Low Flow Discharge Analysis in Slovenia
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FRIEND: Flow Regimes from International Experimental and Network Data (Proceedings of the Braunschweig: Conference, October 1993). IAHS Publ. no. 221, 1994. 119 Low flow discharge analysis in Slovenia M. KOBOLD & M. BRILLY University of Ljubljana, FAGG — Hydraulics Division, Ljubljana, Slovenia Abstract A regional low flow analysis for Slovenia is outlined using the methodology developed by the FRIEND project. Catchment characteris tics are determined by a geographical information system. The low flow statistics, the relationships between low flow statistics and catchment characteristics and the regional equations for evaluating low flows at ungauged catchments are presented. The relationships between different durations are analysed using the mean annual ten day minimum as a key variable. The accuracy of analysis is checked for 11 catchments. INTRODUCTION The region of Slovenia is very varied with influences from the Mediterranean, Pannonian lowland and Alps. Forty percent of its area is composed of limestone as karst land. Slovenia is 20 250 km2 in area and has a lot of water sources and a varied flow regime. The average annual rainfall is 1500 mm and the average annual runoff is 1000 mm. The same environmental concerns exist in protecting the flow regime and water quality. The increasing demand on water resources inevitably leads to a better understanding of low flows and processes. The regional low flow analysis in Slovenia used the methodology described in Regional Low Flow Studies (Gustard et al., 1989). In these studies relationships between flow statistics and catchment characteristics are developed in order to evaluate the main controlling influences on low flows. It also aims to provide methods of estimating low flows at ungauged sites, and regression relationships are presented between single flow statistics, such as the mean annual minimum and the 95 percentile discharge for the flow duration curve, and basin characteristics, such as area, mean annual rainfall and soil type. The relationship between low flows of different durations and different frequencies is also discussed. This method of analysis of low flows was followed in this paper to enable comparison of results. A regression analysis, varying the number of parameters is summarized by Radie (Radie, 1992) with some new accesses (Brilly & Kobold, 1993). The main emphasis was on the catchment geology because the minimum flows are a result of the outflow of groundwater and are hence dependent on catchment geology. CATCHMENT SELECTION AND DETERMINATION OF PARAMETERS The 11 catchments shown in Fig. 1 were selected with areas from 100 to 500 km2. This number is small for statistical analysis, but no more stations with adequate data were available for the preliminary study. The representative stations each have at least 30 years of daily discharge data. Catchment characteristics such as catchment area (AREA), 120 M. Kobold & M. Brilly Fig. 1 Experimental catchments of Slovenia. annual average rainfall for catchment (AAR) and index of geology (GEO) are deter mined by SPANS geographical information system. Data with suitable attributes was digitized and included into a geographical information system (GIS). The determination of average attribute values for catchments was done by computer program. Catchment geology was indexed using values of the baseflow index. The following values for the different types of geology were assumed: alluvium, limestone 0.85-0.95 sandstone, conglomerate, dolomite 0.70-0.80 sandstone and marl 0.50-0.70 marl and clay 0.30-0.50 Using these classes the index of geology for each catchment was determined. Other characteristics such as the length of the main river (MSL), the average slope of the main river (SL), the altitude of the representative gauging station of the catchment (HSTN), median altitude of the catchment (HMEAN) (Radie, 1992) and the average elevation difference (HU), the difference between the median altitude of the catchment and altitude of the gauging station (Brilly & Kobold, 1993) were also determined. The selected catchments with their characteristics are presented in Table 1. CALCULATION AND PRESENTATION OF LOW FLOW STATISTICS Low flow statistics were derived using procedures described in the Low Flow Studies Report (Institute of Hydrology, 1980). Computer programs were written to derive and present flow duration curves (Fig. 2) flow frequency curves (Fig. 3) and baseflow index. Other low flow statistics such as the average flow (ADF), 95 percentile flow (<295) for different durations, mean annual minima (MAM) for different durations, baseflow index Low flow discharge analysis in Slovenia 121 Table 1 List of selected experimental catchments of Slovenia with characteristics. Catchment Gauging Area Period MSL HSTN HMEAN SL HU AAR GEO station (km2) (km) (m) (m) (%) (m) (mm) Pesnica ZamuSani 477.8 1961-1990 58.8 202 270 0.41 68 960 0.46 Paka SoStanj 131.2 1956-1989 27.4 353 630 3.08 277 1210 0.49 Radovna Podhom 132.5 1954-1988 15.0 567 1080 3.54 513 2300 0.70 Soca Krsovec 157.2 1945-1989 19.9 404 1150 3.00 746 2700 0.72 Sora Suha 558.0 1945-1989 43.3 330 580 0.90 250 1850 0.50 Idrijca Hotescek 442.0 1948-1989 54.4 161 580 1.25 419 2250 0.61 Vipava Vipava 109.1 1961-1988 0.4 97 450 0.33 353 2100 0.67 Precna Precna 237.8 1953-1990 35.3 164 290 1.18 126 1190 0.61 Not.Reka Cerk.mlin 332.4 1952-1990 46.3 342 480 0.73 138 1625 0.48 Kolpa Petrina 438.0 1952-1989 26.0 220 470 1.11 250 1900 0.62 Lahinja Gradac 221.3 1952-1988 26.9 129 185 0.08 56 1350 0.64 (BFI) and the 50 percentile recession coefficient (REC50) were also calculated. All low flow statistics are presented in Table 2. The average flow is expressed in m3 s"1, the one day 95 percentile flow, 095(1), and mean annual 1 and 10 day minima, MAM(l) and MAM(10), are expressed in percentages of the average flow to give an easier comparison between catchments. The 10 day duration mean annual minimum (MAMSP) is given in m3 s-- 1 km"2. MAMCV is the coefficient of variation of MAMSP and ARCV the coefficient of variation of annual runoff. REGIONAL ANALYSIS OF LOW FLOW INDICES WITH CATCHMENT CHARACTERISTICS The main parameters affecting low flows were determined from the relationships between low flow indices and catchment characteristics. The results of the regression analysis were similar to those in the FRIEND study of low flows (Gustard et al., 1989). Preliminary regression analysis of the low flow indices with catchment characteristics showed that a logarithmic transformation was appropriate. The relationship between transforming variables is shown in the correlation matrix (Table 3). The correlation coefficients of two key statistics of low flow, <295(1) and MAM(10), with catchment characteristics are almost identical and therefore the regional equations derived from them will be similar. In terms of scale parameters, the catchment area (AREA) is poorly correlated with low flow indices, but the main stream length (MSL) does not show any correlation with low flow indices. The annual rainfall (AAR) and average elevation difference (HTJ) show considerably higher correlation coefficients with <295(1) and MAM(10). In the multivariate regression, the number of parameters was varied and the 122 M. Kobold & M. Brilly 1000: 100 Soca - Krsovec Idrijca - Hotescek Vfpava - Vipava 10 Kolpa - Petrina Uhinja- Gradac NDLReka-Cerk.mlin Radovna-Podhom Sora - Suha Paka - SostanJ Pesnica - Zamusani 5 10 50 80 95 Percentage of time flow exceeded Fig. 2 One day flow duration curves for selected catchments of Slovenia. following nonlinear model obtained: C d (295(1) = a*AREA* AAR GEO MSI/ Sl/fflF (1) By this method, a statistically optimal model with the greatest regression coefficient R and the minimal standard error of estimation was obtained. The regression models for £295(1) and MAM(10) with catchment characteristics are presented in Table 4 and Table 5 with R2 and SE for each model. The comparison of regression coefficients R and standard errors SE shows that the model with all parameters gives the highest R2, while the standard error is minimal for the model excluding main stream length and average slope of river. The regression equation for Slovenia is in the form: Low flow discharge analysis in Slovenia 123 (295(1) = 1.75*10^ AREA148 AAR"139 GEO348 MSL"008 SL"004 HU119 (2) We get the similar equation for the variable MAM(IO): MAM(IO) = 1.10*101 AREA140 AAR"134 GEO326 MSL"004 SL"010 HU118 (3) 60- H Soca - Krsovec • Idrljca - Hotescek o •sn- ca Vipava - Vipava (!) w Kolpa - Petrina c» m A Lahinja- Gradac •*• X Not.Reka - Cerk.mlin > 4(1- D -*• e a ag 30- *"-'+ t A 13 ;en 'Jm-fm t * Per 20- 10- 0- I 1 ! 1 1 1 1 -1.5 -1 -0.5 0 0.5 1 1.5 2.5 Weibull reduced variate 1.25 2 5 10 50 100 Return period (years) 70- X Precna - Precna w Radovna-Podhom 60- * Paka - Sostanj A Sora - Suha " Pesnlca-Zamusanl 1 50 XX % > s ° **°*w« X *xx 30- A A . , ***w.*t***, x x £ 20- ^IÏA 10- -I 1 1 1 1 1— —i— -1.5 -1 -0.5 0 0.5 1 1.5 2.5 Weibull reduced variate 1.25 2 5 10 50 100 Return period (years) Fig. 3 Ten day annual minimum series for selected catchments of Slovenia. 124 M. Kobold & M. Brilly Table 2 Calculated statistics of low flows. Gauging ADF ARCV BFI 295(1) MAM(l) MAM(10) MAMCV MAMSP REC50 station (m3 s"1) (%ADF) (%ADF) (%ADF) (m3 s"1) (km"2) Zamusani 5.50 0.399 0.42 15.2 12.7 14.6 0.438 0.0017 0.901 Sostanj 2.51 0.261 0.51 29.1 15.9 26.5 0.330 0.0051 0.885 Podhom 8.32 0.166 0.63 30.3 22.5 25.2 0.289 0.0158 0.944 Krsovec 12.12 0.228 0.62 28.1 21.7 23.4 0.270 0.0180 0.942 Suha 20.24 0.227 0.50 24.9 18.0 21.7 0.302 0.0078 0.920 Hotescek 24.50 0.195 0.44 26.4 20.7 23.1 0.256 0.0128 0.907 Vipava 6.94 0.149 0.40 23.2 18.1 19.7 0.262 0.0125 0.900 Precna 4.54 0.207 0.67 45.0 34.8 39.1 0.264 0.0075 0.955 Cerk.mlin 8.33 0.283 0.33 8.8 6.4 8.5 0.527 0.0021 0.868 Petrina 26.09 0.157 0.37 16.2 11.2 13.9 0.333 0.0077 0.894 Gradac 5.92 0.233 0.37 10.9 8.0 11.0 0.477 0.0029 0.887 Table 3 Correlation matrix for logarithmic transformation of low flow indices and catchment charac teristics.