VARIACIONES SISTEMÁTICAS EN EL ESTADO DE ESFUERZOS

With exception of the latest development of the school of de Wit model family (GECROS), none of the models listed in section 1.2 is capable of mapping an adequately dynamic interactive reaction of the vegetation cover with respect to carbon dioxide and temperature, as far as it is known to the author.

In this work, a biochemically based assimilation model for C3 grasses (FARQUHAR, VON CAEMMERER AND BERRY 1980) is applied in combination with enhancements concerning the mathematic description of C4 photosynthesis (CHEN ET AL. 1994) and forest growth (FALGE 1997). The photosynthesis model is combined with a model of stomatal conductance (BALL, WOODROW AND BERRY 1987) to enable the hydrological landsurface model PROMET (MAUSER AND BACH 2008) to the simulation of dynamic vegetation processes, including an explicit simulation of the leaf gas exchange. Through the detailed description of biochemical processes

Introduction

within the leaf and the explicit simulation of the gas exchange between leaf and atmosphere, a direct interconnection of the modelled processes to external climate relevant parameters is established. Thus, a dynamic reaction of the modelled biochemistry to environmental parameters like air pressure, radiation, atmospheric carbon dioxide concentration and temperature can be mapped by the model, which is a basic prerequisite for the assessment of climate change effects that are related to vegetation activity.

The scientific aim and central target of this work is the investigation of vegetation behaviour under changing climatic conditions and the quantification of the effect that the vegetation dynamics impose on the hydrology of a river catchment. In this regard it will be of particular interest that diverging processes have to be expected when the canopy is exposed to a changing climate. On one hand rising temperatures and rising atmospheric CO2 concentrations are pointing towards an increase of growth activity, due to the fact that all chemical processes are supposed to be accelerated when exposed to higher temperatures according to the van’t Hoff rule. Also numerous publications have shown that elevated CO2 concentrations may result in somewhat higher plant productivities (WONG 1979, SELLERS ET AL.1996, FIELD ET AL.1995,

KÖRNER 2000, LONG ET AL. 2006), although in the meantime it has been found that these changes are mostly due to changed environmental conditions, while the photosynthetic reaction itself is rapidly adapting to the enriched CO2 supply (KÖRNER 2006). On the other hand, the changing climate not only affects the photosynthetic processes, but also has an impact on the whole landsurface water balance. An increase of the average temperature may result in a lower water supply that may again lead to an increase of drought stress. The future balance of growth enhancing (rising levels of temperature and CO2) and growth inhibiting (increased frequency of water stress, decline of summer precipitation) processes will decide, whether the expected higher vegetation activity will be reduced or even be compensated by drought stress in the near future.

For the investigation of the future behaviour of the vegetation processes, computer aided modelling techniques are applied on a physical basis. As could be shown in section 1.2, a great variety of modelling approaches for different purposes that are related with plant growth already exists. The scientific challenge of this work therefore is not the development of another vegetation model. It rather is the review of existing modelling approaches, the understanding of the concerned processes, the selection of adequate methods and finally their combination and integration into a functioning modelling system that is capable of giving quantitative answers to climate change enquiries. This is a comprehensive task that requires an intensive engagement with different natural sciences on a very detailed level. The awareness of the interconnectedness of natural processes, disregarding the boundaries of single scientific niches, is one of the most determinant advantages that distinguish the science of Geography. The consolidation of methods that descend from different branches of the natural sciences therefore

Introduction

is an appealing task for a universal scientist, who is not afraid of penetrating scientific grounds of neighbour sciences in order to tide over joints in science that actually are an interconnected system in nature.

The spatial extent of this study is limited to the watershed boundary of the Upper Danube catchment (see section 2), while the temporal dimension is separated into two time segments. A so called “reference” period, ranging from 1960 to 2006 and a “scenario” period that comprises the forthcoming 50 years from 2011 to 2060. The reason for choosing a scenario time frame that is relatively short compared to other projections, for example those introduced by the Intergovernmental Panel on Climate Change that partly cover ranges of up to 500 years into the future (IPCC 2001, 2007), is twofold. On one hand the proposed time frame is appealing because a great number of our contemporaries are likely to experience the manifestation of the possible changes in person. The other reason, why a limitation to 50 years was chosen for this task, is founded on restrictions that accompany the generation of artificial meteorology data for the scenario model runs. The stochastic generation of artificial weather data as applied here (see section 5.3.2, MAUSER ET AL.2007) is limited by the data base of measured meteorological

observations that can be integrated into the generation of the chain of artificial weather events. Since the observed regional meteorological data that could be acquired for this study only covers 46 years from 1960 to 2006, saturation effects that manifest in statistic repetitions of observed extremes occur, when the time frame and the connected scenario changes exceed a critical range. The limitation of the spatial extent to the Upper Danube catchment is due to several reasons. Most important, the scientific exchange with the GLOWA-DANUBE cooperative project, founded by the German Ministry of Education and Research (BMB+F), allowed for the acquisition of a comprehensive data base. The Upper Danube catchment is a rewarding subject for investigation, since it is a very heterogeneous landscape ranging from plain periglacial brash fields up to high alpine zones. It is a region that is economically active and densely inhabited, combining a huge variety of land uses that are formed by the antithetic demands of heavy and high tech industry, recreation and intensive agriculture, large forested areas and steep alpine rocks, big cities and rural villages. For hydrological applications, a river basin generally is an appealing entity due to the fact that the water balance can be controlled and verified with help of the measured runoff at the benchmark gauge, where the main stream drains the water basin. Last but not least the Upper Danube Basin is the region that I live in, which is the reason why I personally am most highly interested in the changes that will be of consequence for the environment and the people that are living in this part of central Europe. Concerning the methods, the physically based hydrological landsurface model PROMET (MAUSER AND BACH 2008) forms an excellent basis for this work. The model has already proven

its stability and capacity for hydrological applications on the landscape scale (MAUSER AND

Introduction

modular architecture, additional extensions can easily be integrated, gradually enhancing the models functionality.

Figure 1.01: Cycle of modelled scales. Chloroplast image inspired by MOORE ET AL. (1998).

Besides the actual physical modelling, the scale that is used to comprehend the single modelling steps is a determinant factor. For this work, a modelling approach was applied that finds its beginning at the scale of a landscape or mesoscale, where the meteorology that powers the model is interpolated. The general energy flux then is directed to the leaves of the canopy and from there on to the microscale of the chloroplast level, where the processes of photosynthesis are modelled explicitly, based on mitochondrial activities. The products of the chloroplast photosynthesis then are scaled up to the leaf level and from there are scaled further back to the entire landscape in a last step (fig. 1.01).

USGS definition of a river catchment area: „The land area where precipitation runs off into streams, rivers, lakes, and reservoirs. It is a land

feature that can be identified by tracing a line along the highest elevations between two areas on

a map, often a ridge.”

Introduction

The term landscape in this context refers to the natural entity of a river catchment (fig. 1.02). The drainage basin offers ideal natural boundaries for modelling approaches, due to several reasons. On one hand, the river catchment is a naturally defined area that does not necessarily follow administrative borders and represents a natural landscape unit (HEATHCOTE 1998). On

the other hand, the boundaries of a river catchment allow for a validation of the model results simply because the water balance can be retraced by comparing the model results with measured runoff data from the basin gauge. In comparison with the measurement of evapotranspiration and precipitation respectively, the recording of runoff rates can be accomplished with relatively high precision.