for Susaining he Developmen, Waer Auhoriy Repor. Tripoli. Paerson, K.. An inroducion o Applied Economerics: ime series approach. Palgrave. Phillips, P. C. and Perron, P.1988. Tesing for a Uni Roo in Time Series Analysis, Biomerika, 75():pp. 335-346. Phillips, P.1987. Time Series Regression wih a Uni Roo,Economerica. 11(55):pp.77-31. Thomas, R.1993.Inroducory Economerics: Theory and Applicaions. nd Ediion. UK. 48