estudio de la aplicaciÓn de efluentes tratados de

UNIVERSIDAD DE CASTILLA LA MANCHA

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE

CAMINOS, CANALES Y PUERTOS

DEPARTAMENTO DE INGENIERÍA CIVIL Y DE LA EDIFICACIÓN

ESTUDIO DE LA APLICACIÓN DE EFLUENTES

TRATADOS DE DEPURADORA PARA LA

INUNDACIÓN EN SITUACIONES DE

EMERGENCIA DEL PARQUE NACIONAL DE LAS

TABLAS DE DAIMIEL

TESIS DOCTORAL Beatriz García Fernández DIRECTOR Vicente Navarro Gámir Ciudad Real, Noviembre de 2010

A mis padres, Marcelino y Ramona

A mis hermanos, Ana y David

Agradecimientos

1

Agradecimientos

Con estas líneas quiero expresar mi gratitud a todos aquellos que han hecho posible,

tanto directa como indirectamente, la realización de esta tesis doctoral.

En primer lugar quiero dar las gracias al Ministerio de Ciencia e Innovación y a la

Universidad de Castilla La Mancha, ya que la realización de esta tesis doctoral no

hubiese sido posible sin su apoyo económico.

Gracias a mi director de tesis, Vicente Navarro Gámir. No sólo he aprendido de su

conocimiento y experiencia; su constancia, capacidad de trabajo, dedicación y

entusiasmo han sido un ejemplo a seguir para mí en el futuro. Gracias por haberme

dado la oportunidad de realizar esta tesis, que ha supuesto para mí un periodo de mi

vida en el que he disfrutado con mi trabajo porque sencillamente me gustaba.

Fundamental ha sido la colaboración de Carlos Ruiz de la Hermosa, director del Parque

Nacional de Las Tablas de Daimiel. A él agradezco el haber puesto a nuestra disposición

no sólo la información necesaria para la elaboración de este trabajo, sino todos los

medios técnicos y humanos cuando lo hemos necesitado. Quiero dar las gracias al

personal de Tragsa y sobre todo al personal del Parque, en especial a Bautista, Jesús y

Manuel; su profundo conocimiento del entorno nos ha servido de gran ayuda. Me siento

una privilegiada al haber podido trabajar en este Parque Nacional y sobre todo,

acompañada de todas estas personas.

Quiero agradecer también la colaboración de todos mis compañeros de grupo y de

proyectos de investigación: Ángel, Miguel, Oscar, Laura, Luismi, Juan, Marina, Máximo,

Agradecimientos

2

David, Gema, Mª José, Jesús, Ana, Manuel, Juanjo, Miguel, Elvira, y todos aquellos junto a

los cuales he trabajado; y a Mari Carmen, con la que tantos cafés y charlas he

compartido. Si por algo se caracterizan estos años es por estar plagados de anécdotas

junto a todos ellos, especialmente durante los trabajos de campo en Daimiel. Se me

ocurren tantas que es imposible enumerarlas en estas líneas, y con el paso del tiempo,

al recordarlas, una sonrisa se esboza en mi cara. Momentos así son los que hacen que

estos años sean inolvidables.

La Escuela de Caminos de Ciudad Real es un lugar en el que he pasado muchos años de

mi vida. En ella estudié la carrera junto a mis compañeros de clase, de los que guardo

muy gratos recuerdos. Algunos de ellos junto a los profesores que nos formaron se han

convertido en mis compañeros durante mi etapa doctoral. Con todos ellos he

compartido muchos y muy buenos momentos tanto dentro como fuera de la Escuela. Mi

agradecimiento también a mis amigas por sus constantes ánimos y apoyo, y con

quienes además de grandes días he compartido imborrables noches.

Quisiera agradecer muy especialmente a mi familia el apoyo prestado. A mis abuelos

Vicenta, Elisa y Santiago, y en especial a mi abuelo Ángel, que consideraba la profesión

de Ingeniero de Caminos, Canales y Puertos como una de las más bonitas, y que se

sentiría orgulloso de tener esta tesis entre sus manos. A mis hermanos Ana y David, y a

Emilio, gracias por hacerme sonreír siempre incluso en los malos momentos, haciendo

sin darse cuenta que los problemas pierdan importancia. Y como no, gracias a mis

padres, Marcelino y Ramona, quienes se han esforzado siempre por darme la mejor

formación que ha estado en sus manos, y no sólo hablo de la formación académica, sino

también de la que considero muchísimo más importante, como persona. Todos

vosotros sois el pilar fundamental de mi vida.

Agradecimientos

3

También me gustaría mostrar mi gratitud a todas aquellas personas que no figuran en

estas líneas pero que me han prestado su apoyo de una u otra forma durante estos

años. Con esta tesis doctoral finaliza una etapa de mi vida, y como a todos los finales, le

sigue un nuevo comienzo.

A todos vosotros, MUCHAS GRACIAS.

Beatriz García Fernández

Agradecimientos

4

Índice

5

Índice

Agradecimientos .................................................................................................................................................... 1

Índice .......................................................................................................................................................................... 5

Resumen .................................................................................................................................................................... 7

Lista de Tablas ........................................................................................................................................................ 9

Lista de Figuras ................................................................................................................................................... 11

Introducción ......................................................................................................................................................... 17

I. A general synthetic model of the hydrologic behaviour of Las Tablas de Daimiel

National Park, Central Spain. ......................................................................................................................... 23

II. Characterization of the infiltration rate in Las Tablas de Daimiel National Park,

Central Spain ........................................................................................................................................................ 61

III. An evaluation of the application of treated sewage effluents in Las Tablas de Daimiel

National Park, Central Spain .......................................................................................................................... 97

Conclusiones y futuras líneas de investigación ................................................................................... 143

Índice

6

Resumen

7

Resumen

El Parque Nacional de las Tablas de Daimiel es un humedal que cubre 1,928 ha, situado

sobre el acuífero de Mancha Occidental (5,500 km2). Originalmente el humedal era el

resultado de la inundación generada por los ríos Guadiana y Gigüela en su confluencia,

así como por los aportes del acuífero cuando este descargaba sus aguas en los Ojos del

Guadiana, además de los azudes de 14 molinos que contribuían también al cambio de

condiciones fluviales a lacustres. Las extracciones intensivas del acuífero dieron lugar a

grandes descensos del nivel freático, y en consecuencia, el Parque se ha desconectado

de este, causándose importantes daños ecológicos. En la actualidad se están

contemplando diversas estrategias de aplicación de aportes externos de agua para

mejorar las condiciones de inundación del Parque. Sin embargo hasta ahora no existía

suficiente información para poder evaluar la respuesta del sistema ante esta medida.

Para analizar la viabilidad de estas estrategias de conservación y mejora se debe contar

con un modelo plausible del proceso de inundación del sistema. En este trabajo se ha

planteado un modelo sintético general del comportamiento hidrológico del Parque

Nacional de Las Tablas de Daimiel, centrado en los principales patrones de inundación.

Este modelo está basado en el análisis de las curvas hipsométricas asociadas a distintas

localizaciones de los aportes externos de agua, obtenidas mediante la aplicación de un

algoritmo celda a celda al modelo digital del terreno más reciente. Además, este

algoritmo también permite estimar la visualización aérea de la evolución del área

inundada.

Resumen

8

Tras este análisis morfométrico, en el Parque se han distinguido cuatro Zonas Básicas,

tres de ellas aguas arriba de la presa de Morenillo, área que se caracteriza por su

compleja morfología similar a un conjunto de vasos interconectados (Zonas Guadiana-

Gigüela, Central y Final) y una cuarta entre esta presa y la presa de Puente Navarro

(Zona de Las Cañas). Estas cuatro Zonas Básicas se han subdividido a su vez en 7

Unidades Funcionales.

Una vez obtenido el modelo de inundación, para evaluar la eficiencia del aporte de

efluentes tratados de depuradora, deben identificarse los parámetros que caracterizan

el funcionamiento del sistema. Tanto en la zona de Las Tablas como en la zona de Las

Cañas se han utilizado balances dinámicos de masas de agua. De este modo se han

caracterizado las tasas de infiltración asociadas a cada Unidad Funcional. Para ello se

han aplicado técnicas de identificación de parámetros a las series de datos de

inundación disponibles, facilitados por la guardería del Parque. Una vez identificados

estos parámetros, se estimó la evolución del área inundada del Parque ante la

aplicación de los efluentes tratados de depuradora de que se disponen. Se consideró

que estos efluentes se localizaban en los puntos más deprimidos de la Zona Central

(que fundamentalmente se corresponde con Masegar, la zona de mayor valor ecológico

del Parque) y de Las Cañas. En ambos casos se ha llevado a cabo una predicción de la

serie climática para simulaciones a futuro teniendo en cuenta el efecto del cambio

climático. Los resultados obtenidos en estas simulaciones indican que los efluentes

disponibles son suficientes para mejorar sensiblemente las condiciones de inundación

del Parque, tanto si se aplica en Masegar como en Las Cañas, incluso en la hipótesis de

clima seco. En consecuencia, se pone de manifiesto la conveniencia que, desde un punto

de vista hidrológico, tiene la aplicación de efluentes tratados de depuradora al Parque

Nacional de Las Tablas de Daimiel.

Lista de Tablas

9

Lista de Tablas

II. Table 1. Hydraulic conductivities Ks (in m/s) used to obtain the relative infiltration

rates rir shown in figure 8. ............................................................................................................................. 83

II. Table 2. Regional water table levels (elevation above the sea level) used to find the

relative infiltration rates rir shown in figure 8. ..................................................................................... 83

II. Table 3. Search space of the identification process carried out to characterize the

infiltration parameters. .................................................................................................................................... 84

II. Table 4. Optimum values of ir1, ir2 and ir3. .......................................................................................... 84

III. Table 1. Mean value of the effective hydraulic conductivity and infiltration rate

identified in different areas of Las Tablas. ............................................................................................ 127

III. Table 2. Annual volume of TSEI. ......................................................................................................... 128

III. Table 3. Percent distribution by municipalities of TSEI. .......................................................... 129

Lista de Tablas

10

Lista de Figuras

11

Lista de Figuras

I. Figure 1. Study area: Tablas de Daimiel National Park, Central Spain. Limnimeters are

indicated by black dots with numbers: 1 is Ojillo de Cañada Mendoza, 2 is Quinto de la

Torre, 3 is Isleta de la Fuente, 4 is Tablazo and 5 is Casablanca. .................................................... 47

I. Figure 2. Depression filling scheme in the cell-based used in this work. Point A in a) is

the water source location. Point B in c) is the pour point between the two sub-basins. Zc

in d) is the maximum surface water elevation defined by user. ..................................................... 48

I. Figure 3. Progressive inundation of a sample DEM. a) Hypothetic DEM with elevation

values in meters. The external water supply cell is selected (dark grey). b) First step of

inundation for a surface water elevation of 621 m. c) First step for a surface water

elevation of 622 m. d) Second step. e) Third step. f) First step for a surface water

elevation of 623 m. g) Second step. h) Unique step for a surface water elevation of 624

m. ............................................................................................................................................................................... 49

I. Figure 4 a). Hypsometric curve of the zone upstream of Morenillo dam (Las Tablas)

assuming that the water source is in Molemocho Mill. ....................................................................... 50

I. Figure 4 b). Hypsometric curve of BZ 2 until it merges with BZ 1 assuming that the

water source is at its lowest point (point P3 in figure 1). .................................................................. 50

I. Figure 4 c). Hypsometric curve of Las Cañas zone assuming that the water source is at

its lowest point of elevation (punt P2 in figure 1). ............................................................................... 51

I. Figure 5 a). Basic Units of Las Tablas de Daimiel National Park. ................................................ 52

I. Figure 5 b). Functional Units and connection points located between them......................... 53

Lista de Figuras

12

I. Figure 6. Visualization of the inundation process de la FU 5 (BZ 2) assuming that the

water source is at its lowest point (point P3 in figure 1). .................................................................. 54

I. Figure 7. Inundation data of the limnimeters identified in figure 1........................................... 55

I. Figure 8. A comparison of the historical inundated areas and FUs. The FUs are shown

in white. The bold lines represent the inundation data provided by the TDNP Technical

Staff. .......................................................................................................................................................................... 56

I. Figure 9. A comparison of the historical inundated areas and Functional Units in Las

Cañas Zone. The Functional Units are shown in white. The black contours represent the

data from the inundated areas. ..................................................................................................................... 57

I. Figure 10. Adapted from Dominguez-Castro et al. (2006) and Sánchez-Carrillo et al.

(2001). ..................................................................................................................................................................... 58

I. Figure 11 a). Estimation of the evolution of the inundated area in FU 5. Dry climate

hypothesis. ............................................................................................................................................................. 59

I. Figure 11 b). Estimation of the evolution of the inundated area in FU 5 assuming that

the inundated area is zero at the start of each hydrologic year. Dry climate hypothesis. .... 59

I. Figure 12. Grey line, simulation of the evolution of the inundated area in Las Cañas if

the inundated area is zero at the start of each hydrologic year. Black line, inundation

evolution without external water supplies. Dry climate hypothesis............................................. 60

II. Figure 1 a). Situation of the Upper Guadiana Basin, aquifer 04.04, and Mancha

Húmeda Biosphere Reserve. .......................................................................................................................... 85

II. Figure 1 b). Detailed plan view of the TDNP. ..................................................................................... 86

II. Figure 2. Time series of the inundation data...................................................................................... 87

Lista de Figuras

13

II. Figure 3. Water budget model in Las Cañas. Figure out of scale. Numbers define

elevation above the sea level, in meters. ................................................................................................... 87

II. Figure 4. Hypsometric curve of Las Cañas. ......................................................................................... 88

II. Figure 5. Infiltration rates obtained from the data depicted in figure 2. Values

obtained with equation 2 (black dots), values identified for each of the 8 time series

available (grey dots), and values identified after dividing each of these series into 10

segments (white dots). ..................................................................................................................................... 89

II. Figure 6. Synthetic transect representing the hydrogeologic configuration of Las

Cañas. Figure out of scale. Numbers define elevation above the sea level, in meters. Data

from Aguilera et al., 2009; Domínguez-Castro et al. 2006; García, 1996; and García

Hidalgo et al., 1995............................................................................................................................................. 90

II. Figure 7. Relative infiltration rates rir, and relative volume development rvd. .................. 91

II. Figure 8. Isolines of the RMSE (ha) para ir2=ir2OPT=5mm/day. ............................................. 91

II. Figure 9. Measured data (symbols) and simulation results (lines) of the drying

processes from years 1996 (a), 2000 (b) and 2003 (c). ..................................................................... 92

II. Figure 10 a). Total sewage effluents inflow by year. From 2010 (lower line) to 2027

(upper line) ........................................................................................................................................................... 93

II. Figure 10 b). Precipitation data. .............................................................................................................. 93

II. Figure 11. Differences ∆A between the simulations with Q≠0 and the simulation of

reference (Q=0) for the two climates under consideration. ............................................................. 94

II. 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. ................................................ 95

Lista de Figuras

14

III. Figure 1 a). Situation of the West Mancha aquifer and La Mancha Húmeda Wetlands

(dashed areas)................................................................................................................................................... 130

III. Figure 1 b). Situation of Tablas de Daimiel National Park (TDNP). ..................................... 131

III. Figure 1 c). Detailed plan view of the TDNP. Limnimeter 1 corresponds to Ojillo de

Cañada Mendoza, 2 to Quinto de la Torre, 3 to Isleta de La Fuente, 4 to Tablazo and 5 to

Casablanca. ......................................................................................................................................................... 132

III. Figure 1 d). Digital aerial photography of Las Tablas de Daimiel National Park. ........... 133

III. Figure 2. Evolution of the inundated area with elevation. The water source is

assumed to be at point P1 in figure 1 c. .................................................................................................. 134

III. Figure 3. Functional units (FU) considered in the simplified hydrologic model of Las

Tablas. ................................................................................................................................................................... 135

III. Figure 4. Inundation data of the limnimeters identified in figure 1 c. ................................ 136

III. Figure 5. Variation of the Root Mean Square Error (RMSE, in ha) around the

optimum values of the parameters that define the ir in FU 4. ...................................................... 136

III. Figure 6. Calibration: time series of inundation data (symbols) and model results

(lines) in FU 4. ................................................................................................................................................... 137

III. Figure 7 a). Prediction of the total treated sewage effluent inflow, applied to FU 1-2-

3, FU 4 and FU 5, and precipitation and evaporation series (daily data). b) Total treated

sewage effluents inflow by year. ............................................................................................................... 138

III. Figure 7 b). Total treated sewage effluents inflow by year. From 2009 (lower line) to

2027 (upper line). ............................................................................................................................................ 138

III. Figure 8. Estimation of the evolution of the inundated area in FU 5. .................................. 139

Lista de Figuras

15

III. Figure 9 a). Evolution of the inundated area in FUs 1-2-3 after applying the surplus

TSE resulting from the inundation of FU 5 in addition to the TSE from the Azuer line. .... 140

III. Figure 9 b). Evolution of the inundated area in FU 4 after applying the surplus TSE

resulting from the inundation of FUs 1-2-3 in addition to the TSE from Fuente el

Fresno. .................................................................................................................................................................. 140

III. Figure 10. Estimation of the evolution of the inundated area in FU 5 assuming that

the inundated area is zero at that start of each hydrologic year. ................................................ 141

Lista de Figuras

16

Introducción

17

Introducción

Los humedales son ecosistemas que desempeñan un papel muy importante en los ciclos

de nutrientes, retención de sedimentos, producción biológica, filtración y control de la

erosión entre otros. Además, su riqueza de flora y fauna, especialmente aves acuáticas,

hacen de ellos ecosistemas más valiosos y vulnerables a la acción del hombre que otros

ecosistemas (Costanza et al., 1997; Keddy, 2000; Mitsch and Gosselink, 1993). El

desarrollo de los dos últimos siglos ha hecho que sólo un pequeño porcentaje de los

humedales que existían originariamente permanezcan sin alterar. Por esta razón,

durante los últimos años se ha mostrado gran interés por restaurar los humedales que

han sufrido importantes deterioros (Turner et al., 2000), como es el caso del Parque

Nacional de Las Tablas de Daimiel.

Las Tablas de Daimiel, situado en la provincia de Ciudad Real, es uno de los 14 Parques

que conforman la Red de Parques Nacionales. Los desbordamientos de los ríos

Guadiana y Gigüela, que confluyen en este punto, los aportes subterráneos al

intersectar el nivel freático con la superficie del terreno en los llamados “Ojos del

Guadiana” y las acciones antropogénicas (existen 14 molinos de agua en el entorno de

Las Tablas) daban origen a este humedal (Álvarez-Cobelas and Cirujano, 2007). El

carácter marcadamente estacional de estos aportes y sus distintas características

químicas hacían de Las Tablas de Daimiel un ecosistema de una importante

biodiversidad y riqueza ecológica.

Numerosas son las actuaciones que han ocasionado el deterioro de este ecosistema. En

1956, basándose en la insalubridad de estos terrenos como una fuente de paludismo, se

Introducción

18

aprobó la Ley sobre “Saneamiento y colonización de terrenos pantanosos inmediatos a

los ríos Guadiana, Záncara y Gigüela”. A su amparo se comenzaron los trabajos de

desecación del Parque, la canalización de los ríos Guadiana y Gigüela y la puesta en

regadío de zonas colindantes. Estos trabajos se paralizaron en 1971, cuando ya habían

afectado a un total de aproximadamente 130 Km2 (Álvarez-Cobelas et al., 2001). Con

posterioridad el Parque fue declarado Parque Nacional de Caza en 1966, y finalmente

Parque Nacional en 1973. En 1981 sus fronteras administrativas se ampliaron hasta su

estado actual (1928 ha) y pasó a formar parte de la Mancha Húmeda (Reserva de la

Biosfera, UNESCO). Un año después, en 1982, fue incluida en la convención RAMSAR.

El equilibrio entre los distintos aportes hídricos que hacía de Las Tablas de Daimiel un

ecosistema de gran riqueza ecológica se vio roto cuando las extracciones de la Unidad

Hidrogeológica 04.04 provocaron un importante descenso en el nivel freático. Esto

ocasionó el cese de las descargas por los denominados “Ojos” en el cauce del Guadiana

en 1984 de manera definitiva. A esto hay que sumarle la existencia de series de años

secos que hicieron que los aportes superficiales se redujesen considerablemente, y en

consecuencia, el Parque Nacional de Las Tablas de Daimiel pasase a tener apenas 10 ha

inundadas de las 1928 que lo conforman.

Ante esta nueva situación del Parque se tomaron varias medidas de las cuales la

primera fue la construcción de la presa de Puente Navarro, finalizada en 1985. La

finalidad de esta presa era retener las aguas superficiales que se evacuaban por los

canales de drenaje del río Guadiana. Un año después, en 1986, se acometió la

eliminación de los canales de drenaje con el fin de restituir los cauces y favorecer el

flujo superficial y el encharcamiento de las zonas centrales (Castaño, 2003). En este año

se realizó un Estudio de Viabilidad de un Plan de Regeneración Hídrica, que fue

Introducción

19

aprobado finalmente en 1987. Este estudio contemplaba un conjunto de actuaciones de

las cuales se llevaron a cabo únicamente una batería de sondeos, la realización de

transvases desde el acueducto Tajo-Segura y la construcción en 1988 del dispositivo

hidráulico de Morenillo.

Los aportes provenientes de los transvases han tenido rendimientos considerables en

años en que la climatología ha sido favorable, y muy escasos en aquellos años

extremadamente secos, como lo fueron 1994 y 2007. En estos años el rendimiento fue

del 10 y del 15 por ciento respectivamente, al infiltrarse la mayor parte del volumen

transvasado por el cauce del río Gigüela antes de llegar al Parque. En diciembre de

2009 apenas se tenían 10 ha inundadas de las 500 ha que, como mínimo deben

permanecer inundadas para preservar los principales valores del Parque (Ruano,

1996).

Ante el hecho de que los transvases son aportes meramente puntuales en situación de

emergencia y que su rendimiento es inversamente proporcional a los aportes hídricos

de ese año hidrológico, se planteó una alternativa para mejorar la eficiencia de las

actuaciones destinadas a mejorar el estado del Parque desde un punto de vista

hidrológico. Así se contempló el aporte de los efluentes de depuradoras

convenientemente tratados de los pueblos más cercanos al Parque: Daimiel,

Manzanares, Membrilla, Fuente el Fresno, Herencia, Alcázar de San Juan, Campo de

Criptana, Villarta de San Juan, Arenas de San Juan y Villarrubia de los Ojos.

Anteriormente no se podía evaluar la sensibilidad del Parque ante actuaciones como

esta. No existía un modelo de comportamiento hidrológico del sistema ni unos valores

de tasas de infiltración plausibles que permitiesen predecir la respuesta del Parque

ante distintos aportes en diferentes puntos. Para ayudar a resolver esta carencia, en el

Introducción

20

capítulo I se presenta un modelo general simplificado del comportamiento hidrológico

del sistema. Este modelo se ha generado a partir de la aplicación de un algoritmo celda

a celda al modelo digital del terreno más reciente. Este algoritmo devuelve las curvas

hipsométricas asociadas a diferentes procesos de inundación en función de la

localización del aporte de agua, así como una visualización de la estimación de área

inundada en dicho proceso.

En el capítulo II se ha llevado a cabo un estudio de la eficiencia de la aplicación de

efluentes tratados de depuradora en la zona de Las Cañas. Para ello, basándose en el

modelo del comportamiento hidrológico del Parque del capítulo I, se ha llevado a cabo

una caracterización de la tasa de infiltración mediante técnicas de identificación de

parámetros aplicadas a un balance dinámico de masa de agua. Una vez obtenidos estos

parámetros se ha llevado a cabo una simulación a futuro suponiendo la aplicación de

estos efluentes, en concreto hasta el año 2027, fecha límite para el cumplimiento de la

Directiva Marco del Agua.

En el capítulo III se ha realizado el estudio de la eficiencia de la aplicación de los

efluentes tratados de depuradora en la zona de Las Tablas, aguas arriba de la presa de

Morenillo. Se ha realizado igualmente la caracterización de los parámetros de

infiltración mediante la aplicación de técnicas de identificación de parámetros a un

balance dinámico de masas de agua apoyado en el modelo del comportamiento

hidrológico del sistema que se presenta en el capítulo I. Debido a la compleja

morfometría de esta zona, similar a la de un conjunto de vasos interconectados, la

importancia que adquiere el modelo del comportamiento hidrológico es crucial para

caracterizar de un modo correcto los parámetros asociados a cada subcubeta. Del

mismo modo, se ha realizado una simulación hasta 2027, suponiendo el aporte externo

Introducción

21

de agua en el punto más bajo de Masegar, el área con mayor población de masiega y

una de las que posee mayor valor ecológico del Parque.

Tanto si el efluente se aplica en Las Cañas como en el Masegar, se muestra como estos

aportes son suficientes para crear una superficie inundada que supone un notable

incremento respecto a la situación que presenta el Parque en años extremadamente

secos. Esta tesis se presenta, por lo tanto, como una herramienta que pretende dar

respuesta a la necesidad de mejorar la gestión de los recursos hídricos de que se

dispone, contribuyendo al desarrollo de estrategias eficientes de conservación y

restauración en este Parque Nacional.

Introducción

22

Referencias

Álvarez-Cobelas, M., Cirujano, S., 2007. Multilevel responses of emergent vegetation to

environmental factors in a semiarid floodplain. Aquatic Botany, 87(1): 49-60.

Álvarez-Cobelas, M., Cirujano, S. and Sánchez-Carrillo, S., 2001. Hydrological and

botanical man-made changes in the Spanish wetland of Las Tablas de Daimiel.

Biological Conservation, 97(1): 89-98.

Castaño, S., 2003. Estudio metodológico para el cálculo de la infiltración en el vaso de

Las Tablas de Daimiel. Validación de resultados. Ph.D. Thesis., Universidad

Complutense de Madrid, 112 (plus anexes) pp.

Costanza, R. et al., 1997. The value of the world's ecosystem services and natural

capital. Nature, 387(6630): 253-260.

Mitsch, W.J. and Gosselink, J.G., 1993. Wetlands. John Wiley, New York. pp. 423.

Keddy, P.A. 2000. Wetland Ecology. Principles and conservation. Birks, H. J. B., Wiens J.

A. eds. Cambridge Studies in Ecology. Cambridge University Press. pp 56.

Ruano, P., 1996. Proyecto de captación y aplicación de aguas subterráneas para

situaciones de emergencia en el Parque Nacional de Las Tablas de Daimiel. Memoria del

estudio hidrológico, TRAGSATEC, Madrid.

Turner, R.K., Van der Bergh, J.C.J.M., Söderqvist T., Barendregt A., Van der Straaten J.,

Maltby E., Van Ierland E.C. 2000. Ecological-Economic analysis of wetlands: scientific

integration for management and policy. Ecological economics. 35, special issue: The

values of wetlands: landscape and institutional perspectives, 7-13.

I. A general synthetic model of the hydrologic behaviour of Las Tablas de Daimiel National Park, Central Spain

23

I. A general synthetic model of the

hydrologic behaviour of Las Tablas de

Daimiel National Park, Central Spain.

Beatriz García and Vicente Navarro

Enviado a Water Resources Management (Noviembre de 2010)


24


25

A general synthetic model of the hydrologic behaviour of Las

Tablas de Daimiel National Park, Central Spain.

Beatriz Garcíaa and Vicente Navarrob

a Research Engineer. Geoenvironmental Group, Civil Engineering Department,

University of Castilla-La Mancha, Avda. Camilo José Cela s/n, 13071 Ciudad Real, Spain.

E-mail: [email protected].

b Corresponding author. Associate Professor. Geoenvironmental Group, Civil

Engineering Department, University of Castilla-La Mancha, Avda. Camilo José Cela s/n,

13071 Ciudad Real, Spain. Tel.: +34 926 295 453; fax: +34 926 295 391. E-mail address:

[email protected].

Abstract

Las Tablas de Daimiel National Park, Central Spain, is the most outstanding element of

the Mancha Húmeda, UNESCO’s Biosphere Reserve, to which it has belonged since

1981. In recent years, the Park has undergone both a groundwater level drop and a

decrease in surface inflow. This has disrupted the equilibrium between fluvial,

groundwater and man-made processes, causing the ecological biodiversity to dwindle.

At the present time, several different strategies involving the application of external


26

water sources are being considered in an attempt to improve the system´s current

situation. For an analysis of the feasibility of these strategies, it is necessary to have a

plausible model of the inundation process of the Park. This article presents a model

based on a cell-to-cell algorithm which has been designed to meet this objective.

Firstly, this model has yielded the hypsometric curves associated with different water

source locations. By applying dynamic water budgets, with these curves it is possible to

synthetically characterize the inundation that would be caused by the different water

application strategies. Moreover, the model also provides an aerial visualization of the

evolution of the inundated area. Therefore, in addition to contributing to a better

understanding of the complex hydrologic behaviour of the Park, the model proposed

here is a useful tool that can provide elements of judgement with which to assess the

efficiency of the different improvement strategies.

Keywords

Wetland management, wetland morphometry, inundation model

1. Introduction

Wetlands provide important benefits in water regulation, erosion control, sediment

accretion, soil formation, biological productivity and nutrient and biogeochemical

cycling. In addition, they are known for their distinctive flora and rich spectrum of

wildlife, especially waterfowl, which makes them both more valuable and more prone

to human impact than other ecosystems (Costanza et al., 1997; Keddy, 2000; Qin and

Mitsch, 2009). In wetlands situated in semiarid zones, these values are especially

important. In Spain, similar to what has occurred in other regions of the world, during

the early years of the second half of the 20th Century, a considerable percentage of


27

these wetlands were desiccated, since they were considered to be a source of malaria.

Many others were converted to farmland or suffered environmental damage. This

explains why in recent years sustainable management strategies for wetlands (Turner

et al. 2000) have drawn worldwide attention. This is the case of Las Tablas de Daimiel

National Park (TDNP), Central Spain (figure 1), which in addition to its value as a

wetland, is an area of outstanding significance in terms of cultural heritage.

TDNP was a private hunting park from the 1870s until the 1950s (Settier, 1956 in

Álvarez-Cobelas et al., 2001). In 1956, based on the fact that these lands were

considered to be insalubrious, the following measures were passed: the desiccation of

the Park, the canalization of the Guadiana and Gigüela rivers and the irrigation of

adjacent lands. In 1966 it was considered a National Hunting Park and in 1973 it was

declared a National Park. The Park’s administrative boundaries were extended in 1981

up to the area it currently occupies (1925 ha). The Park has belonged to the UNESCO’s

Mancha Húmeda Biosphere Reserve since 1981, and in 1982 it was included in the

RAMSAR convention.

The fluctuations and states of equilibrium between fluvial, groundwater and man-made

processes (watermill constructions) in TDNP are directly related to its ecological

biodiversity. This equilibrium was disrupted when extractions related to agricultural

activities caused the groundwater level to decrease (Llamas, 1988; Cruces et al., 2000;

Fornés et al., 2000; Bromley et al., 2001; Martinez, 2001; Custodio, 2002, Conan et al.,

2003), and as a consequence, the discharge spilling into the Park ceased as of 1984. In

addition, the occurrence of several consecutive dry years has caused a drop in surface

inflow, which has put TDNP in a critical situation (Álvarez-Cobelas et al., 2001). The

possibility of applying external water is currently under consideration. As the Park is


28

made up of a number of different sub-basins (“lagunas”, Florín, 1993, or “tablazos” as

they are called in the area), the inundation pattern of the Park differs depending on

where the external water supplies are applied. For this reason, a better knowledge of

the Park’s morphometry is a key to understanding the hydrologic behaviour of the

wetland. So, as Lott et al. (2001) and Mitsch et al. (1993) have pointed out, this is the

way to improve water and resource management, which will lead to the

implementation of an efficient restoration project. The purpose of this paper is to work

to achieve these goals.

2. The Tablas de Daimiel National Park structure

The TDNP surface inflow comes mainly from runoff and rainfall during the winter, and

from the streams of the Gigüela and Guadiana rivers as well as seasonal streamflows

from Sierra de Villarrubia. Cachón de la Leona, Cañada Lobosa and Cañada del Gato

(figure 1) can be also considered, but these inflows are comparatively minor due to the

semiarid nature of the region (Castaño-Castaño et al., 2008). The groundwater inflow

is, at the present time, non-existent. However under natural conditions, it is supplied by

the Mancha Occidental aquifer, whose discharge point, called “Ojos del Guadiana”, is

situated in the Guadiana river, upstream of the Park.

Two dams have been built in TDNP. The Puente Navarro dam was constructed in 1985

along the final stretch of the Park. The spillway elevation of the Puente Navarro dam is

606.5 m. This elevation marks the maximum volume of the water collected in “Las

Cañas”, the area located between the Puente Navarro and Morenillo dams. The

Morenillo dam divides the Park into two zones for the purpose retaining water in the

zone upstream of the dam, which has been given the name “Las Tablas”. This is the zone

with the greatest environmental value. Morenillo is an earthfill dam built in 1988 under


29

the TDNP Hydric Regeneration Plan. The dam’s embankment runs parallel to the right

bank of the Guadiana River and the foundations are built over peat soil. An important

characteristic inherent to this type of soil is shrinkage, which accompanies drainage,

one of the main causes of both subsidence if there is vertical shrinkage and soil cracks if

there is horizontal shrinkage. The two phenomena result in a decrease in the base

volume (Oleszczuk and Brandyk, 2008, Schwärzel et al., 2002) and the lack of a rigid

structure to maintain a unique pore distribution (Bradley, 2002). The succession of dry

years and the drop in groundwater level have led to the shrinkage of the peat and the

clayey material of the dam, which have caused high settlements; hence the dam top

elevation is not constant. The minimum elevation of the Morenillo dam top is 607.5 m,

and this determines the maximum volume of water collected in the Tablas zone.

In addition to the evolution of the top of the Morenillo dam and the topographical

variations stemming from the development of the emergent vegetation, the peat

burning episodes of September 1986, March 1987, January 1994 and August –

December 2009 have caused and continue to cause major changes in the

microtopography of the area. The characterization of these processes, however, is

beyond the scope of this article. The information on which this paper is based was

taken from the Digital Elevation Model (DEM) obtained in spring 2007 by the TDNP

Technical Staff (2×2 m cell size). From this DEM, a general synthetic model was

developed to provide an integral description of the hydrologic behaviour of the system.

3. An algorithm to analyze the morphometry of the Park

A cell-to-cell (CTC) algorithm based on the approach proposed by Marks et al. (1984)

and O’Callaghan and Mark (1984) has been developed to characterize the general


30

inundation pattern of TDNP. This approach has been widely used to partition

watersheds into subcatchment areas (Band, 1986; Jenson and Domingue, 1988;

Tarboton et al., 1991; Mackay and Band, 1998; Liang and Mackay, 2000).

The simulation starts by assuming that an external water supply is located in a cell of

the DEM, situated at a local minimum. This cell marks the start of the inundation

process. The rest of the depression points are sorted into ascending order according to

their elevation values and sequential inundation. When the inland catchment of the

depression, where the water is supplied, is filled with flood water, the water surface

therein will rise to a certain level. At this level, flood water starts to overflow through

the pour point to an adjacent depression. This process is repeated successively with the

water flowing towards the rest of the depressions of the system at the different pour

points in each connection between depressions. In this way, the water continues to

inundate the different depressions of the system until the end of the inundation

process, when the surface water rises to an elevation defined as maximum by the user

(figure 2). The increments in the surface water level during this process are also

defined by the user. For each inundation process that has been simulated, the CTC

algorithm yields the corresponding hypsometric curve. It also defines the inundated

cells for each water level increment. The inundated area may thus be represented

graphically, which allows the process to be visualized step by step, as the surface water

level increases. This is illustrated in figure 3.

4. General inundation pattern of Las Tablas de Daimiel National Park

In order to determine the general pattern of the hydrologic behaviour of TDNP, the

evolution of the inundation process of Las Tablas and Las Cañas was simulated by

applying the CTC algorithm to the DEM of the Park.


31

In Las Tablas a water source was assumed to exist at the lowest point of the depression

located next to Molemocho mill (built to prevent water from spilling out of Las Tablas

through the Guadiana riverbed), in the old bed of the Guadiana River (point P1 in figure

1). In Las Cañas a water source was assumed to exist at the lowest point of the

Guadiana channel (point P2 in figure 1). After an analysis of the hypsometric curves

referring to these inundation processes, 4 Basic Zones (BZs) were identified in TDNP,

three of which were located in Las Tablas. Owing to the complex morphometry of the

Park, after a more detailed analysis of the 4 BZs, they were broken down into 7

Functional Units (FUs).

4.1 Guadiana-Gigüela Zone

Figure 4a shows the hypsometric curve representing the inundation process of Las

Tablas from Molemocho mill. A certain degree of continuity can be seen from the

minimum elevation of the inundation up to an elevation of 606.19 m. This is the point

at which the area that had been inundated without any major discontinuities (151 ha)

merges with another area of 106 ha when the surface water elevation rises to 606.20

m.

This discontinuity indicates that the 151 ha inundated up to the height of 606.19 m

comprises a sub-basin that functions independently when it is below this elevation. For

this reason it was considered as a single BZ, called BZ 1 or the Guadiana-Gigüela Zone

(figure 5a). This BZ encompasses the old bed of the Guadiana River, the final stretch of

the Gigüela River and the channels connecting the two.

As a result of a more detailed analysis of the topography and the discontinuities in the

hypsometric curve shown in figure 4a, 3 minor discontinuities were observed in the


32

Guadiana-Gigüela Zone. These discontinuities occurred when the surface water

elevation was equal to 605.23, 605.41 and 606.00 m due to the existence of 4 sub-

basins in this BZ (surface water level below 606.19 m).

At the start of the inundation process depicted in figure 4a, the water inundates the

area surrounding Molemocho mill. The hypsometric curve of the process does not

exhibit any discontinuities until the surface water level reaches 605.23 m. At this point

a discontinuity appears, which indicates that when the water reaches this elevation

level, it overflows into the old Guadiana riverbed as well. The area inundated through

this inundation process from the elevation of the water source cell up to elevation

605.23 m is called FU 1 (figure 5b). The area of this FU only covers 3 ha; however its

cultural heritage value prompted its consideration as an independent FU. Since it is

one of the visitors areas, the artificial inundation of this isolated zone during dry

seasons is very common.

When FU 1 is fully inundated (surface water level above 605.23 m), the water

overflows and inundates the Guadiana riverbed. The hypsometric curve does not show

any substantial discontinuities from elevation 605.23 m to elevation 605.41 m, while

the Guadiana riverbed is being inundated. This discontinuity at 605.41 m indicates that

the water, which was inundating only FU 1 and the Guadiana riverbed, is starting to

inundate the intermediate zone between Gigüela Channel and the Guadiana riverbed.

Hence, the Guadiana riverbed in Las Tablas was associated with FU 2.

When FU 1 and 2 are fully inundated, and the intermediate zone between the Gigüela

Channel and the Guadiana riverbed is being inundated, a discontinuity appears at

elevation 606.00 m. This discontinuity represents the connecting point between the

intermediate area between the two rivers and the final stretch of Gigüela Channel.


33

Thus, the area between Gigüela Channel and the Guadiana riverbed, which is inundated

when the water surface level ranges between 605.41 and 606.00 m, is called FU 3.

When FUs 1-2-3 are fully inundated and the surface water level exceeds 606.0 m, the

inundation process shows a considerable discontinuity at elevation 606.19 m. This is

the point at which the area associated with the final stretch of Gigüela Channel, which

has been related to FU 4, starts to become inundated (figure 5b).

4.2 Central Zone

The 106 ha that merge with the 151 ha of the Guadiana-Gigüela Zone when the surface

water level reaches an elevation of 606.19 m in this inundation process mainly

correspond to the zone known as “El Masegar”. This slightly depressed zone is home to

the largest sawgrass (masiega in Spanish) population in the Park (Álvarez-Cobelas et

al., 2008), and it is called the Central Zone (BZ 2). The perimeter of this Central Zone

encompasses an area of around 200 ha, but only 106 ha are flooded when it is fully

inundated. The remaining area corresponds to small isolated islands, the most

important of which is Los Asnos Island (also called “Generales Island”).

This area, which is the one of greatest environmental value in the Park, has been

studied in detail. The inundation process of El Masegar was simulated with a water

source located in a cell of the DEM corresponding to the local minimum of this

depression with an elevation of 605.41 m (point P3 in figure 1). Figure 4b presents an

illustration of the hypsometric curve of this process until it merges with the Guadiana-

Gigüela zone (surface water level de 606.19 m). This hypsometric curve does not

exhibit any major discontinuities; hence all of the Central Zone has been associated


34

with a single FU, number 5. Figure 6 represents the visualization of the inundation

process, highlighting the activation of the different basins comprising the FU.

4.3 Final Zone

When the surface water level reaches 606.20 m, the Central Zone merges with the

Guadiana-Gigüela Zone through flat channels that surround Pan Island. At this point,

the area which has been called the Final Zone (BZ 3) starts to become inundated. The

Final Zone encompasses both the area between the Guadiana-Gigüela and the Central

Zones and the upper areas of Las Tablas. Starting at an elevation of 606.20 m, there is

also an increase in the speed with which inundated area expands with the elevation,

owing mainly to the flat morphology of the Final Zone. No discontinuities appeared in

the hypsometric curve of the inundation process of the Final Zone (figure 4a). For this

reason, it was not subdivided into sub-basins and the entire area was called FU 6.

4.4 Las Cañas Zone

Unlike what occurred with the hyposometric curve of Las Tablas, in Las Cañas Zone

(figure 4c), the name given to the last BZ, this curve does not exhibit any major

discontinuities. Hence, this zone is considered to comprise a single FU, number 7. Its

structure is similar to that of a reservoir, which greatly simplifies its analysis. As can be

observed in figure 4c, the rate at which the volume increases with the inundated area is

higher than in Las Tablas, which would imply that the latter zone is made up of

shallower areas. In Las Tablas there is a greater extension of inundated area for the

same volume of water collected. Therefore, Las Cañas undergoes fewer losses owing to

both infiltration and evapotranspiration. The water overflows from Las Tablas into Las

Cañas Zone through the spillways of Ojillo de Cañada Mendoza (point 1 in figure 1) and


35

Quinto de La Torre (point 2 in figure 1), through the bottom drains in the Morenillo

dam, or by the seepage flow that runs through and below the dam. Once the water

reaches Las Cañas, it flows over the Guadiana riverbed until it reaches Puente Navarro

dam. It is retained there, where it begins to inundate the upper zones of this BZ until it

reaches the top elevation of this dam, 606.5 m. The water flows from Las Cañas Zone to

Guadiana riverbed downstream of Puente Navarro dam through either the bottom

drains or the dam’s spillways.

5. A comparative analysis of the inundation pattern

This general structure of hydrological behaviour has been contrasted with the

historical inundation data provided by the TDNP Technical Staff.

First of all, it must be noted that similar to what was seen in figure 7, the readings from

the limnimeters located in Las Tablas, in both the Guadiana-Gigüela Zone and Central

Zone, as well as the Final Zone, measure the same surface water level when it exceeds

606.20 m. This is the point at which the Central and Guadiana-Gigüela Zones merge and

FU 6 starts to become inundated, and all of the FUs of Las Tablas begin functioning

jointly. This is what occurs in the general inundation pattern proposed in this paper.

Figure 8 highlights the correlation between the historical inundated area data provided

by the TDNP Technical Staff and the FUs of the model proposed in the Tablas Zone. In

figure 8a it is possible to see that FU 1, when fully inundated, corresponds to the area in

Molemocho Mill on 6/10/2006. Figure 8b represents FUs 1 and 2 along with the

inundated area on 22/06/12007. Figure 8c shows FUs 3 and 4 when the surface water

level reaches 605.8 m along with the inundated area on 27/06/2005. Figure 8d shows

FU 5 fully inundated along with the inundated area on 22/06/2007. Figure 8e


36

illustrates FU 6 when the surface water level reaches an elevation of 606.7 m along

with the inundated area on 3/02/2005. Lastly, figure 8f depicts FU 6 fully inundated

along with the inundated area on 9/06/2004. In all of the above, a good correlation

between the FUs and the inundated zones was observed over the course of the different

years.

As regards the Las Cañas zone, figure 9a represents FU 7 when the surface water

elevation reaches 606.5 m along with the inundated area on 16/6/2003, and figure 9b,

when the surface water level reaches 604.7 m in the same FU along with to the

inundated area on 24/2/2004. After examining the above figures, thanks to the model’s

ability to facilitate the visualization of the inundated area, it was found that the

historical inundation data corroborate the general inundation pattern proposed in this

paper.

As discussed in section 2, the model fits the morphometry of the Park as it was in 2007.

Therefore, the detail of the evolution of the inundated cells, influenced by the

microtopography, is of limited scope. However, the main inundation trends, i.e., that the

FUs are being inundated and the percentage of flooding in each FU, are, in fact, valid.

Moreover, the operational model proposed is also in keeping with the main lithological

domains identified by Domínguez-Castro et al. (2006), and with the sedimentation

pattern described by Sánchez-Carrillo et al. (2000 and 2001). The deposits in the Park

and the lithology of the first 3 meters differ depending on which zones the water, as it

loses energy, overflows into and inundates. For this reason the results synthesized in

figure 10 clearly point to a structure that is consistent with the FUs that comprise the

model presented here. When the water enters the Park from the north, it loses a large

part of the energy it had in the Gigüela riverbed, and in this zone (FU 6), the


37

sedimentation rate is higher. The lithological composition found here consists mainly of

gypsum-rich clays with carophyte layers. This lithology changes when the water

inundates the shallower zones (FUs 3 and 5). These shallow zones exhibit a higher

sedimentation rate of the sandy type. These sediments have a smaller grain size than

those found in FU 6, and the sedimentation peaks occur later than in this FU. Finally,

when the water reaches the channels of both the Guadiana and Gigüela rivers, once

again, it loses its transport capacity. The deposits that appear are made up primarily of

peat and carophyte alternations. As the water flows over the deeper zones (channels of

the Guadiana and Gigüela rivers, FUs 1, 2, 4 and 7), the silty fraction is deposited,

causing the sedimentation peaks to occur after the peaks recorded in FUs 3 and 5.

The surface flow paths of the system, marked by both the main lithological domains

and the sedimentation pattern (Orlandini and Moretti, 2008), do not appear to have

changed during the last millennium and were found to corroborate the general

synthetic model proposed in this paper.

6. Model applications

As indicated in the introduction, owing to the critical situation that has been occurring

in the Park in recent years as a consequence of the lack of both superficial and

groundwater inflow, the application of external water supplies has been considered.

One particular proposition is the use of treated sewage effluents originating from

nearby towns, which would be applied in emergency situations. The effectiveness of

this improvement strategy to be implemented by applying effluents to both El Masegar

(Navarro et al., 2010a) and Las Cañas (Navarro et al., 2010b) has been evaluated.


38

The evaluation of the effectiveness of these interventions in the two cases above was

based on the general synthetic model of the hydrologic behaviour developed in this

paper. The evolution of the inundated area of the different FUs was analyzed using a

dynamic water budget. Fundamental to this analysis was the use of the hypsometric

curves resulting from the application of the CTC algorithm. Field data from the drying

processes from 1996 to 2006 were used to identify the model parameters. For this

purpose a systematic global search was carried out by means of a grid-search algorithm

(Newmaier, 2004). Both papers yielded an infiltration model with a linear decrease of

the infiltration rate with the elevation. By assuming the infiltration rates obtained for

each FU, the system’s response in the event of the application of the available treated

sewage effluent to both El Masegar and Las Cañas Zone was estimated.

In El Masegar (FU 5), the water source was located at its lowest point. A simulation was

conducted to determine the response that this FU would have to these inflows. It was

found that even for dry climates, the available treated sewage effluent is sufficient to

substantially improve the inundation conditions during a large percentage of winters.

Under these hypothetical conditions, the inundated area would be around 100 ha. This

area is called the “net inundated area” (NIA) and includes the area of open water and

the inundated macrophyte cover. Owing to the existence of small elevations, this area is

not continuous. It is possible to define an exterior cover that surrounds the inundated

zones. The area inside this line is the area that will be seen as inundated, including the

NIA and the islands within the wetland that provide the wetland habitat. This area is

called the “equivalent inundated area” (EIA). In FU 5 when the NIA is 100 ha, the EIA is

roughly equal to 192 ha (figure 11a). Another simulation examined what would occur

if, with the same inflow of treated sewage effluent, the inundated area was zero at the

beginning of each hydrologic year (figure 11b). As illustrated by this figure, with the


39

contributions from only one year, it is possible to guarantee a mean inundation of 62%

(62.3 ha) in FU 5 during the worst year.

In the simulation of Las Cañas Zone, the external water supply was also assumed to be

at its lowest elevation point. The simulation was carried out by assuming the

infiltration rate values obtained in the identification of the parameters and a dry

climate hypothesis was considered. The contributions of one year alone will guarantee

an inundation of 125 ha. Although this value is less than the 400 ha that Las Cañas

comprises, it is five times greater than the 25 ha that would be inundated if no external

contribution were applied (figure 12). As the morphology in Las Cañas is similar to

that of a reservoir, the EIA and NIA are practically identical.

The results showed that the amount of available treated sewage effluents is sufficient to

substantially improve the inundation condition in both the areas considered to be high-

priority (El Masegar), and in Las Cañas. However, it is important to remember that

TDNP, as a life system, is a dynamically changing system. Therefore, even if it is

assumed that the infiltration model is valid, there is a certain degree of uncertainty that

cannot be overlooked. For this reason, the results should be considered as a sensitivity

analysis of a system’s ability to respond, contributing information to generate better

speculation (Allen et al., 2003) on the application of treated sewage effluents.

7. Conclusions

This paper has offered a general synthetic model of the hydrological behaviour of Las

Tablas de Daimiel that allows the main trends of inundation process to be reproduced.

A cell-to-cell algorithm based on the one proposed by Marks et al. (1984) and

O’Callaghan and Mark (1984) was designed for this purpose. On the basis of a DEM of


40

the system, this algorithm allows the hypsometric curves to be obtained and also

facilitates the visualization of the inundation process assuming the existence of an

external water supply located in any cell of the DEM.

Based on the results of the analysis of the hypsometric curves with the above

algorithm, TDNP was subdivided into four BZs. In the zone of Las Tablas, which

presents a very complex morphometry, similar to a group of interconnected basins, 3

BZs were identified; the Guadiana-Gigüela, the Central and Final Zones. The Guadiana-

Gigüela Zone, with an area of 151 ha, includes the old bed of the Guadiana River and the

final stretch of the Gigüela River. A more detailed analysis of the discontinuities

observed in the Guadiana-Gigüela Zone prompted its subdivision into 4 sub-basins,

FUs, 1, 2, 3 and 4 (Molemocho mill area, old bed of the Guadiana River, the area

between the Guadiana and Gigüela rivers and the final stretch of the Gigüela River,

respectively). The Central Zone, a total of 200 ha when it is fully inundated (of this area,

106 ha have a surface elevation below that of the water surface when it is fully

inundated), mainly correspond to El Masegar, the area having the most important

ecological value of the Park. The Final Zone includes the upper areas of Las Tablas and

the area connecting the first two.

The area downstream of Morenillo dam is located in Las Cañas Zone, which behaves

like a reservoir, a single basin which simplifies its analysis considerably. As the Central,

Final and Las Cañas Zones do not show any major discontinuities, each one has been

associated with a single FU. The Central Zone was also called FU 5, the Final Zone

corresponds to FU 6 and Las Cañas Zone is FU 7. Hence a total of 7 FUs were identified

in TDNP.


41

The inundation model proposed was compared with the inundation data provided by

the TDNP-Technical Staff, resulting in good fits. Moreover, this model is consistent with

the surface flows which have for centuries comprised the lithological domains existing

in the Park (Domínguez-Castro et al., 2006), and the sedimentation pattern presented

by the system at the present time (Sánchez-Carrillo et. al., 2001). The coherence of the

model proposed in relation to these aspects gives confidence to its ability to estimate

the hydrological evolution of TDNP, now and in the somewhat distant future.

The model has been used to make estimations of this nature in order to assess the

feasibility of the application of treated sewage effluent in El Masegar and in Las Cañas.

The results confirm the usefulness of these types of interventions, providing a very

powerful tool for implementing water management at TDNP.

Acknowledgments

The authors would like to thank the Confederación Hidrográfica del Guadiana for

providing the means and the financial support to carry out this study. In particular we

would like to acknowledge the support provided by Mr. Samuel Moraleda. This

research was also financed in part by a Research Grant awarded to Ms García by the

Spanish Ministry of Science and Education, Research Grant BES-2006-12639. Also

thanks to Ángel Yustres, Laura Asensio and Juan Alonso for their help in the

preparation of this work. The support provided by the staff of the Tablas de Daimiel

National Park, especially by Mr. Carlos Ruiz, is also greatly appreciated. Finally we

would like to thank Dr. Cirujano and Dr. Alvarez-Cobelas for their valuable suggestions.


42

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47

Figures

Guadiana river

LAS CAÑAS

LAS TABLAS

Morenillo damMolemocho

mill

Masegar

Pan Island

0 1 km

N

Asnos Island

Study site

Puente Navarro

dam

Guadiana river

1

2

34

5

P3

P1

P2

Figure 1. Study area: Tablas de Daimiel National Park, Central Spain. Limnimeters are

indicated by black dots with numbers: 1 is Ojillo de Cañada Mendoza, 2 is Quinto de la

Torre, 3 is Isleta de la Fuente, 4 is Tablazo and 5 is Casablanca.


48

A

B

a)

c) d)

b)

Zc

Figure 2. Depression filling scheme in the cell-based used in this work. Point A in a) is

the water source location. Point B in c) is the pour point between the two sub-basins. Zc

in d) is the maximum surface water elevation defined by user.


49

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

625 623 623 623 624 623 623 625 625 623 623 623 624 623 623 625

625 622 621 622 624 623 623 625 625 622 621 622 624 623 623 625

625 622 620 621 622 621 622 625 625 622 620 621 622 621 622 625

625 622 621 623 623 623 623 625 625 622 621 623 623 623 623 625

625 623 623 624 624 623 623 625 625 623 623 624 624 623 623 625

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

a) b)

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

625 623 623 623 624 623 623 625 625 623 623 623 624 623 623 625

625 622 621 622 624 623 623 625 625 622 621 622 624 623 623 625

625 622 620 621 622 621 622 625 625 622 620 621 622 621 622 625

625 622 621 623 623 623 623 625 625 622 621 623 623 623 623 625

625 623 623 624 624 623 623 625 625 623 623 624 624 623 623 625

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

c) d)

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

625 623 623 623 624 623 623 625 625 623 623 623 624 623 623 625

625 622 621 622 624 623 623 625 625 622 621 622 624 623 623 625

625 622 620 621 622 621 622 625 625 622 620 621 622 621 622 625

625 622 621 623 623 623 623 625 625 622 621 623 623 623 623 625

625 623 623 624 624 623 623 625 625 623 623 624 624 623 623 625

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

e) f)

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

625 623 623 623 624 623 623 625 625 623 623 623 624 623 623 625

625 622 621 622 624 623 623 625 625 622 621 622 624 623 623 625

625 622 620 621 622 621 622 625 625 622 620 621 622 621 622 625

625 622 621 623 623 623 623 625 625 622 621 623 623 623 623 625

625 623 623 624 624 623 623 625 625 623 623 624 624 623 623 625

625 625 625 625 625 625 625 625 625 625 625 625 625 625 625 625

g) h)

Figure 3. Progressive inundation of a sample DEM. a) Hypothetic DEM with elevation

values in meters. The external water supply cell is selected (dark grey). b) First step of

inundation for a surface water elevation of 621 m. c) First step for a surface water

elevation of 622 m. d) Second step. e) Third step. f) First step for a surface water

elevation of 623 m. g) Second step. h) Unique step for a surface water elevation of 624

m.


50

Figure 4 a). Hypsometric curve of the zone upstream of Morenillo dam (Las Tablas)

assuming that the water source is in Molemocho Mill.

Figure 4 b). Hypsometric curve of BZ 2 until it merges with BZ 1 assuming that the

water source is at its lowest point (point P3 in figure 1).


51

Figure 4 c). Hypsometric curve of Las Cañas zone assuming that the water source is at

its lowest point of elevation (point P2 in figure 1).


52

BZ 3: Final Zone

BZ 2: Central Zone

BZ 1: Guadiana-Gigüela Zone

BZ 4: Las Cañas Zone

Figure 5 a). Basic Units of Las Tablas de Daimiel National Park.


53

Functional Unit 5

Functional Unit 6

Functional Unit 1

Functional Unit 3

Functional Unit 2

Functional Unit 4

Connection 3-4 (z = 606.00 m)

Connection 2-3 ( z = 605.42 m)

Connection 1-2 ( z = 605.23 m)

Connection 4-5 (z = 606.20 m)

Functional Unit 7

Figure 5 b). Functional Units and connection points located between them.


54

Figure 6. Visualization of the inundation process de la FU 5 (BZ 2) assuming that the

water source is at its lowest point (point P3 in figure 1).


55

Figure 7. Inundation data of the limnimeters identified in figure 1.


56

a) b)

d)c)

e) f)

Figure 8. A comparison of the historical inundated areas and FUs. The FUs are shown in

white. The bold lines represent the inundation data provided by the TDNP Technical

Staff.


57

a)

b)

Figure 9. A comparison of the historical inundated areas and Functional Units in Las

Cañas Zone. The Functional Units are shown in white. The black contours represent the

data from the inundated areas.


58

SEDIMENTATION DOMAINS

MAIN LITHOLOGICAL (ENVIRONMENTAL) DOMAINS

UPPER ZONES WITH MACROPHYTES

FLAT ZONES OR ZONES WITH EMERGENT MACROPHYTES

DEEP CHANNELS

CHAROPHYTE LAYERS UPON GYPSUM-RICH CLAYS

CHAROPHYTE LAYERS WITH VEGETAL REMAINS

PEAT-CHAROPHYTE ALTERNATIONS

DISCHARGE ZONE OF GIGÜELA RIVER

Figure 10. Adapted from Dominguez-Castro et al. (2006) and Sánchez-Carrillo et al.

(2001).


59

Figure 11 a). Estimation of the evolution of the inundated area in FU 5. Dry climate

hypothesis.

Figure 11 b). Estimation of the evolution of the inundated area in FU 5 assuming that

the inundated area is zero at the start of each hydrologic year. Dry climate hypothesis.


60

Figure 12. Grey line, simulation of the evolution of the inundated area in Las Cañas if

the inundated area is zero at the start of each hydrologic year. Black line, inundation

evolution without external water supplies. Dry climate hypothesis.

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

61

II. Characterization of the infiltration rate

in Las Tablas de Daimiel National Park,

Central Spain

Vicente Navarro, Beatriz García and Laura Asensio

Enviado a Hydrological Processes (Noviembre de 2010)


62


63

Characterization of the infiltration rate in Las Tablas de Daimiel

National Park, Central Spain

Vicente Navarroa, Beatriz Garcíab and Laura Asensioc.

a Corresponding author. Associate Professor. Geoenvironmental Group, Civil



[email protected].

b Research Engineer. Geoenvironmental Group, Civil Engineering Department,



c Research Engineer. Geoenvironmental Group, Civil Engineering Department,



Abstract

This article presents the characterization of the infiltration rate in the area known as

“Las Cañas” which is part of Las Tablas de Daimiel National Park, Central Spain.

Available information was used for direct identification and while the results varied


64

widely, it was proven that a functional dependence exists between the infiltration rate

and the inundated area. After examining the structure of this dependence more closely,

the most appropriate model was deemed to be a bilinear model. With these basic

foundations, data from years 1997, 1998, 2003 and 2005 were used to identify the

three parameters of the model. Highly satisfactory identifications were obtained. The

model was then calibrated and the drying processes from 1996, 2000 and 2003 were

simulated with considerable accuracy since the standard deviation was only 4 ha for a

total of 400 inundated ha. The model was used to estimate the evolution that the

inundated area would undergo after introducing contributions of treated sewage

effluents. Even though it was assumed that the results would entail twice as many

errors as the standard deviation, they did, however, allow us to provide a concise

description of the behavior of the system. Consequently, we obtained elements of

judgment that highlight the advisability of the application of treated sewage effluents in

Las Cañas, from a hydrological standpoint.

Keywords

Infiltration, water budget, wetland, inundation.

Introduction

Las Tablas de Daimiel National Park (TDNP) (1,928 ha) is the most outstanding

resource of the wetlands that make up the “Mancha Húmeda” (25,000 ha, Central Spain)

(see figure 1a). This system was declared a Biosphere Reserve in 1980 by the United

Nations Educational, Scientific and Cultural Organization (UNESCO). The intensive

water abstractions carried out in the West Mancha aquifer (regional aquifer beneath

the Mancha Húmeda) have caused the widespread lowering of the water-tables (see for


65

instance Llamas, 1988; Fornés et al., 2000; Cruces et al., 2000; Bromley et al., 2001;

Martínez, 2001; Custodio, 2002; Conan et al., 2003), disconnecting the wetlands from

groundwater inputs. This has led to major environmental damage, particularly in TDNP

(Cirujano et al., 1996; Alvarez-Cobelas et al., 2001). In the past, a number of different

measures were taken in an attempt to mitigate these repercussions. This was the

reason for the construction of the dams of Puente Navarro (in 1985) and Morenillo (in

1988) (see figure 1b). In addition, at different points in time water has been transferred

from the Tagus River. Ten wells were also drilled to supply water to the main “lagunas”

(see Florín et al., 1993) (or “tablazos” in Spanish). However, critical situations continue

to reoccur. In December 2009, out of the 500 ha which, according to the TDNP-

Technical Staff (TDNP-TS), must remain flooded to preserve the area’s most important

values (Pastor, 1996; Ruano, 1996), barely 10 ha were inundated.

Of these 500 ha, 400 ha pertained to the area known as “Las Cañas” (figure 1b), located

between the Puente Navarro and the Morenillo dams. At the present time, the

possibility of using treated sewage effluents from the nearby towns (roughly 10

Mm3/year) is being considered as a means of inundating this zone. The waters to be

used would undergo the appropriate treatment so as to comply with all of the quality

requirements as deemed necessary. The key to determining whether the proposal is

feasible is to obtain a reliable assessment of the resulting inundated area. To this end

and taking into account the experience that has been acquired using water budgets for

effective water-resource and environmental planning and management (see Healy et

al., 2007; Brush et al., 2004; Dalton et al., 2004, among others), the use of a dynamic

water budget was decided on. This would make it possible to describe the evolution of

the inundated area as the water mass balance is calculated (see for instance, Lindley et

al. 1995; Walton et al. 1996; Saxton and Willey, 2006). As will be discussed later, these


66

calculations are based on reliable information regarding the following: morphometry,

run-on R [L3/T] (which comes mainly form the northern area), precipitation p [L/T],

and evapotranspiration e [L/T]. Therefore, as is customary in many hydrologic

problems, the infiltration rate is the term of the water budget that creates the greatest

degree of uncertainty. Fortunately valuable information is available to solve this

problem. The TDNP-TS has been monitoring the inundated area of the Park since 1992.

Specifically, reliable data have been obtained on the drying processes in Las Cañas

during the summer/autumn period of years 1996, 1997, 1998, 2000, 2002, 2003 and

2005 (figure 2). These data comprise the foundations of this paper which characterizes

infiltration rate through the application of identification techniques for the purpose of

obtaining plausible estimations of the evolution of the inundated area.

Characterization of the infiltration rate

The simplest method to obtain information on the infiltration rate ir (L/T) is to make

the computation based on the water budget equation. If the simplified model outlined

in figure 3 is adopted to characterize the water mass balance (similar, for example to

the model used by Saxton and Willey, 2006 in the SPAW program), the generic

expression of the water budget (see Haan et al., 1994) may be detailed in Las Cañas as

follows:

[1] ( ) )( )()()()()()( tAtirtetptRtPNDtMDdt

dV −−++−=

where V is the inundated volume [L3], MD [L3/T] is the inflow rate from Morenillo dam,

PND [L3/T] is the outflow rate that will cross Puente Navarro dam, and A is the

inundated area [L2]. On the basis of the available information on R, p, e, V and A, upon


67

analyzing situations in which both MD and PND [L3/T] were virtually null, if equation 1

is discretized in one-day time steps by using the Euler method (Press et al., 2002), the

following will be obtained:

[2] iii

1iiii

)(ep

A

VVRir −+−+= +

where the subscript “i” indicates the value of the variable on the i-th day. Based on the

data presented in graph form in figure 2, by using the hypsometric curve of figure 4, it is

possible to obtain the infiltration rates shown in figure 5 (black dots). The great

dispersion seen is not a consequence of the method of time discretization, since, for the

problems analyzed here, it was found that the use of one-day time steps, the Euler

method or the fourth order Runge Kutta method (Press et al., 2002) yielded practically

identical results. Nor can it be attributed to the potential distortion introduced owing to

problems with the quality of the data used. With regard to the run-on (calculated

according to the indications found in the National Soil Conservation Service’s National

Engineering Handbook CNEM-4, NRCS, 2003), in 851 of the 854 data entries that were

processed (total number of days in the seven periods under consideration) it had a

value of less than 0.2 mm/day. There were only two days where R was between 0.2

and 0.4 mm/day, and one other day where the value was equal to 2 mm/day.

Evapotranspiration was calculated on the basis of data gathered in the class A

evaporimeter situated in the weather station of the Park (from which the precipitation

data were also taken), and on the basis of the experimental data of transpiration

measured during 1997 and 1998 by Sánchez-Carrillo et al. (2004) for different crops,

percentage of macrophyte cover, open water/macrophyte cover ratio, and evaporation

rate. For this reason, since a reliable digital elevation model (based on recent data,


68

spring, 2007, of the TDNP-TS) was used to obtain the hypsometric curve (figure 4), the

dispersion is probably due to reading errors. The reading of levels shown in figure 2

were taken visually and rounded off to the nearest centimeter. In this operation, the

same criterion was not always applied, as it depended on the person taking the reading.

As a result, an error may have been introduced into terms Vi-Vi+1.

To mitigate this effect, it is advisable to identify the mean values of ir. Therefore, first of

all, a mean value was identified for each of the 7 data series available (summer/autumn

1996, 1997, 1998, 2000, 2002, 2003 and 2005). If the mean value of the inundated area

in each one of these series is drawn versus the value identified, then the result will be

the grey dots of figure 5. Although the dispersion has decreased significantly, it is still

high. Moreover, the use of a single ir value for the entire drying process is debatable, as

is its assignment to the mean inundated area. In order to be able to better consider the

presumed relationship between ir and A, each of the 7 series of available data was

subdivided into 10 segments, and the ir value associated with each one was identified.

This resulted in the white dots drawn in figure 5. In this case, the assignment of ir to the

mean value of A in the segment is acceptable, given that the variation of A in all the

segments considered is always under 50 ha. Again, dispersion is substantially reduced,

although the value is still quite significant. It is important to note that the 25 white dots

drawn are actually only part of the 70 values identified. Since the problem is a simple

one (equation 1 with MD = PND = 0), and by identifying just one parameter (ir), in each

segment a systematic global search was carried out with the help of a grid-search

algorithm (Neumaier, 2004). It was thus found that the values identified were not

associated with local minimums. In this way the selection was made of the 25 values for

which the form of RMSE presented no doubt as to the quality of the identification.


69

Despite the dispersion, all the results of figure 5 indicate that ir decreases when A

increases. This persistent trend would appear to confirm the existence of a functional

dependence between the infiltration rate and the inundated area, which, in keeping

with this figure, may fit a linear model. Before accepting this hypothesis, however, we

decided to conduct a more in-depth analysis of the structure of the variation of the

infiltration rate in Las Cañas.

Structure of the variation of the infiltration rate

When the water level is found at elevation Z (see figure 6), the mean value of the

infiltration rate ir is defined as:

[3] ∫=Z

z

xxPxirZS

Zir0

d )( )(*)(

1)(

where S(Z) is the wet area associated with Z, Zo is the elevation above the sea level of

the Puente Navarro dam foundation, x is an elevation between Zo and Z (see figure 6), P

is the wet perimeter associated with each x, and ir* is the mean value of the infiltration

rate along the contour line associated with x. Given the low topography of the system,

S(Z) is practically equal to A(Z ), and P(x) dx is roughly equal to dA(x). If, like in other

studies of wetlands or infiltration ponds (Lindley et al., 1995; Merritt and Konikow,

2000; Saxton and Willey, 2006), it is assumed that after the contribution of treated

sewage effluents the effective hydraulic conductivity (understood in the sense used by

Vigiak et al., 2006, and equivalent to the hydraulic conductivity of the wet zone of

Bouwer, 1986) reaches a steady value, which remains practically constant throughout

the analysis, ir* may be calculated as follows:


70

[4] ( ))())((1 )()(* AV xLxZZxKxir o +−+=

where KAV is the harmonic mean of the saturated hydraulic conductivity (Ks) in the soil

profile of thickness L:

[5]

∫++

++=

)()()(

0 S4S

4

5S

5

AV321

)(

)()(

)()(

xLxLxL

lK

dl

K

xL

K

xL

xLxK

where L is the distance from the ground level to the regional water table (L = L1 + L2 + L3

+ L4 + L5, see figure 6), which is different for each x. For the inundation depth defined by

Z, the magnitude (1+(Z-(Zo+x))/L(x)) determines the value of the hydraulic gradient for

the points located on the level curve given by x. The thickeners Lk (k=1,..5) correspond,

respectively, to materials M1 (granular soil), M2 (mud), M3 (clay), M4 (peat) and M5

(sediments) identified in figure 6. In reality for any x, these thickeners will have lateral

changes. Therefore, the transect drawn in figure 6 is simply a schematic approximation

of the complex hydrogeologic structure of the soil beneath Las Cañas, which coincides

with the available information (see boreholes of figure 1b). In view of the variability of

the system verified by the TDNP-TS, even when additional information is available,

obtaining a “deterministic” distribution of ir is not a simple task. Nevertheless, it is not

the goal of this analysis, since our intent here was not to define a model that would be

able to quantify ir, instead the idea was to obtain information about its structure.

Therefore, the synthetic transect shown in figure 6 was adopted as a working approach

to the hydrogeologic configuration of Las Cañas.

It is important to point out that soils 1, 2 and 3 have an internal structure associated

with the progressive evolution of a fluvial regime ranging from high energy to low

energy events García-Hidalgo et al., 1995; Domínguez-Castro et al., 2006). This


71

structure can be outlined by assuming that any profile has a “textural evolution”

characterized by the textures of points P1, P2 and P3 (bottom, center and top of soils 1,

2 and 3; figure 6). If P1 has a sandy soil, then P2 will have mud, and P3, clay. This

variability in the properties has been introduced into equation 5 through the integral of

its denominator. It was calculated by assuming a linear variation of the hydraulic

conductivity between P1 and P2, and between P2 and P3. By using this approach, the

geometry defined in figure 6, the hydraulic conductivities of table 1, and the regional

water table levels of table 2, from equation 5, it was possible to obtain upper,

intermediate and lower estimations of KAV for each x. After introducing these

estimations into equation 3 through equation 4, the values of the relative infiltration

rate rir shown in figure 7 were found. The rir was defined as (ir-irMIN)/(irMAX-irMIN). As

can be seen in the three cases, the rir varies in the same way. If the values associated

with low inundation (inundated area lower than 50 ha) are omitted, a decreasing trend

is observed to roughly 280 ha, and after this, there is an increasing trend. It is

interesting to point out that this change also takes place in the relative volume

development rvd (figure 7), defined as (vd-vdMIN)/(vdMAX-vdMIN), where vd is the volume

development ratio defined as 3 dMEAN/dMAX, with dMEAN and dMAX being the mean and the

maximum water depth, respectively (Håkanson, 1982). What is the reason for these

changes? When the inundated area covers 280 ha, the water level reaches the down

stream slope of the Morenillo dam, changing the morphometry of Las Cañas. The

system begins to function as a reservoir, with the water depth increasing more than the

inundated area. The hydraulic gradient also becomes greater causing the infiltration

rate to rise. However, before the 280 ha mark is reached, the varying trend of the

infiltration rate is controlled by the “natural” hydrogeologic configuration, at which

point the reservoir “type” dynamics are imposed. Therefore, instead of introducing the


72

linear model, a bi-linear model of ir according to A was adopted, with the identification

work focussing on the three parameters it is characterized by.

Identification of the infiltration parameters

In order to identify these 3 parameters, a global identification based on a grid-search

algorithm (Neumaier, 2004) was carried out once again. The search space used is

defined in table 3. In this table, ir1, ir2 and ir3 are, respectively, the infiltration rate

values associated with inundated areas of 0, 280 and 434 ha (maximum inundated area

in Las Cañas).

Of the 7 available data series, only 4 were used for identification purposes (years 1997,

1998, 2002 and 2005). The other three series (years 1996, 2000 and 2003, were used

to calibrate the validity of the model.

As can be observed in figure 8, which reflects the isolines of the error topology, the

optimum values were identified (see table 4) with a good degree of certainty. Moreover,

as can be corroborated in figure 9, with the parameters that have been identified, the

dynamic water budget allows for the accurate reproduction of the three data series

used to calibrate the model. It is not only the visual adjustment that is good. The mean

value of the absolute difference between the measured and modeled inundated area is

6.4 ha, with a standard deviation of 4 ha. Therefore, it would seem reasonable to

express confidence in the proposed model.

The most striking result of those presented in table 4 is the low value of ir3, since, in

keeping with figure 7, it would seem logical to expect a higher value than that of ir2.

However, when the system begins to function like a reservoir, the wet area

incorporates zones which, under natural conditions, would seldom be inundated.


73

According to the research carried out by different authors (see, for instance, Scanlon et

al., 1999), in these zones the infiltration rate is lower than the one in topographically

depressed zones where infiltration occurs naturally. As a result, when these zones

become inundated, ir decreases. Figure 7 does not exhibit this behaviour because here

it was allowed that for any given x, the transect drawn in figure 6 is valid even when it

begins to function as a reservoir, which is not true. While the function scheme based on

a bi-linear model is still valid, after 280 ha, ir does not increase.

Simulation of the evolution of the inundated area

Once the ir model has been defined, it is interesting to simulate the evolution of the

inundated area by adding treated sewage effluents to the system. The purpose of this

simulation was to assess the efficiency of applying these effluents, keeping the year

2027 in mind, which is the deadline for full compliance of the EU Water Framework

Directive 2000/60. To do this, equation 1 was formulated as follows:

[6] ( ) )()()()()()( tAtirtetptRtQdt

dV ⋅−−++=

where Q is the inflow of treated sewage effluents. Q was estimated on the basis of data

from the Confederación Hidrográfica del Guadiana (Public Administration responsible

for water management in the West Mancha aquifer), and on data related to water

consumption, the production of wastewaters and population growth from the Instituto

Nacional de Estadística (Public Administration responsible for statistics data

management in Spain). This resulted in the prediction of the effluent flows Q* shown in

figure 10a. In addition to conducting a simulation of reference assuming that Q=0, two

possible cases were considered. In the first it was assumed that there was only a 10%


74

loss of the effluent flows (Q=0.9Q*). In the second, it was considered that before the

water was taken to Las Cañas, it would be treated in a constructed wetland, which was

assumed to produce a 40 % loss of the potential effluent inflow (Q=0.6Q*).

For the three hypotheses of Q the system’s response was simulated considering one

rainy series and one dry series. The former was found by using as “base data” the

information associated with the 17 year series in which the average rainfall was equal

to the 65th percentile of the average rainfall of the 31 series of 17 years that can be

taken from 1961 (the first year in which data are available) to 2009 (see figure 10b).

The latter was determined using the series associated with the 35th percentile. The

potential effect of the climate change was introduced in a simplified manner, following

the indications of Moreno (2005). For this reason the “base data” were modified to

linearly decrease the mean precipitation by 10 mm, and linearly increase the mean

temperature by 2ºC. The data roughness (standard deviation of the series) was not

changed. This type of climate simulation is, of course, an approximation. However, the

methodology was deemed to be sufficient to estimate the sensitivity of the system to

the climate.

Figure 11 shows the differences ∆A between the simulations with Q≠0 and the

simulation of reference (Q=0) for the two climates under consideration. Even in the

worst case scenario (Q=0.6Q* and dry climate), the inundated area is able to be

increased by over 75 ha after the second year of simulation. However this does not

mean that the actual difference will be equal to this value. The simulations of ∆A do not

intend to be realistic. For this to be the case, it is necessary to introduce some kind of

hypothesis regarding values MD and PND into equation 6 rather than considering them

to be null values. The simulations that have been carried out should be used as a tool to


75

generate better speculation (Allen et al., 2003) on the application of treated sewage

effluents. The values of ∆A highlight the system’s sensitivity to the volumes of treated

sewage effluents that can be used, since even if the error in the simulations were such

that it led to an error of 10 ha in ∆A (a value more than twice as high as the standard

deviation shown in figure 9), there still would be a very significant improvement in the

hydrological condition of Las Cañas.

This was also clearly seen by simulating the response the system would have if,

assuming the existence of a dry series, at the start of each hydrologic year, the

inundated area was assumed to be zero. As exhibited in figure 12, with the

contributions of one year only, even in the worst year (hydrologic year 2012-2013) the

inundation is guaranteed to be 125 ha if Q=0.6Q*. While the value is less than the 400 ha

associated with the total inundation of Las Cañas, it is still 5 times greater than the 25

ha that would be inundated if treated sewage effluents were not applied (Q=0). This

simulation is of special interest since it is not just a sensitivity analysis, but rather an

estimation of the actual behavior, making it possible to verify the quick efficiency of the

application of treated sewage effluents. This rapid reaction would take longer if the dry

condition at the outset were associated with a prolonged drought that had caused a

considerable decrease in soil moisture. Under these conditions, both the soil suction

and the existence of cracks would play an important role in infiltration, an effect that

has not been taken into consideration here, and the speed of the response to the

inundation would be slower than the estimation. The application of treated sewage

effluents would help prevent situations of this type, which were quite uncommon in the

typical hydroperiod of TDNP prior to the intensive water abstraction carried out in the

West Mancha aquifer since the late 1970s (Alvarez-Cobelas et al., 2001).


76

Conclusions

After analyzing the application of a dynamic water budget in the area of Tablas de

Daimiel National Park, Central Spain known as “Las Cañas”, it was found that, as is

generally the case, the infiltration rate is the magnitude on which the uncertainty is

focused. Therefore, we decided to carry out an identification process based on the use

of data from 7 drying processes (summer/autumn of years 1996, 1997, 1998, 2000,

2002, 2003 and 2005) to improve its characterization. By adopting the infiltration rate

directly as a parameter to be identified, widely varying identified values were obtained.

The results, however, point to an apparently linear dependence between the infiltration

rate and the inundated area. It was deemed advisable to examine the structure of this

dependence more closely. On the basis of a hydrological model of synthesis and in

keeping with the morphometry of the system, it was considered advisable to use a bi-

linear model. The three parameters of the model were identified by means of the data

series from years 1997, 1998, 2002 and 2005. The results were, quite frankly,

satisfactory and they allowed us to simulate the drying processes from years 1996,

2000 and 2003 with absolute errors that had a standard deviation of only 4 ha. This

conferred a high degree of confidence upon the dynamic water budget proposed. When

it was applied to determine a simulation of how the inundated area would evolve from

2008 to 2027 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 obtained in

years 1996, 2000 and 2003), it was still able to clearly describe the behavior of the

system. In all the cases, a highly significant improvement in the inundation was

achieved. Therefore, the research conducted here provides reliable elements of

judgement with which to assess the opportunity to apply treated sewage effluents to

Las Cañas.


77

Acknowledgements


providing the means and the financial support to carry out this study. Special gratitude

goes out to the support provided by Mr. Samuel Moraleda. This research was also

financed in part by a Research Grant awarded to Ms Garcia by the Spanish Ministry of

Science and Education, Research Grant BES-2006-12639. The support provided by the

staff of the Tablas de Daimiel National Park, especially by Mr. Carlos Ruiz, is also greatly

appreciated.


78

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83

Tables

Table 1. Hydraulic conductivities Ks (in m/s) used to obtain the relative infiltration

rates rir shown in figure 8.

Material Upper Intermediate Lower

P1 1x10-3 5.5x10-4 1x10-4

P2 1x10-5 5.5x10-6 1x10-6

P3 1x10-8 5.5x10-9 1x10-9

M4 2x10-8 1.1x10-8 2x10-9

M5 1x10-3 5.5x10-4 1x10-4

Table 2. Regional water table levels (elevation above the sea level) used to find the

relative infiltration rates rir shown in figure 8.

Water table level (m)

Upper 594

Intermediate 592

Lower 590


84

Table 3. Search space of the identification process carried out to characterize the

infiltration parameters.

min(mm/day) max(mm/day)

ir1 5 10

ir2 3 8

ir3 1 6

Table 4. Optimum values of ir1, ir2 and ir3.

optimum

(mm/day)

ir1 7.25

ir2 5.00

ir3 3.25


85

Figures

N

Aquifer 04.04

Tablas de Daimiel National Park

10 Km

Mancha Húmeda Biosphere Reserve

Upper Guadiana Basin

Figure 1 a). Situation of the Upper Guadiana Basin, aquifer 04.04, and Mancha Húmeda

Biosphere Reserve.


86

Guadiana river

LAS TABLAS

Molemocho mill

Masegar

Pan Island

0 1

km

N

Asnos Island

Puente Navarro

dam

Guadiana river

1

2 34

5

Limnimeters

Weather Station

Tablazo de las Águilas

Pasarelas

Boreholes

LAS CAÑAS

Figure 1 b). Detailed plan view of the TDNP.


87

Figure 2. Time series of the inundation data.

Evapotranspiration Precipitation

Infiltration

Q inflow

Runon

Water table level

Puente Navarro dam

Q outflow

Morenillo dam

606.0

603.9

605.5

607.0

Figure 3. Water budget model in Las Cañas. Figure out of scale. Numbers define

elevation above the sea level, in meters.


88

Figure 4. Hypsometric curve of Las Cañas.


89

Figure 5. Infiltration rates obtained from the data depicted in figure 2. Values obtained

with equation 2 (black dots), values identified for each of the 8 time series available

(grey dots), and values identified after dividing each of these series into 10 segments

(white dots).


90

604.9

601.6

599.2

597.8

regional water table

Zo= 602.0

599.8

598.4

597.2

595.5

M5

607.0

606.0

Surface water level

x

Z

Z, x

M4

M3

M2

M1

L5

L4

L3

L2

L1

P3

P2

P1

Figure 6. Synthetic transect representing the hydrogeologic configuration of Las Cañas.

Figure out of scale. Numbers define elevation above the sea level, in meters. Data from

Aguilera et al., 2009; Domínguez-Castro et al. 2006; García, 1996; and García Hidalgo et

al., 1995.


91

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

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


92

Figure 9. Measured data (symbols) and simulation results (lines) of the drying

processes from years 1996 (a), 2000 (b) and 2003 (c).


93

Figure 10 a). Total sewage effluents inflow by year. From 2010 (lower line) to 2027

(upper line)

Figure 10 b). Precipitation data.


94

Figure 11. Differences ∆A between the simulations with Q≠0 and the simulation of

reference (Q=0) for the two climates under consideration.


95

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.


96

III. An evaluation of the application of treated sewage effluents in Las Tablas de Daimiel National Park, Central Spain

97

III. An evaluation of the application of

treated sewage effluents in Las Tablas de

Daimiel National Park, Central Spain

Vicente Navarro, Beatriz García, David Sánchez and Laura Asensio

Enviado a Journal of Hydrology (Junio de 2010)


98


99

An evaluation of the application of treated sewage effluents in

Las Tablas de Daimiel National Park, Central Spain

Vicente Navarroa, Beatriz Garcíab, David Sánchezc and Laura Asensiod

a Corresponding author. Associate Professor. Geoenvironmental Group, Civil



[email protected].

b Research Engineer. Geoenvironmental Group, Civil Engineering Department,



c Research Engineer. Geoenvironmental Group, Civil Engineering Department,



d Research Engineer. Geoenvironmental Group, Civil Engineering Department,




100

Abstract

At the present time there is not enough information available to develop a quantitative

model on how inundation takes place in the area of Tablas de Daimiel National Park

(Central Spain) located upstream of Morenillo dam. Given that it is the most important

area in the Park from an ecological standpoint, this is a major concern as it has not been

possible to assess the potential effectiveness of the interventions geared towards

improving its current state. As a result, it is not feasible to simulate the hydrologic

response to the application of treated sewage effluents, an initiative recently

implemented by the Public Administration responsible for water management in the

Guadiana river basin, where the Park is located. To help solve this problem, a simplified

model of the hydrologic behaviour of the system has been developed focusing on the

characterisation of the main trends of the inundation process. Field data from 12 drying

processes were used to identify the model parameters. Later, the evolution of the

system was examined after the application of treated sewage effluents, assuming the

hypothesis of a dry climate. The results show that the amount of available effluents is

sufficient to substantially improve the inundation condition of the areas considered to

be high-priority. This therefore demonstrates that, from a hydrologic point of view, it is

highly advisable to use treated sewage effluents.

Keywords

Infiltration, water budget, inundation, wetland.


101

1. Introduction

Tablas de Daimiel National Park (TDNP) is a floodplain wetland which covers 1,928 ha,

located over the West Mancha aquifer (5,500 km2), in Central Spain (figures 1 a and 1

b). Originally the wetland was the result of the overflowing of the rivers Gigüela and

Guadiana, and upwelling waters from the aquifer. Additionally, 14 watermill weirs

contributed to change from riverine to lacustrine conditions (Álvarez-Cobelas and

Cirujano, 2007). TDNP is the most outstanding element of the wetland system known as

“Mancha Húmeda” (25,000 ha), declared a Biosphere Reserve in 1980 by UNESCO

(United Nations Educational, Scientific and Cultural Organization). A large part of the

Mancha Húmeda is also located over the West Mancha aquifer, an area that has been

subject to intensive pumpage since the late 1970s. This has caused the phreatic level to

decrease, as has also been reported by a number of different authors (see, for example,

Bromley et al., 2001; Conan et al., 2003; Cruces et al., 2000; Custodio, 2002; Fornes et

al., 2000; Llamas, 1988; Martinez, 2001). As a result, the wetlands have been cut off

from the regional aquifer, producing major environmental damage. This has had a

serious impact on TDNP (Bromley et al., 2001; Cirujano et al., 1996; Álvarez-Cobelas et

al., 2001). In the past, a number of different measures were taken in an attempt to

mitigate these repercussions, such as the construction of Puente Navarro dam (in 1985)

and Morenillo dam (in 1988) (figure 1 c). The latter dam was built under the Tablas de

Daimiel Hydric Regeneration Plan of 1987, to improve the management of the water

supplied by the aqueduct Tagus-Segura (key structure of the Tagus-Segura diversion).

This water inflow was the main water supply considered into the Regeneration Plan,

but it was not the unique one. Several contingency wells were constructed to supply

water to the main “lagunas” (see Florín et al., 1993) (in the area they are called

“tablazos”) which make up TDNP (figure 1 d). Although water has been transferred


102

from the Tagus river on a number of different occasions since 1989, and pumpage from

some of the contingency wells has been carried out on a relatively regular basis, the

situation of TDNP has gradually worsened. The Confederación Hidrográfica del

Guadiana (the Public Administration responsible for water management in the West

Mancha aquifer) has recently considered the possibility of applying treated sewage

effluents (TSE) to improve the inundation condition. The effluents will be properly

treated at sewage plants to ensure optimum quality when they reach the Park. It is

beyond the scope of this paper to examine how the treatment process itself is carried

out and how optimum quality is defined. Our aim here is to assess the feasibility of this

project from a hydrologic point of view. To do this, the inundation pattern of TDNP

must be simulated; hence, a plausible characterisation of the infiltration rate (ir) is

needed. However this type of information is not currently available.

The first reliable approach to determine the value of the ir was undertaken in 1996.

That year, Ruano carried out the hydrogeologic study of the “Project for the collection

and application of underground waters in emergency situations at Tablas de Daimiel

National Park”. In this study the first daily “water budget” was implemented using a

hypsometric curve to update both the volume and inundated area (“dynamic water

budget”, DWB). The study focussed mainly on Las Cañas (the area of TDNP located

downstream of Morenillo dam, see figure 1 c), where the ir was estimated to be 5

mm/day. The behaviour of the old bed of the Guadiana river near Molemocho mill

(ir=15 mm/day), the Tablazo de las Águilas (ir=6.5 mm/day) and the Pasarelas area (ir

between 24 and 33 mm/day) (figure 1c) were also analysed. Despite its excellent

quality, this technical report was largely overlooked. It was not until Castaño read his

PhD dissertation in 2003 that a detailed ir study was done again. After analysing the

daily water budgets, Castaño identified an infiltration rate of close to 10 mm/day. As


103

Ruano did in 1996, a digital elevation model was also used to obtain the hypsometric

curve. Perhaps the identification model used, based on a local search algorithm, is not

the most appropriate. Moreover, given the complex structure of TDNP (figure 1d), the

assumption that the inundation pattern was equal to a dam reservoir is debatable.

Nevertheless, this work (on which the contribution of Castaño-Castaño et al., 2008 is

based) has served to advance our knowledge considerably.

Ruano (1996), Castaño (2003), and Castaño-Castaño et al. (2008), founded their

research work on DWBs. The use of “water budgets” has been thoroughly used in the

planning and management of wetlands, considering both quantitative and

environmental issues (see, among others, Brush et al., 2004; Dalton et al., 2004; Healy et

al., 2007). Water budgets provide a rational framework to identify the processes by

means of which water moves through the system, an essential point for the calculation

of nutrients, energy and chemical budgets (Lott and Hunt, 2001). The dynamic use of

this method, although not so widely applied, has, however, been solidly validated. This

can be corroborated in the works by Lindley (1995), Walton et al. (1996), Saxton and

Willey (2006) and Gasca and Ross (2009), for example. By applying the DWB, the

inundated surface and volume are updated, thus resulting in a kind of “film” of the

inundation process. However, if the process is not varied very gradually, major errors

could occur, since hydrostatic conditions are assumed to exist in the DWB. To proceed

correctly, the Navier-Stokes equations should be solved using shallow-waters flow

models that include “sink terms” associated with infiltration and evaporation.

Nonetheless, the work presented here was not designed to analyse flash flood

processes, but rather quasi-hydrostatic inundations. For this reason DWB was used as

the first simulation strategy. The line of investigation developed by Ruano (1996) and

Castaño (2003) was followed based on the characterisation of the infiltration rate in the


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application of identification techniques to analyse time series of inundation data

obtained by the TDNP Technical Staff (TDNP-TS).

2. A simplified conceptual model of the hydrologic behaviour of Las Tablas

As discussed in the Introduction, while the area of TDNP situated downstream of

Morenillo dam is known as Las Cañas, the name “Las Tablas” applies specifically to the

zone upstream of the dam (figure 1 c). This is the most environmentally valuable area,

where the largest population of “masiega” (the Spanish name for the sawgrass, Cladium

mariscus), the most characteristic species in the Park, is found. Therefore the inflow of

TSE will be focused on Las Tablas, which is where the characterisation of the ir would

be of greatest interest.

The identification of the ir from inundation data requires an inundation model, which is

none too easy to define in the mixture of water tables which form Las Tablas (Álvarez-

Cobelas and Cirujano, 2007). The field data obtained by the TDNP-TS for floods caused

by the arrival of water from the diversion of the Tagus-Segura through the Gigüela

river, highlights the existence of a complex process of “activation” of small pools which,

in keeping with the experience of the TDNP-TS, is largely controlled by the small

natural weirs formed by the vegetation. In addition, in the Guadiana and Gigüela

riverbeds as well as in Las Cañas, there are important peat deposits, which suffer

structural changes like those reported by Bradley (2002). Even if it were possible to

accurately characterise this microtopography, it would be difficult to predict its

evolution. Therefore, the development of a detailed inundation model has been

disregarded.


105

In contrast, on the basis of experience, it is, in fact, possible to identify a general

inundation pattern. When water arrives by way of the Gigüela river, the first impact is

the inundation of the Central Zone, comprising roughly 100 ha, where El Masegar (a

sawgrass meadow) is located (see figure 1 c). After that, the water reaches the zone

consisting of both the old bed of the Guadiana river and the final stretch of the Gigüela

river (figure 1 c), with an area of around 151 ha (“Guadiana-Gigüela Zone”). At the start

of the inundation of the Gigüela-Guadiana connection, the “Final Zone” is also reached.

This zone covers the area between the two previous zones (which connects through the

flat channels surrounding Pan Island, figure 1 c), as well as the high parts of the Central

Zone.

The topographic data verify the existence of these three basic units. To illustrate this, a

digital elevation model recently developed (spring, 2007) by the TDNP-TS was used to

draw up figure 2. It represents the evolution of the inundated area assuming that it has

a water source situated at the lowest point at Molemocho mill, in the old-bed of the

Guadiana river, point P1 of figure 1 c. The large discontinuity between z = 606.18 m and

z = 606.19 m indicates the elevation at which the Guadiana-Gigüela Zone comes into

contact with the Central Zone and Final Zone, whereby some 100 ha of the centre are

added to the 151 ha of the Guadiana-Gigüela. At this height, there is also an increase in

the speed with which the inundated area expands with the elevation, mainly due to the

flat morphology of the Final Zone.

Figure 2 shows three lesser discontinuities if the elevation is equal to 606.00, 605.41

and 605.23 m, when the water floods only the Guadiana-Gigüela Zone (elevation less

than 606.18 m). This may be attributed to the existence of four “sub-basins”, although


106

between sub-basins 2 and 3 hardly any morphometric discontinuity exists (at 605.41 m,

discontinuity is barely noticeable).

For all of the above reasons, a diagram of the hydrologic behaviour of Las Tablas was

developed using the six Functional Units (FUs) illustrated in figure 3. The first four

pertain to the four sub-basins of the Guadiana-Gigüela Zone. The fifth is associated with

the Central Zone, and the sixth is linked to the Final Zone. In each one of these units, the

evolution of the water depth, inundated area and volume can be characterised by

means of a DWB after obtaining an approximation of their main geometric

characteristics with a hypsometic curve. However, it has been disregarded to estimate

the spatial distribution of the inundation in its interior. To solve this question, the

microtopography of each FU must be described. And, as mentioned, this was not dealt

within the study.

3. Dynamic water budget computation

In all the systems analysed, the dynamic water budget equation was computed as:

[1] ( ) )()()()()()()( tAtirtetptRtQOtQI

dt

dV ⋅−−++−=

where V [L3] is the inundated volume, QI [L3/T] is the inflow that reaches the basin, QO

[L3/T] is the outflow, R [L3/T] is the lateral run-on, p [L/T] is precipitation, e [L/T] the

evapotranspiration, and A [L2] is the inundated area, related to V through the

hypsometric curve. This ordinary differential equation defines an initial value problem,

which may be transformed into a simple algebraic equation by using a derivative

approximation scheme (Finite Differences), and applying certain initial conditions to V

and A. If a scheme based on the day to day discretisation was used, the Euler method


107

and the fourth order Runge Kutta method (Press et al., 2002) were found to produce

practically identical results in Las Tablas. Hence, equation 1 was approached in the

following way:

[2] ( ) ( ) ii1i AirepRQOQIVV ii ⋅−−++−+=+

where subscript “i” indicates the value of the variable on the i-th day.

In the analyses conducted, the values of QI and QO were data. When field data (see

figure 4) was used to calibrate the model, the only series used were those in which both

magnitudes were null. And when making predictions for the future, QI was assumed to

be equal to the TSE that were expected, and QO would be considered practically null in

the emergency situations associated with the application of TSE. Both the average daily

temperature T and p were data too. The historical values were taken from the records

and the predictions were based on the hypothesis on climate (to be described below).

R is calculated from p by means of the curve number method (NRCS, 2003). Since it was

difficult to define the antecedent moisture condition, the possibility of using the

approach proposed by Young and Carleton (2006) was considered. In the end, however,

it has been decided to use the classic formulation based on the five-day antecedent

rainfall, taking into consideration the rainfall range determined by Mitchell et al.

(1993). Although the run-on in Las Tablas is minor as compared to the other terms of

the water budget, it should not be overlooked to consistently introduce into the

simulation of the system heavy rainfall episodes. When run-on is produced, it generally

occurs on the right-hand margin of the Gigüela river, towards FUs 4 and 5.

Water loss by evapotranspiration was defined as (Cesanelli and Guarracino, 2009):


108

[3] ETOKKe ⋅⋅= CS

where ETO is the reference evapotranspiration, KS describes the effect of soil water

stress, and KC is a crop coefficient. The reference evapotranspiration was estimated

from data of a class A evaporimeter located in the weather station of figure 1 c. The

water stress coefficient varies from 0 (dry soil) to 1 (wet condition). In the drying

processes analysed in this paper, KS was assumed to be practically equal to 1. The value

of KC was supposed to be variable according to the inundated area, as can be derived

from the experimental data of transpiration measured during 1997 and 1998 by

Sánchez-Carrillo et al. (2004) for different crops, percentage of macrophyte cover, open

water/macrophyte cover ratio, and evaporation rate. On the basis of these data, it was

inferred that KC has a value of 1 for open water conditions, and 1.2 when the

macrophyte cover prevails over open water conditions. These values are similar to

those put forth by Allen et al. (1998) for the Food and Agriculture Administration (FAO)

of the United Nations. Hence, they were the values finally used in the computations.

Once QI, QO, R, p and e have been defined, the behaviour of the system depends mainly

on the value of ir. As discussed in the Introduction, this is what usually happens in a

substantial number of hydrologic problems. What is, however, new as compared to the

Introduction is that, according to the diagram of the model presented in figure 3, the ir

may, in principle, have a considerably distinct structure for each FU. This makes its

identification more complex than if there were a single magnitude that was valid for all

of Las Tablas.


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4. Infiltration rate characterisation: conceptual model and parameter

identification

If, as is commonly the case in areas of low topography, the infiltration flow is assumed

to be fundamentally vertical (see, for example, Scanlon et al., 2002), the infiltration q

[L/T] in a generic profile “P” may be expressed as:

[4] ( )PPPP )(1 LzZKq −+=

where KP [L/T] is the effective hydraulic conductivity (understood in the sense of Vigiak

et al., 2006; equivalent to the hydraulic conductivity of the wetted zone of Bouwer,

1986) associated with profile P, LP [L] is the profile thickness from the surface to the

groundwater level; Z [L] is the elevation of the water level in the basin where the profile

is located; and zP [L] is the surface elevation of the top of the profile (greater than zMIN

[L], lower topographic elevation of the basin, and less than Z). Similar to what is

common practice in other studies on wetlands or infiltration ponds (Lindley et al.,

1995; Merritt and Konikow, 2000; Saxton and Willey, 2006), here it was assumed that

the effective hydraulic conductivity in the soil profiles under Las Tablas had a steady

value which was roughly the same throughout the process analysed. This allows for the

formulation of the average infiltration rate associated with Z as follows:

[5] ∫∫

−+==

)(

0

PP

)(

0

P 1)(

1)(

1)(

ZA

P

ZA

dAL

zZK

ZAdAq

ZAZir

where, given the low topography, the wetted area is assumed to be practically equal to

the inundated area A. Despite its formal interest, practical difficulties are encountered

in the application of this equation since the distribution of the hydraulic conductivity

must be known. Even if a thorough hydrological field investigation were to be


110

conducted, the existence of preferential infiltration paths may play a very important

role (Brooks, 2005). Due to a number of different circumstances such as the difficulties

encountered in trying to locate these preferential paths, along with the possibility that

their location may change over time (as happens with microtopography); the lack of

information available on bore logs, and the spatial variability of the substrate soil

verified in TDNP-TS, it was deemed best to change the scale of analysis. Taking into

account the scale used in equation 1 to calculate the water mass balance, it has been

decided to work directly with the FUs as a support (in the sense the term is used by

Pachepsky et al., 2006) for hydraulic conductivity. It is interesting to note that this

change in scale enabled us to balance the scale used to model hydraulic conductivity

with the testing scale/field data (figure 4) used to calibrate the model.

Thus, instead of working with the effective hydraulic conductivity linked to each profile,

its mean value KM [L/T] associated with a certain elevation Z of the water level, was

used. Equation 5 was formulated as follows:

[6]

),()()()(

1)(

1)()( GWMM

MIN

ZZiZKzdAzL

zZ

ZAZKZir

Z

z

⋅=

−+⋅= ∫

where i(Z) [dimensionless] is the mean value of the hydraulic gradient, which depends

on the geometry and groundwater level ZGW [L].

By forgoing the possibility of obtaining the ir from the upscaling of q, not only did this

mean giving up the possibility of obtaining a prediction of the ir values through the

hierarchical consideration of the physical processes associated with the infiltration, it

also entailed the loss of a valuable tool to estimate the way in which the effective

hydraulic conductivity varies in relation to the elevation of the water level. It is not easy


111

to define the way in which this variation would take place. On the one hand, it would be

reasonable to expect KM would go from having a low value KM1 when, with low

inundated areas, the sediments from the bottom of the basin have a considerable effect,

to higher values as the water depth increases and the relative importance of the

sediment width diminishes. On the other hand, the low-elevation zones may coincide

with those having a higher interaction with the aquifer, which means that they may

have associated preferential flow paths which make KM1 higher. Therefore, a linear

model between KM1 and KM2 (high inundated area) was adopted as a working

hypothesis without making any impositions as to which of these two values should be

higher.

This model was applied to analyse limnimetric data from Quinto de la Torre when Z

was less than 606.00 m (see figures 1 c and 4). Values of 9 mm/day for KM1-4 (KM1 in FU

4) and 21 mm/day for KM2-4 were identified (see table 1). Drying series from 2000,

2001, 2003 and 2004 were used. A systematic global search by means of a grid-search

algorithm (Neumaier, 2004) was used for identification. Not only did this global

identification avoid potential problems with local minima, it also provided a simple way

to obtain figure 5, in which the quality of the identification becomes evident when the

topography of the error is represented. This figure, as well as the highly satisfactory

adjustment obtained when the field data used to calibrate the identification were

reproduced (1996 and 1999, figure 6), gives confidence to the model, indicating that the

hypothesis of linear variation of KM is a feasible model .

Unfortunately, it was not possible to carry out a similar task in FU 5. This unit marks the

location of “El Masegar”, where the largest population of sawgrass (a species of special

interest) is currently found (Cirujano and Álvarez-Cobelas, 2009, personal


112

communication; Álvarez-Cobelas et al., 2008). However, there are no available data on

this FU separately. The limnimeter closest to FU 5 is in Tablazo (see figure 1 c), situated

at the point of contact between FUs 5 and 6. Hence, limnimetric data of the infiltration

in FU 5 are only available when the water level is above 606.19 m, i.e., when the rest of

the FUs are also inundated. This is why it is not easy to segregate the information

related only to FU 5. Although direct identification is not possible, the available data do,

however, contribute valuable information about the variation ranges of KM in FU 5.

This information was obtained by first analysing the data from Quinto de la Torre when

Z was between 606.18 m and 606.00 m. Hence, once again, by assuming a linear model,

it was possible to identify the variation of “KM-1234”, the value of KM when FUs 1, 2, 3 and

4 are inundated. It is important to note that both in this case and in the identification of

KM1-4 and KM2-4, as well as in the analyses described below, although the information

from Quinto de la Torre was used, a previous verification was made to check the

consistency with the data taken from the other limnimeters (figure 4). Using drying

periods from 1996 and 2003 the values of KM1-1234 and KM2-1234 from table 1 were

identified. The quality of the identification was similar to what was shown for KM1-4 and

KM2-4 in figure 5. The next step was to analyse the drying process which in 1997 affected

all the FUs when the water level went from Z=606.91 m to Z=606.19 m. By applying the

same hypotheses and procedures as in the two cases described above, the values of KM1-

TOT and KM2-TOT in table 1 were identified.

When the water level is between Z=606.18 m and Z=606.19 m, the full inundation of

FUs 1, 2, 3, 4 and 5 is achieved, but with practically no inundation in the zones between

them. Therefore, the mean value of the effective hydraulic conductivity can be

calculated as the weighted average of KM2-1234 (equal to 11 mm/d, inundated area of


113

151.9 ha) and KM2-5 (unknown value, inundated area of 99.6 ha). This weighted value is

assumed to be close to 15 mm/d, i.e., a KM1-TOT . It is deduced that KM2-5 will be equal to

21 mm/d, a value identical to KM2-4.

To estimate the value of KM1-5, given the morphology of FUs 3 and 5, as well as the

nature of their sediments (Sánchez-Carrillo et al., 2001b), it would be reasonable to

assume a certain degree of analogy between the hydrogeologic functioning of FU 5 and

the group formed by FUs 1-2-3 once its function as a floodplain has become more

important than its role as a stream bed. This is assumed to happen above Z=605.45 m. It

is therefore of interest to analyse the drying data from Quinto de la Torre when Z

ranged from 605.99 m to 605.55 m. The data from 2000, 2001 and 2003 were used to

identify the values of KM2-123 and KM1-123 in table 1. The latter value is equal to KM1-4 and

practically equal to KM1-1234. Therefore KM1-5 = 9 mm/d was used.

The characterisation of KM in FU 4, and in the group of FUs 1-2-3, has not only served to

gather more criteria to estimate KM1-5 and KM2-5. As it will be seen in the following

sections, it has also allowed for the evaluation of the potential effectiveness of the

application of TSE in the system.

5. Evaluation of the TSE application: Hypothesis

As pointed out by Cirujano et al. (1996), the basic priority at TDNP is to inundate the

Park. So, once a plausible variation range has been defined for KM in FU5, i.e., once the

variables that affect the hydrologic behaviour of the system have been marked, what

must be evaluated is whether or not the available TSE is able to inundate it. In order to

do this, it has been decided to simulate the long-term effect of TSEs, even though one

must be aware that a simulation of this type has serious drawbacks.


114

The first and perhaps most fundamental question is that it must always bear in mind

that Las Tablas, as a life system, is a dynamically changing system. In fact, in keeping

with the results reported by Sánchez-Carrillo et al. (2001a), its rate of change, (as far as

sedimentation is concerned) is higher than in other wetlands. Although it is true that by

forgoing the execution of a detailed simulation of the spatial distribution of the

inundation, the dependency of the model was substantially reduced with regard to the

changing microtopography, even the simplified model used will be affected by the time

evolution of the system in long-term simulations. Most likely the scale used to simulate

the infiltration will contribute a certain amount of time stability to the model. However,

the data used to estimate the infiltration are associated with a time window of 8 years,

which may be too short. Therefore, even if it is assumed that the infiltration model is

still valid, it must be remembered that associated with the parameters that have been

identified, there is a certain degree of uncertainty that cannot be overlooked.

The long-term simulation also entails another fundamental difficulty. Given that the

response of the system depends on the inundated area, and the inundated area

depends, in turn, on the water inflow, if only the contribution of TSE is considered and

not the discharges from the Gigüela river, the simulations will not be accurate.

Moreover, by trying to estimate the streamflow of the Gigüela, it may be faced a

problem of an even larger scope, since it would entail the precision simulation of the

integrated hydrologic-hydrogeological behaviour of the Upper Guadiana basin (14,000

km2). This is no easy task, given that the amount of information available in the basin is

scarce. Thus, for example, although the group of researchers who are the authors of this

paper, have an integrated hydrologic-hydrogeological model of the Upper Guadiana

basin, its precision (cells of 2.5 × 2.5 km, monthly computational time-steps) is not

appropriate for use in the research proposed here. Therefore, it was not used to


115

simulate the discharges of the Gigüela river. In contrast, another simulation was carried

out based on the contribution of TSE, in the lateral run-on, and on the assumption that

the outflow associated with TSE was null.

For all of the above reasons, the results did not have any “true physic” significance since

they do not indicate the inundation that would take place at a given moment in time.

They should be considered to be a sensitivity analysis of a system’s ability to respond,

contributing information to generate better speculation (Allen et al., 2003) on the

application of TSE.

This sensitivity analysis was designed to be carried out over a period of 18 years from

October-2009 to September-2027. This time period was used, bearing in mind that

September-2027 is the deadline for achieving the objectives of the EU Water

Framework Directive 2000/60.

The treated sewage effluent inflow (TSEI) was estimated using data taken from the

Confederación Hidrográfica del Guadiana, as well as from data on water consumption,

the production of wastewater and population growth provided by the Instituto

Nacional de Estadística (Public Administration responsible for the management of

statistical data in Spain). The resulting predictions are shown in figures 7 a and 7 b and

table 2. TSEI distribution by municipalities is given in table 3. It is not possible to apply

all the effluents to the same place. In accordance with the results from the hydraulic

studies (the program EPA SWMM 5.0; EPA, 2005 was used), in order to transport water

by gravity flow, minimizing earthworks, it is advisable to distribute the effluents evenly

throughout the Park. The water from the towns located on the “Gigüela line” (Campo de

Criptana, Alcázar de San Juan, Herencia, Villarta de San Juan, Arenas de San Juan and

Villarrubia de los Ojos; 57.1 % of the total effluent discharge) will be applied to FU 5,


116

favouring the inundation of the Masegar. The water from the towns of the “Azuer line”

(Membrilla-Manzanares and Daimiel; 40.4 % of the total effluent discharge) will be

directed to FU 1. This will serve to reduce the risk of combustion of the peat deposits

located in the old bed of the Guadiana river. Also, it will ensure the inundation of FU 1,

an area of utmost interest from a cultural heritage point of view, as it is the site of the

Molemocho mill (point P2, figure 1 c), a building of great ethnological value.

The evolution of the climate was simulated with a dry series. The “base data” used were

those associated with the 18-year series in which their average rainfall was equal to the

35th percentile of the average rainfall of all the 18-year series that can be recorded

from 1961 (first year in which daily information was available) to 2007. These “base

data” were later modified in order to linearly decrease the mean precipitation by 10

mm, and linearly increase the mean temperature by 2ºC, during the 18-year simulation.

The data roughness (standard deviation of the series) was not changed. So, the

potential effect of climate change was introduced in a simplified manner (Moreno,

2005). This type of climate simulation is, of course, an approximation (see, for example,

the paper published recently by Candela et al., 2009 on how to obtain climate

forecasting to evaluate the groundwater consequences). However, in view of the

uncertainty associated with the other variables involved in the simulations, the

methodology was deemed to be a good approximation for considering the potential

effect of a dry series in the model.

6. Evaluation of the TSE application: Results

Figure 8 shows the estimation of the response of FU 5 assuming the mean effective

hydraulic conductivity characterised by the values or KM1-5 y KM2-5 recorded in table 1.


117

As indicated, there is enough TSE available to generate a very significant improvement

in the inundation conditions, since full inundation is achieved during a high percentage

of winters. Under these conditions, the area of water covers roughly 100 ha. This area

has been given the name “net inundated area” (NIA), and includes the area of open

water and the inundated emergent macrophyte cover. Owing to the existence of small

elevations, this area is not continuous. It is possible to define an exterior cover that

surrounds the inundated zones. The area inside this line is the area that will be seen as

inundated, including the NIA and the islands within the wetland that provide wetland

habitat. This area is called the “equivalent inundated area” (EIA). In FU 5 when the net

inundated area is 100 ha, the equivalent inundated area is equal to roughly 192 ha. It

never shows values of less than 20 ha of EIA in summer and after the first three years it

has been consistently above 30 ha.

During several episodes, the full inundation of FU 5 (elevation 606.18 m) is reached. In

this case, the water flows naturally into FU 4. However, with a minor intervention, its

flow could occasionally be diverted towards FUs 1-2-3. This intervention would be

reversible and it is planned to go into effect only in cases of emergency inflows and not

on a continuous basis. It is not our intention to distort the functioning of the Park.

Figure 9 a shows what would happen if, in addition to the net effluent inflow from the

Azuer line, this surplus discharge were applied in a “step-by-step application” to FUs 1-

2-3. In this simulation values of KM1-123 and KM2-123 from table 1 were used. Full

inundation (NIA of 99.6 ha, and EIA of about 138 ha) is achieved in approximately half

of the years of simulation. The net inundated area ranges from 30 to 90 ha. This ensures

the full inundation of FU 2, the Guadiana basin where the peat bogs are located.


118

To complete this simulation exercise, calculations were made to find out what would

happen if the net effluent from Fuente el Fresno along with the small surplus discharges

from FUs 1-2-3 (“step-by-step application”) were applied to FU 4. The values of KM1-4

and KM2-4 identified in table 1 were used. The results are given in figure 9 b. Full

inundation (NIA of 26 ha, EIA of 45 ha) is only achieved in extraordinarily heavy rainfall

events. However, the inundation of a minimum area, associated with the surface

depressions of the Gigüela riverbed, is guaranteed.

As discussed in the previous section, although these simulations must be interpreted

with caution, they do, however, illustrate that the application of TSE is sufficient to

substantially improve the state of TDNP.

This fact was also highlighted by simulating the response that FU 5 would have, year by

year, if, in addition to maintaining the TSEI defined above, the inundated area was

assumed to be zero at the start of each hydrologic year. As indicated in figure 10, with

the contributions from only one year, it is possible to ensure that in the worst year

(hydrologic year 2019-2020, precipitation 248.2 mm, 9.7 th percentile with respect to

data from 1959) the mean inundation of FU 5 will be 62.5 % (62.3 ha), whereas the

inundation of FUs 1-2-3 will be 79.3% (73.6 ha), and in FU4 it will be 18 % (4.6 ha).

This simulation is especially interesting, since it is not a sensitivity analysis, but rather

an estimation of the actual behaviour. Despite all the limitations discussed earlier, it

allows for the verification of the rapid efficiency of TSE application. This quick response

would be slowed down if the dry condition at the outset were associated with a drought

that had caused a great reduction in soil moisture. Under these conditions, both soil

suction and the presence of cracks would play an important role in infiltration, an effect

that has not been considered here, and the speed of the inundation response would be


119

lower that expected. The application of TSE would help prevent the occurrence of

situations of this nature, which are highly unusual in the hydroperiod typical of TDNP

prior to the intensive water abstraction that has been carried out in the West Mancha

aquifer since the late 1970s (Álvarez-Cobelas et al., 2001).

To finalise this section, it is interesting to stress the fact that it is difficult to define a

priori the best way for inundation to be carried out. It would probably be of interest to

promote inundation during the first stage of TSE application to favour the recovery of

the aquatic vegetation (Cirujano et al., 1996), although defining the duration of this

stage is no easy task. The management of the inundation process should be defined and

redefined over time, moulding itself to the ecological evolution of TDNP.

7. Conclusions

An analysis of the inundation data and the digital elevation model provided by the

TDNP-TS enabled us to develop a simplified conceptual model of the hydrologic

behaviour of Las Tablas. This model defined six functional units represented in figure 3.

An analysis of the inundation of the first 5 was considered to be of interest. A linear

decrease of the mean effective hydraulic conductivity with the elevation was assumed

to be able to perform the analysis by means of a dynamic water budget. Twelve drying

processes were used to characterise the parameters of this law. Inundation data

gathered by the TDNP-TS was used. The quality of the identification processes (figure

5), as well as the adjustments made to the calibration simulations (figure 6), conferred a

certain degree of confidence on both the parameters identified (table 1) and on the

hydrologic model put forth. For this reason, although, as commonly occurs in ecological

engineering, the variability of the system advises caution in interpreting the results


120

from the simulations, it has been decided to use this model to evaluate the long-term

response of the system to TSE application. Even if a dry climate is assumed to exist,

figures 8, 9 and 10, were obtained, which indicates that the application of TSE will

produce a significant increase in the inundation of FUs 1-2-3, 4 and 5. Therefore,

elements of judgement were introduced that show the advisability, from a hydrologic

point of view, of applying TSE to TDNP.

Acknowledgements


providing the means and the financial support to carry out this study. We are especially

grateful for the support provided by Mr. Samuel Moraleda. This research was also

financed in part by a Research Grant awarded to Ms. Garcia by the Spanish Ministry of

Science and Education research grant BES-2006-12639. Also gratefully acknowledged

is the financial support provided by the Education and Research Department of the

Castilla-La Mancha Regional Government and the European Social Fund within the

framework of the Integrated Operative Programme for Castilla-La Mancha 2000-2006

(approved by Commission Decision C(2001) 525/1) to Mr. Sánchez. The support

provided by the staff of Las Tablas de Daimiel National Park, especially by Mr. Carlos

Ruiz, is also greatly appreciated. Lastly, we thank Dr. Florín, Dr. Cirujano and Dr.

Álvarez-Cobelas for their valuable suggestions.


121

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127

Tables

Table 1. Mean value of the effective hydraulic conductivity and infiltration rate

identified in different areas of Las Tablas.

Parameter Area Value (mm/day) Source

average ir Las Cañas 5 Ruano, 1996

average ir Guadiana, Molemocho 15 Ruano, 1996

average ir Tablazo de las Águilas 6.5 Ruano, 1996

average ir Pasarelas 24-33 Ruano, 1996

average ir Whole Park 10 Castaño, 2003

KM1-4 FU 4 9 This work


KM1-123 FUs 1, 2 and 3 9 This work

KM2-123 FUs 1, 2 and 3 8.5 This work

KM1-1234 FUs 1 to 4 10 This work

KM2-1234 FUs 1 to 4 11 This work

KM1-TOT FUs 1 to 5 15 This work

KM2-TOT FUs 1 to 5 13 This work




128

Table 2. Annual volume of TSEI.

Year

Volume (Mm3)

2009 10.48

2010 10.58

2011 10.67

2012 10.76

2013 10.86

2014 10.95

2015 11.05

2016 11.15

2017 11.25

2018 11.35

2019 11.45

2020 11.55

2021 11.66

2022 11.77

2023 11.87

2024 11.98

2025 12.09

2026 12.21

2027 12.32


129

Table 3. Percent distribution by municipalities of TSEI.

Daimiel 19.4%

Manzanares + Membrilla 21.0%

Villarrubia 13.1%

Alcázar de San Juan + Criptana 37.5%

Herencia 4.7%

Fuente el Fresno 2.5%

Villarta de San Juan 1.0%

Arenas de San Juan 0.8%


130

Figures

N

10 Km

Tablas de DaimielNational Park

Figure 1 a). Situation of the West Mancha aquifer and La Mancha Húmeda Wetlands

(dashed areas).


131

Fuente El FresnoVillarrubia de

los Ojos

Daimiel

Guadiana River

Azuer RiverManzanares

Alcázar de San Juan

Las Tablas de Daimiel National Park

Special protection zone

0 5 km

NHerencia

Malagón

Záncara River

Arenas de San Juan

Villarta de San Juan

Campo de Criptana

Membrilla

Figure 1 b). Situation of Tablas de Daimiel National Park (TDNP).


132

Guadiana river

LAS CAÑAS

LAS TABLAS

Molemocho mill

Masegar

Pan Island

0 1 km

N

Asnos Island

Puente Navarro

dam

Guadiana river

1

2

34

5

Limnimeters

P2

P1

Weather Station

Tablazo de las Águilas

Pasarelas

Figure 1 c). Detailed plan view of the TDNP. Limnimeter 1 corresponds to Ojillo de

Cañada Mendoza, 2 to Quinto de la Torre, 3 to Isleta de La Fuente, 4 to Tablazo and 5 to

Casablanca.


133

Figure 1 d). Digital aerial photography of Las Tablas de Daimiel National Park.


134

Figure 2. Evolution of the inundated area with elevation. The water source is assumed

to be at point P1 in figure 1 c.


135

Functional Unit 5

Functional Unit 6

Functional Unit 1

Functional Unit 3

Functional Unit 2

Functional Unit 4

Connection 3-4

Connection 2-3

Connection 1-2

Connection 4-5

Figure 3. Functional units (FU) considered in the simplified hydrologic model of Las

Tablas.


136

Figure 4. Inundation data of the limnimeters identified in figure 1 c.

KM2-4 (mm/day)

K M 1-4 (mm/day)

Figure 5. Variation of the Root Mean Square Error (RMSE, in ha) around the optimum

values of the parameters that define the ir in FU 4.


137

0.0E+00

2.0E+04

4.0E+04

6.0E+04

8.0E+04

1.0E+05

1.2E+05

1.4E+05

1.6E+05

0.0E+0

1.0E+5

2.0E+5

3.0E+5

4.0E+5

5.0E+5

6.0E+5

7.0E+5

8.0E+5

14/0

7/19

96

03/0

8/19

96

23/0

8/19

96

12/0

9/19

96

02/1

0/19

96

22/1

0/19

96

11/1

1/19

96

01/1

2/19

96

V (m

3 )

A (m

2 )

A-MOD

A-EXP

V-MOD

V-EXP

0.0E+00

2.0E+04

4.0E+04

6.0E+04

8.0E+04

1.0E+05

1.2E+05

1.4E+05

1.6E+05

0.0E+0

1.0E+5

2.0E+5

3.0E+5

4.0E+5

5.0E+5

6.0E+5

7.0E+5

8.0E+5

29/0

6/19

99

19/0

7/19

99

08/0

8/19

99

28/0

8/19

99

17/0

9/19

99

07/1

0/19

99

27/1

0/19

99

V (m

3 )

A (m

2 )

A-MOD

A-EXP

V-MOD

V-EXP

Figure 6. Calibration: time series of inundation data (symbols) and model results (lines)

in FU 4.


138

0

7

14

21

28

35

42

49

56

63

70

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+0522

/02/

2008

06/0

7/20

09

18/1

1/20

10

01/0

4/20

12

14/0

8/20

13

27/1

2/20

14

10/0

5/20

16

22/0

9/20

17

04/0

2/20

19

18/0

6/20

20

31/1

0/20

21

15/0

3/20

23

27/0

7/20

24

09/1

2/20

25

23/0

4/20

27

04/0

9/20

28

p(m

m/d

), E

TO

(mm

/d)

TS

EI (

m3 /

d)

TSEI FU 5 TSEI FU 1-2-3 TSEI FU 4 p ETO

Figure 7 a). Prediction of the total treated sewage effluent inflow, applied to FU 1-2-3,

FU 4 and FU 5, and precipitation and evaporation series (daily data). b) Total treated

sewage effluents inflow by year.

Figure 7 b). Total treated sewage effluents inflow by year. From 2009 (lower line) to

2027 (upper line).


139

Figure 8. Estimation of the evolution of the inundated area in FU 5.


140

Figure 9 a). Evolution of the inundated area in FUs 1-2-3 after applying the surplus TSE

resulting from the inundation of FU 5 in addition to the TSE from the Azuer line.

Figure 9 b). Evolution of the inundated area in FU 4 after applying the surplus TSE

resulting from the inundation of FUs 1-2-3 in addition to the TSE from Fuente el

Fresno.


141

Figure 10. Estimation of the evolution of the inundated area in FU 5 assuming that the

inundated area is zero at that start of each hydrologic year.


142

Conclusiones y futuras líneas de investigación

143

Conclusiones y futuras líneas de

investigación

Conclusiones

La presente tesis doctoral tiene como principal objetivo el estudio de la viabilidad,

desde el punto de vista hidrológico, de aplicar efluentes de depuradora

convenientemente tratados en el Parque Nacional de Las Tablas de Daimiel con el fin de

mejorar su estado actual a medio plazo. Este estudio se ha dividido en tres partes,

donde la primera de ellas se ha centrado en definir un modelo simplificado del

comportamiento hidrológico del Parque. Esto constituye un aspecto fundamental para

poder cuantificar con posterioridad los distintos procesos que caracterizan al sistema

(Good et al., 1978; Greeson et al., 1979; Hunt et al., 1997; Ivanov, 1981; Skalbeck et al.,

2009). Utilizando este modelo como base, se ha caracterizado la infiltración y evaluado

la respuesta del sistema ante aportes externos de agua tanto en la zona de Las Cañas

(capítulo 2) como en la zona de Las Tablas (capítulo 3).

El modelo sintético general del comportamiento hidrológico del Parque Nacional de Las

Tablas de Daimiel desarrollado en el primero de los capítulos permite reproducir los

principales patrones de inundación. Para ello se ha desarrollado un algoritmo celda a

celda basado en el propuesto por Marks et al. (1984) y O’Callaghan and Mark (1984).

Este algoritmo permite, a partir de un modelo digital del terreno, obtener las curvas

hipsométricas y visualizar el proceso de inundación suponiendo un aporte externo de

agua localizado en cualquier celda del MDT.


144

Como resultado del análisis de las curvas hipsométricas obtenidas con dicho algoritmo

y el contraste de estas con los datos históricos de inundación se ha subdividido el

Parque en cuatro Zonas Básicas. En la zona de Las Tablas, debido a su compleja

morfometría similar a la de un conjunto de vasos interconectados, se han distinguido

tres Zonas Básicas: Guadiana-Gigüela, Central y Final. Un análisis más detallado de las

discontinuidades observadas en la Zona Guadiana-Gigüela ha hecho que se subdivida

en 4 sub cubetas, las Unidades Funcionales 1, 2, 3 y 4 (Molino de Molemocho, el lecho

del río Guadiana, el área comprendida entre los ríos Guadiana y Gigüela y el tramo final

del canal del río Gigüela, respectivamente). La Zona Central se corresponde

fundamentalmente con Masegar, y es el área de mayor valor ecológico del Parque. La

Zona Final incluye las áreas más elevadas de Las Tablas y el área de conexión entre las

Zonas Central y Guadiana-Gigüela. Aguas abajo de la presa de Morenillo se encuentra la

Zona de Las Cañas, cuya morfometría es similar a la de un embalse, lo que simplifica

considerablemente su análisis. Puesto que las curvas hipsométricas de las Zonas

Central, Final y Las Cañas no presentan discontinuidades importantes, cada una de

estas Zonas Básicas ha sido asociada a una única Unidad Funcional. A la Zona Central se

le ha llamado Unidad Funcional 5, la Zona Final corresponde a la Unidad Funcional 6 y

la Zona de Las Cañas es la Unidad Funcional 7. Así, en el Parque Nacional de Las Tablas

de Daimiel se han diferenciado un total de 7 Unidades Funcionales.

La obtención de las curvas hipsométricas para cada una de las Unidades Funcionales

caracterizadas permite la integración de la información topográfica disponible en el

análisis hidrológico del sistema, lo que supone una potente herramienta para la gestión

de recursos hídricos (Liang and Mackay, 2000).


145

En la zona de Las Cañas, la ausencia de discontinuidades importantes en la curva

hipsométrica muestra que este área está constituida de un único vaso, lo que simplifica

considerablemente su análisis. La infiltración es el parámetro a identificar, y para

caracterizarlo se utilizaron 7 series de datos correspondientes a procesos de secado,

aplicándolas a balances dinámicos de masas de agua diarios. La calidad de los procesos

tanto de identificación como de calibración otorgaron confianza modelo propuesto, por

lo que se utilizó para estimar la respuesta de este área ante aportes externos de agua.

Al realizar la simulación hasta el año 2027 se comprobó que se obtiene una mejora muy

significativa de las condiciones de inundación en la Las Cañas con el aporte de los

efluentes tratados de depuradora disponibles. Este resultado se obtuvo incluso

considerando la hipótesis de clima seco y suponiendo pérdidas del 40% de los aportes

en tratamientos previos al vertido en el Parque en humedales artificiales.

En el área de Las Tablas las curvas hipsométricas se utilizaron, al igual que en Las

Cañas, para realizar un balance dinámico de masas de agua diario. Se resolvieron 12

procesos de identificación para caracterizar la tasa de infiltración, utilizando los datos

de inundación diarios obtenidos por el personal del Parque. La calidad de los procesos

de identificación así como los ajustes en las simulaciones utilizadas en la calibración,

otorgaron una cierta confianza tanto a los parámetros identificados como al modelo

hidrológico planteado. Por ello, aunque, como es usual en ingeniería ecológica, la

variabilidad del sistema aconseja ser prudentes al interpretar el resultado de las

simulaciones, se decidió utilizar este modelo para evaluar la respuesta del sistema a

largo plazo ante la aplicación de efluentes tratados de depuradora. Aun considerando

un clima seco se pone de manifiesto que la aplicación de efluentes tratados de

depuradora supone un incremento signiicativo de la inundación en las Unidades

Funcionales 1-2-3, 4 y 5.


146

El modelo general del comportamiento hidrológico del Parque Nacional de Las Tablas

de Daimiel se muestra como una potente herramienta para mejorar la gestión de los

recursos hídricos en este sistema. Ha servido de base para la estimación de la eficiencia

de actuaciones destinadas a mejorar las condiciones de inundación en el Parque, en

concreto mediante la aplicación de efluentes tratados de depuradora. Esta medida se ha

mostrado suficiente para producir una notable mejora en las condiciones de

inundación, tanto en Las Tablas como en Las Cañas. Por lo tanto, se aportaron

elementos de juicio que muestran la conveniencia que tiene desde un punto de vista

hidrológico a medio plazo la aplicación de efluentes tratados de depuradoras en el

Parque Nacional de Las Tablas de Daimiel.

Futuras líneas de investigación

A partir de los resultados obtenidos en la presente tesis doctoral se presentan cinco

líneas de investigación:

• Refinar el modelo del comportamiento hidrológico del sistema aplicando el

algoritmo celda a celda a un nuevo Modelo Digital del Terreno más reciente.

• Se plantea el cálculo de unos nuevos parámetros de infiltración del sistema

utilizando nueva información disponible. El año hidrológico 2009-2010 ha sido

muy lluvioso, y el Parque ha registrado niveles de inundación similares a los de

1997. Coincidiendo con estos episodios de lluvias, en enero de 2010 se

instalaron un total de 22 sensores en el Parque para monitorizar la inundación

en todo el sistema. Del total de sensores instalados, 15 se encuentran en Las

Tablas y los 7 restantes en Las Cañas. Estos sensores, de la marca comercial

Schulumberger, están programados para registrar medidas cada 15 minutos.


147

Existen dos tipos de sensores: los barodivers, se encuentran emergidos

midiendo presión atmosférica (7 sensores), y los divers, que están sumergidos

midiendo presión total (15 sensores). La diferencia de presiones nos da el

calado en el instante de la medida. Esta valiosa información, tanto de procesos

de llenado como de procesos de secado, servirá para volver a identificar las

tasas de infiltración aún con mayor precisión.

• Una vez obtenidos estos nuevos parámetros de infiltración, se estará en

disposición de estimar nuevamente la respuesta del sistema ante estos aportes

externos de agua, esta vez de manera más precisa.

• Introducir el modelo de funcionamiento del sistema en un software que permita

la representación espacio-temporal de los procesos de inundación en el Parque.

• Aplicación de la metodología propuesta a otros humedales para mejorar tanto

la gestión de los recursos hídricos disponibles como las estrategias de

restauración y conservación.

En suma, las cinco líneas de investigación que se abren se agrupan fundamentalmente

en un análisis más detallado del Parque Nacional de Las Tablas de Daimiel utilizando la

nueva información disponible, así como la difusión de la metodología propuesta y su

aplicación a otros humedales.


148

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Hunt, R.J., Krabbenhoft, D.P. and Anderson, M.P., 1997. Assessing hydrogeochemical

heterogeneity in natural and constructed wetlands. Biogeochemistry, 39: 271–293.

Ivanov, K.E., 1981. Water movement in Wetlands, Academic, San Diego, CA.

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