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Cameron D, Hartig F, Minnuno F, Oberpriller J, Reineking B, Van Oijen M, Dietze M. Issues in calibrating models with multiple unbalanced constraints: the significance of systematic model and data errors. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14002] [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]
Affiliation(s)
- David Cameron
- UK Centre for Ecology and Hydrology Bush Estate Penicuik UK
| | - Florian Hartig
- Theoretical Ecology University of Regensburg Regensburg Germany
| | - Francesco Minnuno
- Department of Forest Sciences University of Helsinki Helsinki Finland
| | | | | | | | - Michael Dietze
- Department of Earth & Environment Boston University Boston Massachusetts USA
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Halder S, Tiwari YK, Valsala V, Sijikumar S, Janardanan R, Maksyutov S. Benefits of satellite XCO 2 and newly proposed atmospheric CO 2 observation network over India in constraining regional CO 2 fluxes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 812:151508. [PMID: 34762957 DOI: 10.1016/j.scitotenv.2021.151508] [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: 07/14/2021] [Revised: 10/13/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Top-down modeling estimates are among the most reliable information available on the CO2 fluxes of the earth system. The inadequate coverage of CO2 observing stations over the tropical regions adds a limitation to this estimate, especially when the satellite XCO2 is strictly screened for cloud contamination, aerosol, dust, etc. In this study, we investigated the potential benefit of a global ground-based observing station network, 17 newly proposed stations over India, and global satellite XCO2 in reducing the uncertainty of terrestrial biospheric fluxes of Tropical Asia-Eurasia in TransCom cyclo-stationary inversion. The data from selected 80 global ground-based CO2 observation stations, together with two additional stations from India (i.e., Cape Rama and Sinhagad) and satellite XCO2, helps to reduce the temperate Eurasian terrestrial flux uncertainty by 23.8%, 26.4%, and 36.2%, respectively. This further improved to 54.7% by adding the newly proposed stations over India into the inversion. By separating the Indian sub-continent from temperate Eurasia (as inspired by the heterogeneity in the terrestrial ecosystems, prevailing meteorological conditions, and the orography of this vast region), the inversion evinces the capacity of existing CO2 observations to reduce the Indian terrestrial flux uncertainty by 20.5%. The largest benefit (70% reduction of annual mean uncertainty) for estimating Indian terrestrial fluxes could be achieved by combining these global observations with data from the newly proposed stations over India. The existing two stations from India suggest Temperate Eurasia as a mild source of CO2 (0.33 ± 0.57 Pg C yr-1), albeit with prominent anthropogenic influences visible in these two stations during the dry seasons. This implies that the proposed new stations should be cautiously placed to avoid such effects. The study also finds that the newly proposed stations over India also have an impact in constraining nearby oceanic CO2 fluxes.
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Affiliation(s)
- Santanu Halder
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India
| | - Yogesh K Tiwari
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India.
| | - Vinu Valsala
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India
| | - S Sijikumar
- Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India
| | - Rajesh Janardanan
- Satellite Observation Center, Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan
| | - Shamil Maksyutov
- Satellite Observation Center, Earth System Division, National Institute for Environmental Studies, Tsukuba, Japan
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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.
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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
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Wei Y, Shrestha R, Pal S, Gerken T, Feng S, McNelis J, Singh D, Thornton MM, Boyer AG, Shook MA, Chen G, Baier BC, Barkley ZR, Barrick JD, Bennett JR, Browell EV, Campbell JF, Campbell LJ, Choi Y, Collins J, Dobler J, Eckl M, Fiehn A, Fried A, Digangi JP, Barton‐Grimley R, Halliday H, Klausner T, Kooi S, Kostinek J, Lauvaux T, Lin B, McGill MJ, Meadows B, Miles NL, Nehrir AR, Nowak JB, Obland M, O’Dell C, Fao RMP, Richardson SJ, Richter D, Roiger A, Sweeney C, Walega J, Weibring P, Williams CA, Yang MM, Zhou Y, Davis KJ. Atmospheric Carbon and Transport - America (ACT-America) Data Sets: Description, Management, and Delivery. EARTH AND SPACE SCIENCE (HOBOKEN, N.J.) 2021; 8:e2020EA001634. [PMID: 34435081 PMCID: PMC8365738 DOI: 10.1029/2020ea001634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/19/2021] [Accepted: 05/09/2021] [Indexed: 06/13/2023]
Abstract
The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016-2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 h of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT-America data sets.
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Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method. FORESTS 2020. [DOI: 10.3390/f11121369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy.
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Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination. PLoS Negl Trop Dis 2018; 12:e0006674. [PMID: 30296266 PMCID: PMC6175292 DOI: 10.1371/journal.pntd.0006674] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 07/09/2018] [Indexed: 12/27/2022] Open
Abstract
Background Mathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models. Methodology and principal findings We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination. Significance Knowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location. Although parasite transmission models offer powerful tools for predicting the impacts of interventions, there is growing realization that these models can be useful for this purpose only if their governing biological processes are well defined. Recently, model-data assimilation has been applied to address this problem and improve the performance of process-oriented models for ecological forecasting. Here, we developed an analytical framework that allowed the sequential coupling of the three existing lymphatic filariasis (LF) models with longitudinal infection monitoring data collected in field sites undergoing mass drug administrations (MDAs) to examine the relative value of such data for parameterizing these models and for improving their predictions of the required durations of drug interventions to break parasite transmission. We found that data-informed models provided more precise and reliable forecasts of elimination timelines in the study sites compared to model-only predictions, and that data collected up to 5 years post-MDA reduced each model’s predictive uncertainty most. We also found that this improved performance may be intriguingly related to temporal changes in system dynamics. Our results underscore the significance of sequential model-data fusion for enhancing the understanding of LF transmission dynamics, design of surveillance, and generation of reliable model predictions for management decision making.
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Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2018.03.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Shi Z, Xu X, Hararuk O, Jiang L, Xia J, Liang J, Li D, Luo Y. Experimental warming altered rates of carbon processes, allocation, and carbon storage in a tallgrass prairie. Ecosphere 2015. [DOI: 10.1890/es14-00335.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Liu M, He H, Ren X, Sun X, Yu G, Han S, Wang H, Zhou G. The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems. Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2015.01.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Keenan TF, Davidson EA, Munger JW, Richardson AD. Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2013; 23:273-86. [PMID: 23495651 DOI: 10.1890/12-0747.1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Primarily driven by concern about rising levels of atmospheric CO2, ecologists and earth system scientists are collecting vast amounts of data related to the carbon cycle. These measurements are generally time consuming and expensive to make, and, unfortunately, we live in an era where research funding is increasingly hard to come by. Thus, important questions are: "Which data streams provide the most valuable information?" and "How much data do we need?" These questions are relevant not only for model developers, who need observational data to improve, constrain, and test their models, but also for experimentalists and those designing ecological observation networks. Here we address these questions using a model-data fusion approach. We constrain a process-oriented, forest ecosystem C cycle model with 17 different data streams from the Harvard Forest (Massachusetts, USA). We iteratively rank each data source according to its contribution to reducing model uncertainty. Results show the importance of some measurements commonly unavailable to carbon-cycle modelers, such as estimates of turnover times from different carbon pools. Surprisingly, many data sources are relatively redundant in the presence of others and do not lead to a significant improvement in model performance. A few select data sources lead to the largest reduction in parameter-based model uncertainty. Projections of future carbon cycling were poorly constrained when only hourly net-ecosystem-exchange measurements were used to inform the model. They were well constrained, however, with only 5 of the 17 data streams, even though many individual parameters are not constrained. The approach taken here should stimulate further cooperation between modelers and measurement teams and may be useful in the context of setting research priorities and allocating research funds.
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Affiliation(s)
- Trevor F Keenan
- Department of Organismic and Evolutionary Biology, Harvard University, 22 Divinity Avenue, Cambridge, Massachusetts 02138, USA.
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Schaefer K, Schwalm CR, Williams C, Arain MA, Barr A, Chen JM, Davis KJ, Dimitrov D, Hilton TW, Hollinger DY, Humphreys E, Poulter B, Raczka BM, Richardson AD, Sahoo A, Thornton P, Vargas R, Verbeeck H, Anderson R, Baker I, Black TA, Bolstad P, Chen J, Curtis PS, Desai AR, Dietze M, Dragoni D, Gough C, Grant RF, Gu L, Jain A, Kucharik C, Law B, Liu S, Lokipitiya E, Margolis HA, Matamala R, McCaughey JH, Monson R, Munger JW, Oechel W, Peng C, Price DT, Ricciuto D, Riley WJ, Roulet N, Tian H, Tonitto C, Torn M, Weng E, Zhou X. A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis. ACTA ACUST UNITED AC 2012. [DOI: 10.1029/2012jg001960] [Citation(s) in RCA: 241] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Liu S, Bond-Lamberty B, Hicke JA, Vargas R, Zhao S, Chen J, Edburg SL, Hu Y, Liu J, McGuire AD, Xiao J, Keane R, Yuan W, Tang J, Luo Y, Potter C, Oeding J. Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges. ACTA ACUST UNITED AC 2011. [DOI: 10.1029/2010jg001585] [Citation(s) in RCA: 99] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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