1 / 10

Dr. Christos Giannakopoulos Dr. Philippe LeSager Dr. Effie Kostopoulou Dr. Basil Psiloglou

Work progress in WP6.2 Linking impact models to probabilistic scenarios of climate. Dr. Christos Giannakopoulos Dr. Philippe LeSager Dr. Effie Kostopoulou Dr. Basil Psiloglou NATIONAL OBSERVATORY OF ATHENS (NOA). Fire risk model study intercomparison for Europe.

inara
Télécharger la présentation

Dr. Christos Giannakopoulos Dr. Philippe LeSager Dr. Effie Kostopoulou Dr. Basil Psiloglou

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Work progress in WP6.2 Linking impact models to probabilistic scenarios of climate Dr. Christos Giannakopoulos Dr. Philippe LeSager Dr. Effie Kostopoulou Dr. Basil Psiloglou NATIONAL OBSERVATORY OF ATHENS (NOA)

  2. Fire risk model study intercomparison for Europe The two models are compared through inter-correlation and their ability to define the beginning and end of the fire season is assessed. The indices skills are evaluated over the Mediterranean region using real fire observations from Italy for the period 1984-2001. The performance of the indices is also estimated in boreal forest environments in Finland. Aim: To compare the ability of two fire risk models (Canadian and Finnish) to simulate the forest fire riask season across Europe (deliverable report D6.9). National Observatory of Athens

  3. Canadian .vs. Finnish fire indices • The Canadian Fire Weather Index (FWI) noon values of: • Air temperature (shade) • Relative Humidity • Wind speed (at 10 m) • Rainfall (for the previous 24 hours) • FWI is divided into four fire danger classes: • Low 0 – 7 • Medium 8 – 16 • High 17 – 31 • Extreme > 32 • The Finnish Forest Fire Index (FFI) is based on surface moisture estimation and requires: • Potential evaporation from 24-hours • Accumulated 24h rainfall • Flag specifying the presence or absence of snow. • FFI is divided into three fire danger classes: • o Low 1 – 4 • o Medium 4 – 5 • o High > 5 National Observatory of Athens

  4. Canadian .vs. Finnish fire indices Figure to the right shows the local correlation coefficients (FFI/FWI) for two time periods: full year and summer. It shows that correlation decreases where there is no fire risk, and increases in regions known for fire occurrences. National Observatory of Athens

  5. Determining fire season: FWI.vs.FFI (south) • •Calculation of 7-day running averages to smooth daily variability • Defining the beginning of fire season as 2 consecutive weeks with FWI>15. • •Defining the end of fire season as 4 consecutive weeks with FWI<15. • A threshold between 4.5 and 5 for FFI gives results similar to FWI. • The FFI-derived season begins too early in the early 90’s and around 2000. Both indices determine the end of the season at the end of the year 1996. • The fire season is too long with FFI (nearly the full year) something that FWI does not show. Fire seasons according to FWI (black) and FFI (grey) for a location in NW Italy. National Observatory of Athens

  6. Determining fire season: FWI.vs.FFI (north) •FWI shows a more pronounced, quick response to the variation of precipitation events, whilst FFI indicate smoother changes. The largest deviations between the two indices were observed particularly at the beginning (April) and at the end (September) of the study period. FWI system shows a systematic overestimation of extreme fire risk compared to the FFI system. National Observatory of Athens

  7. Conclusions • In general, FWI and FFI determine a fairly similar fire risk. Higher correlations are found especially for locations under significant fire risk. • Both indices show similar features especially during summer, but some deviations are typical during early spring and autumn, as FWI probably overestimates the fire risk. • The comparison with meteorological parameters revealed a quick response of this index to environmental changes, especially to rainfall events. National Observatory of Athens

  8. NOA workplan for heat stress/health • Determine appropriate indices for population stress- Obtain mortality and hospital admission data and classify in cardiovascular and respiratory episodes (at least for Athens) – in progress- Obtain pollution data and relate to met variables for Med cities- in progress • - Determine future health risk using climate model data. National Observatory of Athens

  9. An example of heat stress for Athens • Used 9 years of met data (1993-2001)- heat index takes into account effects of temp and humidity- CAUTION days: Fatigue possible, sunstroke/heat exhaustion possible with prolonged activity National Observatory of Athens

  10. An example of hospital admissions for Athens National Observatory of Athens

More Related