1
|
Chang X, Xing Y, Gong W, Yang C, Guo Z, Wang D, Wang J, Yang H, Xue G, Yang S. Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 875:162601. [PMID: 36882141 DOI: 10.1016/j.scitotenv.2023.162601] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52-0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.
Collapse
Affiliation(s)
- Xiaoqing Chang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Yanqiu Xing
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China.
| | - Weishu Gong
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Cheng Yang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Zhen Guo
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Dejun Wang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Jiaqi Wang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Hong Yang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Gang Xue
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| | - Shuhang Yang
- Centre for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China
| |
Collapse
|
2
|
Krishna DK, Watham T, Padalia H, Srinet R, Nandy S. Improved gross primary productivity estimation using semi empirical (PRELES) model for moist Indian sal forest. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2022.110175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
3
|
Sun W, Berry JA, Yakir D, Seibt U. Leaf relative uptake of carbonyl sulfide to CO 2 seen through the lens of stomatal conductance-photosynthesis coupling. THE NEW PHYTOLOGIST 2022; 235:1729-1742. [PMID: 35478172 DOI: 10.1111/nph.18178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/16/2022] [Indexed: 06/14/2023]
Abstract
Carbonyl sulfide (COS) has emerged as a multi-scale tracer for terrestrial photosynthesis. To infer ecosystem-scale photosynthesis from COS fluxes often requires knowledge of leaf relative uptake (LRU), the concentration-normalized ratio between leaf COS uptake and photosynthesis. However, current mechanistic understanding of LRU variability remains inadequate for deriving robust COS-based estimates of photosynthesis. We derive a set of closed-form equations to describe LRU responses to light, humidity and CO2 based on the Ball-Berry stomatal conductance model and the biochemical model of photosynthesis. This framework reproduces observed LRU responses: decreasing LRU with increasing light or decreasing humidity; it also predicts that LRU increases with ambient CO2 . By fitting the LRU equations to flux measurements on a C3 reed (Typha latifolia), we obtain physiological parameters that control LRU variability, including an estimate of the Ball-Berry slope of 7.1 without using transpiration measurements. Sensitivity tests reveal that LRU is more sensitive to photosynthetic capacity than to the Ball-Berry slope, indicating stomatal response to photosynthesis. This study presents a simple framework for interpreting observed LRU variability and upscaling LRU. The stoma-regulated LRU response to CO2 suggests that COS may offer a unique window into long-term stomatal acclimation to elevated CO2 .
Collapse
Affiliation(s)
- Wu Sun
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA
| | - Joseph A Berry
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA
| | - Dan Yakir
- Earth and Planetary Science, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Ulli Seibt
- Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, 520 Portola Plaza, Los Angeles, CA, 90095-1565, USA
| |
Collapse
|
4
|
Antonarakis AS, Bogan SA, Goulden ML, Moorcroft PR. Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model. GLOBAL CHANGE BIOLOGY 2022; 28:1823-1852. [PMID: 34779555 DOI: 10.1111/gcb.15995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/15/2021] [Accepted: 10/28/2021] [Indexed: 06/13/2023]
Abstract
Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250 m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2 model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737 km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 trees km-2 year-1 in 2010-2011, to ~535 trees km-2 year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.
Collapse
Affiliation(s)
| | - Stacy A Bogan
- Department of Geography, Sussex University, Brighton, UK
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Michael L Goulden
- Department of Earth Sciences, University of California, Irvine, California, USA
| | - Paul R Moorcroft
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| |
Collapse
|
5
|
Sleeter BM, Frid L, Rayfield B, Daniel C, Zhu Z, Marvin DC. Operational assessment tool for forest carbon dynamics for the United States: a new spatially explicit approach linking the LUCAS and CBM-CFS3 models. CARBON BALANCE AND MANAGEMENT 2022; 17:1. [PMID: 35107646 PMCID: PMC8811977 DOI: 10.1186/s13021-022-00201-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a "gain-loss" approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). RESULTS We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year-1. Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source. CONCLUSIONS The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.
Collapse
Affiliation(s)
| | - Leonardo Frid
- Apex Resource Management Solutions Ltd., Ottawa, ON, Canada
| | | | - Colin Daniel
- Apex Resource Management Solutions Ltd., Ottawa, ON, Canada
| | | | | |
Collapse
|
6
|
Fisher JB, Sikka M, Block GL, Schwalm CR, Parazoo NC, Kolus HR, Sok M, Wang A, Gagne‐Landmann A, Lawal S, Guillaume A, Poletti A, Schaefer KM, El Masri B, Levy PE, Wei Y, Dietze MC, Huntzinger DN. The Terrestrial Biosphere Model Farm. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2022; 14:e2021MS002676. [PMID: 35860620 PMCID: PMC9285607 DOI: 10.1029/2021ms002676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 06/15/2023]
Abstract
Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.
Collapse
Affiliation(s)
- Joshua B. Fisher
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- Schmid College of Science and TechnologyChapman UniversityOrangeCAUSA
| | - Munish Sikka
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Gary L. Block
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | | | - Hannah R. Kolus
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Malen Sok
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Audrey Wang
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Shakirudeen Lawal
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Alyssa Poletti
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Kevin M. Schaefer
- National Snow and Ice Data CenterCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulderCOUSA
| | - Bassil El Masri
- Department of Earth and Environmental SciencesMurray State UniversityMurrayKYUSA
| | | | - Yaxing Wei
- Environmental Sciences DivisionOak Ridge National LaboratoryClimate Change Science InstituteOak RidgeTNUSA
| | | | | |
Collapse
|
7
|
A Satellite-Based Method for National Winter Wheat Yield Estimating in China. REMOTE SENSING 2021. [DOI: 10.3390/rs13224680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.
Collapse
|
8
|
COS-derived GPP relationships with temperature and light help explain high-latitude atmospheric CO 2 seasonal cycle amplification. Proc Natl Acad Sci U S A 2021; 118:2103423118. [PMID: 34380737 DOI: 10.1073/pnas.2103423118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In the Arctic and Boreal region (ABR) where warming is especially pronounced, the increase of gross primary production (GPP) has been suggested as an important driver for the increase of the atmospheric CO2 seasonal cycle amplitude (SCA). However, the role of GPP relative to changes in ecosystem respiration (ER) remains unclear, largely due to our inability to quantify these gross fluxes on regional scales. Here, we use atmospheric carbonyl sulfide (COS) measurements to provide observation-based estimates of GPP over the North American ABR. Our annual GPP estimate is 3.6 (2.4 to 5.5) PgC · y-1 between 2009 and 2013, the uncertainty of which is smaller than the range of GPP estimated from terrestrial ecosystem models (1.5 to 9.8 PgC · y-1). Our COS-derived monthly GPP shows significant correlations in space and time with satellite-based GPP proxies, solar-induced chlorophyll fluorescence, and near-infrared reflectance of vegetation. Furthermore, the derived monthly GPP displays two different linear relationships with soil temperature in spring versus autumn, whereas the relationship between monthly ER and soil temperature is best described by a single quadratic relationship throughout the year. In spring to midsummer, when GPP is most strongly correlated with soil temperature, our results suggest the warming-induced increases of GPP likely exceeded the increases of ER over the past four decades. In autumn, however, increases of ER were likely greater than GPP due to light limitations on GPP, thereby enhancing autumn net carbon emissions. Both effects have likely contributed to the atmospheric CO2 SCA amplification observed in the ABR.
Collapse
|
9
|
Fang J, Lutz JA, Wang L, Shugart HH, Yan X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. GLOBAL CHANGE BIOLOGY 2020; 26:6974-6988. [PMID: 32926493 DOI: 10.1111/gcb.15349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/25/2020] [Indexed: 06/11/2023]
Abstract
Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.
Collapse
Affiliation(s)
- Jing Fang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - James A Lutz
- Department of Wildland Resources, Utah State University, Logan, UT, USA
| | - Leibin Wang
- College of Resources and Environment Science, Hebei Normal University, Shijiazhuang, China
- Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang, China
| | - Herman H Shugart
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA
| | - Xiaodong Yan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| |
Collapse
|
10
|
Albert LP, Restrepo-Coupe N, Smith MN, Wu J, Chavana-Bryant C, Prohaska N, Taylor TC, Martins GA, Ciais P, Mao J, Arain MA, Li W, Shi X, Ricciuto DM, Huxman TE, McMahon SM, Saleska SR. Cryptic phenology in plants: Case studies, implications, and recommendations. GLOBAL CHANGE BIOLOGY 2019; 25:3591-3608. [PMID: 31343099 DOI: 10.1111/gcb.14759] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 06/13/2019] [Accepted: 06/16/2019] [Indexed: 06/10/2023]
Abstract
Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.
Collapse
Affiliation(s)
- Loren P Albert
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
| | - Natalia Restrepo-Coupe
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
- School of Life Science, University of Technology Sydney, Ultimo, NSW, Australia
| | - Marielle N Smith
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
| | - Jin Wu
- Biological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, New York, NY, USA
- School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Cecilia Chavana-Bryant
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
- Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA
| | - Neill Prohaska
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
| | - Tyeen C Taylor
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
| | - Giordane A Martins
- Ciências de Florestas Tropicais, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre Simon Laplace, Gif sur Yvette, France
| | - Jiafu Mao
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - M Altaf Arain
- School of Geography and Earth Sciences & McMaster Centre for Climate Change, McMaster University, Hamilton, ON, Canada
| | - Wei Li
- Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre Simon Laplace, Gif sur Yvette, France
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China
| | - Xiaoying Shi
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Daniel M Ricciuto
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Travis E Huxman
- Ecology and Evolutionary Biology & Center for Environmental Biology, University of California, Irvine, CA, USA
| | - Sean M McMahon
- Smithsonian Institution's Forest Global Earth Observatory & Smithsonian Environmental Research Center, Edgewater, MD, USA
| | - Scott R Saleska
- Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
| |
Collapse
|
11
|
Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe. REMOTE SENSING 2019. [DOI: 10.3390/rs11151823] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.
Collapse
|
12
|
Liu D, Wang T, Yang T, Yan Z, Liu Y, Zhao Y, Piao S. Deciphering impacts of climate extremes on Tibetan grasslands in the last fifteen years. Sci Bull (Beijing) 2019; 64:446-454. [PMID: 36659794 DOI: 10.1016/j.scib.2019.03.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/06/2019] [Accepted: 03/07/2019] [Indexed: 01/21/2023]
Abstract
Climate extremes have emerged as a crucial driver of changes in terrestrial ecosystems. The Tibetan Plateau, facing a rapid climate change, tends to favor climate extremes. But we lack a clear understanding of the impacts of such extremes on alpine grasslands. Here we show that extreme events (drought, extreme wet, extreme cold and extreme hot) occurred at a frequency of 0.67-4 months decade-1 during 2001-2015, with extreme precipitation predominantly occurring in June-to-August and extreme temperatures in May. Drought and extreme wet cause opposite and asymmetric effects on grassland growth, with drought-induced reductions greater than increases due to extreme wet. Grassland responses to extreme temperatures, which predominantly occur in May, show a dipole-like spatial pattern, with extreme hot (cold) events enhanced (reduced) growth in the eastern plateau but slightly reduced (enhanced) growth in the western plateau. These opposite responses to extreme temperatures over the eastern plateau are explained by the possibility that the occurrence of extreme cold slows the preseason temperature accumulation, delaying the triggering of spring phenology, while extreme hot hastens the accumulation. In the western plateau, in contrast, positive responses to extreme cold are induced by accompanying high precipitation. Furthermore, high extremeness of climate events generally led to a much lower extremeness in growth response, implying that the Tibetan grasslands have a relatively high resistance to climate extremes. The ecosystem models tested could not accurately simulate grassland responses to drought and extreme temperatures, and require re-parameterization before trust can be placed in their output for this region.
Collapse
Affiliation(s)
- Dan Liu
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Wang
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tao Yang
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhengjie Yan
- School of Life Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yongwen Liu
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Yutong Zhao
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Shilong Piao
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China; College of Urban and Environmental Sciences and Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| |
Collapse
|
13
|
Modeling Carbon and Water Fluxes of Managed Grasslands: Comparing Flux Variability and Net Carbon Budgets between Grazed and Mowed Systems. AGRONOMY-BASEL 2019. [DOI: 10.3390/agronomy9040183] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The CenW ecosystem model simulates carbon, water, and nitrogen cycles following ecophysiological processes and management practices on a daily basis. We tested and evaluated the model using five years eddy covariance measurements from two adjacent but differently managed grasslands in France. The data were used to independently parameterize CenW for the two grassland sites. Very good agreements, i.e., high model efficiencies and correlations, between observed and modeled fluxes were achieved. We showed that the CenW model captured day-to-day, seasonal, and interannual variability observed in measured CO2 and water fluxes. We also showed that following typical management practices (i.e., mowing and grazing), carbon gain was severely curtailed through a sharp and severe reduction in photosynthesizing biomass. We also identified large model/data discrepancies for carbon fluxes during grazing events caused by the noncapture by the eddy covariance system of large respiratory losses of C from dairy cows when they were present in the paddocks. The missing component of grazing animal respiration in the net carbon budget of the grazed grassland can be quantitatively important and can turn sites from being C sinks to being neutral or C sources. It means that extra care is needed in the processing of eddy covariance data from grazed pastures to correctly calculate their annual CO2 balances and carbon budgets.
Collapse
|
14
|
Smith JE, Domke GM, Nichols MC, Walters BF. Carbon stocks and stock change on federal forest lands of the United States. Ecosphere 2019. [DOI: 10.1002/ecs2.2637] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- James E. Smith
- USDA Forest Service, Northern Research Station Durham New Hampshire USA
| | - Grant M. Domke
- USDA Forest Service, Northern Research Station St. Paul Minnesota USA
| | | | - Brian F. Walters
- USDA Forest Service, Northern Research Station St. Paul Minnesota USA
| |
Collapse
|
15
|
Sándor R, Ehrhardt F, Brilli L, Carozzi M, Recous S, Smith P, Snow V, Soussana JF, Dorich CD, Fuchs K, Fitton N, Gongadze K, Klumpp K, Liebig M, Martin R, Merbold L, Newton PCD, Rees RM, Rolinski S, Bellocchi G. The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 642:292-306. [PMID: 29902627 DOI: 10.1016/j.scitotenv.2018.06.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 05/15/2018] [Accepted: 06/02/2018] [Indexed: 06/08/2023]
Abstract
Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m-2 yr-1 (animal density reduction) and -81 ± 74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.
Collapse
Affiliation(s)
- Renáta Sándor
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France; Agricultural Institute, CAR HAS, 2462 Martonvásár, Hungary
| | | | - Lorenzo Brilli
- University of Florence, DISPAA, 50144 Florence, Italy; IBIMET-CNR, 50145 Florence, Italy
| | - Marco Carozzi
- Agroscope Research Station, Climate Agriculture Group, Zurich, Switzerland
| | - Sylvie Recous
- FARE Laboratory, INRA, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Pete Smith
- Institute of Biological & Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, United Kingdom
| | - Val Snow
- AgResearch - Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | | | | | - Kathrin Fuchs
- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092 Zurich, Switzerland
| | - Nuala Fitton
- Institute of Biological & Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, United Kingdom
| | - Kate Gongadze
- Rothamsted Research, Sustainable Soil and Grassland Systems Department, United Kingdom
| | - Katja Klumpp
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France
| | | | - Raphaël Martin
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France
| | - Lutz Merbold
- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092 Zurich, Switzerland; Mazingira Centre, International Livestock Research Institute, 00100 Nairobi, Kenya
| | - Paul C D Newton
- AgResearch Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand
| | - Robert M Rees
- Scotland's Rural College, EH9 3JG Edinburgh, United Kingdom
| | - Susanne Rolinski
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
| | - Gianni Bellocchi
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France.
| |
Collapse
|
16
|
Analysis of Spatiotemporal Dynamics of the Chinese Vegetation Net Primary Productivity from the 1960s to the 2000s. REMOTE SENSING 2018. [DOI: 10.3390/rs10060860] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
17
|
Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems. ATMOSPHERE 2018. [DOI: 10.3390/atmos9030083] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
18
|
Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements. SUSTAINABILITY 2018. [DOI: 10.3390/su10010203] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.
Collapse
|
19
|
Mohammed G, Trolard F, Gillon M, Cognard-Plancq AL, Chanzy A, Bourrié G. Combination of a crop model and a geochemical model as a new approach to evaluate the sustainability of an intensive agriculture system. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 595:119-131. [PMID: 28384568 DOI: 10.1016/j.scitotenv.2017.03.146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/10/2017] [Accepted: 03/16/2017] [Indexed: 06/07/2023]
Abstract
By combining a crop model (STICS) and a geochemical model (PHREEQC), a new approach to assess the sustainability of agrosystems is proposed. It is based upon aqueous geochemistry and the stepwise modifications of soil solution during its transfer from the surface till aquifer. Meadows of Crau (SE France), irrigated since the 16th century, were field monitored (2012-2015) and modelled. Except for N, the mineral requirements of hay are largely covered by dissolved elements brought by irrigation water with only slight deficits in K and P, which are compensated by P-K fertilizers and the winter pasture by sheep. N cycle results in a very small nitrate leakage. The main determinants of the chemical composition changes of water are: concentration by evaporation, equilibration with soil pCO2, mineral nutrition of plants, input of fertilizers, sheep grazing, mineral-solution interactions in superficial formations till the aquifer, including ion exchange. Inverse modelling with PHREEQC allows for quantifying these processes. For groundwater, measured composition fit statistically very well with those computed, validating thus this approach. This long-term established agrosystem protects both soil and water resources: soil nutritional status remains constant with even some P and (minor) K fixation in soils; long-term decarbonatation occurs but it is greatly slowed by saturation of irrigation water by carbonate; P fixation in soil protects groundwater from eutrophication.
Collapse
Affiliation(s)
- Gihan Mohammed
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| | - Fabienne Trolard
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| | - Marina Gillon
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| | - Anne-Laure Cognard-Plancq
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| | - André Chanzy
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| | - Guilhem Bourrié
- UAPV - INRA - UMR 1114 Emmah, Université d'Avignon, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France.
| |
Collapse
|
20
|
Singh UN, Refaat TF, Ismail S, Davis KJ, Kawa SR, Menzies RT, Petros M. Feasibility study of a space-based high pulse energy 2 μm CO 2 IPDA lidar. APPLIED OPTICS 2017; 56:6531-6547. [PMID: 29047943 PMCID: PMC7370220 DOI: 10.1364/ao.56.006531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 07/14/2017] [Indexed: 05/25/2023]
Abstract
Sustained high-quality column carbon dioxide (CO2) atmospheric measurements from space are required to improve estimates of regional and continental-scale sources and sinks of CO2. Modeling of a space-based 2 μm, high pulse energy, triple-pulse, direct detection integrated path differential absorption (IPDA) lidar was conducted to demonstrate CO2 measurement capability and to evaluate random and systematic errors. Parameters based on recent technology developments in the 2 μm laser and state-of-the-art HgCdTe (MCT) electron-initiated avalanche photodiode (e-APD) detection system were incorporated in this model. Strong absorption features of CO2 in the 2 μm region, which allows optimum lower tropospheric and near surface measurements, were used to project simultaneous measurements using two independent altitude-dependent weighting functions with the triple-pulse IPDA. Analysis of measurements over a variety of atmospheric and aerosol models using a variety of Earth's surface target and aerosol loading conditions were conducted. Water vapor (H2O) influences on CO2 measurements were assessed, including molecular interference, dry-air estimate, and line broadening. Projected performance shows a <0.35 ppm precision and a <0.3 ppm bias in low-tropospheric weighted measurements related to column CO2 optical depth for the space-based IPDA using 10 s signal averaging over the Railroad Valley (RRV) reference surface under clear and thin cloud conditions.
Collapse
|
21
|
Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.03.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
22
|
Li Z, Liu S, Zhang X, West TO, Ogle SM, Zhou N. Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
23
|
Divergent projections of future land use in the United States arising from different models and scenarios. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.07.016] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
24
|
Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y, Köhler P, Jung M, Varlagin A, Guanter L. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. GLOBAL CHANGE BIOLOGY 2016; 22:2979-96. [PMID: 26683113 DOI: 10.1111/gcb.13200] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 12/05/2015] [Indexed: 05/22/2023]
Abstract
Mid-to-high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still challenging. In particular, the applicability of existing satellite-based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun-induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME-2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high-latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start-of-season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2-3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space-based SIF measurements to light-use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.
Collapse
Affiliation(s)
- Sophia Walther
- Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre Potsdam, Potsdam, Germany
| | - Maximilian Voigt
- Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre Potsdam, Potsdam, Germany
- Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Tea Thum
- Finnish Meteorological Institute, Helsinki, Finland
| | - Alemu Gonsamo
- Department of Geography, University of Toronto, Toronto, ON, Canada
| | - Yongguang Zhang
- Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre Potsdam, Potsdam, Germany
- International Institute for Earth System Sciences, Nanjing University, Nanjing, 210023, China
| | - Philipp Köhler
- Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre Potsdam, Potsdam, Germany
| | - Martin Jung
- Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Andrej Varlagin
- A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia
| | - Luis Guanter
- Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre Potsdam, Potsdam, Germany
| |
Collapse
|
25
|
Wagle P, Zhang Y, Jin C, Xiao X. Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2016; 26:1211-22. [PMID: 27509759 DOI: 10.1890/15-1434] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based vegetation photosynthesis model [VPM], and a process-based soil canopy observation of photochemistry and energy flux [SCOPE] model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index [EVI] and land surface water index [LSWI]). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.
Collapse
|
26
|
Mekonnen ZA, Grant RF, Schwalm C. Sensitivity of modeled NEP to climate forcing and soil at site and regional scales: Implications for upscaling ecosystem models. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2015.10.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
27
|
Aquatic carbon cycling in the conterminous United States and implications for terrestrial carbon accounting. Proc Natl Acad Sci U S A 2015; 113:58-63. [PMID: 26699473 DOI: 10.1073/pnas.1512651112] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Inland water ecosystems dynamically process, transport, and sequester carbon. However, the transport of carbon through aquatic environments has not been quantitatively integrated in the context of terrestrial ecosystems. Here, we present the first integrated assessment, to our knowledge, of freshwater carbon fluxes for the conterminous United States, where 106 (range: 71-149) teragrams of carbon per year (TgC⋅y(-1)) is exported downstream or emitted to the atmosphere and sedimentation stores 21 (range: 9-65) TgC⋅y(-1) in lakes and reservoirs. We show that there is significant regional variation in aquatic carbon flux, but verify that emission across stream and river surfaces represents the dominant flux at 69 (range: 36-110) TgC⋅y(-1) or 65% of the total aquatic carbon flux for the conterminous United States. Comparing our results with the output of a suite of terrestrial biosphere models (TBMs), we suggest that within the current modeling framework, calculations of net ecosystem production (NEP) defined as terrestrial only may be overestimated by as much as 27%. However, the internal production and mineralization of carbon in freshwaters remain to be quantified and would reduce the effect of including aquatic carbon fluxes within calculations of terrestrial NEP. Reconciliation of carbon mass-flux interactions between terrestrial and aquatic carbon sources and sinks will require significant additional research and modeling capacity.
Collapse
|
28
|
Zhu Q, Zhuang Q. Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux? Ecosphere 2015. [DOI: 10.1890/es15-00259.1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
29
|
Bachelet D, Ferschweiler K, Sheehan TJ, Sleeter BM, Zhu Z. Projected carbon stocks in the conterminous USA with land use and variable fire regimes. GLOBAL CHANGE BIOLOGY 2015. [PMID: 26207729 DOI: 10.1111/gcb.13048] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The dynamic global vegetation model (DGVM) MC2 was run over the conterminous USA at 30 arc sec (~800 m) to simulate the impacts of nine climate futures generated by 3GCMs (CSIRO, MIROC and CGCM3) using 3 emission scenarios (A2, A1B and B1) in the context of the LandCarbon national carbon sequestration assessment. It first simulated potential vegetation dynamics from coast to coast assuming no human impacts and naturally occurring wildfires. A moderate effect of increased atmospheric CO2 on water use efficiency and growth enhanced carbon sequestration but did not greatly influence woody encroachment. The wildfires maintained prairie-forest ecotones in the Great Plains. With simulated fire suppression, the number and impacts of wildfires was reduced as only catastrophic fires were allowed to escape. This greatly increased the expansion of forests and woodlands across the western USA and some of the ecotones disappeared. However, when fires did occur, their impacts (both extent and biomass consumed) were very large. We also evaluated the relative influence of human land use including forest and crop harvest by running the DGVM with land use (and fire suppression) and simple land management rules. From 2041 through 2060, carbon stocks (live biomass, soil and dead biomass) of US terrestrial ecosystems varied between 155 and 162 Pg C across the three emission scenarios when potential natural vegetation was simulated. With land use, periodic harvest of croplands and timberlands as well as the prevention of woody expansion across the West reduced carbon stocks to a range of 122-126 Pg C, while effective fire suppression reduced fire emissions by about 50%. Despite the simplicity of our approach, the differences between the size of the carbon stocks confirm other reports of the importance of land use on the carbon cycle over climate change.
Collapse
Affiliation(s)
- Dominique Bachelet
- Conservation Biology Institute, Western Geographic Science Center, Menlo Park, CA, USA
| | - Ken Ferschweiler
- Conservation Biology Institute, Western Geographic Science Center, Menlo Park, CA, USA
| | - Timothy J Sheehan
- Conservation Biology Institute, Western Geographic Science Center, Menlo Park, CA, USA
| | - Benjamin M Sleeter
- U.S. Geological Survey, Western Geographic Science Center, Menlo Park, CA, USA
| | - Zhiliang Zhu
- U.S. Geological Survey National Center, Renton, VA, USA
| |
Collapse
|
30
|
Optimizing photosynthetic and respiratory parameters based on the seasonal variation pattern in regional net ecosystem productivity obtained from atmospheric inversion. Sci Bull (Beijing) 2015. [DOI: 10.1007/s11434-015-0917-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
31
|
Ecosystem carbon stocks and sequestration potential of federal lands across the conterminous United States. Proc Natl Acad Sci U S A 2015; 112:12723-8. [PMID: 26417074 DOI: 10.1073/pnas.1512542112] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Federal lands across the conterminous United States (CONUS) account for 23.5% of the CONUS terrestrial area but have received no systematic studies on their ecosystem carbon (C) dynamics and contribution to the national C budgets. The methodology for US Congress-mandated national biological C sequestration potential assessment was used to evaluate ecosystem C dynamics in CONUS federal lands at present and in the future under three Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC SRES) A1B, A2, and B1. The total ecosystem C stock was estimated as 11,613 Tg C in 2005 and projected to be 13,965 Tg C in 2050, an average increase of 19.4% from the baseline. The projected annual C sequestration rate (in kilograms of carbon per hectare per year) from 2006 to 2050 would be sinks of 620 and 228 for forests and grasslands, respectively, and C sources of 13 for shrublands. The federal lands' contribution to the national ecosystem C budget could decrease from 23.3% in 2005 to 20.8% in 2050. The C sequestration potential in the future depends not only on the footprint of individual ecosystems but also on each federal agency's land use and management. The results presented here update our current knowledge about the baseline ecosystem C stock and sequestration potential of federal lands, which would be useful for federal agencies to decide management practices to achieve the national greenhouse gas (GHG) mitigation goal.
Collapse
|
32
|
Dasgupta A, Poco J, Wei Y, Cook R, Bertini E, Silva CT. Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2015; 21:996-1014. [PMID: 26357283 DOI: 10.1109/tvcg.2015.2413774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.
Collapse
|
33
|
Keane RE, McKenzie D, Falk DA, Smithwick EA, Miller C, Kellogg LKB. Representing climate, disturbance, and vegetation interactions in landscape models. Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2015.04.009] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
34
|
How well do terrestrial biosphere models simulate coarse-scale runoff in the contiguous United States? Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2015.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
35
|
Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2014.11.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
36
|
Tian H, Chen G, Lu C, Xu X, Hayes DJ, Ren W, Pan S, Huntzinger DN, Wofsy SC. North American terrestrial CO 2 uptake largely offset by CH 4 and N 2O emissions: toward a full accounting of the greenhouse gas budget. CLIMATIC CHANGE 2015; 129:413-426. [PMID: 26005232 PMCID: PMC4439729 DOI: 10.1007/s10584-014-1072-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Accepted: 01/25/2014] [Indexed: 05/05/2023]
Abstract
The terrestrial ecosystems of North America have been identified as a sink of atmospheric CO2 though there is no consensus on the magnitude. However, the emissions of non-CO2 greenhouse gases (CH4 and N2O) may offset or even overturn the climate cooling effect induced by the CO2 sink. Using a coupled biogeochemical model, in this study, we have estimated the combined global warming potentials (GWP) of CO2, CH4 and N2O fluxes in North American terrestrial ecosystems and quantified the relative contributions of environmental factors to the GWP changes during 1979-2010. The uncertainty range for contemporary global warming potential has been quantified by synthesizing the existing estimates from inventory, forward modeling, and inverse modeling approaches. Our "best estimate" of net GWP for CO2, CH4 and N2O fluxes was -0.50 ± 0.27 Pg CO2 eq/year (1 Pg = 1015 g) in North American terrestrial ecosystems during 2001-2010. The emissions of CH4 and N2O from terrestrial ecosystems had offset about two thirds (73 %±14 %) of the land CO2 sink in the North American continent, showing large differences across the three countries, with offset ratios of 57 % ± 8 % in US, 83 % ± 17 % in Canada and 329 % ± 119 % in Mexico. Climate change and elevated tropospheric ozone concentration have contributed the most to GWP increase, while elevated atmospheric CO2 concentration have contributed the most to GWP reduction. Extreme drought events over certain periods could result in a positive GWP. By integrating the existing estimates, we have found a wide range of uncertainty for the combined GWP. From both climate change science and policy perspectives, it is necessary to integrate ground and satellite observations with models for a more accurate accounting of these three greenhouse gases in North America.
Collapse
Affiliation(s)
- Hanqin Tian
- International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 USA
| | - Guangsheng Chen
- International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 USA
| | - Chaoqun Lu
- International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 USA
| | - Xiaofeng Xu
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Daniel J. Hayes
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Wei Ren
- International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 USA
| | - Shufen Pan
- International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 USA
| | - Deborah N. Huntzinger
- School of Earth Sciences and Environmental Sustainability, North Arizona University, Flagstaff, AZ 86011 USA
| | - Steven C. Wofsy
- Department of Earth and Planetary Science, Harvard University, 29 Oxford St., Cambridge, MA 02138 USA
| |
Collapse
|
37
|
Antonarakis A. Uncertainty in initial forest structure and composition when predicting carbon dynamics in a temperate forest. Ecol Modell 2014. [DOI: 10.1016/j.ecolmodel.2014.07.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
38
|
Parazoo NC, Bowman K, Fisher JB, Frankenberg C, Jones DBA, Cescatti A, Pérez-Priego O, Wohlfahrt G, Montagnani L. Terrestrial gross primary production inferred from satellite fluorescence and vegetation models. GLOBAL CHANGE BIOLOGY 2014; 20:3103-3121. [PMID: 24909755 DOI: 10.1111/gcb.12652] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2013] [Revised: 03/23/2014] [Accepted: 04/29/2014] [Indexed: 06/03/2023]
Abstract
Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar-induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7-8 Pg C yr(-1) from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr(-1) ) and enhanced GPP in tropical forests (~3.7 Pg C yr(-1) ). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak-to-trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40-70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well-suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.
Collapse
Affiliation(s)
- Nicholas C Parazoo
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA; Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
39
|
Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models. REMOTE SENSING 2014. [DOI: 10.3390/rs6098945] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
40
|
Zhao S, Liu S. Scale criticality in estimating ecosystem carbon dynamics. GLOBAL CHANGE BIOLOGY 2014; 20:2240-2251. [PMID: 24323616 DOI: 10.1111/gcb.12496] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Accepted: 11/25/2013] [Indexed: 06/03/2023]
Abstract
Scaling is central to ecology and Earth system sciences. However, the importance of scale (i.e. resolution and extent) for understanding carbon dynamics across scales is poorly understood and quantified. We simulated carbon dynamics under a wide range of combinations of resolution (nine spatial resolutions of 250 m, 500 m, 1 km, 2 km, 5 km, 10 km, 20 km, 50 km, and 100 km) and extent (57 geospatial extents ranging from 108 to 1 247 034 km(2) ) in the southeastern United States to explore the existence of scale dependence of the simulated regional carbon balance. Results clearly show the existence of a critical threshold resolution for estimating carbon sequestration within a given extent and an error limit. Furthermore, an invariant power law scaling relationship was found between the critical resolution and the spatial extent as the critical resolution is proportional to A(n) (n is a constant, and A is the extent). Scale criticality and the power law relationship might be driven by the power law probability distributions of land surface and ecological quantities including disturbances at landscape to regional scales. The current overwhelming practices without considering scale criticality might have largely contributed to difficulties in balancing carbon budgets at regional and global scales.
Collapse
Affiliation(s)
- Shuqing Zhao
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | | |
Collapse
|
41
|
Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat Commun 2014; 5:4270. [DOI: 10.1038/ncomms5270] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 06/02/2014] [Indexed: 11/08/2022] Open
|
42
|
Barman R, Jain AK, Liang M. Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis. GLOBAL CHANGE BIOLOGY 2014; 20:1394-1411. [PMID: 24273031 DOI: 10.1111/gcb.12474] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Revised: 09/05/2013] [Accepted: 09/08/2013] [Indexed: 06/02/2023]
Abstract
We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2) yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.
Collapse
Affiliation(s)
- Rahul Barman
- Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | |
Collapse
|
43
|
Liu Z, Bambha RP, Pinto JP, Zeng T, Boylan J, Huang M, Lei H, Zhao C, Liu S, Mao J, Schwalm CR, Shi X, Wei Y, Michelsen HA. Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2014; 64:419-435. [PMID: 24843913 DOI: 10.1080/10962247.2013.816642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
UNLABELLED Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future. IMPLICATIONS Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.
Collapse
|
44
|
Turner DP, Jacobson AR, Ritts WD, Wang WL, Nemani R. A large proportion of North American net ecosystem production is offset by emissions from harvested products, river/stream evasion, and biomass burning. GLOBAL CHANGE BIOLOGY 2013; 19:3516-3528. [PMID: 23824790 DOI: 10.1111/gcb.12313] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 05/29/2013] [Indexed: 06/02/2023]
Abstract
Diagnostic carbon cycle models produce estimates of net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration) by integrating information from (i) satellite-based observations of land surface vegetation characteristics; (ii) distributed meteorological data; and (iii) eddy covariance flux tower observations of net ecosystem exchange (NEE) (used in model parameterization). However, a full bottom-up accounting of NEE (the vertical carbon flux) that is suitable for integration with atmosphere-based inversion modeling also includes emissions from decomposition/respiration of harvested forest and agricultural products, CO2 evasion from streams and rivers, and biomass burning. Here, we produce a daily time step NEE for North America for the year 2004 that includes NEP as well as the additional emissions. This NEE product was run in the forward mode through the CarbonTracker inversion setup to evaluate its consistency with CO2 concentration observations. The year 2004 was climatologically favorable for NEP over North America and the continental total was estimated at 1730 ± 370 TgC yr(-1) (a carbon sink). Harvested product emissions (316 ± 80 TgC yr(-1) ), river/stream evasion (158 ± 50 TgC yr(-1) ), and fire emissions (142 ± 45 TgC yr(-1) ) counteracted a large proportion (35%) of the NEP sink. Geographic areas with strong carbon sinks included Midwest US croplands, and forested regions of the Northeast, Southeast, and Pacific Northwest. The forward mode run with CarbonTracker produced good agreement between observed and simulated wintertime CO2 concentrations aggregated over eight measurement sites around North America, but overestimates of summertime concentrations that suggested an underestimation of summertime carbon uptake. As terrestrial NEP is the dominant offset to fossil fuel emission over North America, a good understanding of its spatial and temporal variation - as well as the fate of the carbon it sequesters ─ is needed for a comprehensive view of the carbon cycle.
Collapse
Affiliation(s)
- David P Turner
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331, USA
| | | | | | | | | |
Collapse
|
45
|
Raczka BM, Davis KJ, Huntzinger D, Neilson RP, Poulter B, Richardson AD, Xiao J, Baker I, Ciais P, Keenan TF, Law B, Post WM, Ricciuto D, Schaefer K, Tian H, Tomelleri E, Verbeeck H, Viovy N. Evaluation of continental carbon cycle simulations with North American flux tower observations. ECOL MONOGR 2013. [DOI: 10.1890/12-0893.1] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
46
|
Kondo M, Ichii K, Ueyama M, Mizoguchi Y, Hirata R, Saigusa N. The role of carbon flux and biometric observations in constraining a terrestrial ecosystem model: a case study in disturbed forests in East Asia. Ecol Res 2013. [DOI: 10.1007/s11284-013-1072-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
47
|
Song X, Tian H, Xu X, Hui D, Chen G, Sommers G, Marzen L, Liu M. Projecting terrestrial carbon sequestration of the southeastern United States in the 21st century. Ecosphere 2013. [DOI: 10.1890/es12-00398.1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
48
|
|
49
|
Hayes D, Turner D. The need for “apples-to-apples” comparisons of carbon dioxide source and sink estimates. ACTA ACUST UNITED AC 2012. [DOI: 10.1029/2012eo410007] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
50
|
Tian H, Chen G, Zhang C, Liu M, Sun G, Chappelka A, Ren W, Xu X, Lu C, Pan S, Chen H, Hui D, McNulty S, Lockaby G, Vance E. Century-Scale Responses of Ecosystem Carbon Storage and Flux to Multiple Environmental Changes in the Southern United States. Ecosystems 2012. [DOI: 10.1007/s10021-012-9539-x] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|