@article{GerlitzSteirouSchneideretal.2019, author = {Gerlitz, Lars and Steirou, Eva and Schneider, Christoph and Moron, Vincent and Vorogushyn, Sergiy and Merz, Bruno}, title = {Variability of the Cold Season Climate in Central Asia. Part II: Hydroclimatic Predictability}, series = {Journal of climate}, volume = {32}, journal = {Journal of climate}, number = {18}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0894-8755}, doi = {10.1175/JCLI-D-18-0892.1}, pages = {6015 -- 6033}, year = {2019}, abstract = {Central Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November-March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Nino. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions.}, language = {en} } @article{MerzVietDungNguyenVorogushyn2016, author = {Merz, Bruno and Viet Dung Nguyen, and Vorogushyn, Sergiy}, title = {Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist?}, series = {Journal of hydrology}, volume = {541}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.07.041}, pages = {824 -- 838}, year = {2016}, abstract = {The repeated occurrence of exceptional floods within a few years, such as the Rhine floods in 1993 and 1995 and the Elbe and Danube floods in 2002 and 2013, suggests that floods in Central Europe may be organized in flood-rich and flood-poor periods. This hypothesis is studied by testing the significance of temporal clustering in flood occurrence (peak-over-threshold) time series for 68 catchments across Germany for the period 1932-2005. To assess the robustness of the results, different methods are used: Firstly, the index of dispersion, which quantifies the departure from a homogeneous Poisson process, is investigated. Further, the time-variation of the flood occurrence rate is derived by non-parametric kernel implementation and the significance of clustering is evaluated via parametric and non-parametric tests. Although the methods give consistent overall results, the specific results differ considerably. Hence, we recommend applying different methods when investigating flood clustering. For flood estimation and risk management, it is of relevance to understand whether clustering changes with flood severity and time scale. To this end, clustering is assessed for different thresholds and time scales. It is found that the majority of catchments show temporal clustering at the 5\% significance level for low thresholds and time scales of one to a few years. However, clustering decreases substantially with increasing threshold and time scale. We hypothesize that flood clustering in Germany is mainly caused by catchment memory effects along with intra- to inter-annual climate variability, and that decadal climate variability plays a minor role. (C) 2016 Elsevier B.V. All rights reserved.}, language = {en} } @article{MurawskiBuergerVorogushynetal.2016, author = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno}, title = {Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin}, series = {Hydrology and earth system sciences : HESS}, volume = {20}, journal = {Hydrology and earth system sciences : HESS}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-20-4283-2016}, pages = {4283 -- 4306}, year = {2016}, abstract = {To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.}, language = {en} } @misc{MurawskiBuergerVorogushynetal.2016, author = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno}, title = {Can local climate variability be explained by weather patterns?}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {525}, issn = {1866-8372}, doi = {10.25932/publishup-41015}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410155}, pages = {24}, year = {2016}, abstract = {To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.}, language = {en} } @article{DuethmannBolchFarinottietal.2015, author = {Duethmann, Doris and Bolch, Tobias and Farinotti, Daniel and Kriegel, David and Vorogushyn, Sergiy and Merz, Bruno and Pieczonka, Tino and Jiang, Tong and Su, Buda and G{\"u}ntner, Andreas}, title = {Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia}, series = {Water resources research}, volume = {51}, journal = {Water resources research}, number = {6}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1002/2014WR016716}, pages = {4727 -- 4750}, year = {2015}, abstract = {Observed streamflow of headwater catchments of the Tarim River (Central Asia) increased by about 30\% over the period 1957-2004. This study aims at assessing to which extent these streamflow trends can be attributed to changes in air temperature or precipitation. The analysis includes a data-based approach using multiple linear regression and a simulation-based approach using a hydrological model. The hydrological model considers changes in both glacier area and surface elevation. It was calibrated using a multiobjective optimization algorithm with calibration criteria based on glacier mass balance and daily and interannual variations of discharge. The individual contributions to the overall streamflow trends from changes in glacier geometry, temperature, and precipitation were assessed using simulation experiments with a constant glacier geometry and with detrended temperature and precipitation time series. The results showed that the observed changes in streamflow were consistent with the changes in temperature and precipitation. In the Sari-Djaz catchment, increasing temperatures and related increase of glacier melt were identified as the dominant driver, while in the Kakshaal catchment, both increasing temperatures and increasing precipitation played a major role. Comparing the two approaches, an advantage of the simulation-based approach is the fact that it is based on process-based relationships implemented in the hydrological model instead of statistical links in the regression model. However, data-based approaches are less affected by model parameter and structural uncertainties and typically fast to apply. A complementary application of both approaches is recommended.}, language = {en} } @article{MerzVorogushynLalletal.2015, author = {Merz, Bruno and Vorogushyn, Sergiy and Lall, Upmanu and Viglione, Alberto and Bl{\"o}schl, G{\"u}nter}, title = {Charting unknown waters-On the role of surprise in flood risk assessment and management}, series = {Water resources research}, volume = {51}, journal = {Water resources research}, number = {8}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1002/2015WR017464}, pages = {6399 -- 6416}, year = {2015}, abstract = {Unexpected incidents, failures, and disasters are abundant in the history of flooding events. In this paper, we introduce the metaphors of terra incognita and terra maligna to illustrate unknown and wicked flood situations, respectively. We argue that surprise is a neglected element in flood risk assessment and management. Two sources of surprise are identified: (1) the complexity of flood risk systems, represented by nonlinearities, interdependencies, and nonstationarities and (2) cognitive biases in human perception and decision making. Flood risk assessment and management are particularly prone to cognitive biases due to the rarity and uniqueness of extremes, and the nature of human risk perception. We reflect on possible approaches to better understanding and reducing the potential for surprise and its adverse consequences which may be supported by conceptually charting maps that separate terra incognita from terra cognita, and terra maligna from terra benigna. We conclude that flood risk assessment and management should account for the potential for surprise and devastating consequences which will require a shift in thinking.}, language = {en} } @phdthesis{Vorogushyn2008, author = {Vorogushyn, Sergiy}, title = {Analysis of flood hazard under consideration of dike breaches}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-27646}, school = {Universit{\"a}t Potsdam}, year = {2008}, abstract = {River reaches protected by dikes exhibit high damage potential due to strong value accumulation in the hinterland areas. While providing an efficient protection against low magnitude flood events, dikes may fail under the load of extreme water levels and long flood durations. Hazard and risk assessments for river reaches protected by dikes have not adequately considered the fluvial inundation processes up to now. Particularly, the processes of dike failures and their influence on the hinterland inundation and flood wave propagation lack comprehensive consideration. This study focuses on the development and application of a new modelling system which allows a comprehensive flood hazard assessment along diked river reaches under consideration of dike failures. The proposed Inundation Hazard Assessment Model (IHAM) represents a hybrid probabilistic-deterministic model. It comprises three models interactively coupled at runtime. These are: (1) 1D unsteady hydrodynamic model of river channel and floodplain flow between dikes, (2) probabilistic dike breach model which determines possible dike breach locations, breach widths and breach outflow discharges, and (3) 2D raster-based diffusion wave storage cell model of the hinterland areas behind the dikes. Due to the unsteady nature of the 1D and 2D coupled models, the dependence between hydraulic load at various locations along the reach is explicitly considered. The probabilistic dike breach model describes dike failures due to three failure mechanisms: overtopping, piping and slope instability caused by the seepage flow through the dike core (micro-instability). The 2D storage cell model driven by the breach outflow boundary conditions computes an extended spectrum of flood intensity indicators such as water depth, flow velocity, impulse, inundation duration and rate of water rise. IHAM is embedded in a Monte Carlo simulation in order to account for the natural variability of the flood generation processes reflected in the form of input hydrographs and for the randomness of dike failures given by breach locations, times and widths. The model was developed and tested on a ca. 91 km heavily diked river reach on the German part of the Elbe River between gauges Torgau and Vockerode. The reach is characterised by low slope and fairly flat extended hinterland areas. The scenario calculations for the developed synthetic input hydrographs for the main river and tributary were carried out for floods with return periods of T = 100, 200, 500, 1000 a. Based on the modelling results, probabilistic dike hazard maps could be generated that indicate the failure probability of each discretised dike section for every scenario magnitude. In the disaggregated display mode, the dike hazard maps indicate the failure probabilities for each considered breach mechanism. Besides the binary inundation patterns that indicate the probability of raster cells being inundated, IHAM generates probabilistic flood hazard maps. These maps display spatial patterns of the considered flood intensity indicators and their associated return periods. Finally, scenarios of polder deployment for the extreme floods with T = 200, 500, 1000 were simulated with IHAM. The developed IHAM simulation system represents a new scientific tool for studying fluvial inundation dynamics under extreme conditions incorporating effects of technical flood protection measures. With its major outputs in form of novel probabilistic inundation and dike hazard maps, the IHAM system has a high practical value for decision support in flood management.}, language = {en} }