Integrated research on the hydrological process dynamics from the Wilde Gera catchment in Germany

Transcription

1 Integrated research on the hydrological process dynamics from the Wilde Gera catchment in Germany Peter Krause and Wolfgang-Albert Flügel 1 Abstract The Wilde Gera is an interflow and snow melt dominated highland headwater catchment in the Thuringian Forest in eastern Germany. Inside the basin the scientific test sites Schmücker Graben and Steinbach are located in which hydrological research is carried out since more than forty years. Due to the long ranging research a comprehensive amount of data for hydrological system analysis, process research is available and was used for hydrological modelling with various models in the last ten years. These modelling results revealed important insights into the runoff generation processes, which have been further investigated and validated with the help of the distributed hydrological modelling system J2 recently. The model was applied in different spatial resolution to investigate the influence on model performance as well as on data, parameter and conceptual uncertainties and sensitivities. J2 was calibrated with discharge time series at the basin s outlet, and multi response validation was carried out by means of time series from recording gauges inside the catchment and additional hydrometeorological data sets, e.g. snow water equivalent and groundwater levels. The findings underpinned the dominant importance of the fast subsurface runoff component for the catchment s runoff generation. They, however clearly indicated the importance of research issues concerning the controlling function of periglacial hillslope sediments and their internal structure on the hydrological process dynamics still unresolved in a regional context. The paper is presenting first results from this research effort and the application of the J2 hydrological modelling system. Special emphasis will be given on model and parameter uncertainties and the definition of interdisciplinary research interfaces for a better understanding of the hydrogeomorphic process dynamics controlling the runoff generation in highland headwater catchments. 2 Introduction As a first approach for an interdisciplinary research project under preparation the J2 model was set up and calibrated on existing stream flow records as a baseline for the further research. For this purpose three different distributions (25, 614 and 3 HRUs) were used. The existing time series ( to ) was split into a calibration ( ) and a validation ( ) period. Model calibration was only performed on the runoff at the catchments outlet. Additional data was used later to check if the internal process representation works correctly. The best results were obtained with the distribution of 614 HRUs which represents a trade-off between model performance and the representation of the basin s variability. This paper will only present the results of this middle resolved distribution. 3 The J2 modelling system The development of the J2 modelling system started in 1997 as a PhD project for process oriented hydrological modelling in large river basins. The reason for the development of a new system was that the distribution concept of the Hydrological Response Units (Flügel 1995), which was selected for the project work, had not been adapted to large scale basins at these times. The model was successfully applied in three large subbasins (Mulde, Unstrut and Schwarze Elster) of the river Elbe in eastern Germany, including a study about landuse change on runoff generation (Krause, 21). A thorough review which was carried out after the first applications revealed that the J2 s systematic concept was not flexible enough to use the system in different environments or for different purposes or scales without further development. In particular, the separation between the system core and the process modules was not perfect in the first version. These limitations led to a new implementation of the whole system within the NetBeans [ environment in Java, which makes the J2 system independent and much more flexible. The precursor to the new development were the possibilities provided by the Modular Modelling System MMS (Leavesley et al. 1996) and the Object Modeling System OMS (Ahuja et al. 24) in such a way that the J2 can be places between these two modelling frameworks. On the one hand it was designed as a modelling framework for hydrological 1

2 purposes and is therefore much more constrained than the OMS, which is implementing a much wider perspective. On the other hand it is more open and flexible compared to the MMS, which was developed as a platform for the Precipitation Runoff Modeling System (PRMS) (Leavesley et al. 1983). For hydrological modelling a set of modules for data correction, radiation calculation, estimation of potential evapotranspiration, interception, snow, soil water, groundwater and flood routing have been implemented. The process description ranges from physically based (e.g. ETP according to Penman- Monteith) to mostly conceptual approaches in the other modules. 4 The Wilde Gera catchment The catchment for the case study, the Wilde Gera, lies in the Thuringian forest in Germany. It is a typical midmountain basin with an area of 13 km². Elevation ranges between 98 m and 56 m. Landcover is dominated by coniferous forest and small parts with decidous forest and agricultural areas near the outlet. The geology is characterised by crystalline schists overlain by loamy to sandy soils. The hydrological regime can be classified as nivo-pluvial with a significant snow melt influence during spring. The mean annual rainfall in the hydrological period 199 to 2 was 146 mm. The potential ETP was calculated as 558 mm and the actual ETP ulated as 499 mm. The erved mean annual runoff is.36 m³/s which equals 883 mm/a. 4.1 Input data For the HRU delineation a digital elevation model with a resolution of 25x25 m was used, together with digital maps of the geology, soil-types and landcover derived from Landsat TM data. From the DEM slope and aspect were derived and classified into 5 slope and 3 aspect (N, S, W&E) classes whereas elevation was aggregated to 1 m bands for the HRU delineation. The HRU delineation resulted in 614 topological connected HRU polygons and 11 river reaches. Daily values of climatological driving data (temperature, relative humidity, wind-speed, sunshine hours) of three climate stations (one nearby and two others about 36 and 87 km away) and 14 precipitation gauges (within a diameter range of about 8 km) were available. The model was calibrated on the runoff measured at the basin s outlet. Moreover, records from two runoff gauges inside the basin, from a groundwater ervation well and snow water equivalent measurements from one climate station were available which were used for model validation only. Figure shows the Wilde Gera catchment, the stream network, the elevation, and the delineated HRU polygons. 4.2 Model setup and calibration For the model application climatological records from November 1989 to October 2 were used. The whole period was split up in a calibration part (11/1989 to 1/1993) and a validation part (11/ Figure 1: The Wilde Gera catchment. Elevation, generated stream network and HRU polygons. The red lines show the borders or the subbasins Schmücker Graben (right) and Steinbach (left) mentioned later in the text.

3 to 1/2) according to Klemes (1986). During preprocessing the measured precipitation was corrected by the method of Richter (1995) which leads to an increase of approximate 15% of the measured rainfall and the potential evapotranspiration was calculated according to Penman-Monteith (Monteith, 1975). For model calibration 19 of the tuneable parameters were used whereas the other 8 were left at their default values. The calibration process itself was carried out manually by trial and error tuning of single parameters. The calibration strategy which was followed was to first ulate a correct long term water balance then balance the distribution of the components and finally fine tune the peaks and low flow conditions. The calibration was ended when a satisfying value (~.8) for the Nash-Sutcliffe efficiency was achieved. 5 Model results 5.1 Calibration period The ulated and erved runoff during the calibration period is shown in Figure 2. Besides the ulated (red) and erved (blue) the mean precipitation of the catchment is shown in the upper panel and the differences between ulated and erved runoff in the lower panel. As can be seen from the plot the J2 modelling system was able to capture the overall hydrological dynamic of the basin. It predicts the high flow conditions, mostly from snow melt in spring as well as the low flow conditions during summer time. Some larger discrepancies can be seen in spring 1992 and In the first one snow melted to early, which leads to overprediction at the beginning and consequently underprediction at the end of the last snow melt period. In April 1993 an overpredicition can be erved which is not compensated by underprediction in the following time of the year. Figure 2: Model results of the calibration period. The upper panel shows precipitation, the middle panel the erved (blue) and ulated (red) runoff, and the lower panel the differences between erved and ulated runoff. 3

4 The performance measures (Table 1) underscore the satisfying model results during calibration. Shown are the Nash-Sutcliffe efficiency (Nash and Sutcliffe, 197) calculated with normal (Eff(Q)) and logarithmic (Eff(ln Q)) values, the coefficient of determination (r²), the index of agreement (IOA) (Willmot 1981, 1984), the gradient of the double sum regression between cumulated erved and ulated runoff (dsgrad), the absolute volume error (VE_abs) in m³/s and the relative volume (VE_rel) error in percent. Table 1: Performance measures of the calibration period. Year Eff (Q) Eff (lnq) r² IOA dsgrad VE_abs VE_rel % % % % total % The normal and log Nash-Sutcliffe efficiencies of the total period show nearly the same value, which indicates that the model is able to reproduce high flow and low flow conditions with the same quality. The relative volume error shows that the model is overpredicting by.43% only which is also a satisfying result. The performance measures of the single years show that all years have results comparable to the total period, despite the hydrological year of In this year the model underpredicts the discharge during spring which resulted in a volume error of -7.25% and lower efficiencies, in particular for the Nash-Sutcliffe efficiency and r² which react very sensitive on peak flow. 5.2 Validation period Validation of the model was performed with the time series of the hydrological years 1994 to 2. The results of the erved and ulated hydrographs, shown in Figure 3, are ilar in regards to the quality as the one from the calibration period. The differences in the lower panel indicate that the model tends to overpredict slightly. From the plot it can be stated, that the model was able to capture the overall flow dynamics also in the validation period quite well. The peak flows during spring time are all predicted, but sometimes on a higher level than the erved ones. This is most obvious at two flow peaks resulting from heavy rain (> 75 mm/d) on the partly snow-covered catchment in 1994 and The comparison of the performance measures of the validation period with those of the calibration period show, that the peak sensitive measures (Eff(Q) and r²) are only slightly reduced for validation. The more low flow sensitive measure Eff(lnQ) for the validation period is significantly lower in contrast to the calibration period. The reason is the overprediction of the runoff during low flow periods. This is also underlined by the higher volume errors. The relative volume error of the validation period (7% compared to the erved runoff) shows a much higher value than that of the calibration period which was below 1%. It has to be noted that the calibration period (mean annual precipitation 13 mm/a) was significantly drier than the validation period (mean annual precipitation 15 mm/a). Because of the drier conditions during calibration some of the parameters like e.g. the ETP reduction might not be tuned very well, which results in the overprediction of the wetter validation period. The performance measures of the validation period are shown in Table 2. The efficiencies of the single years are comparable to the ones of the whole period, despite the lower values of the year This year was relatively dry and therefore had a low variability which reduces the efficiency. In spring 1996 a recorded runoff was not ulated at all. This missing peak is responsible to a large extent for the low efficiency. Its timing indicates that the problems most likely stems from the snow module. In the hydrological year 1997 the lower effiencies are caused by the overprediction in Dec and the underpredicition of one of the peaks in spring. During the remaining time in 1996 and 1997 the model behaves as in the other years which can be seen from the differences plot of Figure 3. 4

5 Figure 3: Model results of the validation period. The upper panel shows precipitation, the middle panel the erved (blue) and ulated (red) runoff, and the lower panel the differences between erved and ulated runoff. Table 2: Performance measures of the validation period. Year Eff (Q) Eff (lnq) r² IOA dsgrad VE_abs VE_rel % % % % % % % total % 5.3 Multi response validation Closer attention was given to two subbasins within the catchment: The Schmücker Graben (Ac: 2.9 km²) and the Steinbach (Ac: 1.26 km²) outlined in red Figure. For both gauges daily runoff data was available for the same studied time period. Because the data from the gauges were not used during model calibration they can be considered as an independent data set for validation. The ulated and erved hydrographs of one year of the validation period from the two subbasins are shown in Figure 4. 5

6 ulated Schmücke erved Schmücke ulated Steinbach erved Steinbach Figure 4: Simulated (gray) and erved (black) runoff of one year of the validation period in the subbasins Schmücker Graben (left) and Steinbach (right). As shown in the runoff plots the model is able to reproduce the hydrological dynamics within the subbasins reasonably, but with an efficiency somewhat less than that obtained for the whole catchment. In the Schmücker Graben (Figure 4, left) some of the flood peaks of the validation period are overpredicted but most of them are underpredicted. The low flows are slightly overpredicted in particular at the end of the validation period. In the Steinbach (Figure 4, right) the low flows are reproduced well by the model throughout the whole period, but most of the peak flows are underpredicted, with only two or three exceptions. A look at the cumulated volume error shows that the runoff in the Schmücker Graben is overpredicted by 32% and is underpredicted by -19% in the Steinbach. As can be seen from Figure the two subbasins together drain the south-western part of the basin. Because they are neighbouring basins it is possible to lump their runoff together. The comparison of the sum of both ulated and erved runoff values results in an overall volume error of +12% which is very close to the VE of the whole catchment. Beside the two stream gauges a groundwater ervation well is situated where the groundwater head levels are recorded once a week. The comparison of the modelled groundwater storage with the ervations is shown in Figure 5. For the plot the head levels are transferred to relative changes and the ulated water storage was multiplied by 1, which can be understand as an effective porosity of the bedrock of GW-table. GW-table Figure 5: Observed groundwater levels (dots) and ulated groundwater storage (line). 6

7 As can be seen from the figure the model is able to reproduce the overall dynamics more or less correct but shows a way to large amplitude. The rising trend of the shown period is reproduced to some extend by the model readings but the dynamics of the single years could not be reproduced by the model. Additional validation of the internal model processes was performed with measured snow water equivalent (SWE) of the climate station Schmücke which lies just outside and south of the catchment. The measured snow-equivalent data was compared with the SWE ulated on a special HRU which was directly put to the location of the climate station. Figure 5 shows the ulated (blue line) and measured (red dots) SWE of the validation period. The figure shows that the model is able to reproduce the development of the snow pack of the catchment most of the times quite well. The rising limb of the snow accumulation period is captured mostly well despite in year The maximum SWE is reproduced well in all years despite 1996 and 1997 in which the modelled SWE is significantly lower. The importance of snow for the hydrological dynamic of the basin is highlighted by the fact, that the two years which showed the lowest efficiencies (1996, 1997) of the validation period are also those when severe problems in the snow modelling are obvious Figure 6: Simulated snow water equivalent on HRU No. 611 and measured SWE from the climate station Schmücke for the validation period. 7

8 6 Discussion The presented application of the J2 model in the Wilde Gera catchment shows good ulation results when only the runoff at the outlet is concerned. Here both the calibration and the validation period were ulated satisfactory. The low flow periods where captured well during the whole time, but some slight overprediction occurs at the end of the validation period. In single years and here in particular during spring time the model performance was not good at all. The comparison of the runoff with records from two gauges inside the basin showed that the internal process representation seems to be more or less correct but that the model performance is lower than in the whole basin. The fact that the lumped runoff of the two subbasins behaves better than the single recordings might indicate that the contributing areas of the two subbasins could not be delineated precisely with the available topographical data. Another explanation might be that maybe subsurface or base flow flows across the basin boundaries. Last but not least it has to be mentioned that the J2 was developed for meso to macro scale basins and is therefore not able to represent all processes which are dominant in micro scale basins like the two subbasins correctly. A reason for the poorer model performance in single years could be found in the snow module. The comparison with measured snow water equivalent showed that the model in some years did predict the snow accumulation and snow melt quite realistic but in other years of the validation period J2 was not able to reproduce the snow accumulation correctly. In particular in the years 1996 and 1997 a major underprediction of the snow pack occurs which lead to errors in the ulated hydrograph. From the comparison no clear reasons for the model errors could be derived. Both years showed medium snow packs comparable to 1995 which was modelled quite well. A thorough review of the precipitation and temperature distributions in those specific years will surely help to track down the problems. The validation of the model with erved groundwater head levels showed that the groundwater dynamics are only reproduced indefinite. The overall long-term trend could be reproduced by the model but the dynamic changes in single years were mostly not captured. Also the factor of.1 which was used to match the modelled groundwater storages with the head level changes seems to be very high. As the groundwater module of the J2 is just a ple storage concept with a linear outflow function it could be not expected that the groundwater head level could be reproduced perfect. Investigations concerning parameter and data uncertainties as well as sensitivities and their influences on the modelling results have not been carried out thoroughly yet but will also be addressed in forthcoming research. As a first check all parameters were changed by plus and minus 1% of their calibrated value and the change of the Nash-Sutcliffe efficiency was monitored. It showed that the parameters of the snow module and those which are responsible for distributing the HRU outflow to interflow or baseflow proofed to be the most sensitive ones. However the calibration showed that the model setup and its parameters are faced with the problem of equifinality (Beven and Binley, 1992). A deep ongoing review of the parameter ranges in different catchments combined with objective sensitivity and uncertainty analysis will provide more knowledge about the validity of specific parameter sets under different conditions and will help to improve the internal process structures. 7 Conclusion and outlook An application of the modular and object-oriented design of the J2 modelling system was shown for a small catchment in the Thuringian Forest in Germany. The catchment was distributed into 614 HRUs which were connected topologically. An 11 years long time period was split up into a calibration part of 4 years and a validation series of 7 years. In both periods the outflow of the basin could be ulated quite satisfying. The comparable performance measures between calibration and validation period showed that the model was calibrated well, despite a light trend to overprediction at the end of the validation period. One possible reason for this behaviour can be found in the different precipitation inputs of the two periods. During the calibration period the mean annual rainfall (MAR) was 13 mm and significantly lower then the MAR (15 mm) of the validation period. The relative small number of calibration parameters (27) compared to other models like e.g. WASIM-ETH (ca. 52) (Schulla et al. 1999), PRMS (ca. 6) (Leavesley et al. 1998) makes the calibration a fast and transparent process. The multi response validation with runoff data from two subbasins showed that the internal process description is valid to some degree, but not able to reproduce the runoff in such basins with the same 8

9 quality as at the outlet. Reasons for this can be seen in the delineation of the subbasins but also in scaling problems. Obviously the model is not able to reproduce all processes which are dominant on such small scale basins. The comparison with measured snow water equivalent data which was not used during calibration showed that the model was able to reproduce the snow accumulation and melt most of the time sufficient. In two years the model predict much less snow that it was monitored which leads to less good ulation results. As snow is a very dominant process in the Wilde Gera catchment an improvement of the snow module will help to increase the overall model performance. The comparison of measured groundwater level heads with the modelled groundwater storage showed that the groundwater dynamics were reproduced only vague. The overall experience of the presented application showed that it is possible to ulate the hydrological dynamics of the Wilde Gera catchment with the J2 model more or less correct. The more specific inspection of the single process representations showed that there are some open questions which have not been resolved yet. In particular the processes which are of increasing dominance in smaller scales are not very well represented by the model concept. To enhance the process understanding of runoff generation and concentration on various scales, ranging from single hillslopes up to mesoscale catchments, integrated and interdisciplinary research which comprises exploration methods and techniques as well as disciplinary expertise from hydrology, soil sciences, hydrogeology and geoinformatics is needed. To address this needs the authors recently initiated an interdisciplinary research project in which the Wilde Gera is one of the test basins. 8 References Ahuja, L.R., Ascough II, J.C., and David, O. 24: Developing natural resource models using the Object Modeling System: feasibility and challenges; in Krause, P., Kralisch, S., Flügel W.-A., Advances in Geosiences (in print) Beven, K. and Binley, A., 1992: The future of distributed models: model calibration and uncertainty prediction; Hydrol. Process. 6, Flügel, W.-A. (1995): Delineating Hydrological Response Units by Geographical Information System Analyses for Regional Hydrological Modelling using PRMS/MMS in the Drainage Basin of the River Bröl, Germany; Hydr. Proc. 9 (3/4) Klemes, V. 1986: Operational testing of hydrological ulation models; Hydrol. Sci. J. 31 (1), Krause, P. (21): Das hydrologische Modellsystem J2 Beschreibung und Anwendung in großen Flußgebieten (The hydrological modelling system J2 Documentation and application in large river basins); Schriften des Forschungszentrums Jülich, Reihe Umwelt/Environment, Band 29 Leavesley, G.H., Lichty, R.W., Troutman, B.M., Saindon, L.G., Precipitation Runoff Modeling System: User s manual, Water Resources Investigations , USGS, Denver, Colorado, Leavesley, G.H., Restrepo, P.J., Markstrom, S.L., Dixon, M., Stannard, L.G., The Modular Modeling System (MMS): User s manual, Open File Report , USGS, Denver, Colorado, Monteith, J.L., 1975; Vegetation and atmosphere, Vol.1, Principles, Academic Press, London Nash, J.E, Sutcliffe, J.V., 197: River flow forecasting through conceptual models; Part I-A discussion of principles; Journal of Hydrology; No. 1, Richter, D. 1995: Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Messfehlers des Hellmann-Niederschlagsmessers; Berichte des Deutschen Wetterdienstes, Nr. 194, Offenbach am Main Schulla, J., Jasper, K. 1999: Model description WaSIM-ETH; Institute of Geography ETH, Zürich Willmott, C.J., On the validation of models. Physical Geography 2, Willmott, C.J., On the evaluation of model performance in physical geography. In: Spatial Statistics and Models, G.L. Gaile, and C.J. Willmott (Editors). D. Reidel, Dordrecht,