Distributed modeling of groundwater recharge at the ...

Ecological Modelling 187 (2005) 15–26

Distributed modeling of groundwater recharge at the macroscale H. Bogenaa,∗ , R. Kunkela , T. Schöbelb , H.P. Schreyb , F. Wendlanda a

Research Center Jülich, Systems Analysis and Technology Evaluation, D-52425 Jülich, Germany b Geological Survey of North Rhine-Westphalia, D-47803 Krefeld, Germany Available online 21 February 2005

Abstract The GROWA model was applied to the entire Federal State of North Rhine-Westphalia (ca. 34,000 km2 ) using a grid resolution of 100 m. It conceptually combines distributed meteorological data (winter and summer precipitation and potential evapotranspiration) with distributed site parameters (land use, soil properties, slope gradient, slope exposure, mean depth to groundwater) to facilitate the calculation of long-term annual averages of total runoff. In the GROWA model groundwater recharge is expressed as a constant proportion (baseflow indices) of the total runoff. This portion depends on certain characteristics of the investigated area, e.g. the slope gradient, soil and hydrogeological properties as well as the degree of surface sealing. In this paper special emphasis is put on the influence of geology on groundwater recharge. In this respect, a new calibration procedure for the parameterization of geology-related parameters is described. In previous applications of the GROWA model baseflow indices have been identified on the basis of observed mean monthly low-water runoff values (MoMLR). Since the MoMLR-values significantly overestimate groundwater recharge in solid rock regions due to high interflow proportions, a more sophisticated hydrograph separation method has been applied. In this study runoff data from about 125 gauging stations within the Federal State of North Rhine-Westphalia were used to derive baseflow indices. The raster-based simulation was carried out using a grid resolution of 100 m. The accuracy of the calculated groundwater recharge values for the period 1979–1999 was verified on the basis of data from gauging stations. A good agreement between observed runoff values from the sub-catchments and model results was achieved. © 2005 Elsevier B.V. All rights reserved. Keywords: Water balance model; Groundwater recharge; GIS; Base flow; Consolidated rock

1. Introduction In 2001 the implementation of the EU-Water Framework Directive (EU-WFD) in the countries of the European Union was initiated. Among things, the EU-WFD requires the establishment of the quantitative status of ∗

Corresponding author. E-mail address: [email protected] (H. Bogena).

0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2005.01.023

the groundwater bodies. Information about groundwater recharge in river basins on a regional basis or on the basis of administrative units like states or countries is an indispensable basis for achieving this goal. The existing studies on the groundwater recharge in the Federal State North Rhine-Westphalia (NRW) cover only individual regions, and as the studies are based on different methods, they are unsuitable for regional comparisons. Therefore, in the study presented in this paper, a model

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H. Bogena et al. / Ecological Modelling 187 (2005) 15–26

approach was used in order to determine groundwater recharge for the whole area of NRW. The development of hydrologic models started in the 1960s with the Stanford Watershed Model (Crawford and Linsley, 1966). In the meantime the number of models and model systems as well as the number of different model concepts has grown considerably as indicated in the overview given by Singh (1995). Most of the models have been developed for a specific scale and the simulation of a specific aspect of the hydrologic cycle. Physically based models like PRMS (Leavesly et al., 1983), TOPMODEL (Beven et al., 1995) or SHE (Abbot et al., 1986), for instance, have been developed for the application in micro- to meso-scale catchments. The application of these models in areas like NRW, which covers an area of ca. 34.000 km2 , is limited not only due to the lack of input data needed to run these models but also because of regionalization issues (e.g. Blöschl and Sivapalan, 1995). Due to these problems models especially designed for macroscale applications have been developed in the last 20 years. These models differ significantly from micro- and meso-scale models with respect to the representation of the relevant processes and the spatial and temporal resolution. The RHINEFLOW model (Kwadijk and van Deursen, 1993), for instance, calculates the water-balance for the entire Rhine basin using an integrated approach on a monthly basis. The macroscaled HBV-model (Bergström, 1995) uses a deterministic approach in order to calculate runoff with a daily resolution. For modeling the long-term groundwater recharge in large catchment areas or regions, empirical models have been shown to provide appropriate results (e.g. Dörhöfer and Josopait, 1980; Renger and Wessolek, 1996; Meinardi, 1994; Kunkel and Wendland, 1998; De Wit et al., 2000). These models allow a reasonable determination of the long-term water-balance using empirical functions that reflect the interaction between the actual land cover and climatological, pedological, topographical and hydrogeological conditions. The increasing availability of high-resolution digital data (e.g. land use, topography, soil physical parameters) as well as the continuous further development of macroscale water-balance models in recent years made it possible to undertake a GIS-based determination of groundwater recharge for the entire State of NRW.

The empirical GROWA model (Kunkel and Wendland, 2002) was chosen for the water-balance calculations, since this model has already proven its reliability in earlier applications, e.g. the Elbe river basin (Kunkel and Wendland, 1998) and the Federal State of Lower Saxony (Dörhöfer et al., 2001), especially for groundwater recharge estimation. In the study presented in this paper GROWA was extended by a new module in order to cope with the special runoff conditions in the consolidated rock areas of NRW.

2. Materials and methods 2.1. The research area The Federal State of NRW is situated in the western central part of Germany and covers an area of about 34,000 km2 . NRW features the highest density of population of Germany (18 Mio inhabitants and 520 persons per km2 ) and is one of the most industrialized federal states in Germany. Therefore, the demand for water is very high, but also the vulnerability of the groundwater resources is a serious problem in this region. The Lower Rhine and Westphalian Basin are characterized by unconsolidated rocks that were formed during the Cenozoic era (see Fig. 1). In the central part of the Westphalian Basin also Mesozoic consolidated rocks are present. The Paleozoic rocks of the Rhenish Massif are entirely consolidated and therefore represent a very low groundwater recharge potential. In the unconsolidated rock regions water supply is based on groundwater withdrawals especially in the northern parts of the Lower Rhine Basin. Since the region of the Rhenish Massif generally exhibits low groundwater quantities because of the low infiltration rates of the consolidated rocks, the water supply is mainly from surface water reservoirs. 2.2. Data base The modeling was based on digital data sets mainly provided by the Geological Survey of North RhineWestphalia. In selecting the data sets it was decisive that they be available for the entire State and that they also display high spatial resolution (see Table 1). The scale of the input data ranges from 1:50,000 to 1:100,000


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Fig. 1. Geologic units of North Rhine-Westphalia.

and from 50 m × 50 m to 1000 m × 1000 m. Due to the high resolution of the data sets, the calculations in this study were carried out on a grid having a resolution of 100 m, making up 3.4 million grid cells in total.

2.3. The GROWA model The GROWA model consists of several modules for determining the long-term annual averages of water-

Table 1 Databases of the GROWA water balance model

Climate data Soil data Soil cover Geology Topography Validation data

Database

Scale/spatial resolution

Data source

Precipitation (May–October) precipitation (November–April) potential evapotranspiration Effective field capacity rooting depth capillary rise groundwater depth influence of perching water CORINE land cover Classes of permeability Slope exposure Catchment areas

1000 m × 1000 m

German Meteorological Survey

1:50000

Geological Survey of NRW

25 ha 1:100000 50 m × 50 m 1:25000

Federal Statistical Office Geological Survey of NRW Geological Survey of NRW Environmental Agencies and Water associations of NRW

Daily runoff 1979–1999

18


balance components, namely actual evapotranspiration, total discharge, direct runoff and groundwater recharge, as described in detail in Kunkel and Wendland (2002) and Bogena et al. (2003). The calculation of total discharge is based on an empirical method developed by Renger and Wessolek (1996), which takes into account various forms of land use and soil cover (arable land, grassland, deciduous forest, coniferous forest) for plain, rural areas at some distance from the groundwater table. In order to cope with limitations of the Renger– Wessolek method, several extensions were developed and implemented in the model scheme, allowing an area-wide application of the GROWA model. In the case of a site being affected by groundwater, actual evaporation is set to a maximal evaporation rate, which is calculated according to Glugla et al. (2002). Mean annual capillary rise is calculated in a vegetation specific way according to Bogena et al. (2003) considering the mean annual rate and the mean duration of capillary rise. In the case of high relief terrains, a correction factor is introduced, which is calculated according to Golf (1981), considering exposure and slope gradient. The

latter are calculated from a digital elevation model. In the case of urban areas a correction factor is introduced to account for the effect of sealing on the actual evaporation rate. The values of these factors are based on investigations by Wessolek and Facklam (1997). The following equation shows the generalized form for the calculation of total discharge (Qtotal ) in the GROWA model: Qtotal = Pyear − hrelief (aPwi + bPsu + c log(Wpl ) +dETpot + e − Dseal )

(1)

Pyear : annual precipitation amount; hrelief : relief correction factor; a–e: land use specific coefficients; Pwi , Psu : winter and summer precipitation amounts, Wpl : plant available soil water content; ETpot : annual potential evapotranspiration; Dseal : term for taking into account the effect of sealing. In the GROWA model groundwater recharge (QGW ) is separated from total discharge using baseflow indices (BFI), which describe groundwater runoff as a constant fraction of the total runoff depending on specific area properties.

Fig. 2. Site characteristics that determine groundwater recharge in GROWA.


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Table 2 BFI values of the unconsolidated rock areas according to (Dörhöfer and Josopait, 1980; Hennings, 2000; Wessolek and Facklam, 1997) Degree of sealing

Groundwater depth

Water logging tendency

Degree of slope (%)

Baseflow indices

>2 m 1.3–2 m

No water logging 1 (very low)

15

1 0.9 0.82 0.67 0.59 0.5 0.44 0.4 0.33 0.28 0.2

I (10–45%)

0.8–1.3 m 0.4–0.8 m 90%)

QGW = BFI · Qtotal

(2)

The allocation of BFI values is determined using a hierarchical approach in which only one site property is regarded as being decisive for the groundwater runoff fraction. Other parameters are considered only if the top level condition is not relevant (see Fig. 2). In general, a distinction can be made between three groups, each of which is associated with a different procedure (Dörhöfer et al., 2001). The urban areas were divided into two levels of sealing and the baseflow indices were identified using the results of Wessolek and Facklam (1997). In unconsolidated rock regions the depth to groundwater, the water-logging tendency of the soil and, to a lesser extent, the local slope gradient were identified as the most determining parameters for the BFI value. In addition, artificial drainage systems can be considered. The BFI values allocated to each site condition are listed in Table 2. It became apparent in previous studies (e.g. Kunkel and Wendland, 1998) that in solid rock regions hydrogeological properties (e.g. permeability of the aquifers) are of special significance for the BFI values

(see Table 3). The soil-related parameters are of much less importance and thus were not taking into account in those areas. By means of a correlation analysis appropriate BFI values were determined on the basis of daily river discharge data. In previous studies baseflow indices have been identified on the basis of observed monthly low-water runoff values (MoLR values). Wundt (1958) showed that a long-term average of MoLR-values of a 20-years period is a good approximation for groundwater recharge in unconsolidated rock areas (MoMLR-method). However, in consolidated rock areas the MoLR values are often affected by interflow leading to a significant overestimation of groundwater recharge. Hence, a more sophisticated hydrograph separation method based on a method developed by Kille (1970) and Demuth (1993), was used in these areas (MoMLRr-method). The MoMLRr-method is a modification of the MoMLR-method, in which the individual MoLRvalues are arranged according to their value. Thus a distribution curve is created, in which the linear zone of the distribution curve is used for fitting a linear trend line (Fig. 3).

Table 3 Classification of the hydrogeological properties of the hard rocks in North Rhine-Westphalia and the associated BFI values obtained by calibration Hydrogeological class

Permeability

ks value (m/s)

Baseflow Indices

I II III IV V VI VII

Very high High Medium Moderate Low Very low Extremely low

>10−2 >10−3 >10−4 >10−5 >10−7 >10−9