Search

Results of bathymetric surveys conducted to examine changes of sand dunes geometry in the Vistula River mouth before, during and after the extreme flood event are presented. A total of 2076 dunes were analysed based on a series of bed elevation profiles obtained along the centreline of about 3.3 km length. Low-steepness dunes characterized by the mean lee-side slopes milder than β10°. Dune height and length are substantially out of phase with progressive changes of water discharge exposing a well-pronounced anti-clockwise hysteresis. Distinct behaviour of dune dimensions reflected in increasing of dune steepness H/λ of about 3-fold and decreasing of about 4-fold were observed during rising and falling discharges, respectively. The bed roughness due to dunes presence showed changes of about 10-fold during the both of limbs and is found to be in range of about kdunes=(1/5÷3/5)Hmean. At the mesoscale region, spectra followed sufficiently by the ‘–3 power law’ for low hydrology, with steeper spectrum slopes close to ‘–4’ during moderate and high water discharges. With the development of the flood, potential of flow separation phenomena was increased of about 9-fold, from 2.2% at the flood beginning phase up to 20% at the flood peak. The obtained results could be used for the improvement of the hydraulic numerical models in sand-bed rivers to predict bedforms evolution, flow resistance and turbulence as well as water levels for proper river system management during flood events.

The classification of characteristics of flood events, like peak, volume, duration and baseflow components is essential for many hydrological applications such as multivariate flood statistics, the validation of rainfall-runoff models and comparative hydrology in general. The basis for estimations of these characteristics is formed by flood event separation. It requires an indicator for the time when a flood peak occurs as well as the definition of the beginning and end of a flood event and a subdivision of the total volume into direct and baseflow components. However, the variable nature of runoff and the multiple processes and impacts that determine rainfall-runoff relationships make a separation difficult, especially an automation of it. We propose a new statistics-based flood event separation that was developed to analyse long series of daily discharges automatically to obtain flood events for flood statistics. Moreover, the related flood-inducing precipitation is identified, allowing the estimation of the flood-inducing rainfall and the runoff coefficient. With an additional tool to manually check the separation results easily and quickly, expert knowledge can be included without much effort. The algorithm was applied to seven basins in Germany, covering alpine, mountainous and flatland catchments with different runoff processes. In a sensitivity analysis, the impact of chosen parameters was evaluated. The results show that the algorithm delivers reasonable results for all catchments and only needs manual adjustment for long timeslots with increasing or high baseflow. It reliably separates flood events only instead of all runoff events and the estimated beginning and end of an event was shifted in mean by less than one day compared to manual separation.

Long-term research has revealed layers with abnormally high concentrations of 137Cs in bottom sediments and alluvial soils in the floodplain of the Yenisei River at various distances (as far as 820 km) downstream from the radioactive discharge point of the Mining-and-Chemical Combine (MCC) of Rosatom. The highest activity concentration of 137Cs in these layers reached 26 000 Bq/kg, which was higher than the 137Cs maximum at the well-known radioactive anomaly in the Yeniseisk riparian zone (330 km downstream from the MCC), which was formed during the 1966 extreme flood event. The radionuclide composition and the 137Cs/152Eu and 137Cs/60Co ratios in the anomalous layers studied were the same as those at the Yeniseisk anomalous site, suggesting that they had the same origin by the transport of sediments from the MCC area during the 1966 extreme flood event. The transport of radioactive bottom sediments over long distances from the MCC discharge point downstream along the Yenisei River during the 1966 extreme flood event may continue to pose a possible radiation hazard to the river ecosystem and residents of riverside villages.

Over the years, flood is one of the natural hazards which occur all over the world and it is critical to be controlled through proper management. Flood in Kelantan is mainly caused by heavy rainfall brought by the Northeast monsoon starting from November to March every year. It is categorized as annual flood as it occurs every year during the Monsoon season. Severe flood events in Kelantan, Malaysia cause damage to both life and property every year and understanding landscape structure changes is very important for planners and decision makers for future land use planning and management. This research aims to quantify the landscape structure near to Kelantan River basin during the flood event using integrated approach of remote sensing (RS), geographic information system (GIS) technique and landscape ecological approach. As a result, this study provide new knowledge on landscape structure that contributes to understand the impact of flood events and provide the best ways to mitigate flooding for helping to protect biodiversity habitat and dwellers. As conclusions, this kind of study will give more benefits to various stakeholders such as Department of Irrigation and Drainage, Department of Environment, state government, fisherman and communities.

The present work is aimed at assessing the aftermath effects of the 2014 flood tragedy on the distribution, pollution status and ecological risks of the heavy metals deposited in the surface river sediment. A series of environmental pollution indexes, specifically the enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), modified degree of contamination (mCd), pollution load index (PLI), potential ecological risk index (PERI) and sediment quality guidelines (SQGs) have been adopted. Results revealed that the freshly deposited sediments collected soon after the flood event were dominated by Cu, Fe, Pb, Ni, Zn, Cr and Cd, with the average concentrations of 38.74, 16,892, 17.71, 4.65, 29.22, 42.36 and 0.29 mg/kg, respectively. According to the heavy metal pollution indexes, Pahang River sediments were moderately to severely contaminated with Pb, Ni, Cu, Zn and Cr, while Cd with the highest risk of 91.09 was the predominant element that illustrated an aesthetic ecological risk to the water body after the tragic flood event. The findings highlighted a critical deterioration of the heavy metals content, driven by the catastrophic flood event, which has drastically altered their geochemical cycles, sedimentary pollution status and biochemical balance of the river's environment. HIGHLIGHTS The 2014 flood has been recognized as the most severe tragedy in the history of Malaysia for the past 30 years.; Heavy metal pollution and sediment geochemical profile of Pahang River were analyzed.; A series of geochemical pollution indexes, including enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), modified degree of contamination (mCd), and pollution load index (PLI) were thoroughly evaluated.; The synergistic ecotoxicological risks in relation to sediment quality were studied using comprehensive potential ecological risk index (PERI) and consensus-based sediments quality guidelines (SQGs).; Establishment of a reliable reference on the sedimentology, geochemistry, pollution status and ecological risk assessments under different environmental settings for future protection, restoration or rehabilitation of the river system after the flood tragedy.

Social network data, utilised as a VGI source, was analysed using the September 2016 flood event in Messinia, South Greece. The flood event led to damage in the urban and rural environment in the general area, and to human deaths. An innovative methodology is based on applying machine learning to classify Twitter content. Tweets were classified into the following ten categories: (i) flood identification, (ii) rain identification, (iii) consequences of the flood, (iv) expressed emotions, (v) ironic attitude to local disaster management authorities, (vi) disaster management information, (vii); volunteer actions, (viii); situation overview, (ix); social effects, and (x); weather information. Some of the categories were divided further, to quantify significant information. The classified output was sequentially geo-referenced by identifying geographic entities within the text of each post (geo-parsing) and replicating each post according to the number of geolocations. The data processing involved various geo-validations and performance metrics. The final output was used to create maps and graphs of different time periods, that provide useful insights into the flood event for disaster management purposes. The applied methodology is an evolution of previous research published by the author, this time providing complete results, based on the analysis of 100 % of the data available, with maps and graphs which demonstrate how the flood event unfolded in different time periods. The methodology is fully automated in terms of data processing, and can be applied using a script developed by the author in the R programming language. This research is a step towards the real-time delivery of advanced information for all disaster management stakeholders, from official authorities and rescue teams, to volunteers and locals who may be situated within the area of a disastrous flood occurrence.

The effects of an extreme storm (501 mm of rainfall in less than five days) were monitored in an alpine show cave to assess the characteristics of its aquifer unsaturated and saturated zones. The selected cave, Bossea (SW Piedmont, Italy), is an excellent test site for hydrological investigation of karst systems, because it hosts an underground karst laboratory since the late 70 s. The investigation was carried out by means of an integrated approach that combined measurements of flow discharge, physico-chemical parameters, major chemical components, and trace elements (metals, Rb, Ba, Sr and REE). The hydrology and hydrochemistry of the main underground river and two of its secondary tributaries (a drip site and a small secondary inflow) draining the unsaturated zone were monitored during the November 2016 flood, an exceptional hydrological event (estimated recurrence time of 200 years) that caused severe damages in the whole southwestern Piedmont region. The results of the 2016 monitoring were compared with those of another less extreme flood occurred in 2011 (recurrence time of 20 years). The karst system showed an impulsive response to flooding at the catchment and at the individual inflow scales, but each site exhibited a characteristic response due to the complex geological and structural setting of the cave system. The development of this karst system is, in fact, related to the lateral and vertical juxtaposition of rocks with different lithologies and mechanical properties, which form different hydrogeologic compartments. Coupling hydrograph, chemograph analysis and REE normalized patterns permitted to assess which compartments were activated during each phase of the floods. In particular, it was possible to recognize a progressive increasing contribution of the non-carbonate lithologies to flow during the peak of the flood for both the two unsaturated inflows and the main underground river, suggesting the activation of larger portions of the aquifer. However, the response of each individual unsaturated inflow is more influenced by its recharge system architecture rather than the magnitude of the meteorological event. Similar complex geological and lithological karst systems are typical of many areas characterized by orogenetic processes (Alps, Rocky Mountains, Caledonides, etc.), but despite them being relatively widespread, the hydrodynamics of these aquifers is still poorly understood. This study affirms that only long-term and well-integrated monitoring and sampling can help unravel the behavior of such complex karst systems.

The catchments of Rishiganga and then Dhauliganga valleys in the Chamoli district of Uttarakhand were impacted by a catastrophic flood triggered due to a massive rockslide, caused by wedge failure on 7th February, 2021. It is estimated that the massive rockslide of ~ 23 million cubic meter volume containing base rock, deposited ice, and snow got detached from the northern slopes of the Trishul mountain range near Ronti Glacier and created a vertical fall of almost 1700 m before severely impacting the Ronti Gad valley located at 1.5 km downstream of Ronti Glacier snout. The huge detached mass of rock and ice (GLIMS ID: G079733E30381N) swiftly moved downstream through the glaciated valley entraining snow, debris, mud on its way, caused rapid fluidization, created massive water/slush waves, and washed away partially or completely the hydel power projects and bridges in its route. It is estimated that ~ 0.93 Peta Joules of potential energy led to the generation of a significant amount of kinetic and thermal energy, good enough to trigger above-mentioned processes. Post-event analysis of high-resolution satellite data shows flood water marks in the valley and on the rock outcrops reaching up to ~ 80–150 m height on the way to Raini Village. The mud and the slush produced through this process led to the formation of a dammed lake and temporarily blocked one of the tributaries of the Rishiganga joining from the northeast. This study provides an insight into the sequence of events as they unfolded, through multi-temporal satellite image analysis, aerial survey, seismological data in conjunction with various other geo-spatial and geo-visualization tools for unraveling the flood event that has happened on February 7, 2021. We also discuss the potential cause of rockslide and the process mechanism of this unique event, causing loss of lives and property besides widespread devastation.

Climate change is causing frequent and large floods, creating a need for information regarding the impact of severe flood disturbances on stream invertebrates. We briefly describe the response of invertebrate assemblages to an extreme flood event in 2018 in the western Ehime Prefecture of Japan. We compared invertebrate data collected in past regional-scale surveys with those gathered by revisiting 27 study sites after the flood and examined the relationship between catchment characteristics and the invertebrate response across the study area. Heavy rainfall was recorded in the catchment of each study site during the flood (range 565–1081 mm). Stream invertebrates may be eliminated by the historical flood, although the degree was small compared with that in previous reports on the effects of severe flood disturbances (abundance − 19.1%, taxon richness − 9.0%), likely owing to the absence of severe bed disturbance. Our results imply that high primary productivity, possibly caused by low elevations and the development of farms, accelerated the recovery of invertebrates after the disturbance. They also suggest that assembling pre-disturbance data is the key to understanding the impact of extreme floods on stream invertebrates and managing stream ecosystems during climate change.

To assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of a land–atmosphere coupled model, the satellite precipitation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) was assimilated into the Weather Research Forecast (WRF) Model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting elements in the coupled model WRF–TOPX. Differing in whether the 4D-Var data assimilation and evaporation were applied, one control experiment and four experiments were performed to simulate a historical flood event that happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture, and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF–TOPX; and 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land–atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on IMERG could improve the Nash–Sutcliffe coefficient of the coupled model WRF–TOPX from 0.483 to 0.521 at the hourly scale. These investigations can provide important implications for the land–atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with IMERG for flood simulation at a large scale.

Flood is one of the severest natural disasters in the world and has caused enormous causalities and property losses. Previous studies usually focus on flood magnitude and occurrence time at event scale, which are insufficient to contain entire behavior characteristics of flood events. In our study, nine behavior metrics in five categories (e.g., magnitude, duration, timing, rates of changes and variability) are adopted to fully describe a flood event. Regional and interannual variations of representative flood classes are investigated based on behavior similarity classification of numerous events. Contributions of geography, land use, hydrometeorology and human regulation on these variations are explored by rank analysis method. Results show that: five representative classes are identified, namely, conventional events (Class 1, 61.7% of the total), low discharge events with multiple peaks (Class 2, 5.3%), low discharge events with low rates of changes (Class 3, 18.1%), low discharge events with high rates of changes (Class 4, 10.8%) and high discharge events with long durations (Class 5, 4.1%). Classes 1 and 3 are the major flood events and distributed across the whole region. Class 4 is mainly distributed in river sources, while Classes 2 and 5 are in the middle and down streams. Moreover, the flood class is most diverse in normal precipitation years (2006, 2008–2010 and 2015), followed by wet years (2007, 2013–2014), and dry years (2011 and 2012). All the impact factor categories explain 34.0%–84.1% of individual flood class variations. The hydrometeorological category (7.2%–56.9%) is the most important, followed by geographical (1.0%–6.3%), regulation (1.7%–5.1%) and land use (0.9%–2.2%) categories. This study could provide new insights into flood event variations in a comprehensive manner, and provide decision-making basis for flood control and resource utilization at basin scale.

In this paper, the catastrophic flash flood event which occurred in the western part of Attica (Greece) in November 2017 is reconstructed. The flood event hit the town of Mandra, causing 24 fatalities and huge damages in the properties and the infrastructure. The flood hydrograph was derived using the two-dimensional hydrodynamic model FLOW-R2D. Attention was drawn on the uncertainties of the model output due to the uncertainty of the estimated parameters such as infiltration, friction and the uncertainty of input data. Due to the computational burden related to the model, a global sensitivity analysis based on Morris method was performed. Then, a Monte Carlo-based uncertainty analysis was performed on the two most influential factors. It was concluded that even the results of the physically based hydrodynamic models are characterised by uncertainties. However, the capability of the hydrodynamic models to describe in detail the dynamics of the overland flow is the main advantage of these models against the conventional hydrological models. It is concluded that the rational use of physically based models for analysing complex storm phenomena with high variable spatial and temporal distribution can lead to a more accurate range of magnitudes of flood peak.

In the last few years, some regions of the Mediterranean area have witnessed a progressive increase in extreme events, such as urban and flash floods, as a response to the increasingly frequent and severe extreme rainfall events, which are often exacerbated by the ever-growing urbanization. In such a context, the urban drainage systems may not be sufficient to convey the rainwater, thus increasing the risk deriving from the occurrence of such events. This study focuses on a particularly intense urban flood that occurred in Palermo (Italy) on 15 July 2020; it represents a typical pluvial flood due to extreme rainfall on a complex urban area that many cities have experienced in recent years, especially in the Mediterranean region. A conceptual hydrological model and a 2D hydraulic model, particularly suitable for simulations in a very complex urban context, have been used to simulate the event. Results have been qualitatively validated by means of crowdsourced information and satellite images. The experience of Palermo, which has highlighted the urgent need for a shift in the way stormwater in urban settlements is managed, can be assumed to be a paradigm for modeling pluvial floods in complex urban areas under extreme rainfall conditions. Although the approaches and the related policies cannot be identical for all cities, the modeling framework used here to assess the impacts of the event under study and some conclusive remarks could be easily transferred to other, different urban contexts.

Dynamic, integrated and complex processes dominate nearshore marine ecosystems. Industrial, agricultural and population growth delivers pollution and excess nutrient and sediment loads to nearshore marine zones. Climate change also poses serious threats to the delicate balance that exists between and within marine ecosystem processes. Methods to monitor, characterise and model nearshore marine systems have been developed to predict responses to changes in the physical, chemical and biological parameters that constitute elements of a model. Such models are useful to management authorities as they provide a tool to mitigate adverse impacts to marine ecosystems. Phytoplankton community structure provides a sensitive early warning for change to marine ecosystems. It is also a vital component of models derived to determine light attenuation and requirements for healthy seagrass and coral habitats. The multifactorial physicochemical drivers of change to marine microalgae abundance and community structure were investigated in Hervey Bay following two floods, a cyclone and destruction of 1000 km2 of seagrass. A water quality gradient from point sources to offshore sites was identified. Correlations between chlorophyll a (Chl a) and both soluble reactive phosphate (SRP) and total suspended solids was revealed. Diatoms dominated the phytoplankton community structure with seasonal and site differences detected amongst 150 phytoplankton species. Chl a, Secchi depth, SRP and pH maximised the rank correlation with phytoplankton assemblage structure (ρ = 0.63) to best ‘explain’ the link between water quality and phytoplankton (BIOENV). Indicator species include Cylindrotheca closterium for the nearshore degraded sites and Rhizosolenia sp. and Guinardia sp. (G. flaccida and G. striatula) for the offshore more pristine habitats. Following the flood in February 1995, Cylindrotheca closterium, Pseudonitzschia sp. and Skeletonema costatum bloomed at Pulgul Creek (a treated sewage effluent site), and Thalassionema frauenfeldii, Cerataulina bicornis and dinoflagellates bloomed at the mouth of the Mary River (agricultural runoff). Discharge-driven flood impact and trigger values were analysed to assist management authorities in their decision making around potential seafood biotoxin problems associated with harmful algal blooms (HAB’s). Results highlight the importance of species determination rather than Chl a assessment when evaluating the health of an ecosystem. Spring blooms of Trichodesium sp. indicate an additional nutrient source during high traffic of commercial whale watching vessels. Flood impacts highlight the need to schedule dredging, construction and other land management and development activities outside of flood periods. Commercial fishing and swimming should be avoided following a flood due to the health risks associated with toxic phytoplankton species (HAB’s) and for the health and safety of swimmers, consumers and other aquatic species. Future research to replicate this study in Hervey Bay and in other estuaries holds much value to further explore the phytoplankton species identified as bio-indicators of ecosystem health in the present study. Research yet to be published, addresses the links between water quality and seagrass epiphytes, light attenuation and the habitat requirements of seagrass in Hervey Bay.

The changing climate and anthropogenic activities raise the likelihood of damage due to compound flood hazards, triggered by the combined occurrence of extreme precipitation and storm surge during high tides and exacerbated by sea-level rise (SLR). Risk estimates associated with these extreme event scenarios are expected to be significantly higher than estimates derived from a standard evaluation of individual hazards. In this study, we present case studies of compound flood hazards affecting critical infrastructure (CI) in coastal Connecticut (USA). We based the analysis on actual and synthetic (considering future climate conditions for atmospheric forcing, sea-level rise, and forecasted hurricane tracks) hurricane events, represented by heavy precipitation and surge combined with tides and SLR conditions. We used the Hydrologic Engineering Center's River Analysis System (HEC-RAS), a two-dimensional hydrodynamic model, to simulate the combined coastal and riverine flooding of selected CI sites. We forced a distributed hydrological model (CREST-SVAS) with weather analysis data from the Weather Research and Forecasting (WRF) model for the synthetic events and from the National Land Data Assimilation System (NLDAS) for the actual events, to derive the upstream boundary condition (flood wave) of HEC-RAS. We extracted coastal tide and surge time series for each event from the National Oceanic and Atmospheric Administration (NOAA) to use as the downstream boundary condition of HEC-RAS. The significant outcome of this study represents the evaluation of changes in flood risk for the CI sites for the various compound scenarios (under current and future climate conditions). This approach offers an estimate of the potential impact of compound hazards relative to the 100-year flood maps produced by the Federal Emergency Management Agency (FEMA), which is vital to developing mitigation strategies. In a broader sense, this study provides a framework for assessing the risk factors of our modern infrastructure located in vulnerable coastal areas throughout the world.

The 2017 Dadin Kowa flood had a more devastating effect in Hore Gare and Tunga areas. The flood occurred as a result of high rainfall, obstruction, siltation and encroachment into flood-prone areas. Monthly maximum and annual rainfall analyses were carried out. The month of June, 2017 received the maximum monthly rainfall of a total of 344 mm which coincided with the month the flood occurred. The analysis of data indicated that flooding in Dadin Kowa town is rainfall-induced and the river channels are expected to be on higher risks of flooding when the channel obstruction capacity coincides with high rainfall intensity. The mean value of annual and maximum rainfall is 834.93 mm and 249.08 mm with standard deviation of 131.95 mm and 49.24 mm respectively at Dadin Kowa town. The recurrence interval of 2, 5, 10, 25, 50, 100 and 200 years of monthly maximum rainfall is 250.70 mm, 292.80 mm, 313.00 mm, 332.70 mm, 344.30 mm, 353.90 mm and 362.00 mm respectively. The frequency analysis of the return period of the maximum monthly rainfall of 344.00 mm is expected to occur in 50 years from 2018 with a magnitude of 344.30 mm. Also, the recurrence interval of 2, 5, 10, 25, 50, 100 and 200 years of annual maximum rainfall is 838.78 mm, 949.40 mm, 1003.60 mm, 1057.70 mm, 1090.30 mm, 1118.10 mm and 1142.10 mm respectively. The frequency analysis of the return period of the

Western Norway suffered major flooding after 4 d of intense rainfall during the last week of October 2014. While events like this are expected to become more frequent and severe under a warming climate, convection-permitting scale models are showing their skill with respect to capturing their dynamics. Nevertheless, several sources of uncertainty need to be taken into account, including the impact of initial conditions on the precipitation pattern and discharge, especially over complex, mountainous terrain. In this paper, the Weather Research and Forecasting Model Hydrological modelling system (WRF-Hydro) is applied at a convection-permitting scale, and its performance is assessed in western Norway for the aforementioned flood event. The model is calibrated and evaluated using observations and benchmarks obtained from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The calibrated WRF-Hydro model performs better than the simpler conceptual HBV model, especially in areas with complex terrain and poor observational coverage. The sensitivity of the precipitation pattern and discharge to poorly constrained elements such as spin-up time and snow conditions is then examined. The results show the following: (1) the convection-permitting WRF-Hydro simulation generally captures the precipitation pattern/amount, the peak flow volume and the timing of the flood event; (2) precipitation is not overly sensitive to spin-up time, whereas discharge is slightly more sensitive due to the influence of soil moisture, especially during the pre-peak phase; and (3) the idealized snow depth experiments show that a maximum of 0.5 m of snow is converted to runoff irrespective of the initial snow depth and that this snowmelt contributes to discharge mostly during the rainy and the peak flow periods. Although further targeted experiments are needed, this study suggests that snow cover intensifies the extreme discharge instead of acting as a sponge, which implies that future rain-on-snow events may contribute to a higher flood risk.

Flood events can have very different generating processes. Floods may originate from high intensity rainfall, long-duration rainfall or snowmelt. Other factors, e.g. initial soil moisture conditions, also affect the spatial and temporal characteristics of floods. Hence, a typology for floods is often used to classify floods according to their runoff generating processes. Having in mind the changing circumstances for floods over the years, like climatic or anthropogenic changes, the question arises in how far these changes affect the occurrence of certain flood types. This does not only concern a change of frequency of flood event types over time, but also spatial changes resulting in different regional patterns. We investigate the changes of the frequency of occurrence of flood event types, their spatial patterns and the consequences for estimating extreme events. Many watershed characteristics are strongly related with specific flood types, e.g. elevation with snowmelt. Short-rain events are limited in their spatial extension and cause floods in smaller catchments, whereas in large river basins a large flood peak requires a long and spatially extended rain event. In this study, the flood typology is based on analyses of the peak-volume relationship allowing to consider long periods of observation with partially only few meteorological data, e.g. snow height. For the resulting flood types, temporal changes and spatial patterns are detected and connected with the generating processes as well as catchment characteristics. Finally, the impact of changes on the flood statistic is investigated by using a seasonal mixture model of partial duration series that is able to model event types and seasons separately and combines them in an annual model.

In this study, we use before and after flood observations of bed sediment lithology to determine how the lithological composition of bed sediment changes in response to an extreme flood event and use the post-flood measurements and observations to estimate values of critical shear stresses needed to erode shale bedrock and protective bank vegetation in the study streams. We present results from measurements of bed sediment lithology taken after the 2013 Colorado Front Range flood and compare these results with the same measurements taken before the flood. These two data sets provide a unique opportunity to determine how bed sediment lithology changes following an extreme flood event and allows insight into sources of channel bed sediment. After the flood, bed sediment lithology shifted distinctly towards the lithological distribution of the total catchment. Before the flood, bed sediment in most locations was primarily composed of granitic material, while after the flood, the streams were more likely to have metamorphic, volcanic, or sedimentary rocks in the bed sediment, reflecting more input from more non-local hillslopes compared to before the flood. Our post-flood measurements and observations allowed us to estimate threshold critical shear stress for the plucking of shale bedrock (212 Pa) and the removal of large cottonwood trees (364 Pa) in downstream sections of the study streams. Bank vegetation plays a critical role in protecting bedrock banks from lateral erosion, such that only the largest flood events can remove vegetation and expose bedrock on channel banks to potential erosion. We found that this large flood event plucked shale bedrock from both the channel bed and banks and that the shale bed sediment then quickly breaks down and is transported away by small flows. As such, this data set allowed us to make conclusions about the effect of large flood events on the long-term evolution of lateral and vertical erosion in bedrock rivers.

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large‐scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar‐based hourly rainfall data, a series of hydrologic‐hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D‐GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km2, and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high‐danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high‐danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high‐performance computing hydrodynamic large‐scale flood simulations.

Social media data generated from individuals provides a unique opportunity to gain valuable insight on information flow, especially for emergency response. However, the inherent limitations associated to these data (particularly, the spatial bias) restrict its precise application. Existing research on spatial bias correction of social media data mainly face two issues: 1) the geographic extent in target domains may be underestimated, and 2) source elements may be transported within inappropriate distance. In this paper, we take 2013 Boulder, Colorado flood event as a case study, and present a new method called subspace optimal transport (SOT). Our proposed SOT aims at transporting biased tweets from dry to real flooded areas with a relatively close distance. Specifically, a comparison between our newly developed SOT and the traditional optimal transport (OT) and geographic optimal transport (GOT) is performed. Experimental results demonstrate that our new SOT method is able to correct the spatially biased geo-referenced tweets, with high precision and excellent computing performance.

Pesticides are the class of compounds with the most dynamic behaviour in their surface water occurrence: their episodic release to surface waters is closely related to the date of application and the following weather conditions and poses substantial challenges to monitoring in order to yield accurate mass transfer figures. Moreover, pesticide use, dose and time of application are largely unknown catchment wide and pose an essential problem as to the realism and reliability of pesticide fate modelling as well as accurate farmer counselling. Spatially and temporally highly resolved monitoring establishing pesticide sources was logistically unthinkable until the advent of passive samplers which combine ease of deployment and continuous sampling. However, because research on passive sampler performance has been mainly driven by analytical precision issues, doubts were high as to whether passive samplers could yield accurate time weighted averages in the field, all the more so that the number of field validations is to this day very limited. Here we present a study that used a combination of spatially distributed passive- and autosamplers to capture the runoff dynamics of pesticides used for maize crops in a 82 km2 catchment in Luxembourg. We demonstrate that passive samplers are capable of accurately monitoring episodic emissions of pesticides through a longitudinal profile in a catchment, thus allowing the identification of pesticide source areas. Thanks to the time-proportional nature of the passive sampling it was furthermore possible to calculate event mean concentrations and loads which were behaving temporally according to the physico-chemical properties of the compounds and to the timing and extent of mobilising discharge.

In the field of hydrology, the Geomorphological Instantaneous Unit Hydrograph (GIUH) is central to describing a watershed response. The application of GIUH is extended to individual hydrological events by accounting for the influence of rainfall spatial distribution. A method is proposed herein to identify the Geomorphological Instantaneous Unit Hydrograph specific to each flood event (i.e. E-GIUH), when runoff of effective rainfall is the dominant process. The E-GIUH is derived from observational data, namely: rainfall field time series, and a hydrograph. Such an identification process is formulated as an inverse problem with parameters such as the E-GIUH velocity and coefficient of dispersion, as well as the hyetograph of rainfall excess. The proposed method is applied to several flood events across two mountainous catchments within the Cevennes-Vivarais Mediterranean Hydrometeorological Observatory territory prone to flash flooding. Results indicate that the E-GIUHs display significant variability over the two basins, and the E-GIUH parameters appear to be correlated with the flood event magnitude. The E-GIUH synthetizes the basin response to rain forcing and can be considered as a signature of flood events. A sensitivity study suggests that E-GIUH identification is fairly robust, even with respect to the a priori hyetograph of effective rainfall.

Weather-related disasters represent a major threat to the sustainable development of society. This study focuses directly on the assessment of the state of spatial information quality for the needs of hydrodynamic modeling. Based on the selected procedures and methods designed for the collection and processing of spatial information, the aim of this study was to assess their qualitative level of suitability for 3D flood event modeling in accordance with the Infrastructure for Spatial Information in the European Community (INSPIRE) Directive. In the evaluation process we entered geodetic measurements and the digital relief model 3.5 (DMR 3.5) available for the territory of the Slovak Republic. The result of this study is an assessment of the qualitative analysis on three levels: (i) main channel and surrounding topography data from geodetic measurements, (ii) digital relief model, and (iii) hydrodynamic/hydraulic modeling. The qualitative aspect of the input data shows the sensitivity of a given model to changes in the input data quality condition. The average spatial error in the determination of a point&rsquo, s position was calculated as 0.017 m of all measured points along a watercourse and its slope foot and slope edge. Although the declared accuracy of DMR 3.5 is assumed to be &plusmn, 2.50 m, in some of the sections in the selected area there were differences in elevation up to 4.79 m. For this reason, we needed a combination of DMR 3.5 and geodetic measurements to refine the input model for the process of hydrodynamic modeling. The quality of the hydrological data for the monitored N annual flow levels was of fourth-class reliability for the selected area.

Flooding events in the Lower Benue valley of Nigeria are often associated with huge damage to properties and loss of life in the adjoining communities. Specific objectives of this study were to (i) examine the characteristics of rainfall and discharge at the major river in the study area—Benue trough of Nigeria; (ii) assess the occurrence of extreme rainfall conditions and other flood-triggering/exacerbating factors; and (iii) determine flood damage extent and available warning system in the area. Specific reference was made to the 2017 flood event in the area. Method used was an integrated environmental approach that combines analysis of rainfall and discharge data with social surveys, remote sensing and geographical information system. Standardized Precipitation Index (SPI), Precipitation Concentration Index (PCI) as well as flood damage curves were analysed with land use/cover change and soil data to establish the nature of the flood and its impacts. Result showed that rainfall has increased in the study area in October–December and February (b ≤ 0.13) but has decreased in the other months, albeit insignificantly (R2

Floods are among the severe weather disasters that cause catastrophic damage to surroundings and adversely impact populations. This study aims to create a one-dimensional (1D) hydraulic model using HEC-RAS for the Rel River in Banaskantha, Gujarat, India. The model has been developed for the extreme flood event of July 2017. A total of hundred cross-sections have been used as geometric data. The peak discharge of 3355 m3/s and the river slope has been applied as upstream and downstream boundary conditions. The model has been calibrated and validated using observed water depth at Railway bridge and Highway bridge. Critical cross-sections have been identified using the 1D hydraulic model. Eight out of the hundred cross-sections were safe for a flood discharge of 3355 m3/s. The villages at high flood risk are identified for this discharge. To mitigating floods, the construction of a retaining wall or levees is recommended to protect these villages. This study can help a disaster management strategy for the cities and town in the River’s vicinity.

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min.

Rainfall patterns have a potential impact on floods, and the accuracy of peak flow determinations can directly affect the accuracy of rainfall warning index values. Therefore, it is necessary to explore the impact of rainfall pattern on the uncertainty of rainfall warning index for a small watershed. Xiawan, in the small Peihe watershed in Henan Province, China, was used as a case study. Based on an analysis of rainfall characteristics, a fuzzy recognition method was used to identify common rainfall patterns in the study area, following which they were compared with the regional design rainfall pattern. Design rainstorm flood calculation and a water level/flow inversion method were used to analyze the rainfall warning indices for different rainfall patterns and to establish the relationship between rainfall patterns and the values of rainfall warning index. The results show that: (1) rainfall pattern has a major impact on rainfall warning index values, and the rationality of the rainfall pattern requires consideration. (2) Deviations in peak flow between different rainfall patterns were large, and the timing of peak rainfall had a considerable influence on peak flood flow; (3) within the same early warning time interval, the rainfall warning indices where the timing of peak rainfall was at the start and in middle of the event were 1.67 times and 1.39 times that where rainfall peaked at the end, respectively. Further study of the rainfall pattern and its impact on rainfall warning index can provide technical support and an empirical reference value for the analysis and calculation of early warning indices for flash flood events in small watersheds.

Flooding is one of the most frequent natural disasters that have significant impact on communities in terms of loss of life, direct and indirect economic losses, and disruption of daily life. Decision makers often depend on flood data inventories to make more informed decisions on the development of flood mitigation plans to protect flood prone communities. A comprehensive inventory that covers multiple aspects of a flood event is critical to identify vulnerable regions, historical trends, and mitigate possible flood impacts. This study proposes an integrated flood data specification to support multi-stakeholder use cases, community-based sustainable domain specific maintenance, and crowdsourced data collection and management. The specification is designed based on comprehensive review of existing global and national repositories, scientific studies and needs and requirements of stakeholders. The specification is designed to include metadata on environmental, economic, and demographic impact, hydraulic, hydrologic, and meteorological features, and detailed location information of a flood event. As a case study, a flood event inventory was compiled for Turkey between 1930 and 2020 using existing national and global data sources and digitized media archives. A total of 2101 flood events with 64 data attributes have been collected over the period of 90 years. An initial statistical analysis of the inventory is also presented for assessment of the seasonal and regional characteristics of flooding in Turkey.

Floods are the most devastating hazards that have significant adverse impacts on people and their livelihoods. Their impacts can be reduced by investing on: 1) improving the forecasting skills of extreme and heavy rainfall events, 2) development of Impacts Based Flood Early Warning System (IBFEWS) and 3) effective communication of impacts from anticipated extreme or heavy rainfall event. The development of IBFEWS however, requires a complete understanding of factors that relates to the formation of extreme or heavy rainfall events and their associated socio-economic impacts. This information is crucial in the development of Impacts Based Flood Forecasting Models (IBFFMs). In this study, we assess the socio-economic impacts of the December 2011 flood event in Dar es Salaam as the preliminary stage in the development of IBFFMs for the City of Dar es Salaam. Data from household survey collected using systematic random sampling techniques and structured questionnaires are used. The survey was conducted to acquire respondent’s views on the causes of floods impacts, adaptive capacity to extreme or heavy rainfall events and adaptation options to minimize flood impact. It is found that the main causes of floods were river overflow due to heavy rainfall and blocked drainage system. Poor infrastructure such as drainage and sewage systems, and ocean surge were identified to be the causes of observed impacts of the December 2011 flood event in Dar es Salaam. Death cases analysis showed that 43 people were reported dead. The flood event damaged properties worth of 7.5 million Tanzania shillings. Furthermore, the Tanzania Government spent a total amount of 1.83 billion Tanzanian shillings to rescue and relocate vulnerable communities that lived-in low-lying areas of Jagwani to high ground areas of Mabwepande in Kinondoni district.

The severe 1374 Rhine river flood represents the largest flood in the past 1,000 years and exceeds the maximum discharge measured at Cologne, Germany, so far. The Dutch water policy is designed such that it should be able to withstand design discharges with a maximum return period of 100,000 years. The question arises whether the current flood protection measures along the Dutch Rhine river branches are sufficient to cope with an extreme event that is as severe as the 1374 flood event. In this study, a 1D-2D coupled model is set up to accurately simulate flood wave propagation with low computational cost. This model is used to simulate the consequences of the 1374 flood event in present times. The study shows that the maximum discharge along the Lower Rhine decreases significantly in downstream direction as a result of dike breaches and overflow. Therefore, the amount of water that reaches the Netherlands through the main channel is restricted to a maximum value. However, overland flow patterns influence the discharge partitioning of the Rhine river branches. Furthermore, the overland flow patterns change the most vulnerable areas in terms of flood risk.

Precipitation is a key factor in controlling the accuracy of runoff simulation, as well as the performance of flood event simulation. Compared with the in-situ rainfall measurement, satellite-based precipitation products provide critical precipitation sources of higher resolution along with detailed depiction of precipitation variability, especially for data-sparse or ungauged regions. This study aims to investigate the impacts of temporal and spatial resolutions of precipitation on flood simulation over a humid region of Southern China. Three versions of Integrated Multi-satellite Retrievals for GPM (IMERG-E, IMERG-L, and IMERG-F) and a gauge-satellite merged precipitation product released by China Meteorological Administration (CMA) at 0.1° and 1 h resolution are used in the study. The lumped hydrological model HBV and semi-distributed hydrological model SWAT are applied to simulate 12 flood events to investigate the impacts of temporal and spatial variabilities of precipitation on flood event simulation. The results show that the spatial resolution of precipitation data affects its capture of characteristics of precipitation events, specifically in magnitude of precipitation variability and the central location of the precipitation event. Furthermore, SWAT shows no improvement compared with HBV in flood event simulation in this case, which may due to the uncertainty of the precipitation spatial variability. The flood events simulated with SWAT indicate that the biases of flood peaks forcing by IMERG-E and IMERG-L increase with the decreasing of precipitation variability, while that forced by IMERG-F are less affected and perform the best among the three IMERG precipitation estimates. The impact of temporal variability of precipitation is conducted with HBV model and the corresponding results are that the higher temporal resolution ensures the better flood event simulation. Furthermore, the CMA source overperforms the other three satellite-based precipitation estimates, and followed by IMERG-F.

Due to climate change and rapid urbanization, urban flooding has become one of the major natural hazards threatening the safety of people and their properties and affecting the overall sustainability of cities across the globe, especially developing countries such as China. Flood modelling has now provided an indispensable tool to support urban flood risk assessment and management, and inform the planning of cities that are more resilient to flooding.Hydraulic structures, e.g. regulation gates and pumping stations, play an important role in urban flood risk management. However, direct simulation of these hydraulic structures is not a current practice in 2D urban flood modelling. This work presents and applies a robust numerical approach to directly simulate the effects of hydraulic structures in a 2D high-resolution urban flood model. An additional computational module is developed and fully coupled to a GPU-accelerated finite volume shock-capturing urban flood model to directly simulate the highly transient flood waves through hydraulic structures. The improved flood model is applied to reproduce a flood event induced by Typhoon “Lekima” in 2019 in Yuhuan, Zhejiang Province, China. At 3m resolution, the model is able to simulate the complete process of the flood event in nearly 3.5 times faster than real time, demonstrating the efficiency and robustness of the new fully coupled model for high-resolution food modelling in cities. Further simulations are performed to systemically investigate the effect of hydraulic structures and different operational regulations on flood dynamics and associated risks, demonstrating the importance of directly considering hydraulic structures and their operations in 2D high-resolution urban flood modelling.

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multi-dimensional statistical modeling approach. Amongst different machine learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created which can be deployed in the future for flash flood forecasting. The results confirm that although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of sub-basin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins which could most benefit from distributed hydrologic modeling.

While drought represents a serious threat to the Pacific southwestern United States, floods represent an equally formidable threat. This risk is so significant that the U.S. Geological Survey created the ARkStorm Project. This project aims to prepare California for a future storm(s) on the scale of the disastrous A.D. 1861–1862 events. Unfortunately, our knowledge of premeasurement floods in the Pacific southwestern United States is sparse. To date, the best paleoflood record consists of flood layers in the Santa Barbara Basin, spanning the past 9000 calendar yr B.P. (cal yr B.P.). As an alternative to marine archives, the lakes of the Pacific southwestern United States represent untapped resources for possible premeasurement flood reconstructions. Here, we present evidence for a flood between ca. 4860 and 4820 cal yr B.P. using sediment from Lake Elsinore core LEGC03-4. Core LEGC03-4 is predominantly clayey silt with occasional sandy silt units of variable centimeter-scale thickness. Here, we focus on a specific core section between 350 and 315 cm, where an ~11-cm-thick “unusual” sediment unit (330–319 cm) is well preserved and complete. The core section was analyzed for a variety of physical and chemical properties, including magnetic susceptibility, loss-on-ignition (LOI) at 550 °C and 950 °C, grain size, Corg:Ntotal ratios, and δ13C(bulk organic matter). The unit is characterized by an erosional basal contact and microflame structures. It is normally graded, with laminae occurring in the upper section of the unit. It contains predominantly terrestrial organic matter, and the upper boundary is gradational. It is coeval with the fourth highest sand peak in a previously dated central basin core. Consequently, it is our conclusion that the unusual sediment unit represents a turbidite associated with a large flood-producing precipitation event with a maximum limiting age between 4860 and 4820 cal yr B.P.

Common bottlenose dolphins, Tursiops truncatus, can suffer health complications from prolonged freshwater exposure, however, little is known about how dolphins behaviorally respond to flood events. We investigated whether dolphins mitigated their freshwater exposure by moving south towards the estuary mouth and/or towards deeper areas with higher salinities in response to a record-breaking flood in Pensacola Bay, Florida. In total, 144 dolphin groups observed during 45 population dynamic surveys were analyzed across two flood-impacted sampling sessions and their respective seasonal control sessions. Kernel density estimates demonstrated southern movement towards the estuary mouth during flood-impacted sessions, but this distribution change was limited. Species distribution models showed that dolphins did not move to deeper areas after the flood and dolphin distribution was not substantially altered by flood-induced salinity changes. The estuary system exhibits strongly stratified waters with broad salinity ranges even during the flood. Dolphins may have mitigated the severity of freshwater exposure by capitalizing on these stratified areas as they continued to use habitat affected by the flood. A lack of avoidance of low salinity could result in this dolphin population being at greater risk for health problems, which should be considered in future population management and conservation.

In the framework of LEXIS (Large-scale EXecution for Industry & Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1° spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25° spatial resolution.The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.

The growth of periphyton in streams is enhanced by phosphorus (P) in baseflow. The likely control of P concentrations in baseflow can be approximated by the equilibrium P concentration (EPC0) of bed sediments. However, sediment composition changes with spatial scale and flood events. It is unknown if this affects EPC0. We sampled sediments in a main stem and headwater tributary of an agricultural catchment in Reporoa, New Zealand, before and after a scouring flood event (99th percentile of flows recorded since 1962). The tributary was chosen for its low slope and predominantly single land use (intensive dairying), which minimised the number of factors likely to affect the influence of the storm event. EPC0 values were significantly correlated to dissolved reactive P (DRP) in baseflow before, and after, the flood event, despite a decrease in the proportion of fines and total P in bed sediments. Both EPC0 and DRP concentrations increased towards the catchment outlet. This increase likely reflected new P-enriched sediments from dairy-farm runoff, but hyporheic zone samples suggested that shallow groundwater may also have played a role in supplying P to the water column. Despite diel variations in dissolved oxygen, DRP concentrations showed little variation during the day and matched EPC0 estimates. This work suggests that despite changes in sediment composition due to flood events, EPC0 is a useful reflection of daytime baseflow-DRP concentrations at sites along a stream network. However, further work is required to clarify if sediment-P exchange or groundwater control baseflow-DRP concentrations. These data also inform our understanding of the influence of sediment on delaying farm and catchment efforts to decrease in-stream DRP concentrations.

In April 2019, large parts of Khuzestan province in Iran were affected by intense record rainfall in the Zagros mountains. Persian Gulf catchment received approximately 30% of its long-term average rainfall over the course of a few days. Karkheh and Dez, two of the major rivers in this catchment, overflowed their banks. As several dams, including Karkheh, with the country's largest capacity, reached their limits, the water had to be released from the reservoirs, which resulted in flooding downstream of the dams. Several cities and more than 200 villages were flooded, and many people had to be evacuated. Many of the dams affected by the 2019 flood were embankment dams, previously reported to exhibit post-construction settlements, at places reaching 13 cm/yr. Therefore, during and after the flood, significant concerns were raised about their health and stability.In this study, we use Sentinel-1 InSAR to monitor embankment dams' response in Khuzestan to the 2019 flood event. We process the full archive of Sentinel-1 using the Small Baseline Subset approach and estimate the time series of displacement for three different embankment dams in Khuzestan province. The first two studied dams are Karkheh and Gotvand, which have the country's largest capacities and became operational in 2001 and 2012, respectively. The third studied dam is the Masjed-Soleyman dam, previously reported to sustain a high displacement rate since its operation in 2002.The Sentinel-1 InSAR displacement results indicate that all observed dams exhibit long-term post-construction settlement before the flood, with rates varies from approximately 1 cm/yr for the Karkheh dam to 5 cm/yr for Gotvand dam and 8 cm/yr for Masjed-Soleyman dam. The time series of displacement for Karkheh and Gotvand dams show gentle changes of displacement in response to the increase in water level following the flood. However, for the Masjed-Soleyman dam, the movement accelerates sharply after the flood with more than 2 cm of displacement on the crest in only two months. For the Masjed-Soleyman dam experiencing the most severe effect of the flood, we also analyzed high-resolution data from TerraSAR-X and COSMO-SkyMed. The results provide a detailed picture of the displacement pattern over the crest and the dam's body before and after the flood.

Globally, about a third of all losses related to natural hazards are due to flooding. Many studies focused their attention on the estimation of flood damages to buildings and infrastructures. However, floods cause significant losses to the agricultural sector too and negatively affect rural economies due to their impacts on agricultural productivity.Several tools to quantify flooding economic impacts on the agricultural sector have been proposed, such as the AGRIDE-c conceptual model, and the Joint Research Centre (JRC) depth-damage functions. However, the tools have rarely been validated against data collected from surveys.The aim of this study is the comparison between the flood economic impacts on agriculture computes using both AGRIDE-c and the JRC tool and the ones retrieved from surveys.A questionnaire for estimating flood economic impacts on agriculture was prepared and submitted to farmers shortly after the flooding event. The selected case study area was the town of Nonantola (near the city of Modena, Northern Italy), where a flooding event occurred on 6th December 2020. The flood was caused by the collapse of about 80m levee portion along the right bank of Panaro River resulting in an inundated area around 2000 hectares. The flood involved the Nonantola town where residential buildings and an active industrial area are located, although the dominant land use is agricultural land. The main local crops are represented by forage, wheat, vineyards, fruits (pears and plums) and sugar beet.The questionnaire is divided into four main sections: The first section is related to the generic information on the farm, the second section to the data on the inundation and damage to crops, the third section to the information on past flood events and risk mitigation strategies eventually adopted during past and present events, the fourth section data to the insurance coverage.Two existing crop damage models (AGRIDE-c and the JRC) were calibrated using three types of data: crop yields, crop selling prices and crop cost of production. Crop yields were obtained from the Italian National Statistical Institute (ISTAT), crop selling prices and costs of production were instead available from official sources such as ISMEA and Coldiretti (Italian association of farmers).Finally, the proposed approach will allow the comparison between the damages experienced by farmers evaluated from questionnaires and the damages estimated by the two models in order to evaluate how the models simulate data directly collected from the field surveys.

In March 2010, a sequence of three major rainfall events in New England (United States) led to a record‐breaking flooding event in the Pawtuxet River Watershed with a peak flow discharge of about 500‐year return period. After development of hydrological and hydraulic models, a number of factors that played important roles in the impact of this flooding and other extreme events including river structures (reservoirs, historical textile mill dams, and bridges) were investigated. These factors are currently omitted within risk assessments tools such as flood insurance rate maps. Some management strategies that should be considered for future flood risk mitigation were modeled and discussed. Furthermore, to better understand possible future risks in a warmer climate, another extreme flood event was simulated. The synthetic/hypothetical storm (Hurricane Rhody with two landfalls) was created based on the characteristics of the historical hurricanes that severely impacted this region in the past. It was shown that while the first landfall of this hurricane did not lead to significant flood risk, the second landfall could generate more rain and flooding equivalent to a 500‐year event. Results and the methodology of this study can be used to better understand and assess future flood risk in similar watersheds.

Digital elevation models (DEMs) are important in flood modelling as they present surface information and obstacles giving the direction of water during a flood event. All hydraulic modelling software needs both channel and floodplain elevation data to simulate the water flow from upstream to downstream. Based on the accuracy and representative capacity of the surface, modelling results can be evaluated in terms of hazard and risk analysis. Developments in remote sensing and GIS technology provide many sources to freely obtain DEM data of different resolution and accuracy. However, access to and the processing of high-resolution data associated with large areas is difficult and time-consuming. This study, therefore, focuses on determining the availability of different resolutions and DEM sources in flood modelling by testing unmanned aerial vehicle (UAV) data. For this, freely available DEMs such as ALOS DEMs (12.5 and 30 m), EU-DEM (25 m), ASTER GDEM (30 m), SRTM (30 and 90 m), MERIT DEM (90 m) and TANDEM-X (90 m) were chosen to investigate part of Biga River (Turkey) which features a natural channel. Then, 1D flood modelling was examined using different DEM resolutions, Manning’s values and flood data of various frequencies (10, 100 and 1000 years). Results were tested using high-resolution DEM data (5 cm) gathered from a UAV. It was concluded that EU-DEM is the most suitable dataset in terms of flood extent, depth and velocity. However, depending on the boundary characteristics of the data, ALOS 12.5 DEM and MERIT DEM (90 m) data are the most suitable in their extent, depth and velocity for high-resolution and low-resolution flood modelling worldwide extent.