Modelling impacts of drivers on biodiversity and ecosystems

Purpose of this chapter: Explores key issues in modelling impacts of changes in direct drivers on biodiversity and ecosystems; and critically reviews major types of models for generating outputs that are either directly relevant to assessment and decision-support activities, or are required as inputs to subsequent modelling of nature’s benefits to people. Key findings: 1-Models of biodiversity and ecosystem function are critical to our capability to predict and understand responses to environmental change; 2- There is a need to match biodiversity and ecosystem function model development to stakeholder and policy needs; 3- Biodiversity and ecosystem modelling depends heavily on our understanding of ecosystem structure, function and process and on their adequate representation in models; 4- Uncertainty in ecosystem dynamics is inherent in ecosystem modelling.EEA Santa CruzFil: Brotons, Lluís. InForest jru. Creaf-Ctfc; EspañaFil: Christensen, Villy. The University of British Columbia; Canadá.Fil: Ravindranath, N. H. India Center for Sustainable Technologies. Indian Institute of Science; India.Fil: Cao, Mingchang. Keqiang Zhao; China.Fil: Chun, Jung Hwa. National Institute of Forest Science, Division of Forest Ecology; Corea del SurFil: Maury, Olivier. Institut de Recherche pour le Développement (IRD); Francia.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Proença, Vânia. Instituto Superior Tecnico - UNIU Lisboa; Portugal.Fil: Salihoglu, Baris. Middle East Technical University. Institute of Marine Sciences; Turquí

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together

Global Analysis, Interpretation and Modelling: An Earth Systems Modelling Program

The Goal of the GAIM is: To advance the study of the coupled dynamics of the Earth system using as tools both data and models; to develop a strategy for the rapid development, evaluation, and application of comprehensive prognostic models of the Global Biogeochemical Subsystem which could eventually be linked with models of the Physical-Climate Subsystem; to propose, promote, and facilitate experiments with existing models or by linking subcomponent models, especially those associated with IGBP Core Projects and with WCRP efforts. Such experiments would be focused upon resolving interface issues and questions associated with developing an understanding of the prognostic behavior of key processes; to clarify key scientific issues facing the development of Global Biogeochemical Models and the coupling of these models to General Circulation Models; to assist the Intergovernmental Panel on Climate Change (IPCC) process by conducting timely studies that focus upon elucidating important unresolved scientific issues associated with the changing biogeochemical cycles of the planet and upon the role of the biosphere in the physical-climate subsystem, particularly its role in the global hydrological cycle; and to advise the SC-IGBP on progress in developing comprehensive Global Biogeochemical Models and to maintain scientific liaison with the WCRP Steering Group on Global Climate Modelling

IGBP Northern Eurasia Study: Prospectus for an Integrated Global Change Research Project

This report was prepared by scientists representing BAHC, IGAC, and GCTE. It is a prospectus for an integrated hydrological, atmospheric chemical, biogeochemical and ecological global change study in the tundra /boreal region of Northern Eurasia. The unifying theme of the IGBP Northern Eurasia Study is the terrestrial carbon cycle and its controlling factors. Its most important overall objective is to determine how these will alter under the rapidly changing environmental conditions. (Also available in Russian.

Measuring and modelling fAPAR for satellite product validation

This thesis presents a comprehensive approach to satellite Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) product validation. This draws on 3D radiative transfer modelling and metrology to characterise the biases associated with a satellite fAPAR algorithm and the uncertainty associated with fAPAR estimates. This extends existing approaches which tend to assume that the in situ measurement technique produces the same fAPAR quantity as the satellite product. The validation procedure involves creating a closure experiment where every aspect of the satellite product definition and its associated assumptions can be tested from the perspective of the in situ and satellite sensors. The intrinsic differences created by the satellite product assumptions are also assessed, where a new reference is created. This is known as the “true” fAPAR since it is perfectly knowable within the context of the radiative transfer model used. Correction factors between the in situ and satellite-derived fAPAR are created to correct data collected over Wytham Woods. The results indicate that the corrections reduce differences of >10% to near zero. However, the uncertainty estimates for the satellite-derived fAPAR show that it does not meet the requirements given by Global Climate Observing System (GCOS) (≤(10% or 0.05)). The wider implications of the retrieved uncertainties are also presented showing that it is unlikely that the GCOS requirements associated with downstream applications that use satellite fAPAR can be met currently. This work represents an important step forward in the validation of satellitederived fAPAR because it is the first time that the absence of satellite and in situ data uncertainty and traceability, and satellite product definition differences have been addressed. This paves the way for the improvement of satellite fAPAR products because their uncertainties can now be quantified effectively and their validation conducted fairly, meaning there is now a benchmark to base improvements on