Hydrological processes controlling flow generation in a small Mediterranean catchment under karstic influence

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Hydrological processes controlling flow generationin a small Mediterranean catchment under karsticinfluenceJEAN-LOUIS PERRIN a & MARIE-GEORGE TOURNOUD aa UMR HydroSciences Montpellier, Maison des Sciences de l'Eau , Place EugèneBataillon—CC MSE, F-34095, Montpellier Cedex 5, France E-mail:Published online: 19 Jan 2010.

To cite this article: JEAN-LOUIS PERRIN & MARIE-GEORGE TOURNOUD (2009) Hydrological processes controlling flowgeneration in a small Mediterranean catchment under karstic influence, Hydrological Sciences Journal, 54:6, 1125-1140,DOI: 10.1623/hysj.54.6.1125

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Hydrological Sciences–Journal–des Sciences Hydrologiques, 54(6) December 2009

Open for discussion until 1 June 2010 Copyright © 2009 IAHS Press

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Hydrological processes controlling flow generation in a small Mediterranean catchment under karstic influence

JEAN-LOUIS PERRIN & MARIE-GEORGE TOURNOUD UMR HydroSciences Montpellier, Maison des Sciences de l’Eau, Place Eugène Bataillon – CC MSE, F-34095 Montpellier Cedex 5, France [email protected] Abstract The hydrological behaviour of the River Vène (67 km2, south of France) was assessed through a coupled approach based on field observations and modelling. The origins of runoff were identified by analysing discharge and water conductivity data. During rainfall events, natural areas did not contribute to runoff. In these areas, infiltration fed the karstic aquifer and contributed to spring discharges. Urban areas were characterized by a rather constant runoff coefficient whose value compares well with the extent of urbanisation and explains the total volume of small floods. Agricultural areas did not contribute directly to the flood peak, and subsurface flow in unsaturated areas was the major hydrological process during flood recession. A mathematical model, based on this “perceptual model”, confirmed the hydrological processes identified and helped quantify the functioning thresholds in this intermittent river. Key words hydrological processes; karst; water conductivity; hydrological modelling; intermittent Mediterranean rivers; River Vène; Languedoc-Roussillon, France

Processus hydrologiques contrôlant la génération des débits dans un petit bassin versant Méditerranéen sous influence karstique Résumé Le fonctionnement hydrologique de la Vène (67 km2, sud de la France) est évalué par une approche conjointe basée sur des observations de terrain et une modélisation mathématique. Les origines de l’écoulement sont identifiées par l’analyse de données de débit et de conductivité électrique de l’eau. Durant les événements pluvieux, les zones naturelles du bassin versant ne contribuent pas directement à l’écoulement. L’infiltration sur ces zones permet d’alimenter les aquifères karstiques qui alimentent à leur tour les sources. Les zones urbaines sont caractérisées par un coefficient de ruissellement constant dont la valeur est proche de la densité d’urbanisation et explique la totalité du volume des petites crues. Les zones agricoles ne contribuent pas directement au pic de crue mais les écoulements de sub-surface en zone non-saturée sont le processus hydrologique majeur durant les décrues. Un modèle mathématique, basé sur ce “modèle perceptuel”, confirme l’existence des processus hydrologiques identifiés et permet de quantifier les différents paliers de fonctionnement de cette rivière intermittente. Mots clefs processus hydrologiques; karst; conductivité électrique de l’eau; modélisation hydrologique; rivières intermittentes Méditerranéennes; la Vène; Languedoc-Roussillon, France INTRODUCTION

In Mediterranean regions, there are many small catchments where the flow is intermittent and occurs only briefly during and after heavy rainstorms (Graf, 1988; Bull & Kirkby, 2002). Like many semi-arid climates, the Mediterranean climate is associated with intense rainstorms. Convective thunderstorms are frequently less than 10–14 km in diameter (Renard & Keppel, 1966; Diskin & Lane, 1972), and result in highly concentrated local rainfall events (Sharon, 1972; Thornes, 1994). Rainfall is highly variable both spatially and temporally, and variability may increase with aridity (Bell, 1979). Classically, these intense events generate locally high rates of Hortonian overland flow runoff. But runoff tends to be patchy, as it responds to surface properties such as crusting, stoniness, vegetation and micro-topography. Bare soil areas are particularly vulnerable to infiltration excess runoff, since the energy of the raindrops can re-arrange the soil particles at the surface and form a surface crust. However, much of the runoff re-infiltrates before reaching a channel (Beven, 2002), except in impervious areas of bare rock, or on human-made surfaces, where surface runoff will start immediately and generate significant discharge.

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In semi-arid regions, overland flow may also be due to the saturation excess mechanism (Gallart et al., 1994; Taha et al., 1997; Grésillon & Taha, 1998; Martinez-Mena et al., 1998). These studies based on field data showed that runoff generation may be related more to antecedent wetness and the amount of rainfall rather than to its intensity. Many studies of runoff generation on dryland areas have demonstrated strong nonlinear dependency on antecedent wetness. Nicolau et al. (1996) showed that there is generally a threshold of storm rainfall below which no runoff occurs, while above the threshold runoff of only a few millimetres is observed. The saturation excess mechanism does not imply that the soil profile is completely saturated. The primary control of infiltration into the soil profile may not be at the surface, but at some depth in the soil, associated with vertical variability of the soil hydrodynamic properties, such as a tillage layer in a cultivated zone (Leonard & Andrieux, 1998). In all cases, antecedent wetness controls the infiltration capacity of soil surfaces and the connectivity of surface and subsurface runoff pathways. In these regions, subsurface flow is often ignored in hydrological behaviour, despite the fact that a storm hydrograph can continue for several hours (or several days) after the rain has stopped. The assumption is that subsurface flow is generally low because water can be stored in a dry soil. But it is of interest to ask whether subsurface flow can play an important role in controlling surface runoff generation in semi-arid areas (Beven, 2002). Direct evidence of the importance of subsurface flow in semi-arid areas has been reported, taking into account the analyses of measured hydrochemical characteristics of catchment discharge (Turner et al., 1991; Taha et al., 1997). Taha et al. (1997) suggested that, in wet antecedent conditions, a significant proportion of a storm hydrograph was derived from pre-event water. Moreover, it has been suggested that, except in the most extreme events, runoff production on semi-arid hillslopes may be disconnected from the stream channel response (Yair & Lavee, 1985; De Boer, 1992; Yair, 1992; Bergkamp, 1998). This implies that the main source area for stream runoff at the catchment scale may be subsurface contributions from valley bottom deposits. Studies of dryland rivers have been dominated by investigations into flash floods, single peak events, multiple peak and seasonal floods (Graf, 1988). The characteristics of all flash floods are their short duration, small areal extent, high flood peaks and rapid flow (Bonacci et al., 2006). Low runoff and decreases in discharge downstream due to transmission losses are commonly observed. The most important impact of transmission losses on dryland channels is that flow is rarely continuous throughout the whole catchment. At the start of a storm, the advance of flood waves is limited by channel infiltration or by pools filling up (Dunkerley & Brown, 1999). Total flood discharge therefore increases only slowly, or, in many cases, decreases downstream, and this pattern is reflected in channel geometry. Many of these processes are also observed in humid regions, but in dryland regions the high amount of water deficit significantly changes the importance of each one for runoff generation. Although none of these features is unique in dryland areas, and there are some exceptions, dryland rivers commonly share many characteristics that place them outside the normal range of temperate rivers and merit separate study. This paper presents the results of the analysis of the hydrological behaviour of an intermittent river (the Vène) located in the south of France in a Mediterranean environment. The study was based on hydrological data collected at three gauging stations that have been functioning in the catchment since 2002. The data were completed by the analysis of physico-chemical data (i.e. water conductivity), which enabled us to distinguish the different contributions to the discharge and the main hydrological processes taking place at basin (and sub-basin) scale in a heterogeneous environment (Nakamura, 1971; Matsubayashi et al., 1993; Ribolzi et al., 1997; Ahearn et al., 2004). This analysis, which enabled the hydrological behaviour of the catchment to be determined, is referred to here as a “perceptual model” of a catchment (Beven, 1991). This perceptual model was validated with the help of a hydrological model using a simple spatial and temporal aggregative approach that took into account the spatial variability of the land uses and their associated hydrological processes.

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Hydrological processes controlling flow generation in a small Mediterranean catchment


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WWTP

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Fig. 1 The Vène River basin: land use and hydro-climatological network.

BASIN DESCRIPTION

General features

The Vène River drains a superficial basin with a surface area of 67 km2 (Fig. 1) and has an elevation ranging from 2 to 323 m. The catchment is composed of two main areas determined by the geological bedrock. Soil distribution and land use follow the same pattern (Table 1). The Montbazin-Gigean Valley covers about 40% of the total area of the basin. It is oriented SW–NE and comprises deep Tertiary marly sediment layers. The altitude ranges from 2 m (at the outlet) to 50 m (at the northeast of the basin, near Cournonsec). It is geomorphologically very flat with a slope of the same order of magnitude as the river (around 0.4%). It is situated in the central part of the basin and used for agriculture (mainly vineyards: 21% of the total area of the basin). In this area, the soils consist of deep limestone alluviosoils that are homogeneous in space and depth, mainly sandy-silty soils with 10–30% sand, 60–85% silt and 5–15% clay. Few variations in soil texture were observed from 0 to 1 m depth. Porosity was high, ranging from 50–45% at the top of the profile and 40–35% at a depth of 1 m. Bulk density ranged from 1.4 to 1.6 over the whole

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Table 1 Vène catchment: land-use characteristics. Land use Geology Soil Area

(km2) Area (%)

Altitude min. (m)

Altitude max. (m)

Slope (%)

Natural Jurrassic karstified limestones

none 42 63 4 323 10–20

Agriculture Miocene molasse

Deep alluviosoils(sand+silt)

23 (vineyards 14)

34 2 50 0.4

Urban - Impervious (25%)

2 3 - - -

From CORINE land cover, IGN France (The CORINE land cover database was completed as part of the European Commission programme to COoRdinate INformation on the Environment. It provides consistent information on land cover changes across Europe.). profile, with lower values in the first 10 cm of between 1.2 and 1.3. Organic matter content was low: between 2 and 4% in the whole profile. Some sites, with grassland, had higher organic matter content (around 10%) in the first 10 cm. The Causse d’Aumelas and La Gardiole Jurassic limestone massifs (60% of the total area) are located on either side of Montbazin-Gigean Valley. The altitude ranges from 4 m (near the Issanka karstic spring) to 323 m (at the top of the basin). This zone is characterized by a very thin soil layer (20–30 cm) composed of residuum from the argillaceous limestone and clastic rocks of the neighbouring massif, on a karstic limestone basement. Vegetation is sparse and consists of typical natural Mediterranean garrigue. These Jurassic limestones are characterized by the presence of a large karstic aquifer whose extent exceeds the limits of the catchment. A digital elevation model (DEM) and a land-use map, with a 50-m mesh size, are available. Land-use data are divided into seven categories: natural, urban and mining areas plus four agricultural classes. A recent study indicated limited change in urban and natural areas (Sagot, 1999); CORINE land-cover data were used to classify non-agricultural areas. The agricultural land-use map was the result of the supervised classification of a multispectral image obtained on 30 May 1996 by the SPOT 3 satellite, with a spatial resolution of 20 m × 20 m. However, we kept only three main categories of land use for this study: natural, agricultural and urban zones. The river has a 12-km-long course with a regular slope. The river sections have a quasi-trapezoidal shape with a mean width of 2.8 m (maximum 9 m, minimum 1.5 m). The cross-sections present dense riparian vegetation, with abrupt banks (35%), straight banks reinforced with stones (15%), or a mixed pattern. The riverbed consists of stones and gravels in most of the sections. The channel bed morphology is characterised by a succession of sinks that form ponds even during the dry season. During the rainy season, the river is fed by two springs flowing out of the Jurassic karstified massifs: Cournonsec Spring upstream and Issanka Spring in the lower part of the basin. The Vène catchment is sparsely populated: about 12 000 inhabitants live in four small villages. Urban areas cover 3% of the total basin area. Three wastewater treatment plants (WWTP) in Cournonsec, Montbazin and Gigean continuously feed the river. Measurement network The hydroclimatological measurement network (Fig. 1) includes three raingauges (Montbazin, operational since 1994, Mas de Plagnol since 2002, and Les Clashs since 2003), where data are available at a 5-min time step. Concomitant discharge data at three streamgauge stations, K (1.8 km2), S (35 km2) and V (67 km2), are available at the same time step. Station V is located at the outlet of the basin (operational since 1994); Station K (operational since 2002) controls the upstream Cournonsec karstic spring plus a small catchment area; and Station S (operational since 2002) controls a nested catchment between the spring and the outlet. Rating curves are available for the three stations. Since 2003, the three streamgauges have been coupled with continuous water conductivity measurements available at a 1-h time step. Water conductivity measures the ability of

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water to pass an electrical current; this is affected by the presence of inorganic dissolved solids. Water conductivity is affected primarily by geology, but also by discharge to streams such as WWTP effluents. Hydroclimatic features During the last 11 years (1994–2004): – the annual precipitation measured at the Montbazin raingauge (Table 2) varied from 520 mm

(1998/99) to 890 mm (2002/03), with an average of 659 mm (standard deviation, SD = 94 mm). At Montpellier weather station (Météo-France), for a 10-year return period, rainfall amounts were estimated at 81 mm for a 6-h time period and at 126 mm for 24-h.

– at Montpellier weather station (Météo-France), annual potential evapotranspiration (PET), calculated on the basis of the Penman formula, varied slightly from 1268 mm (1995/96) to 1386 mm (1994/95) with an average of 1338 mm (SD = 24 mm).

The seasons are well marked (Table 3). For a given season the total amount of rainfall is highly variable from one year to another (except in spring, the ratio SD/mean always exceeds 50%). Nevertheless summer is the driest period, with a mean rainfall of 64 mm (SD = 34 mm) at the Montbazin station. Autumn and the winter are the wettest periods, with a mean rainfall of 256 mm (SD = 109 mm) and 198 mm (SD = 98 mm), respectively. The PET attains its maximum in summer (mean value 576 mm; SD = 41 mm) and minimum in winter (mean value 143 mm; SD = 16 mm). At the outlet of the basin (Gauging station V), annual runoff varied considerably during the last 11 years (Table 2): from a minimum of 46 mm (in 1998/99), to a maximum of 736 mm (in 2003/04). The mean annual runoff (calculated on the basis of the topographic catchment) was 365 mm (SD = 228 mm). Variations in the runoff coefficient (annual runoff/annual rainfall at the Montbazin station) followed the same pattern: minimum value 9% (in 1998/99), maximum value 91% (in 2003/04) as shown by Grillot (2006). It is worth noting that this high runoff coefficient is explained by the external contribution of the karstic springs to the river flow. In 1998/99 the karst did not contribute at all to the river flow. Table 2 Inter-annual hydroclimatic characteristics (1994–2004). Annual features Mean SD Maximum Minimum Rainfall* (mm) 659 94 886 (2002/03) 517 (1998/99) PET † (mm) 1338 24 1386 (1994/95) 1268 (1995/96) Runoff ‡ (mm) 365 228 736 (2003/04) 46 (1998/99) Runoff coef. (%) 52 27 91 (2003/04) 9 (1998/99) * Montbazin raingauge. † Montpellier weather station. ‡ Streamgauge V. Table 3 Seasonal hydroclimatic characteristics (1994–2004). Seasonal features Mean SD Autumn Winter Spring Summer Autumn Winter Spring Summer Rainfall* (mm) 256 198 173 64 109 98 42 34 PET † (mm) 252 143 346 576 15 16 20 41 Runoff ‡ (mm) 95 189 108 4 128 143 88 4 Runoff coef. (%) 27 91 63 12 31 47 50 16 * Montbazin raingauge. † Montpellier weather station. ‡ Streamgauge V.

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Fig. 2 Hydroclimatic characteristics. Comparison of cumulative rainfall and cumulative runoff

Comparison of cumulative rainfall and cumulative runoff revealed three contrasting types of hydrological years (Fig. 2): the wettest years with heavy rainfall events during autumn and spring, the driest years without any heavy rainfall events at all, and intermediate hydrological years characterized by intense rainstorms in autumn and frequent light rainfall events in spring. Seasonal evolution of runoff corresponded to the seasonal evolution of both rainfall and PET (Table 3), but inter-annual variability was very high. The highest runoff value was observed in winter (mean value 189 mm; SD = 143 mm), and the lowest in summer (mean value 4 mm; SD = 4 mm). In the driest years, runoff was very low even in autumn and winter (in 1998/99, 1 mm and 2 mm respectively in autumn and winter), as shown by Grillot (2006). The runoff coefficient varied greatly from one year to another during each season: the lowest values were observed in summer, due to low rainfall as well as high PET rates, and also in autumn when the soil water content increased. In some years, the seasonal runoff coefficient appeared to be higher than 100% (e.g. in 1995/96 during winter and spring), due to the considerable external contribution of the karstic springs. FLOW GENERATION PROCESSES

General features

Over the year, the River Vène alternated between long dry periods and flood events (Fig. 3). During summer the riverbed became completely dry, except in some sections where shallow pools remained due to the discharge of waste waters. Only two kinds of flood were observed: small floods without any karstic influence at the outlet, and karstic floods when one or two karstic springs were contributing. Low-flow period The length of the period with very low flow (<60 L/s) at the outlet varied from one year to another. It usually lasted around six months but could last up to 11 months (1998/99). The river is not connected to a sustainable aquifer able to generate significant baseflow. At the end of summer, the river was still flowing in the last downstream reaches, fed by anthropogenic inputs. The rest of the river was completely dry and only some pools remained (for example at Station S). At Station S, water conductivity varied in summer from 1200 to 1500 µS/cm, similar to that measured at regular time steps at the outlet of the Montbazin WWTP which feeds into the river just upstream of the station. The water conductivity registered at the outlet of the basin (850 µS/cm) did not follow the same pattern because the river is fed by the Gigean WWTP located upstream from the station, and by fresh legal compensation water supplied at Issanka by the karstic aquifer, which has a dilution effect (Fig. 3).

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Fig. 3 Water conductivity and discharge: (a) at gauging stations K and S, and (b) at gauging stations S and V.

Flash floods of urban origin Small floods are characterized by their very short duration (less than one day) with a lag time of less than 2 h. They occurred mainly in September, and were due to very intense storm events (e.g. 22 September 2003, 125 mm in 7 h; 13 September 2004, 156 mm in 4.5 h). Peak flows can reach up to 20 m3/s at the outlet in about two hours. The runoff coefficients at the outlet ranged from 0.02 to 6% (mean value 0.5%; SD = 0.9%). These small floods were mainly generated by urban runoff. The urban zones comprise four villages (Cournonsec, Montbazin, Gigean and Poussan), which represent only 3% of the total catchment area. The total volume of these small floods can be fully explained by runoff over

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Fig. 4 Water conductivity and discharge at stations S and V: 13–15 September 2004.

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urbanized zones (taking into account their areas and a constant runoff coefficient, whose value compares well with the density of urbanisation), as noted by several authors (Arnell, 1982; Desbordes, 1987; Boyd et al., 1993; Brulé et al., 1997; Chocat, 1997). These floods showed a significant drop in conductivity values to below 150 µS/cm (Figs 3 and 4) and corresponded to surface runoff water conductivities (as observed at S and V stations, from 13–15 September 2004, and measured during some rainfall events at the outlet of the urban water drainage system in Montbazin). It is worth noting that rainfall conductivity values measured during some events were about 45 µS/cm. Conductivity values reacted sharply, even in the case of very small rainfall–runoff flood events, and increased very rapidly (in a few hours) to reach initial values. The difference observed in the shape of the curves at stations S and V (Fig. 4) are due to difference in flow propagation specificity between these stations, as mentioned previously. The morphology of the river channel also plays a significant role in the hydrological beh-aviour of the river at the beginning of the first floods after a long dry period. Dunkerley & Brown (1999) and Shannon et al. (2002) define different conceptual ephemeral channel flow domains. The first (i.e. detention storage) shows a succession of disconnected pools (totally or partially empty) and seems to be the most important in the case of the River Vène. At the beginning of a storm, the propagation of the flood wave is disrupted by the filling up of empty reaches. Several floods may not propagate very far downstream (for example between the streamgauge stations S and V where a significant proportion of reaches were completely dry at the end of summer).

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Nevertheless, these events are important to better understand the reconnection processes of the flow at the beginning of the rainy season. In contrast, transmission losses due to bed infiltration were negligible, based on regular discharge measurement campaigns made along the main river course. Transmission losses due to bank infiltration are difficult to quantify but probably limited, because a large part of the banks of the river course are reinforced with stones. Large floods of karstic origin Natural karstic areas did not contribute directly to surface runoff. The limestone massif is highly permeable, so no surface runoff was observed in these zones even during intense rainfall events (e.g. in December 2002 and 2003). The infiltration in these zones directly fed the large karstic aquifer that extends beyond the limits of the Vène topographic catchment. Karstic floods are characterized by a short rising period (less than one hour), but quite a long lag time in comparison with flash floods of urban origin (about eight hours after the end of the rainfall event), and a very long recession period (from several days to several weeks). These floods have significant runoff coefficients (over 80%) due to external supplies of water. They occur with a return period of less than one year and only if more than 120 mm of rain falls within a few days preceding the flood. On an annual scale, karstic inputs represented more than 75% of total flow during the wettest years, but less than 10% during the driest ones. At the flood-event scale, during the wettest years, this proportion ranged from 98% at the beginning of the rainy season to 70% at the end. The beginning of the major floods generally showed a first peak due to surface runoff in the urban zone, and a marked decrease in water conductivity (reaching 150 µS/cm) as observed for flash floods. When karst contributed to river discharge, conductivity values ranged between 500 and 600 µS/cm and corresponded specifically to karstic water, as measured at the outlet of the two karstic springs (Fig. 5). During the karstic spring recession, conductivity increased slowly. When the spring stopped flowing, conductivity increased sharply and reached a value of 1100 µS/cm corresponding to subsurface flow as measured during specific campaigns at the outlet of the Vène River tributaries. Role of agricultural zones

Significant contributions of agricultural areas were only observed for some floods. In semi-arid regions, overland flow is considered to be essentially due to the saturation excess mechanism (Gallart et al., 1994; Taha et al., 1997; Grésillon & Taha, 1998; Martinez-Mena et al., 1998). But in agricultural areas, the rainfall–runoff processes are controlled by agricultural practices. In the study catchment, tillage greatly influences local surface runoff, increasing infiltration and surface storage by altering soil hydrological properties and surface roughness. The very flat topography of these zones also has a significant influence on runoff processes. Infiltration excess surface runoff (the process that occurs during high-intensity rainfall events), or saturation excess surface runoff (the process that occurs after long and significant rainfall events) occur only locally in these zones. Moreover, a large part of the contributing areas is not well connected to the main drainage system and does not contribute to the flood peak, as shown by other studies in semi-arid environments (Yair & Lavee, 1985; Turner et al., 1991; de Boer, 1992; Yair, 1992; Taha et al., 1997; Bergkamp, 1998). The significant depth of the soils (>1.50 m) and the high porosity values have the same influence. As observed by many authors, subsurface flow in the unsaturated zone thus appears to be the major hydrological process in agricultural zones. At the flood event scale, subsurface flow processes represented 10–20% of the total flood volume. Such processes only appeared during major storm events and after significant rainfall amounts (100 mm). It is worth noting that Gaume et al. (2003) proposed the same range of threshold values for neighbouring sites in the south of France. However, subsurface flow can contribute significantly to discharge during flood recession and also during baseflow periods through inputs from tributaries and bank exfiltration.

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Perceptual model

Rainfall variability controls the hydrological behaviour of the catchment. The hydrological balance varies greatly from one year to another; consequently, the influence of each hydrological process varies from one year to another. At the beginning of the rainy season, urban areas play a major role in flow generation, producing flash floods of high amplitude and short duration. Agricultural areas do not contribute directly to runoff, because of the infiltration capacity of the tilled soils and because potential contributing areas are not well connected to the river. However, after significant cumulative rainfall amounts, these areas are able to generate subsurface flow that is drained by tributaries or ditches. Natural areas do not produce any runoff. Rainfall is entirely infiltrated, feeding the underlying karstic aquifers. But it is worth noting that the annual hydrological balance is highly affected by karstic inputs (of both external and internal origin), which produce floods of high amplitude and long duration. These springs also control the recession period up to the spring depletion. At the beginning of the dry period, some contributions of subsurface flow remain but rapidly decrease when the proportion of inputs from wastewater treatment plants becomes predominant. At the end of the dry period, the riverbed is completely dry except in some reaches that are continuously fed by these anthropogenic inputs, where pools of water remain. Based on this “perceptual model” (Beven, 1991) of catchment behaviour, our rainfall/runoff model enabled us to check and validate the hypothesis of hydrological functioning and to quantify, the contribution of each hydrological process to river discharge through long-term simulations. MODELLING RAINFALL/RUNOFF PROCESSES

To study catchment heterogeneity and the spatial variability of rainfall, we used a distributed modelling approach to simulate the rainfall/runoff processes of the Vène catchment. An aggregative method was used, in time (from event to long-term simulations) and in space (for the two defined nested catchments). A “trial and error” procedure was used to calibrate the parameters. A split-sample test (Klemeš, 1986) and a multi-criterion approach (Beven, 1991) were used to validate the model. The concepts of the MERCEDES distributed model (see Appendix) were applied on a grid with a 50-metre mesh. The drainage model was directly extracted from the DEM and corrected taking into account catchment specificities. Simulations were run with a 5-min calculation time step (at the event scale) and with a 1-h time step (for long-term simulations). For all the simula-tions presented below, only one set of transfer parameters was used (V = 1.4 m/s and K0 = 0.76, see Appendix). For the whole catchment, three production functions were defined taking land uses into account. To better simulate the hydrological processes at the catchment scale, two kinds of floods were modelled separately: – the first flash floods without any karstic influence at the S and V gauging stations; and – the other floods: (i) at Station S under the influence of the upstream karstic spring

(continuously monitored at Station K); and (ii) at Station V under the influence of the ungauged Issanka karstic spring.

It is worth noting that, given its structure (disconnected meshes and transfer of the produced water directly to the outlet), the model is not able to reproduce the filling up of empty reaches at the beginning of the first floods after a long dry period. Flash floods Due to the general shape of the first floods, we assumed that these floods are only produced by runoff from urban zones. The simulation concept is based on a simple reservoir (Fig. 6(a))

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Proportional losses(stoc - STOurb) * (1 - wurb)

Initial losses Reservoir recession dsurb

Discharge contribution Surface runoff

(stoc - STOurb) * wurb

stoc

Initial lossesReservoir recession dsagr

Discharge contribution Sub-surface runoff

Reservoir recession dsagr

STOagr

STOsprSTOurb

Storage and constant lossesReservoir recession dsspr

Discharge contribution Reservoir recession dsspr

(a) (b) (c)

Proportional losses(stoc - STOurb) * (1 - wurb)

Initial losses Reservoir recession dsurb

Discharge contribution Surface runoff

(stoc - STOurb) * wurb

stoc

Initial lossesReservoir recession dsagr

Discharge contribution Sub-surface runoff

Reservoir recession dsagr

STOagr

STOsprSTOurb

Storage and constant lossesReservoir recession dsspr

Discharge contribution Reservoir recession dsspr

(a) (b) (c)

Fig. 6 Conceptualisation of production functions of urban, agricultural and karstic natural zones.

0.0

0.5

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Fig. 7 Simulation results of two flash floods at gauging stations S and V: (a) calibration, (b) validation. controlled by three parameters: STOurb, the reservoir capacity to simulate initial losses; wurb, the runoff coefficient; and dsurb, the exponential recession coefficient of the reservoir. Values of STOurb = 7 mm, wurb = 0.14 and dsurb = 0.41 d-1 give quite good results in terms of discharge generation dynamics, for calibration (Fig. 7(a)), as well as for validation (Fig. 7(b)). Some problems remained in terms of volume and peak discharge due to the high spatial and temporal heterogeneity of rainfall at a 5-min time step. However, the values of these parameters are acceptable and relevant for the local characteristics of urban zones. A low runoff coefficient explains simply the runoff volumes produced during these flash floods and corroborates the assumption of the “perceptual model”. Large floods of karstic origin Gauging station S To simulate these floods, the discharge recorded at gauging station K was input directly in the distributed model at the location of the spring. To simulate the contribution of the catchment located between gauging stations K and S, we hypothesized that only the agri-cultural zones contribute to runoff generation through subsurface flow. This hypothesis implies that the natural karstic zones do not contribute to runoff, as observed during field inspections. The simulation concept is based on a simple reservoir (Fig. 6(b)) controlled by two parameters: STOagr, the maximum water level which controls initial losses before subsurface flow occurs; and dsagr, the exponential recession coefficient of the reservoir for initial losses and subsurface runoff generation.

(a) (b) (c)

(a) (b)

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Year 2003Year 2002-2003 Calibration Year 2002 Validation Validation1-hour time step

86

4

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V

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char

ge (m

/s)

3

Fig. 8 Long-term simulations at gauging stations S and V—calibration and validation periods.

The two parameters were calibrated on the biggest flood observed (between the beginning of December 2002 to the end of February 2003) and values of STOagr = 95 mm and dsagr = 0.02 d-1 gave good results for flood simulation at a 1-h time step (Fig. 8). These calibrated parameters allow a good fit between observed and simulated hydrographs for the other floods, even if some problems remained. First, for some floods generated by high intensity rainfall events, peak discharge was not well simulated (November 2002 and March 2003). Saturation processes appeared at a local scale in agricultural zones, but these phenomena were difficult to locate and quantify because of the high variability of the rainfall during these events. Secondly, some recession periods (October and November 2002) were not well simulated. This was essentially due to the high sensitivity of the dsagr parameter. Simulation with dsagr = 0.03 d-1 enhanced the results. The assumptions of the “perceptual model” were validated by the conceptual mathematical model; only taking into account the urban and the agricultural zones, the model was able to correctly reproduce the dynamics and the succession of floods at Station S.

Gauging station V To simulate the hydrographs at gauging station V, we aggregated to the S conceptualisation another production function to simulate the discharge of Issanka Spring. Because the karstic aquifer extends beyond the limits of the watershed, the contributing area of Issanka Spring is unknown. Tracing studies were only able to demonstrate that Cournonsec and Issanka springs are connected (Ladouche et al., 2001). We assumed that the natural karstic zones within the basin explain the discharge at the Issanka Spring, in order to simulate the general shape of the spring hydrographs and particularly the beginning and end of the spring discharge. In addition, a simple scaling factor (surface of the contributive zones, Sf) was used to obtain a good estimation of the total volumes. The simulation concept is based on a quick flush reservoir (Fig. 6(c)) controlled by: STOspr, the water level to start the flush effect (discharge stops when STOspr reaches 0 mm); and dsspr, the exponential recession coefficient of the reservoir for discharge generation. The two parameters were calibrated on the biggest flood observed (between the beginning of December 2002 and the end of February 2003) and values of STOspr = 110 mm, dsspr = 0.07 d-1 and Sf = 34 km2 gave good results for flood simulation at a 1-h time step (Fig. 8).

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It is worth noting that this very simple conceptualisation of the karstic processes gives good results for long-term simulations (for low flow but also for flood simulation) during both calibration and validation periods. The main differences noticed between the observed and the calculated hydrographs were due to the imperfection of the agricultural zone production function, as detailed above. Furthermore, the model allowed us to estimate the contributive areas of Issanka spring which represent 90% of the total area of natural zones within the basin. Water budget during floods and for an annual period at the catchment scale

From 2 July 2002 to 15 November 2003, the calculated discharge volumes compared well with volumes observed at the gauging station S (1.5%) but also at Station V (6%). Table 4 shows the contribution of each defined production function from 2 July 2002 to 31 March 2004. Simulation of each discharge component enabled the conceptual model of the Vène catchment to be validated using the water conductivity data as a validation criterion. Taking into account the water conductivity of each component (150 µS/cm for surface runoff, 600 µS/cm for karstic water, 1100 µS/cm for subsurface flow and 1400 µS/cm for WWTP effluents), a simple mix of the identified volumes enabled correct simulation of water conductivity dynamics at gauging stations S and V (Fig. 9). Observed and simulated water conductivity compared well. Only variations in water conductivity in the dry period were not correctly simulated at Station S, but these variations are due to changes in water conductivity of WWTP effluents during summer, when only the WWTP effluents feed the river course. Table 4 Contribution to the Vène discharge from 2 July 2002 to 31 March 2004. Gauging station Urban zones Agricultural zones Issanka spring Cournonsec spring S 1.5% 23% - 75.5% V 1% 16% 49% 34%

0

2

4

6

8

10

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er C

ondu

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ity (µ

S/cm

)

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800

400

600

1000

1200

1600

200

M 03 A 03 M 03 J 03 J 03 A 03 S 03 O 03 N 03 D 03 J 03 F 04 M 04 A 04

Station S

Observed

Simulated

Fig. 9 Long-term simulation of water conductivity at gauging station S.

CONCLUSIONS

The information available on the hydrological behaviour and structure of the Vène research catchment, which is representative of many Mediterranean basins, was used to develop a simple

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modelling approach at catchment and sub-catchment scales. A simple conceptual model gave quite satisfactory results for the main discharge components, given the very parsimonious para-meterization of the model. The classification of land units, based on relevant physiographic criteria, and the hydrological behaviour of each unit, based on the analysis of both hydrological and physico-chemical data, were validated at different spatial and temporal scales. The procedure defined for this catchment would be easy to adapt to another region, provided that the water conductivity is sufficiently distinct to identify the associated hydrological processes. Despite this requirement, this study indicates that a relatively simple model based on realistic assumptions and approximations can provide a consistent representation of discharge generation. This modelling exercise clearly demonstrates the usefulness of such multivariable and multi-scale approaches for the validation of distributed models. Progress in this direction depends directly on the availability of good quality, complete, long-term data sets for the variables and parameters involved in the water cycle at the catchment scale, which are still limited in Mediterranean regions. This is particularly true when we want to link modelling of water quality with modelling of hydrological behaviour. Such data sets could be obtained with major efforts using well-designed research catchments, including sets of nested sub-catchments. Acknowledgements This project was funded by the European Commission, EESD Specific Programme (tempQsim EVK-CT-2002-00112) and by the PNEC Programme (IFREMER-CNRS-INSU-IRD-BRGM) Chantier Lagunes Méditerranéennes. REFERENCES Ahearn, D. S., Sheibley, R. W., Dahlgren, R. A. & Keller, K. E. (2004) Temporal dynamics of stream water chemistry in the

last free-flowing river draining the western Sierra Nevada, California. J. Hydrol. 295, 47–63. Arnell, V. (1982) Estimating runoff volumes from urban areas. Water Resour. Bull. 18, 383–387. Bell, F. C. (1979) Precipitation. In: Arid Land Ecosystems (ed. by D. W. Goodall & R. A. Perry), 373–393. Cambridge

University Press, Cambridge, UK. Bergkamp, G. (1998) A hierarchical view of the interactions of runoff and infiltration with vegetation and microtopography in

semiarid shrublands. Catena 33, 201–220. Beven, K. J. (1991) Spatially distributed modelling: conceptual approach to runoff prediction. In: Recent Advances in the

Modelling of Hydrologic Systems (ed. by D. S. Bowles & P. E. O’Connell), 373–387. NATO ASI Series, Series C, vol. 345. Kluwer Academic, Dordrecht, The Netherlands.

Beven, K. J. (2002) Runoff generation in semi-arid areas. In: Dryland Rivers: Hydrology and Geomorphology of Semi-arid Channels (ed. by L. J. Bull & M. J. Kirkby), 57–105. John Wiley & Sons Ltd, Chichester, UK.

Bonacci, O., Ljubenkov, I. & Roje-Bonacci, T. (2006) Karst flash floods: an example from the Dinaric karst (Croatia). Nat. Hazards Earth Syst. Sci. 6, 195–203.

Bouvier, C. & Delclaux, F. (1996) ATHYS: a hydrological environment for spatial modelling ad coupling with a GIS. In: Application of Geographic Information Systems in Hydrology and Water Resources Management (ed. by K. Kovar & H. P. Nachtnebel) (Proc. HydroGIS Conf., Vienna, Austria), 19–28. IAHS Publ. 235, IAHS Press, Wallingford, UK.

Bouvier, C., Fuentes, G. & Dominguez, R. (1994) MERCEDES, un modèle de hydrologique d’analyse et de prevision de crues en milieu hétérogène. In: Crues et Inondations (23èmes Journées de l’hydraulique, Congrès de la Société Hydrotechnique de France), 257–260, Société Hydrotechnique de France, Paris.

Boyd, M. J., Bufill, M. C. & Knee, R. M. (1993) Pervious and impervious runoff in urban catchments. Hydrol. Sci. J. 36, 463–478. Brulé, D., Blanchet, F. & Rouselle, J. (1997) Etudes des pertes au ruissellement sur surfaces imperméables en milieu urbain.

Rev. Sci. Eau 10, 147–166. Bull, L. J. & Kirkby, M. J. (2002) Dryland river characteristics and concept. In: Dryland Rivers: Hydrology and

Geomorphology of Semi-arid Channels (ed. by L. J. Bull & M. J. Kirkby), 4–15. John Wiley & Sons Ltd, Chichester, UK. Chocat, B. (1997) Encyclopédie de l’Hydrologie Urbaine et de l’Assainissement. Lavoisier, Collection TecDoc, Paris, France. De Boer, D. H. (1992) Constraints on spatial transference of rainfall–runoff relationships in semi-arid basins drained by

ephemeral streams. Hydrol. Sci. J. 37, 491–504. Desbordes, M. (1987) Contributions à l’analyse et à la modélisation des mécanismes hydrologiques en milieu urbain. Thèse de

Doctorat d’Etat, Université Montpellier 2, Montpellier, France. Diskin, M. H. & Lane, L. J. (1972) A basinwide stochastic model of ephemeral stream runoff in south-estern Arizona. Hydrol.

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full) flow event in an Australian desert stream. Hydrol. Processes 13, 1577–1588. Gallart, F., Llorens, P. & Latron, J. (1994) Studying the role of old agricultural terraces on runoff generation in a small

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Graf, W. L. (1988) Fluvial Processes in Dryland Rivers. Springer-Verlag, Berlin, Germany. Grésillon, J. M. & Taha, A. (1998) Les zones saturées contributives en climat méditerranéen: condition d’apparition et

influence sur les crues. Hydrol. Sci. J. 43, 267–282. Grillot C. (2006) Fonctionnement hydrologique et dynamique des nutriments d’une rivière intermittente méditerranéenne en

étiage et en crue. Analyse spatiale et temporelle. Thèse de Doctorat, Université de Montpellier 2, Montpellier, France. Klemeš, V. (1986) Operational testing of hydrological simulation models. Hydrol. Sci. J. 31, 13–24. Ladouche, B., Bakalowicz, M., Courtois, N., Doerfliger, N., Pinault, J. L., Chemin, P. & Anus, S. (2001) Etude du pourtour de

l’étang de Thau, Phase II: Fonctionnement hydrogéologique du bassin karstique de Thau. BRGM Pep. RP-50787-FR, Bureau de Recherches Géologiques et Minières, Orléans, France.

Léonard, J. & Andrieux, P. (1998) Infiltration characteristics of soils in Mediterrnean vineyards in southern France. Catena 32, 209–223.

Martinez-Mena, M., Albaladejo, J. & Castillo, V. M. (1998) Factors influencing surface runoff generation in a Mediterranean semi-arid environment. Chicamo watershed, SE Spain. Hydrol. Processes 12, 741–754.

Matsubayashi, U., Velasquez, G. T. & Takagi, F. (1993) Hydrograph separation and flow analysis by specific conductance of water. J. Hydrol. 152, 179–199.

Nakamura, R. (1971) Runoff analysis by electrical conductance of water. J. Hydrol. 14, 197–212. Nicolau, J. M., Solé-Benet, A., Puigdefàbregas, J. & Gutiérrez, L. (1996) Effects of soil and vegetation on runoff along a catena

in semi-arid Spain. Geomorphol. 14, 297–309. Renard, K. G. & Keppel, R. V. (1966) Hydrographs of ephemeral streams in the southwest. J. Hydraul. Div. ASCE 92(HY2),

33–52. Ribolzi, O., Moussa, R., Gaudu, J. C., Vallès, V. & Voltz, M. (1997) Etudes des crues de transition entre période sèche et

période humide, par traçage naturel sur un bassin versant méditerranéen cultivé. C. R. Acad. Sci., Série IIa, 324, 985–992. Sagot, O. (1999) Conception et réalisation d’une couche d’informations géoréférencées sur l’occupation des sols des zones

humides et de leurs bassins versants du littoral de la région Languedoc-Roussillon. Mémoire de DEA, Université Louis Pasteur, Strasbourg, France.

Shannon, J., Richardson, R. & Thornes, J. (2002) Modelling event based fluxes in ephemeral streams. In: Dryland Rivers: Hydrology and Geomorphology of Semi-arid Channels (ed. by L. J. Bull & M. J. Kirkby), 129–172. John Wiley & Sons Ltd, Chichester, UK.

Sharon, D. (1972) The spottiness of rainfall in a desert area. J. Hydrol. 17, 161–175. Taha, A., Grésillon, J. M. & Clothier, B. E. (1997) Modelling the link between hillslope water movement and stream flow:

application in a small Mediterranean forest watershed. J. Hydrol. 203, 11–20. Thornes, J. B. (1994) Catchment and channel hydrology. In: Geomorphology of desert environments (ed. by A. D. Abrahams &

A. J. Parsons), 257–287. Chapman and Hall, London, UK. Turner, J. V., Bradd, J. M. & Waite, T. D. (1991) The conjunctive use of isotopic techniques to elucidate solute concentration

and flow processes in dryland salinized catchments. In: Proc. Int. Symp. on the Use of Isotopes in Water Resources Development, 33–59. International Atomic Energy Agency Series, Vienna, Austria.

Yair, A. (1992) The control of headwater area on channel runoff in small arid watershed. In: Overland Flow: Hydraulics and Erosion Mechanisms (ed. by A. J. Parsons & A. D. Abraham), 53–68. UCL Press, London, UK.

Yair, A. & Lavee, H. (1985) Runoff generation in arid and semi-arid zones. In: Hydrological Forecasting (ed. by M. G. Anderson & T. P. Burt), 183–220. John Wiley & Sons Ltd, Chichester, UK.

APPENDIX

The MERCEDES model (http://www.athys-soft.org)

These treatments were integrated in the ATHYS package (Bouvier & Delclaux, 1996). The basin is discretized in regular square meshes, each independent of the others. Rainfall is interpolated in space by a Thiessen method. Each mesh provides a contribution to each time step calculated by a production function (Fig. A1) defined based on natural criteria (here land uses). This elementary contribution is transferred to the catchment outlet by a specific translation-storage routine (Bouvier et al., 1994): the time of translation Tm of a mesh m to the outlet is calculated according to a transfer velocity (V) on the meshes of the trajectory denoted by arrows; storage is modelled by a linear reservoir (K0), whose capacity Km is a linear function of the time of translation Tm. Thus:

tVl

Tk

k

km d

60⋅⋅=∑ and mm TKK ⋅= 0 (A1)

where lk and Vk represent the length and transfer velocity of the trajectory meshes. Consequently, if the contribution of a mesh m between t0 and t0 + Δt time steps is equal to Vol(Δt), the hydrograph of this elementary contribution at the outlet is given by:

0)( =tQ if mTtt +< 0 (A2)

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Transfer function

TF 1 (e,f,g,h)12121212

TF 1 (e,f,g,h)11111111

Contribution at each time step Discharge

Production functionPF 1 (a,b,c,d)11111111

PF 1 (a,b,c,d)12121212

PF 3 (a,b,c,d)31313131

PF 6 (a,b,c,d)61616161

R1R2

Rainfall

Effectiverainfall

Fig. A1 Elementary production and transfer functions of the MERCEDES model.

m

m0

KTtt

mKttQ

)(

e)Vol(Δ)(+−⋅−

⋅= if mTtt +< 0 (A3)

The complete flood hydrograph is finally obtained by summation of the various elementary contributions, calculated for each mesh and each time step. Received 26 June 2007; accepted 19 May 2009

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Documents

Hydrological processes controlling flow generation in a small Mediterranean catchment under karstic influence