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Ondanks de grote uitbreiding van de Ecologische Hoofdstructuur (EHS) en grote investeringen in natuurherstel en natuurontwikkeling blijven er veel soorten uit Nederland verdwijnen. Dit rapport wijst beleidsmakers voor natuur, landschap en water, in dienst van ministeries en provincies, op de mogelijkheden om ruimte en budgetten beter te benutten. Binnen het sectoraal natuurbeleid kan dit door de EHS om te bouwen tot een Ark die alle doelsoorten beschermt in een gewenst veelvoud van locaties. Vervolgens kan het beleid voor natuur, landschap en water worden geintegreerd met een Integrale Ark, die naast de Hotspots van doelsoorten ook de Hotspots beschermt van het landschap en het watersysteem. Zo'n Integrale Ark versterkt bovendien de recreatie- en woonomgeving

In het project zijn audits uitgevoerd voor 31 modellen en databestanden die binnen DLO worden gebruikt bij de uitvoering van taken voor het Milieu- en Natuurplanbureau. Daartoe is een gemeenschappelijke methodiek ontwikkeld die uitgaat van 5 kwaliteitsaspecten: wetenschappelijke inhoud, wiskundige vertaling en resolutie, statistische aspecten, kwaliteitsborging en communicatieve aspecten. Per aspect zijn criteria uitgewerkt, waarop de modellen en databestanden zijn getoetst. De uitkomsten van de individuele audits zijn vertaald in een algemene aanpak waarmee de kwaliteitsborging structureel kan worden verbeterd. Deze aanpak heeft betrekking op: de organisatie van het proces van kwaliteitsborging; de relatie met ISO-9001; de formulering van twee algemene kwaliteitsniveau’s A en AA en de daarbij behorende criteria en op de wijze waarop de keuze voor een gewenst kwaliteitsniveau kan worden afgestemd op de toepassing en de daarmee samenhangende risico’s. Ook wordt aangegeven hoe de ontwikkelde aanpak binnen DLO kan worden geïmplementeerd.

An iterative search procedure was used to 'invert' the soil chemical model SMART2. This 'inverted' form of SMART2 was used to estimate atmospheric nitrogen deposition at the critical conditions for 139 terrestrial vegetation associations. The critical conditions are the lower end of the pH range, and the upper end of the nitrogen availability range for each association, estimated on the basis of Ellenberg values of vegetation relevees. The resulting critical load values were subjected to an uncertainty analysis. The estimation of nitrogen availability on the basis of Ellenberg's indicator for N has the largest contribution to the uncertainty. The critical load over all vegetation types and soil types is estimated to be 22 + 8 kg N ha-1.y-1. This is a rather 'hard' value, however critical loads per vegetation type are less 'hard', and it is not possible to determine critical load values per site. The uncertainties can only be reduced if more data become available on the abiotic response per species under field conditions. The critical loads found in this study were compared to the 'herijking' and 'SMB' critical loads and to empirically derived values. The 'SMB' critical loads appeared to be far lower than all other critical loads, which were in the same order of magnitude

Bayesian statistics may constitute the core of a consistent and comprehensive framework for the statistical aspects of modelling complex processes that involve many parameters whose values are derived from many sources. Bayesian statistics holds great promises for model calibration, provides the perfect starting point for uncertainty analysis and provides an excellent starting point for decision support. The purpose of this paper is to draw attention to problems and possible solutions. It is not our intention to introduce ready-for-use methods

This paper reviews design-based estimators for two-stage and three-stage sampling designs to estimate the mean of finite populations. This theory is then extended to spatial populations with continuous, infinite populations of sampling units at the latter stages. We then assume that the spatial pattern is the result of a spatial stochastic process, so the sampling variance of the estimators can be predicted from the variogram. A realistic cost function is then developed, based on several factors including laboratory analysis, time of fieldwork, and numbers of samples. Simulated annealing is used to find designs with minimum sampling variance for a fixed budget. The theory is illustrated with a real-world problem dealing with the volume of contaminated bed sediments in a network of watercourses. Primary sampling units are watercourses, secondary units are transects perpendicular to the axis of the watercourse, and tertiary units are points. Optimal designs had one point per transect, from one to three transects per watercourse, and the number of watercourses varied depending on the budget. However, if laboratory costs are reduced by grouping all samples within a watercourse into one composite sample, it appeared to be efficient to sample more transects within a watercourse.

The availability of high-quality models is considered a critical success factor for Alterra. To answer the complex questions of policy makers it is often necessary to link models that have been developed initially to study more limited questions. Whenmodels are linked, error propagation may enlarge the uncertainty of the model results. However, the quantification of uncertainty propagation may become more complex. This problem of uncertainty propagation in model chains is explored using a chain of the models SMART2/SUMO, P2E and NTM that predicts the potential nature conservation value of natural areas. Two methods have been explored to study the uncertainty propagation in the model chain: a regression-free method that estimates the uncertainty contributions of groups of sources of uncertainty, and an analysis by means of linear regression approximations of the sub-models of the model chain. The final analysis was done with a regression-free method. The results are presented as the contributions ofthe various sources of uncertainty to the uncertainty of the potential conservation value. From the results of this study, lessons are learned for the analyses of error propagation in model chains.

A scalar model output Y is assumed to depend deterministically on a set of stochastically independent input vectors of different dimensions. The composition of the variance of Y is considered; variance components of particular relevance for uncertainty analysis are identified. Several analysis of variance designs for estimation of these variance components are discussed. Classical normal-model theory can suggest optimal designs. The designs can be implemented with various sampling methods: ordinary random sampling, latin hypercube sampling and scrambled quasi-random sampling. Some combinations of design and sampling method are compared in two small-scale numerical experiments.

Het gebruik van simulatiemodellen voor de evaluatie van beleid is zeer bewerkelijk vanwege grote databehoefte en veel modeloutput. Een alternatief hiervoor is het gebruik van metamodellen, die zijn afgeleid van resultaten van simulatiemodellen. Resultaten van de WSV-vermestingstudie zijn gebruikt voor de afleiding van metamodellen voor de `gemiddelde' nitraatconcentratie in het grondwater, N-afvoer en P-afvoer naar het oppervlaktewater in de toekomst. De metamodellen hadden allemaal een verklaarde variantie van meer dan 75%. Er werden ook modellen afgeleid voor de spreiding rond de `gemiddelde' situatie. Bij de toepassing van de metamodellen moet rekening worden gehouden met het bereik van de dataset waarvan de modellen zijn afgeleid. Het betrouwbaarheidsinterval wordt groter aan de randen van deze range.

The TOXSWA model calculates the acute exposure concentration and the average exposure concentrations (AECs) of pesticides in ditches after 4, 21 and 28 days. In a (global) sensitivity analysis based on the Monte Carlo method the most sensitive processparameters (pesticide properties) and the most sensitive system parameters (e.g. ditch dimensions) with respect to AECs were determined. The process parametes "transformation rate in the water layer", "coefficient for sorption to macrophytes" and "Henry coefficient" were identified as contributing most to the varation in the AECs. The full range of pesticides was characterized by these three parameters and subsequently the flow velocity, water depth and dry weight of macrophyutes were identified sas sensitive system parameters with respect to the AECs. Analytically, the water depth, bottom width of the ditch and the sorption coefficients for sorption to macrophytes and suspended solids were identified as sensitive parameters with respect to the acute exposure concentration.

Uncertainties in major input variables in water and solute models were quantified and their effects on simulated functional aspects of soil behaviour were studied using basic soil properties from an important soil map unit in the Netherlands. Two sources of uncertainty were studied: spatial variability of basic soil properties, and the use of pedotransfer functions to predict soil hydraulic functions. Depending on the type of process studied, variability of basic soil properties explained between 40 and 99.9% of the total variance. Uncertainty of pedotransfer functions is of importance mainly when physical soil behaviour (e.g. workable days and aeration status) is studied.