The Hydrological Predictions for the Environment (HYPE) model is a semi-distributed, physically based catchment model, which simulates water flow and substances on their way from precipitation through different storage compartments and fluxes to the sea (Lindström et al., 2010).

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 change assessments with high spatial resolution, also for ungauged conditions, making use of new technology and many different data sources. The HYPE model has been applied operationally in Sweden since 2008.

The HYPE code (Lindström et al., 2010) is physically based and distributed when describing hydrological processes in different subbasins, although the algorithms are not purely based on physical laws but of more conceptual nature. 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 often resulting in further code development to address various hydro-climatic features. HYPE results for Sweden are available for inspection and download at vattenwebb and world-wide on our HYPEweb applications’s page. The source code was released for open access in 2011 and both code and model set-up is further described on our open source model page.

Historical evolution of the HYPE model
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. Pechlivanidis and Arheimer, 2015; Andersson et al., 2017). Model picture from Hundecha et al., 2016.