In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

: Human settlements have historically thrived near rivers for navigation, trade, and availability of water supply and resources. Night light data, representing economic activities provide a novel approach to studying the interactions between human activity and rivers over time. Here, we use the Defense Meteorological Satellite Program (DMSP) stable night light data from 2000-2013 as a proxy for human presence and activities to quantify the statistical relationships between night light presence and intensity in the Indus Basin, Asia. We test how these data are affected by proximity to trunk channels and by channel type (single/multi-thread) in the study area. We find that night light presence is enhanced by 26% within a 0 to 5 km proximity range of the Indus River and its tribu-taries, relative to the basin as a whole. We interpret this to represent increased human presence and activity within this zone. However, the mean intensity is lower near the river and higher away from the river signifying denser settlements, such as towns and cities which are preferentially located away from the Indus and its tributaries. Moreover, the enhancement of lit pixels signifying human presence and activities is increased by 18% near single-thread sections of the Indus River, compared to segments of the Indus displaying multi-thread morphologies. We suggest that this is due to the enhanced stability of single-threaded channels, relative to mobile multi-threaded channel reaches. This study demonstrates how night lights are an important tool to constrain the relationship between human presence and river dynamics in large catchments such as the Indus, and we suggest this data will have an important role in assessing differential flood spatial and social vulnerability at a regional scale.

Abstract: Floods can be defined as one of the most frequent and destructive disasters, that cause significant damage to people, while droughts present significant challenges in first place to most vulnerable communities. In consequence, state public policies are essential to manage the risk in the face of both phenomena and contribute to the construction of resilient communities. As a request of the National Water Commission of Mexico (CONAGUA) for the Regional Water Plan (PHR), we designed and applied a methodology based on virtual consultation fora in conjunction with digital survey tools to understand the population's perception of these phenomena. The workshops were organized in the five municipalities of Baja California Sur. Then, we performed an interdisciplinary analysis that combines hydrological considerations with the most critical social, economic, environmental, and law components, applying the PESTEL analysis. The results indicate that there is a clear and widespread awareness that floods and droughts increase the risk to the livelihoods of the population; however, there are deficiencies in different areas, which complicates risk management. Timely information and the inclusion of communities in mitigation and adaptation proposals would allow for achieving more successful cases with the efforts to minimize vulnerabilities and increase the resilience of the population in the face of a changing climate. This must be a collaborative work of the entire society, governmental, social, and private stakeholders.

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

Advancements in Machine Learning techniques, availability of more data-sets, and increased computing power have enabled a significant growth in a number research areas. Predicting, detecting and classifying complex events in earth systems which by nature are difficult to model is one of such areas. In this work, we investigate the application of different machine learning techniques for detecting and identifying extreme rainfall events in a sub-catchment within Pangani River Basin, found in Northern Tanzania. Identification and prediction of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations which have been labeled for the whole sub-catchment. In order to assess which Machine Learning technique suits better for rainfall identification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting Random Forest and XGBoost as the ones with best overall performance. However, since the class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin

The research study assessed the level of household exposure, sensitivity, and capacity to cope with flood hazards in Kasese municipality, Kasese district, Uganda. The study used an indicator-based methodology. About 210 respondents were randomly sampled for interview. Individual weights for each indicator were allocated using Principal Component Analysis. Vulnerability indices were constructed at the household level and then aggregated at the division level. A Chi Square test at a significance level of 5% was used to test for differences in the level of household vulnerability. The results revealed that Nyamwamba division was most exposed while Central was least exposed to floods. The Central division was also found most sensitive while Bulembia was least sensitive to floods. Central division had better capacity to cope with floods while Bulembia had the least capacity. Results revealed a significant difference in the level of households’ vulnerability across the divisions. However, overall, Nyamwamba was found most vulnerable and Central least vulnerable to floods. About 43.8% of the households in Kasese municipality were found highly vulnerable to floods. Therefore, urgent attention by the government through policy action measures towards climate change adaptation should be given to address the high levels of vulnerability.

The growth of urban population, combined with an increase of extreme events due to climate changes call for a better understanding and representation of urban floods. Rainfall and infiltration are two important factors that affect the watershed response to a given precipitation event. In this paper, we evaluate the influence of the representation of infiltration and spatially variable rainfall on the computer simulation of the floods that affected the city of Hull, UK in June 2007. This work compares a uniform rainfall with one generated using Kriging with External Drift and a constant infiltration equal to the soil hydraulic conductivity with a neglected infiltration. The results of the four simulations are then compared with the flood extents observed by public authorities. It results that the computer model is able the reproduce the general dynamic of the flood and identify the main inundated areas. We found that neglecting the infiltration induce a better representation of this flood event. Furthermore, the use of radar rainfall results in an accuracy similar to the one obtained with a constant rainfall. This study indicates that when the spatial resolution of the rainfall data is low compared to the catchment size and the precipitation distribution is uniform, the spatial variability of the rainfall might not add significant information.

Three significant changes have occurred in the winter climate in Europe recently: increased UK flooding; Iberian drought; and warmer temperatures north of the Alps. The literature links all three to a persistent, significant increase in sea level pressure over Southern Europe, the Mediterranean, Iberia and the Eastern Atlantic (SEMIEA) which changes the atmospheric circulation system: forcing cold fronts to the north away from Iberia; and creating a south westerly flow around the northern perimeter of the high-pressure region bringing warmer, moist air from the subtropical Atlantic to the UK and Europe which increases precipitation in the UK and raises the temperature in Europe. I use the Last Millennium Ensemble, MERRA-2 and Terra-NCEP data to demonstrate that the extreme, anthropogenic, West African aerosol Plume (WAP) which only exists from December to April perturbs the northern, regional Hadley Circulation creating the high pressure in the SEMIEA. I also show that the anthropogenic WAP has only existed in its extreme form in recent decades as the two major sources of the WAP aerosols: biomass burning; and gas flaring have both increased significantly since 1950 due to: a four-fold increase in population; and gas flaring rising from zero to 7.4 billion m3/annum and note that this time span coincides with the changes in the three elements of the winter climate of Europe. I also suggest that it may be possible to eliminate the WAP and return the winter climate of Europe to its natural state after the crucial first step of recognising the cause of the changes is taken.

This study posits that for appropriately explaining the complex charland (mid-channel island) processes and formulating policy and planning measures, a comprehensive understanding of the dynamic characteristics of the geomorphological, ecological, and human systems holistically is essential. This is also valid for the territorial and maritime areas of Bangladesh. The objectives of this study are: (i) to analyze the salient features and characteristics of the geomorphological and riparian systems of the Bengal Delta; (ii) to analyze the evolutionary discourse of the legal systems concerning eroded (diluvion) and accreted (alluvion) land in Bangladesh; and (iii) to assess characteristics of coping and adaptation strategies of the charland inhabitants. The findings reveal that the delta-building processes, characterized by the dynamic shifts of river channels, and erosion and accretion of charlands have made the land and water systems of the territory very dynamic and unstable – resulting in consistent displacement of settlers and serious deterioration of their socioeconomic status. The historical evolution of land laws and regulations concerning the accreted land favoured vested interests. As no effective institutional framework and structure presently exists in Bangladesh for resettlement planning, formulation of a comprehensive national resettlement policy is therefore urgently needed.

Flash floods repeatedly threaten Barcelona, damaging the city infrastructure and endangering the safety of the population. The city’s urban planning and socioeconomic distribution, associated with the topography and other geographic factors, means that these flood events do not affect the entire city in the same way. This is a key point for surveillance and emergency tasks, which need some patterns and models to improve response capacity. This work aims to gain a better understanding of such events, to add valuable information on how to predict and manage these situations. For this purpose, both radar and ground observational data have been combined to identify the most important precipitation episodes in Barcelona between 2013 and 2018. To make the analysis easier, a new algorithm has been developed to determine the thunderstorm hotspots. Episodes with a higher impact have been analysed in depth. The final objective is to improve the actions taken by the organisation responsible for managing urban floods, which have seen Barcelona recognised as a model city for flood resilience by the United Nations.

This study aims to unravel the paradox of perceptions and knowledge of the flood victims towards the causes of the disaster in both internal and external context. Internal context comprises of a comparison of perceptions and knowledge based on individual characteristics (age, gender, education and income). Whereas, the external context includes the factors of the awareness of the victims towards the amount of rainfall, the impact of land use changes as well as the negligence of the responsible parties. The main objective of this study is to determine the differences of perception and knowledge of December 2014 flood victims in Kelantan towards the factors that lead to the flood. This disaster had resulted in huge amount of money loss as well as traumatize the victims in which can be felt to this day. Since that incident, there were various points of view and different perceptions in finding the cause of the disaster occurred. Besides that, the study found that the level of perception and knowledge as to the cause of the disaster is different in the internal context (individual characteristics). This difference has a significant influence on the awareness of the causes of the floods that occurred in the external context. Significant relationships at the level of p <0.05 has existed between perception and knowledge of the causes of the disaster victims affected by environmental changes in the last 10 years. This indicates that although the victim is aware of the physical environment changes happening around them, but all that is seen as not a major contributing factor to the cause of the floods in Kelantan in 2014.

The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The study assesses the impact of land use and land cover (LULC) changes between 1980 and 2020 and extreme rainfall on peak discharge and sediment yield in the Upper Jhelum Basin (UJB), Kashmir Himalaya, India using KINEROS2 model. Analysis of LULC change revealed a notable shift from natural LULC to more intensive human-modified LULC, including a decrease in vegetative cover, deforestation, urbanization, and improper farming practices. The findings revealed a strong influence of the LULC changes on peak discharge, and sediment yield relative to the 2014 timeframe, which coincided with the catastrophic September 2014 flood event. The model predicted a peak discharge of 115101 cubic feet per second (cfs) and a sediment yield of 56.59 tons/ha during the September 2014 flooding, which is very close to the observed peak discharge of 115218 cfs indicating that the model is reliable for discharge prediction. The model predicted a peak discharge of 98965 cfs and a sediment yield of 49.11 tons/ha in 1980, which increased to 118366 cfs and, 58.92 tons/ha respectively in 2020, showing an increase in basin’s flood risk over time. In the future, it is anticipated that the ongoing LULC changes will make flood vulnerability worse, which could lead to another major flooding in the event of an extreme rainfall as predicted under climate change and, in turn compromise achievement of sustainable development goals (SDG). Therefore, regulating LULC in order to modulate various hydrological and land surface processes would ensure stability of runoff and reduction in sediment yield in the UJB, which is critical for achieving many SDGs.

Flash flooding is one of the most devastating natural events that leads to enormous and recurring loss of life. Kuwait was subjected to severe rainstorms in the winter of 2018 and 2020 followed by an extreme violent flood that had not been known in Kuwait since 1976. It resulted in several geomorphological and environmental impacts in urban and desert areas. This produced some positive results, such as geomorphological activity in landforms, the flow of some valleys and the prosperity of wildlife in the Kuwaiti desert. Negative results included some problems in the metropolitan area and destruction of some road networks that intersect the main valleys, and which were not equipped with crossings for avoiding floods. There was also the emergence of some problems in the infrastructure. Study of flash floods requires the involvement of all scientific and executive bodies to avoid environmental risk. The study aims to: 1- Monitor geomorphological and environmental changes. 2- Assess the impact of floods in the urban areas and on infrastructure. 3- Modeling the impact. 4- Creating solutions and adaptions to the flash flood. The study uses several methods such as remote sensing (RS), geographic information systems (GIS), hydrologic modeling and fieldwork to evaluate the impact of flash flood hazards on the sustainable urban development of Kuwait state. This approach is rarely used in Kuwait. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban developments in Kuwait, and help them to manage flood water, by identifying the most appropriate places for storage to exploit water in agriculture and drinking.

The Siret River crosses NE Romania from the north to the south and it discharges into the Danube, near the city of Galati. During the period, 17^th June - 10^th July 2010, significant amounts of precipitations in the mountainous basin of Siret were recorded. The floods comprised two periods with four bimodal cycles and they were counted among the strongest on the Romanian territory. The exceptional floods occurred in the rivers of Siret, Suceava, Moldova, Bistrita, Trotus and so on. The most important compound flood wave was determined by the precipitations which fell within the period, 29^th June to 1^st July 2010, when significant amounts of rain were recorded, sometimes, exceeding 80 mm. The high discharges on the Bistrita River – downstream from the Bicaz Reservoir – were controlled by complex hydro technical works. The maximum discharge for the year 2010 summer floods was recorded at Dragesti hydrometric station: 2,884 m³/s (historic discharge) compared to the preceding historic discharge of the year 2008 (2,850 m³/s). The effects of floods were strongest in the counties of Suceava, Neamt, and Bacau. The floods on the main course of the Siret River were analyzed in correlation with the tributaries within the mountainous sector.

Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.

In mega cities such as Seoul in South Korea, it is very important to protect the city from the flooding even for the short time of period due to the enormous amount of economic damage. In impervious area of the city, stormwater pipe network is commonly applied to discharge rainfall to the outside of catchment. Therefore, the stormwater pipe network in urban catchment should be carefully designed to discharge the runoff quickly and efficiently. In this study, different types of structures in stormwater pipe network were evaluated using the relationship between the peaks rainfall and runoff in urban catchments in South Korea. More than 400 historical rainfall events were applied in five urban catchments to estimate peak runoff from different type of network structures. Linear regression analysis was implemented to estimate peak runoffs. The coefficient of determination of the regressions were higher than 0.9 which means the regression model represent very well the relationship between the two peaks. However, the variation of the prediction becomes large as the peak rainfall increases and the variation become even larger when the network structure is branched. Therefore, it depends on the structure of stormwater pipe network. When the water paths in the pipe network is unique (branched network), the increased amount of rainfall is congested around the rainwater inlets and the uncertainty of peak runoff prediction is increased. If there are many possible water paths depending on the amount of discharge (looped network), the increased rainfall is discharged more quickly through the many water paths. This can be a way to represent the reliability of the stormwater pipe network. The structures of stormwater pipe network is evaluated using drainage density which is the length of pipes over the unit catchment area and 95% confidence interval. As a result, the 95% confidence interval is increased as the drainage density is increased because the accuracy of peak runoff prediction is decreased. As mentioned earlier, because the looped networks have many alternative water flowing paths, elimination time of rainfall from the catchments become short, the 95% confidence interval become narrow, and the reliability of peak runoff prediction become high. Therefore, it is beneficial to install looped stormwater pipe network within the affordable budget. It is important factor to determine the amount of complexity in stormwater pipe network to decrease the risk of urban flooding.

The report [1] shows that in many European cities the sewer system is older than 40 years, potentially reducing their ability to cope with more intense pluvial flooding [2] addition to climate change, urbanization is an important factor increasing the flooding risk, as it increases of impervious surfaces. Flash floods are particularly troublesome, causing not only the overloading of drainage systems [3], but also urban transport disruption, affecting human health, and contributing to pollution due to untreated sewage discharges [4]. Pluvial floods in urban areas are caused by short-duration precipitation, high intensity and on small scale. Such rainfall is difficult to predict and does not always apply to catchment areas. In Poland, heavy rainfall lasts relatively short, from a few minutes to a few, at most, several hours [5]. The method of the Analytic Hierarchy Process (AHP) was used to rank the importance of criteria characterizing exposure in urban areas. The ranking is based on expert judgements. The article focuses on exposure which is one of the components of risk. Geographical Information System (GIS) is integrated with Multicriteria Decision Analysis (MCDA) to evaluate exposure of urban area on pluvial floods.

Several cities are experiencing an increase in flood hazard and exposure, and consequentially in flood risk, attributable to the coupled effect of climate change and urbanization. Non-structural protection strategies demonstrate significant potential in the short term for mitigating hydraulic risk in urban areas, particularly where the implementation of structural measures is impeded by a complex urban environment. This work proposes an Early Warning System (EWS) for fluvial floods developed for the city of Palermo (Italy). It is based on the preliminary definition of po-tential Flood Event Scenarios (FESs) as a function of typical potential precursors. Each FES pre-dicts first points of flooding, flood areas and water depths, also providing specific hazard maps for people, vehicles, and buildings. The EWS uses rainfall depth-duration thresholds to assess the expected hydrograph as a function of rainfall forecasts and the water stage observed in real time at an upstream section of the river crossing the city. This last also allows for estimating the ante-cedent wetness conditions. Based on this information, the EWS retrieves an expected FES from a pre-built library with a preannouncement time up to 36 h. The EWS is here successfully tested on a historical flood event.

Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They often result in floods and snowfalls in winter disrupting economic activities. This paper examines the evolution and circulation patterns associated with severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against ECMWF’s ERA Interim reanalyses and GPM precipitation at 24-hour timesteps. We present five recent (2016-2019) severe COLs that had high impact and found higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM underestimated precipitation mainly due to inaccurate placing of COL centers and areas of heavy rainfall by up to 5° of latitude away from the actual location, due to the poor formulating of cumulus and microphysics schemes in the model. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.

In the last decades, climate and environmental changes have highlighted the fragility and vulnerability of the landscape, especially in mountain areas where the effects are most severe. The strategy for hazard mitigation must deploy synergistic actions based on a thorough knowledge of the territory and its phenomena to enable integrated and effective land planning and management through suitable communication tools. This study promotes the methodological setup of a Landscape Digital Twin to establish a multi-disciplinary and multi-scalar hazards overview according to a matrix framework implementable over time and space. The original contribution to the research addresses a holistic vision that combines meaningfully qualitative with quantitative approaches to generate risk maps by integrating various indicators within a multi-hazard framework from the socio-economic perspective. This contribution presents road network risk analysis by exploiting flooding and landslide scenarios. The critical road segments or nodes most vulnerable or impacted by network performance and accessibility can be identified with minimal preprocessing from credible open-source sources. The method's applicability is tested in a Piedmont Region, northern Italy case study. The integration proposed will help generate comprehensive risk analysis maps that effectively portray the interconnectedness among natural hazards, infrastructure, and socio-economic factors fostering more resilient decision-making processes.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

As a result of decades of austerity, the economies of the Eurozone are experiencing under-investment and crumbling infrastructures in an era of extremely severe emergencies as the cli-mate change and aggressive global competition. The private savings glut of the rich and the strict fiscal rules suggest a crowding-in approach in partnership with big finance in a new age of in-fra-structures. If governments mint public infrastructures into investable assets, they can gen-erate steady returns for investors and asset managers who look after pensions and insurances schemes and unlock trillions now devoted to welfare state. For the big finance to accept to be partner, however, governments must take the task of de-risking returns. Innovation technology and macro-financial literacy can synergically act to enhance this new de-risking role to address in a financially sustainable way the intense phenomena of Natural Hazards (NHs) that can severely compromise economic growth. This is precisely the aim of our Public-Private Partnership (PPP) among the Department of Economics, Management and Institutions of the University of Naples and a Knowledge Transfert Management, the Department of Engineering and the Aca-demic Spin-Off “STRAIN”, of the University of Campania. Our PPP has been contributing to the eval-uation and mitigation of NHs such as floods, landslides and earthquakes since 2012. The ex-tremely advanced results of this multi-stakeholder partnership ensure a better knowledge of NHs which, as evidenced in the paper, is the fundamental input for the desired Public-Private In-surance Programmes (PIPPs).