The current and future climate change, which is still superimposed by global change, has a serious influence on landscapes in general and agricultural landscapes in particular. To meet the challenges for society and agriculture, well-planned and sustainable adaptation measures to climate change are needed. For agriculture, the following question has to be answered: How will climate change affect regional agriculture and ecosystems and what could be possible adaptation strategies, taking into account local site potentials and the specific structure of farms and agricultural enterprises? To answer this question, both farmers and other stakeholders might need the help of computer-based decision support systems. The information and decision support system described here, called LandCaRe-DSS, is such a helping instrument. The LandCaRe-DSS is designed as a user-friendly, interactive, model-based and spatial-oriented information and decision support system. It can be used on different spatial scales while being fully interactive. It supports long-term spatial scenario simulations, multi-ensemble and multi-model simulations at the regional level as well as complex impact assessments of potential adaptation strategies for land use at the local level. All simulations are carried out with a high spatial resolution and include coupled climate and agro-economic scenarios. The easy-to-use software is controlled via a zoomable user interface known to many users from for instance Google Maps®. The system can be extended with further modules for different tasks.The article describes the structure and use of the LandCaRe-DSS in detail, including the components and modules of the system, the system framework, the operation principles, the climate and geodatabases, the scale-specific ecological impact models and the IT realisation. The LandCaRe-DSS offers different data analysis and visualisation tools, a help system for users and a farmer information system for adaptation of agriculture to climate change. Using concrete examples, the different fields of application are presented: the analysis of climate data, the analysis of phenology and ontogenesis and the climate change impact assessment on national, regional and local resp. farm scales. In particular, the LandCaRe-DSS supports strategic planning in agriculture and the sustainable development of rural areas (regions) and provides answers to the impacts and costs of possible adaptation measures to climate change. The LandCaRe-DSS was developed as a prototype and is parameterised and validated for the region Uckermark (dry lowland, 2,600 km2) in the Federal State of Brandenburg and for the region Weisseritzkreis (wet mountain area, 400 km2) in the Free State of Saxony. In the last 10 years, the system underwent slow albeit continuous changes and has been adapted to further regions and extended for the needs of research projects.

Sap flow measurements of trees are today the most common method to determine transpiration at the tree and the forest canopy level. They provide independent measurements for flux comparisons and model validation. Sap flow measurements of Norway spruce (Picea abies) which were conducted in the mountain range of the Fichtelgebirge (Germany) are reviewed with respect to analyses of structure-function relationships, atmospheric control and horizontal heterogeneity of stand transpiration in the Lehstenbach catchment, and local vertical gradients from the forest floor over various heights in the tree up to the atmosphere above. It is concluded that sap flow measurements have importance for the analysis of physiologically controlled plant process at the organism and community level and could first demonstrate age dependence of managed forest stands. The measurements also supplement long-term micrometeorological measurements and have the potential for cross validation of independent measurements of water vapor fluxes and the water equivalent of latent heat fluxes from eddy covariance technique. For short-term comparisons, the time lag between sap flow and evaporation and effects of changes in tree water storage need to be considered.

Decision support for developing practicable, resilient climate change adaptation strategies for the sustainable use of agro-landscapes encompasses a wide range of options and issues. So far, only a few suitable tools and methods have been available to farmers, regional planners and other stakeholders to support decision-making processes in this direction. The model-based interactive spatial information and decision support system, LandCaRe-DSS, closes this methodical gap. This system does not only support interactive scenario simulations and multi-ensemble and multi-model simulations at the regional level by providing information about the complex long-term impacts of climate change. It also helps different stakeholders to find suitable, sustainable agricultural adaptation strategies to climate change (crop rotation, soil tillage, fertilisation, irrigation, price and cost changes etc.) at the local or farm level. LandCaRe-DSS uses different ecological impact models, including for crop yield, erosion risk, regional evapotranspiration, total water flow-out and irrigation water demand. At the local level, a farm economy model is directly coupled with both the biophysical-based agro-ecosystem model MONICA and the statistical-based crop yield model YIELDSTAT to simulate the economic consequences of regional climate change and of proposed agricultural adaptation strategies. Due to the modular architecture and innovative design of LandCaRe-DSS, alternative or new impact models can easily be incorporated into the system. Scenario simulation runs can be realised in a reasonable amount of time. The interactive LandCaRe-DSS prototype offers a variety of data analysis and visualisation tools and an information system for climate adaptation in agriculture. This article describes the conceptual framework, the structure, the methodology and basic principles of operating LandCaRe-DSS. A number of selected examples demonstrate the versatility of LandCaRe-DSS applications. Using different scales and regions as examples, the impact of climate change is shown on: the ontogenesis of winter wheat for Muncheberg, Germany; the start, end and duration of the vegetation period in two German regions Uckermark (dry lowlands, 2600 km(2)) and Weisseritz (humid mountain area, 400 km(2)); irrigation water demand in Thuringia, Germany and the winter wheat yield in the Prenzlau region, Germany. Using LandCaRe-DSS up to 2075 for the Uckermark und Weisseritz regions, the effects and impacts of different agricultural adaption strategies were analysed taking into account irrigation, the absence of soil tillage and two different cropping ratios (actual cropping ratio vs. cropping ratio enriched with energy maize). Thanks to the modular structure of LandCaRe-DSS, little effort is required to adapt the whole system to geo-data valid for other regions or countries; incorporate other static or dynamic impact models; switch to other climate scenarios and implement other interface communication languages. The LandCaRe-DSS is constantly being developed, updated and adapted in different research projects such as the REGKLAM project for agricultural regions of Saxony, Germany, and the CARBIOCIAL project for regions within the Mato Grosso and Para states of Brazil. It has already been used in a number of climate scenario studies for the Federal States of Thuringia, Brandenburg and Saxony. In the years ahead, international cooperative activities will be initiated with institutions from St.Petersburg, Russia, and Pulawy, Poland, in order to use, adapt and advance this system.

The Penman-Monteith (PM) equation is a state-of-the-art modelling approach to simulate evapotranspiration (ET) at site and local scale.However, its practical application is often restricted by the availability and quality of required parameters.One of these parameters is the canopy conductance.Long term measurements of evapotranspiration by the eddy-covariance method provide an improved data basis to determine this parameter by inverse modelling.Because this approach may also include evaporation from the soil, not only the 'actual' canopy conductance but the whole surface conductance (g c ) is addressed.Two full cycles of crop rotation with five different crop types (winter barley, winter rape seed, winter wheat, silage maize, and spring barley) have been continuously monitored for 10 years.These data form the basis for this study.As estimates of g c are obtained on basis of measurements, we investigated the impact of measurements uncertainties on obtained values of g c .Here, two different foci were inspected more in detail.Firstly, the effect of the energy balance closure gap (EBCG) on obtained values of g c was analysed.Secondly, the common hydrological practice to use vegetation height (h c ) to determine the period of highest plant activity (i.e., times with maximum g c concerning CO 2 -exchange and transpiration) was critically reviewed.The results showed that h c and g c do only agree at the beginning of the growing season but increasingly differ during the rest of the growing season.Thus, the utilisation of h c as a proxy to assess maximum g c (g c,max ) can lead to inaccurate estimates of g c,max which in turn can cause serious shortcomings in simulated ET.The light use efficiency (LUE) is superior to h c as a proxy to determine periods with maximum g c .Based on this proxy, crop specific estimates of g c,max could be determined for the first (and the second) cycle of crop rotation: winter barley, 19.2 mm s -1 (16.0 mm s -1 ); winter rape seed, 12.3 mm s -1 (13.1 mm s -1 ); winter wheat, 16.5 mm s -1 (11.2 mm s -1 ); silage maize, 7.4 mm s -1 (8.5 mm s -1 ); and spring barley, 7.0 mm s -1 (6.2 mm s -1 ).

Climate change is expected to have a strong influence on agricultural systems in the future. It will be important for decision makers and stakeholders to assess the impact of climate change at the farm and regional level in order to facilitate and maintain a sustainable and profitable farming infrastructure. Climate change impact studies have to incorporate aspects of uncertainty and the underlying knowledge is constantly expanding and improving. Decision support systems (DSS) with flexible data bases are therefore a useful tool for management and planning: different models can be applied under varying boundary conditions within a conceptual framework and the results can be used e.g. to show the effects of climate change scenarios and different land management options. Within this project, the already existing LandCaRe DSS will be further enhanced and improved. A first prototype had been developed for two regions in eastern Germany, mainly to show the effects of climate change on yields, nutrient balances and farm economy. The new model version will be tested and applied for a region in north-western Germany (Landkreis Uelzen) where arable land makes up about 50% of overall land-use and where 80 % of the arable land is already irrigated. For local decision makers, it will be important to know how water demand and water availability are likely to change in the future: Is more water needed for irrigation? Is more water actually available for irrigation? Will the existing limits for ground water withdrawal be sufficient for farmers to irrigate their crops? How can the irrigation water demand be influenced by land management options like the use of different crops and varieties or different farming and irrigation techniques? The main tasks of the project are (I) the integration of an improved irrigation model, (II) the development of a standardized interface to apply the DSS in different regions, (III) to optimize the graphical user interface, (IV) to transfer and apply the DSS in an example region in north-west Germany and (V) to expand the underlying data base of climate change models and scenarios. The project is funded by the Bundesministeriums fur Bildung und Forschung (BMBF), Forderkennzeichen Forderkennzeichen: 02WQ1304.

•Conceptual framework for a model-based spatial decision support system to develop adaptation measures to climate change.•Combination of climatic and economic scenarios.•Applicable at the regional, field and farm level.•Participative approach including agricultural and regional stakeholders.

A better understanding of ecosystem water-use efficiency (WUE) will help us improve ecosystem management for mitigation as well as adaption to global hydrological change. Here, long-term flux tower observations of productivity and evapotranspiration allow us to detect a consistent latitudinal trend in WUE, rising from the subtropics to the northern high-latitudes. The trend peaks at approximately 51°N and then declines toward higher latitudes. These ground-based observations are consistent with global-scale estimates of WUE. Global analysis of WUE reveals existence of strong regional variations that correspond to global climate patterns. The latitudinal trends of global WUE for Earth's major plant functional types reveal two peaks in the Northern Hemisphere not detected by ground-based measurements. One peak is located at 20° ~ 30°N and the other extends a little farther north than 51°N. Finally, long-term spatiotemporal trend analysis using satellite-based remote sensing data reveals that land-cover and land-use change in recent years has led to a decline in global WUE. Our study provides a new framework for global research on the interactions between carbon and water cycles as well as responses to natural and human impacts.

Der Bericht Bereitstellung des regional angepassten Entscheidungshilfesystems reprasentiert das REGKLAM-Produkt 3.3.1g. Das Entscheidungshilfesystem LandCaRe-DSS (Land, Climate and Resources Decision Support System) ist eine modellbasierte Wissensplattform, die die explizite Simulation von regionalen und lokalen Auswirkungen des Klimawandels anhand von regionalen Klimaprojektionen ermoglicht. Damit soll das Wissen um mogliche Klimafolgen erganzt und die Ableitung von Anpassungsmasnahmen in der Landwirtschaft unterstutzt werden. Ziel des Teilprojektes (TP3.3.1g) war die Ubertragung des Systems auf die Modellregion Dresden.

• Attempts to combine biometric and eddy-covariance (EC) quantifications of carbon allocation to different storage pools in forests have been inconsistent and variably successful in the past. • We assessed above-ground biomass changes at five long-term EC forest stations based on tree-ring width and wood density measurements, together with multiple allometric models. Measurements were validated with site-specific biomass estimates and compared with the sum of monthly CO₂ fluxes between 1997 and 2009. • Biometric measurements and seasonal net ecosystem productivity (NEP) proved largely compatible and suggested that carbon sequestered between January and July is mainly used for volume increase, whereas that taken up between August and September supports a combination of cell wall thickening and storage. The inter-annual variability in above-ground woody carbon uptake was significantly linked with wood production at the sites, ranging between 110 and 370 g C m(-2) yr(-1) , thereby accounting for 10-25% of gross primary productivity (GPP), 15-32% of terrestrial ecosystem respiration (TER) and 25-80% of NEP. • The observed seasonal partitioning of carbon used to support different wood formation processes refines our knowledge on the dynamics and magnitude of carbon allocation in forests across the major European climatic zones. It may thus contribute, for example, to improved vegetation model parameterization and provides an enhanced framework to link tree-ring parameters with EC measurements.

Decision support to develop viable climate change adaptation strategies for agriculture and regional land use management encompasses a wide range of options and issues. Up to now, only a few suitable tools and methods have existed for farmers and regional stakeholders that support the process of decision-making in this field. The interactive model-based spatial information and decision support system LandCaRe DSS attempts to close the existing methodical gap. This system supports interactive spatial scenario simulations, multi-ensemble and multi-model simulations at the regional scale, as well as the complex impact assessment of potential land use adaptation strategies at the local scale. The system is connected to a local geo-database and via the Internet to a climate data server. LandCaRe DSS uses a multitude of scale-specific ecological impact models, which are linked in various ways. At the local scale (farm scale), biophysical models are directly coupled with a farm economy calculator. New or alternative simulation models can easily be added, thanks to the innovative architecture and design of the DSS. Scenario simulations can be conducted with a reasonable amount of effort. The interactive LandCaRe DSS prototype also offers a variety of data analysis and visualisation tools, a help system for users and a farmer information system for climate adaptation in agriculture. This paper presents the theoretical background, the conceptual framework, and the structure and methodology behind LandCaRe DSS. Scenario studies at the regional and local scale for the two Eastern German regions of Uckermark (dry lowlands, 2600 km(2)) and Weisseritz (humid mountain area, 400 km(2)) were conducted in close cooperation with stakeholders to test the functionality of the DSS prototype. The system is gradually being transformed into a web version (http://www.landcare-dss.de) to ensure the broadest possible distribution of LandCaRe DSS to the public. The system will be continuously developed, updated and used in different research projects and as a learning and knowledge-sharing tool for students.The main objective of LandCaRe DSS is to provide information on the complex long-term impacts of climate change and on potential management options for adaptation by answering "what-if" type questions. (C) 2013 Elsevier Ltd. All rights reserved.

The impact of climate change on agriculture is a matter of great debate. Adaptation of agriculture to climate change requires knowledge of the impact at the regional or local level. Future climate is described by different climate projections. Projections of the same area and time period differ with respect to boundary conditions of the global climate models, the approaches of nested regional climate models, the underlying emission scenarios, the initial conditions of model runs or the statistical basis of climate models. Further, the performance of weather differs in climate projections. Up to now it is unclear, in how far the variation of climate projections for the same study period results in a similar, larger or smaller variation of plant response. During the last years, the model-based decision support system LandCaRe (Land, Climate and Resources) DSS has been developed. It consists of data bases related to past and future regional climate or climate projections, agricultural and ecological models, plant parameters and GIS-based data of land use, topography, hydrology and soil characteristics. The statistical and process-based models are for example predicting information on climate statistics, plant phenology, crop ontogeny, and crop yield. The DSS further allows to investigate plant production and other processes at field, farm and regional level. Here we present the impact of different climate projections and weather realisations on plant production in agricultural ecosystems of Saxony, Germany. The representation and effect of “model weather” in climate projections compared to measured weather is discussed.

At the study site Tharandt Anchor Station in Saxony/Germany sap flow measurements are conducted in an old Norway spruce (Picea abies [L.] KARST.) stand. During the study period from 2001 to 2007 several events like thinning, long and short drought periods and a winter storm significantly affected the amount of canopy water use. We show that intra-annual variation of Ec is strongly related to VPD and PPFD. While there is a non-linear relationship between daily Ec and VPD, daily Ec is limited by daily integrated PPFD indicating stomatal control of Ec through photosynthesis. On a monthly or seasonal basis, reduction of Ec is not only related to high VPD and non-saturating PPFD, but also to higher frequencies of precipitation. In comparison to this, nearly 55% of canopy precipitation and 20% of available energy were used for transpiration during the growing season. Intensive seasonal soil water measurements at the site revealed that on average about 74% of soil water removal within the rooting zone can be related to tree water uptake. A good correlation was found between annual Ec and Ec(max), usually occurring in June or July. Further, the monthly sums of June plus July were good predictors of annual Ec. Within the study period, the extreme drought in 2003 revealed a clear threshold of soil water content by 9.5 vol% and had the most pronounced effect on annual Ec followed by a stand thinning. The winter storm "Kyrill" in January 2007 had caused loss of green needles and twigs. It is assumed that the observed reduction in Ec during spring was related to the reduced leaf biomass and potentially to root damage of bended trees. Excluding the effect of extreme drought and forest management, a mean inter-annual variation in Ec of +/- 15% and in Ec/VPD of +/- 8% remained. It is concluded that lag-effects of drought and the winter storm add lacking explanation to the inter-annual variability of canopy transpiration besides the typical variation of atmospheric conditions. (C) 2011 Elsevier B.V. All rights reserved.

Der globale Wandel zeigt sich, wie eingangs ausgeführt, regional in unterschiedlicher Ausprägung. Die Faktoren, die den Umweltwandel in der Fokusregion beeinflussen, sind selbst Teil des globalen Wandels. Hierzu zählen Einflussfaktoren des Klimas wie die zunehmende Konzentration von Treibhausgasen in der Atmosphäre, ein vermehrter Aerosoleintrag aus dem urbanen Gebiet, aber auch die Reduktion von Luftverunreinigungen durch entsprechende Maßnahmen. Einflüsse auf den Wasserhaushalt betreffen Veränderungen des Klimas selbst sowie Landnutzungsänderungen oder ein sich änderndes Wassernutzungsverhalten aufseiten von Landwirtschaft, Privathaushalten und Industrie. Um den Umweltwandel analysieren und bewerten zu können, sind sowohl die Einflussfaktoren als auch die Veränderungen innerhalb der Ökosysteme systematisch zu erheben. Beobachtungssysteme für die regionale und lokale Ausprägung der Umweltveränderungen, die Analyse der Variabilität und Wechselwirkungen der Veränderungen sowie die Beachtung der regionalen Spezifika sind eine wesentliche Voraussetzung für eine nachhaltige Entwicklung (NKGCF 2005).