LISFLOOD: A GIS-based Distributed Model for River Basin Scale Water Balance and Flood Simulation

International Journal of Geographical Information Science, Volume 24, Issue 2 February 2010 , pages 189 – 212

J. M. Van Der Knijff; J. Younis; A. P. J. De Roo

“In this paper we describe the spatially distributed LISFLOOD model, which is a hydrological model specifically developed for the simulation of hydrological processes in large European river basins. The model was designed to make the best possible use of existing data sets on soils, land cover, topography and meteorology. We give a detailed description of the simulation of hydrological processes in LISFLOOD, and discuss how the model is parameterized. We also describe how the model was implemented technically using a combination of the PCRaster GIS system and the Python programming language, and discuss the management of in- and output data. Finally, we review some recent applications of LISFLOOD, and we present a case study for the Elbe river.”

Estimation of Solar UV Radiation in Maritime Antarctica using a Nonlinear Model including Cloud Effects

International Journal of Remote Sensing, Volume 31, Issue 4 April 2010 , pages 831 – 849

K. Láska; P. Prošek; L. Budík; M. Budíková; G. Milinevsky

“A new approach to the estimation of erythemally effective ultraviolet (EUV) radiation for all sky conditions that occur in maritime Antarctica is reported. The spatial variability of the total ozone content (TOC) and attenuation of the EUV radiation in the atmosphere are taken into consideration. The proposed nonlinear regression model of EUV radiation is described by a hyperbolic transmission function. The first results and the model validation for Vernadsky Station (formerly the British Faraday Station) during the period 2002-2005 show very good agreement with the measured values (R2 = 99.2). The developed model was evaluated using daily doses of EUV radiation with respect to solar elevation angle and cloudiness. The mean average prediction error (MAPE) for cloudy (4.1-7.0 oktas) and overcast skies (7.1-8.0 oktas) varied between 4.0% and 4.3%, while for partly cloudy days (0-4.0 oktas) with high variability of cloud types during a day, MAPE reached 5.9%.”

Spatial Modelling of Car Ownership Data: A Case Study from the United Kingdom

Applied Spatial Analysis and Policy, Volume 3, Number 1 / March 2010

Stephen Clark and Andrew O. Finley

“In this paper a model is formulated to estimate the strength of the relationship between household car ownership and income using cross-sectional data. Whilst reports of such studies are not uncommon in the transport literature, this study is different in that it takes explicit account of the spatial distribution of the data. By incorporating this spatial element in the model formulation, the residual errors in the model are uncorrelated and hence allows for the estimation of parameters that are, in a statistical sense, the best available. These spatial models are fitted to a large data set provided by the United Kingdom Office for National Statistics, covering the area of England and Wales. The recommended model form is a Hierarchical Bayesian spatial regression model with the parameters in the model estimated using the technique of Markov Chain Monte Carlo (MCMC). A common feature of all the spatial models is that the estimate of the elasticity of car ownership with respect to income is seen to be larger than that from a non-spatial model.”