PyCatch: component based hydrological catchment modelling

Noemí Lana-Renault, D. Karssenberg

Abstract


Dynamic numerical models are powerful tools for representing and studying environmental processes through time. Usually they are constructed with environmental modelling languages, which are high-level programming languages that operate at the level of thinking of the scientists. In this paper we present PyCatch, a set of components for process-based dynamic hydrological modelling at the catchment scale, built within the PCRaster Python framework. PCRaster Python is a programming tool based on Python, an easy-to-learn programming language, to which components of the PCRaster software have been added. In its current version, PyCatch simulates the processes of interception, evapotranspiration, surface storage, infiltration, subsurface and overland flow. The model represents those hydrological processes as a series of interconnected stores, and it is structured in such a way that the exchange of water fluxes between the stores is easily performed. The modular structure of PyCatch makes it easy to replace or adapt components (such as a snow melt component or a soil erosion and sediment transport component) according to the aim of the study.


Keywords


dynamic modelling, environmental modelling language, PCRaster Python, hydrological model,

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References


Allen, R.G., Pereira, L.S., Raes, D., Smith, M. 1998. Crop evapotranspiration. FAO Irrigation and Drainage Paper No. 56, FAO, Roma.

Beck, M.B., Jakeman, A.J., Mcaleer, M.J. 1993. Construction and evaluation of models of environmental systems. In Modelling Change in Environmental Systems, M.B. Beck, A.J.

Jakeman, M.J. McAleer (Eds.), Wiley, New York, pp. 3-35.

Burrough, P.A., McDonnell, R.A. 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford.

Bracken, L.J., Croke, J. 2007. The concept of hydrological connectivity and its contribution to understanding runoff-dominated geomorphic systems. Hydrological Processes 21, 1749-1763.

Brolsma R.J., Karssenberg, D., Bierkens, M.F.P. 2010. Vegetation competition model for water and light limitation. I: Model description, one-dimensional competition and the influence of groundwater. Ecological Modelling 221, 1348-1363.

Chow, V.T., Maidment D.R., Mays, L.W. 1988. Applied Hydrology. McGraw-Hill, New York.

Dam, O. 2000. Modelling incoming Potential Radiation on a land surface with PCRaster: POTRAD5.MOD manual. Utrecht University, Utrecht, 6 pp.

Feddes, R.A., Kowalik, P., Jaradny, H. 1978. Simulation of field water use and crop yield. Simulation Monographs, Pudoc, Wageningen, 189 pp.

Karssenberg, D., 2002. The value of environmental modelling languages for building distributed hydrological models. Hydrological Processes 16, 2751-2766.

Karssenberg, D., De Jong, K. 2005a. Dynamic environmental modelling in GIS: 1. Modelling in three spatial dimensions. International Journal of Geographical Information Science 19, 559-579.

Karssenberg, D., De Jong, K., 2005b. Dynamic environmental modelling in GIS: 2. Modelling error propagation. International Journal of Geographical Information Science 19, 623-637.

Karssenberg, D., De Jong, K., Van der Kwast, J. 2007. Modelling landscape dynamics with Python. International Journal of Geographical Information Science 21, 483-495.

Mulligan, N., Wainwright, J. 2004. Modelling and model building. In Environmental Modelling: Finding Simplicity in Complexity, N. Mulligan, J. Wainwright (eds.), John Wiley, Chichester, 430 pp.

PCRaster, January 2013. PCRaster internet site. Available online at: http://www.pcraster.eu/.

Pebesma, E.J., De Jong, K., Briggs, D. 2007. Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: an air quality example. International Journal of Geographical Information Science 21, 515-527.

Python, January 2013. Python Programming Language internet site. Available online at: http:// www.python.org.

Schimtz, O., Karssenberg, D. 2009. Framework for Spatio-Temporal Modelling: Supporting Deterministic and Stochastic Modelling, Data Assimilation and Model Calibration. Faculty of Geosciences, Utrecht University, Utrecht, 24 pp.

Van Deursen, W.P.A. 1995. Geographical Information Systems and Dynamic Models. Koninklijk Nederlands Aardrijkskundig Genootschap/Faculteit Ruimtelijke Wetenschappen. Universiteit Utrecht, Utrecht.

Wesseling, C.G., Karssenberg, D., van Deursen, W.P.A., Burrough, P.A. 1996. Integrating dynamic environmental models in GIS: the development of a Dynamic Modelling language. Transactions in GIS 1, 40-48.




DOI: https://doi.org/10.18172/cig.1993

© Universidad de La Rioja, 2013

ISSN 0211-6820

EISSN 1697-9540