High spatial resolution phenological modelling at continental scales

Understanding phenological variability

escience methodology

High spatial resolution phenological modelling at continental scales

High spatial resolution phenological modelling at continental scales

Understanding phenological variability

Phenology studies the timing of recurring plant and animal biological phases, their causes, and their interrelations. This seasonal timing varies from year to year and from place to place because it is strongly influenced by weather and climatic variability.

Understanding phenological variability is critical to quantify the impact of climate change on the global biogeochemical cycles (e.g. changes in the carbon and water cycles) as well as to manage natural resources (e.g. timing of animal migration), food production (e.g. timing of agricultural activities), public health (e.g. timing of hay fever), and even for tourism (e.g. timing of excursions).

A major obstacle in phenological modeling is the computational intensity and the extreme data size when working at continental scale and with high spatial resolution grids of explanatory variables (e.g. weather and remotely sensed data). We believe that moving our phenological modelling workflows to a modern big-data platform such as Spark will allows us to more easily experiment with novel analytical methods to generate phenological metrics at high spatial resolution (1 km) and to identify phenoregions (i.e. regions with similar phenology) by clustering time series of phenological metrics.

Image by: Chris Devers

Technical Lead Data Management & Analytics Dr. Willem van Hage

Willem´s main research topics in the past 10 years are semantics, augmented sense making, visual analytics, information integration, and text mining.

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eScience Research Engineer Dr. Romulo Pereira Gonçalves

Romulo works on the strategic collaboration between NLeSC and CWI Database group to research, design and implement a data management layer for Big Data problems.

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