PROGRESS. Predictive modelling versus empirical data collision numbers in relation to flight activity in 55 German wind farm seasons

Transcription

1 , Hanna Timmermann, Marc Reichenbach ARSU GmbH, Oldenburg, Germany PROGRESS Predictive modelling versus empirical data collision numbers in relation to flight activity in 55 German wind farm seasons

2 Project Part of PROGRESS (see talk of Grünkorn et al.) Approach: Comparison of: estimated numbers of collisions based on fatality searches vs. collision numbers calculated with Band model Relation between registered flight activity and estimated collision numbers derived from carcass searches Reverse calculation of avoidance rates Validation of the use of the Band model for ordinary onshore windfarm sites in Germany

3 Collision risk modelling Band et al. 2007, SNH 2000 Number of birds colliding per period = Stage 1 The estimated number of birds flying through the rotor swept zone (RSZ) in a given period Stage 2 Calculated likelihood of a bird being hit by the rotor blades as it passes through the RSZ Stage 3 Correction factor for taking into account, among others, avoidance

4 Study design 55 wind farm seasons at 47 different sites (spring and autumn) Flight observations simultaneously with the carcass searches (on the same days). 3 surveys per day (2 VPs) each lasting 60 min in combination with the fatality searches 12 dates in a weekly interval ~36 observation hours per VP (which is equal to the minimum recommendation of SNH) Σ 3,500 observation hours

5 Rotor Swept Zone Three levels of flight height were recorded below, within and above the rotor swept zone (RSZ) (1)

6 How many observations are enough? observed birds within the wind farm areas (all VP sessions added up) of these in RSZ Collision events can hardly be observed! But only 1 observed HIT of a buzzard not even deadly!

7 Number of birds present Number of birds killed

8 Results Geese high avoidance rate (for wintering geese at onshore wind farms it is set at 99.8% - SNH 2014) Raptors flight activity +- homogenous 9

9 Comparison of estimated numbers 568 scanned turbines in 55 wind farm seasons added together Recorded Expected without any avoidance recorded carcass estimated number of collisions upper lower lapwing buzzard red kite best estimate of collision (birds per 12 weeks) assume no avoidance BAND CRM Σ Σ of all wind farm seasons on the basis of observed flight activity Σ lapwing buzzard red kite Σ

10 Back-calculation of avoidance rates Recorded Expected without any avoidance recorded carcass estimated number of collisions upper lower % 16 lapwing buzzard red kite -68% -17% Realistic avoidance rates: best estimate of collision (birds per 12 weeks) assume no avoidance BAND CRM Σ Buzzard heavily underestimated lapwing buzzard red kite 95,0% 9,47 1,92 0,96 97,5% 4,74 0,96 0,48 99,0% 1,89 0,38 0,19 99,9% 0,19 0,04 0,02 Σ Σ

11 Why? Our flight activity data don t represent the actual collision risk Methodological reasons? Check for: Observation effort Day time Height classification 12

12 Observation effort Example VP with highest buzzard activity Decreasing variability within 79,4 ha 35 h Σ 109,36 min flight activity within and RSZ 77% -62% Mean 3,06 3,16 min / observation hour Values of the upper and lower 95% CI calculated as the percentage difference of each relative to the mean Asymptote at 23 hrs Observed (o) and fitted (-) Associated error VP sessions are likely to be inherently variable - BAND CRM don t include variability Variability in utilization reached asymptote with increasing number of observation hours

13 Observation effort VP2 Mean 1,1 min [CI 0 3,03] At a distance of 900 m Asymptote at 17 hrs hours was the minimum observation period for which the calculated probability reaches 5% 175% -100% 37,07 min flight activity within and in RSZ Flight activity of the same species in various parts of a wind farm can be very different and only one season in one year should not be treated as definitive

14 Flight activity per time of day Observation effort MEZ Time of day observation effort [hours] January February March April May June July August September October November December Total ,25 2, , ,00 7,40 8,78 2,13-2, , ,48 38,78 27,05 2,87 1,50 16,82 22, , ,00 1,70 5,22 89,75 93,13 56,10 3,25 1,83 40,23 62,12 23,20-377, ,72 2,33 7,50 81,82 85,62 58,62 5,30 1,28 51,80 101,15 65,82 9,95 473, ,28-9,57 70,98 91,02 66,43 8,65 0,88 43,03 65,03 67,85 16,42 444, ,40 16,95 65,42 92,28 57,37 1,80 1,75 35,40 44,53 43,15 4,88 363, ,03 3,10 9,75 70,73 79,03 46,12 4,25 2,08 45,27 52,70 33,83 5,50 353, ,88 0,50 16,23 56,70 60,60 48,27 7,37 0,67 33,97 65,10 44,68 9,48 348, ,22 0,97 12,85 106,38 108,77 75,48 4,67 0,88 49,15 65,17 51,07 7,72 484, ,70 2,20 12,30 73,70 83,83 43,22 2,42 4,87 31,42 54,03 59,40 5,25 376, ,20 0,93 14,25 14,08 13,80 8,63 1,27 0,25 11,22 23,95 27,33 5,83 124, ,33-7,20 5,37 3,05 1,93 1,15-3,28 3,50 2,63 1,00 30, ,82 0, , , , ,00 Total Maybe thermal circling in the afternoon not sufficiently covered

15 MEZ Time of day observation effort [hours] January February March April May June July August September October November December Total ,25 2, , ,00 7,40 8,78 2,13-2, , ,48 38,78 27,05 2,87 1,50 16,82 22, , ,00 1,70 5,22 89,75 93,13 56,10 3,25 1,83 40,23 62,12 23,20-377, ,72 2,33 7,50 81,82 85,62 58,62 5,30 1,28 51,80 101,15 65,82 9,95 473, ,28-9,57 70,98 91,02 66,43 8,65 0,88 43,03 65,03 67,85 16,42 444, ,40 16,95 65,42 92,28 57,37 1,80 1,75 35,40 44,53 43,15 4,88 363, ,03 3,10 9,75 70,73 79,03 46,12 4,25 2,08 45,27 52,70 33,83 5,50 353, ,88 0,50 16,23 56,70 60,60 48,27 7,37 0,67 33,97 65,10 44,68 9,48 348, ,22 0,97 12,85 106,38 108,77 75,48 4,67 0,88 49,15 65,17 51,07 7,72 484, ,70 2,20 12,30 73,70 83,83 43,22 2,42 4,87 31,42 54,03 59,40 5,25 376, ,20 0,93 14,25 14,08 13,80 8,63 1,27 0,25 11,22 23,95 27,33 5,83 124, ,33-7,20 5,37 3,05 1,93 1,15-3,28 3,50 2,63 1,00 30, ,82 0, , , , ,00 Total Maybe autumn migration period is biased

16 Height classification Compare trainees' height estimates (below, within and above the rotor swept) with those recorded by the GPS and altimeter of a copter and additionally rangefinder data. N correct false 100 HK Σ 148 deviation + 48% 213 HK % 201 HK % ~ 74% correct assignment

17 Conclusion The mismatch between estimated (based on fatality searches) and expected collision numbers (based on CRM) does not or only partly seem to be caused by methodical problems. Results of PROGRESS are in line with the critical discussion of the Band model in the literature Weather is also likely to influence collision risk Main problem seems to be the only vague relation of recordable fligth activity to collision risk

18 Conclusion If a quantitative CRM for ordinary onshore wind farms is not reliable enough, a semi-quantitative or qualitative ecological judgement should be prefered: Identify important areas for vulnerable species Identify spatial functions like regularly used flight paths and feeding areas Take ecological variability into account (change of nest site, presence of conspecifics or competitors, change of agriculture ) Take flight behaviour into account (flight height in different situations, active avoidance ) Don t rely just on numbers (counted or estimated)!

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20 Contact GmbH (ARSU) Escherweg 1 D Oldenburg t: +49 (0) f: +49 (0) m: info@arsu.de w: arsu.de Presented by: M.Sc. Biology weitekamp@arsu.de