@article{NguyenNghiaHungDelgadoGuentneretal.2014,
author = {Nguyen Nghia Hung, and Delgado, Jos{\´e} Miguel Martins and G{\"u}ntner, Andreas and Merz, Bruno and Bardossy, Andras and Apel, Heiko},
title = {Sedimentation in the floodplains of the Mekong Delta, Vietnam. Part I: suspended sediment dynamics},
series = {Hydrological processes},
volume = {28},
journal = {Hydrological processes},
number = {7},
publisher = {Wiley-Blackwell},
address = {Hoboken},
issn = {0885-6087},
doi = {10.1002/hyp.9856},
pages = {3132 -- 3144},
year = {2014},
abstract = {Suspended sediment is the primary source for a sustainable agro-ecosystem in the Mekong Delta by providing nutrient input for the subsequent cropping season. In addition, the suspended sediment concentration (SSC) plays an important role in the erosion and deposition processes in the Delta; that is, it influences the morphologic development and may counteract the deltaic subsidence and sea level rise. Despite this importance, little is known about the dynamics of suspended sediment in the floodplains of the Mekong Delta. In particular, quantitative analyses are lacking mainly because of data scarcity with respect to the inundation processes in the floodplains. In 2008, therefore, a comprehensive in situ system to monitor the dynamics of suspended sediment in a study area located in the Plain of Reeds was established, aiming at the characterization and quantification of suspended sediment dynamics in the deeply inundated parts of the Vietnamese part of the Mekong Delta. The monitoring system was equipped with seven water quality-monitoring stations. They have a robust design and autonomous power supply suitable for operation on inundated floodplains, enabling the collection of reliable data over a long period of time with a high temporal resolution. The data analysis shows that the general seasonal dynamics of suspended sediment transport in the Delta is controlled by two main mechanisms: the flood wave of the Mekong River and the tidal backwater influences from the coast. In the channel network, SSC decreases exponentially with distance from the Mekong River. The anthropogenic influence on SSC could also be identified for two periods: at the start of the floodplain inundation and at the end of the flood period, when subsequent paddy rice crops are prepared. Based on the results, we recommend an operation scheme for the sluice gates, which intends to distribute the sediment and thus the nutrients equally over the floodplain.},
language = {en}
}
@article{VorogushynApelKemteretal.2022,
author = {Vorogushyn, Sergiy and Apel, Heiko and Kemter, Matthias and Thieken, Annegret},
title = {Analyse der Hochwassergef{\"a}hrdung im Ahrtal unter Ber{\"u}cksichtigung historischer Hochwasser},
series = {Hydrologie und Wasserbewirtschaftung},
volume = {66},
journal = {Hydrologie und Wasserbewirtschaftung},
number = {5},
publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde},
address = {Koblenz},
issn = {1439-1783},
doi = {10.5675/HyWa_2022.5_2},
pages = {244 -- 254},
year = {2022},
abstract = {The flood disaster in July 2021 in western Germany calls for a critical discussion on flood hazard assessment, revision of flood hazard maps and communication of extreme flood scenarios. In the presented work, extreme value analysis was carried out for annual maximum peak flow series at the Altenahr gauge on the river Ahr. We compared flood statistics with and without considering historical flood events. An estimate for the return period of the recent flood based on the Generalized Extreme Value (GEV) distribution considering historical floods ranges between about 2600 and above 58700 years (90\% confidence interval) with a median of approximately 8600 years, whereas an estimate based on the 74-year long systematically recorded flow series would theoretically exceed 100 million years. Consideration of historical floods dramatically changes the flood quantiles that are used for the generation of official flood hazard maps. The fitting of the GEV to the time series with historical floods reveals, however, that the model potentially inadequately reflects the flood population. In this case, we might face a mixed sample, in which extreme floods result from very different processes compared to smaller floods. Hence, the probabilities of extreme floods could be much larger than those resulting from a single GEV model. The application of a process-based mixed flood distribution should be explored in future work.
The comparison of the official HQextrem flood maps for the AhrValley with the inundation areas from July 2021 shows a striking discrepancy in the affected areas and calls for revision of design values used to define extreme flood scenarios. The hydrodynamic simulations of a 1000-year return period flood considering historical events and of the 1804 flood scenario compare much better to the flooded areas from July 2021, though both scenarios still underestimated the flood extent.
Particular effects such as clogging of bridges and geomorphological changes of the river channel led to considerably larger flooded areas in July 2021 compared to the simulation results. Based on this analysis, we call for a consistent definition of HQextrem for flood hazard mapping in Germany, and suggest using high flood quantiles in the range of a 1,000-year flood. Flood maps should additionally include model-based reconstructions of the largest, reliably documented historical floods and/or synthetic worst-case scenarios. This would be an important step towards protecting potentially affected population and disaster management from surprises due to very rare and extreme flood events in future.},
language = {de}
}
@article{ThiekenApelMerz2015,
author = {Thieken, Annegret and Apel, Heiko and Merz, Bruno},
title = {Assessing the probability of large-scale flood loss events: a case study for the river Rhine, Germany},
series = {Journal of flood risk management},
volume = {8},
journal = {Journal of flood risk management},
number = {3},
publisher = {Wiley-Blackwell},
address = {Hoboken},
issn = {1753-318X},
doi = {10.1111/jfr3.12091},
pages = {247 -- 262},
year = {2015},
abstract = {Flood risk analyses are often estimated assuming the same flood intensity along the river reach under study, i.e. discharges are calculated for a number of return periods T, e.g. 10 or 100 years, at several streamflow gauges. T-year discharges are regionalised and then transferred into T-year water levels, inundated areas and impacts. This approach assumes that (1) flood scenarios are homogeneous throughout a river basin, and (2) the T-year damage corresponds to the T-year discharge. Using a reach at the river Rhine, this homogeneous approach is compared with an approach that is based on four flood types with different spatial discharge patterns. For each type, a regression model was created and used in a Monte-Carlo framework to derive heterogeneous scenarios. Per scenario, four cumulative impact indicators were calculated: (1) the total inundated area, (2) the exposed settlement and industrial areas, (3) the exposed population and 4) the potential building loss. Their frequency curves were used to establish a ranking of eight past flood events according to their severity. The investigation revealed that the two assumptions of the homogeneous approach do not hold. It tends to overestimate event probabilities in large areas. Therefore, the generation of heterogeneous scenarios should receive more attention.},
language = {en}
}
@article{DelgadoMerzApel2014,
author = {Delgado, Jose Miguel Martins and Merz, Bruno and Apel, Heiko},
title = {Projecting flood hazard under climate change: an alternative approach to model chains},
series = {Natural hazards and earth system sciences},
volume = {14},
journal = {Natural hazards and earth system sciences},
number = {6},
publisher = {Copernicus},
address = {G{\"o}ttingen},
issn = {1561-8633},
doi = {10.5194/nhess-14-1579-2014},
pages = {1579 -- 1589},
year = {2014},
abstract = {Flood hazard projections under climate change are typically derived by applying model chains consisting of the following elements: "emission scenario - global climate model - downscaling, possibly including bias correction hydrological model - flood frequency analysis". To date, this approach yields very uncertain results, due to the difficulties of global and regional climate models to represent precipitation. The implementation of such model chains requires major efforts, and their complexity is high. We propose for the Mekong River an alternative approach which is based on a shortened model chain: "emission scenario - global climate model - non-stationary flood frequency model". The underlying idea is to use a link between the Western Pacific monsoon and local flood characteristics: the variance of the monsoon drives a non-stationary flood frequency model, yielding a direct estimate of flood probabilities. This approach bypasses the uncertain precipitation, since the monsoon variance is derived from large-scale wind fields which are better represented by climate models. The simplicity of the monsoon-flood link allows deriving large ensembles of flood projections under climate change. We conclude that this is a worthwhile, complementary approach to the typical model chains in catchments where a substantial link between climate and floods is found.},
language = {en}
}
@article{NguyenVietDungMerzBardossyetal.2015,
author = {Nguyen Viet Dung, and Merz, Bruno and Bardossy, Andras and Apel, Heiko},
title = {Handling uncertainty in bivariate quantile estimation - An application to flood hazard analysis in the Mekong Delta},
series = {Journal of hydrology},
volume = {527},
journal = {Journal of hydrology},
publisher = {Elsevier},
address = {Amsterdam},
issn = {0022-1694},
doi = {10.1016/j.jhydrol.2015.05.033},
pages = {704 -- 717},
year = {2015},
abstract = {The hydrological load causing flood hazard is in many instances not only determined by peak discharge, but is a multidimensional problem. While the methodology for multivariate frequency analysis is well established, the estimation of the associated uncertainty is rarely studied. In this paper, a method is developed to quantify the different sources of uncertainty for a bivariate flood frequency analysis. The method is exemplarily developed for the Mekong Delta (MD), one of the largest and most densely populated river deltas worldwide. Floods in the MD are the basis for the livelihoods of the local population, but they are also the major hazard. This hazard has, however, not been studied within the frame of a probabilistic flood hazard analysis. The nature of the floods in the MD suggests a bivariate approach, because the societal flood severity is determined by both peak discharge and flood volume. The uncertainty caused by selection of statistical models and parameter estimation procedures are analyzed by applying different models and methods. For the quantification of the sampling uncertainty two bootstrapping methods were applied. The developed bootstrapping-based uncertainty estimation method shows that large uncertainties are associated with the estimation of bivariate flood quantiles. This uncertainty is much larger than the model selection and fitting uncertainty. Given the rather long data series of 88 years, it is concluded that bivariate flood frequency analysis is expected to carry significant uncertainty and that the quantification and reduction of uncertainty merit greater attention. But despite this uncertainty the proposed approach has certainly major advantages compared to a univariate approach, because (a) it reflects the two essential aspects of floods in this region, (b) the uncertainties are inherent for every bivariate frequency analysis in hydrology due to the general limited length of observations and can hardly be avoided, and (c) a framework for the quantification of the uncertainties is given, which can be used and interpreted in the hazard assessment. In addition it is shown by a parametric bootstrapping experiment how longer observation time series can reduce the sampling uncertainty. Based on this finding it is concluded that bivariate frequency analyses in hydrology would greatly benefit from discharge time series augmented by proxy or historical data, or by causal hydrologic expansion of time series. (C) 2015 Elsevier B.V. All rights reserved.},
language = {en}
}
@article{NguyenNghiaHungDelgadoGuentneretal.2014,
author = {Nguyen Nghia Hung, and Delgado, Jos{\´e} Miguel Martins and Guentner, Andreas and Merz, Bruno and Bardossy, Andras and Apel, Heiko},
title = {Sedimentation in the floodplains of the Mekong Delta, Vietnam Part II: deposition and erosion},
series = {Hydrological processes},
volume = {28},
journal = {Hydrological processes},
number = {7},
publisher = {Wiley-Blackwell},
address = {Hoboken},
issn = {0885-6087},
doi = {10.1002/hyp.9855},
pages = {3145 -- 3160},
year = {2014},
abstract = {Deposition and erosion play a key role in the determination of the sediment budget of a river basin, as well as for floodplain sedimentation. Floodplain sedimentation, in turn, is a relevant factor for the design of flood protection measures, productivity of agro-ecosystems, and for ecological rehabilitation plans. In the Mekong Delta, erosion and deposition are important factors for geomorphological processes like the compensation of deltaic subsidence as well as for agricultural productivity. Floodplain deposition is also counteracting the increasing climate change induced hazard by sea level rise in the delta. Despite this importance, a sediment database of the Mekong Delta is lacking, and the knowledge about erosion and deposition processes is limited. In the Vietnamese part of the Delta, the annually flooded natural floodplains have been replaced by a dense system of channels, dikes, paddy fields, and aquaculture ponds, resulting in floodplain compartments protected by ring dikes. The agricultural productivity depends on the sediment and associated nutrient input to the floodplains by the annual floods. However, no quantitative information regarding their sediment trapping efficiency has been reported yet. The present study investigates deposition and erosion based on intensive field measurements in three consecutive years (2008, 2009, and 2010). Optical backscatter sensors are used in combination with sediment traps for interpreting deposition and erosion processes in different locations. In our study area, the mean calculated deposition rate is 6.86kg/m(2) (approximate to 6mm/year). The key parameters for calculating erosion and deposition are estimated, i.e. the critical bed shear stress for deposition and erosion and the surface constant erosion rate. The bulk of the floodplain sediment deposition is found to occur during the initial stage of floodplain inundation. This finding has direct implications on the operation of sluice gates in order to optimize sediment input and distribution in the floodplains.},
language = {en}
}
@article{MetinNguyenVietDungSchroeteretal.2018,
author = {Metin, Ayse Duha and Nguyen Viet Dung, and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno},
title = {How do changes along the risk chain affect flood risk?},
series = {Natural hazards and earth system sciences},
volume = {18},
journal = {Natural hazards and earth system sciences},
number = {11},
publisher = {Copernicus},
address = {G{\"o}ttingen},
issn = {1561-8633},
doi = {10.5194/nhess-18-3089-2018},
pages = {3089 -- 3108},
year = {2018},
abstract = {Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.},
language = {en}
}
@article{DuyHeidbuechelMeyeretal.2018,
author = {Duy, Nguyen Le and Heidb{\"u}chel, Ingo and Meyer, Hanno and Merz, Bruno and Apel, Heiko},
title = {What controls the stable isotope composition of precipitation in the Mekong Delta?},
series = {Hydrology and earth system sciences : HESS},
volume = {22},
journal = {Hydrology and earth system sciences : HESS},
number = {2},
publisher = {Copernicus},
address = {G{\"o}ttingen},
issn = {1027-5606},
doi = {10.5194/hess-22-1239-2018},
pages = {1239 -- 1262},
year = {2018},
abstract = {This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables delta O-18, delta H-2, and d-excess of precipitation at the station of measurement. The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R-2 = 0.8) compared to single-factor linear regression (R-2 = 0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (similar to 70 \%) compared to local climatic conditions (similar to 30 \%); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not sig-nificant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for delta O-18 and delta H-2, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors. The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstruction of paleoclimates based on isotopic records.},
language = {en}
}
@article{SeibertMerzApel2017,
author = {Seibert, Mathias and Merz, Bruno and Apel, Heiko},
title = {Seasonal forecasting of hydrological drought in the Limpopo Basin},
series = {Hydrology and earth system sciences : HESS},
volume = {21},
journal = {Hydrology and earth system sciences : HESS},
publisher = {Copernicus},
address = {G{\"o}ttingen},
issn = {1027-5606},
doi = {10.5194/hess-21-1611-2017},
pages = {1611 -- 1629},
year = {2017},
abstract = {The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Nino and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42\% explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.},
language = {en}
}
@article{MerzApelDungNguyenetal.2018,
author = {Merz, Bruno and Apel, Heiko and Dung Nguyen, Viet-Dung and Falter, Daniela and Guse, Bj{\"o}rn and Hundecha, Yeshewatesfa and Kreibich, Heidi and Schr{\"o}ter, Kai and Vorogushyn, Sergiy},
title = {From precipitation to damage},
series = {Global flood hazard : applications in modeling, mapping and forecasting},
volume = {233},
journal = {Global flood hazard : applications in modeling, mapping and forecasting},
publisher = {American Geophysical Union},
address = {Washington},
isbn = {978-1-119-21788-6},
issn = {0065-8448},
doi = {10.1002/9781119217886.ch10},
pages = {169 -- 183},
year = {2018},
abstract = {Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale.},
language = {en}
}