Report on an intercomparison study of modelled, Europe-wide forest fire risk for present day conditions

Transcription

1 Contract number GOCE-CT RT6/WP6.2 - Linking impact models to probabilistic scenarios of climate Deliverable D6.9 Report on an intercomparison study of modelled, Europe-wide forest fire risk for present day conditions C. Giannakopoulos*, P.LeSager, E. Kostopoulou National Observatory of Athens Greece A. Vajda, A. Venäläinen Finnish Meteorological Institute Finland Responsible partner: National Observatory of Athens (NOA) * cgiannak@meteo.noa.gr

2 1- Introduction Several fire indices have been used to estimate forest fire risk. However, their construction varies widely from one index to another, reflecting different approaches. In addition, the reliability of an index may depend on the region where it is applied, since indices or their readings are usually fine tuned for specific regions of interest. In the present study, two indices are considered: the Canadian Fire Weather Index (FWI) and the Finish Forest Fire Index (FFI). The Canadian FWI depends on temperature, precipitation, relative humidity and wind measurements, while the Finnish FFI relies on potential evaporation, precipitation and snow coverage. The two indices 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 The performance of the indices is also estimated in boreal forest environments. Daily forest fire indices (FFI and FWI) have been computed for three selected locations in Finland over the fire season (April-September). 2- Brief description of the fire indices The Canadian Fire Weather Index (FWI) is based on weather readings taken at noon standard time and rates fire danger at the mid afternoon peak from 2:00 4:00 pm. Meteorological variables required are: Air temperature (in the shade) Relative Humidity (in the shade) Wind speed (at 10 m above ground, averaged over at least 10 minutes) Rainfall (for the previous 24 hours) The FWI System consists of six components: three fuel moisture codes (Fine Fuel 2

3 Moisture Code, Duff Moisture Code, Drought Code) and three fire behaviour indices (Initial Spread index, Build Up Index, Fire Weather Index). Calculation of the index requires previous day records of the fuel moisture codes. FWI is divided into four fire danger classes: o Low 0 7 o Medium 8 16 o High l7 31 o Extreme > 32 The calculation of the Finnish Forest Fire Index (FFI) is based on surface moisture estimation and requires the following input: Potential evaporation from 24-hours centred on time of calculation Accumulated precipitation for 24 hours Flag specifying the presence or absence of snow cover Previous day records of the above are required to compute the index. FFI is divided into three fire danger classes: o Low 1 4 o Medium 4 5 o High > 5 It is noteworthy that both indices reflect similar weather conditions, since relative humidity, temperature and wind can be combined to determine evaporation (Singh and Xu, 1997). Both indices depend on previous day conditions regarding one or more of their components and they both define fire danger classes. 3- Correlation between fire indices in the Mediterranean A subset of ERA-40 reanalyses meteorological data is used to compare FWI and FFI through correlation on a wide domain. The subset of data is centred over the Mediterranean region, on a 1 by 1 grid, and consists of 6-hourly data of temperature, 3

4 precipitation, evaporation and wind for the year Daily values of the variables have been utilised to compute the fire indices. Relative humidity required for computing FWI is estimated by the Romanenko equation as suggested by Singh and Xu (1997). No snow coverage has been considered when computing FFI. In all following correlations, the sea locations have been neglected. Considering all locations and all days, the average correlation coefficient (r) between FFI and FWI equals 0.75, reflecting high correlation between the two indices. Figure 1 shows the daily correlations between the two indices for the entire region. The noticeable positive trend might be due to differences in the spin up applied to index computation (one year for FWI, none for FFI) and suggest that the first 100 days of the year show biased lowered correlations. Preliminary investigation showed no dependence of FFI on the spin up period, but further analysis is needed to confirm this statement. Moreover, the particularly low fire risk in winter possibly makes the correlation unreliable. Figure 1. Daily correlations between FWI and FFI for the Mediterranean region for the year

5 To get a clearer picture, a spatial analysis was also performed. Figure 2 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. This is particularly evident in Figure 3, which shows the local r between danger classes derived from both indices. Correlation is undefined in areas with no fire risk all year around, and no colour shading is shown. Figure 2. Correlation coefficients between time series of FFI and FWI at each location of the data set. Top: full year, bottom: summer 5

6 Figure 3. As in Figure 2 only here the time series of danger classes derived from FFI and FWI are presented Figure 4 shows the time series at two different locations, one with and one without fire risk. The time series of the indices in the first graph (Figure 4a) are characterised by similar behaviour as opposed to the second graph (Figure 4b), which suggests that in cases with very low fire risk, a spin up may be needed when computing FFI. These findings confirm that regions (or time period) with low fire risk show low correlation (if any) between FWI and FFI. The lack of a spin up period when computing FFI lowers even more the correlation. 6

7 (a) (b) Figure 4. Typical time series of FFI and FWI in regions with (a) and without (b) fire risk 7

8 3.1 - Comparison of Fire Indices against observations In this section, regional fire observations (burnt areas, number of fires) from 15 Italian stations (Figure 5) are compared with fire indices derived from meteorological observations at the same locations. Data are available for the period Figure 5. Locations of the studied stations in Italy Monthly correlations were estimated between real data and the fire indices and presented in Figure 6. Blue lines show the correlation between the two indices and confirm the findings from previous section. Note that a spinning is automatically applied to FFI, since more than one year of data is available. The trend detected in Figure1 is not shown in the graphs of Figure 6. The black/red lines represent the correlation between FWI/FFI with the number of fires. As expected, the correlations are quite low revealing the large uncertainties involved in fire predictions. None of the indices performs particularly well at predicting fire, since high fire risk is not always associated with fire occurrence. External factors, such as human behaviour (land use policy, fire suppression strategy etc) 8

9 are not taken into account in the indices computation. Similar results are obtained for the monthly correlation between areas burnt and fire indices (Appendix: Figure 13). Figure 7 shows that areas burnt and fire indices are not correlated in any year. Annual correlations for the number of fires are also low (Appendix: Figure 14). Figure 6. Monthly correlations between number of fires and fire indices (FWI: black, FFI: red) and between the two fire indices (blue) for each of the 15 Italian stations 9

10 Figure 7. Annual correlations between areas burnt and fire indices (FWI: black, FFI: red) and between the two fire indices (blue) for each available year Fire Indices Comparison: Determining Fire Seasons Based on the Italian data, the skill of both indices was assessed in terms of their ability to define fire seasons. According to Good et al. (2005) and Moriondo et al. (2006), defining fire seasons with FWI is more robust than with temperature and allows avoiding false alarm. The method consists of: Calculation of 7-day running averages to smooth daily variability Use FWI=15 as threshold for beginning and end of the season Defining the beginning of fire season as 2 consecutive weeks with FWI>15 (start- 10

11 period) Defining the end of fire season as 4 consecutive weeks with FWI<15 (end-period) The four parameters (threshold, running average period, start- and end-period) were further analysed so as to become applicable for use in FFI as well. Figure 8 provides the fire seasons according to both indices at station code1544, and for a set of season parameters. The y-axis represents day numbers. A threshold between 4.5 and 5 for FFI gives results similar to FWI (as expected by the danger classes). A running average period between 5 and 7 days seems to be more reliable. Under these conditions, the FFIderived season begins too early in the early 90 s and around Both indices determine the end of the season at the end of the year This is definitively too late, and the period used to define the end of the season may need to be shorter. Figure 8. Fire seasons according to FWI (black) and FFI (grey) for location code In all cases, start- and end-periods are 14 and 28 days respectively. In each column, a different running average period is applied to both FWI and FFI. In each row, a different threshold for FFI is used to define the fire period (it is fixed to 15 for FWI) 11

12 Figures 15 to 17 in the Appendix provide some preliminary results as far as the parameters of the fire season are concerned. They suggest shorter end-period (approx. 21 days instead of 28) and longer start-period (approx. 21 days instead of 14) might provide more reliable seasons. It becomes notable by comparing Figures16 and 17 that the season end depends on the season beginning. Figure 9 shows the fire season at a different station, where FFI performs better than FWI at defining fire season. FWI cannot identify any fire season during the period On the other hand, the fire season around 2000 is too long with FFI (the full year!) something that FWI does not show. Additional investigation is needed to understand the reasons for such differences in this station. The dependence on location calls for a systematic analysis of fire season using larger datasets such as the ERA-40 reanalysis data. Figure 9. As in Figure 8 only here the results for location code 1531 are presented 12

13 4- Temporal and spatial variation of fire indices in Finland. Daily forest fire indices (FFI and FWI) have been computed for three locations in Finland (Figure 10). The research focused upon the fire season (April-September) over the period for Helsinki-Vantaa and Jyväskylä and over for Sodankylä. 70 o N Sodankylä Jyväskylä 60 o N Helsinki Vantaa 10 o E 20 o E 30 o E 40 o E Figure 10. Locations of the studied stations in Finland Considering the occurrence of days with forest fire index value 3 during the period (Helsinki-Vantaa, Jyväskylä) and (Sodankylä) the two indices indicate roughly similar occurrence frequency with a gradual decrease in dissimilarities and in fire risk conditions northwards: between 37-45% of total number of days in fire season (April-September) in Helsinki-Vantaa and 20-21% in Sodankylä. This spatial pattern of fire risk can be explained by the decrease of potential evaporation northward and by the decrease in the length of fire period (due to the longer duration of snow cover) in Lapland compared with southern Finland. The annual variation of differences between the FFI and FWI values was considerable at each location (Figure 11). The occurrence of both index values above the defined 13

14 threshold (moderately dry for FFI and high risk for FWI) coincides in most of the cases (56-66% of total occurrence). The number of days when only the FFI indicate decreased moisture conditions is 23-25% of total count. The number of days with high fire risk indicated only by FWI varies considerably among the three locations tending to increase northwards (8.9% for Helsinki Vantaa and 20.7% for Sodankylä). On a monthly basis the values indicate good resemblance of the two fire risk rating indices in summer season (May-August), while during April the occurrence of FWI high fire risk exceed significantly that of FFI moderately dry conditions for all the studied stations. The apparent south-to-north as well as the monthly variation in the occurrence of the high FFI and FWI values suggests a slight overestimation of high and extreme fire risk for humid weather condition by the FWI index, i.e. in Finnish Lapland, particularly in early spring and autumn period. Helsinki-Vantaa Jyväskylä only FWI only FFI FWI and FFI only FWI only FFI FWI and FFI No. of cases No. of cases Year Year Sodankylä only FWI only FFI FWI and FFI No. of cases Year Figure 11. The temporal variation of occurrence of days with FWI indicating high and extreme fire risk and FFI 3. 14

15 The daily variation of FFI and FWI was analysed in comparison with the variation of fire risk defining meteorological parameters for the 1997 fire season at Helsinki-Vantaa station (Figure 12). In this case May represents the threshold, where FFI and FWI values reach the high risk and extreme risk classes in most days, indicating the start of fire season. In general, 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. In early April, shortly after the snowmelt, the soil moisture is high and it is partly enhanced by the frequent rainfall events and the low daily mean temperature (Figure 12a, b). Such meteorological conditions are usually associated with low fire danger. It seems that the Canadian method has some deficiencies in predicting fire risk for this period, with considerable variations between low and high fire risk classes. Furthermore the FWI system shows a systematic overestimation of extreme fire risk compared to the FFI system: during the 1997 fire period none of extreme fire risk (FFI=6) and only 22 cases of high fire risk (FFI=5) were predicted by the Finnish index, while the Canadian index predicted extreme fire risk in 34 cases (Figure 12c, e). During the studied period, the Fine Fuel Moisture Code (FFMC) values ranges from 27.3 to 93, with a mean of About 47% of observed values were ranged in the high and extreme classes (Figure 12f). The relationship between the FFMC and flammability is highly nonlinear, thus small variations in FFMC at the high end of the scale represent important variations in flammability (Harrington et al., 1983). This suggests that almost half of days during the fire season demonstrate conditions suitable for fire occurrence. The Drought Code (DC) responds slowly to environmental changes. In spring, its initial value depends on the intensity of precipitation occurred during the preceding autumn and winter (Dimitrakopoulos and Bemmerzuk, 1998), and the value is at its lowest (DC=4). As the season progresses DC begins rising, and reaches its largest values in autumn (DC=333), which range within the high risk class (Figure 12g). 15

16 35 a Precipitation 25 b Temperature Precipitation (mm) Temperature ( C) /4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date -5 97/4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date 6 c FFI 100 d Relative humidity Index value /4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date Relative humidity (%) /4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date 4 e FWI 100 f f FFMC 3 80 Index value 2 1 Code value /4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date 20 97/4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date Code value g g DC /4/1 97/4/21 97/5/11 97/5/31 97/6/20 97/7/10 97/7/30 97/8/19 97/9/8 97/9/28 Date Figure12. The variation of daily total precipitation (a), mean air temperature (b), Finnish Forest Fire Index (c), relative humidity (d), Canadian Fire Weather index (e), Forest Fuel Moisture Code (f) and Drought Code (g) at Helsinki-Vantaa for the period April- September

17 The spatial distribution tendencies of the daily FFI and FWI indices are generally similar (Figure18 Appendix), although several differences can be observed in local features. For example, slightly overestimated fire risk by FFI compared to the FWI values is observed during June in Lapland. In general, significant deviations occur in prediction of the moderate fire danger conditions i.e. the regions marked with moderate risk by FWI correspond to moderately wet conditions (FFI=3) by the Finnish method that indicates an underestimation of fire risk by FFI. 5- Conclusions and future plans In general, FWI and FFI determine a fairly similar fire risk for a set of weather readings ( r approx. 0.7). Higher correlations are found especially for locations under significant fire risk. The results improve for the lower values of fire risk if a spinning period is used in the computation of FFI. Similar results were obtained for a boreal forest environment. 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. It needs to be highlighted that fire indices do not constitute fire predictors, however they can be used to define the fire season as an advanced alternative to temperature-based determination. It is found that fire-season determination based on indices is satisfactory but needs to be fine-tuned. Namely, cautious adjustment of the start and end periods is required. Neither FFI nor FWI provided a robust definition at any location. Hence, a systematic study using ERA-40 data is planned to be the topic of a future study, where FFI, FWI, and particularly their components will be investigated in relation to weather data. 6- Acknowledgements Special thanks go to Drs Marco Bindi and Marco Moriondo from the University of Florence, Italy for the provision of Italian stations forest fire data. 17

18 References Dimitrakopoulos A, Bemmerzuk A, Evaluation of the Canadian Forest Fire Danger Rating System (CFFDRS) and the Keetch-Byram Index (KBDI) in the Mediterranean Climate of Greece. III International Conference on Forest Fire Research. 14 th Conference on Fire and Forest Meteorology. Vol Y Good P, Moriondo M, Giannakopoulos C, Bindi M, The meteorological conditions associated with extreme fire risk in Italy and Greece: relevance to climate model studies. Climate Research, submitted. Harrington J, Flannigan M, Van Wagner C, A study of the relation of components of the Fire Weather Index to monthly provincial area burned by wildfire in Canada Canadian Forest Service, Chalk River, Ontario. Information Report PI-X-25. Moriondo M, Good P, Durao R, Bindi M, Giannakopoulos C, and Corte Real J, Potential impact of climate change on forest fire risk in Mediterranean area, Climate Research, Special issue 13, Vol. 31, Singh V, Xu C, Sensitivity of mass transfer-based evaporation equations to errors in daily and monthly input data, Hydrological processes, 11,

19 Appendix Figure13. Monthly correlations between areas burnt and fire indices (FWI: black, FFI: red) and between the two fire indices (blue) for each of the 15 Italian stations 19

20 Figure 14. Annual correlations between number of fires and fire indices (FWI: black, FFI: red) and between the two fire indices (blue) for each available year 20

21 Figure 15. As in Figure 8 here with a smaller period to define the end of the season (14 instead of 28 days) 21

22 Figure 16. As in Figure 8 here with a smaller period to define the end of the season (21 instead of 28 days) 22

23 Figure 17. As in Figure 16 here with a longer period to define the beginning of the season (21 instead of 14 days) 23

24 very dry dry moderately dry moderately wet wet very wet Figure 18. Spatial variation of the Finnish Forest Fire Index values (a) and the Canadian Fire Weather Index values (b) for the period 16-25th of June,

25 extreme high moderate low Figure 18. (continued) 25