& Context Projecting changes in forest productivity in Europe is crucial for adapting forest management to changing environmental conditions. & Aims The objective of this paper is to project forest productivity changes under different climate change scenarios at a large number of sites in Europe with a stand-scale processbased model. & Methods We applied the process-based forest growth model 4C at 132 typical forest sites of important European tree species in ten environmental zones using climate change scenarios from three different climate models and two different assumptions about CO 2 effects on productivity. & Results This paper shows that future forest productivity will be affected by climate change and that these effects depend strongly on the climate scenario used and the persistence of CO 2 effects. We find that productivity increases in Northern Europe, increases or decreases in Central Europe, and decreases in Southern Europe. This geographical pattern is mirrored by the responses of the individual tree species. The productivity of Scots pine and Norway spruce, mostly located in central and northern Europe, increases while the productivity of Common beech and oak in southern regions decreases. It is important to note that we consider the physiological response to climate change excluding disturbances or management. & Conclusions Different climate change scenarios and assumptions about the persistence of CO 2 effects lead to uncertain projections of future forest productivity. These uncertainties need to be integrated into forest management planning and adaptation of forest management to climate change using adaptive management frameworks. Keywords 4C (FORESEE). CO 2 effects. Environmental change. Level-II plots. Process-based modelling. Uncertainties

Abstract. The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all.Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation.In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.

Abstract. The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.

ABSTRACT. Scenarios have become a key tool for supporting sustainability research on regional and global change. In this study we evaluate four regional scenario assessments: first, to explore a number of research challenges related to sustainability science and, second, to contribute to sustainability research in the specific case studies. The four case studies used commonly applied scenario approaches that are (i) a story and simulation approach with stakeholder participation in the Oum Zessar watershed, Tunisia, (ii) a participatory scenario exploration in the Rwenzori region, Uganda, (iii) a model-based prepolicy study in the Inner Niger Delta, Mali, and (iv) a model coupling-based scenario analysis in upper Thukela basin, South Africa. The scenario assessments are evaluated against a set of known challenges in sustainability science, with each challenge represented by two indicators, complemented by a survey carried out on the perception of the scenario assessments within the case study regions. The results show that all types of scenario assessments address many sustainability challenges, but that the more complex ones based on story and simulation and model coupling are the most comprehensive. The study highlights the need to investigate abrupt system changes as well as governmental and political factors as important sources of uncertainty. For an in-depth analysis of these issues, the use of qualitative approaches and an active engagement of local stakeholders are suggested. Studying ecological thresholds for the regional scale is recommended to support research on regional sustainability. The evaluation of the scenario processes and outcomes by local researchers indicates the most transparent scenario assessments as the most useful. Focused, straightforward, yet iterative scenario assessments can be very relevant by contributing information to selected sustainability problems.

Coupling land-use change and hydrologic models for quantification of catchment ecosystem services