Analyzing the Operational Performance of the Hydrological Models in an Alpine Flood Forecasting System
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Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2012 Analyzing the operational performance of the hydrological models in an alpine flood forecasting system Achleitner, S ; Schöber, J ; Rinderer, M ; Leonhardt, G ; Schöberl, F ; Kirnbauer, R ; Schönlaub, H Abstract: During recent years a hybrid model has been set up for the operational forecasting of flood discharges in the 6750 km2 Tyrolean part of the River Inn catchment in Austria. The catchment can be characterized as a typical alpine area with large variations in altitude. The paper is focused on the error analysis of discharge forecasts of four main tributary catchments simulated with hydrological water balance models. The selected catchments cover an area of 2230 km2, where the non-glaciated and glaciated parts are modeled using the semi-distributed HQsim and the distributed model SES, respectively. The forecast errors are evaluated as a function of forecast lead time and forecasted discharge magnitude using 14 events from 2007 to 2010. The observed and forecasted precipitation inputs were obtained under operational conditions. The mean relative bias of the forecasted discharges revealed to be constant with regard to the forecast lead time, varying between 0.2 and 0.25 for the different catchments. The errors as a function of the forecasted discharge magnitude showed large errors at lower values of the forecast hydrographs, where errors decreased significantly at larger discharges being relevant in flood forecasting. DOI: https://doi.org/10.1016/j.jhydrol.2011.07.047 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-53444 Journal Article Accepted Version Originally published at: Achleitner, S; Schöber, J; Rinderer, M; Leonhardt, G; Schöberl, F; Kirnbauer, R; Schönlaub, H (2012). Analyzing the operational performance of the hydrological models in an alpine flood forecasting system. Journal of Hydrology, 412-3:90-100. DOI: https://doi.org/10.1016/j.jhydrol.2011.07.047 1 Analyzing the operational performance of the hydrological 2 models in an alpine flood forecasting system 3 4 S. Achleitner1, J. Schöber2,3, M. Rinderer4, G. Leonhardt5, F. Schöberl3, R. 5 Kirnbauer6, H. Schönlaub7 6 1 Unit of Hydraulic Engineering, University of Innsbruck, Austria 7 2 alpS – Centre for Climate Change Adaptation Technologies, Innsbruck, Austria 8 3 Institute of Geography, University of Innsbruck, Austria 9 4 Hydrology and Climate Unit, Department of Geography, University of Zurich, 10 Switzerland 11 5 Unit of Environmental Engineering, University of Innsbruck, Austria 12 6 Institute of Hydraulic Engineering and Water Resources Management, Vienna 13 University of Technology, Austria 14 7 TIWAG – Tiroler Wasserkraft AG, Austria 15 16 Correspondence to: Stefan Achleitner (Unit of Hydraulic Engineering, University of 17 Innsbruck, Austria, Technikerstr. 13, 6020 Innsbruck, [email protected]) 18 19 Abstract 20 During recent years a hybrid model has been set up for the operational forecasting of 21 flood discharges in the 6750 km2 Tyrolean part of the River Inn catchment in Austria. 22 The catchment can be characterized as a typical alpine area with large variations in 23 altitude. The paper is focused on the error analysis of discharge forecasts of four 24 main tributary catchments simulated with hydrological water balance models. The 25 selected catchments cover an area of 2230 km2, where the non-glaciated and 26 glaciated parts are modeled using the semi-distributed HQsim and the distributed 27 model SES, respectively. 1 1 2 The forecast errors are evaluated as a function of forecast lead time and forecasted 3 discharge magnitude using 14 events from 2007 to 2010. The observed and 4 forecasted precipitation inputs were obtained under operational conditions. The mean 5 relative bias of the forecasted discharges revealed to be constant with regard to the 6 forecast lead time, varying between 0.2 and 0.25 for the different catchments. The 7 errors as a function of the forecasted discharge magnitude showed large errors at 8 lower values of the forecast hydrographs, where errors decreased significantly at 9 larger discharges being relevant in flood forecasting. 10 11 1 Introduction 12 The work presented in this paper deals with the evaluation of the flood forecasting 13 performance of four representative tributaries discharging to the River Inn in Tyrol (a 14 western province of Austria). These catchments are part of the flood forecasting 15 system “HoPI” (Hochwasserprognose für den Tiroler Inn). The total River Inn 16 catchment covers an area of 6750 km2 along the 200 km-long stretch of the river Inn 17 between the Engadin (Switzerland) and Bavaria (Germany). The selected 18 hydrologically modelled tributary catchments account for 2230 km2 (33%) of the total 19 catchment. 20 Runoff from tributaries to the River Inn is dominated by snow melt and ice melt from 21 glaciated parts of the basin. Therefore the hydrological models of the tributaries are, 22 depending on the catchment, composed of (1) the Snow- and Icemelt Model SES for 23 the hydrology of glaciated catchments (Asztalos et al., 2007; Blöschl et al., 1991a; 24 Blöschl et al., 1991b; Schöber et al., 2010a) and (2) HQsim for the hydrology of non- 25 glaciated catchments or catchment parts (Achleitner et al., 2009; Kleindienst, 1996). 26 The River Inn itself is modeled with the FluxDSS/DESIGNER/FLORIS2000 (Reichel et al., 27 2000) a 1D hydraulic model. Forecasted discharges downstream are provided as 28 input to the forecast model for Bavaria, Germany (Ehret et al., 2009). 29 The HQsim and SES hydrological models are semi-distributed and distributed water- 30 balance models, respectively. Other examples of forecasting systems in alpine area 31 using water balance models include Jasper et al. (2002) or Verbunt et al. (2007), to 2 1 list two Swiss examples, and the Kamp model in Lower Austria (Blöschl et al., 2008). 2 The use of water balance models is a vital part, since the continuously obtained 3 system states (e.g. soil saturation, snow cover) have a significant impact on the 4 runoff formation (Marchi et al., 2010; Schöber et al., 2010b). 5 In the evaluation of the flood forecasting performance of past flood events the 6 meteorological input uncertainty is thereby considered to be the largest source of 7 uncertainty (Rossa et al., 2010a). For the continuous simulations hourly 8 meteorological station data provided in real time was utilized. For the forecast period, 9 the results of the meteorological forecast tool INCA (Integrated Nowcasting through 10 Comprehensive Analysis) (Haiden et al., 2010) is used as primary input. All input 11 data used in the investigation remained as provided under operational conditions, 12 having the full spectrum of possible errors included. The focus was on the 13 quantification of errors in the produced discharge forecast. The errors were evaluated 14 as a function of the forecast lead time and the forecasted discharge magnitude. 15 16 2 Methods 17 2.1 Model description 18 2.1.1 Catchments and meteorological Data 19 For this study, we analyzed four different tributary catchments which are 20 representative for the main landscape types of the Inn catchment. Figure 1 provides 21 an overview on the location and extend of the four from totally 49 tributary 22 catchments. The difference in elevation of the model area ranges from 3762 m to a 23 minimum elevation of 500 m at the boarder to Germany. In 2009 the Austrian Inn 24 catchment had a glaciated area of roughly 200 km² (estimated from a Landsat Image 25 of August 2009). The flow from the glaciated headwaters of the Inn catchment is 26 modeled using SES model (see details below). Two of the considered tributaries 27 (Ötztal and Sanna) are influenced by glaciated headwaters (see Figure 1). The 28 largest tributary, Ötztaler Ache, has four different SES model-areas included, and the 29 runoff of each model serves as input for the routing routine of the HQsim model (see 30 details below). 3 1 2 3 Figure 1: Total catchment of the Tyrolean River Inn with its tributary caatchments. The four 4 catchments assessed in this paper are marked in green (non-glaciated parts) and gray 5 (glaciated parts) 6 The main reason for the selection of the model areas was the availability of gauge 7 measurements (see Figure 1) and the location of the catchments – one of the 8 selected watersheds is located in thhe western part of the country, two are located 9 south of the Inn valley and one is situated north of the Inn valley. Table 1 10 summarizes the main catchment feattures including the estimated rranges of rainfall- 11 runoff concentration times during obseerved medium and large stormm events. 12 Table 1: Characteristics of the investigated tributary catchments Area Elevation [m.a.s.l] Glaciation tc [km2] min max [km2] [%] [h] Brandenberger Ache 280 508 2250 0 0 7-14 Brixentaler Ache 330 500 2466 0 0 3-8 Sanna 727 769 3397 9 1,2 3-8 Oetztaler Ache 893 673 3762 90 10 5-15 13 tc…Concentration time 14 Catchments in the north-western and south-eastern parts of the Inn catchment are 15 affected differently by the predominantly western frontal weather system. 16 Comparable meteorological conditions can be expected for the catchment areas of 17 the Brandenberger Ache and the Brixentaler Ache, and differences in runoff may be 18 due to differing geology. In contrast, the glaciated Sanna or Ötztaler Ache 4 1 catchments show differing runoff regimes and, consequently, different types of 2 floods. 3 In the operational mode, the flood warning system is generating forecasts on an 4 hourly basis using remotely transmitted meteorological and hydrological data.