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Learning from global disasters — understanding what happened, the successes that prevented impacts from being worse, and the opportunities to reduce risk to future events — is critical if we are to protect people from increasingly extreme weather. Population growth is overtaxing ecosystems and climate change is creating new and intensifying existing climate hazards. Proactive and collaborative efforts are needed between all levels, from local to international, and across sectors connecting social science, economics, policy, infrastructure and the environment, to address these challenges. Perhaps most urgently, however, is the need to harness humanitarian response, development, disaster risk reduction, and climate change adaptation to work in concert – we can no longer afford to deliver these needs in isolation.

In March and April 2019 Cyclones Idai and Kenneth – two of the most destructive and powerful cyclones to ever hit southeast Africa – resulted in a widespread humanitarian disaster in Malawi, Mozambique and Zimbabwe, the impacts of which continue today in terms of livelihoods lost, food insecurity, and loss of permanent shelter for thousands. Damages were intensified by the novel nature of the impacts – the storms brought with them climate threats that were new to the areas and people impacted, leading to greater failure of existing preparedness and response mechanisms than might have been expected.

This talk will present learnings from a study conducted by members of the Zurich Flood Resilience Alliance on the impacts of Cyclones Idai and Kenneth, highlighting opportunities for building multi-hazard resilience to future events. In particular, we will highlight the opportunities we found for strengthening resilience, even when challenged by entirely new climate hazards, through strengthening early warning systems and climate services, building capacity and resourcing for early action, supporting the construction of resistant homes and development of more diverse farming practices, and, most crucially, by better connecting humanitarian response and Disaster Risk Reduction (DRR) efforts.

These lessons are part of a series of Post-event Review Capability (PERC) learnings conducted by Zurich since 2013. The PERC methodology (available at: https://www.floodresilience.net/perc) supports broad, multi-sectoral resilience learning from global disaster events and identifies key actions for reducing future harm.

How to cite: MacClune, K. and Norton, R.: When the unprecedented becomes precedented: Lessons from Cyclones Idai and Kenneth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1802, https://doi.org/10.5194/egusphere-egu21-1802, 2021.

Record-breaking hydrometeorological disasters such as Hurricane Maria in 2017 have once again highlighted the severe disaster risk exacerbated by a changing climate facing Caribbean small island states. Hurricane rainfall is a key cause of flooding in many Caribbean islands. Yet, despite the projected changes in hurricane rainfall under climate change and the attribution of a climate change signal in particular hurricane rainfall events, estimates of current and future flooding associated with hurricane rainfall are limited in the Caribbean. This research outlines a method for assessing current and future flood risk estimates in the Caribbean, producing an event-based pluvial model using hydrodynamic model LISFLOOD-FP to simulate flood hazard in Puerto Rico for present day, 1.5^oC and 2^oC Paris Agreement climate projections. An event set of 59,000 hurricane rainfall estimates from a synthetic hurricane rainfall model was applied as the rainfall input to the hydrodynamic model, simulated across time (2-hour timesteps) and space (0.1-degree spatial resolution). The event-based pluvial model was run at the island-scale in Puerto Rico (9104km²) at 20m and 90m resolution to produce event-based flood hazard estimates under present day, 1.5^oC and 2^oC climate change projections. The flood hazard event set was then combined with population data to get estimates of exposure exceedance under present day, 1.5^oC and 2^oC climate change projections. The results of this research will provide useful information for both the hydrology and disaster risk reduction communities regarding the potential changes in population exposure to hurricane rainfall-induced flood events in Puerto Rico, as well as how particular characteristics of hurricane rainfall affect flood hazard under 1.5^oC and 2^oC climate change. This research will also highlight how an event-based pluvial flood model can be utilised to assess a range of possible current and future flood scenarios, as well as how important different aspects of the modelling are for these small island studies. This information is crucial for both current and future disaster risk reduction and climate change adaptation planning in the Caribbean – an ever-increasingly urgent task. 

How to cite: Archer, L., Neal, J., Bates, P., Vosper, E., and Mitchell, D.: Assessing current and future flood risk estimates associated with hurricane rainfall under the 1.5°C and 2°C Paris Agreement goals in Puerto Rico, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5607, https://doi.org/10.5194/egusphere-egu21-5607, 2021.

This contribution shows a multi-disciplinary forensic investigation of the catastrophic flood event that took place in the northern part of Spain between the 22^nd and 23^rd of October 2019. The case study has been selected for three reasons. Besides flash-floods and floods in several parts of the region of interest, the event also recorded windstorms, one tornado, snowfalls, thunderstorms, strong sea surges, and landslides, that made a good paradigm of a compound and multi-hazard event. The second reason is because the event has been analyzed from three approaches. Firstly, the hydrometeorological perspective, which includes monitoring through meteorological radar and observation stations, as well as the consequences on the surface. Second, from the socio-economic perspective, both in terms of economic impact and in terms of social perception, for which a citizen science experiment was designed with the FLOODUP tool and in collaboration with the Museu de la Vida Rural de l'Espluga de Francolí (a museum located in the village the event hit the most). FLOODUP is an App developed to improve the population risk awareness and sensibilization face to climate change, that can be also used to collect information. In this case it was used during the period of home confinement due to the pandemics to collect information about the emergency management and impacts. This part also includes the cascading effects, as well as what the Covid-19 pandemic meant in the difficulty of recovery. A third approach analyzes the early warning, emergency management and recovery, in addition to various human initiatives that were carried out. Finally, the third consideration follows the example of pair-events comparison developed in the framework of the IAHS Panta Rhei hydrological decade 2013- 2022 like (ex: Kreibich et al., 2017). In this case, the October 2019 event is compared with the floods of October 1994, specifically regarding the Francolí basin. Maximum precipitation recorded in this last event was 410 mm between 9 and 11 October, with a maximum discharge of the Francolí River of 900 m³/s in Montblanc. As a consequence of it, 10 bridges were destroyed, 10 people lost their life and more than 230 € millions in damages were produced. On the 2019 event maximum precipitation was of 293 mm between 22 and 23 October, the maximum discharge in Montblanc was of 544 m³/s and 5 people lost their life and damages were above 44 € millions. Finally, the event is contextualised in the flood trend observed in the region due to climate and environmental changes.  The presentation concludes with the discussion on the potential measures of adaptation that have been already applied or could be applied.

This work has been done in the framework of the M-CostAdapt (CTM2017-83655-C2-1&2-R) research project, funded by the Spanish Ministry of Science and Innovation (MICINN-AEI/FEDER, UE).

Reference. Kreibich, H., S. Vorogushyn, J.C.J.H. Aerts, et al. 2017. Adaptation to flood risk – results of international paired flood event studies. Special collection “Avoiding Disasters: Strengthening Societal Resilience to Natural Hazards” in the journal Earth’s Future. Earth’s Future,5,953–965, doi:10.1002/2017EF000606

How to cite: Llasat, M. C., Caballero-Leiva, I., Llasat Botija, M., Esbrí, L., Rigo, T., Díaz, A., Martín Vide, J. P., and Iglesias, A.: The floods recorded in the North of Spain in October 2019. A paradigm of compound and multi-hazard event in the framework of climate and environmental change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8482, https://doi.org/10.5194/egusphere-egu21-8482, 2021.

The steady increase of economic losses and social consequences caused by flood events in Europe is triggering the development of updated and efficient technologies for assessing flood hazard over large areas, where detailed hydrologic-hydrodynamic numerical models are resource intensive and therefore scarcely suitable. In this context, the EIT-Climate KIC SaferPLACES (https://saferplaces.co) project aims at exploring and developing innovative and simplified modelling techniques to assess and map pluvial, fluvial and coastal flood hazard and risk under current and future climates, mainly based on LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models) raster-based analysis. Within the SaferPLACES activities, a fast-processing Hierarchical Filling-&-Spilling Algorithm (HFSA), named Safer_RAIN (see Samela et al., 2020; https://www.mdpi.com/2073-4441/12/6/1514/htm), has been recently developed for mapping pluvial flooding in large urban areas by accounting for spatially distributed rainfall inputs and infiltration processes. Although it does not incorporate any detailed description of the dynamics of overland flow and water-depth routing, previous applications have shown Safer_RAIN to be an effective tool for a rapid and consistent identification of pluvial-hazard hotspots under different rainfall and land-use scenarios.

Although Safer_RAIN has been conceived for pluvial flooding hazard assessment, its structure suggests its suitability for delineating flooded areas and computing water depth in the aftermath of fluvial inundation (i.e. once the dynamic components of the inundation process become negligible) in predominantly flat floodplains. To this aim, a given flood volume can be assigned to the pixels coinciding with the fluvial flooding point-sources (e.g. simulated levee breach or overtopping) as the input to Safer_RAIN, which is then used for flooding the downstream floodplain portion according to a HSFA approach. We present a first test of the fluvial-application of Safer_RAIN for the case study of the Pisciatello river (Northern Italy, floodplain area of approximately 1300 hectares). Results for different flood scenarios obtained with Safer_RAIN at 1m resolution are compared with the corresponding flooding scenarios simulated with the fully two-dimensional numerical model HEC-RAS at 1m and 5m resolutions. The outcomes of both models are compared in terms of flooded area extent and water depth distribution, highlighting potential and limitations of Safer_RAIN for identifying fluvial flooding hazard.

How to cite: Persiano, S., Carisi, F., Wang, H., Luzzi, V., Mazzoli, P., Bagli, S., and Castellarin, A.: Assessing fluvial flooding hazard with a DEM-based Hierarchical Filling-&-Spilling Algorithm: a case study in Northern Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2894, https://doi.org/10.5194/egusphere-egu21-2894, 2021.

The study of flood impacts on the different sectors that compose the built environment and the society is crucial to implement actions of prevention, mitigation, and cautious planning. In such a context, the sector of businesses assumes a critical role, both for its importance for the wellbeing of the society and because of the high losses it suffers in case of inundations. Nevertheless, flood damage modelling to businesses is still a challenging task: the large number of different commercial activities, their specific geographical and economic contexts and the few observed damage data are just some of the reasons for that. In Italy, for example, a shared methodology to assess damage to enterprises is not present; building knowledge about types and dimensions of impacts of flood events to economic activities is then even more impelling. This contribution presents the analysis of about a thousand observed damage records regarding industrial and commercial activities, collected by four research groups after different flood events in Italy: the inundation occurred in the town of Lodi (Lombardia Region) in 2002, the one in Sardegna Region in 2013, and the floods caused in the Emilia-Romagna Region by Secchia (2014) and Enza (2017) Rivers. Data retrieved from the local and regional authorities responsible for damage compensation present different levels of detail and aggregation, according to the case study investigated. In all cases, they refer to the direct damage only and, for each case study, they have been first organised according to the activity types (e.g. trade, manufacturing, construction, finance) and per affected components: i.e. structure, equipment and stock. Data analysis has been led by some questions, we identified as key to start developing knowledge for damage modelling:  are there similarities in the different case studies? Which are the more affected business sectors in case of flood? Which component suffers the highest damage among structure, equipment, and stock? Is there an empirical trend of damage with hazard parameters? Results were first compared with the socio-economic context of the affected area, to have a first confirmation of data quality and reliability; then, the analysis focused on searching information and relationships between damage and activity type, activity dimension and water level. Results support the identification of the more vulnerable elements within the business sector, orienting modellers’ and decision-makers’ choices.

How to cite: Galliani, M., Carisi, F., Domeneghetti, A., Menduni, G., Molinari, D., Sterlacchini, S., Zazzeri, M., and Ballio, F.: An analysis of Italian damage data to economic activities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10086, https://doi.org/10.5194/egusphere-egu21-10086, 2021.

In September 2019, a weather phenomenon known in Spain as “DANA” brought rainfall accumulations of up to 452 mm in 48 h to the south-eastern part of Spain, triggering numerous flash floods and a severe fluvial flood in the Segura river. As a consequence, seven people died, over 5000 were evacuated, and the economic losses exceeded 2.2 billion Euros.

During such devastating events, early warning systems (EWSs) are a key element for the effective mitigation of impacts. They provide emergency responders (e.g. civil protection authorities) with essential information for the coordination of the flood response.

In Europe, emergency responders co-operate on different spatial scales: National and regional civil protection authorities collaborate in monitoring and applying specific actions, such as evacuations, road closures, or the installation of mobile flood barriers. For this task, they require location-specific information in high spatiotemporal resolution. At a larger scale, the Emergency Response Coordination Centre of the European Union (ERCC) monitors the entire continent for upcoming emergencies and supports the regional and national authorities with information and resources. Such international actors prefer order-of-magnitude statements over large spatial domains to make informed decisions. The different requirements of end-users operating at different spatial scales need to be taken into account for the development of EWSs.

Traditionally, flood EWSs are designed to predict the hazard component of the flood (e.g. in terms of river discharge). In recent years, however, a number of methods were developed that automatically translate the flood hazard into the corresponding socio-economic impacts (e.g. the number of people affected). Such impact-based EWSs enhance the decision support for the emergency responders and thus facilitate an effective flood response.

In this work, we analyse the DANA event of 2019 from the perspective of impact-based early warning. We present, validate, and compare rapid flash flood impact assessments from the following two methods:

Firstly, the ReAFFIRM method (Ritter et al., 2020) generating quantitative flash flood impact estimates in high resolution to support decisions at local and regional scales. Secondly, a newly developed method (named ReAFFINE) that qualitatively estimates flash flood impacts with pan-European coverage, as decision support for end-users operating over large spatial domains.

Simulation results for the DANA event show that the flash flood impact assessments from the pan-European method (ReAFFINE) correspond well to reported impacts and to the results from the regional method (ReAFFIRM) while providing more context-specific information for end-users operating at the international level.

References:

Ritter, J., Berenguer, M., Corral, C., Park, S., Sempere-Torres, D., 2020. ReAFFIRM: Real-time Assessment of Flash Flood Impacts – a Regional high-resolution Method. Environ. Int. 136, 105375. https://doi.org/10.1016/j.envint.2019.105375

How to cite: Ritter, J., Berenguer, M., Park, S., and Sempere-Torres, D.: Rapid flash flood impact assessments at different spatial scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14444, https://doi.org/10.5194/egusphere-egu21-14444, 2021.

Understanding disaster risk is the first priority for action of the Sendai Framework for Disaster Risk Reduction (SFDRR) and is the essential information needed to guide disaster governance and achieve disaster risk reduction. Flooding is a natural hazard that causes the highest number of affected people due to disasters. In Ecuador from 1970 to 2019 flooding caused the highest amount of loss and damage to housing, and from 2016 to 2019 there were 1263 flood events reported. However, the differentiated impacts in flood exposed areas and what can be done to reduce risk and its impacts are still not well understood. In this research, we explored the different dimensions of flood risk, namely hazard, exposure, and vulnerability, and investigated the drivers of risk in different ecological regions of Ecuador. The assessment was conducted at the parish level, the smallest administrative scale, for three selected provinces of Bolivar, Los Ríos, and Napo, representing not only the country’s three main ecological regions but also commonly affected territories due to flooding. Using an automated flood detection procedure based on Sentinel-1 synthetic aperture radar data, flood hazard information was derived from flood frequency and flood depth for the years 2017, 2018, and 2019. The drivers of exposure and vulnerability were derived from scientific literature and further evaluated and complemented during a participatory workshop with over 50 local experts from the different regions. Centered on this exercise, an indicator library was created to inform the data selection from various sources and provides the basis for deriving a spatially explicit flood risk assessment using an indicator-based approach. Impact data are available to validate the risk assessment at the parish level and with this reveal key drivers of flood risk in different ecological regions of Ecuador. This information will provide the basis to derive targeted measures for disaster risk reduction.

How to cite: Mena-Benavides, M., Urrutia, M., Scheffczyk, K., Valdiviezo-Ajila, A. A., Mendoza, J., Diaz, G., Riembauer, G., and Walz, Y.: Flood risk assessment for Ecuador, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14853, https://doi.org/10.5194/egusphere-egu21-14853, 2021.

The Sendai Framework for Disaster Risk Reduction (SFDRR) provides a concrete agenda for evidence-based policy for disaster risk reduction as a key component of the post-2015 global development agenda. However, the progress of implementing the seven Global Targets of the SFDRR at the national level via the monitor of a set of thirty-eight indicators is obstructed due to a lack of available, accessible, and validated data on disaster-related loss and damage, especially in developing countries. This weakens the accuracy, timeliness, and quality of the Sendai monitoring process. In the case of floods, which account for the highest number of people affected by hazards,[WY1]  there is a strong need for innovative and  appropriate tools for monitoring and reporting flood impacts.

The country of Ecuador and their validated national flood loss and damage database, which stretches back to 1970, is a stark counterpoint to the norm and serves as the case study for this research. In this research we develop a geospatial model approach, which combines earth observation-based information products with additional geospatial data to result quantitative measures for selected indicators of the SFDRR and validate them based on an existing database on flood loss and damage in Ecuador. Specifically, we build on automated  derivation of flood event characteristics from a full year of Sentinel-1 synthetic aperture radar data to assess flood hazard in Ecuador, and complement this with geospatial data on flood-related exposure and vulnerability to model selected indicators of the SFDRR in a spatially explicit way. The validation process of this geospatial model is conducted in reference to in situ loss and damage data related to flooding for the years 2017, 2018, and 2019. The derivation of information products is conducted in close collaboration with the National Service for Risk and Emergency Management of the Government of Ecuador, the country office of the United Nations Development Program, and the United Nations Office for Disaster Risk Reduction. It is thereby assured that the development and validation of this methodology is in line with the national and international approach of implementing the SFDRR.

How to cite: Urrutia, M., Riembauer, G., Valdiviezo-Ajila, A. A., Jímenez, S., Andrade, A. R., and Walz, Y.: Development and validation of a geospatial model for monitoring indicators of the Sendai framework using the example of flooding in Ecuador, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14720, https://doi.org/10.5194/egusphere-egu21-14720, 2021.

Weather extremes can have high socio-economic impacts. Better impact forecasting and preventive action help to reduce these impacts. In Switzerland, the winter windstorms caused high building damage, felled trees and interrupted traffic and power. Events such as Burglind-Eleanor in January 2018 are a learning opportunity for weather warnings, risk modelling and decision-making.

We have developed and implemented an operational impact forecasting system for building damage due to wind events in Switzerland. We use the ensemble weather forecast of wind gusts produced by the national meteorological agency MeteoSwiss. We couple this hazard information with a spatially explicit impact model (CLIMADA) for building damages due to winter windstorms. Each day, the impact forecasting system publishes a probabilistic forecast of the expected building damages on a spatial grid.

This system produces promising results for major historical storms when compared to aggregated daily building insurance claims data from a public building insurer of the canton of Zurich. The daily impact forecasts were qualitatively categorized as (1) successful (2) miss or (3) false alarm. The impacts of windstorm Burglind-Eleanor and five other winter windstorms were forecasted reasonably well, with four successful forecasts, one miss and one false alarm.

 The building damage due to smaller storm extremes was not as successfully forecasted. Thunderstorms are not as well forecasted with 2 days’ lead time and as a result the impact forecasting system produces more misses and false alarms outside the winter storm season. For the Alpine-specific southerly Foehn winds, the impact forecasts produce many false alarms, probably caused by an overestimation of wind gusts in the weather forecast.

The forecasting system can be used to improve weather warnings and allocate resources and staff in the claims handling process of building insurances. This will help to improve recovery time and costs to institutions and individuals. The open-source code and open meteorological data makes this implementation transferable to other hazard types and other geographical regions.

How to cite: Röösli, T. and Bresch, D. N.: Building damage impact forecasting for winter windstorms in Switzerland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7253, https://doi.org/10.5194/egusphere-egu21-7253, 2021.

Requiring spatial and temporal quantified information on landslide hazard over a large area is a prerequisite to forecast them. However, in many cases, the quantification remains partial, because of a lack of information on the phenomena, on predisposing and triggering factors or because the scientific approaches used in research domain are complex to apply in a regulatory framework. Thus, in this context, for many sites and end-users, the documents produced by empirical methods are used, without quantification of hazards.

In 2019, a collaboration between the DIMENC Geological Survey Service of New-Caledonia (South-Pacific) and the BRGM planed the development of a global methodology of landslide hazard assessment at the 1:25,000 scale of work according to the recommendations of the JTC-1. Indeed, landslide hazard in New Caledonia is insufficiently assessed and few taken into account in land-use planning. However, this large mountainous island is regularly affected by different type of instabilities (i.e. rock-falls; rock-slides; slides; debris-flows) due to intense rainfalls. The consequences can be material and human, as in 2016 for the municipality of Houaïlou, where debris-flows occurred, inducing 5 deaths, 3 missing persons, 8 injuries along with large material damages. Few heuristic landslide hazard maps based on expert opinion are available, but the methodology is not homogeneous and harmonized. Therefore, even if these maps constitute a solid base of knowledge, their valorization for land use planning remains difficult.

To overcome these shortcomings, the methodology chosen is quantitative, taking into account the susceptibility of the territory (i.e. spatial probability of phenomena occurrence with discrimination of initiation and run-out), the temporal probability of occurrence (i.e. from diachronic analyses) and the phenomena intensity (i.e. through the considered velocity of runout and the potential of induced damages). The methodology is declined by type of phenomena and is based on a comprehensive inventory. Six main steps are defined with:

• An inventory of the events by visual remote sensing and field observations;
• Discriminated mapping of bedrock and surficial formations (i.e. regolith: weathered formations and gravitational deposits);
• Computation of each landslide initiation susceptibility by a bivariate method;
• Integration of the temporal occurrence probability;
• Computation of the phenomena runout by a numerical approach taking into account the reach angle;
• Integration of the intensity of the phenomena according to the estimated volumes and/or velocity to quantify landslide hazard.

The classes of spatial and temporal probabilities are based on the JTC-1 agreement and allow obtaining quantified hazard maps. The validation of the results is performed by a field validation, by phenomena not used for the computations, and by statistical tests. The method is tested in the municipality of Mont-Dore (643 km²), which was heavily impacted in 1988 by cyclone 'Anne'. Beyond the fact that the methodology will be applied throughout the territory in an operational framework and will allow the adaptation of local planning, the project allows the improvement of:

• Knowledge of the different kind of landslides in a volcano-sedimentary and metamorphic context strongly weathered;
• Knowledge of the regolith, which newly integrated for this type of analysis for the island’s municipalities.

How to cite: Colas, B., Thiery, Y., Guyomard, Y., Mengin, M., Monge, O., Mardhel, V., and Vandromme, R.: Towards a quantified and global landslide hazard assessment for New-Caledonia (South Pacific) in a regulatory mapping context, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7862, https://doi.org/10.5194/egusphere-egu21-7862, 2021.

The Model Of InTegrated Impact and Vulnerability Evaluation of climate change (MOTIVE) project (2014 - 2020) develops an integrated assessment platform including health, water (quantity and quality of water, aquatic ecology), agriculture (productivity, suitability, greenhouse-gas emissions), forest (net ecosystem exchanges, soil carbon content, landslide, forest fire), land-ecosystem (species diversity, habitat), ocean (flood area by the typhoon), and fishery (gross primary productivity, catch) sectors. The MOTIVE assesses the societal impact and vulnerability of climate change in the 2030s, 2050s, and 2080s. The 1 km high-resolution Representative Concentration Pathways climate scenarios (RCPs) are predicted by the dynamically downscaling from the Community Earth System Model (CESM) by Korea Environment Institute and the Unified Model (UM) by Korea Meteorological Administration. The user-friendly webpage is designed with the DataBase Management System (DBMS) to visualize the results of MOTIVE. This DBMS-MOTIVE aims to provide the scientific-knowledge for adaptation planning the local community to national scales. This study is supported by “Basic Study on Improving Climate Resilience” (2021-001-03), conducted by the Korea Environment Institute (KEI) upon the request of the Korea Ministry of Environment.

How to cite: Hong, J.-W., Yoo, H.-G., Yu, M., and Song, Y.-I.: DataBase Management System (DBMS) of Model Of InTegrated Impact and Vulnerability Evaluation for Climate Change (MOTIVE), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15656, https://doi.org/10.5194/egusphere-egu21-15656, 2021.

Arctic extensively urbanized centers are subject to the impact of many negative environmental phenomena progressing in terms of global climate change and regional development in Yakutia in the context of poor and missing databases. For this reason, the modeling of the risk exposures is based on combining the remote sensing, and local knowledge of inhabitants. According to the occurrences of the natural hazards, the territorial management and the decision-making system require the identification and assessment of natural risks to which the rural populations localized in the towns and villages are exposed, for example, in the urban center of Khamagatta located at 70km to the North from Yakutsk near the Lena River. The main environmental vulnerability exposures are seasonal: springtime floods between May and June, the forest fires from June to August, the cyclic permafrost degradation, and river erosion impacts.

The current vulnerability impacts, damages to the lands and the settlements, and the populations risk exposures are analyzed from the maps of vulnerabilities created from remote sensing satellite Sentinel 2A/B series, with the local knowledge of the inhabitants of Khamagatta who lived and perceived all events. All the data generated, maps, models of vulnerability exposures, and local knowledge are integrated, combined, and merged into the geographic information system (GIS). The GIS modeling combines the risk of natural hazards and the damages, and the risk knowledge and perceptions of the inhabitants. Land uses, Landscape classification, and the land cover is made by Object-Based Image Analysis (OBIA) using an optical time series of Sentinel 2 images (2015-2020) including the population knowledge for the recognition of the environmental vulnerabilities. The methodological approach included the participation of local people in workshops through discussion and participatory mapping, questionnaires, and interviews in two stages. The first stage included the development of the knowledge database for a comprehensive understanding of the life of the local population, including the forms of adaptation to the negative natural phenomena. The collected information is delocalized and integrated into the GIS. The second stage consisted of validation and discussion, including stakeholders (municipality and rescue services) to increase the reliability and legitimacy of the research results.

Perceptions of the inhabitants of Khamagatta are correlated with the maps of risk exposures generated by remote sensing to increase the accuracy of the environmental process modeling and landscape classification. The combination of the environmental change dynamics, the impacts on the towns and villages with the human perception and experience constitutes the main base supporting the prevention mapping of the natural hazards. This data could be very useful in planning the development of Arctic towns and villages and proposing evolution scenarios and urban planning models and strategies for increasing their resilience and adaptation to climate change consequences.

How to cite: Gadal, S., Zakharov, M., Kamicaityte, J., Savvinova, A., and Danilov, Y.: Environmental Vulnerability Modeling in the Extensively Urbanized Arctic Center Integrating Remote Sensing, Landscape Mapping, and Local Knowledge, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16268, https://doi.org/10.5194/egusphere-egu21-16268, 2021.