PREDICTING TREE DIVERSITY ACROSS THE UNITED STATES AS A FUNCTION OF MODELED GROSS PRIMARY PRODUCTION

2008 ◽  
Vol 18 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Joanne M. Nightingale ◽  
Weihong Fan ◽  
Nicholas C. Coops ◽  
Richard H. Waring
Keyword(s):  
United States ◽  
Primary Production ◽  
Gross Primary Production ◽  
The United States ◽  
Tree Diversity

scholarly journals Primary Production of the Central Grassland Region of the United States

Ecology ◽  
1988 ◽  
Vol 69 (1) ◽  
pp. 40-45 ◽  
Author(s):  
O. E. Sala ◽  
W. J. Parton ◽  
L. A. Joyce ◽  
W. K. Lauenroth
Keyword(s):  
United States ◽  
Primary Production ◽  
The United States

Primary Production and Abiotic Controls in Forests, Grasslands, and Desert Ecosystems in the United States

Ecology ◽  
1983 ◽  
Vol 64 (1) ◽  
pp. 134-151 ◽  
Author(s):  
Warren L. Webb ◽  
William K. Lauenroth ◽  
Stan R. Szarek ◽  
Russell S. Kinerson
Keyword(s):  
United States ◽  
Primary Production ◽  
The United States ◽  
Desert Ecosystems ◽  
Abiotic Controls

Primary Production and Abiotic Controls in Forest, Grasslands, and Desert Ecosystems in the United States

Ecology ◽  
1983 ◽  
Vol 64 (3) ◽  
pp. 623
Author(s):  
Warren L. Webb ◽  
William K. Lauenroth ◽  
Stan R. Szarek ◽  
Russell S. Kinerson
Keyword(s):  
United States ◽  
Primary Production ◽  
The United States ◽  
Desert Ecosystems ◽  
Abiotic Controls

scholarly journals A time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in eastern United States

2018 ◽  
Author(s):  
Qinchuan Xin ◽  
Yongjiu Dai ◽  
Xiaoping Liu
Keyword(s):  
United States ◽  
Time Series ◽  
Steady State ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Gross Primary Production ◽  
Eastern United States ◽  
Area Index ◽  
Time Stepping

Abstract. Terrestrial plants play a key role in regulating the exchange of energy and materials between the land surface and the atmosphere. Robust terrestrial biosphere models that simulate both time series of leaf dynamics and canopy photosynthesis are required to understand the vegetation-climate interactions. This study proposes a time stepping scheme to simulate leaf area index (LAI), phenology, and gross primary production (GPP) simultaneously via only climate variables based on an ecological assumption that plants allocate leaf biomass till an environment could sustain to maximize photosynthetic reproduction. The method establishes a linear function between the steady-state LAI and the corresponding GPP, which is used to track the suitability of environmental conditions for plant photosynthesis, and applies the MOD17 algorithm to form simultaneous equations together, which can be solved numerically. To account for the time lag in plant responses of leaf allocation to environment variation, a time stepping scheme is developed to simulate the LAI time series based on the solved steady-state LAI. The simulated LAI time series is then used to derive the timing of key phenophases and simulate canopy GPP with the MOD17 algorithm. The developed method is applied to deciduous broadleaf forests in eastern United States and has found to perform well on simulating canopy LAI and GPP at the site scale as evaluated using both flux tower and satellite data. The method could also capture the spatiotemporal variation of vegetation LAI and phenology across eastern United States as compared with satellite observations. The developed time-stepping scheme provides a simplified and improved version of our previous modeling approach and forms a potential basis for regional to global applications in future studies.

Land cover change-induced decline in terrestrial gross primary production over the conterminous United States from 2001 to 2016

2021 ◽  
Vol 308-309 ◽  
pp. 108609
Author(s):  
Yulong Zhang ◽  
Conghe Song ◽  
Taehee Hwang ◽  
Kimberly Novick ◽  
John W. Coulston ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Primary Production ◽  
Land Cover Change ◽  
Gross Primary Production

scholarly journals Tidal Wetland Gross Primary Production Across the Continental United States, 2000–2019

2020 ◽  
Vol 34 (2) ◽  
Author(s):  
R. A. Feagin ◽  
I. Forbrich ◽  
T. P. Huff ◽  
J. G. Barr ◽  
J. Ruiz‐Plancarte ◽  
...  
Keyword(s):  
United States ◽  
Primary Production ◽  
Gross Primary Production ◽  
Tidal Wetland

scholarly journals An Assessment of Street Tree Diversity: Findings and Implications in the United States

2020 ◽  
Vol 56 ◽  
pp. 126826
Author(s):  
Bingqian Ma ◽  
Richard J. Hauer ◽  
Hongxu Wei ◽  
Andrew K. Koeser ◽  
Ward Peterson ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Tree Diversity ◽  
Street Tree

scholarly journals Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3434
Author(s):  
Maryia Halubok ◽  
Zong-Liang Yang
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Spatial Variability ◽  
Great Plains ◽  
Gross Primary Production ◽  
European Space Agency ◽  
The United States ◽  
Soil Moisture Data ◽  
The Us

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

scholarly journals Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1735
Author(s):  
Xiaocui Wu ◽  
Xiangming Xiao ◽  
Jean Steiner ◽  
Zhengwei Yang ◽  
Yuanwei Qin ◽  
...  
Keyword(s):  
United States ◽  
Winter Wheat ◽  
Great Plains ◽  
Gross Primary Production ◽  
The United States ◽  
Grain Production ◽  
Wheat Grain ◽  
Global Food Security ◽  
The Usa ◽  
Data Layer

Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008-2018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPPVPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (>20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPPVPM was linearly related with grain production of winter wheat, with an R2 value of 0.68 across the CONUS. The relationships between grain production and GPPVPM in those counties without a substantial difference (<20%) between planted and harvested area were much stronger and their harvest index (HIGPP) values ranged from 0.2-0.3. GPPVPM in May could explain about 70%-90% of the variance of winter wheat grain production. Our findings highlight the potential of GPPVPM in winter wheat monitoring, especially for those high harvested/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public.

scholarly journals A simple time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in the eastern United States

2019 ◽  
Vol 16 (2) ◽  
pp. 467-484 ◽  
Author(s):  
Qinchuan Xin ◽  
Yongjiu Dai ◽  
Xiaoping Liu
Keyword(s):  
United States ◽  
Time Series ◽  
Steady State ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Gross Primary Production ◽  
Eastern United States ◽  
Area Index ◽  
Time Stepping

Abstract. Terrestrial plants play a key role in regulating the exchange of energy and materials between the land surface and the atmosphere. Robust models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. This study proposes a simple time-stepping scheme to simulate leaf area index (LAI), phenology, and gross primary production (GPP) when forced with climate variables. The method establishes a linear function between steady-state LAI and the corresponding GPP. The method applies the established function and the MOD17 algorithm to form simultaneous equations, which can be solved together numerically. To account for the time-lagged responses of plant growth to environmental conditions, a time-stepping scheme is developed to simulate the LAI time series based on the solved steady-state LAI. The simulated LAI time series is then used to derive the timing of key phenophases and simulate canopy GPP with the MOD17 algorithm. The developed method is applied to deciduous broadleaf forests in the eastern United States and is found to perform well for simulating canopy LAI and GPP at the site scale as evaluated using both flux tower and satellite data. The method also captures the spatiotemporal variation of vegetation LAI and phenology across the eastern United States compared with satellite observations. The developed time-stepping scheme provides a simplified and improved version of our previous modeling approach to simulate leaf phenology and can potentially be applied at regional to global scales in future studies.

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