Fluvial deposits of the Ahr river (western Germany) reveal recurring high‐magnitude flood events over the last 1,500 years

Historical flood events occurred before the begin of systematic flow records, represent valuable information that should be considered in flood frequency analyses. However, in most cases historical data is still stored in printed volumes and therefore not easily accessible and ready-to-use for hydrological analyses. The aim of this study is to compare the largest historical floods in the Danube and Main catchments to modern (i.e. between 1950 and 2022) floods in terms of their spatial, temporal and causal carachteristics. Here we collect, digitize, and compile a dataset of the largest historical flood events in the Danube and Main catchment between 1845 and 1950. The newly developed dataset contains daily and peak discharge and water level measurements observed at several locations for 13 and 9 flood events in the two catchments. Flood hydrographs were also recovered at several locations in the Danube catchment. Using the developed dataset, we compare the characteristics of the historical flood events to the characterisitcs of modern large flood events. The findings show that the historical flood discharges are among the largest ever measured in the two catchments and that large floods occur more frequently in summer than in the past. In summary, this work reviews the spatial, temporal and causal characteristics of these very large historical events in comparison with recent events and discusses the implications for flood hazard assessment.

Floods are among the most devastating and recurring natural hazards and have caused extensive economic losses to human lives and infrastructures around the world. Swat valley in northern Pakistan is prone to frequent floods and was severely affected by the Flood2010 in the recent past. Flood hazard assessment is a non-structural strategy for flood mitigation in addition to the structure measure. In this study, 60 km long reach of the River Swat (Khwazakhela Bridge–Chakdara Bridge) was modeled using the HEC-RAS 2D model and high-resolution 12-m WorldDEM. The model was calibrated and validated for only historical maximum flood event, i.e., Flood2010 using Manning’s ‘n’ values, flood stage at the Chakdara Bridge and satellite imagery-based Flood2010-observed extent. In addition, flood model sensitivity to the DEM was carried out and simulated maximum depth was 12, 13, 14, and 25 m for the 12-m WorldDEM, 30-m SRTM, 30-m ALOS and 30-m ASTER DEMs, respectively. Designed hydrographs were prepared for 2, 5, 10, 25, 50, and 100-year return periods based on the Flood2010-observed hydrograph. Finally, the model was simulated for 2, 5, 10, 25, 50, and 100-year return periods with full momentum equation as the calculation method. Simulated extents based on the 12-m WorldDEM were used for the preparation of flood hazard maps. Landcover exposure to the designed flood events shows that agriculture including orchards is the major potential affected class with affected areas up to 55 Km2. The developed flood hazard maps will enable the policy makers to mainstream flood hazard assessment in the planning and development process for mitigating flood hazard in Swat Valley.

Abstract. Flood is one of the natural disasters which has a high intensity in terms of occurrence. Despite the loss value in each event which is not as high as some natural disasters, such as earthquakes or tsunamis, the high occurrence of floods may cause high loss in total. Floods damage property and infrastructure, disrupt economic activity, displace people, harm communities, and degrade ecosystems. This study aims to compare the flood hazard model using Geomorphic Flood Index (GFI) and Multi-criteria Decision Analysis (MCDA). GFI is an established method which already used globally to identify the flood-prone area and the depth of the flood. The parameter needed to calculate GFI are the elevation, river network, and historical flood event. Meanwhile, the MCDA method tries to combine environmental, physical, and hydrographic factors, such as land use/land cover, precipitation, and runoff. The study area is Jatinangor District in Sumedang Regency which part of West Java Province, Indonesia. This location is chosen based on historical and potential flood events. Besides, Jatinangor District is the center of industry and commerce which Sumedang Regency is very dependent on. The finding of this study is expected to identify suitable methods for assessing flood hazards in Jatinangor or other areas with similar characteristics, between GFI and MCDA.

Climate change and rapid urbanization have increased the risk of urban flooding, making timely and accurate flood prediction crucial for disaster response. However, conventional physics-based models are limited in real-time applications due to their high computational costs. Recent advances in deep learning have enabled the development of efficient surrogate models that capture complex nonlinear relationships in hydrological processes. This study presents a deep learning-based surrogate model designed to efficiently reproduce the spatiotemporal evolution of urban pluvial flooding using data from physics-based models. For the Oncheon-cheon catchment in Busan, the spatiotemporal evolution of inundation at a 10 m spatial resolution was simulated using the physics-based model for various synthetic inundation scenarios to train the deep learning model based on a Convolutional Neural Network (CNN). The training dataset was constructed using synthetic rainfall scenarios based on probabilistic rainfall data, while the model was validated using both a synthetic flood event and a historical flood event from July 2020 with observed ground-based rainfall measurements. The model’s performance was evaluated using quantitative metrics, including the Hit Rate (HR), False Alarm Ratio (FAR), and Critical Success Index (CSI), by comparing results against both synthetic and real (historical) flood events. Validation results demonstrated high reproducibility, with a CSI of 0.79 and 0.73 for the synthetic and real experiments, respectively. In terms of computational efficiency, the deep learning model achieved a speedup 16.4 times the parallel version and 82.2 times the sequential version of the physics-based model, demonstrating its applicability for near real-time flood prediction. The findings of this study contribute to the advancement of urban flood prediction and early warning systems by offering a cost-effective, computationally efficient alternative to conventional physics-based flood modeling, enabling faster and more adaptive flood risk management.

This study aimed to assess the differences in modelling disaster risks results when using historical precipitation and when using simulated precipitation associated with future Intergovernmental Panel on Climate Change (IPCC) climate scenarios. Subsequently, the relationship between climate change and climate hazards was analyzed in this study. The secondary data analyzed included historical precipitation (1983-2017), flood and landslide events records, and Providing Regional Climates for Impacts Studies (PRECIS) regional climate model (RCM):A1B, A2 and B2 scenarios. By comparing the historical precipitation data with the RCM scenarios, the results showed that the precipitation was correlated with A1B scenario (r= 0.695). The relationship between climate change and hazards was identified to be a positive correlation. The historical daily precipitation (1983-2017) showed a positive correlation with flood and landslide events (r= 0.530, r = 0.797, respectively). As for prediction of climate hazards, the RCM A1B, A2 and B2 scenarios showed correlations with flood event: r= 0.648, 0.384 and 0.417, respectively. Similar results were obtained for landslide and the RCM A1B, A2 and B2 scenario: r = 0.498, 0.751 and 0.654, respectively. Precipitation simulation by PRECIS RCM indicated increased levels of precipitation in the Cameron Highlands for the 2018 - 2069. Commensurate with this, great possibility of increasingly serious consequential hazards such as flood and landslide events are expected.

<p>One of the most severe floods that has ever been registered in the catchment of the Upper Danube River in Central Europe is the one that took place in June/July 1572. This flood was caused by a prolonged precipitation event related to a so-called Vb cyclone. Such cyclones develop either over the Bay of Biscay or the Mediterranean (Genoa region), move eastward via Italy and the Adriatic Sea, and subsequently turn northeast. Vb cyclones bring extreme weather conditions with sustained precipitation over the northern side of the European Alps and Central Europe.</p><p>The impacts of the Vb cyclone in 1572 severely affected transport routes and local economies as indicated for instance by salt transport data from the Salzach River, one tributary stream (via the Inn River) of the Danube River. Different means of remembrance as historical flood level markers or memorial stones at several cities in Central Europe suggest that contemporaries considered the outcome of the cyclone as catastrophic. The modern quantification of the effects of such an extreme meteorological event helps to increase the understanding of the human-nature relationship in a period when manmade, modern changes of riverbeds and protection structures against floods or debris flows did not exist or did so only to a very limited extent. However, quantifying the effects of a historical regional-scale flood event in terms of degree of devastation at local-sale is normally outright impossible due to lack of detailed data.</p><p>In the Styrian Provincial Archive in Graz, Austria, a detailed damage inventory referring to the cyclone of 1572 exists. The purpose of the inventory was to reduce taxes for the Benedictine Abbey of Admont. The interdisciplinary analysis (historian, geographer) of the source enabled a local-scale insight into the effects of the cyclone at Admont. The inventory contains a list of 355 subjects of the abbey distributed over 12 administrative units that suffered minor to severe (complete destruction) damage related to flooding (main river or tributary creeks), debris-flows or landslides.</p><p>Further historical sources and geographical data such as land registers and cadastres allowed the localization of 150 damaged buildings at cadastral scale in the valley surrounding the abbey. Our analyses show that most of the properties were located near watercourses at alluvial fans or at slopes above the Enns valley bottom. A significantly greater amount of damage was revealed for properties, which would be nowadays located in moderate- and high-risk hazard zones (according to the Austrian Federal Service for Torrent and Avalanche Control). However, only 18.7% of the properties damaged in 1572 are located inside modern hazard zones. Modern hazard zone maps are commonly based on runoff modelling using design flood events. Our analysis suggests, nevertheless, that previously undetected or unconsidered sources might contribute substantially to the understanding of the spatial pattern of potential damage in an entire valley region during an exceptional cyclone at a local and even cadastre scale. This achievement is possible despite obvious changes in geomorphological, hydrographical, building structure and protective measure conditions since 1572. </p>

Flood hazards are common in Bhutan as a result of torrential rainfall. Historical flooding events also point to flooding during the main monsoon season of the year, which has had a huge impact in many parts of the country. To account for climate change patterns in flood hazards in Bhutan, 116 historical flood events between 1968 and 2020 for 20 districts were retrieved and reviewed. The preliminary review revealed that the frequency of flood occurrence has increased by three times in recent years. In this study, seven flood vulnerability (FV) indicators were considered. Five are the attributes of historical floods, classified into a number of incidents for flood events, fatalities, affected population, and infrastructure damages including economic losses. Additionally, the highest annual rainfall and existence of a flood map were other two indicators considered. Using historical data, flood hazard and impact zonation were performed. The analytic hierarchy process (AHP) method was employed to derive a multi-criteria decision model. This resulted in priority ranking of the seven FV indicators, broadly classified into social, physical/economic, and environmental. Thereafter, an indicator-based weighted method was used to develop the district flood vulnerability index (DFVI) map of Bhutan. The DFVI map should help researchers understand the flood vulnerability scenarios in Bhutan and use these to mediate flood hazard and risk management. According to the study, FVI is very high in Chhukha, Punakha, Sarpang, and Trashigang districts, and the index ranges between 0.75 to 1.0.

This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales.

Floods are the most prominent and lethal event of this century. Unfortunately, the lack of a robust flood prediction framework has led to the loss of human life and infrastructures. Flood prediction models are very important for assessing and managing extreme flood events. They provide robust and accurate predictions that can be utilized for policy analysis and evacuation planning. Due to the limitations of long-term observed rainfall and runoff dataset, which are the necessity of the traditional statistical and physical models, the use of data-driven models has increased. Modern data-driven models, such as those developed by machine learning, are more advantageous due to their ability to develop models with minimal inputs. They can also numerically formulate flood nonlinearity using historical data. Therefore, a support vector machine (SVM) algorithm was used to develop the flood prediction model in this study. The developed flood prediction model was then used to simulate the flood at three gauging stations namely, Purna, GR Bridge and Yelli of the Godavari river basin. The model was calibrated with four historical flood events and validated with two flood events. Historical gridded rainfall data from IMD is given as input to the model for the simulation of floods. The performance evolution of the developed SVM model was done with the NSE, Error in Peak and Error in time to Peak. In calibration, the average NSE was obtained as 0.96, 0.97 and 0.96 at the G.R. Bridge, Purna and Yelli, respectively, and in validation, it was found to be 0.93, 0.95 and 0.87 respectively. The error in time to peak was found to be zero except for one event at Yelli. The error in magnitude of peak flow at each gauging location was less than 10% in both calibration and validation, which shows that the developed SVM models were good enough to predict future flood events at respective gauging locations. The bias corrected rainfall of best performing GCM and multimodel ensemble (MME) mean of CMIP6 GCM rainfall were used to generate future flood events in the Godavari river basin. A considerable flood peak reduction around 10%–37% was observed for the future flood events under both the scenarios for the MME mean rainfall and best performing GCMs rainfall in comparison to historical floods. Future bias corrected MME mean was on low side compared to the historical rainfall and this results the reduction of future flood peaks in the study area. The HEC-RAS model was also used to prepare the flood inundation maps from the output of the SVM model. Future flood inundation maps were generated for the simulated flood events from MME mean over the period 2070–2100. The future flood inundation maps showed that the river reach from GR Bridge to Yelli is vulnerable to floods. The extent of flood maps is more for the SSP585 than SSP245 for the period of 2070–2100. Nanded town, on the bank of river Godavari is going to inundate for the above-mentioned period under high emission scenario (SSP585). The results of this study may help the local stakeholders and organizations to better understand the flood behaviour in the Godavari river basin and are useful to form mitigation strategies for the future.

<p>The flood event in July 2021 caused more than 180 death tolls in Germany and several billion Euro damage. In particular, the Ahr valley was severely hit by the flood, which lead to unprecedented inundation, houses and bridge destruction. Flood response was based on the official flood hazard maps, which were designed in the course of the EU Flood Directive. We revisit flood hazard in the Ahr valley by considering historical flood events in the extreme value statistics. A state-of-the-art bootstrapping approach is applied to estimate flood quantiles considering uncertainties in estimating historical discharges. Inclusion of historical floods dramatically changes estimation of flood quantiles. Nevertheless, we observe significant deviations between empirical and theoretical flood distributions due to singular outstanding extremes and discuss the suitability of the GEV model. We further drive a 2D hydraulic model with the estimated quantiles to compute inundation areas, depths and flow velocities. Human stability indicator is additionally computed. We conclude that the consideration of historical events is paramount for assessing flood hazard. We recommend to revisit flood hazard maps and include historical floods either into extreme value statistics or as indication of potential extreme inundation.</p>

<p><strong>Abstract</strong></p><p>Flood frequency curves are usually fitted to short time series of observations, leading to great uncertainties mainly for high return periods. However, reliable estimates are required for designing and assessing safety of hydraulic infrastructure, such as bridges and dams. Therefore, flood frequency analyses based on instrumental data collected at gauging stations can be improved by incorporating available information about historical floods before the beginning of the systematic period. This study presents how to identify and integrate all the information available, in order to improve flood frequency curve estimates. The Cuevas de Almanzora Dam located in southeast Spain is selected as case study.</p><p>The Cuevas de Almanzora Dam catchment has an area of 2122 km<sup>2</sup> with a mean annual precipitation of 316 mm. However, daily precipitation can be higher, such as 600 mm for the 1973 flood event. Flood data are available at a gauging station located in the River Almanzora upstream of the dam, with a draining catchment of 1850 km<sup>2</sup>. The systematic period is 1963-2008 with information about 36 annual maximum floods. The largest flood in the 20<sup>th</sup> century was recorded at the gauging station in 1973. A two-dimensional (2D) hydrodynamic model of the River Almanzora was calibrated with such information.</p><p>Historical information about floods has been collated from local newspapers, books, chronicles, research papers, photographs, national archives of historical floods, and municipal archives. The three largest floods in the River Almanzora between 1830 and 1963 were identified, extending the systematic period to a total period of 191 years. Information about water depths and flood extensions at different cross sections of the River Almanzora were collected. The 2D hydrodynamic model was used to estimate the peak discharges in such historical flood events.</p><p>After the end of the systematic period, the hydrograph of the great 2012 flood event was estimated from the data recorded at the Cuevas de Almanzora reservoir. A rainfall-runoff model was calibrated in the catchment with 1-h precipitation data to estimate the flood hydrograph at the gauging station.</p><p>The five historical floods that exceed the perception threshold in the period 1830-2020 were integrated with the annual maximum floods extracted from the systematic data, using five techniques to incorporate historical information in the flood frequency curve. The Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distribution functions were considered. The best fit was selected considering the accuracy and the uncertainty of estimates by a stochastic procedure. Flood quantiles for the highest return periods triple the estimates obtained by using only the systematic data.</p><p>The methodology proposed can improve the reliability of flood quantile estimates, mainly in arid regions where the lack of information about the rare greatest flood, which can exceed several times the mean magnitude of floods in the systematic period, can lead to strong underestimates for the highest return periods that are needed to design and assess the safety of hydraulic infrastructure.</p><p><strong>Acknowledgments</strong>: This research has been supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.</p>

Grain size analysis of flood sediments is a key method for understanding the sedimentary environments of rivers worldwide; however, there is limited knowledge of how to effectively reflect the sedimentary environment of lower Yellow River (Huang He) flood events using grain size parameters. In this study, two widely used grain size analysis methods, the graphic method (GM) and moment method (MM), were compared, and their applicability to flood sediment analysis in the lower Yellow River was evaluated. Modern flood sediments (n = 143) in the lower Yellow River featured a fine-grained texture and were classified as silty sand (4.95 ≤ Φ ≤ 5.03) characterized by an inadequate sorting ability. The grain size distribution patterns obtained using the GM and MM revealed positive and extremely positive deviations with sharp and flat peaks, respectively. A strong correlation (0.6966 ≤ R2 ≤ 0.9961) was observed between the mean grain size and the sorting coefficient obtained using the GM and MM. Thus, both methods were deemed suitable and could be used interchangeably. Our results indicate that the MM should be applied to assess skewness because it provided comprehensive information regarding flood sediments in the lower Yellow River, whereas the GM is recommended for kurtosis analysis, as it highlighted the primary sedimentary dynamics during flood events. Methods must be selected based on the sedimentary environment when analyzing grain size parameters.

Design discharges corresponding to large return periods are generally highly uncertain because of the relatively short data set of measured discharges. The uncertainty in these discharge predictions can be decreased by extending the data set of annual maximum discharges with historic flood events. However, efficient model approaches, in terms of com­ putational times and model accuracy, are required to reconstruct historic flood events. In this study, the suitability of three data-driven surrogate models are evaluated, namely: a linear regression model, an artificial neural network and a support vector machine. The Rhine river flood event of 1809 is used as a case study. Although all types of surrogate models are capable of reproducing the maximum discharge during the 1809 flood event, the use of an artificial neural network resulted in the smallest 95% uncertainty interval.

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.

The Lake Tian E Zhou (TEZ, an oxbow lake) was formed during the rerouting of the Changjiang River in 1972, with strong influences from the main river channel and flood events. Herein, a sediment core was collected from the Lake TEZ for the measurements of carbon isotopes and biomarkers, including stable carbon isotopes (δ13C), radiocarbon composition (Δ14C), and lignin phenols, as well as lead-210 to reconstruct recent heavy flood events over the past 70 years. At the 24–26 cm interval, the sediment contained the highest OC%, TN%, and lignin phenols content, as well as significantly depleted 13C but enriched 14C, corresponding to the extreme flood event in 1998. In addition, statistics from t-test showed that lignin phenols normalized to OC (Λ8), the concentration of 3, 5-dihydroxy benzoic acid (3, 5-BD), and the ratio of p-hydroxy benzophenone to total hydroxyl phenols (PHB/HP) were all significantly different between the layers containing flood deposits and the layers deposited under normal non-flood conditions (p<0.05). These results indicate that the later three parameters are highly related to flood events and can be used as compelling proxies, along with sediment chronology, for hydrological changes and storm/flood events in the river basin and coastal marine environments.