Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems

Renwick, Katherine M.; Fellows, Aaron W.; Flerchinger, G. N.; Lohse, Kathleen A.; Clark, Patrick E.; Smith, William K.; Emmett, Kristen; Poulter, Benjamin

doi:10.1016/j.agrformet.2019.04.003

Cited by 23 publications

(27 citation statements)

References 63 publications

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“…The second assumption is that remote sensing GPP products capture the dynamics of vegetation production in our study area. This work builds on lessons learned from previous efforts to estimate GPP in the same study area Pandit et al, 2019;Renwick et al, 2019), all of which recommend additional investigations into model development within drylands.…”

Section: Resultsmentioning

confidence: 99%

“…However, an understanding of DGVM capabilities and limitations in an ecosystem is necessary to capture uncertainties. An evaluation of a DGVM should include model parametrization, sensitivity analysis (SA), calibration, and evaluation (Fer et al, 2018;Keenan et al, 2013;Kuppel et al, 2012;Pandit et al, 2019;Post et al, 2017;Renwick et al, 2019;Santaren et al, 2007;Wang et al, 2001). There is an information gap regarding DGVM evaluation in drylands and more specifically in regions where the drivers in ecosystem processes may vary across an elevation gradient.…”

Section: Resultsmentioning

confidence: 99%

“…There is an information gap regarding DGVM evaluation in drylands and more specifically in regions where the drivers in ecosystem processes may vary across an elevation gradient. Renwick et al (2019) implemented the LPJ-GUESS DGVM (Smith et al, 2001) and modified the phenology routine in this model. They calibrated the model by minimizing the objective function of all sites along an elevation gradient simultaneously.…”

Section: Resultsmentioning

confidence: 99%

“…This approach may be limiting in ecosystems in which GPP capacity and activity are controlled by different processes across a study area . Many studies have investigated the correlation between simulated GPP and GPP estimated from EC towers or remote sensing (Antonarakis et al, 2014;Pandit et al, 2019;Renwick et al, 2019;Trugman et al, 2016), which is more applicable to GPP capacity rather than activity. More explicit metrics of GPP activity are required to fully capture the performance of models in terms of vegetation dynamics.…”

Section: Resultsmentioning

confidence: 99%

“…RCEW is located in the western US (Figure 3.1). The elevation gradient in RCEW is 900-2200 m. With increasing elevation, the mean annual precipitation increases and temperature decreases Renwick et al, 2019). In this study, we used three EC towers located at different elevations in RCEW (Figure 3.1, (Flores et al, 2016).…”

Section: Study Area and Datamentioning

confidence: 99%

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Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Dashtiahangar¹

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

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Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Dashtiahangar¹

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Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Steiner,

Scott,

et al. 2024

Ecosphere

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Savannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

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Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability

Mahmud

Scott

Litvak³

et al. 2022

Preprint

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems

Cited by 23 publications

References 63 publications

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability

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