On the use of the bayesian approach for the calibration, evaluation and comparison of process-based forest models

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaForest ecosystems have been experiencing fast and abrupt changes in the environmental conditions, that can increase their vulnerability to extreme events such as drought, heat waves, storms, fire. Process-based models can draw inferences about future environmental dynamics, but the reliability and robustness of vegetation models are conditional on their structure and their parametrisation. The main objective of the PhD was to implement and apply modern computational techniques, mainly based on Bayesian statistics, in the context of forest modelling. A variety of case studies was presented, spanning from growth predictions models to soil respiration models and process-based models. The great potential of the Bayesian method for reducing uncertainty in parameters and outputs and model evaluation was shown. Furthermore, a new methodology based on a combination of a Bayesian framework and a global sensitivity analysis was developed, with the aim of identifying strengths and weaknesses of process-based models and to test modifications in model structure. Finally, part of the PhD research focused on reducing the computational load to take full advantage of Bayesian statistics. It was shown how parameter screening impacts model performances and a new methodology for parameter screening, based on canonical correlation analysis, was presente

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe

Bayesian methods for quantifying and reducing uncertainty and error in forest models

Purpose of review: Forest models are tools for analysis and prediction of productivity and other services. Model outputs can only be useful if possible errors in inputs and model structure are recognized. However, errors cannot be quantified directly, making uncertainty inevitable. In this paper, we aim to clarify terminological confusion around the concepts of error and uncertainty and review current methods for addressing uncertainty in forest modelling. Recent findings: Modellers increasingly recognize that all uncertainties—in data, model inputs and model structure—can be represented using probability distributions. This has stimulated the use of Bayesian methods for quantifying and reducing uncertainty and error in models of forests and other vegetation. The Achilles’ heel of Bayesian methods has always been their computational demand, but solutions are being found. Summary: We conclude that future work will likely include (1) more use of Bayesian methods, (2) more use of hierarchical modelling, (3) replacement of model spin-up by Bayesian calibration, (4) more use of ensemble modelling and Bayesian model averaging, (5) new ways to account for model structural error in calibration, (6) better software for Bayesian calibration of complex models, (7) faster Markov chain Monte Carlo algorithms, (8) more use of model emulators, (9) novel uncertainty visualization techniques, (10) more use of graphical modelling and (11) more use of risk analysis

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

Do bacteria thrive when the ocean acidifies? Results from an off-­shore mesocosm study

Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 µatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean

Scaling the effects of warming on metabolism from organisms to ecosystems

Understanding the impact of warming on organisms, communities and ecosystems is a central problem in ecology. Although species responses to warming are well documented, our ability to scale up to predict community and ecosystem properties is limited. Improving understanding of the mechanisms that link patterns and processes over multiple levels of organisation and across spatial and temporal scales promises to enhance our ability to predict whether the biosphere will exacerbate, or mitigate, climate warming. In this thesis, I combine ideas from metabolic theory with a variety of experimental approaches to further our understanding of how warming will impact photosynthesis and respiration across scales. Firstly, I show how phytoplankton can rapidly evolve increased thermal tolerance by downregulating rates of respiration more than photosynthesis. This increased carbon-use efficiency meant that evolved populations allocated more fixed carbon to growth. I then explore how constraints on individual physiology and community size structure influence phytoplankton community metabolism. Using metabolic theory, I link community primary production and respiration to the size- and temperature- dependence of individual physiology and the distribution of abundance and body size. Finally, I show that selection on photosynthetic traits within and across taxa dampens the effects of temperature on ecosystem-level gross primary production in a set of geothermal streams. Across the thermal-gradient, autotrophs from cold streams had higher photosynthetic rates than autotrophs from warm streams. At the ecosystem-level, the temperature-dependence of gross primary productivity was similar to that of organism-level photosynthesis. However, this was due to covariance between biomass and stream temperature; after accounting for the effects of biomass, gross primary productivity was independent of temperature. Collectively, this work emphasises the importance of ecological, evolutionary and physiological mechanisms that shape how metabolism responds to warming over multiple levels of organisation. Incorporating both the direct and indirect effects of warming on metabolism into predictions of the biosphere to climate futures should be considered a priority

Constraining the carbon budgets of croplands with Earth observation data

Cropland management practices have traditionally focused on maximising the production of food, feed and fibre. However, croplands also provide valuable regulating ecosystem services, including carbon (C) storage in soil and biomass. Consequently, management impacts the extents to which croplands act as sources or sinks of atmospheric carbon dioxide (CO2). And so, reliable information on cropland ecosystem C fluxes and yields are essential for policy-makers concerned with climate change mitigation and food security. Eddy-covariance (EC) flux towers can provide observations of net ecosystem exchanges (NEE) of CO2 within croplands, however the tower sites are temporally and spatially sparse. Process-based crop models simulate the key biophysical mechanisms within cropland ecosystems, including the management impacts, crop cultivar, soil and climate on crop C dynamics. The models are therefore a powerful tool for diagnosing and forecasting C fluxes and yield. However, crop model spatial upscaling is often limited by input data (including meteorological drivers and management), parameter uncertainty and model complexity. Earth observation (EO) sensors can provide regular estimates of crop condition over large extents. Therefore, EO data can be used within data assimilation (DA) schemes to parameterise and constrain models. Research presented in this thesis explores the key challenges associated with crop model upscaling. First, fine-scale (20-50 m) EO-derived data, from optical and radar sensors, is assimilated into the Soil-Plant-Atmosphere crop (SPAc) model. Assimilating all EO data enhanced the simulation of daily C exchanges at multiple European crop sites. However, the individually assimilation of radar EO data (as opposed to combined with optical data) resulted in larger improvements in the C fluxes simulation. Second, the impacts of reduced model complexity and driver resolution on crop photosynthesis estimates are investigated. The simplified Aggregated Canopy Model (ACM) – estimating daily photosynthesis using coarse-scale (daily) drivers – was calibrated using the detailed SPAc model, which simulates leaf to canopy processes at half-hourly time-steps. The calibrated ACM photosynthesis had a high agreement with SPAc and local EC estimates. Third, a model-data fusion framework was evaluated for multi-annual and regional-scale estimation of UK wheat yields. Aggregated model yield estimates were negatively biased when compared to official statistics. Coarse-scale (1 km) EO data was also used to constrain the model simulation of canopy development, which was successful in reducing the biases in the yield estimates. And fourth, EO spatial and temporal resolution requirements for crop growth monitoring at UK field-scales was investigated. Errors due to spatial resolution are quantified by sampling aggregated fine scale EO data on a per-field basis; whereas temporal resolution error analysis involved re-sampling model estimates to mimic the observational frequencies of current EO sensors and likely cloud cover. A minimum EO spatial resolution of around 165 m is required to resolve the field-scale detail. Monitoring crop growth using EO sensors with a 26-day temporal resolution results in a mean error of 5%; however, accounting for likely cloud cover increases this error to 63%

Forests and Carbon: A Synthesis of Science, Management, and Policy for Carbon Sequestration in Forests

The goal of this volume is to provide guidance for land managers and policymakers seeking to understand the complex science and policy of forest carbon as it relates to tangible problems of forest management and the more abstract problems of addressing drivers of deforestation and negotiating policy frameworks for reducing CO2 emissions from forests. It is the culmination of three graduate seminars at the Yale School of Forestry & Environmental Studies focused on carbon sequestration in forest ecosystems and their role in addressing climate change