@phdthesis{Reusser2011, author = {Reusser, Dominik Edwin}, title = {Combining smart model diagnostics and effective data collection for snow catchments}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-52574}, school = {Universit{\"a}t Potsdam}, year = {2011}, abstract = {Complete protection against flood risks by structural measures is impossible. Therefore flood prediction is important for flood risk management. Good explanatory power of flood models requires a meaningful representation of bio-physical processes. Therefore great interest exists to improve the process representation. Progress in hydrological process understanding is achieved through a learning cycle including critical assessment of an existing model for a given catchment as a first step. The assessment will highlight deficiencies of the model, from which useful additional data requirements are derived, giving a guideline for new measurements. These new measurements may in turn lead to improved process concepts. The improved process concepts are finally summarized in an updated hydrological model. In this thesis I demonstrate such a learning cycle, focusing on the advancement of model evaluation methods and more cost effective measurements. For a successful model evaluation, I propose that three questions should be answered: 1) when is a model reproducing observations in a satisfactory way? 2) If model results deviate, of what nature is the difference? And 3) what are most likely the relevant model components affecting these differences? To answer the first two questions, I developed a new method to assess the temporal dynamics of model performance (or TIGER - TIme series of Grouped Errors). This method is powerful in highlighting recurrent patterns of insufficient model behaviour for long simulation periods. I answered the third question with the analysis of the temporal dynamics of parameter sensitivity (TEDPAS). For calculating TEDPAS, an efficient method for sensitivity analysis is necessary. I used such an efficient method called Fourier Amplitude Sensitivity Test, which has a smart sampling scheme. Combining the two methods TIGER and TEDPAS provided a powerful tool for model assessment. With WaSiM-ETH applied to the Weisseritz catchment as a case study, I found insufficient process descriptions for the snow dynamics and for the recession during dry periods in late summer and fall. Focusing on snow dynamics, reasons for poor model performance can either be a poor representation of snow processes in the model, or poor data on snow cover, or both. To obtain an improved data set on snow cover, time series of snow height and temperatures were collected with a cost efficient method based on temperature measurements on multiple levels at each location. An algorithm was developed to simultaneously estimate snow height and cold content from these measurements. Both, snow height and cold content are relevant quantities for spring flood forecasting. Spatial variability was observed at the local and the catchment scale with an adjusted sampling design. At the local scale, samples were collected on two perpendicular transects of 60 m length and analysed with geostatistical methods. The range determined from fitted theoretical variograms was within the range of the sampling design for 80\% of the plots. No patterns were found, that would explain the random variability and spatial correlation at the local scale. At the watershed scale, locations of the extensive field campaign were selected according to a stratified sample design to capture the combined effects of elevation, aspect and land use. The snow height is mainly affected by the plot elevation. The expected influence of aspect and land use was not observed. To better understand the deficiencies of the snow module in WaSiM-ETH, the same approach, a simple degree day model was checked for its capability to reproduce the data. The degree day model was capable to explain the temporal variability for plots with a continuous snow pack over the entire snow season, if parameters were estimated for single plots. However, processes described in the simple model are not sufficient to represent multiple accumulation-melt-cycles, as observed for the lower catchment. Thus, the combined spatio-temporal variability at the watershed scale is not captured by the model. Further tests on improved concepts for the representation of snow dynamics at the Weißeritz are required. From the data I suggest to include at least rain on snow and redistribution by wind as additional processes to better describe spatio-temporal variability. Alternatively an energy balance snow model could be tested. Overall, the proposed learning cycle is a useful framework for targeted model improvement. The advanced model diagnostics is valuable to identify model deficiencies and to guide field measurements. The additional data collected throughout this work helps to get a deepened understanding of the processes in the Weisseritz catchment.}, language = {en} } @misc{PradhanFischerVelthuizenetal.2015, author = {Pradhan, Prajal and Fischer, G{\"u}nther and Velthuizen, Harrij van and Reusser, Dominik Edwin and Kropp, J{\"u}rgen}, title = {Closing yield gaps}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {491}, issn = {1866-8372}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408105}, pages = {18}, year = {2015}, abstract = {Global food production needs to be increased by 60-110\% between 2005 and 2050 to meet growing food and feed demand. Intensification and/or expansion of agriculture are the two main options available to meet the growing crop demands. Land conversion to expand cultivated land increases GHG emissions and impacts biodiversity and ecosystem services. Closing yield gaps to attain potential yields may be a viable option to increase the global crop production. Traditional methods of agricultural intensification often have negative externalities. Therefore, there is a need to explore location-specific methods of sustainable agricultural intensification. We identified regions where the achievement of potential crop calorie production on currently cultivated land will meet the present and future food demand based on scenario analyses considering population growth and changes in dietary habits. By closing yield gaps in the current irrigated and rain-fed cultivated land, about 24\% and 80\% more crop calories can respectively be produced compared to 2000. Most countries will reach food self-sufficiency or improve their current food self-sufficiency levels if potential crop production levels are achieved. As a novel approach, we defined specific input and agricultural management strategies required to achieve the potential production by overcoming biophysical and socioeconomic constraints causing yield gaps. The management strategies include: fertilizers, pesticides, advanced soil management, land improvement, management strategies coping with weather induced yield variability, and improving market accessibility. Finally, we estimated the required fertilizers (N, P2O5, and K2O) to attain the potential yields. Globally, N-fertilizer application needs to increase by 45-73\%, P2O5-fertilizer by 22-46\%, and K2O-fertilizer by 2-3 times compared to the year 2010 to attain potential crop production. The sustainability of such agricultural intensification largely depends on the way management strategies for closing yield gaps are chosen and implemented.}, language = {en} } @article{LissnerReusserScheweetal.2014, author = {Lissner, Tabea Katharina and Reusser, Dominik Edwin and Schewe, Jacob and Lakes, T. and Kropp, J{\"u}rgen}, title = {Climate impacts on human livelihoods: where uncertainty matters in projections of water availability}, series = {Earth system dynamics}, volume = {5}, journal = {Earth system dynamics}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {2190-4979}, doi = {10.5194/esd-5-355-2014}, pages = {355 -- 373}, year = {2014}, abstract = {Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions - and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions.}, language = {en} } @article{LandholmHolstenMartellozzoetal.2018, author = {Landholm, David M. and Holsten, Anne and Martellozzo, Federico and Reusser, Dominik Edwin and Kropp, J{\"u}rgen}, title = {Climate change mitigation potential of community-based initiatives in Europe}, series = {Regional environmental change}, volume = {19}, journal = {Regional environmental change}, number = {4}, publisher = {Springer}, address = {Heidelberg}, issn = {1436-3798}, doi = {10.1007/s10113-018-1428-1}, pages = {927 -- 938}, year = {2018}, abstract = {There is a growing recognition that a transition to a sustainable low-carbon society is urgently needed. This transition takes place at multiple and complementary scales, including bottom-up approaches such as community-based initiatives (CBIs). However, empirical research on CBIs has focused until now on anecdotal evidence and little work has been done to quantitatively assess their impact in terms of greenhouse gas (GHG) emissions. In this paper, we analyze 38 European initiatives across the food, energy, transport, and waste sectors to address the following questions: How can the GHG reduction potential of CBIs be quantified and analyzed in a systematic manner across different sectors? What is the GHG mitigation potential of CBIs and how does the reduction potential differ across domains? Through the comparison of the emission intensity arising from the goods and services the CBIs provide in relation to a business-as-usual scenario, we present the potential they have across different activities. This constitutes the foundational step to upscaling and further understanding their potential contribution to achieving climate change mitigation targets. Our findings indicate that energy generation through renewable sources, changes in personal transportation, and dietary change present by far the highest GHG mitigation activities analyzed, since they reduce the carbon footprint of CBI beneficiaries by 24\%, 11\%, and 7\%, respectively. In contrast, the potential for some activities, such as locally grown organic food, is limited. The service provided by these initiatives only reduces the carbon footprint by 0.1\%. Overall, although the proliferation of CBIs is very desirable from a climate change mitigation perspective it is necessary to stress that bottom-up initiatives present other important positive dimensions besides GHG mitigation. These initiatives also hold the potential of improving community resilience by strengthening local economies and enhancing social cohesion.}, language = {en} } @article{RybskiReusserWinzetal.2016, author = {Rybski, Diego and Reusser, Dominik Edwin and Winz, Anna-Lena and Fichtner, Christina and Sterzel, Till and Kropp, J{\"u}rgen}, title = {Cities as nuclei of sustainability?}, series = {Environment and Planning B: Urban Analytics and City Science}, volume = {44}, journal = {Environment and Planning B: Urban Analytics and City Science}, number = {3}, publisher = {Sage Publ.}, address = {London}, issn = {2399-8083}, doi = {10.1177/0265813516638340}, pages = {425 -- 440}, year = {2016}, abstract = {We have assembled CO2 emission figures from collections of urban GHG emission estimates published in peer-reviewed journals or reports from research institutes and non-governmental organizations. Analyzing the scaling with population size, we find that the exponent is development dependent with a transition from super- to sub-linear scaling. From the climate change mitigation point of view, the results suggest that urbanization is desirable in developed countries. Further, we compare this analysis with a second scaling relation, namely the fundamental allometry between city population and area, and propose that density might be a decisive quantity too. Last, we derive the theoretical country-wide urban emissions by integration and obtain a dependence on the size of the largest city.}, language = {en} } @misc{ReusserBlumeSchaeflietal.2009, author = {Reusser, Dominik and Blume, Theresa and Schaefli, Bettina and Zehe, Erwin}, title = {Analysing the temporal dynamics of model performance for hydrological models}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45114}, year = {2009}, abstract = {The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.}, language = {en} }