Major Flood Events Research Articles

Nest traits and major flooding events influence nest survival of Emperor Geese while regional environmental variation linked to climate does not

Abstract The reproductive ecology of geese that breed in the Arctic and subarctic is likely susceptible to the effects of climate change, which is projected to alter the environmental conditions of northern latitudes. Nest survival is an important component of productivity in geese; however, the effects of regional environmental conditions on nest survival are not well understood for some species, including the Emperor Goose (Anser canagicus), a species of conservation concern that is endemic to the Bering Sea region. We estimated nest survival and examined how indices of regional environmental conditions, nest traits (nest age, initiation date, and maximum number of eggs in the nest), and researcher disturbance influenced daily survival probability (DSP) of Emperor Goose nests using hierarchical models and 24 years of nest monitoring data (1994–2017) from the Yukon–Kuskokwim Delta (Y–K Delta) in western Alaska. Our results indicate that overall nest survival was generally high (µ = 0.766, 95% CRI: 0.655–0.849) and ranged from 0.327 (95% CRI: 0.176–0.482) in 2013 to 0.905 (95% CRI: 0.839–0.953) in 1995. We found that DSPs of nests were influenced by nest traits, negatively influenced by major tidal flooding events and by researcher disturbance, but were not influenced by regional indices of spring timing, temperature and precipitation during nesting, or fox and vole abundance on the Y–K Delta. However, the number of nests found each year was negatively related to our index of fox abundance, suggesting nests that failed as a result of fox predation may have never been discovered due to our limited nest-searching efforts during egg laying. Our results suggest that regional environmental variation had minimal influence on the nest survival of Emperor Geese, although major flooding events were important. Nevertheless, we suspect that within-year variation in local weather conditions and local abundance of predators and alternative prey may be important and should be considered in future studies.

Impact of a Tropical Cyclone on Terrestrial Inputs and Bio-Optical Properties in Princess Charlotte Bay (Great Barrier Reef Lagoon)

In January 2013, tropical cyclone Oswald caused widespread flooding in the North-East coast of Australia, and large and highly episodic inputs into Princess Charlotte Bay (PCB, northern Great Barrier Reef). Freshwater outflows from the Normanby and Kennedy rivers, the two main rivers draining the adjacent catchments, resulted in drastic changes in physical, biogeochemical and optical properties within PCB. On 31 January, 2 days after the peak riverine discharge from the Normanby river, nutrients and dissolved organic matter contents peaked under the influence of large outflows from the Kennedy river into the western section of the bay (5.8 μM for dissolved inorganic nitrogen, 6.9 g m−3 for dissolved organic carbon and 6.1 m−1 for the colored dissolved organic matter absorption coefficient at 412 nm). In the eastern section of the bay, the situation appeared more ‘mixed’, with a suspended solids concentration reaching 23.1 g m−3 close to the Normanby river mouth. The main phytoplankton bloom occurred in the transition zone between the Kennedy and Normanby flood plumes, and was dominated by diatoms with a chlorophyll a concentration reaching 14.6 mg m−3. This study highlights the need to better describe the critical spatial and temporal scales of variability of key biogeochemical and optical properties after a major flood event. The data collected is key to improve the accuracy of ocean color remote sensing algorithms and regional biogeochemical budgets following highly episodic inputs.

An Effective Approach for Automatic River Features Extraction Using High-Resolution UAV Imagery

The effects of climate change are causing an increase in the frequency and extent of natural disasters. Because of their morphological characteristics, rivers can cause major flooding events. Indeed, they can be subjected to variations in discharge in response to heavy rainfall and riverbank failures. Among the emerging methodologies that address the monitoring of river flooding, those that include the combination of Unmanned Aerial Vehicle (UAV) and photogrammetric techniques (i.e., Structure from Motion-SfM) ensure the high-frequency acquisition of high-resolution spatial data over wide areas and so the generation of orthomosaics, useful for automatic feature extraction. Trainable Weka Segmentation (TWS) is an automatic feature extraction open-source tool. It was developed to primarily fulfill supervised classification purposes of biological microscope images, but its usefulness has been demonstrated in several image pipelines. At the same time, there is a significant lack of published studies on the applicability of TWS with the identification of a universal and efficient combination of machine learning classifiers and segmentation approach, in particular with respect to classifying UAV images of riverine environments. In this perspective, we present a study comparing the accuracy of nine combinations, classifier plus image segmentation filter, using TWS, also with respect to human photo-interpretation, in order to identify an effective supervised approach for automatic river features extraction from UAV multi-temporal orthomosaics. The results, which are very close to human interpretation, indicate that the proposed approach could prove to be a valuable tool to support and improve the hydro-geomorphological and flooding hazard assessments in riverine environments.

Large-scale dynamics moderate impact-relevant changes to organised convective storms

Larger organised convective storms (mesoscale-convective systems) can lead to major flood events in Europe. Here we assess end-of-century changes to their characteristics in two convection-permitting climate simulations from the UK Met Office and ETH-Zürich that both use the high Representative Concentration Pathway 8.5 scenario but different approaches to represent atmospheric changes with global warming and different models. The UK Met Office projections indicate more frequent, smaller, and slower-moving storms, while ETH-Zürich projections show fewer, larger, and faster-moving storms. However, both simulations show increases to peak precipitation intensity, total precipitation volume, and temporal clustering, suggesting increasing risks from mesoscale-convective systems in the future. Importantly, the largest storms that pose increased flood risks are projected to increase in frequency and intensity. These results highlight that understanding large-scale dynamical drivers as well as the thermodynamical response of storms is essential for accurate projections of changes to storm hazards, needed for future climate adaptation.

Temporal evolution of plutonium concentrations and isotopic ratios in the Ukedo - Takase Rivers draining the Difficult-To-Return zone in Fukushima, Japan (2013–2020)

In 2011, the Fukushima Dai-Ichi Nuclear Power Plant (FDNPP) accident released significant quantities of radionuclides into the environment. Japanese authorities decided to progressively reopen the Difficult-To-Return Zone after the decontamination of priority reconstruction zones. These areas include parts of the initially highly contaminated municipalities located to the north of the FDNPP, including Namie Town, an area drained by the Ukedo and Takase Rivers. Eleven years after the accident, research focused on the spatial distribution of plutonium (Pu) and radiocesium (Cs) isotopes at contrasted individual locations. To complement previous results, the current research was conducted on flood sediment deposits collected at the same locations after major flooding events during eleven fieldwork campaigns organised between 2013 and 2020 at the outlet of the Ukedo and Takase Rivers (n = 22).The results highlighted a global decrease of the Pu and 137Cs contents in sediment with time during the abandonment phase in the region, from 2013 (238.20 fg g−1) to 2020 (4.28 fg g−1). Furthermore, based on the analysis of the 240Pu/239Pu isotopic ratios, the plutonium transiting these rivers (range: 0.166 – 0.220) essentially originated from the global fallout (0.180 ± 0.014 (Kelley et al., 1999)). Sediment showed contrasted properties in the two investigated rivers, which is likely mainly the result of the occurrence of Ogaki Dam on upper sections of the Ukedo River as it strongly impacts the material supply from this river to the Pacific Ocean. A statistical analysis highlighted the strong correlation between Pu activity concentrations and 137Cs activities in both rivers, confirming that both radionuclides are transported with a similar pathway. Despite it was detected early after the accident (2011–2013), the current research demonstrates that plutonium originating from FDNPP is no longer detected in these rivers draining the Difficult-To-Return Zone at the onset of the reopening of the area to its former inhabitants.

APPLICATION OF MIKE FLOOD MODEL FOR INUNDATION SIMULATION IN YEN BAI CITY

Flood is a natural disaster that can cause loss of life and damage to property. The objective of this study is to create a flood map of Yen Bai city, where flood occurs regularly during heavy rain and Thao River's water level rises. The study used Mike Flood model based on the connection of 1D and 2D hydrodynamic models. The model parameters were calibrated and validated againts observed water level data of two major flood events in August 2008 and July 2009 which were measured by Bao Ha and Yen Bai hydrological stations, in addition with historical flood survey data in 2008. Flood map was developed corresponding to the alarm levels. The results show that the most affected areas by flood are 4 communes of Au Lau, Hop Minh, Tuy Loc, and Hong Ha wards.

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids

Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning.

Parameter estimation for models of major rainfall induced floods in ungaged mountain basins of Colorado

Study regionRocky Mountains in Colorado including Front and San Juan ranges Study focusHydrologic modeling of ungaged mountain basins plays an important role in ensuring the safety of Colorado’s dams. Recent research has shown that infiltration-excess runoff, saturation-excess runoff, and subsurface stormflow can all contribute to streamflow generated by extreme rainfall in Colorado’s mountains, and the soil moisture accounting (SMA) method in HEC-HMS has been suggested as an appropriate approach to model these mechanisms. However, SMA requires estimation of parameters that have not been previously considered for large floods. The objective of this work is to develop methods to estimate SMA parameters for modeling the response to extreme rainfall in ungaged mountain basins of Colorado where calibration to observed discharges is not possible. New hydrological insights for the regionThe proposed parameter estimation methods are tested for five basins in the Front Range and three basins in the San Juan Range that have streamflow data available for major flood events. For these historical events, the proposed uncalibrated models produce the same streamflow generation processes as calibrated models, and the predicted peak discharges from the uncalibrated models usually have 25% errors or smaller. For design storms, the peak discharges from the uncalibrated models can differ substantially from the peak discharges from calibrated models but are conservative relative to the envelope of observed peak discharges.

Surface Water Extent Mapping in Denmark: Comparing Airborne Thermal Imagery and Satellite Earth Observation

Mapping and prediction of inundated areas are increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observe flooding patterns for training, calibration, validation, and benchmarking. At the regional to continental scales, satellite earth observation (EO) is the established method for surface water extent (SWE) mapping, and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared with low-resolution SWE maps derived from satellite earth observation and with potential flooded areas derived from the high-resolution digital elevation model. We conclude that UASs have significant potential for SWE mapping at intermediate scales (up to a few square kilometers), can bridge the scale gap between ground observations and satellite EO, and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Anthropogenic influence of open pit mining on river floods, an example of the Blessem flood 2021

Large scale anthropogenic relief changes result in a non-equilibrium landscape and such landscapes are prone to catastrophic erosion events during river floods. An example is the major flood event in western German in July 2021. During this event, the Erft River flooded an open gravel pit near the village of Erftstadt-Blessem, resulting in a massive erosional event, including the catastrophic destruction of parts of the settlement. Three different geomorphological periods occurred during the event: I) rapid backward erosion caused the destruction of the overlaying soils and floodplain sediments. Consequently, the gravel pit was filled with water and sediments. Three different pathways of backward erosion were activated during this period. II) Sedimentation in a temporary lake and the formation of alluvial fans. III) The Erft River moved towards the gravel pit due to the erosional processes followed by a second flood. Understanding of such complex erosional and depositional processes in open pit mining areas might serve as a blueprint for processes and hazards in anthropogenic influenced landscapes.

A Comprehensive Methodology for Evaluating the Economic Impacts of Floods: An Application to Canada, Mexico, and the United States

In 2020, we developed a comprehensive methodology (henceforth, the methodology) to assess flood-related economic costs. The methodology covers direct damages, indirect effects, and losses and additional costs across 105 social, infrastructure, economic, and emergency response indicators. As a companion paper, this study presents findings from analysis of applying the methodology to investigate economic costs for major flood events between 2013 and 2017 and to assess gaps in the existing datasets across Canada, Mexico, and the United States. In addition, we conducted one case study from each country for an in-depth examination of the applicability of the methodology. Applying the methodology, Mexico showed the most complete flood indicator data availability and accessibility among the three countries. We found that most flood-related economic cost assessments evaluated only direct damages, and indirect effect data were rarely included in datasets in the three countries. Moreover, few of the records from Canada and the United States captured the losses and additional costs. Flood-related economic cost data at the municipal or county level were easily accessible in Mexico and the United States. Mexico’s National Center for Prevention of Disasters (Centro Nacional de Prevención de Desastres), unique among the three nations, provided access to centralized and comprehensive flood cost data. In the United States and Canada, data collection by multiple agencies that focus on different jurisdictions and scales of flood damage complicated comprehensive data collection and led to incomplete economic cost assessments. Our analysis strongly suggests that countries should aim to expand the set of data indicators available and become more granular across space and time while maintaining data quality. This study provides significant insights about approaches for collating spatial, temporal, and outcome-specific localized flood economic costs and the major data gaps across the three countries.

“The 20 July 2021 Major Flood Event” in Greater Zhengzhou, China: A Case Study of Flooding Severity and Landscape Characteristics

Climate change and rapid urbanization are two global processes that have significantly aggravated natural disasters, such as drought and flooding. Urbanization without resilient and sustainable planning and execution could lead to undesirable changes in landscapes and stormwater regulation capacity. These changes have exacerbated the effects of extreme climatic events with disastrous consequences in many cities worldwide. Unfortunately, the major storm in Zhengzhou, China on 20 July 2021 was one of these examples. This event provided a rare opportunity to study the key roles of green infrastructures (GI) in mitigating flooding risks in a major urban center after a devasting flood event. Using the data from high-resolution images collected via two satellites, a comprehensive study of the Jialu System in Greater Zhengzhou was conducted to systematically compare how far the river water had reached before and after the 20 July 2021 major storm in order to identify the main weak links in the city’s GI and stormwater management system. A flood inundation intensity index (FI) in the Upper (UJLR), Middle (MJLR), and Lower (LJLR) Regions of the Jialu River System was generated. Bivariate Moran’s I, a correlation coefficient between FI and landscape characteristics, was calculated and used to identify problem areas for future improvements. Our results showed that the MJLR had the severest flooding impacts. LJLR had the biggest change in how far the river water reached after flooding, ranging from 4.59 m to 706.28 m. In UJLR, the percentages of mine, crop land, and green space had the highest global bivariate Moran’s I correlation coefficients. In MJLR, the percentages of vacant land, impervious surfaces, and water body had the highest global bivariate Moran’s I correlation coefficients. In LJLR, the percentages of vacant land, water body, and crop land had the highest global bivariate Moran’s I correlation coefficients. The total percentages of both high landscape characteristics indices-high flood inundation intensity indices and low landscape characteristics indices-high flood inundation intensity indices areas are 12.96%, 13.47%, and 13.80% in UJLR, MJLR, and LJLR, respectively. These land cover composition types identified for each region can be treated as areas of primary focus. However, GeoDector Model (GDM) analyses showed that our eight variables of landscape characteristics were not independent. Hence, a more comprehensive approach integrating all eight variables is still necessary in future flood mitigation efforts.

Facies architecture and paleogeography evolution of regressive wave-dominated shorelines transitioning into tide-dominated estuaries: Early Devonian Subbat Member, Jauf Formation, Saudi Arabia

AbstractThe Paleozoic succession on the northern Arabian Plate was deposited during several regressive and transgressive events. The Early Devonian Subbat Member of the Jauf Formation comprises several smaller-scale intervals of the Paleozoic succession that were interpreted based on large-scale observations from outcrop and subsurface data. This study utilizes process-based sedimentology and investigates facies stacking, lateral continuity of sand bodies, and ichnofacies to interpret an open marine wave-dominated forced regressive system, that is followed by transgressive shorelines.This study integrates a total of 417 meters of the Devonian stratigraphy from four outcrops and two shallow cores. This dataset records a third-order sequence which developed through an extensive intra-plate siliciclastic influx in between two carbonate units during the deposition of the Subbat Member. This study illustrates the evolution of a falling-stage systems tract that is characterized by shoreface sand bodies and an erosional-based delta front in the lower Subbat Member. These sediments overlie a regressive surface of marine erosion (RSME), extending for hundreds of kilometers and transitioning to an overall transgression in the upper parts of the Subbat Member.This study interprets a total of seven facies associations (FAs): i) offshore, ii) wave-dominated delta, iii) shoreface to offshore transition, iv) fluvial channels, v) crevasse splays, vi) paleosol, and vii) estuarine facies associations. In the lower part of the Subbat Member, the wave-dominated delta and shoreface to offshore transitional FAs overlie the marine shelf strata of the offshore FA and develop a RSME. Fluvial channels and crevasse splays FAs are interpreted.Unique assemblages of trace fossils, in terms of intensity and diversity, ranging from the Nereites Ichnofacies to Skolithos Ichnofacies, play a major role in the understanding of the overall water depth and depositional setting. Distinctive terrestrial Prototaxites fossils are present in sheet-like bodies and are interpreted as part of extensive crevasse splays that formed during major river flooding events. This study provides a unique integrated approach using ichnology, sedimentology, and sequence stratigraphy to better understand the spatial and temporal facies distribution of a forced regressive sequence and refine the paleogeography of northern Arabia during Early Devonian time.

Assessing the housing price capitalization of non-destructive flooding events

Flooding poses significant costs to communities and is expected to vary in intensity and severity in the future. Previous research has shown that major flooding events significantly affect housing markets; however, perceptions of non-destructive flooding events have not been adequately studied. In this paper, I examine the housing price capitalization of elevated river levels that did not reach the major flood stage to measure their influence on flooding risk perceptions. Estimates from a property fixed effects hedonic model provide evidence that housing prices respond heterogeneously to non-destructive flooding, with a negative and significant effect in the 100 year floodplain, but not the 500 year floodplain or other areas. These results show that a major event is not necessary for residents to update their beliefs and can inform future extreme natural event policy.

Riverine flood vulnerability assessment and zoning using geospatial data and MCDA method in Aq’Qala

Aq’Qala county has been categorized in high flood-prone areas in Iran, therefore it faced many major flood events in last two decades (e.g. in 2001, 2002, 2007, 2012, 2017, and 2019). Forecasting of flood-prone areas would be essential for planning of flood management strategies. Hence the main aim of the current study is to produce and validate flood vulnerability rate (FVR) map relying on geospatial data. Vulnerability criteria were identified through flood expert's consultations, field surveys and taking into consideration a flood event of 2019. Subsequently, the criteria clustered into five main classes: topography, climatology, human factors, geology and morphology. All sub-criteria were analysed using an analytical hierarchy process (AHP) questionnaire and fuzzification by triangular fuzzy number (TFN-AHP) in order to weighting. The FVR maps indicated that 26.1% (AHP map) to 29.66% (TFN-AHP map) of Aq’Qala have been located in very-high vulnerability rate. The least difference between AHP and TFN-AHP were 12.99% and 17.41% in very-high and high vulnerability rates, respectively. Moreover the least difference between the FVR maps and remote sensing flood mapping were 6.55% (AHP) and 2.33% (TFN-AHP). Base on time series satellite images-derived there was high compatibility between TFN-AHP map and flooding map of 2019 flood event. Results demonstrated that application of remote sensing data and Multi-Criteria methods simultaneously can provide reliable outputs.

We don't want your water: Broadening community understandings of and engagement in flood risk and mitigation

Many communities across the USA and globally lack full understanding of the flood risk that may adversely impact them. This information deficit can lead to increased risk of flooding and a lack of engagement in mitigation efforts. Climatic changes, development, and other factors have expedited changes to flood risk. Due to these changes, communities will have an increased need to communicate with a variety of stakeholders about flood risk and mitigation. Lafayette Parish, Louisiana, USA, having recently experienced a major flood event (the 2016 Louisiana Floods), is representative of other communities experiencing changes to flood impacts. Using focus groups, this study delves into better understanding the disconnect between individual and community perceptions of flood risks, and how emerging hydroinformatics tools can bridge these gaps. Using qualitative analysis, this study evaluated the resources individuals use to learn about flooding, how definitions of community impact flood mitigation efforts, how individuals define flooding and its causes, and where gaps in knowledge exist about flood mitigation efforts. This research demonstrates that individuals conceive of flooding in relationship to themselves and their immediate circle first. The study revealed division within the community in how individuals think about the causes of flooding and the potential solutions for reducing flood risk. Based on these results, we argue that helping individuals reconceive how they think about flooding may help them better appreciate the flood mitigation efforts needed at individual, community, and regional levels. Additionally, we suggest that reducing gaps in knowledge about mitigation strategies and broadening how individuals conceive of their community may deepen their understanding of flood impacts and what their community can do to address potential challenges.

Case Study on Damage and Recovery of Water Supply Related Facilities Damaged by Recent Flood Disasters

In recent years, several large-scale flood disasters occurred in Japan. The large-scale flooding causes not only direct damage but also indirect damage due to the closure of lifelines such as water, electricity, roads, and railways, and both impact the affected region’s resilience. Therefore, it is important to understand the current level of disaster resilience and consider non-structural measures accordingly to prepare for the next disastrous event. To assess current disaster resilience requires understanding how each building or lifeline structure is damaged in a disaster and the recovery time. In this study, water supply related facilities are selected from various kinds of lifelines, and a questionnaire survey was conducted to investigate facility damage and recovery after being affected by major flood events occurring in Japan after 2015. A comprehensive study on the damage and recovery of water supply related facilities due to flood has never been conducted, not only in Japan but also worldwide. This is because of difficulty in collecting enough data from many facilities, and thus, there have been reports of damage and recovery of an individual facility, but not more. However, occurrence of several severe flooding disasters in recent years in Japan made it possible to do so. This study dug into not only facility-level damage and recovery but also component level ones based on the quantity of data collected (97 components from 61 facilities) through the questionnaire survey. We found that control panels are most likely to be damaged by flood, and the number of days required for temporary recovery strongly depends on how quikly replacement equipment/parts for failed equipment can be procured.

The behaviour of property prices when affected by infrequent floods

The reduction in property values following a major flood event is significant to individuals, communities and governments alike. Property owners who purchase properties just before a major flood risk significant personal loss on their largest single asset. The costs of a major event are shared across communities through government funded recovery efforts in the short term, and changes to insurance premiums and house values in the long term. Designing protections, adaptations and insurance mechanisms that can equitably ameliorate future impacts requires a detailed understanding of how flood risk affects property prices. This paper explores patterns of discounting of property prices following infrequent flooding events. It relates property value discounting in response to floods to various theories of market behaviour. There are alternative behavioural theories which explain why agents may react in this manner. We use a behavioural framework proposed in earlier literature to construct econometric measurements that provide the opportunity to make statistical inferences on whether the expected behaviours are supported by the data. The patterns found are consistent with a market where buyers are either unaware of the risk of flood or unable to evaluate it probabilistically.

Reconstruction of historical suspended particulate matter contributions of Rhône River tributaries to the Mediterranean Sea

Total metal concentrations are usually used as geochemical tracers to identify sources of suspended particulate matter (SPM) and sediment deposits, but their non-conservative behaviour can lead to large uncertainties. This non-conservatism may be a significant issue when total metal concentrations of contemporary SPM are used to determine historical sediment sources, as diagenesis processes and/or past anthropogenic inputs modify metal concentrations in sediment core profiles. For the Rhône River, which is the main supplier of SPM to the Mediterranean Sea, there is no information on historical SPM source contributions from its main tributaries during major flooding events. The objective of this study was to highlight the relevance of using tracers in the non-reactive fraction to estimate the historical relative SPM contributions of the main tributaries of the Rhône River. These historical SPM contributions (1981–2017) were reconstructed based on a sediment core sampled at the outlet of the river basin and on sampling of contemporary SPM (2011–2019, Rhône Sediment Observatory monitoring network) from each main tributary. Fine stratigraphic resolution enabled us, using this original fingerprinting method, to reconstruct historical tributary contributions to sediment deposits at the scale of past flooding events over the last 37 years. When using tracers in the total fraction of SPM/sediments, the Isère and Ardèche rivers were the main contributors of SPM (32 % and 22 % on average, respectively) during the 1981–2017 period, whereas when using tracers in the non-reactive fraction, the main SPM source was the Durance River (52 %). The results obtained by this fingerprinting approach using the non-reactive fraction are fully consistent with literature data estimated using other methods (e.g. mean interannual SPM contribution of 53 % for the Durance River), and with documented historical flood events. This study showed that using tracers in the non-reactive fraction is more reliable than using tracers in the total fraction, especially for the deepest layers of the sediment core that are influenced by historical anthropogenic inputs.