Predicting the time of green up in temperate and boreal biomes

Kaduk, Jörg; Los, S.O.

doi:10.1007/s10584-010-9951-1

Cited by 22 publications

(12 citation statements)

References 48 publications

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“…timing of spring onset. Previous research using both ground and satellite remotely sensed data has suggested that models incorporating chilling tend to outperform models based solely on forcing requirements (Chiang & Brown, 2007;Kaduk & Los, 2011;Jeong et al, 2012). However, these studies have consistently used arbitrary start dates with no specific biological basis (e.g., January 1) to accumulate forcing.…”

Section: Discussionmentioning

confidence: 99%

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Melaas

Friedl

Richardson

2016

Global Change Biology

110

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Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model-based phenology representations fail to capture local- to regional-scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground-based observations to estimate models that better represent how community-level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing-based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species-specific models in combination with species composition information to 'upscale' model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species-specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.

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Section: Discussionmentioning

confidence: 99%

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Melaas

Friedl

Richardson

2016

Global Change Biology

110

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“…Different GDD-based phenology models have been well studied Fu et al, 2012a,b;Linkosalo et al, 2008). Intercomparison studies have reported that phenology models that include chilling generally outperform the GDD only based in temperate and boreal biomes (Kaduk & Los, 2011;Vitasse et al, 2011;Jeong et al, 2012). However, to our knowledge, none of the existing phenology models has explicitly considered the effect of precipitation, despite its recognized importance in grassland ecosystem (Stewart & Dwyer, 1994;Yuan et al, 2007;Piao et al, 2011a).…”

Section: Implications For Phenology Modellingmentioning

confidence: 99%

Unexpected role of winter precipitation in determining heat requirement for spring vegetation green‐up at northern middle and high latitudes

Piao

Zhao

et al. 2014

Global Change Biology

167

120

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Heat requirement, expressed in growing degree days (GDD), is a widely used method to assess and predict the effect of temperature on plant development. Until recently, the analysis of spatial patterns of GDD requirement for spring vegetation green-up onset was limited to local and regional scales, mainly because of the sparse and aggregated spatial availability of ground phenology data. Taking advantage of the large temporal and spatial scales of remote sensing-based green-up onset data, we studied the spatial patterns of GDD requirement for vegetation green-up at northern middle and high latitudes. We further explored the correlations between GDD requirement for vegetation green-up and previous winter season chilling temperatures and precipitation, using spatial partial correlations. We showed that GDD requirement for vegetation green-up onset declines towards the north at a mean rate of 18.8 °C-days per degree latitude between 35°N and 70°N, and vary significantly among different vegetation types. Our results confirmed that the GDD requirement for vegetation green-up is negatively correlated with previous winter chilling, which was defined as the number of chilling days from the day when the land surface froze in the previous autumn to the day of green-up onset. This negative correlation is a well-known phenomenon from local studies. Interestingly, irrespective of the vegetation type, we also found a positive correlation between the GDD requirement and previous winter season precipitation, which was defined as the sum of the precipitation of the month when green-up onset occur and the precipitation that occurred during the previous 2 months. Our study suggests that GDD requirement, chilling and precipitation may have complex interactions in their effects on spring vegetation green-up phenology. These findings have important implications for improving phenology models and could therefore advance our understanding of the interplay between spring phenology and carbon fluxes.

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“…For simulating natural vegetation, global TBMs use cumulative growing‐degree days to identify leaf onset (Lawrence et al, ; Krinner et al, ). To improve phenological predictions, rule‐based models employ chilling requirements for leaf onset (e.g., Kaduk & Los, ; Picard et al, ), add day of year as an additional parameter (e.g., Fisher et al, ), or incorporate soil water and photoperiod for the timing of senescence (e.g., Borchert et al, ; Dahlin et al, , ; Lawrence et al, ).…”

Section: Introductionmentioning

confidence: 99%

Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4)

Haynes

Baker

Denning

et al. 2019

J Adv Model Earth Syst

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Grasslands grow in a sequence of seasonal growth stages that respond to both climate and weather, and these relationships can be used to establish a strategy for predicting plant phenology. Current plant states (phenophase) can be represented as one of established growth stages that dictate carbon allocation and leaf photosynthetic capacity. Calculating daily phenophases from climate and environmental relationships allows for sequential growth stages (i.e., well-defined seasonal cycles with a single growth period) or dynamic growth stages (i.e., multiple growth periods during a growing season). Senescence results from biomass mortality in response to environmental conditions. This approach uses a single mechanistic framework to represent grassland ecology, removing the dependence on satellite-based vegetation indices and individual site tuning of parameters. Rather than being specified, a variety of properties emerge, from allometric relationships such as root-shoot ratios, to behavior across moisture gradients, to interannual variability in growing season lengths, carbon stores, and land surface fluxes. Using dynamic phenology stages to link biophysical and biogeochemical processes provides a mechanism to predict self-consistent land-atmosphere exchanges of carbon, water, energy, radiation, and momentum, as well as carbon storage in cascading pools of biomass; and describing these processes in a mathematically determinate model makes them clear, testable, and usable for predictions. This paper describes this new phenology method as it is implemented in the Simple Biosphere Model Version 4 (SiB4), and a companion paper evaluates this method at grassland sites worldwide.Plain Language Summary Every year, grasslands grow in a sequence of seasonal growth stages that have evolved in nature. For grasslands, climate and weather patterns can be used to establish a strategy for predicting these stages. Using five different possible stages, the current stage can be used to determine which part of the plant grows the fastest. This approach uses a single set of equations to represent grassland ecology, removing the dependence on satellite-based vegetation information. Rather than being specified, a variety of properties emerge, such as different growth patterns in regions that receive more rainfall and different growth rates per year. This approach links plant processes and provides a way to model plant growth and its interaction with the atmosphere. This paper describes this new method as it is implemented in the Simple Biosphere Model Version 4 and provides an example at a grassland site.

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Predicting the time of green up in temperate and boreal biomes

Cited by 22 publications

References 48 publications

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Unexpected role of winter precipitation in determining heat requirement for spring vegetation green‐up at northern middle and high latitudes

Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4)

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