Navigation: Home ›  The model ›  Components and Algorithms

Components and Algorithms

A detailed description of the components and algorithms implemented in WaSiM is deliberately omitted at this point. Please refer to the detailed WaSiM documentation.

model componentAlgorithm
Precipitation correctiontemperature and wind dependent correction after Sevruk (1986); Correction is done separately for rain and snow
Interpolation of meteorologic input data(1) inverse distance weighting (IDW) interpolation
(2) altitude dependent regression, using externally preprocessed parameter files
(3) combination of (1) und (2)
(4) interpolation with Thiessen polygones
(5) bilinear interpolation
(6) bilinear interpolation of gradients/lapse rates and residuals
(7) bi-cubic spline interpolation
(8) bi-cubic spline interpolation of gradients/lapse rates and residuals
(9) read grids according to the name in a grid list file
(10) altitude dependent regression of Stationdata directly in WaSiM (like method 1)
(11) regression and IDW from station data (equivalent to method 3
(12) Thiessen combined with given lapse rates
Regional superpositionAllows application of different interpolation methods for different regions or different parameters for identical methods but different regions or even the application of multiple interpolation methods for the same region (with weighted superposition)
Shading, slope and aspect dependent correction for direct radiation and temperatureMethods proposed in Oke (1987) and some internally developed algorithms (for T-correction)
Evapotranspiration(1) Penman-Monteith (Monteith & U., 1990), also for layered vegetation
(2) approach after Wendling (1975)
(3) approach after Hamon (1961)
(4) approach after Haude (1955)
Snow accumulation and melt(1) temperature index method (degree day)
(2) temperature wind index method
(3) simple energy balance method (Anderson 1973)
(4) extended energy balance method (Braun 1985)
Glacier melt and glacier runoff(1) temperature index method
(2) approach after Hock (1999) with radiation impact
Glacier dynamicGlatscherwachstum oder -Rückgang kann mit den Algorithmen nach Stahl (2008) modelliert werden
InterceptionIntegrated into multi layer vegetation model
Dynamic phenologydynamic computing of phenological phases by using one of four approaches (thermal time models and sequence models; also with regard to dormancy
Runoff generation(1) Topmodel approach after Beven and Kirkby (1979)
(2) implicit runoff generation with Richards (1931) approach, see following components of soil water and groundwater dynamic
Silting-upusing approaches that estimate the soil sealing dependent on soil grain sizes, kinetic energy of the rain, soil and crop type and other parameters
Soil water dynamicvertical water movement in the unsaturated zone of the soil based on the Richards-Equation (1931) with parameterization after van Genuchten (1980)
Heat transferFinite Differences Method;
implicit solution of the heat transport equation in the soil, 1D-vertical;
Including advection (heat transport by infiltrating water), Regarding freezing up and thawing of the soil including the impacts on the hydraulic properties of the soil
Groundwater dynamicFinite differences method;
iterative solution of flow and transport equation;
consideration of multiple aquifers (confined and unconfined)
Runoff concentration(1) Single linear reservoir series considering translation times (translation-retention approach)
(2) Kinematic wave apporoach for routing surface runoff from cell to cell, thus modelling runoff concentration for each sub basin physically based
runoff channel routingKinematic wave approach (Lighthill & Witham, 1955)
Tracer and substance transportConsideration of conservative (non-radiactive) and radioactive tracers, volatile and non-volatile tracers as well as salts (which can even fall out);
Calculation of (mixing-)concentrations in almost all sub models


back top home



new patch availabe:
WaSiM 9.10.01 -> More »

Third WaSiM User Conference in Munich, October 18-19 2017:
Announcement -> More »