Using dynamic vegetation models to simulate plant range shifts

Peer reviewedPublisher PD

Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO2 mixing ratio towers and chambers

Herramientas de simulación para el apoyo de toma de decisiones en la gestión forestal adaptativa en Europa

In forest management there is a tendency towards measuring less and simulating more. In this context the development of reliable, user friendly forest simulators has become economically relevant. The objective of this perspective paper is to highlight the recent trends in forest simulation and to identify the remaining challenges to make forest simulation a reliable tool for forest policy and management. Experiences with forest simulators for various purposes in different geographical contexts illustrate how the important challenges of forest decision support can be addressed through flexible customization for different end-user categories, offering spatially explicit approaches at the landscape scale, and integrating empirical and mechanistic models in hybrid and bayesian simulation approaches. Recent development trends in forest simulation for decision support are mainly related to the ever increasing calculation speed and capacity of computers, facilitating the development of robust tools with comfortable user interface and realistic functions and options. Another trend is the combination of simulation tools with optimization and choice algorithms fading away the difference between simulators and decision support systems. The remaining challenges are basically in the high expectations of stakeholders concerning the ability of simulators to predict a range of outcomes in terms of ecosystem services and sustainability indicators, as well as the quality of their outcome in terms of output credibility to stakeholders. Need for accepted and realistic model validation and verification methods preferably using empirical data is crucial in this matter.En la planificación de la gestión forestal existe la tendencia a medir menos y similar más. En este contexto, el desarrollo de simuladores forestales es económicamente relevante para el gestor. El objetivo de este artículo es el de discutir y enfatizar tendencias en el ámbito de la simulación forestal e identificar retos importantes para que la simulación forestal sea una herramienta fiable en el proceso decisorio de la planificación forestal y en el de desarrollo de políticas. Varios ejemplos de simuladores forestales existentes y que responden a objetivos y escalas geográficas distintas, ilustran como la capacidad analítica de los gestores puede mejorar sustancialmente para responder a los grandes retos en el proceso decisorio de la gestión forestal. El artículo presenta simuladores que responden a necesidades de diferentes usuarios y hacen frente a distintas cuestiones, utilizando distintos enfoques y herramientas; desde simuladores espacialmente explícitos basados en modelos empíricos que son integrados con herramientas de optimización, hasta simuladores mecanísticos o basados en enfoques híbridos y bayesianos. El desarrollo más reciente en simulación forestal esta sobretodo relacionado con el incremento de capacidad de cálculo de los computadores, lo que ha facilitado el desarrollo de herramienta robustas y visuales, fáciles de utilizar por los usuarios finales. Otra tendencia, importante es la combinación de herramientas de simulación con técnicas de optimización númerica, lo cual posibilita el desarrollo de los mas modernos sistemas de soporte a la decisión. Uno de los retos más importantes es el de colmar las altas expectativas de los principales agentes y centros decisores forestales en relación a la capacidad de los simuladores y sistemas de apoyo a la decisión para proporcionar información relevante en relación a los servicios ecosistémicos e indicadores de sostenibilidad. En este contexto, es necesaria la validación de los diferentes modelos que configuran los simuladores haciendo uso de información empírica disponible

LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model

Dynamic global vegetation models are a common tool to assess the effect of climate and land use change on vegetation. Though most applications of dynamic global vegetation models use plant functional types, some also simulate species occurrences. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. Pollen studies have shown that a number of plant species lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive at and establish in the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed for the simulation of either only small areas or have been implemented as a post-process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for simulations of large areas. Both distribute seeds between grid cells, leading to individual establishment. The first method uses an approach based on fast Fourier transforms, while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows. We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1&thinsp;km, all dispersal simulations need to be performed at maximum at that scale. However, with the currently available computational power it is not feasible to simulate the local vegetation dynamics of a large area at that scale. We present an option to decrease the required computational costs through a reduction in the number of grid cells for which the local dynamics are simulated only along migration transects. Evaluation of species patterns and migration speeds shows that simulating along transects reduces migration speed, and both methods applied on the transects produce reasonable results. Furthermore, using the migration transects, both methods are sufficiently computationally efficient to allow for large-scale DGVM simulations with migration.</p

Upscaling of solar induced chlorophyll fluorescence from leaf to canopy using the DART model and a realistic 3D forest scene

Non peer reviewe

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication

Quantifying terrestrial ecosystem carbon dynamics with mechanistically-based biogeochemistry models and in situ and remotely sensed data

Terrestrial ecosystem plays a critical role in the global carbon cycle and climate system. Therefore, it is important to accurately quantify the carbon dynamics of terrestrial ecosystem under future climatic change condition. This dissertation evaluates the regional carbon dynamics by using upscaling approach, mechanistically-based biogeochemistry models and in situ and remotely sensed data. The upscaling studies based on FLUXNET network has provided us the spatial and temporal pattern of the carbon fluxes but it fails to consider the atmospheric CO2 effect given its important physiological role in carbon assimilation. In the second chapter, we consider the effect of atmospheric CO2 using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of net ecosystem exchange (NEE) and the derived gross primary productivity (GPP) to the conterminous United States. We found that atmospheric CO 2effect on GPP/NEE exhibited a great spatial and seasonal variability. Further analysis suggested that air temperature played an important role in determining the atmospheric CO2 effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO2 failed to detect ecosystem responses to droughts in part of the US in 2006. The study suggested that the spatially and temporally varied atmospheric CO2 concentrations should be factored into carbon quantification when scaling eddy flux data to a region. The process-based ecosystem models are useful tools to predicting future change in the terrestrial ecosystem. However, they suffer the great uncertainty induced by model structure and parameters. The carbon isotope (13C) discrimination by terrestrial plants, involves the biophysical and biogeochemistry processes and exhibits seasonal and spatial variations, which may provide additional constraints on model parameters. In the third chapter, we found that using foliar 13C composition data, model parameters were constrained to a relatively narrow space and the site-level model simulations were slightly better than that without the foliar 13C constraint. The model extrapolations with three stomatal schemes all showed that the estimation uncertainties of regional carbon fluxes were reduced by about 40%. In addition, tree ring data have great potentials in addressing the forest response to climatic changes compared with mechanistic model simulations, eddy flux measurement and manipulative experiments. In the fourth chapter, we collected the tree ring isotopic carbon data at 12 boreal forest sites to develop a linear regression model, and the model was extrapolated to the whole boreal region to obtain the water use efficiency (WUE) and GPP spatial and temporal variation from 1948 to 2010. Our results demonstrated that most of boreal regions except parts of Alaska showed a significant increasing WUE trend during the study period and the increasing magnitude was much higher than estimations from other land surface models. Our predicted GPP by the WUE definition algorithm was comparable with site observation, while for the revised light use efficiency algorithm, GPP estimation was higher than site observation as well as land surface model estimates. In addition, the increasing GPP trends estimated by two algorithms were similar with land surface model simulations