Conceptual model building inspired by field-mapped runoff generation mechanisms

Alberto Viglione 1 , Magdalena Rogger 1 , Herbert Pirkl 2 , Juraj Parajka 1  and Günter Blöschl 1
  • 1 Institute for Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Karlsplatz,, Vienna, Austria
  • 2 Technical Office for Geology Dr. Herbert Pirkl, Plenergasse,, Vienna, Austria


Since the beginning of hydrological research hydrologists have developed models that reflect their perception about how the catchments work and make use of the available information in the most efficient way. In this paper we develop hydrologic models based on field-mapped runoff generation mechanisms as identified by a geologist. For four different catchments in Austria, we identify four different lumped model structures and constrain their parameters based on the field-mapped information. In order to understand the usefulness of geologic information, we test their capability to predict river discharge in different cases: (i) without calibration and (ii) using the standard split-sample calibration/ validation procedure. All models are compared against each other. Results show that, when no calibration is involved, using the right model structure for the catchment of interest is valuable. A-priori information on model parameters does not always improve the results but allows for more realistic model parameters. When all parameters are calibrated to the discharge data, the different model structures do not matter, i.e., the differences can largely be compensated by the choice of parameters. When parameters are constrained based on field-mapped runoff generation mechanisms, the results are not better but more consistent between different calibration periods. Models selected by runoff generation mechanisms are expected to be more robust and more suitable for extrapolation to conditions outside the calibration range than models that are purely based on parameter calibration to runoff data.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Abbott, M., Bathurst, J., Cunge, J., O’Connell, P., Rasmussen, J., 1986. An introduction to the European Hydrological System - Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system. Journal of Hydrology, 87, 45-59.

  • Bai, Y., Wagener, T., Reed, P.M., 2009. A top-down framework for watershed model evaluation and selection under uncertainty. Environemental Modelling and Software, 24, 901-916.

  • Beven, K.J., 2001. How far can we go in distributed hydrological modelling? Hydrology and Earth System Sciences, 5, 1-12.

  • Beven, K.J., 2006. A manifesto for the equifinality thesis. Journal of Hydrology, 320, 1-2, 18-36. DOI: 10.1016/j.jhydrol.2005.07.007.

  • Blöschl, G., 2005. Rainfall-runoff modeling of ungauged catchments. In: Anderson, M.G. (Ed.): Encyclopedia of Hydrological Sciences. John Wiley & Sons, Chichester, pp. 2061-2080.

  • Blöschl, G., 2006. Hydrologic synthesis: Across processes, places, and scales. Water Resources Research, 42, 3, W03S02. DOI: 10.1029/2005WR004319. Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., Savenije, H.H., 2013. Runoff Prediction in Ungauged Basins - Synthesis across Processes, Places and Scales. Cambridge University Press, Cambridge, 484 p.

  • Caylor, K.K., D’Odorico, P., Rodriguez-Iturbe, I., 2006. On the ecohydrology of structurally heterogeneous semiarid landscapes. Water Resources Research, 42, 7. DOI: 10.1029/2005WR004683.

  • Ehret, U., Gupta, H.V., Sivapalan, M., Weijs, S.V., Schymanski, S.J., Blöschl, G., Gelfan, A.N., Harman, C., Kleidon, A., Bogaard, T.A., Wang, D., Wagener, T., Scherer, U., Zehe, E., Bierkens, M.F.P., Di Baldassarre, G., Parajka, J., van Beek, L.P.H., van Griensven, A., Westhoff, M.C., Winsemi

  • us, H.C., 2014. Advancing catchment hydrology to deal with predictions under change. Hydrology and Earth System Sciences, 18, 649-671. DOI: 10.5194/hess-18-649-2014.

  • Falkenmark, M., Chapman, T., 1989. Comparative Hydrology: An Ecological Approach to Land and Water Resources. The Unesco Press, Paris, 479 p.

  • Fenicia, F., Kavetski, D., Savenije, H.H., 2011. Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resources Research, 47, 13. DOI:10.1029/2010WR010174.

  • Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., Blöschl, G., 2012. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resources Research, 48, W04511. DOI: 10.1029/2011WR011509.

  • Grayson, R.B., Blöschl, G. (Eds.) 2000. Spatial Patterns in Catchment Hydrology: Observation and Modelling, Cambridge University Press, Cambridge, 404 p.

  • Gutknecht, D., Jolánkai, G., Skinner, K., 2008. Patterns and processes in the catchment. CAB International, Chapter 2, pp. 18-29.

  • He, X., Højberg, A.L., Jørgensen, F., Refsgaard, J.C., 2015. Assessing hydrological model predictive uncertainty using stochastically generated geological models. Hydrological Processes, 29, 19, 4293-4311. DOI: 10.1002/hyp.10488.

  • Hellebrand, H., Müller, C., Matgen, P., Fenicia, F., Savenije, H., 2011. A process proof test for model concepts: Modelling the meso-scale. Physics and Chemistry of the Earth, 36, 42-53.

  • Hogue, T.S., Bastidas, L.A., Gupta, H.V., Sorooshian, S., 2006. Evaluating model performance and parameter behavior for varying levels of land surface model complexity. Water Resources Research, 42, 8. DOI: 10.1029/2005WR004440.

  • Holländer, H.M., Blume, T., Bormann, H., Buytaert, W., Chirico, G.B., Exbrayat, J.-F., Gustafsson, D., Hölzel, H., Kraft, P., Stamm, C., Stoll, S., Blöschl, G., Flühler, H., 2009. Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data. Hydrology and Earth System Sciences, 13, 2069-2094. DOI: 10.5194/hess-13- 2069-2009.

  • Hrachowitz, M., Fovet, O., Ruiz, L., Euser, T., Gharari, S., Nijzink, R., Freer, J.E., Savenije, H.H.G., Gascuel-Odoux, C., 2014. Process consistency in models: The importance of system signatures, expert knowledge, and process complexity. Water Resources Research, 50, 9, 7445-7469. DOI: 10.1002/2014WR015484.

  • Kavetski, D., Kuczera, G., Franks, S.W., 2006a. Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42, 3, W03407. DOI: 10.1029/2005WR004368.

  • Kavetski, D., Kuczera, G., Franks, S.W., 2006b. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resources Research, 42, 3, W03408. DOI: 10.1029/2005WR004376.

  • Klemes, V., 1986. Operational testing of hydrological simulation models. Hydrological Sciences Journal - des Sciences Hydrologiques, 31, 1, 13-24. DOI: 10.1080/02626668609491024.

  • Merz, R., Blöschl, G., 2009. A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria, Water Resources Research, 45, W01405. DOI: 10.1029/2008WR007163.

  • Merz, R., Blöschl, G., Parajka, J., 2006. Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331, 3-4, 591-604. DOI: 10.1016/j.jhydrol.2006.06.008.

  • Milly, P.C.D., Dunne, K.A., 2002. Macroscale water fluxes 2. Water and energy supply control of their interannual variability. Water Resources Research, 38 10, 24-1-24-9. DOI: 10.1029/2001WR000760.

  • Mullen, K., Ardia, D., Gil, D., Windover, D., Cline, J., 2011. DEoptim: an R package for global optimization by differential evolution. Journal of Statistical Software, 40, 6, 1-26.

  • Müller, C., Hellebrand, H., Seeger, M., Schobel, S., 2009. Identification and regionalization of dominant runoff processes - a GIS-based and a statistical approach. Hydrology and Earth System Sciences, 13, 779-792.

  • Nester, T., Kirnbauer, R., Parajka, J., Blöschl, G., 2012. Evaluating the snow component of a flood forecasting model. Hydrology Research, 43, 6, 762-779. DOI: 10.2166/nh.2012.041.

  • Nijzink, R.C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H.H.G, Hrachowitz, M., 2016. The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models. Hydrology and Earth System Sciences, 20, 1151-1176. DOI: 10.5194/hess-20- 1151-2016.

  • Parajka, J., Merz, R., Blöschl, G., 2003. Estimation of daily potential evapotranspiration for regional water balance modeling in Austria. In: 11th. International Poster Day Transport of Water, Chemicals and Energy in the Soil - Crop Canopy - Atmosphere System. Slovak Academy of Sciences, Bratislava, pp. 299-306.

  • Pirkl, H., 2009. Hydrogeologische und geohydrologische Grundlagen für die ausgewählten Leiteinzugsgebiete - Unveröffentl. Bericht im Rahmen Projekt Hochwasser Tirol (HOWATI). Technical Report, Vienna.

  • Pirkl, H., 2012. Untergrundabhängige Abflussprozesse. Kartierung und Quantifizierung für das Bundesland Tirol. Flächendeckende Aufnahme Osttirols. Endbericht. Unveröffentl. Bericht, Technical Report, Vienna.

  • Rogger, M., Kohl, B., Pirkl, H., Viglione, A., Komma, J., Kirnbauer, R., Merz, R., Blöschl, G., 2012a. Runoff models and flood frequency statistics for design flood estimation in Austria - Do they tell a consistent story? Journal of Hydrology, 456-457, 30-43. DOI: 10.1016/j.jhydrol.2012.05.068.

  • Rogger, M., Pirkl, H., Viglione, A., Komma, J., Kohl, B., Kirnbauer, R., Merz, R., Blöschl, G., 2012b. Step changes in the flood frequency curve: Process controls. Water Resources Research, 48, W05544. DOI: 10.1029/2011WR011187.

  • Rosero, E., Yang, Z.-L., Wagener, T., Gulden, L.E., Yatheendradas, S., Niu, G.-Y., 2010. Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season. Journal of Geophysical Research- Atmospheres, 115, D3. DOI: 10.1029/2009JD012035.

  • Salinas, J.L., Kiss, A., Viglione, A., Viertl, R., Blöschl, G., 2016. A fuzzy Bayesian approach to flood frequency estimation with imprecise historical information. Water Resources Research, 52, 9, 6730-6750. DOI: 10.1002/2016WR019177.

  • Samuel, J.M., Sivapalan, M., Struthers, I., 2008. Diagnostic analysis of water balance variability: A comparative modeling study of catchments in Perth, Newcastle, and Darwin, Australia. Water Resources Research, 44, 6. DOI: 10.1029/2007WR006694.

  • Savenije, H.H.G., 2009. The art of hydrology. Hydrology and Earth System Sciences, 13, 157-161.

  • Savenije, H., 2010. Topography driven conceptual modelling (FLEX-Topo), Hydrology and Earth System Sciences, 14, 12, 2681-2692. DOI: 10.5194/hess-14-2681-2010, HESS Opinions.

  • Van den Bos, R., Hoffmann, L., Juilleret, J., Matgen, P., Pfister, L., 2006. Regional runoff prediction through aggregation of first-order hydrological process knowledge a case study, Hydrological Sciences - Journal - des Sciences Hydrologiques, 51, 1021-1038. van Werkhoven, K., Wagener, T., Reed, P., Tang, Y., 2008. Characterization of watershed model behavior across a hydroclimatic gradient. Water Resources Research, 44, W01429. DOI: 10.1029/2007WR006271.

  • van Werkhoven, K., Wagener, T., Reed, P., Tang, Y., 2009. Sensitivity- guided reduction of parametric dimensionality for multi- objective calibration of watershed models. Advances in Water Resources, 32, 8, 1154-1169. DOI: 10.1016/j.advwatres.2009.03.002.

  • Wagener, T., Sivapalan, M., Troch, P., Woods, R., 2007. Catchment Classification and Hydrologic Similarity, Geography Compass, 1, 4, 901-931. DOI: 10.1111/j.1749-8198.2007.00039.x.

  • Winter, T.C., 2001. The concept of hydrologic landscapes. Journal of the American Water Resources Association, 37, 2, 335-349. DOI: 10.1111/j.1752-1688.2001.tb00973.x.

  • Wolock, D.M., Winter, T.C., McMahon, G., 2004. Delineation and evaluation of hydrologic-landscape regions in the United States using geographic information system tools and multivariate statistical analyses. Environmental Management, 34, 1, S71-S88. DOI: 10.1007/s00267-003-5077-9.


Journal + Issues