Estimation of flow parameters applying hydrogeological

Transcription

1 FRIEND: Flow Regimes from International Experimental and Network Data (Proceedings of the Braunschweii Conference, October 1993). IAHS Publ. no. 221, Estimation of flow parameters applying hydrogeological area information S. DEMUTH & I. HAGEMANN Department of Hydrology, University of Freiburg, Werderring 4, D Freiberg, Germany Abstract In the past years many researchers have stressed the important role of geology on river flows and have pointed out in particular the problem of developing a numerical index of geology. The objective of this paper is to present an approach to developing a geological index. The method is based on a classification scheme including both basin geology and hydrogeological data such as daily available water yield and groundwater capacity. The resulting hydrogeological classes (HG-classes) are linked to the recession constant by a multiple regression analysis, in which the recession constant has been calculated by a simple linear exponential model. Based on a set of 57 catchments within the county of Baden-Wurttemberg in the Federal Republic of Germany, the newly established hydrogeological index (GEO) was successfully tested, together with other basin characteristics, in a multiple regression model estimating baseflow at the ungauged site. INTRODUCTION Variables describing geological conditions in a catchment are hard to establish and difficult to quantify. Until now, especially in connection with regionalization, this problem has not been solved. The difficulty is not only the lack of large scale, extensive, geological or hydrogeological maps, but also the problem of developing an index which describes the impact of geology on runoff. In the past decades several authors have pointed out the important role of basin geology in particular on low flow estimation, e.g. Farvolden (1963) for northern Nevada, Knisel (1963) for south-central United States, Narbe (1968) for east Germany, Weyer & Karrenberg (1970) for west Germany, Wright (1970) for Scotland, Grant (1971) for Northern Ireland, Wright (1974) for southeast England, Klassen et al. (1975) for New South Wales, Musiake et al. (1975) for Japan, Einsele (1978) for Germany, Gustard et al. (1993) for the UK, Toussaint (1981) for Hesse in Germany, Pereira & Keller (1982) for the Pre-Alps, Reed & Warne (1985) for the River Wye in Wales, Gustard et al. (1987) for Scotland, Wilcock & Hanna (1987) for Northern Ireland, Demuth (1989) for northwest Europe, Demuth (1994) for west Germany. Most of these studies investigated the influence of geology on low flows by first grouping the catchments according to their geology. In a further step the low flow behaviour has been compared for different geological conditions, either by simple correlations or graphically. However, most of the authors did not attempt to include geology as a basin characteristic in regression models. Only a few authors developed a geological index, which was with varying degrees of success included in regression models (Wright, 1970,1974; Pereira & Keller, 1982).

2 152 S. Demuth & I. Hagemann Wright (1970) outlines the necessity of considering a numerical system of describing the hydrogeological characteristics of each formation to assess the influence of geology on river flows. Thus, a geological index was calculated and used as a catchment characteristic together with runoff, slope and catchment area. Applying multiple regression analysis, an equation was developed for estimating lowest annual flows from these catchment characteristics. Several years later the geology index was modified for southeast England and used, as before, in regression analysis to estimate lowest annual flows. There, catchment size and geology were found to be the most dominant variables in explaining the variance of the low flow parameters (Wright, 1974). Pereira & Keller (1982) developed geological indices based on bedrock permeabilities. They were used as basin properties in multiple regression analysis and were found to be fundamental in explaining recession behaviour in particular. The present study describes a procedure which allows geology to be indexed using a composite numerical index. The study took place in the county of Baden-Wûrttemberg in the Federal Republic of Germany which is characterized by a wide variety of geological units. HYDROGEOLOGICAL CLASSIFICATION The development of a geological index was carried out in several steps. The first step involved the derivation of hydrogeological data such as daily available water yield, groundwater capacity, and groundwater location from hydrogeological maps in the scale 1: The hydrogeological maps of Baden-Wurttemberg show the distribution of 18 different hydrogeological associations, of which 14 associations occur in the considered basins. The proportional areas of the hydrogeological components for each catchment were derived manually by overlaying a digitized boundary map (scale 1: ) onto the hydrogeological map. In addition to the hydrogeological map, a 1: scale geological map was included in the next step. The geological formations present were then compared and combined with the 14 derived hydrogeological classes (HG1 to HG14). The resulting relationships between hydrogeology and geology are summarized in Table 1. Table 1 Relationship between hydrogeology and geology, obtained by comparison of catchment boundary maps with components boundaries. Geology Hydrogeology Quaternary HG1, HG2, HG3, HG6, HG9 Tertiary HG9, HG10 Jurassic: white HG8, HG14 brown HG4, HG7, HG11 black HG4, HG7, HG11 Triassic: Keuper HG4, HG7, HG11 Coquina HG5, HG8, HG14 New Red Sandstone HG12 Crystalline bedrock HG13

3 Estimation of flow parameters applying hydrogeological area information 153 White Jurassic limestone and coquina develop karst forms, and so the hydrogeological classes HG5, HG8 and HG14 consist mainly of karstic limestone. The combination scheme shown in Table 1 between the hydrogeological and the geological information produces the basis for the classification scheme of hydrogeology (Table 2). There, the hydrogeological classes are grouped in a horizontal direction according to the geological formation. Additionally the hydrogeological properties of each class are shown in a vertical direction. Thus a combination of both factors is obtained. The different hydrogeological classes are distributed unequally both in the catchments and over the entire study area. The numbers in brackets in Table 2 give percentage areas of HG-classes in the catchments considered. The whole area covered by the 58 catchments considered is km 2. Thus, for example the 1.03 % for HG2 indicates that this HG-class covers km 2 and HG13 with 25.12% covers km 2. DETERMINATION OF THE HYDROGEOLOGICAL INDEX The determination of a hydrogeological index is based on the relationship between the 14 hydrogeological classes and the recession constant. The recession constant was calculated for the summer and winter seasons separately, applying a simple exponential model (Demuth, 1989,1994). In a multiple regression model the hydrogeological classes Table 2 Hydrogeological classification. Daily available water yield Geological formation of the aquifer groundwater Groundwater location Quaternary Tertiary Jurassic white brown black Triassic Keuper coquina New Red Sandstone Bedrock > m 3 high HG1 (0.06) < m 3 good HG2(1.03) < 1000 m 3 low HG3 (4.42) HG4(1.48) HG5 (5.81) <500m 3 scarce HG6(1.68) HG8 (1.68) HG7 (9.48) HG8 (1.68) < 100 m 3 HG9 (7.20) HG10 (0.75) HG11 (11.17)' HG12 (14.53) HG13 (25.12) 0-20 m 3 none HG14 (15.59 ) HG14 (15.59)

4 154 S. Demuth & I. Hagemann HG1 to HG14 were used as independent variables to estimate the recession constant (dependent variable). In order to include all 14 hydrogeological classes in the regression model a step-wise MAXR-procedure was applied. The dependent and independent variables have been log transformed and the multiple regression analysis resulted in two equation (equation (1) for the summer season and equation (2) for the winter season). The general form of the model is: K s = 0.84 (HGl + ir 2-67 (HG2 + 1) 0 ' 28 (HG3 + 1) 0 ' 16 (HG4 + I)" 019 (HG5 + l) 0-18 (HG6 +1) 005 (HG7 +l) 0-12 (i) (HG8 + 1) 0-39 (HG9 + 1) 0-18 (HG10 + 1) 008 (HG11+1) 0J0 (HG12 + 1) 0-15 (HG13 + 1) 0-17 (HG14 + 1) 0-16 K w = 0.87 (HG1 + 1) 0-72 (HG2 + 1) 0-05 (HG3 + 1) 0-12 (HG4 +1)' 0-09 (HG5 +1) 0 ' 15 (HG6 +l) 0-08 (HG7 +1) 0-08 (2) (HG8 + 1) 016 (HG9 + 1) 0-11 (HG10 + 1) 007 (HG11 + 1) 008 (HG12 + 1) 0-09 (HG13 + 1) 0 ' 11 (HG14 + 1) 0-12 The coefficient of determination for the model of the winter season (equation (2)) was increased by using the number of recession limbs as weighting factors (R 2 = 66%). The model for the summer season (equation (1)) resulted in a coefficient of determination of 70%, so that an inclusion of the number of recession limbs did not improve the estimate. The accuracy and validity of both models were evaluated by investigating the predictive power of the regression equations in estimating the recession constant. Thus, the statistical parameters were closely examined, the deviation of the estimates was considered, the computed residuals were plotted and tested for regional clusters. A data splitting test was introduced in order to test random effects and sample errors and to prove the general validity of the derived relationships. The results of these tests showed that the models chosen were adequate and accurate. The close relationship found between the recession constants and the HG-classes, as illustrated in the selected equations, reflects the important role of the recession constant as an index of catchment response. Based on the relationship between the recession constant Kand the HG-classes the hydrogeological index (GEO) was derived, simply by replacing the dependent variable K s by GEO s and K w by GEO w. By using this procedure equations (1) and (2) could be considered as models describing the composed index (GEO). The final models were used to calculate the hydrogeological index (GEO) for each catchment in Baden-Wurttemberg by inputting the associated HG-classes in the respective equation. For example, a catchment entirely on HG13 would have an estimated hydrogeological index (GEO) of for the summer seasons and for the winter seasons. APPLICATION OF THE HYDROGEOLOGICAL INDEX (GEO) TO ESTIMATE THE BASEFLOW The role of the newly established hydrogeological index (GEO) in regression models estimating baseflow was studied in the county of Baden-Wurttemberg in the Federal Republic of Germany (Demuth, 1994). The low flow parameters and the basin characteristics used in the multiple regression analysis were taken from a subset of 57 selected

5 Estimation of flow parameters applying hydrogeological area information 155 stations from the European Water Archive. The hydrogeological index (GEO) was calculated for the summer and the winter season (equations (1) and (2)). The catchment characteristics used as independent variables in the regression analysis are area (AREA), altitude of station (HTSTN), drainage density index (FOLIS), mainstream length (MSL), stream slope (SL1085), percentage of urbanization (URBAN), percentage of area covered by forest (FOREST), annual average rainfall (AAR) and two day rainfall with ten year return period (M10-2D) (Gustard et al. (1989), vol. II). The catchment characteristics quoted are summarized in Table 3. The catchment size of the 57 basins varies considerably, although the maximum size is limited to 500 km 2 due to the selection criteria. The mean catchment size is about 150 km 2. The percentage of forest also varies considerably, between 9% and 93% with a mean of 50%. The basins with a high percentage of forest are mainly located in the mountainous areas of the Black Forest (24 catchments) and the Swabian Alb (14 catchments). An attempt has been made to establish a relationship between the basin properties including the hydrogeological index (GEO) and the baseflow (BF) calculated according to Demuth (Demuth, 1994). The dependent and independent variables have been log transformed and the stepwise multiple regression analysis resulted in the following equation: BF = AREA 094 AAR 132 GEO 7 / 74 (3) The independent variables are significant at the 95% level and explain 86% of the variation of the baseflow. The resulting regression model (equation (3)) shows that the newly developed hydrogeological index (GEO f ) has a significant influence on the low flow parameter, baseflow, in the study area of Baden-Wiirttemberg. The calculated and estimated baseflows are compared in Fig. 1. In general the estimates of baseflow are satisfactory especially in the area of the Black Forest. Table 3 Statistical parameters of basin descriptive variables for 57 stations in Baden-Wurttemberg Catchment characteristics Catchment area [km 2 ] Drainage density index" Mainstream length [km] Altitude of gauging station [m] Percentage of urbanization Percentage of forest [%] Two day duration rainfall with return period 10 years [mm] Annual rainfall [mm] Geological index summer Geological index winter Acronym AREA FOLIS MSL HTSTN URBAN FOREST M10-2D AAR GEO, GEO, Minimum Mean Maximum a: Number of streams intersecting a circle of 30 cm diameter laid on a 1: scale topographical map.

6 156 S. Demuth & I. Hagemann 8 ôstl. L Fig. 1 Comparison of calculated and estimated baseflow in Baden-Wurttemberg (black bars according to Demuth, dotted bars estimated according to equation (3)). CONCLUSION This study shows the possibility of producing a hydrogeological index which can be used in statistical regional analysis. The hydrogeological associations which occurred in the 58 selected catchments in Baden-Wurttemberg werefirstcombined with areal geological information and a new classification scheme based on both hydrogeological and geological information was developed resulting in 14 different hydrogeological classes. This classification scheme is a dynamic scheme which can be adapted to other regions of interest, providing physical and geological properties are available. The relationship between the hydrogeological classes for different seasons resulted in a hydrogeological index (GEO) which has been included with other basin characteristics in a multiple regression model estimating baseflow. The introduction of the hydrogeological index (GEO) in the regression analysis was a success and stresses the important role of geology especially in low flow estimation. Further studies should concentrate on the validation of the classification scheme in other geographical regions. Acknowledgements The research presented in this paper was carried out as a part of the German contribution to the FRIEND (Flow Regimes from International Experimental and Network Data) project. Support was given by the National Committee of the Federal Republic of Germany for the International Hydrological Programme (IHP). The authors would like to thank the members of the FRIEND Low Flow Group for their many valuable discussions.

7 REFERENCES Estimation of flow parameters applying hydrogeological area information 157 Demuth, S. (1989) Small research basin studies. In: Flow Regimes fromexperimentalandnetworkdata (FREND), vol. I: Hydrological Studies, Institute of Hydrology, Wallingford, Oxfordshire, UK. Demuth, S. (1994)MethodischeUntersuchungenzum Niedrigwasser und Entwicklung regionaler Ubertragungsfunktionen in West-Europa (Low flow studies and regionalization in western Europe). Freiburger Schrifien zur Hydrologie 3 (in press). Einsele, G. (1978) Neubildung und Abflufl von Grundwasser in verschiedenen geologisch definierten Landschaftstypen (Recharge and discharge of groundwater in different geological areas). DVWK 10, Hydrologie Fortbildungskurs, Vortrag 13. Karlsruhe. Farvolden, R. N. (1963) Geologic controls on groundwater storage and baseflow. J. Hydro!. (NZ) 1, Grant, P. J. (1971) Low flow characteristics on three rock types of the east coast, and the translation of some representative basin data. J. Hydro!. (NZ) 10(1), Gustard, A., Marshall, D. C. W. & Sutcliffe, M. F. (1987) Low flow estimation in Scotland. Institute of Hydrology, Wallingford, Low Flow Studies Report no. 4. Gustard, A., Roald, L. A., Demuth, S., Lumadjeng, H., Gross, R. &. Arnell, N. W. (1989) Flow Regimes from Experimental and Network Data (FREND), vol. I: Hydrological Studies; vol. II: Hydrological Data. Institute of Hydrology, Wallingford, Oxfordshire, UK. Gustard, A., Bullock, A. & Dixon, J. M. (1992) Low flow estimation in the United Kingdom. Report 108, Institute of Hydrology, Wallingford, Oxfordshire, UK. Klaasen, B. E. & Pilgrim, D. H. (1975) Hydrograph recession constants for New South Wales streams. The Institution of Engineers, Australia, Civil Engineering Transactions, Knisel,W. G. (1963) Baseflow recession analysis for comparisonof drainage basin and geology./. Geophys Res.(&(\2), Musiake, K., Inokuti, S. &. Takahasi, Y. (1975) Dependence of low flow characteristics on basin geology in mountainous areas of Japan. In: The Hydrological Characteristics ofriver Basins (Proc. Tokyo Symp., December 1975), IAHSPubl.no Narbe, S. (1968) The discharge recession as criterion on the retention capacity of basins. In: Hydrological Aspects of the Utilisation of Water, IAHS Publ. no. 76. Pereira, L. S. & Keller, H. M. (1982) Recession characterization of small mountain basins, derivation of master recession curves and optimization of recession parameters. In: Hydrological Aspects of Alpine and High Mountain Areas (Proc. Exeter Symp., July 1982), IAHS Publ. no Reed, D. W. & Warne, D. W. (1985) Low flow forecasting to aid regulation of the river Wye. Report to Welsh Water Southeastern Division. Toussaint, B. (1981) Ermittlung der Leerlaufkoeffizienten nach Maillet und des effektiv nutzbaren Gesteinshohlraumes in hessischenfluftgebietendurchauswertungderabfliisseimtrockenjahr 1976 (Evaluationof the recession coefficients according to the method of Maillet and of the groundwater storage volume in rocks in Hessian River catchment areas by analysis of runoff in the dry weather year 1976). Deutsche Gewasserkundliche Mitteilungen 25(3/4), Weyer, K. U. & Karrenberg, H. (1970) Influence of fractured rocks on the recession curve in limited catchment areas in hill country: a result of regional research and a first evaluationof runoff at hydrogeological experimental basins./. Hydrol. (NZ) 9(2), Wilcock, D. N. & Hanna, J. E. (1987) Derivationof flow duration curves in Northern Ireland. Proc. Instn Civ. Engrs, Part 2,83, Wright, C. E. (1970) Catchment characteristics influencing low flow. Wat. Wat. Eng, Wright, C. E. (1974) The influenceof catchment characteristicsupon low flows in South-East England. Water Services, July 1974,

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