J. Kros

Geographical variation in terrestrial nitrogen budgets across Europe

Nitrogen (N) budgets of agricultural systems give important information for assessing the impact of N inputs on the environment, and identify levers for action.

Regional Calibration of a Steady-State Model to Assess Critical Acid Loads

The Model to Assess Critical Acid Loads (MACAL) has been developed for assessing and mapping critical acid loads on a national scale. MACAL simulates soil solution concentrations of major ions in a forest soil at any given depth at steady state for a given deposition level. The critical acid load is calculated from defined critical values for the A13+ concentration and the Al3+/Ca2+ ratio by inverse modelling. In order to minimize the uncertainty in the critical load computations, which is due to insufficient knowledge of parameter values, a multi-signal calibration of poorly defined important model parameters was performed using a data set on soil solution concentrations of 150 forest stands in the Netherlands. Since no detailed data was available on site scale (i.e. individual forest stands), a regional calibration was preferred. The cumulative distribution functions (CDF) of the model outputs for the 150 forest stands where fitted to those of the associated measurements. All model parameters could be identified with the objective function used except for forest filtering factors for nitrogen deposition. The calibration showed to be useful to reduce parameter ranges for some of the important model parameters, resulting in a lower uncertainty in model predictions.

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.

Models and scale aspect

Hydrological modelling requirements for scenario studies are discussed and it is noted that physically based distributed models have an appropriate structure for predicting the hydrological regimes associated with various land use change scenarios. The capabilities and limitations of current physically based models are discussed: uncertainties in predictions derive primarily from unresolved sub-grid scale heterogeneity and from inadequatedata bases for parameter identification. A rigorous validation framework for assessing predictions of land usev change effects is described which can account for various sources of prediction uncertainty. A case study of land use planning in the Tyne basin, UK is described in outline in which predictions of land use patterns generated by an agro-economic model have been used to generate predictions of flow and water quality using the physically based modelling system SHETRAN.