4-9 September 2022, Bonn, Germany
OSA2.4
Weather impacts for built infrastructure

OSA2.4

Weather impacts for built infrastructure
Conveners: Silvana Di Sabatino, Kirsti Jylhä, Ulpu Leijala, Virve Karsisto, Clemens Drüe, fraser ralston | Co-convener: Kjersti Gisnås
Orals
| Thu, 08 Sep, 09:00–10:30 (CEST), 11:00–13:00 (CEST)|Room HS 5-6
Posters
| Attendance Thu, 08 Sep, 14:00–15:30 (CEST) | Display Thu, 08 Sep, 08:00–Fri, 09 Sep, 14:00|b-IT poster area

Orals: Thu, 8 Sep | Room HS 5-6

Chairpersons: Kirsti Jylhä, Ulpu Leijala, Virve Karsisto
Reducing weather risks to transport
09:00–09:15
|
EMS2022-113
|
CC
|
Online presentation
Joe Eyles, Alice Lake, Hannah Susorney, and Henry Odbert

Surface transport forecasting has traditionally focused on deterministically categorising the state - for example, dry, damp, or icy - of a road surface, to provide decision support to winter gritting services. However, with the emerging Connected Autonomous Vehicle (CAV) sector, the ability to accurately describe a broader range of weather conditions, at a much smaller temporal and spatial scale, is becoming equally important to ensuring public safety. Localised conditions such as road spray, rain and fog can all degrade the performance of CAV sensors, whilst settled snow also has the potential to impede navigation by obscuring road markings. The difficulty of deterministically forecasting such precise localised conditions requires us to quantify uncertainty, driving a move towards probabilistic, risk-based forecasting. Therefore, the Met Office is currently developing a new Surface Transport Forecasting (STF) post-processing system, designed to accommodate these future user requirements.

The new STF post-processing system is centred on the Joint UK Land Environment System (JULES); a community model used as the land-surface component of the Met Office Unified Model (UM), but which can also be used – as we do here – as a stand-alone land-surface model driven by forecast output from Numerical Weather Prediction (NWP) models. To produce probabilistic forecasts for locations within the United Kingdom, we are using output from the Met Office regional ensemble model MOGREPS-UK to drive JULES. MOGREPS-UK is a 2.2km resolution 18-member ensemble which provides forecasts out to 5 days. It is generated by time-lagging over 6 hours, initialising three new ensemble members every hour from perturbed initial conditions. By running JULES for each ensemble member, we create a set of possible road forecast outputs. Considering these predictions in aggregate allows us to generate probabilistic forecasts of road weather conditions.

Our ensemble-driven STF system has been verified using standard ensemble verification techniques, including rank histograms and reliability plots. Initial analysis of results, using data from approximately 300 locations in the United Kingdom for which good quality road weather observations are available between 2015 and 2021, indicate that observed road surface states are generally captured within the spread of predictions. The new system has been compared with the current deterministic STF system.  In particular, in challenging meteorological conditions (for example, where there is variable cloud cover, scattered showers, or on marginal temperature nights) the probabilistic approach allows us to quantify the uncertainty in the road state forecast in a way that the deterministic approach does not. Further work will focus on the most effective way to communicate probabilistic forecasts to end-users, ensuring they are able to apply the output to enhance their decision-making.

How to cite: Eyles, J., Lake, A., Susorney, H., and Odbert, H.: Developing Forward-looking Probabilistic Road Weather Forecasts at the Met Office, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-113, https://doi.org/10.5194/ems2022-113, 2022.

09:15–09:30
|
EMS2022-49
|
Onsite presentation
Virve Karsisto and Matti Horttanainen

Spatially accurate road condition forecasts are important for road maintenance personnel to keep roads safe in changing weather conditions. Road surface temperature can vary significantly depending on the road surroundings. Buildings, trees, and terrain features can block solar radiation and cause the temperature to be lower than in open surroundings. On the other hand, the long wave radiation from the surrounding objects can make the road warmer on clear nights. Sky view factor and shadowing algorithm were added to the Finnish Meteorological Institute’s road weather model to take the surrounding features into account in road weather forecasts. Sky view factor means the portion of the sky that is visible at a certain point and shadowing algorithm determines whether the road point receives direct solar radiation at a certain time using local horizon angles. The local horizon angles and sky view factors were determined from the digital surface model (DSM) that was generated from National Land Survey of Finland’s laser scanning data. The data’s resolution is at average 0.5 points per square meter. The DSM differ from digital elevation model (DEM) as it includes vegetation and buildings. The DSM was generated in pieces to cover a 150 km long motorway from Helsinki to Turku in southern Finland. The sky view factors and local horizon angles were calculated for road points located every 50 m on both west and east leading carriageways. Some small-scale road structures like light and electricity poles and road signs caused problems as they appeared too bulky in the DSM. An overlay covering the carriageways was taken from a 2m resolution DEM and was added over the DSM to smooth the problematic features. As this was not enough to remove all the small-scale structures at the side of the road, the local horizon angles went through an algorithm that smoothed too large spikes and bulky features. The produced local horizon angles and sky view factors were given to the road weather model. The forecasts are generated once in an hour for all the selected road points on the motorway. Spatial differences in the forecasted surface temperatures can be seen especially in autumn and spring when the sun is low. At many places the northern carriageway leading to the west is warmer in these situations, as the trees or rock cuttings prevent the east leading southern carriageway from getting direct solar radiation. The forecast system is now in testing phase but has potential to improve spatial accuracy of road surface temperature forecasts.

How to cite: Karsisto, V. and Horttanainen, M.: Updated road weather forecast system using sky view factor and screening implemented on a motorway in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-49, https://doi.org/10.5194/ems2022-49, 2022.

09:30–09:45
|
EMS2022-455
|
Onsite presentation
|
Nico Becker, Henning W. Rust, and Uwe Ulbrich

Weather conditions can affect the probability of road accidents. However, the effect can be different, depending on the vehicle type, the type of collision or road characteristics at the location of the accident. The aim of this study is to quantify the combined effects of traffic volume and different meteorological parameters on hourly probabilities of different accident types using generalized additive models. These models are based on police reports, which are available at the level of administrative districts, as well as reanalysis data and radar-based precipitation. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different accident types in case of precipitation, sun glare and high wind speeds.

The largest effect of snow is found in case of single-truck accidents, while rain has a larger effect on single-car accidents. Precipitation particularly increases the relative risk of run-off-road accidents, as well as accidents at curves and descending road sections. A increasing effect of sun glare on accident probability was found in case of multi-car accidents, in particular in case of rear-end crashes. High wind speeds increase the risk of single-truck accidents and, for all vehicle types, the risk of collisions with objects blown on the road. A comparison of the predictive skill of models with and without meteorological variables shows an improvement of scores of up to 24%. This makes the models suitable for applications in real-time traffic management or impact-based warning systems and have the potential to improve risk perception and behavior of warning recipients.

How to cite: Becker, N., Rust, H. W., and Ulbrich, U.: Weather impacts on different types of road accidents, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-455, https://doi.org/10.5194/ems2022-455, 2022.

Building design and energy consumption
09:45–10:00
|
EMS2022-106
|
CC
|
Onsite presentation
Carla Mateus, Keith Lambkin, Barry Coonan, Seánie Griffin, Jonathan McGovern, Simon McGuinness, Ken Murphy, Matthew Eames, and Orla Coyle

The climate of Ireland is changing. Consequently, climate maps to support building design standards are being updated to inform national Technical Guidance Documents. Additionally, weather files, comprising test reference years and design summer years, are being produced for locations in Ireland to assess the risk of overheating in buildings. This project is carried out by Met Éireann under its strategy ‘Making Ireland Weather and Climate Prepared’ and funded by the Department of Housing, Local Government and Heritage. The outputs of this project are part of Ireland’s Climate Action Plan 2021 under action 197 – develop specific climate maps and data for use in building design to enhance resilience in support of climate change adaptation. The climate services produced in this project will support a wide range of stakeholders, currently collaborating with Met Éireann, which include the National Standards Authority of Ireland, ARUP, Transport Infrastructure Ireland, Office of Public Works, Sustainable Energy Authority of Ireland, Kavanagh Mansfield & Partners, and the Department of Housing, Local Government and Heritage. These climate services will also inform policy in delivering key national infrastructure, including housing, drainage, transport networks and building renovation.

 

The presentation will focus on the importance, data, methodology and outputs of the project’s work packages:

  • Work package 1: Maps of driving rain intensity (maps of spell index following two orientations: N as per ISO 15927-3:2009 and SW as the predominant wind direction in Ireland).
  • Work package 2: Maps of isotherms of the highest maximum shade air temperature (50, 100 and 120 years return periods). Maps of isotherms of the lowest minimum shade air temperature (50, 100 and 120 years return periods).
  • Work package 3: Maps and gridded dataset of estimation of point rainfall frequencies based on the depth-duration-frequency model with durations varying from 1 to 25 days, 24 hours to 15 minutes, and shorter than 15 minutes (50, 100 and 120 years return periods).
  • Work package 4: Maps of isotherms of the lowest 10cm soil temperature (50, 100 and 120 years return periods). Maps of snow loads (50, 100 and 120 years return periods).
  • Work package 5: Current and future weather data sets (Test Reference Years and Design Summer Years) to assess overheating risk in buildings.

 

The produced climate services will be made available open-access through the Met Éireann and the Department of Housing, Local Government and Heritage websites.

How to cite: Mateus, C., Lambkin, K., Coonan, B., Griffin, S., McGovern, J., McGuinness, S., Murphy, K., Eames, M., and Coyle, O.: Climate maps and data to support building design standards in Ireland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-106, https://doi.org/10.5194/ems2022-106, 2022.

10:00–10:15
|
EMS2022-395
|
Onsite presentation
Kathrin Wehrli, Stefanie Gubler, Gianrico Settembrini, Franz Sidler, and Sven Kotlarski

Buildings constructed today will be exposed to climatic changes during their lifetime. Changes in the outdoor climate also affect the indoor climate in buildings where people spend a substantial part of their day. In addition, the building sector significantly contributes to energy demand and greenhouse gas emissions. Therefore, it is vital to design buildings in a way that ensures comfortable indoor temperatures today and in the future while minimizing energy use. For Switzerland, an additional warming of 2-3 degrees Celsius by mid-Century is projected without climate change mitigation. According to the Swiss Climate Scenarios CH2018, heatwaves are set to increase significantly in frequency, intensity and duration, whereas coldwaves will be less common. To adapt to these changes and design buildings optimally for a warmer future climate, climate projections tailored to the needs of the building sector are required.

To serve this demand and together with partners from universities and the industry, MeteoSwiss has created a reference dataset for the future climate for use in building simulations. For this, observations were combined with climate change information from the CH2018 scenarios. The resulting reference data represent the possible future climate at locations across Switzerland. Hourly and physically consistent data for the future for the variables temperature, humidity, wind and solar radiation have been calculated. These represent typical years and warm summer years. The data are available for the near future and the middle of the century and are based on two different greenhouse gas emission scenarios: 'no climate change mitigation' (RCP8.5) and 'concerted climate change mitigation efforts' (RCP2.6). The data are also available for four extra-urban and urban station pairs, which allows evaluating the urban heat island effect.

The described reference data were compiled in close collaboration with users and allows them to design buildings tailored to a warmer climate. Findings from using this data in building simulations allow inferring requirements for the architecture and operation of the buildings and show an increasing demand for cooling systems in the future.

How to cite: Wehrli, K., Gubler, S., Settembrini, G., Sidler, F., and Kotlarski, S.: Deriving future climate reference data for the Swiss building sector, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-395, https://doi.org/10.5194/ems2022-395, 2022.

10:15–10:30
|
EMS2022-653
|
Online presentation
Natalia Korhonen, Azin Velashjerdi Farahani, Juha Jokisalo, Risto Kosonen, Sami Lestinen, and Kirsti Jylhä

According to the current National Building Code of Finland, the room temperature in new residential buildings should not exceed the threshold of 27 °C by more than 150 degree hours per year. The code also demands that new buildings pass the nearly zero-energy requirements for energy performance and conformity. The fulfilment of these requirements in the design phase of the new buildings needs to be verified by dynamic building performance simulation tools that use specific meteorological reference year weather data sets as hourly input. In addition to commercial purposes, such simulation software’s, like the IDA Indoor Climate and Energy (ICE) tool, may be used for education and research.

Here we describe several sets of hourly weather data that have been recently used in Finland as input to IDA-ICE simulations. TRY2020 data sets describe typical present-day weather conditions during twelve months that originate from different years of the period 1989-2018. They were selected based on a standard methodology (ISO 15927-4) with a few modifications. The same 30-year period was used to choose new cooling design days (1% risk) for Finland on the basis of another standard method (ISO 15927-2). HWS2018 data set, in turn, consists of weather observations of heatwave summer 2018.

All the data sets include the following variables: air temperature, relative humidity, global, diffuse and normal direct solar radiation as well as wind speed and direction. While the data sets describing the recent past climate are based on observations, their counterparts corresponding to future climates are based on CMIP5 climate model projections and delta-change methods.

The IDA-ICE tool was used, among others, to simulate the risk of too warm indoor conditions. According to the results, already nowadays during a prolonged heat wave the annual degree hours above 27 °C in apartment buildings without mechanical cooling may be several times the limit of 150. In the future, hotter summers are expected to become more common and the risk of overheating of dwellings will continue to increase. This would cause an increase in the heat-related health and mortality risks. Although solar protection windows and openable windows were found to be effective in reducing indoor temperatures, the results show that active cooling systems will be needed to eliminate the overheating risk.

Ref: Velashjerdi Farahani et al. 2020, https://doi.org/10.3390/app11093972

How to cite: Korhonen, N., Velashjerdi Farahani, A., Jokisalo, J., Kosonen, R., Lestinen, S., and Jylhä, K.: Weather impacts on simulated indoor conditions, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-653, https://doi.org/10.5194/ems2022-653, 2022.

Coffee break
Chairpersons: Ulpu Leijala, Virve Karsisto
11:00–11:15
|
EMS2022-139
|
Online presentation
|
Ren-Yun Li, Liu-Chen Wang, and Tzu-Ping Lin

The architectural design is heavily influenced by the base's climatic conditions, such as window opening, daylighting, ventilation, etc.In the past, weather information obtained from the existing climate data may be too far from the base, too wide a range of coverage, or too wide a difference in geographical features, making it difficult to obtain seasonal information.With the increasing degree of urbanization, the heat island effect has intensified, and considering Taiwan's fluctuating terrain, it is even more important to quickly obtain detailed and accurate environmental background information.The TMY3 standard meteorological year data available in Taiwan has been established for only eight out of 23 counties and cities in Taiwan.

Taiwan's historical climate reconstruction data (TReAD) based on the Taiwan Climate Change Estimation Information and Adaptive Knowledge Platform (TCCIP) of the Ministry of Science and Technology (MOCT).The historical simulated climate data can show the climatic background of a location to some extent after verification by the present study and the Central Meteorological Administration of Meteorology.

Based on the climate background data of the base, this study compares TReAD data to a health examination table: A microclimate health checklist is made to determine the climate background of a base based on the data of various meteorological parameters.A website on Taiwan's Historical Climate Map has also been set up to provide a convenient way for users to access climate information and to use it as a reference for architectural design and urban planning.Furthermore, the site may be expanded to understand the relationships and other applications of the various factors affecting the historical climate.

Downscaling, close-knit climate data will help bring in the various stages of the architectural and urban life cycle as background data, such as open windows and airflow simulations.It is hoped that the link between urban and microclimate information can be further enhanced, creating a more pleasant architectural or urban environment, and effectively enhancing green architecture.

How to cite: Li, R.-Y., Wang, L.-C., and Lin, T.-P.: Establishment and Application of Platform Based on Downscaling Climate Reanalysis Data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-139, https://doi.org/10.5194/ems2022-139, 2022.

11:15–11:30
|
EMS2022-100
|
Online presentation
Cing Chang, Chieh-Yu Chen, and Tzu-Ping Lin

As urbanization expands, the urban heat island (UHI) effect caused by high temperatures is becoming obvious increasingly. Previous researches have shown that urban types and anthropogenic heat emission are the main influencing factors. However, the differences in the types of urban areas lead to different UHI intensity between regions. In this context, more and more studies begin to explore the impact of meteorological data on building energy consumption. The temperature of the air conditioner setting and the time of use account for a large percentage of the total building energy consumption. Cooling degree hours (CDH) is the cumulative value of the temperature difference between the outside air temperature and the base temperature of the cooled room. This study was used the ASHRAE standard cold room temperature of 23°C. Information such as climate data, building morphology, zoning and building electricity collection can be integrated through the Geographic Information System (GIS). This study used existing electricity consumption data and GIS maps to estimate energy consumption information in Taichung, and combine the historical climate reconstruction data of Taiwan from The Taiwan Climate Change Projection and Information Platform Project (TCCIP) to analyze the relationship between urban high temperature and residential energy consumption.

The study area is Taichung City, Taiwan, excluding the mountainous areas with low temperature. This study divided Taichung into 500×500 m2 grids, and analyzed the estimated residential energy consumption with climate data. The highest average temperature in Taiwan is in July, so this study collects temperature data for July 2021 in Taichung. According to the analysis, it can calculate the Taichung average temperature from July is between 26-28°C and the average RH is 80-85%. CDH in the suburbs below 80°C -hour. City center CDH is between 105-180°C -hour. After integrating building form and land use with GIS, the average residential energy consumption of the grid was calculated. The average residential energy consumption of Taichung is about 3,000MWh.Get rid of the grid without building and compare the energy consumption of the building, CDH increases as the energy consumption increases. However, when the energy consumption exceeds 4,000 MWh, the CDH approaches 50°C-hour. Based on this result, the energy consumption below 1,000MWh is the slope maximum. The energy consumption between 1,000 MWh and 3,000 MWh is smaller slope value. The slope of energy consumption above 3,000 MWh tends to be nearly 0, revealing that the energy consumption was less affected by the increasing CDH. The results can be used in future urban energy planning to develop regional improvement strategies for areas with higher energy consumption.

How to cite: Chang, C., Chen, C.-Y., and Lin, T.-P.: Analysis of residential energy consumption based on urban climate patterns, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-100, https://doi.org/10.5194/ems2022-100, 2022.

Extreme weather and nuclear facilities
11:30–11:45
|
EMS2022-646
|
CC
|
Onsite presentation
Kirsti Jylhä, Taru Olsson, Mika Rantanen, Jenni Rauhala, Terhi Laurila, Ulpu Leijala, Anna Luomaranta, Jani Särkkä, and Meri Virman

More than 80 % of total electricity consumption in Finland is covered by domestic production. While 52 % of the Finnish electricity production in 2020 was based on renewable energy sources, 34 % of electricity was generated with nuclear power. Currently there are five nuclear power plant (NPP) units in the country, two in Loviisa in the south and three in Olkiluoto in the west, all along the seashore. Because of the importance of the NPPs to national electricity production, no external or internal events should hamper their normal operation.

As defined by the Finnish Radiation and Nuclear Safety Authority (STUK), overall safety management over the life cycle of an NPP unit requires, among others, probability estimates for external events triggered by exceptional weather events, such as very high and low atmospheric temperatures, high winds including tornadoes and downbursts, rain, snow, hail, freezing rain, lightning, and drought. The probability estimates are used in the safety assessments of existing and new NPP units and in the design of new safety features.

This presentation shows some results from our weather-related studies conducted within the Finnish Research Programme on Nuclear Power Plant Safety (SAFIR2022). A closely related presentation in this session, given by Leijala et al., focuses on sea level research relevant for nuclear safety.

Our current topics include the climatology of convective sea-effect snowfall, heavy precipitation jointly with high sea level, large-scale windstorms and derechoes, i.e., clusters of downbursts.  Changes in the seasonal cycle of sea-effect snowfall have been studied based on reanalysis data (ERA5) and trends in the frequency of the compound precipitation and sea level events using observational data. In addition, we have examined tracks and clustering of large-scale windstorms based on ERA5 and occurrence of derechoes based on the FMI network of meteorological stations and weather radar data.

Even very low annual probabilities of occurrence are of relevance for the NPPs. Therefore, meteorological and climatological research conducted for other applications, including building regulations, land use planning and infrastructure protection, is supportive but typically not sufficient. The topics discussed here have been selected based on feedback from STUK and the Finnish NPP companies. For example, although intense snowfall does not pose a direct threat to the safety systems of the NPPs, it might hamper the normal operation of the support systems and their interface with the environment, e.g., by blocking ventilation air intakes.

References:

Leijala et al.: Examining extreme sea levels for the support of nuclear power plant safety in Finland.

Official Statistics of Finland (OSF): Production of electricity and heat [e-publication].

How to cite: Jylhä, K., Olsson, T., Rantanen, M., Rauhala, J., Laurila, T., Leijala, U., Luomaranta, A., Särkkä, J., and Virman, M.: Exceptional weather and nuclear power plant safety in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-646, https://doi.org/10.5194/ems2022-646, 2022.

11:45–12:00
|
EMS2022-651
|
Onsite presentation
Ulpu Leijala, Milla M. Johansson, Kirsti Jylhä, Marko Laine, Mika Rantanen, Jani Räihä, Olle Räty, and Jani Särkkä

Urbanized low-lying coastal areas become evidently increasingly vulnerable in the future climate, where higher mean sea level and more powerful storm surges are expected. Safe planning and operation particularly of critical coastal infrastructure, such as nuclear power plants, requires careful evaluation of external events due to extreme weather and sea level conditions. Finland is located in the northeast part of the Baltic Sea, where sea level variations are driven by short-lasting phenomena (storm surges, internal oscillations in the Baltic Sea, tides), long-term mean sea level change (global mean sea level, the post-glacial land uplift and the Baltic Sea water balance), and wind waves. In Finland, there are altogether five nuclear power plant units along the coast and approximately one third of electricity production in the country is grounded on nuclear energy.

The Finnish Meteorological Institute has studied weather, climate and sea level hazards potentially posing risks to nuclear power plants since 2007 in several research projects. In this presentation, we will introduce some preliminary results of studies on extreme sea levels on the Finnish coast, which are conducted in the PREDICT project (https://en.ilmatieteenlaitos.fi/predict). Their focus is on the short-term sea level variations, which might be several meters in the Baltic Sea, even if the tides in the region are mostly negligible.

In the first study, Bayesian hierarchical modelling is used to estimate return levels of annual sea level maximum on the Finnish coast and non-stationarity in the related sea level extremes. Our model setup enables sharing information on sea level extremes between the neighboring tide gauge stations. Additionally, it accounts temporal variations in the distribution parameters by incorporating climate indices such as North Atlantic oscillation (NAO) in the model. Preliminary results suggest that hierarchical approach reduces the range of uncertainty in the estimated parameters.

The second study tackles a question “What is the most severe flooding that could occur in the Baltic Sea coast in the present climate if the weather conditions are optimal?”. In this study, effects of low-pressure intensity, speed, direction and point of origin on the sea level extremes is examined by making simulations with numerical sea level model combined with the synthetic cyclones for the Finnish tide gauge locations. Tentative results indicate that the highest sea level extremes on the Finnish coast are caused by large and slowly propagating wind storms.

How to cite: Leijala, U., Johansson, M. M., Jylhä, K., Laine, M., Rantanen, M., Räihä, J., Räty, O., and Särkkä, J.: Examining extreme sea levels for the support of nuclear power plant safety in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-651, https://doi.org/10.5194/ems2022-651, 2022.

Vulnarability and preparedness,
12:00–12:15
|
EMS2022-688
|
Onsite presentation
Dynamic Vulnerability of built infrastructure - How previous storm events effect the damage of residential buildings in Germany.
(withdrawn)
Andreas Trojand, Henning Rust, and Uwe Ulbrich
12:15–12:30
|
EMS2022-648
|
Online presentation
Sara Filla, Tuukka Rautio, Mikko Sane, and Noora Veijalainen

The effects of floods are increasing as a result of global warming. Coastal, marine and watershed flooding, cause damage to people, the environment and economic activity. The damage to the economy is primarily affected by urban areas close to the coast and inland waterways, where the technological and underground infrastructure of buildings contribute to the damage of structures. The floods result in damages of more than EUR 5 million annually in Finland, for example for property owned by municipalities. 

Extreme weather events are intensifying as a result of climate change, as well as rising sea levels, will pose increasing risks to flood-sensitive areas. Consequently, damages and other costs from flooding will also increase. It is therefore important to consider adaptation measures when mapping flood risk areas in order to minimise the damage and costs of flooding. As a solution to this, we used a GIS-based solution that allows us to combine flood risk areas and flooding costs for each sector through an analysis of spatial datasets. 

It is important to highlight how the combination of flood data and statistical data provides added value, e.g., evaluating industry specific impacts at an aggregated level. The focus of the presentation is the importance of accurate spatial data and statistical data in examining the impacts on different regional levels. 

In the KUITTI-project, we used SYKE's Flood dataset, which describes, among other things, the population of the flood hazard area and the floor area of buildings at 250x250meter rasters for different types of floods corresponding to the current climate. 

Rasters have been calculated by overlapping analysis of flood hazard zones and building and apartment registry ranges separately for each flood probability (recurrence time). The location of the rasters are equivalent to that of Statistics Finland's regional division of municipalities. 

We attached the data from Statistics Finland's raster database to SYKE's flood risk raster dataset. In this way, we were able to examine, for example, which industry the potential impact is being applied to. 

This highlights the importance of spatio-temporally high-resolution datasets, which form a base for further regional and national analyses. 

How to cite: Filla, S., Rautio, T., Sane, M., and Veijalainen, N.: Added value of spatially high-resolution flood and statistical data: Starting from Assessment of the cost of inaction regarding climate change, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-648, https://doi.org/10.5194/ems2022-648, 2022.

12:30–12:45
|
EMS2022-575
|
Onsite presentation
Monika Lakatos, Beatrix Izsák, Olivér Szentes, and Zita Bihari

The rainfall intensity for various return periods are commonly used for hydrological design. In this study, we focus on rare, short-term precipitation extremes and related return values which are relevant durations in the planning and operating demands of drainage and sewerage systems in Hungary.

Hungarian Meteorological Service together with the National Water Directorate (Municipal Water Management Department) with the professional assistance of the Hungarian Chamber of Engineers (Water Management and Water Construction Section) developed a user service for design purposes.  The user can download the return levels of the short-term rainfall intensity for the closest meteorological station to the location of the planned object specified with geographical coordinates.

Automatic stations replaced the ombrometer in many places in Hungary, particularly from the late 1990s. The change of the measurement practice do not allow simply merging the data recorded form the registering paper in the past and the recent 10 minutes measurements.   The most intense 5, 10, 20, 30, 60, 180 min sub-totals per rainfall events were recorded from the ombrometer registering paper before atomization, typically until 1993. By contrast, the 10 min precipitation sum from the AWSs are stored in the meteorological database of the Hungarian Meteorological Service from automatization. In order to join together the older and the AWS measurements it was necessary to develop a method to make this possible. Therefore, we downscaled the 10 min data in time. The sampling of the AWSs is one minute, although the 1-minute data are available only for some stations in the digital database. We applied a linear regression model to downscale the 10-minute data for 1 min. After this, we can derive the most intense sub-totals per events from the AWS data as if they have been measured with the ombrometers. Therefore, several station data series can be made longer, thus the confidence intervals of the return level estimates are narrowing, and the quality of the service is improving.

How to cite: Lakatos, M., Izsák, B., Szentes, O., and Bihari, Z.: Analysis of short-term precipitation extremes for design purposes in Hungary, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-575, https://doi.org/10.5194/ems2022-575, 2022.

12:45–13:00
Display time: Thu, 8 Sep 08:00–Fri, 9 Sep 14:00

Posters: Thu, 8 Sep, 14:00–15:30 | b-IT poster area

Chairpersons: fraser ralston, Silvana Di Sabatino
P15
|
EMS2022-412
|
Online presentation
Albert Aparicio, Vicent Altava-Ortiz, Laura Barberia, Antoni Barrera-Escoda, and Anna Rius

Future climate projections for the Mediterranean area point out to an important increase in temperature during this century, independently on the considered emission scenario. This projected increase will have a significant impact on temperature-related climate indices, such as heating and cooling degree-days, and also, the economic costs to maintain climate comfort within buildings, especially for summer. These indices are key outside temperature-based indices to evaluate the energy consumption of buildings. 

In this work, a very high spatial resolution database for heating (HDD) and cooling degree-days (CDD) has been performed from different temperature data: The network of automatic weather station from the Meteorological Service of Catalonia from 2006 to 2015 (138 stations in 32,000 km2), and non-automatic weather station data from 1971 to 2015. It has been taking as a baseline 15 and 18 °C for HDD, and 21 and 25 °C as thresholds for CDD. 

Data from automatic weather stations allow us to compute these indices with a high temporal accuracy (hourly or sub-hourly time scales), but they are only available with a highly-dense network for the last few 15 years. Otherwise, data from non-automatic weather stations allows us to analyse a wider temporal coverage, but only with daily-mean temperature data. Thus, in areas with a complex topography, as it is the case of Catalonia, important differences at annual scale can appear when computing HDD and CDD from daily or hourly-mean values, especially for zones prone to thermal inversion situations. Hourly data leads to increase HDD and CDD for each considered threshold. The differences among them reach annual-mean values higher than 200 ºC-days for heating degree-days and 150 ºC-days for cooling degree-days. 

Taking into account statistically downscaled climate projections at 1-km spatial resolution from 3 IPCC-AR5 global climate simulations forced by RCP4.5 and RCP8.5 emission scenarios, it is expected a significant decrease in HDD for the Pyrenees, up to 2000-2400 ºC-days in 2100, for 15 and 18 ºC thresholds, respectively. Meanwhile, it is projected an important increase in CDD for the Ebro Valley, up to 400-600 ºC-days in 2100, for 25 and 21 ºC thresholds, respectively.

 

How to cite: Aparicio, A., Altava-Ortiz, V., Barberia, L., Barrera-Escoda, A., and Rius, A.: A very high spatial resolution database for heating and cooling degree-days in Catalonia (1971-2100): observations and climate projections., EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-412, https://doi.org/10.5194/ems2022-412, 2022.

Supporters & sponsors