L’invarianza spaziale dei parametri di un modello afflussi-deflussi può rivelarsi una soluzione pratica e valida nel caso si voglia stimare la disponibilità di risorsa idrica di un’area. La simulazione idrologica è infatti uno strumento molto adottato ma presenta alcune criticità legate soprattutto alla necessità di calibrare i parametri del modello. Se si opta per l’applicazione di modelli spazialmente distribuiti, utili perché in grado di rendere conto della variabilità spaziale dei fenomeni che concorrono alla formazione di deflusso, il problema è solitamente legato all’alto numero di parametri in gioco. Assumendo che alcuni di questi siano omogenei nello spazio, dunque presentino lo stesso valore sui diversi bacini, è possibile ridurre il numero complessivo dei parametri che necessitano della calibrazione. Si verifica su base statistica questa assunzione, ricorrendo alla stima dell’incertezza parametrica valutata per mezzo di un algoritmo MCMC. Si nota che le distribuzioni dei parametri risultano in diversa misura compatibili sui bacini considerati. Quando poi l’obiettivo è la stima della disponibilità di risorsa idrica di bacini non strumentati, l’ipotesi di invarianza dei parametri assume ancora più importanza; solitamente infatti si affronta questo problema ricorrendo a lunghe analisi di regionalizzazione dei parametri. In questa sede invece si propone una procedura di cross-calibrazione che viene realizzata adottando le informazioni provenienti dai bacini strumentati più simili al sito di interesse. Si vuole raggiungere cioè un giusto compromesso tra lo svantaggio derivante dall’assumere i parametri del modello costanti sui bacini strumentati e il beneficio legato all’introduzione, passo dopo passo, di nuove e importanti informazioni derivanti dai bacini strumentati coinvolti nell’analisi. I risultati dimostrano l’utilità della metodologia proposta; si vede infatti che, in fase di validazione sul bacino considerato non strumentato, è possibile raggiungere un buona concordanza tra le serie di portata simulate e osservate. / Spatial homogeneity of rainfall-runoff model parameters can be a practical and valuable solution in order to assess water availability of a region. Hydrological simulation is indeed an handy tool but it is critical as it usually requires some degree of calibration. Calibration of spatially distributed models, that are particularly useful to describe the variability of physical processes that play a role in runoff generation, is challenging because of the high number of involved parameters. But some parameters can be homogeneous in space, therefore allowing one to reduce their total amount when multiple basins are considered. This assumption is verified on a statistical ground, making use of an MCMC algorithm to assess the parameter uncertainty; as a result the parameter distributions are with varying degrees comparable on the different catchments. When one wants to simulate the hydrological response of ungauged catchments, the hypothesis of spatial homogeneity of parameters has even more relevance; a long regionalization technique is usually applied, but we propose a cross-calibration procedure to be used at regional level. With this procedure model parameters are calibrated making use of hydrological information coming from gauged basins that are more similar to the site of interest. We want to analyze the trade-off between assuming the parameters homogeneous in space and adding new information as the cross-calibration evolves. Results show that the cross-calibration is a process well worth using; in validation in fact a good agreement is reached between observed and simulated discharges.

The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses. Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed. In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option. The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast? Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.

Flood disasters are a major cause of fatalities and economic losses, and several studies indicate that global flood risk is currently increasing. In order to reduce and mitigate the impact of river flood disasters, the current trend is to integrate existing structural defences with non structural measures. This calls for a wider application of advanced hydraulic models for flood hazard and risk mapping, engineering design, and flood forecasting systems. Within this framework, two different hydraulic models for large scale analysis of flood events have been developed. The two models, named CA2D and IFD-GGA, adopt an integrated approach based on the diffusive shallow water equations and a simplified finite volume scheme. The models are also designed for massive code parallelization, which has a key importance in reducing run times in large scale and high-detail applications. The two models were first applied to several numerical cases, to test the reliability and accuracy of different model versions. Then, the most effective versions were applied to different real flood events and flood scenarios. The IFD-GGA model showed serious problems that prevented further applications. On the contrary, the CA2D model proved to be fast and robust, and able to reproduce 1D and 2D flow processes in terms of water depth and velocity. In most applications the accuracy of model results was good and adequate to large scale analysis. Where complex flow processes occurred local errors were observed, due to the model approximations. However, they did not compromise the correct representation of overall flow processes. In conclusion, the CA model can be a valuable tool for the simulation of a wide range of flood event types, including lowland and flash flood events.

In order to handle Natural disasters, emergency areas are often individuated over the territory, close to populated centres. In these areas, rescue services are located which respond with resources and materials for population relief. A method of automatic positioning of these centres in case of a flood or an earthquake is presented. The positioning procedure consists of two distinct parts developed by the research group of Prof Michael G. H. Bell of Imperial College, London, refined and applied to real cases at the University of Bologna under the coordination of Prof Ezio Todini. There are certain requirements that need to be observed such as the maximum number of rescue points as well as the number of people involved. Initially, the candidate points are decided according to the ones proposed by the local civil protection services. We then calculate all possible routes from each candidate rescue point to all other points, generally using the concept of the "hyperpath", namely a set of paths each one of which may be optimal. The attributes of the road network are of fundamental importance, both for the calculation of the ideal distance and eventual delays due to the event measured in travel time units. In a second phase, the distances are used to decide the optimum rescue point positions using heuristics. This second part functions by "elimination". In the beginning, all points are considered rescue centres. During every interaction we wish to delete one point and calculate the impact it creates. In each case, we delete the point that creates less impact until we reach the number of rescue centres we wish to keep.

La Comunità Europea, alla luce dei recenti eventi alluvionali occorsi nei Paesi Membri ed al progressivo aumento dei danni economici da essi provocati, ha recentemente emanato una direttiva (Direttiva Europea 2007/60/CE, Flood Directive) per la valutazione e la predisposizione di piani di gestione del rischio idraulico alluvionale. Con riferimento a tale contesto l’attività di ricerca condotta si è concentrata sulla valutazione delle potenzialità offerte dalla modellistica numerico-idraulica mono e bidimensionale quale strumento per l’attuazione della Direttiva 2007/60. Le attività sono state affrontate ponendo particolare attenzione alla valutazione dei termini di incertezza che caratterizzano l’applicazione dei modelli numerico-idraulici, esaminando i possibili effetti di tale incertezza sulla mappatura della pericolosità idraulica. In particolare, lo studio si concentra su diversi tratti fluviali del corso medio inferiore del Fiume Po e si articola in tre parti: 1) analisi dell’incertezza connessa alla definizione delle scale di deflusso in una generica sezione fluviale e valutazione dei suoi effetti sulla calibrazione dei modelli numerici quasi-bidimensionali (quasi-2D); 2) definizione di mappe probabilistiche di allagamento per tratti fluviali arginati in presenza di tre sorgenti di incertezza: incertezza nelle condizioni al contorno di monte, nelle condizioni di valle e nell’identificazione delle eventuali brecce arginali; 3) valutazione dell’applicabilità di un modello quasi-2D per la definizione, a grande scala spaziale, di strategie alternative al tradizionale rialzo dei manufatti arginali per la mitigazione del rischio alluvionale associato a eventi di piena catastrofici. Le analisi condotte, oltre ad aver definito e valutato le potenzialità di metodologie e modelli idraulici a diversa complessità, hanno evidenziato l’entità e l’impatto dei più importanti elementi d’incertezza, sottolineando come la corretta mappatura della pericolosità idraulica debba sempre essere accompagnata da una valutazione della sua incertezza. / In the light of recent catastrophic flood events and the steadily increase of economic losses associated with inundations in Europe, the European Community recently issued the Flood Directive (2007/60/EC), which requires Member States to evaluate and map flood-risk and to develop flood risk management plans. Concerning these issues, the present dissertation focuses on the development and testing of mono and quasi two-dimensional (quasi-2D) numerical hydraulic models for the implementation of Directive 2007/60. The activities are carried out paying particular attention on the evaluation of the main sources of uncertainty that characterize the application of numerical models, examining their possible effect on the flood hazard mapping. The study considers various river branches of the River Po and is structured into three main parts: 1) analysis of rating-curve uncertainty in a given river section and evaluation of its effects on the calibration of roughness coefficients for a quasi-2D, 2) flood hazard mapping for a diked river reach considering three major sources of uncertainties: uncertainties in upstream and downstream boundary conditions and uncertainties in the dike-failure location and breach morphology; 3) development and testing of a quasi-2D hydraulic model to support the large-scale identification of optimal flood-risk mitigation strategies relative to the 500-year flood. The analysis assesses the potential of methodologies and numerical-hydraulic models for flood hazard mapping and highlights the impact of the most important elements of uncertainty, pointing out how a correct flood hazard map should always be accompanied by a throughout uncertainty assessment.

Tradizionalmente, l'obiettivo della calibrazione di un modello afflussi-deflussi è sempre stato quello di ottenere un set di parametri (o una distribuzione di probabilità dei parametri) che massimizzasse l'adattamento dei dati simulati alla realtà osservata, trattando parzialmente le finalità applicative del modello. Nel lavoro di tesi viene proposta una metodologia di calibrazione che trae spunto dell'evidenza che non sempre la corrispondenza tra dati osservati e simulati rappresenti il criterio più appropriato per calibrare un modello idrologico. Ai fini applicativi infatti, può risultare maggiormente utile una miglior rappresentazione di un determinato aspetto dell'idrogramma piuttosto che un altro. Il metodo di calibrazione che viene proposto mira a valutare le prestazioni del modello stimandone l'utilità nell'applicazione prevista. Tramite l'utilizzo di opportune funzioni, ad ogni passo temporale viene valutata l'utilità della simulazione ottenuta. La calibrazione viene quindi eseguita attraverso la massimizzazione di una funzione obiettivo costituita dalla somma delle utilità stimate nei singoli passi temporali. Le analisi mostrano come attraverso l'impiego di tali funzioni obiettivo sia possibile migliorare le prestazioni del modello laddove ritenute di maggior interesse per per le finalità applicative previste. / In the majority of rainfall-runoff modelling applications, the objective function to be minimised in the parameterisation procedure is based on a measure of the goodness-of-fit that maximized the fit of the simulated data to the overall observed data, taking partially into account the specific model applications. The present dissertation focuses on the development and testing of an objective function based on the expected utility of the rainfall-runoff model. The method is based on the evidence that the performances of a hydrological model closely depend on the purpose of the application. For istance, the simulated data caught have different utility in a water resources management system or in a flood forecasting system. In the proposed method, at each time step, the comparison between simulated and observed data is carried out by using an “ad-hoc” utility function. The calibration is performed by maximizing the overall estimated utility of the simulated data. Different utility functions are tested and the results are compared against those obtained with traditional procedure. The results reveal that an adequate utility function allows an improvement of the model performances in the reproduction of the discharges considered most important to the purpose of the modeling application.

This dissertation focuses on the assessment of projected changes on water resources by the end of this century (2071-2100), considering an ensemble of high resolution future climate scenarios, the effects of hydrological parameterisation, and the bias of the hydrological model in representing different streamflow magnitudes. Quantification of the impacts of climate change on water resources will depend on the emission scenario, climate model, downscaling technique and impact model used to drive the impact study. In particular, hydrological impact studies involve important decisions (e.g., model structure, parameterisation, input data) whose effects are reflected into the final impacts. As a result, quantification of impacts of climate change have to be seen as a "cascade of uncertainty", in which decisions taken in every step of the assessment process convey uncertainties that are unavoidably propagated to subsequent levels. At the other hand, uncertainties in projections of climate models and those involved in the quantification of their hydrological response limit the understanding of those future impacts and hamper the assessment of mitigation policies. The Soil and Water Assessment Tool (SWAT) hydrological model was set up for daily simulations of the western part of the Ebro River basin (~ 42000 km2) in Spain, during the control period 01/Jan/1961 to 31/Dec/1990, and two subcatchments were selected for testing the methodology proposed in this dissertation. A sensitivity analysis with Latin Hypercube One-factor-At-a-Time (LH-OAT) was carried out in order to identify parameters with a high effect on simulated streamflows. Then, an uncertainty analysis was carried out using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, in order to select parameter sets that can be considered as acceptable simulators of the system, adopting a re-scaled Nash-Sutcliffe efficiency as "less formal" likelihood, and a cut-off threshold equal to zero to discriminate between behavioural and non-behavioural simulators. Afterwards, a Latin Hypercube (LH) sampling strategy was implemented within GLUE, in order to reduce the number of model runs required to obtain a good exploration of the parameter space. The 95% of the cumulative distribution of each predicted output, weighted by the re-scaled likelihood of each behavioural parameter set, was used to compute the predictive uncertainty bounds, both during the control and future scenarios. Bias-corrected daily time series of precipitation and air temperature, for the future period 2071-2100, were derived from an ensemble of six high-resolution climate change scenarios, selected from the EU FP5 PRUDENCE project. Long-term averages of precipitation and air temperature fields were computed for the control period, and projected anomalies for the future scenarios were computed as well, in an annual, seasonal and monthly basis, including expected changes for different elevation bands within the basin. The same bias-corrected time series were then used to drive daily hydrological simulations during the future period on the two selected catchments. For each climate scenario, a number of simulations equal to the number of behavioural parameter sets obtained during the uncertainty analysis was carried out. Resulting streamflows were used to compute daily flow duration curves (FDCs) to provide a qualitative assessment of the relative importance of uncertainties coming from the choice of the driving RCM and from hydrological parameterisation. In addition, streamflows derived from running each climate scenario with its corresponding behavioural parameter sets, were used to compute empirical cumulative density functions (ECDFs) of three selected percentiles, representing different flow magnitudes, in order to provide a quantitative assessment of the projected changes in streamflows. We observed that the hydrological parametric uncertainty was larger than the uncertainty coming from the driving RCM, during the complete future period and each one of the four seasons, for the two selected catchments. However, this result can not be generalised, because it is conditional to decisions taken during the uncertainty analysis and to the ensemble of RCMs considered. Empirical CDFs computed for projected values of low (Q5), medium (Q50) and high (Q95) flows show that there is a general projected decrease in all the streamflow magnitudes, but bias in the representation of the streamflows during the control period 1961-1990 hamper the assessment of reliable quantitative projections for low and medium flows, whereas projected decreases for high flows range from 0 to 60%, depending on the catchment and the climate scenario considered.

Permafrost degradation in high mountain environments is one of the effects of climate change in the Alpine region (IPCC, 2007). The consequences may be manyfold, ranging from rock falls and debris flows, to structural damages in infrastructures located on high mountains. The exceptional rock-fall activity during the summer 2003 is likely an indication of this rapid destabilization that takes place as an almost immediate reaction to extreme warming (Gruber et al., 2004a). The understanding and prediction of such phenomena requires ï¬ rst the localization of permafrost affected areas, and then the monitoring of permafrost sites through proper measurement and modeling techniques. However, the modeling of alpine permafrost is not an easy task because of a variety of causes that contribute to increase the complexity. In particular, the crucial factors dominating alpine permafrost are (1) topography and soil type heterogeneity, (2) snow insulating effect, (3) presence of ice in the ground and (4) high thermal inertia for temperature change at depth. These disturbances could be dealt with through a physically based approach that accounts for the topographical characteristics of the basin, allows heterogeneous parameterization of thermal and hydraulic properties of the ground, solves snow accumulation and melting, and calculates temperature, water and ice content in the ground. GEOtop (Rigon et al., 2006) is a distributed physically-based hydrological model that appears suitable to deal with the above outlined requirements, as it solves coupled water and energy budgets, allows heterogeneous input parameters in the form of maps and includes a snow module that calculates accumulation-melting of snow through a multilayer discretization of the snowpack (Endrizzi, 2007). The model, at the beginning of this work, was lacking of a freezing-soil module capable to account for phase change and heat advection in the soils, extremely important in permafrost affected areas (Roth and Boike, 2001). The inclusion of this part, however, needs a deep thermodynamical analysis of the system, in order to derive the relations between pressure and temperature in a ground subject to freezing conditions. Furthermore, the solution of the energy equation requires a robust numerical scheme, which has to cope with the high non-linearities present in the apparent heat capacity formulation for phase change (Hansson et al., 2004). Finally, the snow-soil thermal interactions require a special attention, as they command the energy flux in input to the ground when the snow is present. The objectives of this thesis are to develop a new freezing soil module inside GEOtop, to test the model against analytical solutions, experimental data and ï¬ eld observations, and to apply the model to investigate the influence of coupled heat and water flow in arctic and alpine permafrost areas.

Water resources availability and its variability is one of the most pressing global problems. Hydrological models are useful to understand the water balance of a basin, providing information for water resource forecast, assessment, and management. The effectiveness of the models in estimating the freshwater space-time availability and variability, however, depends on concurrent and explicitly modelling of all water budget components instead of a single component estimation and optimization. The whole water budget modelling at basin scale requires a combined solution from hydrological and spatial information tools, in-situ and remote sensing data. The present dissertation describes an effort to improve estimation of each water budget component, and water budget closure at various spatial and temporal scales, by combining JGrass-NewAge model system, GIS spatial toolbox, in-situ and remote sensing data. JGrass-NewAge is a system which deploys modern informatics to facilitate models maintainability and reproducible research. It integrates advanced GIS features and the Object Modelling System version 3 infrastructures, which allow for a component-based modelling experience. This means that JGrass-NewAGE is not actually a model, but a set of elements (the components) that can be combined just before runtime to produce various modelling solutions. Topics like calibration of processes, the interpolation forcing and the assessment of forecasting errors can therefore be faced with consistent and solid approaches. In this context also the use of some remote sensing resources can be inserted appropriately and with new techniques. For all the analysis, two significantly different basins, in terms of size and hydrological processes, are considered as case studies. These are Posina river basin in northeast Italy (small size basin) and Upper Blue Nile basin(large size basin) are used as case study. The uDig Spatial Toolbox (uST) GIS infrastructure that is used for generating the hydromorphological parameters is described in the second chapter. A large number of tools are included in uST for terrain analysis, river network delineation, and basin topology characterisation. In addition, the geomorphological settings necessary to run JGrass-NewAGE are shown. The third chapter studies the effect of spatial discretisation and the hillslope size on basin responses. The possible epistemic uncertainty exerted by the use of sub basin spatial discretisation of topographic information in the semi-distributed hydrological modelling has been studied. The use of different spatial representation in hydrological modelling context has been also studied by comparing JGrass-NewAGE with a model configuration called PeakFlow. The latter is an implementation of the geomorphological unit hydrograph based on the width function. The experiment indicates that the Peak-Flow model, with a more accurate spatial representation, reproduce the storm events slightly better than the JGrass-NewAGE model. In the fourth chapter, the thesis set-up JGrass-Newage modelling solution for the estimation of hydrological modelling inputs (rainfall, snow, temperature data) and estimates them, as well as with their errors. Regards to the meteorological forcings (mainly temperature and precipitation), in Posina river basin where there are relatively dense meteorological stations, the effects of different interpolation schemes were evaluated. Since the hydrological processes from rainfall is different from snowfall, a new method of separating rainfall and snowfall was introduced using MODIS imagery data. In the fifth chapter, JGrass-NewAGE was used to estimate the whole set of water balance components. For evapotranspiration (ET) estimation, the Priestley-Taylor component of JGrass-NewAGE is used. In order to calibrate its parameter a new method based on the water budget was implemented. This method uses two different hypothesis on available data (budget stationarity "Budyko hypothesis", and local proportionality of actual evapotranspiration to soil moisture availability). Finally the spatial and temporal dynamics of water budget closure of Posina river basin is presented. The sixth chapter concerns about the inputs data, particularly precipitation, for water balance modelling in a region where ground-based gauge data are scarce. Five high-resolution satellite rainfall estimation (SRE) products were compared and analysed using the available rain gauge. The basin rainfall is investigated systematically, and it was found that, at some locations, the difference in mean annual rainfall estimates between these SREs very high. In addition to the identification of the best performing products, the chapter shows that a simple empirical cumulative distribution (ecdf) mapping bias correction method can provide a means to improve the rainfall estimation of all SREs, and the highest improvement is obtained for CMORPH. In the seventh chapter, using the capability of JGrass-NewAGE components and different remote sensing data, the spatio-temporal water budget of Upper Blue Nile basin is simulated. The water budget components (rainfall, discharge evapotranspiration, and storage) were analysed for about 16 years at daily time step using the modelling solution and remote sensing data set. For the verification of the approaches followed, wide ranges of remote sensing data (MODIS ET product MOD16, GRACE, and EUMETSAT CM SAF cloud fractional cover) are used.

Stream ecosystems form an active component of the carbon (C) cycle, and are identified as “hotspots” for carbon dioxide (CO2) emissions. However, the mechanisms driving CO2 emissions from streams are not completely understood. Beside the input of C in the form of CO2 from groundwater, streams receive organic matter from aquatic and terrestrial origins which is partly mineralized to inorganic nutrients and CO2. Future predictions suggest enhanced input of terrestrial organic matter into streams. As such, surrounding land use may highly influence dissolved organic matter (DOM) composition and turnover in streams. The quality, i.e. bioavailability or lability, of aquatic and terrestrial organic matter, as well as which quality feature provides which bioavailability, is controversially discussed and the research is still in its infancy. Thus, the main goal of my thesis is to enhance the understanding of the role of organic matter quality as a potential driver for organic matter turnover in stream ecosystems. A further goal is to shed light on C dynamics with main focus on CO2 of streams surrounded by different land use. The presented work is based on an experimental approach in the laboratory, supported by seasonal field studies and a developed model in order to explore C dynamics and the corresponding drivers in stream ecosystems. The underlying mechanisms and the importance of DOM quality as a main driver was assessed on the small scale in laboratory experiments. The C emissions from streams were quantified and the influence of DOM quality was examined on a stream reach scale by investigating two stream types with different organic matter quality inputs. By developing a process-based model, the understanding of the daily and seasonal scale of C turnover in stream ecosystems was amplified. The results from the experiment under controlled conditions demonstrate that DOM quality governs microbial metabolism (i.e. respiration and bacterial protein production). Moreover, I revealed significant quality differences between two terrestrial DOM sources, while respiration and bacterial protein production increased with the available proportion of the labile DOM source. The molecular weight of DOM was the strongest predictor of bacterial protein production and respiration, while among others, the concentration of low molecular weight substances was another highly influential predictor. The importance of molecular size/weight and DOM quality for microbial metabolism was further confirmed on the stream reach scale where we demonstrated among others a significant linkage between molecular size of DOM and pCO2 across agricultural and forest streams. Moreover, agricultural streams contained significantly higher pCO2 compared to forest streams during all seasons. However, CO2 emissions measured with the powerful drifting chamber method were not significantly different between the stream types. Modeled dissolved oxygen (O2) and CO2 dynamics calibrated with field data resulted in respiratory quotients (RQ = mole of CO2 produced per mole of O2 consumed), which are intimately linked to the elemental composition of the respired compounds across four seasons and two stream types. RQ values were not related to adjacent land use or season. Nevertheless, I found significant relationships between RQ values and DOM quality indicators, such as fluorescing component characteristic for higher plant material and molecule size of DOM in agricultural streams. In conclusion, this thesis demonstrates that DOM quality is an important driver for organic matter turnover in streams. Consequently, my results indicate that ongoing and future land use change and enhanced terrestrial DOM input into streams may influence CO2 emissions, and underline the status of streams as C turnover “hotspots”. Thus, my thesis contributes to the mechanistic understanding of organic matter cycling in stream ecosystems and their role in the regional and global C cycle. Therefore, organic matter quality should be considered in future models and studies with respect to C cycling.