The use of dynamic global vegetation models for simulating hydrology and the potential integration of satellite observations

Dynamic global vegetation models (DGVMs) offer explicit representations of the land surface through time and have been used to research large-scale hydrological responses to climate change. These applications are discussed and comparisons of model inputs and formulations are made among and between DGVMs and global hydrological models. It is shown that the configuration of process representations and data inputs are what makes a given DGVM unique within the family of vegetation models. The variety of available climatic forcing datasets introduces uncertainty into simulations of hydrological variables. It is proposed that satellite-derived data, validated thoroughly, could be used to improve the quality of model evaluations and augment ground-based observations, particularly where spatial and temporal gaps are present. This would aid the reduction of model uncertainties and thus potentially enhance our understanding of global hydrological change.

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Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

Biogeosciences ◽

10.5194/bg-11-1449-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 1449-1459 ◽

Cited By ~ 7

Author(s):

I. N. Fletcher ◽

L. E. O. C. Aragão ◽

A. Lima ◽

Y. Shimabukuro ◽

P. Friedlingstein

Keyword(s):

Forest Fires ◽

Large Scale ◽

Fire Spread ◽

Small Scale ◽

Burnt Area ◽

Active Fire ◽

Fractal Properties ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

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Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Biogeosciences ◽

10.5194/bg-18-95-2021 ◽

2021 ◽

Vol 18 (1) ◽

pp. 95-112

Author(s):

Peter Horvath ◽

Hui Tang ◽

Rune Halvorsen ◽

Frode Stordal ◽

Lena Merete Tallaksen ◽

...

Keyword(s):

High Latitude ◽

Vegetation Cover ◽

Land Surface ◽

Snow Water Equivalent ◽

Radiative Properties ◽

Community Land Model ◽

Precipitation Seasonality ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

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

Geoscientific Model Development ◽

10.5194/gmd-12-893-2019 ◽

2019 ◽

Vol 12 (3) ◽

pp. 893-908 ◽

Cited By ~ 5

Author(s):

Veiko Lehsten ◽

Michael Mischurow ◽

Erik Lindström ◽

Dörte Lehsten ◽

Heike Lischke

Keyword(s):

Large Scale ◽

Fourier Transforms ◽

Grid Cells ◽

Large Area ◽

Climate Conditions ◽

Small Areas ◽

Seed Supply ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

Abstract. 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 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.

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Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

10.5194/bg-2020-149 ◽

2020 ◽

Author(s):

Peter Horvath ◽

Hui Tang ◽

Rune Halvorsen ◽

Frode Stordal ◽

Lena Merete Tallaksen ◽

...

Keyword(s):

High Latitude ◽

Vegetation Cover ◽

Land Surface ◽

Radiative Properties ◽

Environmental Predictors ◽

Distribution Models ◽

Data Set ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

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Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2

International Journal of Biometeorology ◽

10.1007/s00484-015-1087-6 ◽

2015 ◽

Vol 60 (7) ◽

pp. 945-955 ◽

Cited By ~ 3

Author(s):

L. F. C. Rezende ◽

B. C. Arenque ◽

S. T. Aidar ◽

M. S. B. Moura ◽

C. Von Randow ◽

...

Keyword(s):

Plant Physiology ◽

Atmospheric Co2 ◽

Elevated Atmospheric Co2 ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

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Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models

Brazilian Journal of Biology ◽

10.1590/1519-6984.14414 ◽

2016 ◽

Vol 76 (2) ◽

pp. 341-351

Author(s):

L. F. C. Rezende ◽

B. C. Arenque-Musa ◽

M. S. B. Moura ◽

S. T. Aidar ◽

C. Von Randow ◽

...

Keyword(s):

Data Mining ◽

Native Species ◽

Classification And Regression Tree ◽

Northeastern Brazil ◽

Semiarid Region ◽

Global Changes ◽

Data Mining Techniques ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

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Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0315 ◽

2018 ◽

Vol 373 (1760) ◽

pp. 20170315 ◽

Cited By ~ 34

Author(s):

Cleiton B. Eller ◽

Lucy Rowland ◽

Rafael S. Oliveira ◽

Paulo R. L. Bittencourt ◽

Fernanda V. Barros ◽

...

Keyword(s):

Tropical Forest ◽

El Niño ◽

Optimization Model ◽

El Nino ◽

Soil Drought ◽

Forest Sites ◽

Dynamic Global Vegetation Models ◽

Vegetation Models ◽

Global Vegetation

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration ( E ) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

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Where is the carbon going? - A multi-model comparison

10.5194/egusphere-egu21-12422 ◽

2021 ◽

Author(s):

Michael O'Sullivan ◽

Pierre Friedlingstein ◽

Stephen Sitch

Keyword(s):

Model Comparison ◽

Net Primary Productivity ◽

Relative Increase ◽

Historical Period ◽

Residence Times ◽

Vegetation Carbon ◽

Dynamic Global Vegetation Models ◽

Spatio Temporal ◽

Vegetation Models ◽

Global Vegetation

Net terrestrial carbon uptake is primarily driven by increases in net primary productivity (NPP) and/or the residence time of carbon in vegetation and soil. As such, it is of critical importance to accurately quantify spatio-temporal variation in both terms and determine their drivers. Both NPP and residence times are modulated by changing environmental conditions, including climate change and variability, atmospheric CO2, and Land Use and Land Cover Changes (LULCC). For the historical period, 1901-2019, outputs from a suite of Dynamic Global Vegetation Models (DGVMs) from the TRENDY consortium, driven with observed changes in climate, CO2, and LULCC are analysed. Changes in global and regional carbon fluxes, stocks, and residence times are quantified, as well as an attribution to the underlying drivers. We find that over the historical period the majority of models simulate an increase in NPP, predominantly driven by enhanced atmospheric CO2 concentrations. This generally leads to increased carbon storage in both vegetation and soils, however there is no agreement across models on the partitioning between vegetation and soils. This increased storage also acts to reduce soil carbon residence times due to a relative increase in carbon allocated in the faster decomposing soil pools. LULCC over this period has acted to reduce carbon inputs to the system and reduce vegetation carbon residence times due to conversion of forests to shorter vegetation. We find there is a large variation in simulated global and regional fluxes, stocks, and residence times in resonse to changes in climate, implying there are considerable uncertainties in current DGVMs. We therefore use long-term global observations of productivity and biomass change to constrain model estimates and provide insight into a process attribution for biospheric change as well as highlighting areas for future model improvement.

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