90 research outputs found
Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries
No full textWhen river and coastal floods coincide, their impacts are often worse than when they occur in isolation; such floods are examples of ‘compound events’. To better understand the impacts of these compound events, we require an improved understanding of the dependence between coastal and river flooding on a global scale. Therefore, in this letter, we: provide the first assessment and mapping of the dependence between observed high sea-levels and high river discharge for deltas and estuaries around the globe; and demonstrate how this dependence may influence the joint probability of floods exceeding both the design discharge and design sea-level. The research was carried out by analysing the statistical dependence between observed sea-levels (and skew surge) from the GESLA-2 dataset, and river discharge using gauged data from the Global Runoff Data Centre, for 187 combinations of stations across the globe. Dependence was assessed using Kendall’s rank correlation coefficient () and copula models. We find significant dependence for skew surge conditional on annual maximum discharge at 22% of the stations studied, and for discharge conditional on annual maximum skew surge at 36% of the stations studied. Allowing a time-lag between the two variables up to 5 days, we find significant dependence for skew surge conditional on annual maximum discharge at 56% of stations, and for discharge conditional on annual maximum skew surge at 54% of stations. Using copula models, we show that the joint exceedance probability of events in which both the design discharge and design sea-level are exceeded can be several magnitudes higher when the dependence is considered, compared to when independence is assumed. We discuss several implications, showing that flood risk assessments in these regions should correctly account for these joint exceedance probabilities
Compound flood potential from river discharge and storm surge extremes at the global scale
No full textThis dataset presents the results presented in Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale. For more information about the methods, please refer to the paper. This dataset was created using as input time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014.
If using this data, please cite:
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p
Measuring compound flood potential from river discharge and storm surge extremes at the global scale
Full text linkThe interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.Water Resource
A comparison of two global datasets of extreme sea levels and resulting flood exposure
No full textEstimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The DIVA model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population and is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure
Droughts and Decisions: Pastoralism, Decision Junctures and Rain Forecasting
No full textThe livelihood of the Maasai pastoral communities in Longido District of Northern Tanzania are impacted by droughts regularly, with expectations of increasing variability in rainfall patterns the coming years due to climate change. The goal of this research is to explore if weather forecast and remote sensing data can be tailored to existing coping strategies and decision-making. Furthermore, it is assessed if this tailored information provides enough skill to effectively complement local knowledge and drought management strategies. The study generated important methodological and theoretical findings, both of which have practical implications for policy and technological development. An ethnographic and participatory approach, including four months of immersion with local families, was used to document local knowledge and strategies, and understand what specific, weather information may benefit pastoralists. The study focused on alamei periods, which refers to times of drought and scarcity in the Maasai language. It revealed that weather information around particular important ‘decision junctures’ is most relevant. On the one hand, decisions to move livestock during vulnerable times are based on current water and grass availability; on the other hand, families also consider expectations of rainfall in their decisions. The research determined that at very specific junctures throughout respective seasons, key, timely decisions must be made to maintain household resiliency. It is at these junctures that rainfall predictions become crucial. Using NDVI data and the ECMWF weather model, it was assessed if the onset of rains at such junctures can be predicted with enough skill to support livestock movement decisions. It revealed both optimism and scepticism about the role of current remote sensing and weather prediction technologies vis-à-vis variable, dryland ecologies and pastoral livelihoods
De vormgeving van de vroege Friese geschiedschrijving
No full textAbstractBegun in 1568, the revolt of the Netherlands against the Spanish stimulated every Dutch province to strive to attain the greatest possible autonomy and independence from the dominant province of Holland. One of the arguments forwarded for pursuing this independent course was how ancient a region was (laudatio ex vetustate). Incidentally, it was Holland with its Batavian myth that had a strong suit in hand in this matter. To counter this, historiographers were appointed to confirm their region's age. In this capacity, the States of Friesland designated Suffridus Petrus (1527-1597), Bernardus Furmerius (1542-1616) and Pierius Winsemius (1586-1644) consecutively. Relying on traditional accounts, which they believed were ancient, Petrus and Furmerius established a line of legendary Frisian monarchs, beginning with Friso - banished from India - who was said to be a descendant of Noah's son Sem. The results of their scholarly research were published in small-scale, unillustrated books in Latin. Not officially commissioned as a historiographer, around 1597 Martinus Hamconius (c. 1550-1620), wrote an acrostic on the name of Suffridus Petrus, which comprised an ekphrasis with an animated description of the legendary Frisians. In 1606 he also devised a table (fig. i) in which all the characters who played a role in the illustrious history of Friesland are described in Latin. This cast of characters was published again in 1617, this time in Dutch (fig. 2). A lost copy of this edition featured illustrations (fig. 3), which were reused in an edition of Hamconius' Frisia (1620) (figs. 17, 20, 21). The tableau of 1617 includes several old Frisian traditional costumes (fig. 10). All the prints were made by Pieter Feddes of Harlingen. A second set of illustrations of the Frisian princes was etched by Simon Wynhoutsz. Frisius around 1617. These prints, known only from Pierius Winsemius' Chronique of 1622 (figs. 15, 18, 19), originally constituted a consecutive series (fig. 13), doubtless intended to illustrate Hamconius' treatise and probably made for his publisher Jan Lamrinck, who (according to the author's hypothesis) could not use it and thus cut down the plates and included them in Winsemius' Chronique, which he also published. A third, incomplete series of illustrations (fig. 14), again by Pieter Feddes, was likewise made to illustrate Hamconius' series, but may have been rejected and likewise used in the Chronique. Some details in four of the figures in both series (figs. 15-23) seem to point to the iconographic tradition of the free Frisian countryman.
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Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic-hydrodynamic model coupling framework
No full textFluvial flood events are a major threat to people and infrastructure. Typically, flood hazard is driven by hydrologic or river routing and floodplain flow processes. Since they are often simulated by different models, coupling these models may be a viable way to increase the integration of different physical drivers of simulated inundation estimates. To facilitate coupling different models and integrating across flood hazard processes, we here present GLOFRIM 2.0, a globally applicable framework for integrated hydrologic-hydrodynamic modelling. We then tested the hypothesis that smart model coupling can advance inundation modelling in the Amazon and Ganges basins. By means of GLOFRIM, we coupled the global hydrologic model PCR-GLOBWB with the hydrodynamic models CaMa-Flood and LISFLOOD-FP. Results show that replacing the kinematic wave approximation of the hydrologic model with the local inertia equation of CaMa-Flood greatly enhances accuracy of peak discharge simulations as expressed by an increase in the Nash-Sutcliffe efficiency (NSE) from 0.48 to 0.71. Flood maps obtained with LISFLOOD-FP improved representation of observed flood extent (critical success index C = 0:46), compared to downscaled products of PCR-GLOBWB and CaMa-Flood (C = 0:30 and C = 0:25, respectively). Results confirm that model coupling can indeed be a viable way forward towards more integrated flood simulations. However, results also suggest that the accuracy of coupled models still largely depends on the model forcing. Hence, further efforts must be undertaken to improve the magnitude and timing of simulated runoff. In addition, flood risk is, particularly in delta areas, driven by coastal processes. A more holistic representation of flood processes in delta areas, for example by incorporating a tide and surge model, must therefore be a next development step of GLOFRIM, making even more physically robust estimates possible for adequate flood risk management practices.</p
Benchmarking flexible meshes and regular grids for large-scale fluvial inundation modelling
No full textDamage resulting from flood events is increasing world-wide, requiring the implementation of mitigation and adaption measures. To facilitate their implementation, it is essential to correctly model flood hazard at the large scale, yet fine spatial resolution. To reduce the computational load of models, flexible meshes are an efficient means compared to uniform regular grids. Yet, thus far they have been applied only for bespoke small-scale studies requiring a high level of a priori grid preparation. To better understand possible advantages as well as shortcomings of their application for large-scale riverine inundation simulations, three different flexible meshes were derived from Height Above Nearest Drainage (HAND) data and compared with regular grids under identical spatially explicit hydrologic forcing by using GLOFRIM, a framework for integrated hydrologic-hydrodynamic inundation modelling. By means of GLOFRIM, output from the global hydrologic model PCR-GLOBWB was passed to the hydrodynamic model Delft3D Flexible Mesh. Results show that applying flexible meshes can be beneficial depending on the envisaged purpose. For discharge simulations, similar model accuracy was obtained between flexible and regular grids, with the former generally having shorter run times. For inundation extent simulations, however, the coarser gridding of flexible meshes in upstream areas results in a poorer performance if assessed by contingency maps. Moreover, while the ratio between minimum and maximum spatial resolution of flexible meshes has limited impact on discharge simulations, water level estimates may be stronger influenced by the application of larger grid cells.. As this study presents only a small set of possible realizations, additional research needs to unravel how the data and methods used as well as the choices for discretizations influence model performance. Generally, the application and particularly discretization process of flexible meshes involves more options, bringing more responsibilities for the user. Once an a priori decision is made on the model purpose, flexible meshes can be a valuable addition to modelling approaches where short run times are essential, facilitating large-scale flood simulations, ensemble modelling or operational flood forecasting
Correction to: Mode of action-based risk assessment of genotoxic carcinogens
Full text linkThe author would like to thank N. Bakhiya, S. Hessel-Pras, B. Sachse, and B. Dusemund for their support in the chapter about pyrrolizidine alkaloids
River Flood Detection Using Passive Microwave Remote Sensing in a Data-Scarce Environment: A Case Study for Two River Basins in Malawi
No full textDetecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting rcm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (rcm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: rcmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, rcmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R2 = 0.369) than the latter (R2 = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.</p
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