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AQUIFER OVEREXPLOITATION AND DESERTIFICATION DYNAMICS IN THE GUADALENTIN BASIN.

A MODEL SIMULATION APPROACH

Martínez Vicente, S.1; López Bermúdez, F.2; Ibáñez Puerta, J.3;

Alonso Sarriá, F. [P]2; Martínez Valderrama, J.1

Abstract

The Guadalentín basin is located in South East Spain; the climate is semiarid, being one of the driest areas of Europe, with high inter-annual variability in rainfall and constant aridity. Under these conditions, water becomes a key factor. Socioeconomic and natural systems have strong links that interact and influence each other. The way in which socioeconomic systems have evolved has been closely related to environmental factors, but also in response to political and technological changes. Land use historical changes are related with climate and the exploitation of water resources.

The model proposed has three submodels: socioeconomic, groundwater, and surface water. Such submodels are connected by demand (use of water) and supply (water resources). Water supply is modelled in groundwater and surface water submodels; water demand in the socioeconomic submodel. A DSS (Decision Support System) application has been developed to make it easier to manage the model. Qualitative and quantitative indicators are used to reflect the dynamic behaviour of significant magnitudes such as soil quality and depth, population, employment, rural incomes, water quality and quantity (groundwater and surface water) and water consumption.

This work is part of the Project “Procedimiento de Alerta y Seguimiento de la Desertificación en España”, REN2000-1507-CO3-01 y 03/GLO, funded by CICYT. We are grateful for that funding.

Key words: Upper Guadalentín, desertification, simulation models, aquifer overexploitation

1 Instituto de Economía y Geografía del CSIC. C/Pinar,25, 28006 Madrid 2 Universidad de Murcia. Área de Geografía Física. Campus de La Merced, 30001 Murcia 3 Dpto. de Estadística y Métodos de Gestión en Agricultura de la Universidad Politécnica de Madrid. ETSI Agrónomos, Ciudad Universitaria s/n, 28040 Madrid

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1. Introduction.

The Guadalentín basin is located in South-East Spain and covers an area of 3300 km2 .The climate is semi-arid, being one of the driest areas of Europe. Rainfall is less than 300 mm per year, with a high inter-annual variability and 900-1000 mm of potencial evapotranspiration in most of the territory, being the average temperature around 17 ºC. With these climatic conditions, and taking into account the relief characteristics of the territory, Guadalentin river has an extremely irregular hydrologic regime, being mainly a dry channels system, but experiencing episodic catastrophic flash floods. Natural vegetation is severely limited by climate, and most of the semi-natural ecosystems are shrublands of diverse types, except in the mountains covered by Pinus halepensis forests.

Water has always been an important factor in socio-economic development. However, in the 20th century the situation became more critical. The territory is affected by severe desertification processes as results of deforestation, overgrazing, soil erosion and salinization (López Bermúdez et al., 1999). Socio-economic systems and natural systems have strong links that interact with and influence each other. The way in which socio-economic systems have evolved has been closely related to environmental factors, but also according to changing politics an technology. Historical changes in land use in this basin are in strong relation with climate and the exploitation of water resources (López Bermúdez et.al, 1998, 2002).

The most important change in land use during the 20th century, has been the exhaustive use of water resources, associated with three main processes:

(i) Complete regulation of Guadalentín river by the Valdeinfierno and Puentes reservoirs has made the traditional irrigated lands of the valley more sustainable causing the expansion of permanently irrigated lands;

(ii) Complete regulation of the Guadalentín headwaters, the next possibility to provide new water resources was from groundwater. The over-exploitation has serious ecological effects, being soil salinization, an important process of desertification, the main one;

(iii) Use of water resources from other basins. The area of irrigated land has spread as new water resources have been exploited, so its share in the value of agricultural production has risen to 90%. At the same time drylands and semi-natural ecosystems have become more and more marginal from an economic point of view. Crops in dryland are increasingly maintained by subsidies, even if production has become almost unprofitable.

Each of these processes is associated with problems for the long term sustainability of water resources. In terms of sustainable agriculture a solution must be found to conserve water in terms of quantity and quality. Water is the key factor required to sustain both the human society and the natural environment in the basin. The scale of water consumption on the irrigated lands cannot be sustained, as the climate does not provide sufficient rainfall and the exploitation of the aquifers has reached unacceptable depths. Nowadays, in the Guadalentín basin, the overexploitation of groundwater and soil salinization associated are the main factors of desertification of territory.

In this paper, a Decision Support System (DSS) called HISPASED is presented. It is a tool which combines a Geographic Information System (GIS) with a dynamic simulation model in order to be useful in helping to manage the water resources in the Guadalentín basin. Its general architecture is presented in the following section. Some general issues regarding the integration of GIS and dynamic models are exposed in section 3. The key functional

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relationships of HISPAMED are briefly described in section 4. Section 5 explains the computer support given to the DSS and shows some of its screens, that for the scenarios specification and those for the presentation of results.

2. General architecture of the model.

Figs. 1, 2 and 3 help us to understand the general architecture of HISPASED. The Fig. 1 refers to the thematic modules in which the model is structured.

The socioeconomic module determines population dynamics, water demand, both for irrigation and other purposes, and calculates rural income. The land module determines soil quantity, and its degree of salinity and humidity, as well as the evolution of three of its uses: for dry land, irrigated land, and natural vegetation. The vegetable coverage module determines the evolution of vegetation in dry land areas. The water module models the quantity and quality of the supply of this resource, from both surface and groundwater sources.

In Fig. 2, HISPASED is organised according to three operative submodels: the socioeconomic submodel, comprised of the socioeconomic, land and vegetable coverage modules; the surface, and groundwater submodels, into which the above mentioned water module is divided.

The central causal loop that interrelates the three submodels can be seen in Fig. 2. By taking the difference between the overall availability of water and the demand for this resource, the agricultural uses of the land can then be determined. Finally, according to such uses, the evolution of the quantity and quality of the water resources can be determined.

To a large degree, HISPASED treats the two municipalities that form the Alto Guadalentín area separately: Lorca and Puerto Lumbreras. The model uses similar equation structures for both of these towns. Of course, the parameters take on different values, and as a result, the simulations generate different and specific results for each town. The only exception is the groundwater submodel that reproduces the operation of a single hydrological unit, the Alto Guadalentín Aquifer System. The submodel is comprised of a single structure of equations. As regards the surface water submodel, within the disaggregation per town there is a similar disaggregation for drainage sub-basins.

This disaggregation according to municipalities is due, above all, to the fact that these constitute the smallest spatial units for which an acceptable amount of statistical information is available, although there is of course a lack of municipal data for certain aspects. This is particularly the case for environmental factors (for example, average rainfall, the most representative lithological features, average land slope, available surface waters in the sub-basins in each town). Thus a GIS has been used in order to obtain these variables for each town. In turn, the results obtained with HISPASED for each town are fed into the GIS so as to generate dynamic maps of the most prominent variables. Fig. 3 sets out the relationships between the GIS and HISPASED model.

3. GIS-dynamic modelling integration

3.1.- General perspective

Environmental modelling strategies have been using a GIS approach to deal with the spatial components of models, to pre-process input variables, to run the model or to analyze outputs

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(Goodchild et al., 1993, 1996) specially for hydrological modelling (Maidment & Djokic, 2000; Lyon, 2003).

Dynamic systems approach deals with components that are related each other by matter, energy or information fluxes. The magnitude of those fluxes depends, in an objective, mathematical way of a set of parameters and the values of some input and control variables. When those components represent portions of the earth surface, the problem that arises is to obtain a accurate and representative values for both parameters and variables.

In this case the modelling process implies the integration of continuous environmental variables and socio-economic variables sampled by administrative spatial units as municipality boundaries, scarcely related with environmental units as watershed catchments also to be integrated.

A GIS approach can help to avoid the problem because GIS programs include tools to deal with such ambivalent nature of space as continuous and as a set of discrete features.

3.2.-Case study

A simple case of loose GIS-Dynamic modelling integration systems is presented here. Two separate systems, GIS and a Vensim model, just exchange data. More thigh integration schemas as, memory space sharing or embedded models as GIS modules, have been developed (Fedra, 2003).

In this case study the objective, from a GIS point of view, is to obtain parameters for environmental (catchments) and administrative (municipalities) features, from a set of spatially continuous variables. The basic layers of information included in the GIS database used to feed the model are:

1. Administrative boundaries (municipalities),

2. Digital Elevation Model (DEM),

3. Soil map,

4. Location of Rain-gauges linked to a climatic database incluiding rainfall series,

5. Location of soil sampling points linked to a soil properties database.

Watershed were extracted from the DEM following standard procedures of flow routing through the elevation grid (Lyon, 2003). The watershed boundaries were stored as polygons. Soil properties relevant to infiltration were interpolated using a clasification method (Burrough & McDonnell, 1998) using soil classes as spatial units which are the main factor to explain soil properties variability. Once new layers containing soil properties were interpolated, the curve number method was used to estimate runoff coefficient maps (Chow et al, 1988).

Rainfall values integrated in municipalities were also calculated as spatio-temporal averages from rainfasll series (most of them ranging from 1950 to 2000) in the different raingauges.

Once those environmental factor layers were created, they were sampled using polygons to obtain estimations from each watershed and municipalities as a parameter to be used in the modelling process.

4.- Key functional relationships of HISPAMED

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4.1.- Socioeconomic submodel

Firstly, the main relationships that determine the dynamics of land use are described: dry land, irrigated land, and natural vegetation. Fig. 4 illustrates this description.

There are two basic behaviour relationships: the one that determines the fraction of dry land that is transformed into irrigated land and the other that explains the fraction of natural vegetation that is broken up and transformed into dry land. The first fraction is obtained by an equation that contains, as the explanatory variable, the expected amount of available water for irrigation purposes (the <water supply> variable of Fig. 4 includes the expectations for new water resources). The increase in expectations for water availability produces an increase in the irrigated land surface, although this may later be reverted in the event that the expectations are not fulfilled. The fraction of broken up vegetation manages, as the explanatory variable, the subsidies for dry land crops (almond, olive, cotton, etc.).

Fig.5 describes the population dynamics. As is usual in these kinds of models, the key variable is immigration, which is made to depend on the difference between the working rural population and the offers of jobs in the agricultural sector.

Fig. 6 describes the determination of agricultural revenue as the difference between income and costs. Incomes are derived from dry land crops and irrigated land crops, and are calculated by multiplying the unitary incomes per respective surfaces and adding the dry land subsidies. The costs are obtained by adding together the costs of both dry land and irrigated land. The latter are affected by the evolution of the piezometric level. Obviously, the lower the level of underground aquifers, the higher the cost of water extraction, and of the unitary costs in irrigated land, accordingly.

Fig. 7 shows the basic relationships of the equation for land loss. As can be seen, the variables that explain such a loss are: vegetable coverage, the quantity of torrential rain (rainfall peak), the index of land erosion (related to the topography and structure of the land) and conservation practices. The basic relationships that determine the dynamics of land humidity are shown in Fig. 8. As can be seen, humidity varies as a consequence of water input to the land, both from rain or irrigation, and of water output as a result of drainage or evapotranspiration.

The third behaviour equation related to land refers to land salinity. Fig. 9 shows the causal diagram for this level variable. Total salinity accumulates as a result of salt contributions from chemical treatment of crops.

A key variable of the model is water demand, or more exactly, water consumption, which will only equate the former as long as there are no restrictions on demand, in other words, if the water resources are sufficient. Fig. 10 depicts the causal diagram for this variable.

The total consumption of water is obtained from the sum of the consumption for domestic supply and other uses (industrial, services, etc.) and consumption for irrigation purposes, which as a percentage is more important. The consumption of domestic water and other uses is obtained by multiplying the population by the respective standards for water usage. The consumption for irrigation is obtained by taking the irrigated land area and multiplying it by

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the average water supply per hectare. The latter is obtained by taking a theoretical average volume of water and multiplying it by two factors related to rainfall and availability of water.

4.2.- Surface water submodel

With the surface submodel it is possible to calculate the available surface water resources for the different modules of HISPASED and for the two towns under analysis, i.e. Lorca and Puerto Lumbreras. The amount of surface water available is highly dependent on a series of environmental features, which, as already mentioned, vary considerably according to a detailed spatial scale. The use of the GIS is particularly important for this submodel.

The source of the surface water resources differs according to the various land uses. Thus, the water available for dry land and natural vegetation is mainly derived from monthly rainfall. Whereas, water for irrigated land is basically obtained from the groundwater resources from the reservoir in the Guadalentín basin, regulated by the Water Authorities. Therefore, the submodel calculates the water available for each land use separately.

For dry land and natural vegetation, with the GIS it is possible to obtain aggregate series of monthly rainfall for the towns under consideration. Then, with these series, the series of average monthly rainfall is generated for each town, Fig.11.

In the case of dry land, the amount of available water affects the vegetation density and coverage, and at the same time, the probability of erosion processes and their degree of intensity.

In the case of irrigated land, the calculation of available water is quite different. As well as the rainfall factor, other factors should be taken into consideration associated to the different drainage sub-basins, such as the topography and lithology, their degree of regulation or their relationship with the different municipalities. Thus, as well as the municipalities, the surface water submodel takes into consideration, another level of spatial disaggregation corresponding to the drainage sub-basins that make up the Alto Guadalentín basin. All this is supported by the use of the GIS. Fig. 12 describes in a synthesized way how the amount of available surface water is determined for irrigated land.

By way of the GIS, the drainage sub-basins of the Alto Guadalentín can be determined. For each of them, the available rainfall series are selected and the average monthly rainfall series are calculated. Next, the GIS determines the value, in each sub-basin, of the topographic and lithological parameters, and ground use parameters, that intervene in establishing the rainfall-surface run-off conversion factors. These make it possible to calculate the natural surface run-off generated by rainfall in each sub-basin. Another important factor in establishing available water is the regulation coefficient. With this, the proportion of regulated water resources from reservoirs or other catchment works is calculated, and such resources can therefore be diverted and used for irrigation purposes. Lastly, the relationships between the sub-basins and the municipalities are established in order to determine the amount of surface water finally available for irrigated land in each municipality.

4.3.- Groundwater submodel

As has already been mentioned, the groundwater submodel is not spatially disaggregated, as it refers to a single hydrological unit that comprises the Aquifer System of the Alto Guadalentín. The total reserve of groundwater in the said aquifer is modelled by a single level equation, in the same way as in the numerous studies carried out on this matter (e.g.

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Gisser y Sanchez, 1980; Conrad, 1994; Castro et al., 1994; Tobarra, 1995; Hernandez et al., 1997).

The variation of groundwater reserve (GR) is given by

dGR/dt = nr – AD – TP + cp IW

where nr is the natural recharge or infiltration of useful rain, AD is the aquifer discharge, TP is the total pumping rate, cp is the coefficient of deep percolation and IW is the total amount of water used for agricultural irrigation (per unit of time).

It is initially assumed that nr and cp are constant in time. The determination of TP is related to the extraction cost and to the quality of the aquifer water, as well as to the cost and availability of other alternative sources of water supply. IW is obtained by adding together the separate demands that agriculture makes to each of the different water supply sources. Such demands are distributed depending on the relative costs of the various sources. Thus, the determination of both variables constitutes, as has already been said, the central link between the three submodels of HISPASED.

DN is the sum of the water discharged to the surface by the aquifer, by water outcrops, and of the water discharged to other aquifer systems through deep underground connections. The graph in Fig.13 represents the functional relationship between the said discharge and the water reserve RA.

Curve b in Fig. 13 could correspond to an aquifer with both surface and underground discharges, while curve a would correspond to an aquifer with surface discharges only. In this analysis we have adopted the latter option, so DN represents, as long as it does not have a zero value, a flow of surface water whose value is transferred to the surface water submodel.

The average piezometric level in the aquifer, PZ, is also related to the water reserve, GR, by way of a tabulated function. This has been obtained from a series of data provided by the Consejería de Política Territorial y Obras Públicas (Council of Territorial Policy and Public Works) of the Autonomous Government of Murcia (1988) (Fig.14). As already mentioned, PZ is used in the socioeconomic submodel to estimate the average cost of pumping water from aquifers.

5. Computing support for HISPASED

The HISPASED model is programmed in Vensim 4.0D4 and is presented in a DSS (Decision Support System) created with Sable1.05. Figures 4 to 10 have been generated with the Vensim programmed model. Figures 15 to 19 show examples of screens generated with Sable, so as to give an idea of the model simulations and its principle outputs.

4 Vensim is a trademark registered by Ventana Systems. 5 Sable is a trademark registered by BRB Consulting.

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6. Conclusions

The Guadalentin basin is a territorial unit in which physical factors and human actions converge to create one of he severest cases of desertification in Europe. The combination of a whole set of degradation factors (climatic, geological, geomorphological, hydrological and socioeconomic) led to the selection of the basin as one of the target areas for the investigation of desertification processes in Spain.

The modelling approach used for such a research, which combines GIS with system dynamic models, has provided very satisfactory results until now. Firstly, because of the synergic benefits coming from the integration itself: the GIS supplies essential information for the parameter and variable definitions of the dynamic model and this constitutes an underlying and comprehensive theory which allows for a better understanding of all the information contained in the GIS. In addition, the final DSS that results from such integration has provided to be useful to the purposes it was conceived for, that is to say, the rational management of water resources in the Guadalentín basin.

Acknowledgements

This work is part of the Project “Procedimiento de Alerta y Evaluación de la Desertificación en España”, REN2000-1507-CO3-01 & 03/GLO, funded by CICYT. Spanish National Plan of I+R. The support is gratefully acknowledge by the authors.

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References

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CASTRO, J.P.; MARTÍNEZ, C.; RUBIO, S. 1994. Modelo de gestión de un acuífero. En Análisis Económico y Gestión de Recursos Naturales. Azqueta, D.; Ferreiro, A. (Eds.) Alianza Editorial. 259-292.

CHOW,V.T.; MAIDMENT,D.R.; MAYS,L.W., 1988, Applied hydrology. McGraw Hill 583 pp.

CONRAD, J.M. 1994. Economía y gestión de los recursos hídricos: acuíferos. En Análisis Económico y Gestión de Recursos Naturales. Azqueta, D.; Ferreiro, A. (eds.) Alianza Editorial. 249-258.

CONSEJERÍA DE POLÍTICA TERRITORIAL Y OBRAS PÚBLICAS. 1988. El sistema acuífero del Alto Guadalentín. Dirección General de Recursos Hidráulicos. Comunidad Autónoma de la Región de Murcia.

FEDRA, K. 1993. GIS and environmental modelling in Goodchild,M.F. ; Parkx,B.O. & Steyaert,L.T. (Eds.) Environmental Modelling with GIS. Oxford University Press. 35-46.

GISSER, M.; SANCHEZ, D.A. 1980. Competition versus optimal control in groundwater pumping. Water Resources Research, 16 (4). 638-642.

GOODCHILD,M.F. ; PARKS, B.O. & STEYAERT,L.T. (Eds.) 1993 Environmental Modelling with GIS. Oxford University Press. 485 pp.

GOODCHILD,M.F.; STEYAERT,L.T. & PARKS, B.O. (Eds.) 1996 GIS and Environmental Modelling. Progress and Research issues. GIS World books. 486 pp.

HERNÁNDEZ, E.; LÓPEZ, F.; SÁNCHEZ, M.I.; RAMÍREZ, L. 1997. Desarrollo sostenible. Estudio de un caso práctico en la Región de Murcia. Ecología: Economía. Universidad de Murcia.

LYON,J.G. (Ed.) 2003. GIS for water resources and watershed management. Taylor & Francis. 266 pp.

LOPEZ BERMÚDEZ, F.; ROMERO DIAZ, A.; CABEZAS, F.; ROSO SERRANO, L.; MARTINEZ FERNANDEZ, F.;BOER, M.; DEL BARRIO ,G., 1998: The Guadalentin Basin, Murcia, Spain. In P.Mairota, J.Thornes & N.Geeson, Eds. Atlas of Mediterranean Environments in Europe. Wiley .Chichester, pp.130-142

LÓPEZ BERMÚDEZ, F.; BARBERÁ;G.G.; ALONSO SARRÍA,F.; ROMERO DÍAZ,A., 1999: Guadalentín Basin (Murcia, Spain): An area threatened by Desertification. In P. Balabanis, D.Peter, A.Ghazi & M.Tsogas, Eds. Mediterranean Desertification. Research results and policy implications. European Commission. Directorate-General Research. EUR 19303. Brussels, pp. 399-422

LÓPEZ BERMÚDEZ,F.; BARBERÁ, G.G.; ALONSO SARRÍA,F.; BELMONTE SERRATO,F., 2002: Natural Resources in the Guadaletín Basin (South-East Spain): Water as a Key factor. In N.A.Geeseon, C.J. Brandt & J.Thornes, Eds., Mediterranean Desertification: A Mosaic of Process and Responses. Wiley.Chichester., pp. 233-245.

MAIDMENT,D.; DJOKIC,D. (Ed.) 2000. Hydrologic anf hydraulic Modelling Support with Geographic Information Systems. ESRI Press. 216 pp.

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TOBARRA, P. 1995. Estudio del Alto Guadalentín desde la perspectiva económica de la gestión del agua subterránea. Caja de Ahorros del Mediterráneo. Murcia, 351 pp.

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FIGURES

FIGURE 1. HISPASED Thematic Modules

Figure 2. HISPASED Submodules

WATER · Surface · Ground · Quantity · Quality

VEGETABLE COVERAGE · Degree · Diversity · Durability

SOIL · Quantity · Salinity and nitrates· Humidity

Rainfall

Surface run-off Fertility

SOCIOECONOMY · Population · Job market · Economy

Conditioning factors

Supply

Demand

Water Resources

GROUND- WATERS

SURFACE WATERS

Water demand

Area

Town Rural income

Land uses

SOCIOECONOMIC SUBMODULE

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FIGURE 3. Relationships between HISPASED and GIS

irrigated land surface in the area

irrigated land surface dry land - irrigated land transformation

dry land - irrigated land fraction

dry land surface

<Total water consumption in the area>

<Water supply>

broken up vegetation

breaking up fraction

vegetation surface

<Subsidies effect dry land>

<Types dry land subsidies<total surface>

<max irrigated land surface>

Figure 4. Basic relationships in determining land use

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Rural working population Total population

Activity rate Deaths Emigrations

Immigrations Births

Agricultural employment

Emigration rate

<Agricultural activity rate>

<Unitary irrigated land employment>

dry land employment> <Unitary

<irrigated land>

<dry land>

<Mortality rate>

<Birth rate >

Figure 5. Basic relationships in the population model

Agricultural revenue Agricultural costs

Agricultural income

Agricultural subsidies

Unitary irrigated land costs

<Dry land unitary costs >

< Irrigated land agricultural prices>

<Dry land agricultural prices>

<dry land surface>

<irrigated land surface>

<Types subsidies irrigated land>

<Types subsidies dry land> < Piezométrica

level>

Figure 6. Basic relationships in determining agricultural revenue

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auxiliary land loss

<Vegetable coverage> <Land conservation practices factor>

<Topographic factor>

<Rain erosion index>

<Discharge surface>

Unitary land quantity in area

Land quantity

< total surface>

Land Loss

Figure 7. Basic relationships in determining land quantity

Land humidity Water input to land

Water output from land

<Water consumption for irrigation>

<Total rainfall>

Land drainage

Evapotranspiration <temperature>

Figure 8. Basic relationships in determining land humidity

Land salinity

Variation in land salinity

Water consumption for irrigation

Pesticide treatments

Volume per hectare

Theoretical water volume

<Total rainfall >

<irrigated land surface>

<dry land surface>

<Pesticide treatments on irrigated land>

< Pesticide treatments on dry land>

<total surface>

Figure 9. Basic relationships in determining land salinity

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Total water consumption

Water consumption for other uses Water consumption

for irrigation

Water consumption domestic uses

<Total population>

<Water volume per hectare>

<irrigated land surface>

<Standard domestic use>

Annual total water consumption in area Total accumulated water

consumption in area

Total water consumption in area

<Water consumption in other towns>

Figure 10. Basic relationships in determining water consumption

Monthly rainfall series meteorological station

Average monthly rainfall

per town

Available water for Natural Vegetation

Available water for Dry Land

Aggregate series per municipality

GIS

Figure 11. Determination of available water for dry land and natural vegetation

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useful water from surface run-off

per sub-basin

Regulation coefficient per

sub-basin

available water per sub-basin

available surface water for irrigation per municipality

monthly rainfall per sub-basin

sub-basins aggregation per

municipality

rainfall series meteorological stations

topographic parameters

lithological factors land uses

Rainfall- surface run-off

conversion factors

GEOGRAPHICAL INFORMATION SYSTEM

ALTO GUADALENTIN SUB-BASINS

MUNICIPALITIES

FIGURE 12. Diagram of the determination of available surface water for irrigated land

AD(GR)

GR GRo

nro

b

a gr*

GR*b GR*

a

FIGURE 13. The natural discharge function AD(GR)

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400

200

0

-200

-400 0 160 320 480 640 800 960 1120 1280 1440 1600

GR (Hm3)

PZ (m.s.n.m.)

FIGURE 14. Relationship between the piezometric level (PZ) and the underground water

reserve (GR)

Source: Alto Guadalentín Aquifer System. 1988. Consejería de Política Territorial y Obras Públicas. CARM.

FIGURE 15. Screen of main menu of the model

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FIGURE 16. Evolution of the vegetable coverage in the area

FIGURE 17. Irrigated land surface per town

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FIGURE 18. Evolution of the quantity, humidity and salinity of the land in the area

FIGURE 19. Evolution of the underground water reserve