@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} } @article{BuergerReusserKneis2009, author = {B{\"u}rger, Gerd and Reusser, Dominik and Kneis, David}, title = {Early flood warnings from empirical (expanded) downscaling of the full ECMWF Ensemble Prediction System}, issn = {0043-1397}, doi = {10.1029/2009wr007779}, year = {2009}, language = {en} } @article{ReusserZehe2011, author = {Reusser, Dominik Edwin and Zehe, Erwin}, title = {Low-cost monitoring of snow height and thermal properties with inexpensive temperature sensors}, series = {Hydrological processes}, volume = {25}, journal = {Hydrological processes}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0885-6087}, doi = {10.1002/hyp.7937}, pages = {1841 -- 1852}, year = {2011}, abstract = {Small, self-recording temperature sensors were installed at several heights along a metal rod at five locations in a case study catchment. For each sensor, the presence or absence of snow cover was determined on the basis of its insulating effect and the resulting reduction of the diurnal temperature oscillations. Sensor coverage was then converted into a time series of snow height for each location. Additionally, cold content was calculated. Snow height and cold content provide valuable information for spring flood prediction. Good agreement of estimated snow heights with reference measurements was achieved and increased discharge in the study catchment coincided with low cold content of the snow cover. The results of the proposed distributed assessment of snow cover and snow state show great potential for (i) flood warning, (ii) assimilation of snow state data and (iii) modelling snowmelt process.}, language = {en} } @article{ReusserZehe2011, author = {Reusser, Dominik Edwin and Zehe, Erwin}, title = {Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity}, series = {Water resources research}, volume = {47}, journal = {Water resources research}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2010WR009946}, pages = {15}, year = {2011}, abstract = {In this paper we investigate the use of hydrological models as learning tools to help improve our understanding of the hydrological functioning of a catchment. With the model as a hypothetical conceptualization of how dominant hydrological processes contribute to catchment-scale response, we investigate three questions: (1) During which periods does the model (not) reproduce observed quantities and dynamics? (2) What is the nature of the error during times of bad model performance? (3) Which model components are responsible for this error? To investigate these questions, we combine a method for detecting repeating patterns of typical differences between model and observations (time series of grouped errors, TIGER) with a method for identifying the active model components during each simulation time step based on parameter sensitivity (temporal dynamics of parameter sensitivities, TEDPAS). The approach generates a time series of occurrence of dominant error types and time series of parameter sensitivities. A synoptic discussion of these time series highlights deficiencies in the assumptions about the functioning of the catchment. The approach is demonstrated for the Weisseritz headwater catchment in the eastern Ore Mountains. Our results indicate that the WaSiM-ETH complex grid-based model is not a sufficient working hypothesis for the functioning of the Weisseritz catchment and point toward future steps that can help improve our understanding of the catchment.}, language = {en} } @article{ReusserBuytaertZehe2011, author = {Reusser, Dominik Edwin and Buytaert, W. and Zehe, Erwin}, title = {Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test}, series = {Water resources research}, volume = {47}, journal = {Water resources research}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2010WR009947}, pages = {14}, year = {2011}, abstract = {The quest for improved hydrological models is one of the big challenges in hydrology. When discrepancies are observed between simulated and measured discharge, it is essential to identify which algorithms may be responsible for poor model behavior. Particularly in complex hydrological models, different process representations may dominate at different moments and interact with each other, thus highly complicating this task. This paper investigates the analysis of the temporal dynamics of parameter sensitivity as a way to disentangle the simulation of a hydrological model and identify dominant parameterizations. Three existing methods (the Fourier amplitude sensitivity test, the extended Fourier amplitude sensitivity test, and Sobol's method) are compared by applying them to a TOPMODEL implementation in a small mountainous catchment in the tropics. For the major part of the simulation period, the three methods give comparable results, while the Fourier amplitude sensitivity test is much more computationally efficient. This method is also applied to the complex hydrological model WaSiM-ETH implemented in the Weisseritz catchment, Germany. A qualitative model validation was performed on the basis of the identification of relevant model components. The validation revealed that the saturation deficit parameterization of WaSiM-ETH is highly susceptible to parameter interaction and lack of identifiability. We conclude that temporal dynamics of model parameter sensitivity can be a powerful tool for hydrological model analysis, especially to identify parameter interaction as well as the dominant hydrological response modes. Finally, an open source implementation of the Fourier amplitude sensitivity test is provided.}, 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} } @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} } @article{GraeffZeheReusseretal.2009, author = {Gr{\"a}ff, Thomas and Zehe, Erwin and Reusser, Dominik and Lueck, Erika and Schroeder, Boris and Wenk, Gerald and John, Hermann and Bronstert, Axel}, title = {Process identification through rejection of model structures in a mid-mountainous rural catchment : observations of rainfall-runoff response, geophysical conditions and model inter-comparison}, issn = {0885-6087}, doi = {10.1002/Hyp.7171}, year = {2009}, abstract = {The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall-runoff responses which occurred up to the mid-1970s regularly in the Schafertal catchment and vanished after the onset of mining activities. Understanding, this process is a first step to understanding the ongoing hydrological change in this area. It is hypothesized that either subsurface stormflow, or fast displacement of groundwater, could cause the second delayed peak. A top-down analysis of rainfall-runoff data, field observations as well as process modelling are combined within a rejectionistic framework. A statistical analysis is used to test whether different predictors. which characterize the forcing. near surface water content and deeper subsurface store, allow the prediction of the type of rainfall-runoff response. Regression analysis is used with generalized linear models Lis they can deal with non-Gaussian error distributions Lis well its a non-stationary variance. The analysis reveals that the dominant predictors are the pre-event discharge (proxy of state of the groundwater store) and the precipitation amount, In the field campaign, the subsurface at a representative hillslope was investigated by means of electrical resistivity tomography in order to identify possible strata as flow paths for subsurface stormflow. A low resistivity in approximately 4 in depth-either due to a less permeable layer or the groundwater surface-was detected. The former Could serve as a flow path for subsurface stormflow. Finally, the physical-based hydrological model CATFLOW and the groundwater model FEFLOW are compared with respect to their ability to reproduce the bimodal runoff responses. The groundwater model is able to reproduce the observations, although it uses only an abstract representation of the hillslopes. Process model analysis as well Lis statistical analysis strongly suggest that fast displacement of groundwater is the dominant process underlying the bimodal runoff reactions.}, language = {en} }