It’s spring time! Or isn’t it ?

The transition from the “dead” winter to the “glorious” spring is linked to a striking annual shift in the behavior of ecological and geophysical systems: plants and trees start putting on leaves or open beautiful flowers, butterflies and other insects appear, songbirds tune their melodies, and many animals either look for partners to breed or give birth to a new generation. Researchers from the Faculty ITC of the University of Twente have released the web-mapping platform Greenwave that shows the start of spring at a continental scale.

Poppy field

One of the many features that make the onset of spring an attractive subject is that it happens at different calendar dates from one year to the next. For instance, this year we are having a very early spring with flowers and butterflies already visible in December! This is because the past winter was one of the warmest on record.

For centuries, the onset of spring has attracted considerable attention from both scientists and the general public. Researchers from the Faculty ITC of the University of Twente have just released a web-mapping platform  that shows the start of spring at a continental scale. Long-term and high spatial resolution gridded maps of the so-called Extended Spring Indices (SI-x) can be explored with this platform. The SI-x are a suite of phenological models developed in the USA by using historical observations of cloned lilacs and honeysuckles, and daily temperature values from nearby weather stations [1-4]. 

Cloud computing

Till very recently the SI-x model outputs were only available at specific locations (typically weather stations) or as coarse gridded products (>100 Km). However, the maps prepared by the ITC researchers have a much finer spatial resolution (grid cells of 1 Km) and cover a sufficiently long period (1980-2014) to be able to analyze changes in the timing of spring arrival and to detect deviations from the average spring. Producing these maps was possible thanks to a Google Faculty Award given to Dr. Raul Zurita-Milla to investigate the so-called “green-wave”. This project opened up Google’s cloud computing infrastructure to the ITC researchers who implemented the SI-x models in Google’s Earth Engine. “The overall logic of the models had to be re-worked but now we can process large datasets in a matter of minutes” says Dr. Emma Izquierdo-Verdiguier and “these spring onset maps allow us to investigate the impact of climate change at a local scale” adds Raul Zurita-Milla.

Supporting citizen science

The ITC researchers are working in close collaboration with the USA and Dutch phenological networks, which coordinate the collection of phenological observations by citizen scientists. Phenology is the science that studies the timing of recurrent biological events like leaf out or bird migration. Gustavo Garcia-Chapeton, a PhD student on geovisual analytics, says: “this web-mapping platform will be improved so that we can overlay citizen observations with model outputs”.  This would allow us to study differences and design better models. “With the infrastructure and know-how developed in this project we can now also think of creating phenological models for the various species observed by citizen scientists”, Raul Zurita-Milla explains.

Greenwave website:

Related publications:

  1. Ault, T., Zurita-Milla, R., and Schwartz, M.D. (2015). A Matlab© toolbox for calculating spring indices from daily meteorological data. Computers and Geosciences 83: 46-53.
  2. Schwartz, M. D. 1997.  Spring index models: an approach to connecting satellite and surface phenology. Phenology in seasonal climates I, 23-38.
  3. E. Izquierdo-Verdiguier, R. Zurita-Milla, T. R. Ault, M. D. Schwartz (2015): Using cloud computing to study trends and patterns in the Extended Spring Indices. Third International Conference on Phenology, Kusadasi, Turkey. 2194-5934.
  4. Ault, T. R., M. D. Schwartz, R. Zurita-Milla, J. F. Weltzin, and J. L. Betancourt (2015): Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices. Journal of Climate 28: 8363-8378.

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