Bridging the gap between data and decisions: A review of process-based models for viticulture

2021 ◽  
Vol 193 ◽  
pp. 103209
Author(s):  
Matthew J. Knowling ◽  
Bree Bennett ◽  
Bertram Ostendorf ◽  
Seth Westra ◽  
Rob R. Walker ◽  
...  
Keyword(s):  
2007 ◽  
Vol 34 (9) ◽  
pp. 1449-1460 ◽  
Author(s):  
Rory Quinn ◽  
Wes Forsythe ◽  
Colin Breen ◽  
Donal Boland ◽  
Paul Lane ◽  
...  

2012 ◽  
Vol 9 (4) ◽  
pp. 4543-4594 ◽  
Author(s):  
A. D. McGuire ◽  
T. R. Christensen ◽  
D. Hayes ◽  
A. Heroult ◽  
E. Euskirchen ◽  
...  

Abstract. Although arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO2 and CH4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990–1999 and 2000–2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of the compilation of flux observations and of inversion model results indicate that the annual exchange of CO2 between arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that arctic tundra acted as a sink for atmospheric CO2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 °C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Similarly, the observations and the applications of regional process-based models suggest that CH4 emissions from arctic tundra have increased from the 1990s to 2000s. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that arctic tundra was a sink for atmospheric CO2 of 110 Tg C yr−1 (uncertainty between a sink of 291 Tg C yr−1 and a source of 80 Tg C yr−1) and a source of CH4 to the atmosphere of 19 Tg C yr−1 (uncertainty between sources of 8 and 29 Tg C yr−1). The suite of analyses conducted in this study indicate that it is clearly important to reduce uncertainties in the observations, process-based models, and inversions in order to better understand the degree to which Arctic tundra is influencing atmospheric CO2 and CH4 concentrations. The reduction of uncertainties can be accomplished through (1) the strategic placement of more CO2 and CH4 monitoring stations to reduce uncertainties in inversions, (2) improved observation networks of ground-based measurements of CO2 and CH4 exchange to understand exchange in response to disturbance and across gradients of hydrological variability, and (3) the effective transfer of information from enhanced observation networks into process-based models to improve the simulation of CO2 and CH4 exchange from arctic tundra to the atmosphere.


2013 ◽  
Vol 10 (8) ◽  
pp. 13097-13128 ◽  
Author(s):  
F. Hartig ◽  
C. Dislich ◽  
T. Wiegand ◽  
A. Huth

Abstract. Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.


2020 ◽  
Author(s):  
Martine M. Savard ◽  
Valérie Daux

Abstract. Climatic reconstructions based on tree-ring isotopic series convey substantial information about past conditions prevailing in forested regions of the globe. However, in some cases, the relations between isotopic and climatic records appear unstable over time, generating the ‘isotopic divergences’. Former reviews have thoroughly discussed the divergence concept for tree-ring physical properties, but not for isotopes. Here we want to take stock of the isotopic divergence problem, express concerns and stimulate collaborative work for improving paleoclimatic reconstructions. There are five main causes for divergent parts in isotopic and climatic series. (1) Artefacts due to sampling and data treatment, relevant for dealing with long-series using sub-fossil stems. (2) Stand dynamics, including juvenile effects mostly occurring in the early part of tree-ring series. (3) Rise in atmospheric pCO2, which can directly influence the foliar behaviour. (4) Change of climate, which may modify the isotope-climate causal links. Finally (5), atmospheric pollution, which may alter leaf and root functions. Future paleoclimate research would benefit from interdisciplinary efforts designed to develop further process-based models integrating multi-proxy inputs, so to help identify causes of isotopic divergences and circumvent some of them in inverse applications.


2005 ◽  
Vol 14 (3) ◽  
pp. 482
Author(s):  
M. A. De Zavala ◽  
I. R. Urbieta ◽  
R. Bravo de la Parra ◽  
O. Angulo

2022 ◽  
Vol 314 ◽  
pp. 108802
Author(s):  
Rui Zhang ◽  
Jianhong Lin ◽  
Fucheng Wang ◽  
Nicolas Delpierre ◽  
Koen Kramer ◽  
...  

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