defi pour l'observation de la terre et pour la modelisation du systeme terrestre Wolfgang Cramer Erdsystemanalyse, Potsdam-Institut für Klimafolgenforschung (PIK), Institut für Geoökologie, Universität Potsdam, Centre Européen de Recherche et d Enseignement des Géosciences de l Environnement (CEREGE), Aix-en-Provence
defi pour l'observation de la terre et pour la modelisation du systeme Avoiding dangerous climate climate change terrestre science-based policy support from observations and modelling From land cover change to land use change Earth system implications of land use change
Avoiding dangerous climate climate change science-based policy support from observations Policy and modelling support for safeguarding the global environment presently concerns Earth System Interactions more than ever: Impacts of climate change on human lifesupport systems on land and in the ocean Feedbacks from changing land (and ocean) use to the atmosphere and climate Interactions between land and ocean
Avoiding dangerous climate climate change science-based policy support from observations Policy support must be and modelling based on international conventions science-based & peer-reviewed use the best available information This demands an assessment process (e.g., IPCC, Millennium Ecosystem Assessment) which takes into account findings from Earth Observation, process modelling and other methods.
From land cover change to land use change
40% of the ice-free land surface is agriculture of grassland Croplands + statistiques Pastures and rangelands Ramankutty et al. 2008
Effects of historical land cover change biogeophysical biogeochemical combined
Earth system implications of land use change Nitrogen fertilizer application Methane emissions from rice paddies Deforestation and forest degradation
Fertilization azotée Importance des émissions de N 2 O sur les zones d'agriculture intensive (Phil Potter) Le capteur SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) sur ENVISAT permet des mesures de la colonne de N 2 O (protoxyde d'azote ou oxyde nitreux) Les études sur la qualité des données sont encore insuffisamment poussées pour pouvoir interpréter les séries temporelles (Dils et al. 2006). Incertitude estimée : 30% (Buchwitz et al. 2004)
Méthane Simulation de la concentration de Méthane par le modèle de chimie atmosphérique KNMI/TM3. Ce modèle considère l'importante émission de méthane des rizières inondées SCIAMACHY mesure la forte concentration de Méthane dans l'atmosphère au dessus des rizières au plus fort de la production rizicole. (Buchwitz et al. 2004)
Estimation of future methane emissions from rice paddies Estimation of (future) climate impacts from rice paddies requires not only the spatial extent, but also number of crop cycles per season flooding calendar selection of cultivars (Monfreda et al, 2008)
The Rice Monitoring Project ESA- NRSCC Dragon Ancillary& ground data Weather data ENVISAT ASAR & MERIS, VGT Remote Sensing Methods ENVISAT SCIAMACHY Methane Measurement Rice Parameters Rice Mapping Local & regional Rice and Biochemistry Models (Thuy Le Toan) Yield estimation Rice Production Methane & CO 2 Fluxes Rice in Carbon cycle
Projekt ESA-NRSCC Dragon - Rice Combination of different satellite systems: Wheat Rice SPOT-VGT -> seasonality (crop cycles) Rice Rice ENVISAT-SAR -> mapping of different cultivars flooding period Track 82: 25 - WSM VV (Alexandre Bouvet)
Other cropping system aspects (beyond greenhouse gas emission) A combination of Earth observation and modelling can contribute to better monitoring and management of irrigation management of crop residues (for biofuel use or for soil carbon storage) interaction between agriculture and coastal water management
Important changes during last decades Population (billions) 6 5 4 3 2 1 0 Population Arable areas Irrigated areas Fertilizer N Wheat yield Rice yield 1860 1900 1940 1980 2000 Year 1.8 0 1.6 0 1.4 0 1.2 0 1.0 0.8 0.6 0.4 0.2 0 Arable or irrigated area (10 9 ha) 80 60 40 20 0 Fertilizer N (10 6 t) 3.6 0 3.2 0 2.8 0 2.4 0 2.0 1.6 1.2 0.8 0.4 0 World average wheat and rice yields (t.ha -1 )
Deforestation Continues with high rates in many countries Leads to major biodiversity loss, soil degradation and loss of biodiversity Still no consistent monitoring scheme worldwide
Minnesota Kansas Germany Bolivia Thailand Brazil NASA, ASTER Team
Global Forest ressource Monitoring The JRC TREES-3 project Global sample of interpreted satellite «imagettes» for forest change estimates from 1990 to 2005 (Eva et al.) Systematic sampling as option for future monitoring -Samples are 10 km by 10 km with 5 km buffer -Data source for monitoring forest changes is 30m resolution satellite data (Landsat) -Reference dates are (1980) 1990 2000 and 2005 Bassin du Congo, red points = hot spots change
Reduced Emissions from Deforestation and Degradation (REDD) If developing countries are to be compensated for their efforts to reduce deforestation, then a credible monitoring instrument is required
Defi for earth observation and modelling? Remote sensing technology development is important, but not the only aspect (high frequency, high spatial and spectral resolution...) Ground-based observations (e.g., agricultural and forest statistics) are equally necessary The connection between space- and ground-based observations occurs through suitable process models Observation-based process models allow for improved scenario analysis and therefore better policy support
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