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The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model
(2019)
In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.
Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
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.
In hydrology, the storage-discharge relationship is a fundamental catchment property. Understanding what controls this relationship is at the core of catchment science. To date, there are no direct methods to measure water storage at catchment scales (10(1)-10(3)km(2)). In this study, we use direct measurements of terrestrial water storage dynamics by means of superconducting gravimetry in a small headwater catchment of the Regen River, Germany, to derive empirical storage-discharge relationships in nested catchments of increasing scale. Our results show that the local storage measurements are strongly related to streamflow dynamics at larger scales (> 100km(2); correlation coefficient=0.78-0.81), but at small scale, no such relationship exists (similar to 1km(2); correlation coefficients=-0.11). The geologic setting in the region can explain both the disconnection between local water storage and headwater runoff, and the connectivity between headwater storage and streams draining larger catchment areas. More research is required to understand what controls the form of the observed storage-discharge relationships at the catchment scale. This study demonstrates that high-precision gravimetry can provide new insights into the complex relationship between state and response of hydrological systems.
Despite its societal relevance, the question whether fluctuations in flood occurrence or magnitude are coherent in space has hardly been addressed in quantitative terms. We investigate this question for Germany by analysing fluctuations in annual maximum series (AMS) values at 68 discharge gauges for the common time period 1932-2005. We find remarkable spatial coherence across Germany given its different flood regimes. For example, there is a tendency that flood-rich/-poor years in sub-catchments of the Rhine basin, which are dominated by winter floods, coincide with flood-rich/-poor years in the southern sub-catchments of the Danube basin, which have their dominant flood season in summer. Our findings indicate that coherence is caused rather by persistence in catchment wetness than by persistent periods of higher/lower event precipitation. Further, we propose to differentiate between event-type and non-event-type coherence. There are quite a number of hydrological years with considerable nonevent-type coherence, i.e. AMS values of the 68 gauges are spread out through the year but in the same magnitude range. Years with extreme flooding tend to be of event-type and non-coherent, i.e. there is at least one precipitation event that affects many catchments to various degree. Although spatial coherence is a remarkable phenomenon, and large-scale flooding across Germany can lead to severe situations, extreme magnitudes across the whole country within one event or within one year were not observed in the investigated period. (C) 2018 Elsevier B.V. All rights reserved.
Sedimentation in the floodplains of the Mekong Delta, Vietnam. Part I: suspended sediment dynamics
(2014)
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.
Sedimentation in the floodplains of the Mekong Delta, Vietnam Part II: deposition and erosion
(2014)
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.
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.
Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
Quantifying the roles of single stations within homogeneous regions using complex network analysis
(2018)
Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.