scholarly journals Optimizing shrub parameters to estimate gross primary production of the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model

2018 ◽  
Author(s):  
Karun Pandit ◽  
Hamid Dashti ◽  
Nancy F. Glenn ◽  
Alejandro N. Flores ◽  
Kaitlin C. Maguire ◽  
...  
Keyword(s):  
Climate Change ◽  
Invasive Species ◽  
Primary Production ◽  
Dynamic Models ◽  
Gross Primary Production ◽  
Ecosystem Dynamics ◽  
Spatiotemporal Variability ◽  
Sagebrush Steppe ◽  
Steppe Ecosystems ◽  
Global Carbon

Abstract. Gross primary production (GPP) is one of the most critical processes in the global carbon cycle, but is difficult to quantify in part because of its high spatiotemporal variability. Direct techniques to quantify GPP are lacking, thus, researchers rely on data inferred from eddy covariance (EC) towers and/or ecosystem dynamic models. The latter are useful to quantify GPP over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. However, such models have also been associated with internal uncertainties and complexities arising from distinct qualities of the ecosystem being analyzed. Widely distributed sagebrush-steppe ecosystems in western North America are threatened by anthropogenic disturbance, invasive species, climate change, and altered fire regimes. Although land managers have focused on different restoration techniques, the effects of these activities and their interactions with fire, climate change, and invasive species on ecosystem dynamics are poorly understood. In this study, we applied an ecosystem dynamic model, Ecosystem Demography (EDv2.2), to parameterize and predict GPP for sagebrush-steppe ecosystems in the Reynolds Creek Experimental Watershed (RCEW), located in the northern Great Basin. Our primary objective was to develop and parameterize a sagebrush (Artemisia spp.) shrubland Plant Functional Type (PFT) for use in the EDv2.2 model, which will support future studies to model estimates of GPP under different climate and management scenarios. To accomplish this, we employed a three-tiered approach. First, to parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, gathered information from existing sagebrush literature, and borrowed values from other PFTs in EDv2.2. Second, we identified the five most sensitive parameters out of thirteen that were found to be influential in GPP prediction based on previous studies. Third, we optimized the five parameters using an exhaustive search method to predict GPP, and performed validation using observations from two EC sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. We expect that, with further refinement, the resulting sagebrush PFT will permit explicit scenario testing of potential anthropogenic modifications of GPP in sagebrush ecosystems, and will contribute to a better understanding of the role of sagebrush ecosystems in shaping global carbon cycles.

scholarly journals Developing and optimizing shrub parameters representing sagebrush (<i>Artemisia</i> spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model

2019 ◽  
Vol 12 (11) ◽  
pp. 4585-4601
Author(s):  
Karun Pandit ◽  
Hamid Dashti ◽  
Nancy F. Glenn ◽  
Alejandro N. Flores ◽  
Kaitlin C. Maguire ◽  
...  
Keyword(s):  
Climate Change ◽  
Great Basin ◽  
Dynamic Models ◽  
Gross Primary Production ◽  
Model Performance ◽  
Field Measurements ◽  
Fire Regimes ◽  
Ecosystem Model ◽  
Sagebrush Steppe ◽  
Steppe Ecosystem

Abstract. Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model.

scholarly journals Gross Primary Production and False Spring: a spatio-temporal analysis

2020 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Raúl Zurita-Milla ◽  
Álvaro Moreno-Martinez ◽  
Gustau Camps-Valls ◽  
Anja Klisch ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Damage Index ◽  
Temporal Analysis ◽  
Environmental Stressors ◽  
Impact Of Climate Change ◽  
False Spring ◽  
Spatial Temporal Analysis

&lt;p&gt;Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.&lt;/p&gt;&lt;p&gt;Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1&lt;sup&gt;st&lt;/sup&gt;). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.&lt;/p&gt;&lt;p&gt;Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.&lt;/p&gt;

scholarly journals High Rates of Ecosytem Metabolism in Five Western Rivers

2011 ◽  
Vol 33 ◽  
pp. 115-118
Author(s):  
Robert Hall ◽  
Jennifer Tank ◽  
Michelle Baker ◽  
Emma Rosi-Marshall ◽  
Michael Grace ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Primary Production ◽  
Carbon Cycling ◽  
Carbon Balance ◽  
Carbon Fixation ◽  
Higher Plants ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Total Rate ◽  
Global Carbon

Primary production and respiration are core functions of river ecosystems that in part determine the carbon balance. Gross primary production (GPP) is the total rate of carbon fixation by autotrophs such as algae and higher plants and is equivalent to photosynthesis. Ecosystem respiration (ER) measures rate at which organic carbon is mineralized to CO2 by all organisms in an ecosystem. Together these fluxes can indicate the base of the food web to support animal production (Marcarelli et al. 2011), can predict the cycling of other elements (Hall and Tank 2003), and can link ecosystems to global carbon cycling (Cole et al. 2007).

scholarly journals Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops

2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Víctor Sánchez-Girón ◽  
Laura Recuero ◽  
César Sáenz ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Balance ◽  
Gross Primary Production ◽  
Meteorological Variables ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Global Carbon

Gross primary production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis and it is the largest flux of the global carbon balance. Our overall objective in this research is to identify and model GPP dynamics and its relationship with meteorological variables and energy fluxes based on time series analysis of eddy covariance (EC) data in two different agroecosystems, a Mediterranean rice crop in Spain and a rainfed cropland in Germany. Crops exerted an important influence on the energy and water fluxes dynamics existing a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops.

Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests

2021 ◽  
Author(s):  
Junbin Zhao ◽  
Holger Lange ◽  
Helge Meissner
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Norway Spruce ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Economic Benefits ◽  
Sink Strength ◽  
Climate Change Scenarios ◽  
Ecosystem Productivity ◽  
Spruce Forests

&lt;p&gt;Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO&lt;sub&gt;2&lt;/sub&gt; uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (R&lt;sub&gt;eco&lt;/sub&gt;). In this study, we used eddy-covariance (EC)-based CO&lt;sub&gt;2&lt;/sub&gt; flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO&lt;sub&gt;2&lt;/sub&gt; sequestration. We found that the net CO&lt;sub&gt;2&lt;/sub&gt; uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the R&lt;sub&gt;eco&lt;/sub&gt; increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO&lt;sub&gt;2&lt;/sub&gt; uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO&lt;sub&gt;2&lt;/sub&gt; sequestration and productivity to gain full ecological and economic benefits.&lt;/p&gt;

scholarly journals Impacts of climate change on the gross primary production of Italian forests

2019 ◽  
Vol 76 (2) ◽  
Author(s):  
Luca Fibbi ◽  
Marco Moriondo ◽  
Marta Chiesi ◽  
Marco Bindi ◽  
Fabio Maselli
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Gross Primary Production ◽  
Impacts Of Climate Change

A new global Gross Primary Production (GPP) dataset based on microwave Vegetation Optical Depth Climate Archive (VODCA)

2020 ◽  
Author(s):  
Benjamin Wild ◽  
Irene Teubner ◽  
Leander Moesinger ◽  
Wouter Dorigo
Keyword(s):  
Carbon Source ◽  
Primary Production ◽  
Optical Depth ◽  
Carbon Sink ◽  
Gross Primary Production ◽  
Operation Time ◽  
Ecosystem Dynamics ◽  
Single Frequency ◽  
Model Input ◽  
Single Sensor

&lt;p&gt;Gross Primary Production (GPP) describes the uptake of C0&lt;sub&gt;2&lt;/sub&gt; by plants through photosynthesis and is essential to monitor and analyze ecosystem dynamics. Teubner et al.&lt;sup&gt;1&lt;/sup&gt; developed a carbon sink-driven approach to estimate GPP on a global scale using Vegetation Optical Depth (VOD), derived from active and passive microwave observations. This allows to analyze GPP variability, complementing existing optical GPP products which are more affected by weather conditions. The short operation time of the individual microwave sensors and the bias between them prohibit analyzing GPP variability. This issue can potentially be overcome by using the Vegetation Optical Depth Climate Archive (VODCA) developed by Moesinger et al.&lt;sup&gt;2&lt;/sup&gt;, which merges multiple VOD products into a single data record. However, the use of a long-running VOD composite for estimating global GPP is challenging because the implications of the VOD aggregation process on the modelling of GPP are difficult to identify a priori.&lt;/p&gt;&lt;p&gt;Here, we present the results of applying the carbon sink-driven GPP estimation approach on the VODCA datasets. As model input for each pixel we used raw VOD from VODCA as well as changes in VOD and median VOD, the latter serves as proxy for vegetation cover. In order to analyze the performance of the carbon sink-driven approach when using VODCA as input, the model is cross-validated against single-sensor (AMSR-E) VOD estimates and commonly used carbon source-driven estimates (MODIS/FLUXCOM). We assessed the ability to model GPP based on single-frequency VODCA (C-, X- and Ku-band) as well as using multiple frequencies as model input.&lt;/p&gt;&lt;p&gt;Overall, the results show that single-band as well as multi-band VODCA performs slightly better in predicting GPP than single-sensor based VOD. Especially in the tropical regions multi-frequency VODCA GPP outperforms single-sensor based estimates. Compared to source-driven approaches, VOD based GPP estimates are higher than FLUXCOM and MODIS GPP. The spatial patterns, however, show good correspondence with the carbon source-driven GPP products, confirming that VODCA can be used to extend the GPP estimates to the past three decades.&lt;/p&gt;&lt;p&gt;&lt;sup&gt;1&lt;/sup&gt;Teubner, I., Forkel, M., Camps-Valls, G., Jung, M., Miralles, Diego, Tramontana, G., van der Schalie, R., Vreugdenhil, M., Moesinger, L., Dorigo, W.:A carbon sink-driven approach to estimate gross primary production from microwave satellite observations, 2019. Remote Sensing of Environment. 229. 100-113. 10.1016/j.rse.2019.04.022.&lt;/p&gt;&lt;p&gt;&lt;sup&gt;2&lt;/sup&gt;Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-42, in review, 2019.&lt;/p&gt;

scholarly journals Spatiotemporal variability of light attenuation and net ecosystem metabolism in a back-barrier estuary

2018 ◽  
Author(s):  
Neil K. Ganju ◽  
Jeremy M. Testa ◽  
Steven E. Suttles ◽  
Alfredo L. Aretxabaleta
Keyword(s):  
Dissolved Oxygen ◽  
Primary Production ◽  
Chlorophyll A ◽  
Gross Primary Production ◽  
Light Attenuation ◽  
Ecosystem Metabolism ◽  
Spatiotemporal Variability ◽  
Net Ecosystem Metabolism ◽  
Enhanced Production ◽  
Strong Control

Abstract. The light climate in back-barrier estuaries is a strong control on phytoplankton and submerged aquatic vegetation (SAV) growth, and ultimately net ecosystem metabolism. However, quantifying the spatiotemporal variability of light attenuation and net ecosystem metabolism over seasonal timescales is difficult due to sampling limitations and dynamic physical and biogeochemical processes. Differences in the dominant primary producer at a given location (e.g., phytoplankton versus SAV) can also determine diel variations in dissolved oxygen and associated ecosystem metabolism. Over a one year period we measured hydrodynamic properties, biogeochemical variables (fDOM, turbidity, chlorophyll-a fluorescence, dissolved oxygen), and photosynthetically active radiation (PAR) at multiple locations in Chincoteague Bay, Maryland/Virginia, USA, a shallow back-barrier estuary. We quantified light attenuation, net ecosystem metabolism, and timescales of variability for several water properties at paired channel-shoal sites along the longitudinal axis of the bay. The channelized sites, which were dominated by fine bed sediment, exhibited slightly higher light attenuation due to increased wind-wave sediment resuspension. Light attenuation due to fDOM was slightly higher in the northern portion of the bay, while attenuation due to chlorophyll-a was only relevant at one channelized site, proximal to nutrient and freshwater loading. Gross primary production and respiration were highest at the vegetated shoal sites, though enhanced production and respiration were also observed at one channelized, nutrient-enriched site. Production and respiration were nearly balanced throughout the year at all sites, but there was a tendency for net autotrophy at shoal sites, especially during periods of high SAV biomass. Shoal sites, where SAV was present, demonstrated a reduction in gross primary production (GPP) when light attenuation was highest, but GPP at adjacent shoal sites where phytoplankton were dominant was less sensitive to light attenuation. This study demonstrates how extensive continuous physical and biological measurements can help determine metabolic properties in a shallow estuary, including differences in metabolism and oxygen variability between SAV and phytoplankton-dominated habitats.

scholarly journals Carbon cycle uncertainty in the Alaskan Arctic

2014 ◽  
Vol 11 (15) ◽  
pp. 4271-4288 ◽  
Author(s):  
J. B. Fisher ◽  
M. Sikka ◽  
W. C. Oechel ◽  
D. N. Huntzinger ◽  
J. R. Melton ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Primary Production ◽  
Net Primary Production ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
The Arctic ◽  
Regional Patterns ◽  
Alaskan Arctic

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

scholarly journals Global Analysis of the Relationship between Reconstructed Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP)

2021 ◽  
Vol 13 (14) ◽  
pp. 2824
Author(s):  
Haiqiang Gao ◽  
Shuguang Liu ◽  
Weizhi Lu ◽  
Andrew R. Smith ◽  
Rubén Valbuena ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Landscape Heterogeneity ◽  
Global Analysis ◽  
Gross Primary Production ◽  
Spatiotemporal Variability ◽  
Spatial And Temporal Scales ◽  
Discriminatory Analysis ◽  
Positive Correlations ◽  
The Relationship

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p < 0.001). The generally linear SIFc–GPP relationships were substantially affected by spatial and temporal scales and SIFc datasets. The GPP/SIFc slope of the evergreen needleleaf forest (ENF) biome was significantly higher than the slopes of several other biomes (p < 0.05), while the other 11 biomes showed no significant differences in the GPP/SIFc slope between each other (p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship.

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