About HYPE

The Hydrological Predictions for the Environment (HYPE) model was developed in the early 2000’s, when introducing the EU Water Framework Directive (WFD) in Sweden (Arheimer and Lindström, 2013). The aim was to provide water information to society for environmental and climate impact assessments with high spatial resolution, also for ungauged conditions, making use of new technology and many different data sources.

The HYPE code (Lindström et al., 2010) is distributed in describing hydrological processes, although the algorithms are not purely physically based. It is meant to be applied in a multi-basin manner to achieve high spatial distribution of flow paths in the landscape. It can be evaluated against point measurements in the river network and against spatially distributed observations, such as Earth Observations or interpolated products from in-situ monitoring.

Over time, the HYPE model has been applied in many different environments (Fig. 1) often resulting in further code development to address various hydro-climatic features. HYPE results are available for inspection and down-load at vattenwebb.smhi.se for Sweden and world-wide at hypeweb.smhi.se. The source code was released for open access in 2011.


Figure 1. The historical evolution of the HYPE model and its applications by SMHI. The HYPE model was first set-up for Sweden (Strömqvist et al., 2012), then the Baltic Sea region (Arheimer et al., 2012) and Europe (Donnelly et al., 2013; 2016), followed by other continents (e.g. Andersson et al., 2015; Pechlivanidis and Arheimer, 2015). Model picture from Hundecha et al., 2016.

Figure 2. Schematic structure of the HYPE code (modified from Arheimer et al., 2011).

Figure 2. Schematic structure of the HYPE code (modified from Arheimer et al., 2011).

In the HYPE code the algorithms for hydrology is embedded in the HYdrological Simulation System (Fig. 2), which is the infrastructural part of the model source code. HYSS handles the simulation instructions, provides the hydrological model with variables containing data on the model set-up, time and current forcing data (i.e. precipitation and air temperature) and writes the result files. It also provides variables for model state, model parameters and output, which are used and set by the model. In this way, the hydrological algorithms are separated from the model infrastructure in its own module. HYSS can thus be coupled to several different hydrological modules with different model structures and process descriptions.

The main motivation for developing the HYPE code was to harmonise the simulation of water balance and flow paths with substance transport and water quality. Previous model concepts at SMHI was using the HBV model (Bergström et al., 1976; Lindström et al., 1997) for nutrient modelling (e.g. Arheimer and Brandt, 1998; Arheimer and Wittgren, 2002; Arheimer et al. 2005), resulting in a very complex and ineffective code, which was trying to reconstruct flow paths from the lumped HBV concept.

With time the HYPE model proved to be efficient also for hydrological forecasting and is currently used operationally in the SMHI flood warning service and for hydrological short-term and seasonal forecasts. Extending the HYPE model to new purposes and geographical domains have resulted in an increased number of lines in the model source code, as well as numbers of FORTRAN files (Fig. 3). Each year 5-10 new model versions are released.

Figure 3. Historical evolution of the HYPE source code when introducing new functions.

Figure 3. Historical evolution of the HYPE source code when introducing new functions.


Below are some results from tests run for new HYPE versions. The tests were been run on a laptop with a linux server. None of these machines are configured for testing runtime of HYPE, other applications may run at the same time. The timing can also depend on the machine.
The run time is defined by:

  • the size of the model (number of subbasins, number of soil-land use-classes)
  • the simulation time
  • if substances are simulated in addition to flow
  • the number of outputs you want also have an influence.
  • The runtime (HYPE ver. 4.13.1) in seconds per subbasin, class and simulation years varies between 0.0002-0.0005 when running five different models of different size (with nutrients) on a laptop:

    Model #subbasins #classes #years #runtime (secs)
    1 43 24 11 6
    2 604 49 2 28
    3 1170 65 13 474
    4 36693 60 1 376-490 (depending on output amount)
    5 35408 75 1 561

    Read more: HYPE Documentation in Doxygen and wiki

    The HYPE code is mainly developed at the Hydrological Research unit of SMHI. We are happy to share experience and collaborate world-wide. Please, give us feedback and share your experience! Make your code extensions part of the official code. Join the Forum!

    Read more about us: Hydrological Research at SMHI



    Andersson, J.C.M., Pechlivanidis, I.G., Gustafsson, D., Donnelly, C., and Arheimer, B., 2015. Key factors for improving large-scale hydrological model performance. European Water 49:77-88.

    Arheimer, B. and Brandt, M., 1998. Modelling nitrogen transport and retention in the catchments of southern Sweden. Ambio 27(6):471-480.

    Arheimer, B. and Lindström, L., 2013. Implementing the EU Water Framework Directive in Sweden. Chapter 11.20 in: Bloeschl, G., Sivapalan, M., Wagener, T., Viglione, A. and Savenije, H. (Eds). Runoff Predictions in Ungauged Basins – Synthesis across Processes, Places and Scales. Cambridge University Press, Cambridge, UK. (p. 465) pp. 353-359.

    Arheimer, B. and Wittgren, H.B., 2002. Modelling Nitrogen Retention in Potential Wetlands at the Catchment Scale. Ecological Engineering 19(1):63-80.

    Arheimer, B., Dahné, J., Donnelly, C., Lindström, G. and Strömqvist, J., 2012.  Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin – influence of input-data quality and scale. Hydrology research 43(4):315-329.

    Arheimer, B., Löwgren, M., Pers, B.C. and Rosberg, J., 2005. Integrated catchment modeling for nutrient reduction: scenarios showing impacts, potential and cost of measures. Ambio 34(7):513-520.

    Arheimer, B., Wallman, P., Donnelly, C., Nyström, K. and Pers, C., 2011.  E-HypeWeb: Service for Water and Climate Information – and Future Hydrological Collaboration across Europe? In: J. Hřebíček, G. Schimak, and R. Denzer (Eds.): ISESS 2011, IFIP AICT 359: 657–666.

    Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Reports RHO, No. 7, Norrköping.

    Donnelly, C, Andersson, J.C.M. and Arheimer, B., 2016. Using flow signatures and catchment similarities to evaluate a multi-basin model (E-HYPE) across Europe. Hydr. Sciences Journal 61(2):255-273, doi: 10.1080/02626667.2015.1027710

    Donnelly, C., Arheimer, B., Capell, R., Dahné, J., and Strömqvist, J., 2013. Regional overview of nutrient load in Europe – challenges when using a large-scale model approach, E-HYPE. IAHS Publ. 361:49-58.

    Hundecha, Y., Arheimer, B., Donnelly, C., Pechlivanidis, I., 2016. A regional parameter estimation scheme for a pan-European multi-basin model. Journal of Hydrology: Regional Studies, Volume 6, June 2016, Pages 90-111. doi:10.1016/j.ejrh.2016.04.002

    Lindström, G., Johansson, B., Persson, M., Gardelin, M. and Bergström, S., 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 20:272-288.

    Lindström, G., Pers, C.P., Rosberg, R., Strömqvist, J., and Arheimer, B., 2010. Development and test of the HYPE (Hydrological Predictions for the Environment) model – A water quality model for different spatial scales. Hydrology Research 41.3-4:295-319.

    Pechlivanidis, I. G. and Arheimer, B., 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case, Hydrol. Earth Syst. Sci., 19, 4559-4579, doi:10.5194/hess-19-4559-2015.

    Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C. and Lindström, G., 2012. Water and nutrient predictions in ungauged basins – Set-up and evaluation of a model at the national scale. Hydrological Sciences Journal 57(2):229-247.