Validation and Over-Parameterization—Experiences from Hydrological Modeling
Models that simulate environmental processes by quantifying fluxes and states vary largely in their complexity and number of parameters. Most models suffer from over-parameterization, meaning that the available information does not allow identification of all model parameters. Over-parameterization is a serious problem in environmental modeling, as it might imply that a model works well, but could do so for the wrong reasons. This can lead to unreliable results when the model is used to make predictions. Model testing, or model validation, is therefore crucial. Usually, in more complex models more, internal variables are explicitly simulated, and, thus, there are more opportunities for model testing against observations than is the case for simple models. Increasing model complexity, however, comes at the cost of more parameters, and therefore the risk for over-parameterization increases as well. In this chapter, we discuss different ways to validate models, which simulate hydrological processes at the catchment scale, and the balance between model testability and over-parameterization.
KeywordsModel validation Catchment modeling Model complexity Parameter identification
- Barnes, C. J., & Bonell, M. (1996). Application of the Unit hydrograph techniques to solute transport in catchments. Hydrological Processes, 10(6), 793–802. https://doi.org/10.1002/(SICI)1099-1085(199606)10:6%3c793:AID-HYP372%3e3.0.CO;2-K.CrossRefGoogle Scholar
- Bathurst, J., Ewen, J, Parkin, G, O’Connell, P., & Cooper, J. (2004). Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses. Journal of Hydrology, 287(1–4), 74–94. https://doi.org/10.1016/j.jhydrol.2003.09.021.CrossRefGoogle Scholar
- Camporese, M., Paniconi, C., Putti, M., & Orlandini, S. (2010). Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resources Research, 46(2). https://doi.org/10.1029/2008wr007536.
- Ebel, B. A., Loague, K., Vanderkwaak, J. E., Dietrich, W. E., Montgomery, D. R., Torres, R., et al. (2007). Near-surface hydrologic response for a steep, unchanneled catchment near Coos Bay, Oregon: 2. Physics-based simulations. American Journal of Science, 307(4), 709–748. https://doi.org/10.2475/04.2007.03.CrossRefGoogle Scholar
- Euser, T., Winsemius, H. C., Hrachowitz, M., Fenicia, F., Uhlenbrook, S., & Savenije, H. H. G. (2013). A framework to assess the realism of model structures using hydrological signatures. Hydrology and Earth System Sciences, 17(5), 1893–1912. https://doi.org/10.5194/hess-17-1893-2013.CrossRefGoogle Scholar
- Fenicia, F., McDonnell, J. J., & Savenije, H. H. G. (2008). Learning from model improvement: On the contribution of complementary data to process understanding. Water Resources Research, 44 (December 2007), 1–13. https://doi.org/10.1029/2007wr006386.
- Finger, D., Vis, M. J. P., Huss, M., & Seibert, J. (2015). The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments. Water Resources Research, 51(1), 1939–1958. https://doi.org/10.1002/2014WR016259.CrossRefGoogle Scholar
- Glaser, B., Klaus, J., Frei, S., Frentress, J., Pfister, L., & Hopp, L. (2016). On the value of surface saturated area dynamics mapped with thermal infrared imagery for modeling the hillslope-riparian-stream continuum. Water Resources Research, 52(10), 8317–8342. https://doi.org/10.1002/2015WR018414.CrossRefGoogle Scholar
- Grayson, R. B., & Blöschl, G. (eds.) (2001). Spatial patterns in catchment hydrology: Observations and modelling. CUP Archive.Google Scholar
- Grayson, R. B., Moore, I. D., & McMahon, T. A. (1992). Physically based hydrologic modeling 2. Is the concept realistic. Water Resources Research, 26(10), 2659–2666.Google Scholar
- Hansen, A. L., Refsgaard, J. C., Christensen, B. S. B., & Jensen, K. H. (2013). Importance of including small-scale tile drain discharge in the calibration of a coupled groundwater-surface water catchment model. Water Resources Research, 49(1), 585–603. https://doi.org/10.1029/2011WR011783.CrossRefGoogle Scholar
- Ivanov, V. Y., Vivoni, E. R., Bras, R. L., & Entekhabi, D. (2004). Catchment hydrologic response with a fully distributed triangulated irregular network model. Water Resources Research, 40(11). https://doi.org/10.1029/2004wr003218.
- James, L. D., & Burges, S. J. (1982). Selection, calibration, and testing of hydrologic models, Hydrologic Modeling of Small Watersheds (C Haan, H Johnson, and D Brakensiek, eds). American Society of Agricultural Engineers: St. Joseph, Mich.Google Scholar
- Jones, J. P., Sudicky, E. A., Brookfield, A. E., & Park, Y. -J. (2006). An assessment of the tracer-based approach to quantifying groundwater contributions to streamflow. Water Resources Research, 42(2). https://doi.org/10.1029/2005wr004130.
- Jones, J. P., Sudicky, E. A., & McLaren, R. G. (2008). Application of a fully-integrated surface-subsurface flow model at the watershed-scale: A case study. Water Resources Research, 44(3). https://doi.org/10.1029/2006wr005603.
- Kirchner, J. W. (2006b). Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research, 42(3), W03S04 https://doi.org/10.1029/2005wr004362.
- Klemeš, V. (1997). Guest editorial: Of carts and horses in hydrologic modeling. Journal of Hydrologic Engineering, 2(2), 43–49. https://doi.org/10.1061/(ASCE)1084-0699(1997)2:2(43).CrossRefGoogle Scholar
- Laudan, L. (1990). Demystifying underdetermination. In C. W. Savage (Ed.), Scientific theories. Minnesota studies in the philosophy of science (pp. 267–297). Minneapolis: University of Minnesota Press. https://doi.org/10.1080/03634528709378635.
- Milzow, C., Krogh, P. E., & Bauer-Gottwein, P. (2011). Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment. Hydrology and Earth System Sciences, 15(6), 1729–1743. https://doi.org/10.5194/hess-15-1729-2011.CrossRefGoogle Scholar
- Mirus, B. B., Ebel, B. A., Heppner, C. S., Loague, K. (2011). Assessing the detail needed to capture rainfall-runoff dynamics with physics-based hydrologic response simulation. Water Resources Research 47(3). https://doi.org/10.1029/2010wr009906.
- Parkin, G., O’Donnell, G., Ewen, J., Bathurst, J. C., O’Connell, P. E., & Lavabre, J. (1996). Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment. Journal of Hydrology, 175(1–4), 595–613. https://doi.org/10.1016/s0022-1694(96)80027-8.CrossRefGoogle Scholar
- Pool, S., Vis, M. J. P., Knight, R. R., & Seibert, J. (2017). Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection. Hydrology and Earth System Sciences, 21(11), 5443–5457.Google Scholar
- Remondi, F., Kirchner, J. W., Burlando, P., & Fatichi, S. (2018). Water flux tracking with a distributed hydrological model to quantify controls on the spatiotemporal variability of transit time distributions. Water Resources Research, 3081–3099. https://doi.org/10.1002/2017wr021689.CrossRefGoogle Scholar
- Robinson, D. A., Campbell, C. S., Hopmans, J. W., Hornbuckle, B. K., Jones, S. B., Knight, R., et al. (2008). Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone Journal, 7(1), 358. https://doi.org/10.2136/vzj2007.0143.CrossRefGoogle Scholar
- Schulze-Makuch, D., Carlson, D. A., Cherkauer, D. S., & Malik, P. (1999). Scale dependency of hydraulic conductivity in heterogeneous media. Groundwater, 37(6), 904–919. https://doi.org/10.1111/j.1745-6584.1999.tb01190.x.CrossRefGoogle Scholar
- Smerdon, B. D., Mendoza, C. A., & Devito, K. J. (2007). Simulations of fully coupled lake-groundwater exchange in a subhumid climate with an integrated hydrologic model. Water Resources Research, 43(1). https://doi.org/10.1029/2006wr005137.
- Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., & Hunkeler, D. (in review). Your work is my boundary condition! Challenges and approaches for a closer collaboration between hydrologists and hydrogeologists. Revised version resubmitted to Journal of Hydrology.Google Scholar
- Turner, J. V., & Barnes, C. J. (1998). Modeling of isotope and hydrogeochemical responses in catchment hydrology. In C. Kendall & J. Mcdonnell (Eds.), Isotope Tracers in Catchment Hydrology (723–760). Elsevier.Google Scholar
- van Huijgevoort, M. H. J., Tetzlaff, D., Sutanudjaja, E. H., & Soulsby, C. (2016). Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model. Hydrological Processes, 30(25), 4761–4778. https://doi.org/10.1002/hyp.10902.CrossRefGoogle Scholar
- Viviroli, D., Mittelbach, H., Gurtz, J., & Weingartner, R. (2009). Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland—Part II: Parameter regionalisation and flood estimation results. Journal of Hydrology, 377(1–2), 208–225. https://doi.org/10.1016/j.jhydrol.2009.08.022.CrossRefGoogle Scholar
- von Freyberg, J., Studer, B., Rinderer, M., Kirchner, J. W. (2018). Studying catchment storm response using event- and pre-event-water volumes as fractions of precipitation rather than discharge. Hydrology and Earth System Sciences, 22, 5847–5865. https://doi.org/10.5194/hess-22-5847-2018.CrossRefGoogle Scholar
- Western, A. W., Grayson, R. B., & Green, T. R. (1999a). The tarrawarra project: High resolution spatial measurement, modelling and analysis of soil moisture and hydrological response. Hydrological Processes, 13(5), 633–652. isi:000079622700002.Google Scholar
- Zehe, E., et al. (2014). HESS Opinions: From response units to functional units: A thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments. Hydrology and Earth System Sciences, 18(11), 4635–4655. https://doi.org/10.5194/hess-18-4635-2014.CrossRefGoogle Scholar