M. van der Perk

Spatial modelling of transfer of long-lived radionuclides from soil to agricultural products in the Chernigov region, Ukraine.

Abstract Within the RESTORE project (‘restoration strategies for radioactive contaminated ecosystems’) funded by the European Commission Nuclear Fission Safety Programme, environmental models are being developed to identify regions that are vulnerable to increased radionuclide transfer as a consequence of the Chernobyl nuclear power plant accident and nuclear weapons testing at the Semipalatinsk test site in Kazakhstan. Since radionuclide transfer varies in space and time depending on deposition processes, soil type, land use, and resulting contamination in food products, the radionuclide transfer through food chains derived from a variety of ecosystems is analysed by the use of models embedded in a Geographical Information System. The Chernigov region in northern Ukraine was affected by the Chernobyl fallout resulting in deposition levels ranging from 15 to 300 kBq m −2 . GIS-based steady state and dynamic transfer models within an environmental decision support system (EDSS) were run for this region to model radiocaesium transfer from soil to various agricultural products on a collective farm level and on a district level within this region using spatial data sets of soil attributes, soil contamination and land use. Observed agricultural product contamination was available for comparison with model predictions. This paper presents examples of radiocaesium transfer from soil to fodder grass and potatoes to make an initial assessment of the radioecological situation in the Chernigov region to identify critical gaps in the model structure and data required for model input and validation. It highlights the feasibility of applying spatial and temporal data sets to make predictions of the present radioecological situation, as an alternative to approaches commonly used which categorise such data sets, thereby losing valuable information.

GIS-based modelling to identify regions of Ukraine, Belarus and Russia affected by residues of the Chernobyl nuclear power plant accident

Within the framework of the EC-financed RESTORE project (EC DGXII Nuclear Fission Safety Programme), a GIS-based Environmental Decision Support System (EDSS) will be developed to help local authorities identify vulnerable collective units (e.g. settlements, collective farms) having population groups with critical radiocaesium doses. The EDSS will include methods for modelling the spatial and temporal variation of radiocaesium in alluvial and peaty soils through flooding, radiocaesium transfer from different soil types to food products, and the collection and intake of food products by the human population. The spatial models will be written in PCRaster, a raster GIS and dynamic modelling toolkit that includes geostatistical analysis, conditional simulation, and topological modelling.

The identifiability of parameters in a water quality model of the Biebrza River, Poland

The identifiability of model parameters of a steady state water quality model of the Biebrza River and the resulting variation in model results was examined by applying the Monte Carlo method which combines calibration, identifiability analysis, uncertainty analysis, and sensitivity analysis. The water quality model simulates the steady state concentration profiles of chloride, phosphate, ammonium, and nitrate as a function of distance along a river. The water quality model with the best combination of parameter values simulates the observed concentrations very well. However, the range of possible modelled concentrations obtained for other more or less equally eligible combinations of parameter values is rather wide. This range in model outcomes reflects possible errors in the model parameters. Discrepancies between the range in model outcomes and the validation data set are only caused by errors in model structure, or (measurement) errors in boundary conditions or input variables. In this sense the validation procedure is a test of model capability, where the effects of calibration errors are filtered out. It is concluded that, despite some slight deviations between model outcome and observations, the model is successful in simulating the spatial pattern of nutrient concentrations in the Biebrza River.

Advances in the spatio-temporal modelling of environment and landscapes (in honour of Professor Peter A. Burrough)

This special issue of the International Journal of Geographical Information Science is dedicated to Professor Peter A. Burrough to mark his retirement from Utrecht University in September 2005 and to honour his work and achievements. Peter Burrough has been one of the leading scientists in the field of geographical information science (GIScience) over the last 30 years with a particular focus on the spatio-temporal analysis and modelling of environmental processes. Peter received his BSc (First Class Honours) in chemistry from the University of Sussex, Brighton in 1965, and his DPhil from the University of Oxford in 1969 for the thesis entitled ‘Studies of Soil Survey Methodology’. From 1970 until 1973, Peter was employed as senior research scientist at the Land Resources Division of the Ministry of Overseas Development, Tolworth, London, and during this period he worked on a major reconnaissance soil survey of the state of Sabah, Malaysia. In 1973, he went to Sydney, Australia to become Lecturer in Soil Science at the University of New South Wales and then in 1976 moved to The Netherlands where he became based for nearly three decades. He first worked in Wageningen as a Senior Research Scientist at STIBOKA (the Dutch Soil Survey) from 1976 to 1980, and then he moved to Wageningen Agricultural University, where he became Senior Lecturer in Spatial Analysis and Soil Science at the Department of Soil Science. Throughout this period in the UK, Australia, and Wageningen, Peter was engaged in the development and implementation of advanced computer and statistical methods for practical soil survey. It was in September 1984, that Peter was appointed Professor of Physical Geography and Geographical Information Systems at the Geographical Institute of Utrecht University. Research activities at the Physical Geography department in Utrecht before then had mainly focused on geomorphology. Under Peter’s leadership, GIS, geostatistics, environmental modelling, and remote sensing were introduced to both the teaching curriculum of Physical Geography and the research agenda of the department. Between 1984 and 1994, he was the initiator and chairman of two major 5-year research programmes: LAMIRU (Landscape, Environment, and Land Use) from 1984 to 1988 and GISLA (Geographical Information Systems and Landscape Analysis) from 1988 to 1993.

Calibration and identifiability analysis of a water quality model to evaluate the contribution of different processes to the short‐term dynamics of suspended sediment and dissolved nutrients in the surface water of a rural catchment

A method that combines calibration and identifiability analysis of a dynamic water quality model to evaluate the relative importance of various processes affecting the dynamic aspects of water composition is illustrated by a study of the response of suspended sediment and dissolved nutrients to a flood hydrograph in a rural catchment area in the Netherlands. Since the water quality model simulates the observed concentrations of suspended sediment and dissolved nutrients reasonably well, the most important processes during the observed flood hydrograph could be determined. These were erosion, exchange between dissolved phase and bed sediments and denitrification. It is concluded that the method is very useful for identifying the most significant model parameters and processes that are essential for water quality modelling.

Spatial Variation of Chemicals in Sediments in Small Aquatic Systems

An unbalanced nested sampling has been carried out to study the scale, form and magnitude of spatial variation of concentrations in stream bed sediments. Concentrations in the sediment are so variable that spatial interpolation is senseless at scales below 1000 m.

Model parametrization and data needs

The data crisis in scenario studies embodies topics such as (i) the relevance of existing data for current and forthcoming models; (ii) deciding on the use ofexisting soil data or sampling new data ; (iii) the sensivity of models to several sources of uncertainty in model inputs and consequences for data sampling; (iv) pragmatic approaches to the data crisis; and (v) the presentation of uncertain model results. In all these topics, the magnitude of the data crisis with respect to any parameter is a function of the spatial and temporal variability of this parameter, and of the sensivity the simulation model shows to this variability. The five topics are addressed using case studies and literature rev iews for illustrative purposes. Conclusions drawn with respect to the above topics, are : (i) soil data have merely an identification function when used with conceptual models, but have an estimation function too with implemented models; (ii) existing soil data may not be representing soil bodies unbiasedly, in which case probability sampling in combination with simulation modelling is a proposed method to select representative data based on soil behaviour; (iii) an uncertainty analysis of a model is a useful method to obtain sampling priorities; (iv) Pragmatic solutions to the data cris is exist in the field of exogenous model inputs, model initialisation and obtaining values for process parameters; (v) the presentation of results of scenario studies in terms of probabilities enables the incorporation of the effects of several sources of uncertainty and hence is a powerful method to be further developed.