Abstract. Simulations with a hydrological model for the river Rhine for the present (1960–1989) and a projected future (2070–2099) climate are discussed. The hydrological model (RhineFlow) is driven by meteorological data from a 90-years (ensemble of three 30-years) simulation with the HadRM3H regional climate model for both present-day and future climate (A2 emission scenario). Simulation of present-day discharges is realistic provided that (1) the HadRM3H temperature and precipitation are corrected for biases, and (2) the potential evapotranspiration is derived from temperature only. Different methods are used to simulate discharges for the future climate: one is based on the direct model output of the future climate run (direct approach), while the other is based on perturbation of the present-day HadRM3H time series (delta approach). Both methods predict a similar response in the mean annual discharge, an increase of 30% in winter and a decrease of 40% in summer. However, predictions of extreme flows differ significantly, with increases of 10% in flows with a return period of 100 years in the direct approach and approximately 30% in the delta approach. A bootstrap method is used to estimate the uncertainties related to the sample size (number of years simulated) in predicting changes in extreme flows.

Abstract. The concept and structure of the Spatial Decision Support System AFFOREST sDSS dealing with environmental performance (EP) of afforestation on agricultural land in northwestern Europe, is presented. EP is defined in terms of three environmental impact categories: (1) carbon sequestration (2) groundwater recharge and (3) nitrate leaching. The core of the sDSS is a raster based geographical database which allows for queries addressing 14 types of questions on where, how and how long to afforest in order to reach a desired EP or change in EP due to afforestation of the agricultural land. First the study area is differentiated according to the site conditions (based on soil texture, soil drainage, initial land use, yearly average precipitation, and yearly average N deposition. Then the EP for every site class is computed as a function of time using the VSAM metamodel. VSAM results from a conceptual simplification of an existing mechanistic point model, the forest process model SMART2. Input data for the metamodel are limited to the classified site conditions, the tree species used for afforestation, the afforestation strategy and the evaluation time. Besides limiting the data requirements, the metamodel approach allows for rapid and flexible computations on large numbers of pixel classes. Finally, depending on the type of question, the sDSS creates georeferenced outputs based on SQL-type spatial or attribute queries and more advanced multiple goal programming techniques.

Afforestation objectives vary from one country to another and even within countries. Apart from the objectives, the specific conditions from a biophysical, environmental and socioeconomic point of view should always be considered throughout the entire afforestation process, from policy decisions through location of the new forest, establishment and management, and the final utilisation of the forest. Decisions on how and where to afforest, and how much these decisions will affect the environmental impacts should ultimately be a compromise between the site quality in terms of climate, soil and preceding land-use, the initial goals set by planners and managers, and the stakeholders' preferences. The focus of AFFOREST has been on building knowledge and capacity to support decisions regarding afforestation of former arable land with respect to changes in C and N pools and fluxes and changes in 250 K. HANSEN ET AL. water recharge. The guidelines in this chapter are based on literature reviews, the experimental data from chronosequences of afforested stands in Denmark, Sweden, and the Netherlands (described in Chapter 2, 3 and 4), and on the developed mechanistic metamodel (METAFORE) and the spatial Decision Support System (AFFOREST-sDSS) (Chapter 7, 8, 9 and 10). The structure of the guidelines is based on questions and corresponding answers under the main themes of water recharge, nitrate leaching, C sequestration, diversity of understory vegetation and complex questions involving more than one of the first three issues. Hopefully, the guidelines will be helpful and inspire landscape and forest planners in planning how and where afforestation should take place.

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ABSTRACT. Using the systems approach framework (SAF), a coupled model suite was developed for simulating land-use decision making in response to nutrient abatement costs and water and nutrient fluxes in the hydrological network of the Scheldt River, and nutrient fluxes in the estuary and adjacent coastal sea. The purpose was to assess the efficiency of different longterm water quality improvement measures in current and future climate and societal settings, targeting nitrogen (N) load reduction. The spatial-dynamic model suite consists of two dynamically linked modules: PCRaster is used for the drainage network and is combined with ExtendSim modules for farming decision making and estuarine N dispersal. Model predictions of annual mean flow and total N concentrations compared well with data available for river and estuary (r² ≥ 0.83). Source apportionment was carried out to societal sectors and administrative regions; both households and agriculture are the major sources of N, with the regions of Flanders and Wallonia contributing most. Load reductions by different measures implemented in the model were comparable (~75% remaining after 30 yr), but costs differed greatly. Increasing domestic sewage connectivity was more effective, at comparatively low cost (47% remaining). The two climate scenarios did not lead to major differences in load compared with the business-as-usual scenario (~88% remaining). Thus, this spatially explicit model of water flow and N fluxes in the Scheldt catchment can be used to compare different long-term policy options for N load reduction to river, estuary, and receiving sea in terms of their effectiveness, cost, and optimal location of implementation.