HS7.5 | Hydro-meteorological Extremes and Hazards: Vulnerability, Risk, Impacts and Mitigation
EDI
Hydro-meteorological Extremes and Hazards: Vulnerability, Risk, Impacts and Mitigation
Co-organized by NH1/NP8
Convener: Francesco Marra | Co-conveners: Nadav Peleg, Elena Cristiano, Federica Remondi, Efthymios Nikolopoulos
Orals
| Mon, 24 Apr, 14:00–18:00 (CEST)
 
Room 2.44
Posters on site
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
Hall A
Posters virtual
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
vHall HS
Orals |
Mon, 14:00
Mon, 10:45
Mon, 10:45
Extreme hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the triggers of these hazards and the related aspects of vulnerability, risk, and mitigation.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from academia, the industry (e.g. insurance) and government agencies (e.g. civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications
This session is linked to an active special issue in Natural Hazards and Earth System Sciences (NHESS): https://nhess.copernicus.org/articles/special_issue1203.html

Orals: Mon, 24 Apr | Room 2.44

Chairpersons: Francesco Marra, Elena Cristiano
14:00–14:05
14:05–14:25
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EGU23-10255
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solicited
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On-site presentation
Virginia Ruiz-Villanueva

Floods are one of the most relevant natural hazards, causing significant socio-economic damage every year globally. They will likely continue to increase for various reasons: the climate and global changes, two relevant ones. More importantly, our still limited capability to predict river response to flooding and anticipate the consequences by designing proper and sustainable risk mitigation measures. A recent example was Europe's floods in July 2021, the highest recorded. They led to many casualties and economic losses (i.e., 180 fatalities and billions of Euros). Extreme long, high-intensity rainfall resulted in extreme flows, particularly in small tributaries, but this could not solely explain the devastating impacts. Geomorphological changes, bank erosion and channel widening, sediment erosion and transport, and uprooted and transported trees and instream large wood accumulated at bridges played a significant role. However, these cascade processes are rarely quantified or considered in flood hazard and risk analysis. This is the focus of this talk. Case studies showing a combination of modelling approaches will illustrate how quantifying the supply and transport of instream large wood is essential in river reaches crossing infrastructures like bridges to assess flood-related hazards and risks.

How to cite: Ruiz-Villanueva, V.: Cascading flood hazards: the role of large wood transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10255, https://doi.org/10.5194/egusphere-egu23-10255, 2023.

14:25–14:35
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EGU23-14062
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On-site presentation
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Jose Luis Salinas Illarena, Ludovico Nicotina, Shuangcai Li, and Arno Hilberts

Accurate real and near-real time forecasting of extreme flood events has lately become more and more important for the insurance and re-insurance industry (e.g., for claims allocations, Insurance Linked Securities and Catastrophe Bonds…). Examples of such events triggering significant losses in recent years are low-pressure system Bernd (July 2021, eastern Belgium, western Germany, and north-eastern France), hurricane Ida (August-September 2021, Louisiana and Northeastern United States), or hurricane Ian (September 2022, Florida). In order to estimate overall flood risk and flood losses in near-real time, a precipitation product released with a short latency is necessary.

This study analyses the use of the near-real time precipitation products NOAA’s Climate Prediction Center (CPC) and Multi-Radar/Multi-Sensor System (MRMS) for flood forecasting, the latter having a higher spatial and temporal resolution than the former. We investigate and compare their different rainfall characteristics in terms of their ability to capture rainfall extremes, their suitability as input for hydrological/inundation models, and the effect that they have on overall economic losses for a series of selected historical events over the Conterminous United States. Finally, we include in the comparison the more stablished, long-latency dataset North American Land Data Assimilation System (NLDAS), more frequently used for event reconstruction c.a. 1 week after the event.

How to cite: Salinas Illarena, J. L., Nicotina, L., Li, S., and Hilberts, A.: Suitability of near-real time precipitation products for Flood Risk Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14062, https://doi.org/10.5194/egusphere-egu23-14062, 2023.

14:35–14:45
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EGU23-2462
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ECS
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On-site presentation
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Andrea Pozo, Matthew Wilson, Emily Lane, Fernando Méndez, and Marwan Katurji

Floods are the most common hazard in New Zealand, the second most costly and they will change rapidly in frequency and intensity, become more extreme as the impacts of climate change become realized. At the same time, we are undergoing an intense urban development and growing population lives in floodplains, increasing the risk for people’s households and wellbeing. Additionally, computers have limited power and capacity, so there is a limitation in the number of flood scenarios that can be assessed and in the complexity of the hydrodynamic modelling process. This research project, which is part of the 5-year multi-stakeholder research programme “Reducing flood inundation hazard and risk across Aotearoa/New Zealand”, supported by the New Zealand Government and led by the National Institute of Water and Atmospheric Research (NIWA); investigates the feasibility of using a hybrid hydrodynamic/machine learning model to reduce the numerical modelling load and enable probabilistic modelling. The study site is the Wairewa catchment (Little River, Canterbury, New Zealand), working closely with the Wairewa Rūnanga based there. A sample of flooding scenarios is constructed based on the characteristics of the main inundation driver (spatial and temporal characteristics of rainfall extreme events) and other inundation drivers (lake level and antecedent conditions in the catchment). Selected scenarios from this sample will be modelled through a previously calibrated hydrodynamic model and the resultant inundation maps (maximum water depth map for each event) will be used to train a machine learning algorithm to produce the maps for the remaining events. The hybrid model would provide for any flooding scenario (defined by a specific number of variables) the corresponding inundation map in a fast and accurate way, avoiding the hydrodynamic modeling process (very time and computationally expensive). Results from this research will be used to develop a Mātauranga Māori approach to flood resilience and flood related policies by the local and central governments.

How to cite: Pozo, A., Wilson, M., Lane, E., Méndez, F., and Katurji, M.: Towards a method of rapid flood scenario mapping using hybrid approaches of hydraulic modelling and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2462, https://doi.org/10.5194/egusphere-egu23-2462, 2023.

14:45–14:55
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EGU23-17047
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Highlight
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On-site presentation
Christine Hatch, Seda Salap-Ayca, Christian Guzman, and Eve Vogel

In the Northeastern U.S., the most costly damages from intense storm events were impacts to road-stream crossings.  In steep post-glacial terrain, erosion by floodwater and entrained sediment is the largest destructive force during intense storms, and the most likely driver of major morphological changes to riverbanks and channels.  Steam power analysis is a tool that can successfully quantify floodwater energy that caused damages, however, prediction of which reaches or watersheds may experience future impacts remains uncertain. Downstream, in urban areas, floodwaters increasingly occupy larger geographic extents that spill well beyond traditionally mapped flood and hazard zones. Limiting these maps are critical biases: Often more information is available for coastal and urban areas (missing steeper terrain geomorphic hazard zones), base functional assumptions (that flood risk is dominantly inundation risk from a specific depth of water, ignoring the force of moving water, sediment or erosion), their concentration around the highest-value infrastructure (lower-value and lower-density development or undeveloped areas have little or no map coverage) and how these maps are utilized for regulatory purposes (e.g. mortgage and insurance requirements). Compounding the physical destruction of flooding is the unequal distribution of these impacts on socially vulnerable populations that are least able to recover from them.  We strive to improve the co-generated mapping of social vulnerability and flood risk by (1) utilizing measures of social vulnerability with greater social and geographical insight and nuance, including self-organizing maps (SOM) that cluster overlapping metrics, (2) applying modified flood hazard maps that accurately represent fluvial geomorphic hazards, urban flooding hazards, and climate change considerations, and (3) overlapping these to understand what factors influence current maps and policy practice; what populations and places may be overlooked or under-resourced relative to vulnerability; and use this collective insight to help inform and develop improved map products and policy approaches.  Integration of this information directly with practitioners’ resources allows communities to prioritize and make land-use decisions and flood-response and preparedness decisions that are informed by the specific vulnerabilities of their populations as well as the fluvial geomorphic workings of the larger watershed, and that have powerful local implications.  Outreach and educational programs focused on social vulnerability and fluvial systems for river practitioners and politicians at all levels align communities’ attitudes about flooding and rivers can ultimately result in ecologically sound, socially just, and more flood resilient policies and practices.

How to cite: Hatch, C., Salap-Ayca, S., Guzman, C., and Vogel, E.: A just map: community and fluvial science working together for flood hazard vulnerability mapping in Massachusetts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17047, https://doi.org/10.5194/egusphere-egu23-17047, 2023.

14:55–15:05
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EGU23-7772
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ECS
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Highlight
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On-site presentation
Mina Yazdani, Paola Salvati, Mauro Rossi, Cinzia Bianchi, and Fausto Guzzetti

Flood events are among the most damaging natural disasters, with billions of people being directly exposed to the risk of intense flooding worldwide. The economic and societal consequences of these events are expected to increase in the coming years. Flood societal risk can be determined by analyzing the relationship between the frequency of fatal flood events and the magnitude of the resulting consequences to the population (evaluated by the number of fatalities due to the event). Here, we test an approach previously proposed for landslides to estimate the flood societal risk in Italy, using historical sparse data on flood fatalities, available through national catalogues. Such an approach is based on the use of the Zipf distribution, which has previously been widely adopted for the modeling of societal risk for different natural hazards. The model allowed the evaluation of the spatial and temporal distribution of societal flood risk over the Italian territory over a regularly spaced grid. Different risk scenarios are presented and discussed.  

How to cite: Yazdani, M., Salvati, P., Rossi, M., Bianchi, C., and Guzzetti, F.: Societal Flood Risk in Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7772, https://doi.org/10.5194/egusphere-egu23-7772, 2023.

15:05–15:15
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EGU23-11966
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On-site presentation
Bruno Merz, Mostafa Farrag, Xiaoxiang Guan, Björn Guse, Li Han, Heidi Kreibich, Dung Nguyen, Nivedita Sairam, Kai Schröter, and Sergiy Vorogushyn

Flood risk assessments are an important basis for risk management. For larger regions, these assessments are often based on small-scale modelling, which is subsequently compiled into a large-scale picture. However, this approach neglects spatial interactions, such as decreasing risk due to upstream dike breaches, and does not provide realistic risk statements for larger regions. This paper presents the ‘derived flood risk analysis’ as an alternative approach and its implementation for Germany. A model chain consisting of hydrological, hydraulic, and damage models simulates the occurrence of extreme runoff, inundation, and direct economic damages. This model chain is driven by a weather generator that provides spatially consistent fields of climate variables. The generation of very long (several thousand years) time series with daily resolution allows the estimation of extreme runoff and corresponding damages. The consideration of the spatial relations in all model components, from the weather generator to the damage model, is able to provide consistent large-scale risk statements. This avoids the significant overestimates typical of many large-scale flood risk assessments.

How to cite: Merz, B., Farrag, M., Guan, X., Guse, B., Han, L., Kreibich, H., Nguyen, D., Sairam, N., Schröter, K., and Vorogushyn, S.: Spatially consistent flood risk assessment for Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11966, https://doi.org/10.5194/egusphere-egu23-11966, 2023.

15:15–15:25
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EGU23-10453
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ECS
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On-site presentation
Long Yang

Characterizing the upper tail of flood peak distributions remains a challenge due to the elusive nature of extreme floods, particularly the key elements of flood-producing storms that are responsible for them. Here I examine the upper tail of flood peaks over China based on a comprehensive flood dataset that integrates systematic observations from 1759 stream gaging stations and 14,779 historical flood surveys. I show that flood peak distributions over China are associated with a mixture of rainfall-generation processes. The storms responsible for the upper-tail floods (with the recurrence intervals beyond 50 years) are characterized with anomalous moisture transport and/or synoptic configurations, with respect to those responsible for annual flood peaks. Anomalous moisture transport (in terms of intensity, pathways, and durations) dictates the space-time rainfall dynamics (relative to the drainage networks) that subsequently lead to anomalous basin-scale flood response. I provide physical insights into extreme flood processes based on downscaling simulations using the Weather Research and Forecasting model driven by the 20th Century Reanalysis fields. Modeling analyses for a collective of extreme flood events highlight the role of interactions between complex terrain and large-scale environment in determining the spatial and temporal variability of extreme rainfall. My analyses contribute to improved understanding of the unprecedented flood hazards over China by establishing the nexus between atmospheric processes and basin-scale flood response. These knowledge gains can be potentially used to the upper tail of flood peak distributions.

How to cite: Yang, L.: Hydrometeorological processes and controls of the upper-tail floods over China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10453, https://doi.org/10.5194/egusphere-egu23-10453, 2023.

15:25–15:35
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EGU23-2000
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ECS
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On-site presentation
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Stefano Basso, Ralf Merz, Larisa Tarasova, and Arianna Miniussi

Notwithstanding hundreds of years of efforts, flooding is still the most common natural disaster. A reliable assessment of the impending flood hazard is indeed an outstanding challenge with severe consequences. Mistaken estimates of the odds and magnitude of extreme floods especially result in huge economic losses due to widespread destruction of infrastructure and properties.

We show here that we can infer the propensity of rivers to generate extreme floods by means of two hydroclimatic and geomorphic descriptors of watersheds, which embody the spatial organization of the stream network and the characteristic streamflow dynamics of the river basin. These features are main determinants of a sharp increase of the magnitude of the rarer floods and of the flood value for which this marked growth of magnitude occurs, which we term flood divide as it separates ordinary from extreme floods. Their relevance is suggested by a novel ecohydrological approach to flood hazard assessment and confirmed by observations from hundreds of watersheds in the USA and Germany.

We first ascertained the capability of the method to distinguish between basins which do not and exhibit a flood divide, and its ability to dependably estimate its magnitude. We then applied a dimensional reduction tool to pinpoint key physioclimatic controls of the occurrence of flood divides, verifying our results against data. Finally, we utilized descriptors of these controls (namely the hydrograph recession exponent and streamflow variability) within binary logistic regression to predict the possible occurrence of flood divides and extreme floods in river basins. Repeated analyses for independent realizations of subsets of data indicate good prediction accuracy.

The identified controls of the propensity of rivers to generate extreme floods are readily estimated from primary hydroclimatic variables. The tool thus allows for inferring cases where extreme events shall be expected from short records of ordinary events, providing valuable information to raise awareness of the peril of floods in river basins.

This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

How to cite: Basso, S., Merz, R., Tarasova, L., and Miniussi, A.: Foreseeing the propensity of rivers to extreme floods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2000, https://doi.org/10.5194/egusphere-egu23-2000, 2023.

15:35–15:45
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EGU23-6689
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ECS
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Highlight
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Virtual presentation
Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

In a world of increasing global flood hazards, vulnerable populations (very young and elderly) are disproportionately affected by flooding due to their low self-reliance, weak political voice and insufficient inclusion in climate adaptation and emergency response plans. These individuals account for most flood casualties and often rely on emergency services due to flood-induced injuries, exacerbated medical conditions, and requiring evacuative assistance. However, emergency service demand often exceeds the potential capacity whilst flooded roads and short emergency response timeframes decrease accessibility, service area, and population coverage; but how does this compare across the globe and what will the future hold?

To answer this question, a global analytical framework has been created to determine the spatial, temporal, and demographic variability of emergency service provision during floods. This is based on global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP 8.5 climate scenarios). The framework includes an accessibility analysis to identify emergency service accessibility to vulnerable populations based on restrictions of flood barriers and response-time frameworks, a vulnerability analysis to compare the difference in emergency service provision between key demographic groups, and a hotspot analysis to identify the extent and distribution of flood hazards and at-risk vulnerable populations.

Research findings include the identification that (based on the scenario of 2050 riverine flooding at a 100-yr return period under RCP8.5 and a 30-minute response time):

  • Globally, 64% of schools are always accessible to the ambulance service and 56% of schools are always accessible to the fire service
  • Globally, 29% of schools are never accessible to the ambulance service and 38% of schools are never accessible to the fire service.
  • Globally, approximately 20% fewer people are accessible to emergency services than under non-flood conditions.
  • Africa and Asia experience the greatest accessible population reductions (14-27% and 24-25%) whilst Europe experiences the least accessible population reductions (8-9%).
  • Priority hotspot countries are primarily located in central North America (e.g., Belize), northern South America (e.g., Guyana) and west-central Africa (e.g., Liberia).

The highlighted geographical and temporal differences in emergency service provision globally and between regions, in addition to the framework itself, can be used by national and international organisations to inform strategic planning of emergency response operations and major investments of infrastructure, services, and facilities to maximise the benefit to the disproportionately affected vulnerable populations. This includes the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high vulnerability but low emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6689, https://doi.org/10.5194/egusphere-egu23-6689, 2023.

Coffee break
Chairpersons: Nadav Peleg, Federica Remondi, Francesco Marra
16:15–16:20
16:20–16:30
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EGU23-12932
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ECS
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Highlight
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On-site presentation
Elisa Ragno and Amir AghaKouchak

The concept of return period (recurrence interval) of extreme events is widely used in engineering practice and in the media. In engineering design and risk assessment, the concept of return period is used to determine the expected magnitude(s) of one or more extreme weather events – i.e., the expected magnitude of an event that, if occurred, might cause the failure of a structure. In the media, the concept of return period is used to communicate to the general public the severity of an event. For example, the 2021 summer flood in Northwestern Europe was reported in the news as a one-in-400-year event – an event expected on average once in 400 years. The strength of return period as a metric (in years) to describe the severity of events resides in the straightforward comparison between the average occurrence in years of an event with the average number of years a person can experience and recollect events.

Generally, the return period of a rare event and its magnitude (known as return level) is inferred from limited observations - often derived by extrapolating from a distribution function fitted to the available observations. The distribution is often greatly influenced by the length of observations. These factors make the concept of return period prone to misinterpretation as extreme events are rarely observed in existing records.

Here we provide a new perspective on the return period of extremes determined not only by its exceedance probability but also in relation to the observations used to describe the underlying distribution. Our method offers a straightforward metric, independent of the type of statistical distribution adopted, to quantify and communicate the likelihood of having observed the event of interest in the available observations, ranging from unlikely to very likely. This metric can provide a measure of confidence in the statistical inference of return periods based on the length of record used for inference. We argue that this additional information on likelihood offers important information for designers, planners, and decision-makers.

How to cite: Ragno, E. and AghaKouchak, A.: Communicating the return period of extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12932, https://doi.org/10.5194/egusphere-egu23-12932, 2023.

16:30–16:40
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EGU23-15836
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Virtual presentation
Renji Remesan and Akshay Pachore

Flash droughts are generally considered a subset of seasonal drought events. In the present study, we have characterized the flash drought events based on soil moisture index (SMI) using daily ERA5 reanalysis data having a spatial resolution of 0.250 * 0.250 from 1960 till 2021. Flash drought events were identified when SMI drops below the 20th percentile within less than 3 next pentads, and it terminates when SMI goes above the 20th percentile and stays there for the next 2 pentads. Flash drought time series was prepared and the Mann-Kendall trend test was applied to investigate the evidence of the statistically significant trends. To assess the atmospheric drivers (precipitation, PET) of flash drought, standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) were calculated during the occurrence of each flash drought event at each grid pixel. For calculating SPI and SPEI, ERA5 reanalysis data of precipitation and PET (potential evapotranspiration) was used. Seasonal analysis of results showed that the flash drought frequency observed during the pre-monsoon season (March-April-May) shows considerable variation when compared to the monsoon (July-August-September) and post-monsoon (October-November-December) seasons. Results of Mann-Kendall statistics show the increasing trend of flash drought over semi-arid regions like Marathwada and Vidarbha. Both SPI and SPEI shows spatially varying similarity with the flash drought events. When observed on a seasonal scale, it is observed that SPEI shows a higher degree of similarity with flash drought events during pre-monsoon season as compared to SPI as evaporative demand is high during this period.  

How to cite: Remesan, R. and Pachore, A.: Analysis of Spatio-temporal variability and atmospheric drivers of the flash drought over Godavari river basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15836, https://doi.org/10.5194/egusphere-egu23-15836, 2023.

16:40–16:50
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EGU23-10096
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ECS
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Highlight
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On-site presentation
Ze Jiang, Dipayan Choudhury, and Ashish Sharma

Over the past six years, Australia has experienced significant fluctuations in rainfall, including prolonged dry conditions and extensive bushfires, followed by two consecutive years of heavy rainfall in the east. Could such anomalies be predicted many years in advance is the question this study hopes to answer. A prediction framework that combines empirical and physically-based approaches using CMIP decadal prediction, and a novel spectral transformation approach is presented. When tested in a hindcast experiment, this framework shows significant prediction skill for rainfall up to five years in the future across all regions and climate zones in Australia. This framework was used to project from 2018 to 2022, covering the years of bushfires and extreme floods in Australia, as an added blindfolded validation of the prediction approach used. Following this, a blind projection of the precipitation anomalies over the continent for the coming five years is presented, to assess whether the anomalies for the past five years were, indeed, anomalies, or part of a pattern of what can be expected into the future. It is shown that this decadal framework has great potential for predicting whether the next few years will be wetter or drier, extending the predictive accuracy beyond a few months into the future. This can be valuable for managing water resources, prioritizing demands, protecting vulnerable systems, and reducing uncertainty in hydrological decision-making.

How to cite: Jiang, Z., Choudhury, D., and Sharma, A.: Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10096, https://doi.org/10.5194/egusphere-egu23-10096, 2023.

16:50–17:00
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EGU23-16630
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ECS
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On-site presentation
Wenting Wang, Shuiqing Yin, Zeng He, Deliang Chen, Hao Wang, and Andreas Klik

Five CMIP6 models were selected to project changes in rainfall erosivity of China for two future periods (the near-term in 2041-2065, the long-term in 2076-2100) under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios. Models’ capacity in estimating two erosivity indices, annual average rainfall erosivity (R-factor) and the storm erosivity at 10-year return level (10-year storm EI) were evaluated by comparing the model derived indices for the historical period with the state-of-the-art reference erosivity maps of China interpolated with hourly observations. Results show that GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL outperform the other two models with higher NSEs and better spatial correlation, especially in the water erosion regions. R-factor and 10-year storm EI estimated using MMEs (the arithmetic means of the aforementioned three models) for the historical period are generally underestimated, and the median biases are 0.80 and 0.66, respectively. Biases for each grid were determined as the bias-correction factors for future erosivity projection. Generally, most areas in eastern and central China are expected to experience larger rainfall erosivity. Under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, R-factor over mainland China is projected to increase by 18.9% and 19.8% for the near-term and 26.0% and 46.5% for the long-term, respectively; and 10-year storm EI is projected to increase by 14.2% and 17.4% for the near-term, and 14.9% and 45.0% for the long-term, respectively. The projected increases in rainfall erosivity are mainly due to the increasing probability of extreme precipitation. This implies that soil and water conservation measures in China need to be further strengthened to meet the challenges brought by the increasing number and magnitude of extreme events in the context of global warming.

How to cite: Wang, W., Yin, S., He, Z., Chen, D., Wang, H., and Klik, A.: Projection of future rainfall erosivity over China under global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16630, https://doi.org/10.5194/egusphere-egu23-16630, 2023.

17:00–17:10
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EGU23-16753
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On-site presentation
Jie Tang, Wenting Wang, and Yun Xie

Evaluating the characteristics of long-term dry and wet climate changes under the background of global climate change is important for regional water resources security, ecosystem security and socio-economic development. Based on the daily meteorological data of 1680 meteorological stations in China from 1971 to 2019, the reference evapotranspiration (ET0) was estimated with the FAO-56 Penman–Monteith equation. Based on which, the temporal and spatial variations of humidity index (HI), precipitation (P), reference evapotranspiration (ET0) and the driving factors of which were further analyzed. Results showed that HI significantly increased in the northwest China of arid area, the northeast China of subhumid area and the Huang-Huai region of humid area, while it significantly decreased in the southwest and southeast China of humid areas. The change of HI can be mainly attributed to the change of ET0 while no significant trends has been detected for P for most regions of China. During the past 50 years, the increasing rate of ET0 was 3.76 mm/10a. But the temporal variation of ET0 are different from regions, and the increasing and decreasing area were mainly dominated by climate different factors. For region of Huang-huai and northern Northeast China, ET0 showed significant downward trend. Among factors that relating to ET0, wind speed contributes most to the significant decrease of ET0. For all rest regions of China, ET0 showed significant upward trends, and relative humidity contribute most to the increase.

 

Key words: Dry and wet climatic change, humidity index, reference evapotranspiration, contribution, climatic factors.

How to cite: Tang, J., Wang, W., and Xie, Y.: Dry and wet climatic change and its driving factors in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16753, https://doi.org/10.5194/egusphere-egu23-16753, 2023.

17:10–17:20
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EGU23-7000
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On-site presentation
Alessia Flammini, Jacopo Dari, Carla Saltalippi, and Renato Morbidelli

In the hydraulic structures design against extreme events a proper estimate of the areal reduction factor (ARF) is required. Specifically, rainfall-runoff models widely used need to be fed with information on areal-average rainfall over a watershed surface, while rainfall data is typically available at a point scale. The ARF allows to convert rainfall data from point to areal scale.

In this work, a new fixed-area and deterministic approach for estimating the ARF is proposed; it involves ratios between observed annual maxima with specific duration of average rainfall occurring in a specific area and those referring to all the available point rainfalls in the same area. The approach was applied to the Umbria region in Central Italy where, using high-quality and validated rainfall data (with a temporal resolution of 1 minute), a parametric relation expressing ARFs as function of duration and area was found. The outcomes were then compared with those of the most widespread empirical approaches available in literature, often applied when rainfall data are lacking, obtaining substantial over- or underestimation of empirical ARFs. This confirms that the transposition of ARF relations from a geographic area to another could have not-negligible impacts on the design of hydraulic structures. In addition, indications aimed at selecting the most suitable method to be applied for ARF estimation are provided. Specifically, the proposed approach is suitable when a limited number of years of rainfall observations is available. In this regard, the robustness of the methodology was tested by varying the length of the rainfall observation period; a minimum number of about 6 years was found to make the derived empirical formulation sufficiently accurate in a specific area.

How to cite: Flammini, A., Dari, J., Saltalippi, C., and Morbidelli, R.: Areal reduction factor assessment for extreme rainfalls through a new empirical fixed-area formulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7000, https://doi.org/10.5194/egusphere-egu23-7000, 2023.

17:20–17:30
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EGU23-16619
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On-site presentation
Impacts on flood risk metrics by implementing mitigation measures on building and catchment scale
(withdrawn)
Punit Bhola, Margot Doucet, and Bernhard Reinhardt
17:30–17:40
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EGU23-4537
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ECS
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On-site presentation
John Sekajugo, Grace Kagoro-Rugunda, Rodgers Mutyebere, Clovis Kabaseke, David Mubiru, Esther Namara, Violet Kanyiginya, Bosco Bwambale, Liesbet Jacobs Jacobs, Olivier Dewitte, and Matthieu Kervyn

Geo-hydrological hazards (landslides and floods) are often associated with significant damages on physical infrastructure like buildings and roads. Understanding the factors controlling the extent of damage is a prerequisite for quantitatively estimating risk and its spatial distribution, and advising on measures to reduce vulnerability. In this study we document the impact of 64 landslide and six flood events in four selected districts in western Uganda for the period May 2019 - March 2021 through extensive fieldwork. We quantify in economic value the physical damage of landslide and flood hazards on exposed buildings, roads and bridges. We then analyse the physical vulnerability based on damage ratios and determine the factors  (building material, hazard characteristics and age of the building) that control the degree of damage using fractional logistic regression. Out of the 91 buildings affected by landslides, 54% were totally destroyed, and only 10% not or minorly damaged, for an average damage cost of 3,179 USD/building. For the 212 documented buildings affected by floods, 35% were totally destroyed, 28% had severe to moderate damage and the rest were minorly or not affected, with an average damage costs of 1,755 USD/building. The physical vulnerability of buildings to landslides depends on the size of the landslide, age of the building, type of building wall material and the steepness of the slope cut to establish an artificial foundation platform. On the other hand, the physical vulnerability of buildings to flood hazards is largely controlled by the flood depth, the distance from the river channel, slope, size of flooded area and type of floor material. The physical vulnerability functions developed in this study are being used as a new inputs into a regional quantitative model of geo-hydrological risks. Combining the hazard estimates with the most accurate information on exposure of physical infrastructure, will facilitate the identification of the types of events and the locations that require most attention for risk reduction.

How to cite: Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Mubiru, D., Namara, E., Kanyiginya, V., Bwambale, B., Jacobs, L. J., Dewitte, O., and Kervyn, M.: What controls physical vulnerability to geo-hydrological hazards? A contribution to quantitative assessment of landslide and flood risk in western Uganda, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4537, https://doi.org/10.5194/egusphere-egu23-4537, 2023.

17:40–17:50
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EGU23-3734
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ECS
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On-site presentation
Yuan-Hung Chiu, Colin P. Stark, and Hervé Capart

In many mountain valleys, communities and infrastructure are exposed to high risks of damage due to debris fan aggradation. To assess such risks, two questions must be addressed: (1) What will be the extent and thickness of deposition over the fan for a given volume of debris delivered from the upstream catchment? (2) How large could debris flow volumes be for a single event or a sequence of events? In this contribution, we propose a methodology to address both questions. Its first component is a simplified model of debris fan morphology, based on assuming a fan-slope-distance relationship along paths affected by topographic obstacles like steep valley sides. Using a computationally efficient algorithm, this model can be used to reconstruct past fan volumes from terrace remnants resolved on high resolution DEM topography, and to simulate large numbers of possible future events. Its second component is a stochastic model for the evolution of fan volume framed as a form of random walk. To take into account the episodicity of debris delivery, we model this random walk as a gamma-subordinated Wiener process aka a variance-gamma process. To calibrate the model parameters, we exploit both short-term and long-term data: for the slope-distance relationship, topographic data from recent and Holocene debris-fan remnants; for the stochastic process parameters, reconstructed fan-volume changes associated with recent flood events and with older radiocarbon-dated fan surfaces. We illustrate the approach with an application to the Laonong River in southern Taiwan. In this valley, an important roadway link has been repeatedly damaged by debris-flow aggradation. To guide road and bridge reconstruction, it is essential to assess fan aggradation risk for different design alternatives on a decadal time scale or more. The model provides a basis for optimizing the layout and height of such infrastructure.

How to cite: Chiu, Y.-H., Stark, C. P., and Capart, H.: Modeling risk to infrastructure due to episodic debris fan aggradation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3734, https://doi.org/10.5194/egusphere-egu23-3734, 2023.

17:50–18:00
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EGU23-1542
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ECS
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Virtual presentation
Stefano Luigi Gariano, Giuseppe Esposito, Rocco Masi, Stefano Alfano, and Gaetano Giannatiempo

The Campania region, in Southern Italy, is affected by hundreds of wildfires every year, mainly during the summer season. Starting from the month of September, mountain watersheds including those hit by wildfires are impacted by even more frequent intense rainstorms. In such conditions, the high sediment availability, lack of recovered vegetation and a likely stronger soil water repellency increase the likelihood of surface runoff and soil erosion, leading to potential post-fire debris flows downstream.

This work provides information on more than 100 post-fire debris flows (PFDFs) that occurred in Campania between 2001 and 2021, with a particular focus on the triggering rainfall conditions. Rainfall measurements at a high temporal resolution (10 min) were gathered from a dense rain gauge network, with an average distance between sensors and PFDFs initiation areas of 2.6 km. Information on the occurrence of PFDFs was obtained from web news, social networks, and reports produced by the Fire Brigades. The collection of accurate information related to the debris flow timing and location allowed retrieving and analyzing properties of the triggering rainfall inputs, by identifying the minimum triggering conditions with rainfall thresholds. Moreover, to evaluate the temporal structure and type of the storms associated with the PFDFs (e.g., convective or frontal systems), the standardized rainfall profiles of the triggering events were defined. The return times of the peak cumulative rainfall of the bursts in 10, 20, and 30 minutes were also calculated.

Results show that the triggering rainfall events are very short (37 minutes on average), with high average intensity (73.2 mm/h and 49 mm/h in 10 and 30 minutes, respectively), and mostly associated with severe convective systems (i.e., thunderstorms). The estimated return times are quite low, with 75° percentiles of the related distribution ranging from 2.7 to 3.2 years, indicating that these rainfall events are neither rare nor extreme, as also observed by other authors worldwide. Differences are observed in return times and the spatial distribution of the events that occurred in July-September (higher rainfall magnitudes and longer return times) rather than in October-December. The time window in which PFDFs are more likely to occur in the study area has an extension of four months, from September to December. According to the defined triggering rainfall threshold, a rainfall of 11.4 mm in 30 minutes (corresponding to an average intensity of 22.8 mm/h) is likely sufficient to trigger a PFDF in the study area.

These research outcomes provide reliable and effective support to inform decision-makers engaged in hazard assessment and risk management, in order to implement suitable countermeasures in terms of monitoring and early warning systems. It is worth noting that PFDFs often occur in small-scale watersheds characterized by very short concentration times, in response to intense bursts of less than 60 minutes. This means insufficient lead time to fully develop an effective emergency response. This and other criticalities represent serious challenges requiring additional work.

How to cite: Gariano, S. L., Esposito, G., Masi, R., Alfano, S., and Giannatiempo, G.: Triggering rainfall conditions of post-fire debris flows in Campania, Southern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1542, https://doi.org/10.5194/egusphere-egu23-1542, 2023.

Posters on site: Mon, 24 Apr, 10:45–12:30 | Hall A

Chairpersons: Elena Cristiano, Nadav Peleg, Efthymios Nikolopoulos
A.123
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EGU23-445
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ECS
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Rui Fagundes Silva, Rui Marques, and José Luís Zêzere

Since the settlement of the São Miguel Island (Azores-Portugal), in the middle of the fifteenth century, there is a record of occurrence of landslides, some with high socio-economic impact. In this work, we carried out a spatial, temporal and impact analysis of landslide events that were registered in the NATHA (Natural Hazards in Azores) database for the period 1900-2020, based on newspapers descriptions. A total of 236 landslide events (a day with one or more landslides identified) that caused human losses, damage to houses or obstruction of roads on São Miguel Island were catalogued. Based on the recorded events, it is verified that there is not a regular increment and/or pattern in the distribution of the events over time, although two main periods can be distinguished: 1900–1994 (1.0 events per year) and 1995–2020 (5.3 events per year). The events were responsible for 82 fatalities, 41 injuries, 66 houses partially or totally destroyed and 305 homeless people. The municipality of Povoação registered 76 landslide events, followed by the municipalities of Ribeira Grande (71 events), Ponta Delgada (69 events), Vila Franca do Campo (47 events), Nordeste (26 events) and Lagoa (21 events). Although there is a relative homogeneity on the distribution of landslide events in the municipalities of Povoação, Ribeira Grande and Ponta Delgada, the same does not apply to the impact caused. In the municipality of Povoação were counted 48 fatalities, 20 injuries, 17 houses destroyed and 109 homeless people, in Ponta Delgada 14 fatalities, 14 injuries, 24 houses destroyed and 173 homeless people and in Ribeira Grande 8 fatalities, 5 injuries, 16 houses destroyed and 21 homeless people. In the municipality of Vila Franca do Campo were counted 7 fatalities and 2 houses destroyed, in Nordeste 3 fatalities and 2 injuries, and in Lagoa 2 fatalities, 7 houses destroyed and 2 were homeless people. Rainfall was the triggering factor responsible for 70% of the catalogued landslide events, followed by sea erosion (8%), anthropogenic actions (4%) and earthquakes (2%). The triggering factor was not possible to identify in 16% of the landslide events. Landslides occurred mostly during the rainiest season (from November to March), which comprise about 78% of the catalogued landslide events.

How to cite: Silva, R. F., Marques, R., and Zêzere, J. L.: Landslides on São Miguel Island (Azores-Portugal) in the period 1900-2020: Analysis of the spatio-temporal distribution, triggering factors and impact based on newspapers press articles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-445, https://doi.org/10.5194/egusphere-egu23-445, 2023.

A.124
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EGU23-2240
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ECS
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Anna Whitford, Hayley Fowler, Stephen Blenkinsop, and Rachel White

Short-duration (3hr) extreme rainfall events can cause significant socioeconomic and structural damage, alongside loss of life, due to their ability to generate dangerous flash floods, particularly in urban areas and small catchments. With the projected future increase in the frequency and intensity of these events due to global warming, it is imperative to improve our ability to provide warning to communities that may be impacted by these floods. Large-scale atmospheric dynamics play a role in generating the conditions conducive to the development of local-scale sub-daily extremes, but our current understanding of these processes is limited. Additionally, large-scale circulations are inherently more forecastable than small-scale features such as convection, therefore, this project focuses on finding connections between the large-scale dynamics and sub-daily extremes.

This study uses the quality-controlled Global Sub-Daily Rainfall dataset to identify past extreme events in western Europe. The atmospheric circulation pattern present on the day of each event is extracted from the UK Met Office’s set of 30 weather patterns (WPs) based on mean sea level pressure. This information is then used to examine the intensity and frequency of extreme events under each WP, leading to analysis of the spatial connections between the WPs and sub-daily extremes.

Results indicate just 5 of the 30 WPs account for 53% of recorded 3hr events above the 99.9th percentile in Europe in summer. The important WPs are a mixture of those showing a cyclonic system (cut-off low) close to or over western Europe and those representing a transitional environment. There are also distinct spatial patterns to the relationships in some cases, for example WP11 (isolated low pressure centred over the south-west UK), is associated with very high frequency of extremes over the UK and Portugal but much lower frequencies elsewhere in Europe. The identification of a select group of WPs as important for the generation of sub-daily extremes has implications for forecasting these events at longer lead times, as the large-scale WPs can be predicted further ahead than local conditions.

The WP-based analysis is supplemented by investigation of the links between the sub-daily rainfall extremes and synoptic scale Rossby wave patterns. The Local Finite Amplitude Wave Activity (LWA) metric is used to identify regions of anomalous cyclonic or anticyclonic wave activity both prior to and during the extreme events. This analysis indicates anomalous cyclonic wave activity at certain locations, including over Alaska, to the west of the British Isles and over northern Siberia, is significantly correlated with extreme rainfall over Europe. It is also possible to trace the LWA in days leading up to the extreme events, enabling identification of wave patterns that evolve into conditions associated with the extremes.

These results offer new evidence on the role of large-scale dynamics associated with sub-daily extreme rainfall, whilst also providing powerful information that could be used in the forecasting of these events.

How to cite: Whitford, A., Fowler, H., Blenkinsop, S., and White, R.: Large-scale dynamical drivers associated with sub-daily extreme rainfall in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2240, https://doi.org/10.5194/egusphere-egu23-2240, 2023.

A.125
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EGU23-3073
Yong-Jun Lin, Hsiang-Kuan Chang, Kai-Yuan Ke, Jihn-Sung Lai, and Yih-Chi Tan

This study adopts the rainfall scenario generated by TCCIP (The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform) based on IPCC AR5, which provides the 95th percentile of Taipei’s maximum 24-hour cumulative rainfall due to climate change. The baseline of this scenario is 404 mm for 1979-2008, and the projected rainfall is 517 mm for the future mid-century (2039-2065).

The flooding potentials of the Taipei Mass Rapid Transit (MRT) stations are obtained by applying the scenarios of rainfalls and the corresponding rainfall patterns of each rainfall station to a two-dimensional flood model. The flooding simulations of baseline and future scenarios show that Jingan Station and Fu-Jen University Station have the highest flooding potential, with a maximum flooding depth of 2 meters. The flooding hazard factors include flooding depth, flow velocity, and rising rate of water surface level. We adopted those factors to analyze the flooding hazard at Banqiao Main Station, which unites Banqiao Railway Station, a high-speed rail station, and Banqiao MRT station. It has a severe flooding potential and a large traffic volume. Because the mid-century rainfall is 1.43 times that of the baseline, the corresponding flooded area of the future scenario is also increased. As a result, the flooding hazards around the exits of Banqiao Main Station are high within the 300 m buffer for the baseline. In contrast, the very high flood hazard was found in a 200m-300m buffer for the future scenario.  

MRT Banqiao Station has 5 entrances/exits, while Banqiao Railway Station has 6 entrances/exits, a total of 11. The average daily ridership at this union station before Covid-19 is 159,239 people/day. The impact ratio of the ridership is set by the degree of flood hazard for each entrance/exit. In the future scenario, the number of affected people is roughly estimated to be 11,611 people/day, which is about 7% of daily ridership before Covid-19.

How to cite: Lin, Y.-J., Chang, H.-K., Ke, K.-Y., Lai, J.-S., and Tan, Y.-C.: Flooding Hazard of Union Station and Impact of Ridership due to Climate Change-an Example of Banqiao Main Station, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3073, https://doi.org/10.5194/egusphere-egu23-3073, 2023.

A.126
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EGU23-4243
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ECS
Rong Gan and Yuting Yang

Do CMIP6 climate models capture rapid shifts between dry and wet extremes?

Authors: Rong Gan1, Yuting Yang1,*

Affiliations: 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

*Correspondence to: Yuting Yang (yuting_yang@tsinghua.edu.cn)

Keywords: CMIP6, climate extremes, compound events

Abstract:

Rapid shifts between dry and wet extremes may impose higher socioeconomic and environmental pressure than single extremes. Whether the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models are capable of capturing the abrupt alternations between dry and wet periods remain elusive. Here we examine such compound events simulated by CMIP6 models based on the state-of-the art reanalysis datasets, namely ERA5, NCEP-NCAR and MERRA-2. The 1-month Standard Precipitation-Evapotranspiration Index (SPEI) were first calculated to identify dry spells (SPEI≤1) followed by wet spells (SPEI≥1), and vice versa. Event characters including frequency, duration and intensity were then evaluated across all CMIP6 models and reanalysis datasets spanning 1980-2014. We find the following:

  • CMIP6 multimodel-ensemble median and reanalysis ensemble give close estimates of event characters on global average, with frequency being about 4.1 and 3.67 (No. events/20-year), duration of 2.50 and 2.55 (months), and intensity around 3 (SPEI mean) for dry-wet events, respectively. Similar values were found for wet-dry events.
  • During 1980-2014, CMIP6 and reanalysis indicate roughly 10% increase in event frequency comparing the first and last 20-year periods, and less than 1% increase in duration and intensity for both dry-wet and wet-dry events.
  • Spatial distribution for event frequency tends to overlap for dry-wet and wet-dry events, as shown by both CMIP6 models and reanalysis. Hot spots were found in North-eastern America, Europe, Eastern Asia, South-western America, and Middle Africa. Higher latitude regions were shown to experience more events. Despite general spatial agreement between CMIP6 and reanalysis, discrepancies can be seen on finer scales within each region.
  • Common spatial patterns for duration were also found between the two types of events based on CMIP6 models, where the events tend to last longer in middle and southern Eurasia, Eastern Africa, northwest of South America and west of Northern and Central America. However, reanalysis indicates longer events also happened in Middle Africa and eastern Australia. Both CMIP6 models and reanalysis indicate longer event duration roughly around the equator.
  • CMIP6 models give much higher dry-wet intensity compared to wet-dry, especially in Australia and Southern and Western Asia. Reanalysis agrees well on this pattern, yet greater magnitude differences were found in eastern South America.

Overall, CMIP6 models are capturing the variations of abrupt dry and wet alternations well when compared to reanalysis. The models are more skilful in simulating event frequency than duration and intensity in general. Caution should be paid assessing such compound events especially on smaller spatial scales and sensitive regions such as Africa for frequency and Australia for duration and intensity. Our results can be further employed to support climate risk adaptation and mitigation.

How to cite: Gan, R. and Yang, Y.: Do CMIP6 climate models capture rapid shifts between dry and wet extremes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4243, https://doi.org/10.5194/egusphere-egu23-4243, 2023.

A.127
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EGU23-4417
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ECS
Marina Refatti Fagundes, Fernando Mainardi Fan, Gean Paulo Michel, Karla Campagnolo, Masato Kobiyama, Ronald Pöppl, and Bruno Henrique Abatti

Trails are one of the main places for ecotourism practitioners’ activities. Many of them are located close to watercourses, and it is often necessary for practitioners to cross them. This often leads to dangerous situations, since critical conditions of water stages and flow velocity can make people lose their walking stability. One way to quantify these hazards is the hazard index (HI) which, in general, is defined as the product of the flow velocity by its depth (Stephenson, 2002). Although many studies have been carried out to determine the HI values as safety limits for people exposed to water flows, none of them analyzed the natural river conditions like those encountered during an ecotourism trail. In these environments, locomotion is hampered due to the surface which is usually highly irregular and often contains slippery rocks and sediments. Thus, that there is a gap related to the HI analysis in natural rivers, and more research becomes necessary, since more people have sought to carry out activities related to ecotourism. The main objective of this research is to apply HI approach in natural rivers so that its results can be utilized in the management of trails containing watercourses crossing. Initially, a bibliographic review was carried out, where some important concerns related to people's loss of stability were analyzed. The results of the bibliographic review were organized within a summary table which permits verifying variables with stronger influence on people's stability, during these walks. After this first stage, three mountain trails located in the Aparados da Serra National Park, in southern Brazil, were selected for field measurements. In all of these trails, measurements of flow depth and velocity were carried out using a small current meter and the granulometry of the river sediments was measured through an adaptation of the Pebble Count method. The measurements were taken at all points where tourists cross the riverbed during the trails, i.e., 23 measurement sites in total. The analysis of these data resulted in preliminar information: (i) an easy-to-interpret diagram that indicates the thresholds values of HI in natural rivers, named Hazard Index Diagram of Natural River (HIDNR); and (ii) list of the main variables responsible for people's loss of stability, in order to contribute to the safety of ecotourism practitioners. One of the next steps of the work is to analyze how the sediment transport and connectivity behaviour could give us insights about hazard levels.

REFERENCES

STEPHENSON, D. (2002). Integrated flood plain management strategy for the Vaal. Urban Water, v. 4, n. 4, p. 423-428.

How to cite: Refatti Fagundes, M., Mainardi Fan, F., Michel, G. P., Campagnolo, K., Kobiyama, M., Pöppl, R., and Abatti, B. H.: Hazard index applied to natural rivers – Preliminary result from a case study of mountain trails in southern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4417, https://doi.org/10.5194/egusphere-egu23-4417, 2023.

A.128
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EGU23-5513
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ECS
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Jannis Hoch, Izzy Probyn, Joe Bates, Oliver Wing, and Christopher Sampson

Intensity–duration–frequency (IDF) curves are representations of the probability that a given rainfall intensity will occur within a given period. At the global scale, however, only for a few locations sub-daily rain gauge data is available from which global IDF curves could be derived. This poses a major challenge for simulations of global pluvial flood hazard and risk which require information of intensity, duration, and probability as boundary conditions. Therefore, efficient yet accurate means for scaling the locally available data to the global extent need to be found.

Consequently, we use available quality-controlled sub-daily precipitation data from the GSDR data set to derive growth curve parameters at around 10,000 locations world-wide. After combining these scale and shape parameters with globally available data of main precipitation drivers, a regionalized machine learning model is first trained and tested and then applied to produce global IDF maps.

Finally, we evaluated these maps against an ensemble of openly available local IDF curves found in literature. By selecting locations spread across the globe, we try to ensure to include as much variability as possible in the evaluation. Additionally, the global IDF curves were benchmarked against available more bespoke IDF data in the USA and UK.

While such data-driven approaches clearly depend on the quality and quantity of available sub-daily rainfall observations, the method still shows to capabilities of current data-driven modelling approaches to scale local data to global data applicable in both flood risk research and practice.

How to cite: Hoch, J., Probyn, I., Bates, J., Wing, O., and Sampson, C.: Global IDF curves created from local observations using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5513, https://doi.org/10.5194/egusphere-egu23-5513, 2023.

A.129
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EGU23-7194
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ECS
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Farzana Mohuya, Claire Walsh, and Hayley Fowler

Dhaka, the capital city of Bangladesh, is one of the most densely populated cities in South Asia. Urban flooding from extreme rainfall is a recurring phenomenon, with historic floods in 1988, 1998, and 2004 amongst the most catastrophic events in Dhaka. Prolonged urban flooding or water logging is a major concern for both Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC) areas. This research investigates how “Citizen Science (CS)” could help individuals, communities, and stakeholders understand and manage the risk of current and future urban flooding, integrating formal flood risk management along with the affected area’s respondents’ self-perceived perception, concerns, experience, awareness, and opinions about flood risk management, and ability to cope with the flood risk. Fieldwork data were collected through the administration of a purposely designed questionnaire to 500 respondents in the water logging affected wards of the two city corporations’ areas in Dhaka. Preliminary findings from the fieldwork revealed that every year approximately 45.6% and 29.4% respondents in the study area experienced 1-3 days of urban flooding/water logging, mostly during the monsoon season (June – September), with a work time loss of 3-4 hours respectively. Respondents in the study area are aware and concerned about flooding and its associated risk, and approximately 36.9% respondents think that the frequency of urban flooding will increase in Dhaka in the next 10 years. In terms of the vulnerability, approximately 51.5% respondents mentioned that they are vulnerable to urban flooding and small business holders (Entrepreneurs) are most affected (61.5% respondents) by flooding. Although almost 61.2% respondents were not familiar with the “Citizen Science” concept, but approximately 42.8% of respondents expressed an eagerness to involve themselves in any Citizen Science based project to promote awareness and mitigation of urban flood risk/water logging issues in their community or in Dhaka City. In addition, preliminary findings from Key Informant Interviews (KII) and Focus Group Discussion (FGD) Meetings suggested that unplanned urbanisation, poor and inadequate drainage system management, and recent extreme rainfall events were the major drivers behind the urban flooding/water logging situation in Dhaka.

The study also explored annual and seasonal trends of rainfall in Dhaka (using observed datasets from the Bangladesh Meteorological Department) over the period from 1953-2019 using extreme precipitation indices [Climate Change Detection and Indices (ETCCDI)]. It is revealed that over these 67 years, Annual Maximum Daily Rainfall has increased during winter (0.021 mm/year) but statistically significantly decreased during the monsoon (-0.636 mm/year). The overall annual rainfall has significantly decreased (-0.718 mm/year). Trends in Consecutive Dry Days, heavy, and very heavy precipitation days indicate an annual increasing rate of 0.158 days/year for CDD, 0.077 days/year with >= 10 mm rainfall and 0.019 days/year with >= 20 mm rainfall, respectively. Results from the rainfall datasets are now being integrated with the fieldwork findings and other secondary datasets to set up a Hydrodynamic Model (CityCAT) to investigate current and future flood risk in Dhaka in more detail.

How to cite: Mohuya, F., Walsh, C., and Fowler, H.: Urban Flood Risk in Dhaka, Bangladesh, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7194, https://doi.org/10.5194/egusphere-egu23-7194, 2023.

A.130
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EGU23-10474
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ECS
Gigi Pavur, Venkataraman Lakshmi, and James H Lambert

On September 28, 2022, Hurricane Ian made landfall in Florida as the 5th strongest tropical cyclone on record for the United States of America. Preliminary damage assessments conducted by the National Oceanic and Atmospheric Administration (NOAA) estimated over $50 billion USD in insured and uninsured losses from the event. The extensive environmental and socioeconomic consequences of recent hydrometeorological extremes in Florida indicate an urgent need to improve understanding of hydrological and socioeconomic vulnerability in the region to inform future investments to increase resilience to events like Hurricane Ian. This study conducts an interdisciplinary risk analysis of both hydrological and socioeconomic variables before and after Hurricane Ian to improve understanding of the region’s hydrological and socioeconomic vulnerability to hydrometeorological extremes. A variety of publicly available satellite-based remote sensing data are leveraged for the hydrological analysis, specifically precipitation data from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), soil moisture data from Soil Moisture Active Passive (SMAP), synthetic aperture radar data from Sentinel-1, optical imagery from Landsat 8, and Global Navigation Satellite System Reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) are utilized. Additionally, high-resolution commercial satellite data from Planet, Maxar, and Capella are used to further identify infrastructure damages from Hurricane Ian. To support the socioeconomic risk analysis, publicly available demographic and economic data are used from the U.S. Census Bureau and State of Florida. Results from this work can be used to improve understanding of hydrological and socioeconomic risk in Florida due to hydrometeorological extremes. Additionally, this work can be used to inform priorities and strategy aimed to decrease risk and increase resilience in this region towards major tropical cyclones. 

How to cite: Pavur, G., Lakshmi, V., and Lambert, J. H.: A hydrological and socioeconomic risk assessment of tropical cyclone disasters by leveraging space-based Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10474, https://doi.org/10.5194/egusphere-egu23-10474, 2023.

A.131
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EGU23-11439
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ECS
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Highlight
Eleonora Panizza, Yared Abayneh Abebe, and Roberto Rudari

The frequency and intensity of floods in the Intergovernmental Authority on Development (IGAD) region in Eastern Africa have increased over the years because of climate variability and change. Sudan is one of the IGAD countries most affected by these extreme events. In August 2022, the country experienced the fourth consecutive year of major flooding, which extensively damaged buildings and impacted people’s livelihoods. Floods also cause the displacement of thousands of people every year in Sudan due to direct damage to houses and impacts on livelihoods, critical services, and infrastructure. The effects of these events on people’s lives are worsened by contextual socio-economic, political, and individual vulnerabilities. In this regard, assessing flood impacts on displacement is crucial to increase people’s resilience and risk reduction capacities.

In this poster, we present the design, execution, and results of a data collection campaign focused on a pilot area in the Khartoum State of Sudan. These data will support the next phase of research, which is an agent-based modeling (ABM) study. The aims of the broader study are to better understand the nexus between flood events and displacement patterns in the area, including flood perception, preparedness, and displacement duration, and to evaluate the impact of different risk reduction policies. The overall goal of the effort is to strengthen local resilience and capacity, and to support policymakers in identifying effective mitigation and management strategies.

Considering that there could not be a one-size-fits-all solution for different contexts, first-hand data were collected at the local level to capture specific information about the area and its population. Questionnaires were administered to a statistically significant sample of residents in the pilot area, focusing on household characteristics, their experience regarding floods and displacement, and their risk perception. Among the results, it was found that 67% of the surveyed population was displaced due to flooding at least once, most of them for a period ranging from 1 to 5 months. The main reason for the decision to move was the damage to the house, followed by flood impacting livelihood. Displacements occurred most often during the event itself, showing a lack of preparedness. Data showed that 81% of the respondents perceived that they lived in a flood-prone area, while 56% of them believed they were at high risk of being displaced due to flood events. To gain a broader understanding of flood risk reduction policies and implementation contexts, representatives of Sudanese institutions and relevant organizations were interviewed. Policy options were explored, including housing policy and Early Warning Systems.  Both questionnaires and interviews are being used to inform the construction of the ABM.

The research is therefore relevant to understand the main elements that affect displacement decisions and to support the design of strategies for mitigating the risk of involuntary mobility in the area, and for increasing people’s resilience and capacity to cope with flood events and displacement risks.

How to cite: Panizza, E., Abebe, Y. A., and Rudari, R.: Assessing floods impacts on population displacement in Sudan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11439, https://doi.org/10.5194/egusphere-egu23-11439, 2023.

A.132
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EGU23-14903
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ECS
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Marc-André Falkensteiner, Gregor Ehrensperger, Thorsten Simon, and Tobias Hell

Knowledge about extreme values of severe hail plays an important role in engineering and insurance. The estimation of return levels of severe hail events is challenging, as hail is locally rare and documentation about hail events is not available in a unified way. For instance for the state of Austria GeoSphere provides radar based probabilities of hail (POH) and maxima of expected hail size (MEHS) that only span a period from 2010 onward.

Based on this sparse data the application of classical extreme value theory, such as Block-Maxima or Peak over Threshold might be invalid. Instead we use a version of the metastatistical extreme value distribution (MEVD), which was shown to work reasonably well in the context of extreme precipitation events, even with a rather small number of available years used for the estimation in comparison to the recurrence time. More precisely we make an assumption about the underlying probability distribution of the daily maximum POH values. The parameters of the distribution are then modeled as smooth functions of the day of the year and the year of observation, thus employing the framework of generalized additive models for location, scale and shape (GAMLSS). Furthermore we add topographic information (longitude, latitude, altitude) to our model, resulting in a full spatiotemporal model across the whole domain of Austria, from which the return values of the POH, respectively MEHS are calculated.

This framework allows for the incorporation of an arbitrary number of additional covariables, as long as they are available on the same grid as the desired output. To illustrate this we use the information of daily precipitation extremes to enrich the model with additional atmospheric information.

How to cite: Falkensteiner, M.-A., Ehrensperger, G., Simon, T., and Hell, T.: Modelling severe hail events over Austria using the metastatistical extreme value distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14903, https://doi.org/10.5194/egusphere-egu23-14903, 2023.

A.133
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EGU23-17459
Sunil Kumar De, Arindam Chowdhury, and Milap Chand Sharma

The Sikkim Himalaya, similar to other mountain regions, has lost considerable ice cover over the years owing to the changing climatic factors leading to enlargement of glacier-fed lakes, and thus posing a potential threat to downstream communities in the mountain and Tarai (foothills) region in case of breach anytime in the future. The Chhombo Chhu Watershed (CCW) of the Tista Basin in the Sikkim Himalaya, located between the Greater Himalayan Range and the Tethyan Sedimentary Sequence, is the storehouse of number of glacial lakes with large areas and volumes. In this study, we mapped the glacial lakes' changes between 1975–2018 and assessed their dynamics based on manual analysis of optical satellite images using KeyHole-9 Hexagon (∼4 m), Landsat Series (∼15-30 m), and Sentinel 2A-MSI (∼10-20 m) imagery and verified during field surveys. The results show that the number of lakes has increased from 62 to 98, and its total area expanded significantly by 34.6 ± 5.4%, i.e., from 8.5 ± 0.2 km2 in 1975 to 11.4 ± 0.6 km2 by 2018, at an expansion rate of 0.8 ± 0.1% a–1. Lake outburst susceptibility result reveals that a total of twenty-seven potentially dangerous glacial lakes exist in the watershed; 5 have a status of ‘high’ outburst probability, 17 ‘medium’ and 5 ‘low’. The majority of the proglacial lakes in the watershed have significantly enlarged due to the faster melting and calving processes as a result of accelerating increasing long term average annual trend of temperature (+0.283° Ca–1; 95% confidence level) and homogeneous or slightly declining precipitation.

How to cite: De, S. K., Chowdhury, A., and Sharma, M. C.: Inventory, Classification, Evolution, and Potential Outburst Flood Assessment of Glacial Lakes in the Chhombo Chhu Watershed (Sikkim Himalaya, India), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17459, https://doi.org/10.5194/egusphere-egu23-17459, 2023.

Posters virtual: Mon, 24 Apr, 10:45–12:30 | vHall HS

Chairpersons: Efthymios Nikolopoulos, Federica Remondi, Francesco Marra
vHS.17
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EGU23-3005
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ECS
Christian Dominguez and Alejandro Jaramillo

Tropical cyclones (TCs) are among the most hazardous hydrometeorological phenomena. Mexico is affected by TCs from the North Atlantic and Eastern Pacific oceans, and they originate 86.5% of domestic disasters. The natural hazards associated with TCs are extreme precipitation events, floods, storm surges, and landslides. In the present preliminary study, we focus on exploring how El Niño-Southern Oscillation (ENSO) modulates the frequency and magnitude of extreme precipitation events and floods caused by TCs. We use the CHIRPS dataset for determining the extreme precipitation events (defined by the 95th percentile of daily precipitation) and Mexican rain gauge stations from May to November during the 1981-2013 period. We find that TCs are responsible for ~60% of floods in coastal regions, but this percentage decreases inland. Under El Niño conditions, most floods occur over southwestern Mexico. During neutral conditions, the western coast of Mexico is mainly affected. Under La Niña conditions, most floods occur over the eastern coast of Mexico. Additionally, trends in floods are explored. We conclude that local decision-makers need this information to decrease the hydrometeorological risk before the tropical cyclone season begins. Implementing this information on Early Warning Systems for TCs is also discussed.

How to cite: Dominguez, C. and Jaramillo, A.: Variations in floods associated with Tropical Cyclones over Mexico under ENSO conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3005, https://doi.org/10.5194/egusphere-egu23-3005, 2023.

vHS.18
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EGU23-15819
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ECS
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Isly Issac, Dr N.K. Goel, and Nityanand Rai

In Indian Himalayas, many hydroelectric projects are now under construction due to the availability of a perennial water source and a natural head for hydropower generation. Hydropower plants often require significant investments, design lifetimes, and lengthy repayment. Indian Himalayan states are now developing State Action Plans on Climate Change, with policies for climate change mitigation and adaptation at the subnational level. These plans recognize GLOFs as a significant climate change-related flood to be considered for the safety of River Valley Projects. The snow-fed catchment area of these projects has many glacial lakes, and there is a high likelihood of breaching for lakes located at the glacier's snout. In general, potentially dangerous lakes are located near the end of a glacier in the lower part of the ablation area. A large mother glacier can create potentially hazardous lakes. These moraine dams could likely breach due   to   piping   or   overtopping   due   to   their porous soil content inside dam body. A sudden discharge of significant magnitude could endanger the safety of the downstream HE hydroelectric project. It is suggested, the glacial lake outburst flood (GLOF) and the design flood be simultaneously considered while assessing the spillway capacity of new hydropower projects to ensure that they are hydrologically secure.

Bajoli-Holi Hydroelectric Project, located on river Ravi in the Himachal Pradesh state of India, is studied, to analyze its spillway capacity considering both GLOF and Inflow Design flood. BIS published the guidelines for fixing spillway capacity. As per the codal provisions, the Bajoli-Holi dam qualifies for PMF as its Inflow design flood.

The hydrology of a particular basin or project undergoes certain changes due to factors such as climate change, urbanization, deforestation, soil erosion, a heavy spell of short-duration rainfall, etc. With the aid of the most recent methods, including hydrodynamic modeling and a hydro meteorological approach, the design flood and GLOF for the dam have been evaluated in this study.

There are a total of 83 glacial lakes identified and mapped in this catchment area. It is further critically analysed to find the effect of the most critical glacial lake which is glacial Lake-52 having an area of 14.5 ha at a distance of 26.5km from the project location. River cross sections spaced 400 m apart has been considered. The upper envelope of the PMF is calculated to be 15,303 cumecs, average envelope is 6247cumecs and the lower envelope value is 2551 cumecs. The combined GLOF peak attenuated after hydrodynamic channel routing at the project site and the PMF analysed, will be taken as the inflow flood for analyzing the spillway requirements for the Bajoli-Holi project. The study results can be applied to similar hydro-meteorologically similar basins of the Himalayas in India which are under the influence of glacial lake outbursts and PMF.

How to cite: Issac, I., Goel, D. N. K., and Rai, N.: Approach and methodology for estimating combined glacial lake outburst flood (GLOF) and PMF design flood for Bajoli Holi hydro-electric project in the Indian Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15819, https://doi.org/10.5194/egusphere-egu23-15819, 2023.