Integration of various data sources for transient

Journal of Hydrology 306 (2005) 71–96 www.elsevier.com/locate/jhydrol

Integration of various data sources for transient groundwater modeling with spatio-temporally variable fluxes—Sardon study case, Spain Maciek W. Lubczynskia,*, Jacek Gurwinb a

Department of Water Resources, International Institute for Geo-Information Science and Earth Observation (ITC-International Training Center), P.O. Box 6, Enschede 7500 AA, The Netherlands b Department of Hydrogeology, University of Wroclaw, Poland Received 15 December 2003; revised 10 August 2004; accepted 27 August 2004

Abstract Spatio-temporal variability of recharge (R) and groundwater evapotranspiration (ETg) fluxes in a granite Sardon catchment in Spain (w80 km2) have been assessed based on integration of various data sources and methods within the numerical groundwater MODFLOW model. The data sources and methods included: remote sensing solution of surface energy balance using satellite data, sap flow measurements, chloride mass balance, automated monitoring of climate, depth to groundwater table and river discharges, 1D reservoir modeling, GIS modeling, field cartography and aerial photo interpretation, slug and pumping tests, resistivity, electromagnetic and magnetic resonance soundings. The presented study case provides not only detailed evaluation of the complexity of spatio-temporal variable fluxes, but also a complete and generic methodology of modern data acquisition and data integration in transient groundwater modeling for spatio-temporal groundwater balancing. The calibrated numerical model showed spatially variable patterns of R and ETg fluxes despite a uniform rainfall pattern. The seasonal variability of fluxes indicated: (1) R in the range of 0.3–0.5 mm/d within w8 months of the wet season with exceptional peaks as high as 0.9 mm/d in January and February and no recharge in July and August; (2) a year round stable lateral groundwater outflow (Qg) in the range of 0.08–0.24 mm/d; (3) ETgZ0.64, 0.80, 0.55 mm/d in the dry seasons of 1997, 1998, 1999, respectively, and !0.05 mm/d in wet seasons; (4) temporally variable aquifer storage, which gains water in wet seasons shortly after rain showers and looses water in dry seasons mainly due to groundwater evapotranspiration. The dry season sap flow measurements of tree transpiration performed in the homogenous stands of Quercus ilex and Quercus pyrenaica indicated flux rates of 0.40 and 0.15 mm/d, respectively. The dry season tree transpiration for the entire catchment was w0.16 mm/d. The availability of dry season transpiration measurements considered as root groundwater uptake (Tg), allowed estimation of dry season catchment groundwater evaporation (Eg) as 0.48, 0.64, 0.39 mm/d for 1997, 1998 and 1999, respectively. q 2004 Elsevier B.V. All rights reserved. Keywords: Data acquisition and integration; Recharge; Groundwater evapotranspiration; Spatio-temporal groundwater flux modeling

* Corresponding author. Tel.: C31 53 4874277; fax: C31 53 4874336. E-mail addresses: [email protected] (M.W. Lubczynski), [email protected] (J.Gurwin). 0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2004.08.038

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M.W. Lubczynski, J. Gurwin / Journal of Hydrology 306 (2005) 71–96

1. Introduction Ground water modeling is recognized nowadays as the best tool to support management of groundwater resources. In the last 20 years, there has been a rapid development of the numerical codes (models). In most cases these codes are very sophisticated, being able to address a wide range of water-related problems very efficiently, provided the appropriate data are available to calibrate models in a reliable manner. However, such data usually are not available. The knowledge related to reliable data acquisition, data integration and data extrapolation, particularly regarding spatial data upscaling and spatio-temporal data integration is far less developed than modeling techniques themselves. Numerical modeling when applied for groundwater water recourses assessment in fractured rocks, is fraught with problems of non-continuity, anisotropy and heterogeneity of the medium, thus leading to a scale effect (Sanchez-Vila et al., 1996), i.e. increase of effective transmissivity with increasing scale of observation. If a model cell is in order of several hundred meters, which often is the case, a single borehole pumping test result in heterogeneous, particularly fractured media, cannot be reliably extrapolated to such a cell size. If several pumping test data within a single cell are available then still it is difficult to decide what sort of averaging process has to be applied. Thus, parametric, pumping test data upscaling is not straightforward. Fluxes such as recharge (R) and groundwater evapotranspiration (ETg) are generally less spatially variable than parameters but their upscaling is more complex due to their spatio-temporal nature and variability as discussed below. Routine steady state numerical modeling procedures applied to porous media with hydraulic conductivity (K)/transmissivity (T) set in the model as an independent variable and net recharge as a dependent, calibrated variable, are usually not applicable as such in groundwater modeling of fractured rock aquifers. In such conditions, pumping test parameters (transmissivity and storativity) are usually uncertain and representative only for small areas around boreholes, often differing even among the piezometers recording the responses of the same well abstraction. Therefore, it is important not only to rely on T/K data but also to provide net

recharge (RnZRKETg) or, even better, recharge (R) and groundwater evapotranspiration (ETg) separately, to the best possible level of knowledge, to minimize the non-uniqueness of models. For example, Stoertz and Bradbury (1989) and Batelaan et al. (2003) constructed and calibrated steady state models against temporally invariant but spatially variable recharge, where cells either discharged or recharged groundwater. This was a follow up of the Toth (1971) flow system concept in which groundwater recharge was allocated in the high elevated areas and groundwater discharge in the lower elevated areas, characterized by the occurrence of phreatophytic vegetation (Winter, 1999; Klijn and Witte, 1999; Rosenberry et al., 2000). However, in semi-arid and arid areas, particularly where plant roots reach aquifers (Canadell 1996; Le Maitre, 2000), such schematic patterns where certain areas either recharge or discharge groundwater are often not applicable. To survive, plants withdraw groundwater not only from the areas considered as discharge areas but also from those considered as recharge areas, and the rate of withdrawal is often considerable and seasonally dependent. Moreover, intensive rainfall, depending on the hydrogeological conditions, can also provide substantial recharge to the aquifer locations considered as discharge areas. This means that in semiarid and arid conditions, certain areas (model cells) may change from recharge to discharge status and vice versa, depending on the temporal variability of fluxes. This indicates that standard steady-state understanding of recharge–discharge area allocation cannot be directly applied in the environments with large temporal variability of fluxes such as most of semi-arid and arid locations. The hydrogeological analysis of the processes in such environments requires a transient modeling approach with spatiotemporally variable R and ETg. Having time as the fourth dimension, transient models provide also the additional, time dependent calibration constraint, so the models can be calibrated with less degrees of freedom and therefore more reliably. In the standard transient model solutions, the temporal head variability is only due to the change of aquifer storage driven by stresses, e.g. well abstractions, whereas the fluxes R and ETg are time invariant, similar to steady state solutions. Such transient


solutions, termed also as partially transient or quasitransient (Lubczynski, 2000), can provide adequate results for groundwater management, but only if: (1) stress responses (e.g. drawdowns) are substantially larger than the natural system responses (e.g. natural groundwater table fluctuation); (2) well abstractions and related drawdowns are recorded adequately spatially and temporally. Among transient models, the most reliable but also the least explored, probably due to the demanding input data requirements, are the models with temporally variable R and ETg, also termed as fully transient models (Lubczynski, 2000). In these models the temporal variability of heads is dependent not only on the temporal variability of aquifer storage but also on the temporal variability of fluxes. These fluxes are dependent on the processes occurring at the ground surface and in the unsaturated zone. Therefore, coupling of surface and groundwater processes is an important issue, which as yet, to our knowledge has no modeling solution that at the same time would be efficient, accurate and numerically stable. The most widely known examples of coupled surface-groundwater numerical models are the complex and data demanding MIKE SHE code (Demetriou and Punthakey, 1999) and the relatively simpler IGSM code (version 5), which, despite many management applications in the US still has a number of computational inaccuracies as reported by La Bolle (2003). Less sophisticated, but perhaps more efficient, is a deterministic semi-coupled type of modeling solution, where the net recharge obtained as a result of unsaturated distributed modeling is applied as state variable input in groundwater model solutions. Examples of net recharge determination with unsaturated models such as TOPOG_IRM, SMILE, HELP3 and SWAT with further application to MODFLOW (McDonald and Harbaugh, 1996) are presented by Zhang et al. (1999), Beverly et al. (1999), Jyrkama et al. (2002), Sophocleous et al. (1999) and Sophocleous and Perkins (2000). Relatively the simplest and the most efficient are direct solutions where input fluxes are adjusted in the calibration process following the temporal system responses (e.g. groundwater levels). An example of such a solution is presented by Ting et al. (1998), who linearly lumped the relations between rainfall, net recharge and head variability at the field monitoring points and obtained spatio-temporal net recharge in

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the process of model calibration. Recent advancements in GIS, remote sensing (RS) and automated field monitoring techniques created a unique opportunity to improve the direct type of modeling solutions. Integration of such techniques can provide preliminary spatio-temporal distribution of recharge, which if further integrated in the numerical model, can enhance the efficiency and reliability of the model calibration. In line with this strategy, Brodie (1999) used GIS to define the net recharge input for a steady state model and Lubczynski (2000) used GIS and RS to define spatially variable R and ETg in a quasi-transient model used for groundwater resources management in Serowe area in Botswana. The Sardon study presents also the direct type of approach, although in contrast to the previous study cases, the data acquisition and data integration is spatio-temporal allowing for fully transient model calibration. All the steps of that procedure are methodologically explained and concluded by spatio-temporal groundwater flux analysis and discussion.

2. Flux modeling background In the MODFLOW code, R is simulated with the Recharge Package and ETg with the Evapotranspiration Package. In the Recharge Package, R can be assigned either as time invariant flux (steady state and partially transient solutions), usually taken as a long-term average (e.g. tracer approach), or as a temporally variable flux in fully transient solutions where recharge varies among stress periods but is constant within each of the stress periods. In the Evapotranspiration Package, ETg can be assigned not only as a space and/or time variable but also as a depth variable flux, decreasing linearly down to the predefined depth called extinction depth, below which ETg ceases. If the Evapotranspiration Package is not active the Recharge Package calculates automatically the net recharge (Rn). The recharge flux (R) has been a point of research interest for many years. The three most popular categories of recharge assessment are: (1) tracer, (chemical and isotopic) mass balance related methods, which due to the slow and complex nature of the mixing processes, provide long term average estimates of recharge; therefore they are often

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used as input for steady state numerical models; the results of these methods can either be interpolated using geostatistical methods or extrapolated using for example remote sensing upscaling techniques; (2) hydrological monitoring methods, based on the temporal measurements of hydrological system responses such as groundwater table, river discharge, soil moisture and temporal variability of some chemical constituents (e.g. chloride content), provide temporal variability of recharge, directly applicable as input for transient modeling; (3) distributed, transient groundwater modeling technique, which in contrast to other two methods, represents indirect water balance solution, in which recharge is considered as unknown. The first two categories of direct recharge assessment focus either on spatial or on temporal recharge aspects, whereas the transient groundwater modeling solution provides opportunity to integrate spatial and temporal fluxes. Therefore, the most appropriate seem to be combinations of direct methods with distributed transient groundwater modeling in which the spatio-temporal recharge distribution obtained through groundwater modeling simulations, can be adjusted and verified by the two direct methods. The most recent and complete overview of recharge advances can be found in the special issue of the Hydrogeology Journal vol. 10 no.1 from 2002. The groundwater evapotranspiration (ETg) is a groundwater-related component of total evapotranspiration (ET) as shown in Eq. (1). ET consists of: (1) ETs–surface evapotranspiration (leaf evaporation, direct evaporation from terrain surface, evaporation from water bodies etc.); (2) ETu– unsaturated zone evapotranspiration, which consists of transpiration from unsaturated zone (Tu) by root water uptake and evaporation from unsaturated zone (Eu) by upward flux originated from soil moisture of unsaturated zone excluding capillary zone; (3) ETg–groundwater evapotranspiration, which consists of transpiration from groundwater (Tg) by root water uptake, and evaporation from groundwater (Eg) by upward flux originated from saturated zone and/or capillary zone. ET Z ETs C ETu C ETg Z ETs C ðTu C Eu Þ C ðTg C Eg Þ

(1)

The groundwater evapotranspiration (ETg ) flux, the main point of interest in groundwater modeling, is still largely unknown and its importance is usually underestimated (Lubczynski, 2000). The ETg was first investigated and defined using tank experiments and groundwater table measurements by White (1932) in the Escalante Valley (Utah) and by Robinson (1970) in Humbold River Valley (Nevada). The relatively recent development of micrometeorology, allowed Nichols (1994) to estimate ETg for the shrubs of the Great Basin Desert (Nevada) using the Bowen ratio method applied in the dry season when ETsZ 0. By plotting the Bowen ET from several investigation sites against the corresponding groundwater table depths (zw) ranging from 1.7 to 6.4 m b.g.s. (below ground surface), he defined the relation of ETg declining exponentially with zw for phreatophyte shrubs in the investigated research area. However no guidelines were found in this study on how generic the proposed formula was and on how good was the assumption neglecting Tu in the dry season (disregarding Eu could possibly be justified if unsaturated moisture can be neglected). These assumptions seem to be of critical importance in semi-arid and arid areas, as found in the similar climatic conditions in the Serowe study case on the Kalahari (Botswana), where even with a groundwater table deeper than 10 m b.g.s., the measured dry season tree transpiration consisted not only of the root groundwater uptake (Tg) but also of the soil moisture root water uptake (Tu) contribution (Lubczynski, 2000). The presence of ETuZEuCTu even in the peak dry season, forbade the use of the convenient groundwater modeling assumption that dry season ETZETg. Therefore in the Botswana study, the spatially variable dry season ET (ETZETuCETg) obtained from the remote sensing (RS) solution of energy balance (Bastiaansen et al., 1998; Timmermans and Meijerink, 2000) had to be scaled down in the numerical model calibration (Lubczynski, 2000). The problem of the partitioning of ETu and ETg as well as Tu and Tg, has no solution as yet.


3. The study area The location of the Sardon catchment study area situated in the central-western part of Iberian Peninsula (Fig. 1–this map and all the others are presented in the UTM coordinate system in zone 30), was selected mainly because of suitable small size of the catchment of w80 km2 characterized by welldefined boundaries, low human population and therefore low human impact, semi-arid conditions, typical fractured granite rocks and land cover with standard hard rock hydrology problems. The topographic boundaries of the catchment, are marked by outcropping and shallow subcropping of massive nonfractured rocks (Fig. 2) composed of: impermeable schists and massive granites at the southern boundary,

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massive granites at the western boundary and northern boundary, and fractures filled with quartzite material at the eastern boundary. The upland boundary areas are relatively flat (Tertiary planations) in contrast to the central lowland areas, characterized by steeper slopes due to the incision of the Sardon river and its tributaries. The altitude difference between the uplands and lowlands varies from 840 m a.s.l. at the highest southern boundary to 740 m.a.s.l. at the Sardon river outlet point. The land cover in the study area is characterized by natural woody-shrub vegetation. The area is used mainly for pasture because the soils contain large proportions of weathered granite, which make them generally unsuitable for agriculture. There are only two types of tree species in the study area: evergreen

Fig. 1. Location of the study area.

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Fig. 2. Surface litho-hydrostratigraphic units.

oak Quercus ilex and broad-leafed deciduous oak Quercus pyrenaica. Both trees, as indicated by Silva et al. (2003) and examined in the study area by deep excavations, have sufficient rooting systems to tap shallow groundwater directly from the aquifer. Except for the trees, there are grasses and abundant shrub Cytisus scoparius vegetation also known as Scotch Broom. Wherever Cytisus scoparius grows, it spreads to form pure stands at the expense of grasses and young trees. Local interviews and three excavations suggest that the roots of that species are too shallow to reach the phreatic surface and that these plants have a minor role in groundwater uptake. However, considering uncertainty of that judgment related to low number of sampling observations, very thin root structure which is difficult to detect and general recognition problem due to the large variability of plants similar to that species type, more root depth

investigations are needed to finally disregard (or to confirm) the eventual contribution of Cytisus scoparius to direct groundwater uptake. The climate in the study area is semi-arid and is typical for the central part of the Iberian Peninsula. The long-term 23-years mean rainfall, estimated on the base of six Spanish Meteorological Institute rain gauges located in the surroundings of the study area (none inside the study area) was w500 mm/yr. The warmest and the driest months in the study area are July and August when the average temperature is w22 8C, potential evapotranspiration (PET) is on average w5 mm/d and rainfall is less than 20 mm/month. The coldest months are January and February with an average temperature w5 8C, the wettest November and December with rainfall above 100 mm/month and the lowest PET is in December and January, on average w0.5 mm/d.


The drainage network in the study area is dense and is largely influenced by the intermittent river Sardon. This river is dry from mid-June to midOctober and in the rest of the year performs a role of a drain, mainly for direct runoff. Along the river course there is a regional, brittle fault zone (Fig. 2) named here as the Sardon fault. This fault divides the study area into two geomorphologically different parts, a gentler undulating western and a steeper undulating eastern. Along the downthrown western side of the fault, there is an open-fracture zone (Fig. 3), a few tens to more than thousand meters’ wide and a few tens of meters deep, eroded in the rock basement and in-filled with alluvial deposits and weathered rocks. This channel-fill structure was identified by field structural geology investigations and by geophysical methods such as resistivity and electromagnetic sounding and profiling and magnetic resonance sounding (Owuor, 1998; Attanayake, 1999; Tesfai, 2000; Lubczynski and Roy, 2003). The channel-fill structure acts as a groundwater drain all year round. The runoff processes in the study area are typical for semi-arid hard rock catchments. The thin, highly permeable upper unconsolidated layer with low retention capacity and deeply incised and dense drainage network, result in large and rapid overland and subsurface runoff (together called direct runoff) responses to high-intensity rain showers. During and

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shortly after heavy rain showers in the rainy seasons, such responses often result in temporary flooding of the terrain depressions and in temporal saturation of the vadose zone. Groundwater runoff, in contrast to direct runoff is more moderate in motion and quantity and less seasonally influenced. The groundwater flows from the entire catchment towards the central regional drainage line of the Sardon fault, which conducts water towards the northern outlet of the study area. The geology and hydrogeology is largely influenced by the granitic composition of the rocks in the study catchment. The geology of the study area that belongs to the Central Iberian Zone was described by Lopez and Carnicero (1987). The hydrogeology of the study area is strongly influenced by weathering and fracturing processes. Three layers were identified in the study area. The top, unconsolidated layer composed of weathered and alluvial deposits is relatively thin, on average 0–5 m, exceptionally up to 10 m and has a limited spatial extent (Fig. 2). The second, fractured granite layer with intercalations of granodiorites, schists, gneises and quartzites (Attanayake, 1999; Tesfai, 2000), outcrops extensively in the study area. Its depth, constrained by the depth of the granite fracturing, varies from w60 m b.g.s. in the central part of the catchment to a few meters in the upland areas (Tesfai, 2000). The third, massive granite layer with some gneiss inclusions, forms impermeable rock

Fig. 3. Schematic cross-section.

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basement (aquiclude) and is deepest in the center and shallowest at the catchment boundaries where it outcrops locally (Fig. 2). As typical in granitic areas, the groundwater table is shallow, in the river valleys 0–3 m b.g.s. and at the watershed divides 2–6 m b.g.s. By mapping the piezometric heads of 1998, obtained from 45 shallow boreholes, it was found that the groundwater table has a concentric pattern largely influenced by the Sardon fault-river drainage line (Fig. 4). There is a substantial hydraulic gradient with maximum w0.01 in the neighborhood of the Sardon fault-river drainage line. The groundwater pattern is natural because the groundwater use in the study area is negligible, limited mainly to the water use by the cattle farms. These farms are focused on using water from

the man-made, groundwater-bound ponds, some of which dry out in summer due to the seasonal lowering of groundwater table enhanced by surface evaporation.

4. Automated hydrological monitoring Due to the remote location of the project, the hydrological monitoring was performed exclusively with the Automated Data Acquisition Systems (ADAS). ADAS is a remotely controlled system composed of sensors monitoring the desired hydrological variables and of a logger managing the performance of the sensors and providing their output in a digital format. The following hydrological

Fig. 4. Calibrated heads: 1–piezometric head; 2–direction of groundwater flow; 3–groundwater monitoring point; 4–groundwater table measurement point; 5–model grid; 6–DRAIN boundary cell; 7–GHB—general head boundary cell; 8–HZconst cell converted in transient solution to drain boundary; 9–inactive cells.


variables were monitored in the study area: climatic variables, stream discharges and groundwater table. The automated monitoring was performed by two multi-sensor ADAS stations, operated by the two multi-channel loggers, DataHog2 in the Muelledes location and Delta-2e in the Trabadillo location and by a number of electronic devices composed of one logger and one sensor for measuring one specific hydrological variable. (Fig. 1). The whole automated monitoring network was programmed for synchronized data acquisition with 1 h intervals. The climate monitoring refers mainly to rainfall and evapotranspiration. The spatial and temporal variability of rainfall was monitored in the Sardon catchment by six tipping bucket sensors with a sensitivity of 1 mm/tip and by two tipping buckets with a sensitivity of 0.2 mm/tip (Fig. 1), all equipped with data loggers. After 1 year of monitoring, the results were evaluated and no statistically significant differences were found between the monitoring stations and also a good correlation was observed with the rainfall from the Spanish Meteorological stations. Therefore, after 1 year of operation only the two tipping bucket rain gauges with a sensitivity of 0.2 mm/tip, were kept to monitor long-term temporal variability of rainfall in the study area. One was installed in the upper catchment (ADAS Meulledes site) and the other in the lower catchment (ADAS Trabadillo site) as shown in Fig. 1, next to other micro-climatic monitoring instruments. The micro-climatic sensor composition was designed to determine potential evapotranspiration (PET), the component needed in 1D recharge modeling (as explained below). In both ADAS locations the following four PETrelated sensors such as wind speed, solar incoming radiation, relative humidity and temperature with the same technical specifications were installed at the 2 m height as required by the reference Penman–Monteith evapotranspiration algorithm (Allen et al., 1996, 1998). In hydrogeological assessment, the stream discharge monitoring usually focuses on baseflow. To monitor the baseflow in the study catchment, three trapezoidal, calibrated steel flumes, equipped with automatic data recorders were installed: two small size (!49 l/s) flumes in the upper Sardon catchment and one medium size (! 86 l/s) at the northern boundary outlet (Fig. 1). The flume installations were designed for baseflow measurements only, so the discharges above the nominal flume capacities were not intended to be recorded. The measurable, baseflow discharges

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for the Sardon catchment outlet for years 2000 and 2001, each representing 26 days of late spring discharge recessions from w80 l/s to complete river dry out, are presented in Fig. 5. The monitored recession is characterized by a high discharge recession constant (aZ0.07 dK1). The two other, smaller flumes indicated even more rapid discharge recession with aw0.1 dK1. Due to such high a and because of the field observations performed during and after the rain showers, the discharge recessions in the two small streams were regarded as entirely originated from direct runoff. This is also why, after 1 year of automated monitoring, the two upstream flume measurements were abandoned whereas the flume discharge monitoring at the northern outlet location was continued. The groundwater table monitoring in the study area was performed by the two barometrically compensated pressure transducer piezometric sensors operated by the loggers at the two ADAS locations and by eight non-compensated Automated Groundwater Table Recorder (AGTR) devices (combination of sensor and logger in one monitoring unit) measuring absolute underwater piezometric pressure. One extra AGTR was used for barometric correction. Due to the drying of some piezometers, a number of AGTRs’ were occasionally moved from one well location to another to obtain the maximal data coverage. The remoteness of the study area was the main reason for the incomplete data acquisition (Fig. 6). Seasonal fluctuations of the groundwater table in the study area are characterized by steeply rising and quickly recessing groundwater table with an average groundwater table recession constant of w2!10K3 dK1 (Shakya, 2001) and average amplitude in the order of 2 m.

5. Data integration for the numerical model setup Dense fracturing and good connectivity of fractures in the Sardon study area provided the base for applying the equivalent porosity media (EPM) concept (Anderson and Woessner, 1992) in the Sardon numerical MODFLOW model processed within the PMWIN-MODFLOW pre- and post-processing environment (Chiang and Kinzelbach, 2001).

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Fig. 5. Discharge recession measured by the flume at the catchment outlet of the river Sardon.

5.1. Boundary conditions For all the external Sardon catchment boundaries, no-flow boundary conditions (QZ0) were assigned except for w1.3 km wide section of the channel fill outlet at the northern boundary of the catchment where the General Head Boundary (GHB) was assigned (Fig. 4). The GHB head was defined based on the field piezometric data, while GHB conductance was initially assigned based on the shallow borehole slug test data and later adjusted in the model calibration. The hydrogeological role of the Sardon river flowing along the brittle fault zone (Fig. 3), was simulated in the model by assigning the MODFLOW DRAIN boundary condition (Fig. 4). This was permitted because the Sardon fault structure, drains the aquifer all year long, maintaining the groundwater table along that structure at the shallow depth of not more than 0.5 m b.g.s. The DRAIN elevation was set in the model as the elevation of the river bottom, while the DRAIN boundary conductance was estimated based on the slug tests performed within the course of the river channel. To avoid undesirable model constraints in the calibration process, only one, the most northern cell of the Sardon river outlet, was assigned HZconstant in steady state (Fig. 4). In

the later transient solution, even that cell was changed to a head dependent DRAIN boundary condition. 5.2. Hydrostratigraphic units A two-layer model with a regular square grid of 100!100 m was used. The upper unconsolidated layer was assigned as an unconfined MODFLOW layer type 1 and the underlying fractured rock layer as a MODFLOW layer type 3. The top of the nonfractured rock basement was assigned as the impermeable base of the model. In order to define hydrostratigraphic boundaries of the two permeable layers as well as their groundwater table and piezometric surface, first a database structure in GIS was assembled to integrate the following data sources: (1) geological field mapping data, including detailed lineament (fracture) analysis and fracture density mapping based on the interpretation of the multispectral Landsat TM5 images and 1:20,000 aerial photographs; (2) available logs and new boreholes made with a portable percussion drilling engine set; (3) resistivity sounding and profiling; and (4) hydrogeological depth profiles acquired with the new hydrogeophysical technique called Magnetic Resonance Sounding (Lubczynski and Roy, 2003; 2004).


Fig. 6. Measured and simulated heads—location of the groundwater monitoring points marked in Fig. 1.

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The topographical surface assigned as the top of the first model layer, was interpolated from digitized contour lines of 1:25,000 topographical maps. Next, the thicknesses of the first layer, second layer and unsaturated zone were interpolated from the point data and then subsequently subtracted from the smoothed topographical surface to obtain the appropriate layer boundaries and piezometric head surface common for the two permeable layers. The thickness of unsaturated zone and in consequence the piezometric head surface were derived from the averages of the four sets of groundwater table measurements performed in May and September in 1998 and 1999. 5.3. Hydraulic conductivity To obtain data for the spatial distribution of hydraulic conductivity (K) required as MODFLOW model input, the vertical electrical soundings (VES) and shallow borehole investigations with a portable percussion-drilling machine were carried out along the three transects (Fig. 1). Along those transects the three hydrogeological cross-sections were conceptualized in 2D and cross-sectional FLONET (Waterloo Hydrogeologic Inc., 1997) steady state models were calibrated. In the calibration of these models the hydraulic conductivity adjustments were guided by the piezometric slug test results elaborated using the Hvorslev method, by the net recharge estimates from the chloride mass balance method and by the recharge estimates from the 1D modeling of groundwater hydrographs (see below). The resultant K distributions along the three cross-sectional models were integrated in GIS. To provide better spatial coverage of the reference K data in the areas away from the three hydrogeological cross-sections, additional VES investigations and shallow boreholes with slug tests were made. The abundance of the K data in the first unconsolidated layer allowed assigning the K model input directly, by polygonizing zones of uniform K at the background of the point map of K measurements projected in GIS over the digitally processed multispectral Landsat image. For the second layer the assignment of K model input was more difficult because of scarcity and uncertainty of the point borehole data. Therefore, a GIS based cross-overlay procedure was applied. In this procedure the following

three background K attribute raster maps were created: (1) the groundwater slope map representing the spatially variable hydraulic gradient which, according to Darcy law, is inversely proportional to the aquifer transmissivity (the higher the transmissivity the lower the hydraulic gradient and vice versa); (2) lineament density map, assuming that higher lineament density is associated with higher K or T, created on the base of the multispectral Landsat TM5 satellite image processing and verified by 1:20,000 scale aerial photograph interpretation and field fracture mapping; (3) apparent resistivity map interpolated from the field VES measurements, assuming that in a fractured granite environment such as in the Sardon study area, K increases with the lowering of resistivity values from above 1000 Um for non-fractured or negligibly fractured granite to 50–250 Um for fractured granite aquifers saturated with water. The three maps were scored from 1 to 10 with regard to K. The highest relative K rank of 10 was associated with a combination of the lowest hydraulic gradient, the highest lineament density and the lowest apparent resistivity. The lowest relative K rank was associated with the combination of the highest hydraulic gradient, the lowest lineament density and the highest apparent resistivity. The three component maps were finally summed on a pixel basis and reclassified in ILWIS GIS, which resulted in the relative K map. All the point data as well as the data derived from the three cross-sectional FLONET solutions were then plotted on the background of the relative K map and quantified by scaling up the relative K values. That map was finally resampled into the size of the MODFLOW grid and imported to MODFLOW. 5.4. Storage coefficient The assignment of the spatial variability of the storage coefficient (S) was processed in a similar way as the assignment of K. For the first, unconsolidated layer, the spatial distribution of S was defined using large number of the S point data obtained from the shallow borehole samples, for which the laboratory specific yield estimates were determined. For the second fractured layer, only a few S data points from the 1D lumped EARTH water balance model (Van der Lee and Gehrels, 1990) discussed below and from


the magnetic resonance sounding (MRS) tests (Lubczynski and Roy, 2003, 2004) were available. This is why, for the spatial distribution of S in that layer, a GIS based cross-overlay procedure was applied. In this procedure, the two component maps, such as the ranked lineament (fracture) density map and the ranked apparent resistivity map were summed on a pixel basis and reclassified (see above), which resulted in the relative storage coefficient map. The use of the lineament map for the aquifer storage assessment was justified by the assumption that a higher storage coefficient was associated with a larger fracture density while the use of the apparent resistivity map was justified by the assumption that a lower apparent resistivity was associated with a larger contribution of the relatively conductive water in the open fractures of the resistive granite. Finally, all the S point data were plotted at the background of the relative S map, which was quantified by scaling up the relative S values. That map was resampled into the size of the MODFLOW grid and imported into MODFLOW. 5.5. Time discretization The time discretization in the constructed MODFLOW model refers to the period from December 1996 to May 2000 and was chosen after analyzing the temporal variability of the measured groundwater table fluctuation (Fig. 6), temporal variability of rainfall and temporal variability of fluxes simulated with EARTH. This resulted in the identification of 16 irregular stress periods, which adhered to the principle of uniformity of the groundwater regime within each of the stress periods represented by the multiplication of 1 week, considered as a unit time step. The lengths of the stress periods varied from 7 to 20 weeks. The longest stress periods (4–5 months) were assigned to the dry seasons (June–September) characterized by lack of recharge and therefore continuous groundwater table recessions. The shortest stress periods were assigned to the wet seasons, which are characterized by the variable length of recharge episodes. 5.6. Recharge The quantification of recharge in the study area was made by the chloride mass balance method

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(Fetter, 1994) and by the 1D modeling with EARTH (Van der Lee and Gehrels, 1990). The chloride sampling of rainfall and of groundwater was performed in September 2000 at 22 groundwater locations scattered over the study area. Dry chloride deposition was not assessed. The net spatial recharge was calculated according to the chloride mass balance formula and is presented in Fig. 7. The EARTH modelling was performed in 13 points where hydrograph calibration data were present. The EARTH model requires daily rainfall and daily potential evapotranspiration (PET) input and optionally the groundwater table and/or the soil moisture (not assessed in this study) for calibration purpose. As output, the EARTH model provides R and ET also on daily basis. The temporal input data for EARTH calibration was obtained from the ADAS hydrological monitoring network and the model input parameters were estimated on the base of the field measurements. One example of the EARTH calibration results is presented in Fig. 8, which shows measured and simulated hydraulic heads as well as precipitation in the bottom graph and the temporal variability of recharge, potential evapotranspiration and actual evapotranspiration in the top graph. The temporally averaged recharge estimates from the 13 EARTH models are presented in Fig. 7 next to the chloride mass balance estimates. The temporally variable recharge fluxes obtained from the EARTH model supported not only the steady state model solution but also the setup and calibration of the transient MODFLOW model. The chloride mass balance method and the EARTH model results indicated that despite uniform rainfall in the study area, the recharge is highly spatially variable because of the large heterogeneity of land cover and subsurface. Therefore, instead of using geostatistical interpolation methods for spatial allocation of recharge, the GIS relative recharge modeling was used. This was done by GIS cross-overlay procedure for which the following recharge attribute maps were prepared in ILWIS GIS (ILWIS, 2001): (1) geological map (Attanayake, 1999); (2) surface litho-hydrostratigraphic map (Fig. 2); (3) thickness of the upper unconsolidated layer (Tesfai, 2000); (4) soil texture (Cornejo, 2000); (5) vegetation type (Cornejo, 2000); (6) vegetation cover (Cornejo, 2000); (7) depth of groundwater table (Duah, 1999); (8) surface slope

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Fig. 7. Relative recharge and measurements.

(Canadell et al., 1996); (9) drainage density (Cornejo, 2000); (10) fracture density (Cornejo, 2000); (11) flow system dependency map which was divided into recharge and discharge areas (Cornejo, 2000) based mainly on the three cross-sectional FLONET solutions; (12) evapotranspiration map (Worku, 2000). The proposed composition of the maps in the relative recharge assessment was subjective. The relative recharge scoring of the individual maps from 1 to 10, with 1 representing the lowest and 10 the highest relative recharge was also subjective to expert judgement. To deal with the inter-correlation between the maps, first the correlation matrix of the attribute maps was examined to identify and combine together the correlated maps using the GIS cross-table function. After resampling, the combined maps were scored in the same way as all the other uncorrelated

maps. Finally a similar procedure to the vulnerability index overlay method (Aller, 1987) was applied, in which the scored recharge attributes (zones) of the maps were multiplied by the subjectively assigned recharge weights of the individual maps. The maps were then summed on the pixel basis and the result normalized by dividing the overlaid weighted maps by the sum of the weights, giving a relative recharge score on a pixel basis. The resulting mosaic was then simplified by aggregating pixels into five recharge classes to provide relatively simple input in recharge zones for the groundwater model (Fig. 7). These zones were then crosschecked with the available point recharge data (chloride sampling, EARTH) and the attribute ranking and map weighting procedures were continued until relatively good agreement between the recharge zones and the point recharge data was


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Fig. 8. An example of EARTH groundwater hydrograph analysis for Muelledes hydrograph.

achieved. The following recharge variability ranges in mm/y were finally assigned for the recharge zones: very high 100–200, high 50–100, moderate 25–50, low 10–25, very low !10. The obtained recharge

map was finally imported to MODFLOW and optimised with PEST (Doherty, 2000) applying in the optimization procedure the above mentioned recharge variability ranges.

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5.7. Ground water evapotranspiration As yet, there is no direct method to estimate spatiotemporal ETg. In the Sardon study case, three indirect methods were used to support formulation of ETg as input for groundwater modeling: (1) remote sensing solution of the surface energy balance algorithm for land called SEBAL (Bastiaansen et al., 1998), which provides ET spatially; (2) 1D water balance modeling with EARTH which provides ET for the assessed point usually representative for the adjacent area; and (3) direct tree sap flow measurements of transpiration TtZTuCTg per analyzed tree species. Concerning the remote sensing solution of the surface energy balance, the three Landsat TM5 images of 4 April 1999, 23 June 1999 and 11 September 1999 were acquired and processed to obtain the spatial variability of ET (Worku, 2000). The June image represented exceptionally the wettest conditions (typically April image would represent the wettest condition), whereas the September image, as expected, pertained to the driest condition among the three images. The spatial patterns of ET were similar on all the three processed images but the ET rates were different. The highest was in the wet June image (from 0 to 6.1 mm/d) and the lowest in the dry September image (from 0 to 4.3 mm/d). The average ET for pixels representing Quercus ilex stands were 1.7, 1.9, 2.0 mm/d in June, April, and September, and for Quercus pyrenaica stands 0.7, 1.0 and 1.4 mm/d respectively (Worku, 2000). The modeling with EARTH indicated high variability of daily ET values, varying from 0 to 3 mm/d. Yearly means calculated on the basis of at least a oneyear period for the different hydrograph locations varied from 0.3 to 0.7 mm/d. An example of temporally variable ET obtained with EARTH is presented in Fig. 8 model next to PET obtained with PenmanMonteith formula. The sap flow measurements of transpiration were used to provide an idea about the spatio-temporal variability of transpiration fluxes, but also to attempt to scale the dry season SEBAL ET (see below). The sap flow measurement techniques are well described and evaluated by Smith and Allen (1996); Kostner et al. (1998). The sap flow (Qs) is a product of the two separately measured values: sap velocity (vs) and sap wood (xylem) area (Ax). In the Sardon study case, the

sap velocity measurements were carried out using the thermal dissipation probe (TDP) method (Granier, 1985, 1987). The sap velocities were measured in the selected homogenous tree stands (Fig. 1) of Quercus ilex and Quercus pyrenaica during the three field campaigns, in late August 2001 (the end of the dry season-only Quercus ilex measured), in the beginning of June 2002 (the middle of the growing season-Quercus ilex and Quercus pyrenaica measured) and in early September 2003 (the end of the dry season only Quercus pyrenaica measured), see Fig. 9. Each measurement was carried out on trees with various biometric characteristics for a minimum period of 3 days to acquire at least 1 day of clear sky measurement for comparison purpose. The measurements indicated, that similarly to observations made by Kostner et al. (1998), vs did not differ statistically either with stem size (diameter Ds/area As), or with the canopy size (diameter Dc/area Ac) or with xylem area (Ax). The average dry season (late August/early September 2001) vs of the evergreen Quercus ilex, based on the measurements of the 10 randomly selected trees of the homogenous stand, was 4.5 cm/h with a daily maximum value of 10.0 cm/h. For comparison, the measurement on the 35 Quercus ilex trees carried out in the moist June 2002 recognized as the beginning of the growing season, indicated slightly higher average vsZ4.9 cm/h but with different daily vs distribution characterized by a bit higher daily maximum of 10.3 cm/h and by w1 h earlier rise of the velocity in the morning due to the earlier sunrise in June compared to late August (Fig. 9). The vs of the second tree species Quercus pyrenaica was substantially lower than that of Quercus ilex. The average June 2002 sunny day vs, based on the 22 randomly selected trees of the homogenous stand, was 2.5 cm/h with the daily maximum of 4.7 cm/h (Fig. 9). The average, early September (dry season) sunny day vs based on the six tree measurements was 2.0 cm/h with a daily maximum of 3.8 cm/h, and also with w1 h earlier rise of the velocity in June than in September. The xylem area (Ax) is the second parameter necessary for calculation of sap flow. In the Sardon study area, Ax was determined using the increment borer sampling method. This procedure is tedious and tree-invasive therefore only a limited amount of trees were investigated and the appropriate upscaling relationships with biometric characteristics of the


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Fig. 9. Sap flow measurements.

stem area (As) and canopy area (Ac) were established for both Quercus ilex and Quercus pyrenaica. Among different biometric relationships the most applicable were the linear relationships between Ax and Ac, which further allowed upscaling of field measurements of Ax. These relationships were as following: for Quercus ilex AxZ0.0022 Ac as defined on 22 samples with r2Z0.62 and for Quercus pyrenaica AxZ0.0026 Ac as defined on 25 samples with r2Z 0.62. Evaluation of the transpiration flux requires not only Ax and vs but also the aerial tree density per species, which was determined with scanned aerial photographs of 1:20,000 scale. The tree density assessment was carried out in GIS by canopy pixel count in the two 0.5!0.5 km stands of Quercus ilex and Quercus pyrenaica shown in Fig. 1. The average canopy closure for the homogenous Quercus ilex stand was 17% and for the homogenous Quercus pyrenaica stand 11.5%. The average estimate of a clear sky day transpiration flux in a dry season, when total transpiration (Tt) was likely equal to Tg, was 0.40 mm/d for Quercus ilex stand and 0.15 mm/d for Quercus pyrenaica stand. The dry season tree transpiration for the entire study area, estimated on

the base of the vegetation map of Shakya (2001) was 0.16 mm/d. Concerning MODFLOW input, as the ETg, the dry season ET of 11 September 1999, further scaled towards ETg in the MODFLOW calibration process (by sap flow transpiration and EARTH ET) was used. The selection of the dry season image in the study area characterized by low retention capacity of the unsaturated zone assured the minimum possible contribution of ETs and ETu in ET so the best possible correlation between ET and ETg . The SEBAL distribution of ET was aggregated into five classes (Fig. 10) and resampled to the 100!100 m cell (pixel) format of MODFLOW model. The five classes were assigned as follows: 0 (no ET), 0–0.4 (low ET), 0.4–0.8 (moderate ET), 0.8–1.5 (high ET) and O1.5 mm/d (very high ET). This map was further imported to MODFLOW as ‘maximum evapotranspiration rate (MER)’. Next, the ‘Elevation of the ET Surface’ was assigned with artificial, unrealistically low ‘Elevation of the ET Surface’ to set MODFLOW ET as equal to MER and as independent from ‘ET Extinction Depth’. This was done because of the two reasons: (1) unknown contributions and variability of Tg and Eg with depth; (2) MODFLOW model experienced high numerical

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instability during initial model runs with ET variable not only with space and time but also with depth. With the described setup, the MODFLOW ET (ETg) was entirely controlled by one variable, MER, which further was adjusted in the calibration process as ETg.

6. Model calibration and water balance The calibration target in the steady state model was to: (1) match heads calculated by the model with the 45 measured head points; (2) maintain

heads at the likely depth of 0–5 m b.g.s. in the areas without data, by compromising between the predefined average values of R and ETg (see above) and the transmissivity of the second aquifer. In the steady state calibration process, the PEST-MODFLOW optimization procedure was used based on automatic minimizing of the discrepancy between calculated and measured heads. The initial model calibration runs, had indicated that the dry season SEBAL estimates of ET were far too high to provide realistic ETg. This is why the SEBAL ET input was proportionally rescaled down using dry season sap flow measurements and EARTH

Fig. 10. Dry season evapotranspiration of 11 September 1999 verified by sap flow measurements.


estimates as guidelines in the adjustments performed in the process of model calibration. The resultant heads for the first and the second layer, which are nearly identical, are presented in Fig. 4. Other calibration matrices are not presented for reasons of space limitation. The water budget of the steady state model, which represents the long term average water balance of the study area, consists of: recharge (R) of w56 mm/yr (11% of average yearly rainfall), groundwater evapotranspiration (ET g) of w20 mm/yr (4% of rainfall) and groundwater outflow (Qg) of w36 mm/yr (7% of rainfall) equal to net recharge (Rn). The transient model calibration was carried out based on the groundwater hydrograph data from 12 piezometers (Fig. 6). In the first transient calibration step, the PEST code was used to optimize the spatial variability of storage coefficient (S) within the predefined variability ranges on the base of laboratory measurements and tables in the literature. In the second step, improvement and fine-tuning of the model was performed by trial and error adjustments of R and ETg. These adjustments were guided by the 1D EARTH water balance solutions and the sap flow measurements of tree transpiration and controlled by matching calculated and observed heads. In these adjustments however, the aim was not to strive for the lowest mean square error of the difference between calculated and observed heads, but to have the best fit of the pattern of rises and recessions of the groundwater table. The final calibrated and measured hydrograph heads are shown in Fig. 6. The temporal variability of fluxes and the budget of the transient model are shown in Fig. 11.

distribution of data in a more reliable manner than by using interpolation. The fluxes R and ETg, in contrast to system parameters K and S, vary not only spatially but also

7. Discussion Any field data can be either spatially interpolated (e.g. kriging) or spatially extrapolated by using for example RS based GIS map modeling. Interpolation is justified if the data is abundant and spatially correlated. In practice, however, a sufficiently dense data network is quite rare and the spatial data correlation is common only in porous media. Therefore in hard rocks and data scarce areas, the RS based data extrapolation, allows

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Fig. 11. Temporal variability of groundwater fluxes.

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temporally. Therefore they require not only spatial but also temporal data extrapolation. The spatio-temporal extrapolation can be efficiently done by the RS-GIS based integration of ADAS monitoring results in transient numerical models, which provide groundwater fluxes in a spatio-temporal manner. The chloride mass balance used in this study is a convenient method of recharge assessment but is restricted to environments only naturally enriched in chloride from rainfall. Therefore it is highly sensitive to the presence of artificial sources of chloride (pollutions) in groundwater, natural sources of chloride but also to a possible uptake of chloride from groundwater by plants (Tg). Under the assumption that chloride dissolved in groundwater is not taken up by plant roots, the chloride mass balance method provides the net recharge (Rn) rather than the total recharge (R). If however tree roots uptake part of the chloride from groundwater, the concentration in groundwater decreases, so the recharge estimate increases from Rn towards R. Finally, if the root water uptake took place at the same chloride concentration as in groundwater, the method would then provide the estimate of the total recharge just like it does when using C14 and stable isotopes (O18 and H2) as tracers, which remain the isotopic composition of sap water unaltered until it reaches tissues or leaves (Kendall and McDonnell, 1998, p. 166–167). The recharge rates obtained with a chloride mass balance in the Sardon study area were typically higher than those obtained with the 1D modelling solution with EARTH (Fig. 7). The reason of that is not known but it was likely due to inaccuracies related to the less accurate chloride mass balance method. The EARTH modeling (Van der Lee and Gehrels, 1997) is an efficient and quite accurate (Gehrels, 2000) flux assessment tool providing not only R but also ET. However, as with any 1D modeling technique, EARTH assumes that water level rises in the piezometers represent exclusively the system response to recharge from precipitation and not to lateral flow. This assumption results in inaccuracy, which affects mainly simulations in discharge areas, where delayed lateral groundwater flow as a reaction to distant recharge episodes can affect the groundwater table regime. Such behaviour for example was identified in the Trabadillo piezometer at the ADAS station (Fig. 1), located in the discharge area close to

the Sardon fault and the impact of distant recharge was removed from the simulation (Cornejo, 2000). The relative GIS map modeling turned out to be a useful and convenient tool, particularly in spatial recharge assessment. It allowed integrating various data sources in the form of map layers, which after GIS data processing provided a semi-quantitative recharge distribution. That information, after verification with the chloride mass balance assessment and EARTH model, was further used as input for groundwater modelling. The purpose of the use of the relative GIS modelling technique, is not to define recharge distribution in a quantitative manner but to obtain a preliminary distribution pattern and the most likely ranges of recharge which are further adjusted in the numerical model calibration where the quantitative recharge estimate is obtained. The use of the energy balance method as input for groundwater modeling is limited by the fact that it likely overestimates ET and also because the ETZETsCETuCETg differs from the ETg applied in groundwater models. While considering eventual application of ET as ETg, care should be taken about the ETKETgZETsCETu. Even in dry seasons when ETsZ0 can be assumed, the relevance of ETu can be critical, not allowing to assign ET as ETg. This is mainly because of the ever existing and unsolved as yet problem of partitioning of ETu from ETg. The recently developed two source RS based models (Kustas and Norman, 1999; French et al., 2000) allow for algorithm-inherent spatial separation of ETs from overall ET but do not solve the problem of separation of ETg from ETu. However, with field methods like sap flow, transpiration measurement, and isotopic tracing (Adar et al., 1995; Haase et al., 1996; Coudrain-Ribstein et al., 1998), combined together (Thornburn et al., 1993; Cook et al., 1998), the separation of ETu from ETg seems to be possible. Not only ETu from ETg but also Tu from Tg cannot be partitioned as yet. The depth dependent isotope tracing, such as the one provided by Haase et al. (1996), can only confirm whether the root water uptake occurs from groundwater or not. In the Sardon study area, which is characterized by the coarse and fractured unsaturated zone and low water retention capacity, in dry seasons the soil moisture drops down to 0–2% as indicated by monitoring profiles in both


ADAS stations. With such low soil moisture (Tuw0), the convenient, although applicable only in a dry season assumption could have been made, that the measured (by sap flow) tree transpiration (Tt) was equal to Tg. With regard to the transpiration flux assessment, of particular interest in the study area is the abundant, shallow rooted Cytisus scoparius, which remains green even through the peak of the dry season. This plant is known to be able to survive for a long period on a very low quantity of water. It is expected, that not having roots deep enough to reach groundwater, it captures water either from the moisture remaining in the soil, or from dew, or from condensation of vapor moving up and down in the unsaturated zone (Coudrain-Ribstein et al., 1998; De Vries et al., 2000; Scanlon et al., 2003). In the Sardon study case, the assignment of ETg in the MODFLOW numerical model was done indirectly, by applying the ET pattern of the SEBAL RS solution of the energy balance. For that purpose, the dry season image of 11 September 1999 was used assuring the minimum contribution of ETsCETu. In this regard, of particular interest is the comparison of the dry season SEBAL ET with the simultaneous ET values from the EARTH models and with the Tt from sap flow measurements. The SEBAL ET extracted for the pixels representing the Quercus ilex canopy, was 2.0 mm/d and for the Quercus pyrenaica was 1.4 mm/d. The EARTH ET for the various hydrographs for the same period was in the range from 0.35 to 0.85 mm/d, and the sap flow transpiration flux estimates for the comparable periods of September 2001 and 2003 (unfortunately the simultaneous transpiration measurements of 11 September 1999 were not available) for the Quercus ilex and Quercus pyrenaica stands were 0.40 and 0.15 mm/d, respectively. The substantially higher ET determined using SEBAL in comparison with the other methods and also uncertain larger canopy ET in “dry” September than in “wet” June, suggested that the dry condition SEBAL ET fluxes were likely overestimated in a similar way as in the Botswana case study reported by Timmermans and Meijerink (2000) and by Lubczynski (2000). The higher ET flux values obtained with EARTH than with sap flows are because EARTH provides an estimate of total evapotranspiration whereas sap flow measures only its component, tree transpiration.

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Any model calibration has to consider the nonuniqueness problem. In the steady state model solution, long term average Rn (or long term average of R and ETg separately) with no change in aquifer storage is usually assumed, so the main source of nonuniqueness refers to the interchangeability between Rn and T, i.e. the same head configuration can be derived with different combinations of Rn and T. In such case, the strategy of model calibration depends on the quality and confidence in the data available. This means that either Rn or T or both, but at various spatial locations are considered as known, so the unknowns are then calibrated as dependent variables. In the trial and error method, the progress and the calibration results are largely dependent on the expertise of the modeler, who makes hydrogeological judgments and decisions during the calibration process, whereas, the use of an automatic optimization technique such as PEST linked with MODFLOW, allows for more efficient but less interactive calibration of Rn and T at a certain pre-established uncertainty level. In the PEST optimization, also used in the Sardon steady state model calibration, the hydrogeological expertise was mainly required in the process of zonation of the PEST parameters, in establishing the variability ranges of the parameters, and in the judgment of the subsequent PEST solutions based on the PEST uncertainty reports. Automatic optimization with PEST turned out to be an appropriate and convenient tool for the steady state modeling. The use of time as a fourth dimension makes transient model calibration far more complicated than a steady state model calibration, particularly when not only storage but also input fluxes are temporally variable. For such a task, the availability of temporal data is indispensable and this nowadays can be realized at the desired temporal resolution by monitoring with ADAS. The time discretization into stress periods, which largely influences the transient model solution, is a critical modeling step. More stress periods add more temporal variability in the calibration process, allow for a better fit between calculated and measured heads, but also make the calibration task more complicated and more time consuming because more stress periods (and time steps) need more input data and therefore more processing time.

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The transient models, particularly those with temporally variable fluxes are more reliable solution than steady state models. The temporal aspect of calibration reduces the number of degrees of freedom so that the model solution is less non-unique. In that respect, a disadvantage of the transient calibration is that it requires the formulation of the additional parameter of storage coefficient (S), which if poorly known, adds uncertainty to the model solution, although in a limited manner, because S is less spatially variable, less scale dependent and more predictable than T. This is also why it is critically important that T is already well optimized against the long-term average net recharge (recharge and groundwater evapotranspiration separately) in a steady state solution, so that it does not need to be recalibrated again in the transient stage. The transient calibration of the Sardon model was performed in two steps. In the first step, the distribution of S was adjusted with PEST to obtain a general fit in heads. In the second step, the trial-anderror, fine-tuning adjustments of R and ETg fluxes, turned out to be more efficient than the PEST optimization method, allowing for interactive ‘cellby-cell’ and ‘stress-by-stress’ improvement of the model, better honoring hydrogeological knowledge than automatic optimization. The ‘dry’ stress periods of the Sardon transient model, extending over approximately four months a year, are very distinct in the water budget solution (Fig. 11). The short, rare rainfalls in these periods, are almost entirely captured by the surface and unsaturated zone storage, which results in the negligible R and large contribution of the discharge components, such as ETg equal to 0.64, 0.80 and 0.55 mm/d in 1997, 1998, and 1999, respectively, and less important and temporally uniform Qg varying from 0.08 to 0.17 mm/d. Both, ETg and Qg occur largely at the expense of aquifer storage. Assuming that the average dry season catchment transpiration TtZ0.16 mm/d, estimated by sap flow measurements, does not vary between different dry seasons, and that the dry season TuZ0 meaning that TtZTg, then the estimated dry season Eg from EgZETgKTg are 0.48, 0.64, and 0.39 mm/d for 1997–1999, respectively. These estimates indicate that in the Sardon study area, the dry season groundwater evaporation is greater than transpiration. The relevance of dry season Eg in the

groundwater balance of the Sardon study area is areaspecific and is mainly because of the shallow depth of groundwater and coarse/fractured unsaturated zone characterized by very low retention capacity. Unfortunately, it is very difficult to measure and verify Eg experimentally, because neither soil moisture sensors nor soil suction pressure sensors are able to detect the gas form of the water movement. The ‘wet’ stress periods extending approximately over 8 months a year are characterized by temporally scattered recharge with variable rates. Such long wet recharge periods are mainly due to the very low retention capacity of the unsaturated zone, which requires a relatively short time to reach the field capacity for initiation of the recharge process. In the wet recharge periods, usually starting in the beginning of October shortly after the first heavy rains, the distribution of recharge follows the rainfall pattern (Fig. 11) and typically is 0.3–0.5 mm/d and exceptionally w0.9 mm/d (stress periods 1 and 5). During these periods, the ETg is very low !0.05 mm/d, because the tree transpiration demands are satisfied by the surface and unsaturated moisture and because the Eg is practically not active because of the availability of the sufficient amount of moisture in the unsaturated zone. Due to the relatively low transmissivity of the main, fractured aquifer, Q g is low and quite temporally stable, in the range of 0.1–0.2 mm/d. This corresponds to 90–180 l/s of the catchment groundwater outflow at the northern outlet along the Sardon fault zone. The small temporal variability of Qg in the Sardon catchment implies that the large seasonal changes of aquifer storage (Fig. 11) are mainly dependent on largely temporally variable Rn. This has the following implications: (1) in the wet seasons, the excess of R over QgCETg is transferred to aquifer storage, which typically gains 0.2–0.4 mm/d and exceptionally 0.7 mm/d (stress period no. 5), resulting in generally rapid groundwater table rises (Fig. 6) enhanced by the relatively low aquifer storage coefficient (0.01–0.05); (2) in the dry seasons characterized by the lack of recharge, the combined action of ETg and Qg results in the depletion of aquifer storage at a rate of 0.62– 0.86 mm/d, with groundwater table recession constant w10K3–10K2 dK1 (Shakya, 2001); in this process the contribution of ETg (0.55–0.80 mm/d) to the storage depletion is substantially higher as compared to Qg


(0.08–0.17 mm/d); (3) the large discharge recession constant, aZ0.07 dK1 of the Sardon river (Fig. 4), measured by the flume at the catchment outlet, indicates the direct runoff type rather than the baseflow type of recession. This, points at the important, if not dominant role of the Sardon fault as a groundwater conduit, which unfortunately is very difficult to examine in the field.

4.

8. Conclusions 1. The selected Sardon study area represents a small, w80 km2 research catchment composed mainly of fractured granite discontinuously overlain by a few meters thick, highly permeable unconsolidated layer of weathered and alluvial rocks. The groundwater table is shallow and the retention capacity of the unsaturated zone very low. In dry seasons this results in quite fast groundwater table declines due to the enhancement of lateral groundwater outflow by groundwater evaporation and groundwater uptake by tree roots. In the wet seasons, when tree demands are satisfied mainly by surface and unsaturated moisture and when groundwater evaporation is negligible due to the presence of water retained in the unsaturated zone, most of the rainfall events result in recharge. Because of low unsaturated and saturated zone storage, this results in rapid groundwater table rises. 2. The integration of various field, automated monitoring and remote sensing techniques in GIS and the interfacing of them with numerical models, enlarge the capability of the models, providing new, better opportunities in distributing parameters (K/T, S) spatially and in distributing fluxes (R, ETg) spatio-temporally. 3. With reliable, spatially variable, yearly average net recharge (or with yearly averages of R and ETg separately) and with reliable head distribution, a steady-state model can provide a reliable estimate of the overall system transmissivity. In the Sardon case, the spatial recharge was defined by a GIS map modeling method. The subjectivity of attribute ranking and map weighting was minimized by fitting the recharge zone distribution to the available point recharge data taken from the EARTH assessment of the well hydrographs,

5.

6.

7.

93

from the chloride mass balance method, and by adjusting and calibrating such distributions in the numerical model. The ET, determined by the remote sensing solution of surface energy balance (SEBAL method) resulted in higher ET values than obtained by the EARTH model, which in turn was more than the transpiration (Tt) from sap flow measurements. The likely ET overestimation with SEBAL as well as the fundamental difference as compared to ETg, prevented direct application of SEBAL ET in the MODFLOW evapotranspiration package. Instead, the dry season pattern of SEBAL ET was scaled by EARTH ET and by sap flow Tt in numerical model calibration. In the transient model calibration, the following temporal pattern of flux variability was obtained: (a) in the dry periods of about 4 months duration, R was low (in the order of !0.05 mm/d), ETg was high (0.55–0.8 mm/d) and Q g quite low (w0.1 mm/d); the latter two discharge fluxes, ETg and Qg, caused depletion of the aquifer storage; (b) in the wet periods of about 8 months duration, ETg was low (!0.05 mm/d), Qg was low (0.1–0.2 mm/d) but the recharge was quite high, varying per stress period from w0.15 to 0.9 mm/d; the excess of the recharge over the discharge (ETgCQg) was accumulated as aquifer storage. For dry seasons, the clear sky, sap flow transpiration (Tt) measurements indicated 0.40 mm/d in the Quercus ilex stand and 0.15 mm/d in the Quercus pyrenaica stand. The dry season transpiration estimated for the entire catchment was 0.16 mm/d. The sap flow measurements of transpiration as well as the calibrated in MODFLOW Eg, allowed also estimation of dry season Eg from EgZETgKTg under the likely assumption that TtZ Tg. In this way calculated Eg values for the dry seasons 1997, 1998 and 1999 were estimated as 0.48, 0.64 and 0.39 mm/d, respectively. The large contribution of Eg in the groundwater balance of the study area was attributed to the shallow depth to groundwater table and coarse and fractured unsaturated zone characterized by low retention capacity typical for granitic catchments. Despite the large amount of data collected, the Sardon model involved not only the standard

94


spatial non-uniqueness between net recharge and transmissivity typical for steady state solutions but also temporal non-uniqueness, mainly between uncertain ETg and Qg. The uncertainty of ETg was due to the immeasurable Eg and the uncertainty of Qg due to the immeasurable groundwater outflow along the Sardon fault zone. 8. Ground water management can be better done with transient models than with steady state models because transient models are calibrated with less degree of freedom due to the temporal character of data input. The partially transient models with temporally variable aquifer storage but not fluxes, are efficient, but can be successfully realized only if substantial and therefore expensive drawdown schemes are available. The fully transient models with spatio-temporally variable fluxes are at least as accurate as partially transient models and do not require large drawdowns providing best possible evaluation of renewability of groundwater resources. They, however, require time and budget demanding monitoring network installation which is the main condition limiting their applicability.

Acknowledgements We thank Drs Kovacs for initiating the project; Prof. Meijerink, Dr Roy and Dr Schetselaar for scientific support; Dr Kosters and Prof. Hale for financial support; and all ITC students involved, particularly Shakya, Cornejo, Duah, Attanayake, Tesfai for their research contributions. We thank also Dr Batelaan, an anonymous reviewer and the Editor Dr Sophocleous for the revision of the manuscript, which undoubtedly improved its quality.

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Integration of various data sources for transient

Integration of various data sources for transient

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