Characterization of the infiltration rate in Las Tablas de Daimiel National Park, Central Spain

HYDROLOGICAL PROCESSES

(wileyonlinelibrary.com) DOI: 10.1002/hyp.8134

Characterization of the infiltration rate in Las Tablas deDaimiel National Park, Central Spain

Vicente Navarro,* Beatriz Garcıa and Laura AsensioGeoenvironmental Group, Civil Engineering Department, University of Castilla-La Mancha, Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain

Abstract:

This article presents the characterization of the infiltration rate in the area known as ‘Las Canas’ which is part of Las Tablasde Daimiel National Park, Central Spain. Available information was used for direct identification and while the results variedwidely, it was proven that a functional dependence exists between the infiltration rate and the inundated area. After examiningthe structure of this dependence more closely, the most appropriate model was deemed to be a bilinear model. With thesebasic foundations, data from years 1997, 1998, 2003 and 2005 were used to identify the three parameters of the model. Highlysatisfactory identifications were obtained. The model was then calibrated and the drying processes from 1996, 2000 and 2003were simulated with considerable accuracy since the standard deviation was only 4 ha for a total of 400 inundated ha. Themodel was used to estimate the evolution that the inundated area would undergo after introducing contributions of treatedsewage effluents. Even though it was assumed that the results would entail twice as many errors as the standard deviation, theydid, however, allow us to provide a concise description of the behaviour of the system. Consequently, elements of judgementthat highlight the advisability of the application of treated sewage effluents in Las Canas, from a hydrological standpoint, wereobtained. Copyright 2011 John Wiley & Sons, Ltd.

KEY WORDS infiltration; water budget; wetland; inundation

Received 5 November 2010; Accepted 8 April 2011

INTRODUCTION

Las Tablas de Daimiel National Park (TDNP; 1928 ha) isthe most outstanding resource of the wetlands that makeup the ‘Mancha Humeda’ (25 000 ha, Central Spain;see Figure 1a). This system was declared a BiosphereReserve in 1980 by the United Nations Educational, Sci-entific and Cultural Organization and a Ramsar Conven-tion site in 1982. The intensive water abstractions carriedout in the West Mancha aquifer (regional aquifer beneaththe Mancha Humeda) have caused the widespread low-ering of the water tables (see for instance Llamas,1988; Cruces et al., 2000; Fornes et al., 2000; Brom-ley et al., 2001; Martınez, 2001; Custodio, 2002; Conanet al., 2003), disconnecting the wetlands from ground-water inputs. This led to major environmental damage,particularly in TDNP (Cirujano et al., 1996; Alvarez-Cobelas et al., 2001). In the past, a number of differentmeasures were taken in an attempt to mitigate theserepercussions. This was the reason for the constructionof the dams of Puente Navarro (in 1985) and More-nillo (in 1988; see Figure 1b). In addition, at differentpoints in time, water was transferred from the TagusRiver. Ten wells were also drilled to supply water to themain ‘lagunas’ (see Florın et al., 1993; or ‘tablazos’ inSpanish). However, critical situations continue to reoc-cur. In December 2009, of the 500 ha which, according

* Correspondence to: Vicente Navarro, Geoenvironmental Group, CivilEngineering Department, University of Castilla-La Mancha, Avda.Camilo Jose Cela s/n, 13071 Ciudad Real, Spain.E-mail: [email protected]

to the TDNP-Technical Staff (TDNP-TS), must remainflooded to preserve the area’s most important values (Pas-tor, 1996; Ruano, 1996), barely 10 ha were inundated.

Of these 500 ha, 400 ha pertained to the area knownas ‘Las Canas’ (Figure 1b), located between the PuenteNavarro and the Morenillo dams. At the present time,the possibility of using treated sewage effluents from thenearby towns (roughly 10 Mm3/ year) is being consid-ered a means of inundating this zone. The waters to beused would undergo the appropriate treatment so as tocomply with all the quality requirements as deemed nec-essary. The key to determine whether the proposal isfeasible is to obtain a reliable assessment of the resultinginundated area. To this end and taking into account theexperience that has been acquired using water budgetsfor effective water-resource and environmental planningand management (see Brush et al., 2004; Dalton et al.,2004; Healy et al., 2007, among others), the use of adynamic water budget was decided on. This would makeit possible to describe the evolution of the inundated areaas the water mass balance is calculated (see for instance,Lindley et al., 1995; Walton et al., 1996; Saxton and Wil-ley, 2006). As will be discussed later, these calculationsare based on reliable information regarding the follow-ing: morphometry, run-on R (L3/T; which comes mainlyfrom the northern area), precipitation p (L/T) and evapo-transpiration e (L/T). Therefore, as is customary in manyhydrologic problems, the infiltration rate is the term ofthe water budget that creates the greatest degree of uncer-tainty. Fortunately valuable information is available tosolve this problem. The TDNP-TS has been monitoring

Copyright 2011 John Wiley & Sons, Ltd.

Hydrol. Process. , 367–Published online 12 May 2011 in Wiley Online Library

26 378 (2012)

V. NAVARRO, B. GARCIA AND L. ASENSIO

10 Km

Aquifer 04.04

Mancha HúmedaBiosphere Reserve

Tablas de DaimielNational Park

Upper GuadianaBasin

N

Guadiana river

LASTABLAS

Morenillo dam

Molemocho mill

Masegar

Pan Island

0

km

Cañada del G

ato

Cachón de la Leona

Cañada Lobosa

Asnos Island

Gigüela channel

PuenteNavarro

dam

Guadiana river

1

2 34

5

LimnimetersWeather Station

Tablazo delas Águilas

Pasarelas

Boreholes

LAS CAÑAS

Gigüela river

0

N

(a)

(b)

Figure 1. (a) Situation of the Upper Guadiana Basin, aquifer 04Ð04, and Mancha Humeda Biosphere Reserve and (b) detailed plan view of the TDNP

the inundated area of the Park since 1992. Specifically,reliable data have been obtained on the drying processesin Las Canas during the summer/autumn period of years1996, 1997, 1998, 2000, 2002, 2003 and 2005 (Figure 2).These data comprise the foundations of this article whichcharacterizes infiltration rate through the application ofidentification techniques for the purpose of obtainingplausible estimations of the evolution of the inundatedarea.

CHARACTERIZATION OF THE INFILTRATIONRATE

The simplest method to obtain information on the infil-tration rate ir (L/T; which, like p and e, are areal averagevalues associated to the inundated area A) is to make thecomputation based on the water budget equation. If thesimplified model outlined in Figure 3 is adopted to char-acterize the water mass balance (similar, e.g. to the model


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Hydrol. Process. 26, 367–378 (2012)

INFILTRATION RATE IN TDNP

used by Saxton and Willey, 2006, in the SPAW (Soil-Plant-Air-Water) program), the generic expression of thewater budget (see Haan et al., 1994) may be detailed in

Figure 2. Time series of the inundation data

Las Canas as follows:

dV

dtD MD�t� � PND�t� C R�t�

C [p�t� � e�t� � ir�t�] A�t� �1�

where V is the inundated volume (L3), MD (L3/T) isthe inflow rate from Morenillo dam, PND (L3/T) is theoutflow rate that will cross Puente Navarro dam and Ais the inundated area (L2). On the basis of the availableinformation on R, p, e, V and A, on analysing situationsin which both MD and PND (L3/T) were virtually null, ifEquation (1) is discretized in 1-day time steps using theEuler method (Press et al., 2002), the following will beobtained:

iri D Ri C �Vi � ViC1�

AiC pi � ei �2�

where the subscript ‘i’ indicates the value of the variableon the ith day. Based on the data presented in graph formin Figure 2, using the hypsometric curve of Figure 4, it ispossible to obtain the infiltration rates shown in Figure 5(black dots). The great dispersion seen is not a conse-quence of the method of time discretization, since, forthe problems analysed here, it was found that on the use

Water surface elevation above sea level (m)

Are

a (h

a)

Vol

ume

(Mm

3 )

Figure 4. Hypsometric curve of Las Canas

Puente Navarrodam

Q outflow

Evapotranspiration Precipitation

Runon

Q inflow

Infiltration

Morenillo dam

Figure 3. Schematic representation of the water budget model in Las Canas. Figure out of scale


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Hydrol. Process. 26, 367–378 (2012)


of 1-day time steps, the Euler method or the fourth-orderRunge Kutta method (Press et al., 2002) yielded prac-tically identical results. Nor can it be attributed to thepotential distortion introduced owing to problems withthe quality of the data used. With regard to the run-on (calculated according to the indications found in theNational Soil Conservation Service’s National Engineer-ing Handbook Section 4, Natural Resources ConservationService (NRCS), 2003), in 851 of the 854 data entriesthat were processed (total number of days in the sevenperiods under consideration), it had a value of less than0Ð2 mm/day. There were only two days where R wasbetween 0Ð2 and 0Ð4 mm/day, and one other day wherethe value was equal to 2 mm/day. Evapotranspiration wascalculated on the basis of data gathered in the class Aevaporimeter situated in the weather station of the Park(from which the precipitation data were also taken) andon the basis of the experimental data of transpiration mea-sured during 1997 and 1998 by Sanchez-Carrillo et al.(2004) for different crops, percentage of macrophytecover, open water/macrophyte cover ratio and evapora-tion rate. For this reason, since a reliable digital elevationmodel (based on recent data, spring, 2007, of the TDNP-TS) was used to obtain the hypsometric curve (Figure 4),the dispersion is probably due to reading errors. The read-ing of levels shown in Figure 2 were taken visually androunded off to the nearest centimetre. In this operation,the same criterion was not always applied, as it dependedon the person taking the reading. As a result, an error mayhave been introduced into terms Vi � ViC1.

To mitigate this effect, it is advisable to identify themean values of ir. Therefore, first of all, a mean valuewas identified for each of the seven data series available(summer/autumn 1996, 1997, 1998, 2000, 2002, 2003 and2005). If the mean value of the inundated area in eachone of these series is plotted versus the value identi-fied, then the result will be the grey dots in Figure 5.Although the dispersion has decreased significantly, itis still high. Moreover, the use of a single ir value for

Figure 5. Infiltration rates obtained from the data depicted in Figure 2.Values obtained with Equation (2) (black dots), values identified for eachof the eight time series available (grey dots) and values identified after

dividing each of these series into ten segments (white dots)

the entire drying process is debatable, as is its assign-ment to the mean inundated area. To be able to betterconsider the presumed relationship between ir and A,each of the seven series of available data was subdividedinto ten segments, and the ir value associated with eachone was identified. This resulted in the white dots drawnin Figure 5. In this case, the assignment of ir to the meanvalue of A in the segment is acceptable, given that thevariation of A in all the segments considered is alwaysunder 50 ha. Again, dispersion is substantially reduced,although the value is still significant. It is important tonote that the 25 white dots drawn are actually only partof the 70 values identified. Since the problem is a sim-ple one [Equation (1) with MD D PND D 0], and byidentifying just one parameter (ir), in each segment, asystematic global search was carried out with the helpof a grid-search algorithm (Neumaier, 2004). It was thusfound that the values identified were not associated withlocal minimums. In this way, the selection was made ofthe 25 values for which the form of root mean squareerror (RMSE) presented no doubt as to the quality of theidentification.

Despite the dispersion, all the results in Figure 5 indi-cate that ir decreases when A increases. This persistenttrend would appear to confirm the existence of a func-tional dependence between the infiltration rate and theinundated area, which, in keeping with this figure, mayfit a linear model. Before accepting this hypothesis, how-ever, it was decided to conduct a more in-depth analysisof the structure of the variation of the infiltration rate inLas Canas.

STRUCTURE OF THE VARIATION OF THEINFILTRATION RATE

Like in other studies on wetlands or infiltration ponds(Lindley et al., 1995; Merritt and Konikow, 2000; Saxtonand Willey, 2006), it was assumed that after the contri-bution of treated sewage effluents, the effective hydraulicconductivity (understood in the sense used by Vigiaket al., 2006, and equivalent to the hydraulic conductiv-ity of the wet zone of Bouwer, 1986) reaches a steadyvalue, which remains practically constant throughout theanalysis. Therefore, provided a surface water level Z, theinfiltration rate irŁ�X, Z�, associated with an elevation X,was calculated as follows:

irŁ�X, Z� D KAV[1 C �Z � X�/L�X�] �3�

where Z defines the surface water level (elevation abovesea level) and X is an elevation between 602Ð0 m (eleva-tion of the Puente Navarro Dam foundation, see Figure 6)and Z. L is the distance from the ground level to theregional water table. It is defined as L D L1 C L2 C L3 CL4 C L5. The thicknesses Lk �k D 1, . . . , 5� correspond,respectively, to materials M1 (granular soil), M2 (mud),M3 (clay), M4 (peat) and M5 (sediments) identified inFigure 6. In reality for any X, these thicknesses will havelateral changes. Hence, the transect drawn in Figure 6 is


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Figure 6. Synthetic transect representing the hydrogeological configuration of Las Canas. Figure out of scale. Numbers define elevation above sealevel, in meters. Data from Aguilera et al. (2009), Domınguez-Castro et al. (2006), Garcıa (1996) and Garcıa Hidalgo et al. (1995)

simply a schematic approximation of the complex hydro-geological structure of the soil beneath Las Canas, whichcoincides with the available information (see boreholesin Figure 1b). For each Z, different profiles associatedwith the same X would probably have associated differ-ent infiltration rates. Consequently, irŁ is not a functionof X. In view of the variability of the system verified bythe TDNP-TS, even when additional information is avail-able, obtaining a ‘deterministic’ distribution of irŁ is nota simple task. However, the objective of this analysis isto characterize the structure of the infiltration, rather thanobtaining an expression to calculate it in an exact way. Itwas assumed that by introducing in Equation (3) the val-ues of Lk indicated in Figure 6, it is obtained some kindof characteristic value of the infiltration rate associatedwith the X-contour line which provides a fair/workingapproximation of this structure. So that, KAV (the har-monic mean of the saturated hydraulic conductivity, KS)was computed as follows:

KAV�X� D L�X�

L5�X�

KS5C L4�X�

KS4C

∫ L1�X�CL2�X�CL3�X�

0

dl

KS�l��4�

After defining irŁ �X, Z�, a mean value of the infiltra-tion rate ir associated with Z can be defined as:

ir�Z� D 1

S�Z�

∫ Z

602ir�X, Z� P�X� dX

³ 1

A�Z�

∫ Z

602ir�X, Z� dA�X� �5�

where S(Z) is the lake-floor area (wet area) associatedwith Z and P is the shore line related with each X.A(Z) is the surface area associated with Z. Given thelow topography of whole Las Canas system (land slopeof 0Ð03%), S(Z) is practically equal to A(Z), and P(X)dX is roughly equal to dA(X). Both approximations areintroduced in the last term of Equation (5), which defineshow ir(Z) was computed.

Table I. Hydraulic conductivities Ks (in m/s) used to obtain therelative infiltration rates rir shown in Figure 8

Material Description Upper Intermediate Lower

P1 Sandy soil 1 ð 10�3 5Ð5 ð 10�4 1 ð 10�4

P2 Mud 1 ð 10�5 5Ð5 ð 10�6 1 ð 10�6

P3 Clay 1 ð 10�8 5Ð5 ð 10�9 1 ð 10�9

M4 Peat 2 ð 10�8 1Ð1 ð 10�8 2 ð 10�9

M5 Sediments 1 ð 10�3 5Ð5 ð 10�4 1 ð 10�4

Table II. Regional water table levels (elevation above sea level)used to find the relative infiltration rates rir shown in Figure 8

Water table level (m)

Upper 594Intermediate 592Lower 590

It is important to point out that soils 1, 2 and 3 have aninternal structure associated with the progressive evolu-tion of a fluvial regime ranging from high- to low-energyevents (Garcıa-Hidalgo et al., 1995; Domınguez-Castroet al., 2006). This structure can be outlined by assumingthat any profile has a ‘textural evolution’ characterized bythe textures of points P1, P2 and P3 (bottom, centre andtop of soils 1, 2 and 3; Figure 6). If P1 has a sandy soil,then P2 will have mud, and P3, clay. This variability inthe properties was introduced into Equation (4) throughthe integral of its denominator. It was calculated byassuming a linear variation of the hydraulic conductivitybetween P1 and P2 and between P2 and P3. Using thisapproach, the geometry defined in Figure 6, the hydraulicconductivities of Table I and the regional water tablelevels of Table II, from Equation (5), it was possibleto obtain upper, intermediate and lower estimations ofKAV for each x. After introducing these estimations intoEquation (5) through Equation (3), the values of the rel-ative infiltration rate rir shown in Figure 7 were found.The rir was defined as �ir � irMIN�/�irMAX � irMIN�. As


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Figure 7. Relative infiltration rates rir and relative volume development rvd

can be seen in the three cases, the rir varies in the sameway. If the values associated with low inundation (inun-dated area lower than 50 ha) are omitted, a decreasingtrend is observed to roughly 280 ha, and after this, thereis an increasing trend. It is interesting to point out that thischange also takes place in the relative volume develop-ment rvd (Figure 7), defined as �vd � vdMIN�/�vdMAX �vdMIN�, where vd is the volume development ratio. Thisis obtained as vd D 3 dMEAN/dMAX, where dMEAN isthe mean water depth and dMAX is the maximum waterdepth. As higher vd is, more flat is the lake morphol-ogy (see Hutchinson, 1957; Hakanson, 1982). In LasCanas, vdMAX D 0Ð877 and vdMIN D 0Ð040. This lowvalue occurs for reduced inundation areas, where the exis-tence of localized holes has a relevant impact. What is thereason for these changes? When the inundated area cov-ers 280 ha, the water level reaches the downstream slopeof the Morenillo dam, changing the morphometry of LasCanas. The system begins to function as a reservoir, withthe water depth increasing more quickly than the inun-dated area. The hydraulic gradient also becomes greater,causing the infiltration rate to rise. However, before the280-ha mark is reached, the varying trend of the infil-tration rate is controlled by the ‘natural’ hydrogeologicalconfiguration, at which point the ‘reservoir-type’ dynam-ics are imposed. Therefore, instead of introducing thelinear model, a bilinear model of ir according to A wasadopted.

IDENTIFICATION OF THE INFILTRATIONPARAMETERS

The identification work was focused on the three parame-ters ir1, ir2 and ir3 that characterize the bilinear model ofir : the infiltration rates associated with a 0 inundated area

Table III. Search space of the identification process carried outto characterize the infiltration parameters

Minimum (mm/day) Maximum (mm/day)

ir1 5 10ir2 3 8ir3 1 6

(ir1) and an inundated area of 280 ha (inundated area inLas Canas at which the water level reaches the down-stream slope of the Morenillo dam and the system beginsto function as a reservoir; ir2) and 434 ha (maximuminundated area in Las Canas; ir3).

To identify these three parameters, a global identifica-tion based on a grid-search algorithm (Neumaier, 2004)was carried out once again. The search space used isdefined in Table III. Of the seven available data series,only four were used for identification purposes (years1997, 1998, 2002 and 2005). The other three series (years1996, 2000 and 2003) were used to calibrate the validityof the model.

In Figure 8, the isolines of the RMSE between themodelled and the experimental data are represented. Ascan be observed, the optimum values (minimum error)of the parameters (Table IV) were identified with a gooddegree of certainty. Moreover, as can be corroboratedin Figure 9, with the parameters that have been identi-fied, the dynamic water budget allows for the accuratereproduction of the three data series used to calibrate themodel. It is not only the visual adjustment that is good.The mean value of the absolute difference between themeasured and modelled inundated area is 6Ð4 ha, witha standard deviation of 4 ha. Therefore, it would seemreasonable to express confidence in the proposed model.


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Figure 8. Isolines of the RMSE (ha) for ir2 D ir2OPT D 5 mm/day

Table IV. Optimum values of ir1, ir2 and ir3

Optimum (mm/day)

ir1 7Ð25ir2 5Ð00ir3 3Ð25

The most striking result of those presented in Table IVis the low value of ir3, since, in keeping with Figure 7,it would seem logical to expect a higher value than thatof ir2. However, when the system begins to function likea reservoir, the wet area incorporates zones which, undernatural conditions, would seldom be inundated. Accord-ing to the research carried out by different authors (see,for instance, Scanlon et al., 1999), in these zones, theinfiltration rate is lower than the one in topographicallydepressed zones where infiltration occurs naturally. As aresult, when these zones become inundated, ir decreases.Figure 7 does not exhibit this behaviour because here itwas allowed that for any given x, the transect drawn inFigure 6 is valid even when it begins to function as areservoir, which is not true. While the function schemebased on a bilinear model is still valid, after 280 ha, irdoes not increase.

SIMULATION OF THE EVOLUTION OF THEINUNDATED AREA

Once the ir model was defined, it was interestingto simulate the evolution of the inundated area byadding treated sewage effluents to the system. Thepurpose of this simulation was to assess the efficiencyof applying these effluents, keeping the year 2027 inmind, which is the deadline for full compliance of theEU Water Framework Directive 2000/60. To do this,Equation (1) was formulated as follows:

dV

dtD Q�t� C R�t� C [p�t� � e�t� � ir�t�] Ð A�t� �6�

where Q is the inflow of treated sewage effluents. Toestimate the total sewage effluent, ten villages near the

Park have been considered. Q was estimated on thebasis of data from the Confederacion Hidrografica delGuadiana (Public Administration responsible for watermanagement in the West Mancha aquifer) and on datarelated to water consumption, the production of wastewa-ters and population growth from the Instituto Nacional deEstadıstica (Public Administration responsible for statis-tics data management in Spain). This resulted in theprediction of the effluent flows QŁ, 10 Mm3 in 2010 thatrise to 12 Mm3 in 2027, monthly distribution as shownin Figure 10a.

The sewage effluents must be treated appropriately toreach an optimal set of chemical parameters (whose def-inition is outside the scope of this article) before beingapplied to the Park. Two hypotheses were considered.In the first one, an improved chemical treatment in thesewage treatment plants is considered. Afterward, thewater will be transported to Las Canas by a pipe. So, itwas assumed that there was only a 10% loss of the efflu-ents (Q D 0Ð9QŁ). In the second one, a treatment by nat-ural processes in a constructed wetland (EnvironmentalProtection Agency, 1988), after the existing conventionaltreatments in the sewage treatment plants, is contem-plated. This water must be of a quality good enough notto affect the aquifer when infiltrating in the wetland. Itwas assumed that both this infiltration and the evapotran-spiration will produce a 40% loss of the potential effluentinflow (Q D 0Ð6QŁ). Both approaches (improved chemi-cal and wetland treatments) must guarantee the requiredremoval of nutrients and organic matter. Therefore, theeffect of clogging in Las Canas is likely to be small.

In addition to these two hypotheses of water loss(inflow Q of 0Ð9QŁ and 0Ð6QŁ), it was consideredan additional ‘reference simulation’, in which it wasassumed no water inflow (Q D 0).

For the three hypotheses of Q (0, 0Ð6QŁ and 0Ð9QŁ), thesystem’s response was simulated considering one rainyseries and one dry series. The former was found usingas ‘base data’ the information associated with the seriesformed by the 17 years in which the average rainfallwas equal to the 65th percentile of the average rainfallof the 31 series of 17 years that can be taken from1961 (the first year in which data are available) to 2009(Figure 10b). The latter was determined using the seriesassociated with the 35th percentile. The potential effect ofthe climate change was introduced in a simplified manner,following the indications of Moreno (2005). For thisreason, the ‘base data’ were modified to linearly decreasethe mean precipitation, starting from a 0 decrease in2010 and reaching a decrease of 10 mm in 2027. Asimilar approach was followed to modify the temperature,increasing in this case the mean temperature from 0 °Cin 2009 to 2 °C in 2027. The data roughness (standarddeviation of the series) was not changed. This typeof climate simulation is, of course, an approximation.However, the methodology was deemed to be sufficientto estimate the sensitivity of the system to the climate.

Figure 11 shows the increment of inundated area Abetween the simulations with Q 6D 0 (Q D 0Ð9QŁ and


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Figure 9. Measured data (symbols) and simulation results (lines) of the drying processes from years (a) 1996, (b) 2000 and (c) 2003

0Ð6QŁ) and the simulation of reference (not consideringexternal water supplies, Q D 0). The two climate periods(wet and dry) were considered. Even in the worst casescenario (Q D 0Ð6QŁ and dry climate), the inundated areacan be increased by over 75 ha after the second year ofsimulation. However, this does not mean that the actualdifference will be equal to this value. The simulationsof A do not intend to be realistic. For this to be thecase, it is necessary to introduce some kind of hypothesisregarding values MD and PND into Equation (6) ratherthan considering them to be null values. The simulationsthat have been carried out should be used as a tool togenerate better speculation (Allen et al., 2003) on theapplication of treated sewage effluents. The values ofA highlight the system’s sensitivity to the volumes oftreated sewage effluents that can be used, since even if the

error in the simulations were such that it led to an errorof 10 ha in A (a value more than twice as high as thestandard deviation shown in Figure 9), there still wouldbe a very significant improvement in the hydrologicalcondition of Las Canas.

This was also clearly seen by simulating the responsethe system would have if, assuming the existence of a dryseries, at the start of each hydrologic year the inundatedarea was assumed to be zero. As exhibited in Figure 12,with the contributions of one year only, even in theworst year (hydrologic year 2012–2013), the inundationis guaranteed to be 125 ha if Q D 0Ð6QŁ. While the valueis less than the 400 ha associated with the total inundationof Las Canas, it is still five times greater than the 25 hathat would be inundated if treated sewage effluents werenot applied (Q D 0). This simulation is of special interest


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Figure 10. (a) Total sewage effluents inflow by year. From 2010 (lower line) to 2027 (upper line) and (b) precipitation data

since it is not just a sensitivity analysis, but rather anestimation of the actual behaviour, making it possible toverify the quick efficiency of the application of treatedsewage effluents. This rapid reaction would take longerif the dry condition at the outset were associated witha prolonged drought that had caused a considerabledecrease in soil moisture. Under these conditions, boththe soil suction and the existence of cracks would playan important role in infiltration. This effect has not beentaken into consideration and so the speed of the responseto the inundation would be slower than the estimation.The application of treated sewage effluents would helpprevent situations of this type, which were uncommon inthe typical hydroperiod of TDNP prior to the intensive

water abstraction carried out in the West Mancha aquifersince the late 1970s (Alvarez-Cobelas et al., 2001).

CONCLUSIONS AND FUTURE DEVELOPMENTS

After analysing the application of a dynamic water budgetin the area of TDNP, Central Spain, known as ‘LasCanas’, it was found that, as is generally the case, theinfiltration rate is the magnitude on which the uncertaintyis focused. Therefore, it was decided to carry out anidentification process based on the use of data fromseven drying processes (summer/autumn of years 1996,1997, 1998, 2000, 2002, 2003 and 2005) to improveits characterization. By adopting the infiltration rate


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Figure 11. Differences A between the simulations with Q 6D 0 and the simulation of reference (Q D 0) for the two climates under consideration

Figure 12. Simulation of the evolution of the inundated area if the inundated area is zero at the start of each hydrologic year. Dry climate hypothesis

directly as a parameter to be identified, widely varyingidentified values were obtained. The results, however,point to an apparently linear dependence between theinfiltration rate and the inundated area. It was deemedadvisable to examine the structure of this dependencemore closely. On the basis of a hydrological model ofsynthesis and in keeping with the morphometry of the

system, it was considered advisable to use a bilinearmodel. The three parameters of the model were identifiedby means of the data series from years 1997, 1998,2002 and 2005. The results were satisfactory, and theymade it possible to simulate the drying processes fromyears 1996, 2000 and 2003 with absolute errors thathad a standard deviation of only 4 ha. This conferred a


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high degree of confidence on the dynamic water budgetproposed. When it was applied to determine a simulationof how the inundated area would evolve from 2008 to2027 after the application of treated sewage effluents,while errors of 10 ha were found in the simulations(over twice as high as the standard deviation obtainedin years 1996, 2000 and 2003), it was still able toclearly describe the behaviour of the system. In all thecases, a highly significant improvement in the inundationwas achieved. Therefore, the research conducted hereprovides reliable elements of judgement with which toassess the opportunity to apply treated sewage effluentsto Las Canas.

This conclusion is based on a quite simplified hydro-logical formulation. To improve this model, it is neededto improve the characterization of the infiltration rate.Therefore, more inundation data are required. For thatreason, during the wet period of the hydrologic year2009–2010, four electronic limnigraphs and twoelectronic barometers were installed for monitoring theinundation and desiccation processes in Las Canas, withmeasurements read every 15 min. In addition, five elec-tronic limnigraphs and ten electronic barometers wereinstalled in Las Tablas for the same purpose. Waterlevel measurements read by the electronic limnigraphsare compensated for atmospheric pressure measurementsobtained by the electronic barometer.

The new data of the inundation and desiccation pro-cesses, in addition to an updated digital elevation model,make it possible to improve the quality of the infiltrationparameters. After doing so, improved hydrologic modelswill be applied.

ACKNOWLEDGEMENTS

The authors would like to thank the Education andResearch Department of the Castilla-La Mancha RegionalGovernment for providing the means and the financialsupport to carry out this study (research grant POII-0119-3381). This research was also financed by the Confed-eracion Hidrografica del Guadiana. Special gratitude goesout to the support provided by Mr Samuel Moraleda.The research grants awarded by the Spanish Ministry ofScience and Education to Ms Garcıa (BES-2006-12639)and to Ms Asensio (AP2009-2134) are also grateful. Thesupport provided by the staff of the Tablas de DaimielNational Park, especially by Mr Carlos Ruiz, is alsogreatly appreciated.

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Documents

Characterization of the infiltration rate in Las Tablas de Daimiel National Park, Central Spain