Kristin Böttcher

ESLab application to a boreal watershed in southern Finland: preparing for a virtual research environment of ecosystem services.

Abstract We report on preparatory work to develop a virtual laboratory for ecosystem services, ESLab, and demonstrate its pilot application in southern Finland. The themes included in the pilot are related to biodiversity conservation, climate mitigation and eutrophication mitigation. ESLab is a research environment for ecosystem services (ES), which considers ES indicators at different landscape scales: habitats, catchments and municipalities and shares the results by a service that utilizes machine readable interfaces. The study area of the pilot application is situated in the boreal region of southern Finland and covers 14 municipalities and ten catchments including forested, agricultural and nature conservation areas. We present case studies including: present carbon budgets of natural ecosystems; future carbon budgets with and without the removal of harvest residues for bioenergy production; and total phosphorus and nitrogen future loads under climate and agricultural yield and price scenarios. The ESLab allows researchers to present and share the results as visual maps, statistics and graphs. Our further aim is to provide a toolbox of easily accessible virtual services for ES researchers, to illustrate the comprehensive societal consequences of multiple decisions (e.g. concerning land use, fertilisation or harvesting) in a changing environment (climate, deposition).

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle- and deciduous broad-leaf, was obtained for the period 2003–2011 from regional JSBACH (Jena Scheme for Biosphere–Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.

On the Use of In-Memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets

The need to apply complex algorithms on large volumes of data is boosting the development of technological solutions able to satisfy big data analytics needs in Cloud and HPC environments. In this context Ophidia represents a big data analytics framework for eScience offering a cross-domain solution for managing scientific, multi-dimensional data. It also exploits an in-memory-based distributed data storage and provides support for the submission of complex workflows by means of various interfaces compliant to well-known standards. This paper presents some applications of Ophidia for the computation of climate indicators defined in the CLIPC project, the WPS interface used for the submission and the workflow based approach employed.

Enhancing Remotely Sensed Low Resolution Vegetation Data for Assessing Mediterranean Areas Prone to Land Degradation

An enhanced long term remote sensing based data set for Green Vegetation Fraction (GVF) was created for the Mediterranean area. The dataset contains 10-day composites of GVF for the time period 1989–2005 on a scale of 0.01°, covering the Mediterranean basin. The MEDOKADS data set was employed to create mixture triangles of NDVI and surface temperature, of which three abundances, the “vegetated”, “non-vegetated” and “cold” abundance were derived. The vegetated abundance was eventually converted to GVF. Compared to NDVI, clear improvements have been made for GVF, in particular in respect to the mitigation of undesired effects of bad atmospheric conditions. GVF can be derived in an almost fully operational way, which enables it as base data for monitoring vegetation and related purposes. The data has been successfully employed in two case studies on olive farming intensity and rural land abandonment.