Geoscience Reference
In-Depth Information
TOPMODEL Since 1974, many variants of TOPMODEL have been developed at Leeds, Lancaster and
elsewhere but never a “definitive” version. This has been quite intentional. TOPMODEL is not intended
to be a traditional model package but is more a collection of concepts that can be used where appropriate.
It is up to the user to verify that the assumptions made are appropriate. The version of the program
discussed in Box 6.1 is best suited to catchments with shallow soils and moderate topography which
do not suffer from excessively long dry periods. Ideally, predicted contributing areas should be checked
against what actually happens in the catchment (at least qualitatively).
There are a number of sources of TOPMODEL Software. The Lancaster University demonstration ver-
sion available at www.lec.lancs.ac.uk/research/catchment and aquatic processes/software.php was de-
scribed in some detail in the first edition of this topic. This program is intended as a demonstration version
of TOPMODEL for Windows (32 bit only) and has been developed from versions used for teaching pur-
poses. It includes an option for making Monte Carlo runs of the model that link to the Windows GLUE
software (see software for Chapter 7). A Windows digital terrain analysis package, DTM-Analysis, is
also available. The DTM-ANALYSIS program is used to derive a distribution of
ln
(
a/tanB
) values
from a regular raster grid of elevations for any catchment or subcatchment using the multiple direc-
tion flow algorithm of Quinn
et al.
(1995). Output from the program is a histogram of the distribution
of the
ln
(
a/tanB
) values and a map file of
ln
(
a/tanB
) values that can be used for map output in the
TOPMODEL program.
A version of the code for TOPMODEL is available as an R package through CRAN sites (see
http://rwiki.sciviews.org/doku.php?id=packages:cran:topmodel). It is also part of a wider R package
called R-Hydro that is currently under development by Wouter Buytaert and Dominik Reusser (see
http://r-forge.r-project.org/projects/r-hydro/).
Chapter 7 Sensitivity Analysis, Model Calibration and
Uncertainty Estimation
A more detailed guide to software for random number generation and uncertainty estimation methods
can be found at www.uncertain-future.org.uk.
GLUE Lancaster University provides a Windows (32 bit only) demonstration GLUE package
as a teaching aid. It provides tools for sensitivity analysis and uncertainty estimation using the
results of Monte Carlo simulations. It
is available from www.lec.lancs.ac.uk/research/catchment
and aquatic processes/software.php.
A MATLAB version of GLUE has been developed by Marco Ratto at the EU Joint Research Centre,
Ispra, Italy. This allows for more parameters and moreMonte Carlo runs than theWindows demonstration
version and can be downloaded from http://eemc.jrc.ec.europa.eu/Software-GLUEWIN.htm.
DYNIA, with GLUE, is included in the Imperial College Monte Carlo toolbox for MATLAB that can
be found at www3.imperial.ac.uk/ewre/research/software/toolkit.
PEST is a suite of software for parameter estimation, sensitivity analysis and uncertainty estimation,
developed by John Doherty
et al.
It is applicable to a wide range of models including highly parame-
terised distributed models. It has a variety of regularisation techniques to reduce the parameter dimen-
sions. The main inversion technique is based on a weighted least squares approach. It is available from
www.pesthomepage.org/Home.php.
UNCERTML OGC-compatible standards for communicating uncertainty in variables and model results
are being developed under the UncertML project. See www.uncertml.org.
