Contributors: Hans Hooyberghs (VITO), Julie Berckmans (VITO), Filip Lefebre (VITO), Koen De Ridder (VITO)
Aedes albopictus
Background
We focus on Aedes albopictus or tiger mosquito (Figure 1). This mosquito occurs in South-East Asia and is an invasive species. Its natural habitat is a small pond of water, which is very similar to the artificial habitat of car tyres that are imported into Europe by shipping containers. Therefore, cities located close to a port area will be confronted more easily with the arrival of these invasive species. The Aedes albopictus is an important vector for the transmission of the two main viral pathogens in Europe, dengue fever and Chikungunya fever.
Figure 1: Aedes albopictus (tiger mosquito)
Suitability of survival
Suitability maps of Aedes albopictus are generated based on the Multi-criteria decision analysis after ECDC (ECDC 2009). This approach considers empirical suitability functions, which link a number of (aggregated) climate variables to the suitability of a habitat for a given vector species,
e.g. for a species to be active a minimum threshold of temperature is required below which the species is not active. These suitability functions are presented by sigmoidal functions with intervals ranging between 0 and 255 (Figure 2).
More specifically, the suitability was reduced to zero when the annual rainfall was lower than 450 mm, and maximum suitability was reached when the annual rainfall was higher than 800 mm. For summer temperatures, the suitability was zero when temperatures were lower than 15 °C and higher than 30 °C, and maximum between 20 °C and 25 °C. For January temperatures, the suitability was zero when temperatures were lower than - 1°C and maximum when temperatures were higher than 3 °C.
The different suitability functions are then entered into a weighted linear combination approach and the results were rescaled to a range between 0 and 100.
Figure 2: Suitability functions for Aedes albopictus (Caminade et al. 2012).
Season length
The season length is defined along a GIS-based seasonal activity model as the time when insect's eggs hatch after winter until when the eggs are no longer hatching (going in diapause) in autumn. The model of Medlock et al. (2006) is used, that is based on the overwintering criterion with weekly temperatures and photoperiods to simulate the weeks of activity. The suitability of the mosquito to survive the winter is based on the January temperature and the annual rainfall. If the January temperature is below 0 °C and the annual rainfall below 500 mm, then it is not suitable on this specific location.
The egg hatching in spring is determined based on two criteria: the photoperiod should be above 11.25 hours and the spring temperature should be above 10.5 °C. The autumn diapause of the mosquito is determined by the autumn temperature that should be below 9.5 °C and the photoperiod that should be below 13.5 hours.
Limitations
It is important to keep in mind that this is not a presence data driven model. The model isn't forced with occurrence data of the vector. This is therefore not a species distribution model. The multicriteria decision analysis and the GIS-based seasonal activity model shows where the species could occur and how long it could be active, based on different parameters for rainfall, temperature and photoperiod described above and is not based on its current distribution and seasonal activity.
Future climate data
Input data
We use a particular product, containing bias-adjusted EURO-CORDEX model output for 2 metre air temperature. This data was developed within the CLIM4ENERGY project (https://climate.copernicus.eu/clim4energy). The bias correction method is called IPSL-CDFT22 using the reference observational dataset of WFDEI (Weedon et al., 2014) for the period of 19792005. The bias correction methodology uses the general Cumulative Distribution Function transform method (CDFt) explained in Vrac et al. (2012). The bias adjustment was done for 4 Regional Climate Models (RCMs) coupled to 1 Gerenal Circulation Model (GCM), and 1 RCM coupled to 4 GCMs, so a total of 8 models or model-combinations at a horizontal resolution of 0.11 x 0.11 degrees under two scenarios RCP4.5 and RCP8.5 (Table 1).
Table 1: The models used within the CLIM4ENERGY project that were bias-corrected using the same method of CDFt
Scenario | Period | RCM | Driving model (GCM) |
RCP4.5/ RCP8.5 | 19710101-21001231 | WRF331F | IPSL-IPSL-CM5A-MR |
ARPEGE51 | CNRM-CERFACS-CNRM-CM5 | ||
HIRHAM5 | ICHEC-EC-EARTH | ||
RACMO22E | ICHEC-EC-EARTH | ||
RCA4 | IPSL-IPSL-CM5A-MR | ||
CNRM-CERFACS-CNRM-CM5 | |||
ICHEC-EC-EARTH | |||
MPI-M-MPI-ESM-LR |
Data processing
Overview
The data processing uses several steps:
- Calculation of daily temperature, daily accumulated precipitation and photoperiod time series
- Computation of yearly indicators
- Climate averages over 30 years
- Ensemble averages and standard deviations
- Regridding to regular latitude-longitude grid
In the following paragraphs, each step is described in more detail.
Calculation of daily temperature time series
At first, the hourly time series are converted to daily mean temperature, daily accumulated precipitation and photoperiod time series for the period 1971 - 2100.
Computation of yearly indicators
In a next step, the two indicators for suitability and season length are calculated for each year of the period 1971 – 2100, for all the RCMs and scenarios under consideration.
The output consists of yearly time series of the two indicators per model and scenario, which will be further processed in the following steps.
Climate averages over 30 years
To retrieve the climate signal from the annual time series, we take a running average over 30 years. The year-labels always refers to the middle of the 30 year period; we thus report the average of the statistics in the period [x – 15, x +15] for year x. Consequently, the results are only available for the 100-year time frame 1986 – 2085.
Ensemble averages
To obtain an ensemble average, we calculate for each year the mean over the eight models under consideration. We assume that all the models have an equal probability and that their results are independent from each other1 and thus apply uniform weights.
Apart from the average, for each year also the standard deviation over the models is calculated. Since the standard deviation has large interannual variations, we further smooth the standard deviation over 20 years. For the period 1986 – 1995 we use the value of 1995, while for the period 2076 – 2085, the value of 2076 is applied.
Regridding
The original projection from the bias-adjusted EURO-CORDEX data is a rotated pole grid with 424 grid cells in the longitudinal direction and 412 grid cells in the latitudinal direction (Christensen et al., 2014). This format is unfortunately unsuitable to be used in the Climate Data Store toolbox, which can only deal with regular longitude-latitude grids for the time being. Therefore, we reproject the ensemble averages and standard deviations bilinearly to a longitude-latitude grid (coordinate system EPSG:4326 / WGS84) with a resolution of 0.1 x 0.1 degrees. The detailed characteristics are given in Table 2.
Table 2: Grid characteristics of the final output grid.
Attribute | Meta data description | Meta data value |
grid_lon_res | longitudinal resolution of regular grid | 0.1 degree |
grid_nlon | number of longitude cells in regular grid | 599 |
grid_lat_res | latitudinal resolution of regular grid | 0.1 degree |
grid_nlat | number of latitude cells in regular grid | 425 |
grid_westb | west bound of regular grid | -24.85 |
grid_eastb | east bound of regular grid | 34.95 |
grid_northb | north bound of regular grid | 72.55 |
grid_southb | south bound of regular grid | 30.05 |
References
Caminade C., J.M. Medlock, E. Ducheyne, K.M. McIntyre, S. Leach, M. Baylis, A.P. Morse (2012): Suitability of European climate for the Asian tiger mosquito Aedes albopictus: recent trends and future scenarios, J. R. Soc. Interface, Vol. 9, 75, pp. 2708-2717. doi: 10.1098/rsif.2012.0138
Christensen, O.B., W.J. Gutowski, G. Nikulin and S. Legutke (2014): CORDEX Archive Design, https://is-enes-data.github.io/cordex_archive_specifications.pdf
ECDC (2009) Development of Aedes albopictus Risk Maps, Technical Report 0905. See https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/0905_TER_Development_of_Aedes_Albopictus_Risk_Maps.pdf
Medlock J.M. , D. Avenell, I. Barrass, S. Leach (2006): Analysis of the potential for survival and seasonal activity of Aedes albopictus in the United Kingdom, J. Vect. Ecol., Vol. 31, pp. 292-304
Vrac, M., P. Drobinski, A. Merlo, M. Herrmann, C. Lavaysse, L. Li and S. Somot (2012): Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., Vol. 12, pp. 2769-2784
Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best and P. Viterbo (2014): The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., Vol. 50, pp. 7505-7514
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