Precipitation as the central meteorological feature for agriculture, water security, and human well-being amongst others, has gained special attention ever since. Lack of precipitation may have devastating effects such as crop failure and water scarcity. Abundance of precipitation, on the other hand, may as well result in hazardous events such as flooding and again crop failure. Thus, great effort has been spent on tracking changes in precipitation and relating them to underlying processes. Particularly in the face of global warming and given the link between temperature and atmospheric water holding capacity, research is needed to understand the effect of climate change on precipitation. The present work aims at understanding past changes in precipitation and other meteorological variables. Trends were detected for various time periods and related to associated changes in large-scale atmospheric circulation. The results derived in this thesis may be used as the foundation for attributing changes in floods to climate change. Assumptions needed for the downscaling of large-scale circulation model output to local climate stations are tested and verified here. In a first step, changes in precipitation over Germany were detected, focussing not only on precipitation totals, but also on properties of the statistical distribution, transition probabilities as a measure for wet/dry spells, and extreme precipitation events. Shifting the spatial focus to the Rhine catchment as one of the major water lifelines of Europe and the largest river basin in Germany, detected trends in precipitation and other meteorological variables were analysed in relation to states of an ``optimal'' weather pattern classification. The weather pattern classification was developed seeking the best skill in explaining the variance of local climate variables. The last question addressed whether observed changes in local climate variables are attributable to changes in the frequency of weather patterns or rather to changes within the patterns itself. A common assumption for a downscaling approach using weather patterns and a stochastic weather generator is that climate change is expressed only as a changed occurrence of patterns with the pattern properties remaining constant. This assumption was validated and the ability of the latest generation of general circulation models to reproduce the weather patterns was evaluated. % Paper 1 Precipitation changes in Germany in the period 1951-2006 can be summarised briefly as negative in summer and positive in all other seasons. Different precipitation characteristics confirm the trends in total precipitation: while winter mean and extreme precipitation have increased, wet spells tend to be longer as well (expressed as increased probability for a wet day followed by another wet day). For summer the opposite was observed: reduced total precipitation, supported by decreasing mean and extreme precipitation and reflected in an increasing length of dry spells. Apart from this general summary for the whole of Germany, the spatial distribution within the country is much more differentiated. Increases in winter precipitation are most pronounced in the north-west and south-east of Germany, while precipitation increases are highest in the west for spring and in the south for autumn. Decreasing summer precipitation was observed in most regions of Germany, with particular focus on the south and west. The seasonal picture, however, was again differently represented in the contributing months, e.g.\ increasing autumn precipitation in the south of Germany is formed by strong trends in the south-west in October and in the south-east in November. These results emphasise the high spatial and temporal organisation of precipitation changes. % Paper 2 The next step towards attributing precipitation trends to changes in large-scale atmospheric patterns was the derivation of a weather pattern classification that sufficiently stratifies the local climate variables under investigation. Focussing on temperature, radiation, and humidity in addition to precipitation, a classification based on mean sea level pressure, near-surface temperature, and specific humidity was found to have the best skill in explaining the variance of the local variables. A rather high number of 40 patterns was selected, allowing typical pressure patterns being assigned to specific seasons by the associated temperature patterns. While the skill in explaining precipitation variance is rather low, better skill was achieved for radiation and, of course, temperature. Most of the recent GCMs from the CMIP5 ensemble were found to reproduce these weather patterns sufficiently well in terms of frequency, seasonality, and persistence. % Paper 3 Finally, the weather patterns were analysed for trends in pattern frequency, seasonality, persistence, and trends in pattern-specific precipitation and temperature. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years were considered. Thus, the assumption of a constant link between patterns and local weather was tested rigorously. This assumption was found to hold true only partly. While changes in temperature are mainly attributable to changes in pattern frequency, for precipitation a substantial amount of change was detected within individual patterns. Magnitude and even sign of trends depend highly on the selected time period. The frequency of certain patterns is related to the long-term variability of large-scale circulation modes. Changes in precipitation were found to be heterogeneous not only in space, but also in time - statements on trends are only valid for the specific time period under investigation. While some part of the trends can be attributed to changes in the large-scale circulation, distinct changes were found within single weather patterns as well. The results emphasise the need to analyse multiple periods for thorough trend detection wherever possible and add some note of caution to the application of downscaling approaches based on weather patterns, as they might misinterpret the effect of climate change due to neglecting within-type trends.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

Ecosystem services (ESs) are defined as the contributions that ecosystems make to human wellbeing and are increasingly being used as an approach to explore the importance of ecosystems for humans through their valuation. Although value plurality has been recognised long before the mainstreaming of ESs research, socio-cultural valuation is still underrepresented in ESs assessments. It is the central goal of this PhD dissertation to explore the ability of socio-cultural valuation methods for the operationalisation of ESs research in land management. To address this, I formulated three research objectives that are briefly outlined below and relate to the three studies conducted during this dissertation. The first objective relates to the assessment of the current role of socio-cultural valuation in ESs research. Human values are central to ESs research yet non-monetary socio-cultural valuation methods have been found underrepresented in the field of ESs science. In regard to the unbalanced consideration of value domains and conceptual uncertainties, I perform a systematic literature review aiming to answer the research question: To what extent have socio-cultural values been addressed in ESs assessments. The second objective aims to test socio-cultural valuation methods of ESs and their relevance for land use preferences by exploring their methodological opportunities and limitations. Socio-cultural valuation methods have only recently become a focus in ESs research and therefore bear various uncertainties in regard to their methodological implications. To overcome these uncertainties, I analysed responses to a visitor survey. The research questions related to the second objective were: What are the implications of different valuation methods for ESs values? To what extent are land use preferences explained by socio-cultural values of ESs? The third objective addressed in this dissertation is the implementation of ESs research into land management through socio-cultural valuation. Though it is emphasised that the ESs approach can assist decision making, there is little empirical evidence of the effect of ESs knowledge on land management. I proposed a way to implement transdisciplinary, spatially explicit research on ESs by answering the following research questions: Which landscape features underpinning ESs supply are considered in land management? How can participatory approaches accounting for ESs be operationalised in land management? The empirical research resulted in five main findings that provide answers to the research questions. First, this dissertation provides evidence that socio-cultural values are an integral part of ESs research. I found that they can be assessed for provisioning, regulating, and cultural services though they are linked to cultural services to a greater degree. Socio-cultural values have been assessed by monetary and non-monetary methods and their assessment is effectively facilitated by stakeholder participation. Second, I found that different methods of socio-cultural valuation revealed different information. Whereas rating revealed a general value of ESs, weighting was found more suitable to identify priorities across ESs. Value intentions likewise differed in the distribution of values, generally implying a higher value for others than for respondents themselves. Third, I showed that ESs values were distributed similarly across groups with differing land use preferences. Thus, I provided empirical evidence that ESs values and landscape values should not be used interchangeably. Fourth, I showed which landscape features important for ESs supply in a Scottish regional park are not sufficiently accounted for in the current management strategy. This knowledge is useful for the identification of priority sites for land management. Finally, I provide an approach to explore how ESs knowledge elicited by participatory mapping can be operationalised in land management. I demonstrate how stakeholder knowledge and values can be used for the identification of ESs hotspots and how these hotspots can be compared to current management priorities. This dissertation helps to bridge current gaps of ESs science by advancing the understanding of the current role of socio-cultural values in ESs research, testing different methods and their relevance for land use preferences, and implementing ESs knowledge into land management. If and to what extent ESs and their values are implemented into ecosystem management is mainly the choice of the management. An advanced understanding of socio-cultural valuation methods contributes to the normative basis of this management, while the proposal for the implementation of ESs in land management presents a practical approach of how to transfer this type of knowledge into practice. The proposed methods for socio-cultural valuation can support guiding land management towards a balanced consideration of ESs and conservation goals.