scholarly journals Moderate forest disturbance as a stringent test for gap and big-leaf models

2014 ◽  
Vol 11 (7) ◽  
pp. 11217-11248 ◽  
Author(s):  
B. Bond-Lamberty ◽  
J. Fisk ◽  
J. A. Holm ◽  
V. Bailey ◽  
C. M. Gough
Keyword(s):  
Tree Mortality ◽  
Net Primary Production ◽  
Forest Disturbance ◽  
Structural Complexity ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Area Index ◽  
C Cycle ◽  
Key Factor ◽  
Big Leaf Model

Abstract. Disturbance-induced tree mortality is a key factor regulating the carbon balance of a forest, but tree mortality and its subsequent effects are poorly represented processes in terrestrial ecosystem models. In particular, it is unclear whether models can robustly simulate moderate (non-catastrophic) disturbances, which tend to increase biological and structural complexity and are increasingly common in aging US forests. We tested whether three forest ecosystem models – Biome-BGC, a classic big-leaf model, and the ED and ZELIG gap-oriented models – could reproduce the resilience to moderate disturbance observed in an experimentally manipulated forest (the Forest Accelerated Succession Experiment in northern Michigan, USA, in which 38% of canopy dominants were stem girdled and compared to control plots). Each model was parameterized, spun up, and disturbed following similar protocols, and run for 5 years post-disturbance. The models replicated observed declines in aboveground biomass well. Biome-BGC captured the timing and rebound of observed leaf area index (LAI), while ED and ZELIG correctly estimated the magnitude of LAI decline. None of the models fully captured the observed post-disturbance C fluxes. Biome-BGC net primary production (NPP) was correctly resilient, but for the wrong reasons, while ED and ZELIG exhibited large, unobserved drops in NPP and net ecosystem production. The biological mechanisms proposed to explain the observed rapid resilience of the C cycle are typically not incorporated by these or other models. As a result we expect that most ecosystem models, developed to simulate processes following stand-replacing disturbances, will not simulate well the gradual and less extensive tree mortality characteristic of moderate disturbances.

scholarly journals Moderate forest disturbance as a stringent test for gap and big-leaf models

2015 ◽  
Vol 12 (2) ◽  
pp. 513-526 ◽  
Author(s):  
B. Bond-Lamberty ◽  
J. P. Fisk ◽  
J. A. Holm ◽  
V. Bailey ◽  
G. Bohrer ◽  
...  
Keyword(s):  
Primary Production ◽  
Tree Mortality ◽  
Net Primary Production ◽  
Forest Disturbance ◽  
Gross Primary Production ◽  
Structural Complexity ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Area Index ◽  
C Cycle

Abstract. Disturbance-induced tree mortality is a key factor regulating the carbon balance of a forest, but tree mortality and its subsequent effects are poorly represented processes in terrestrial ecosystem models. It is thus unclear whether models can robustly simulate moderate (non-catastrophic) disturbances, which tend to increase biological and structural complexity and are increasingly common in aging US forests. We tested whether three forest ecosystem models – Biome-BGC (BioGeochemical Cycles), a classic big-leaf model, and the ZELIG and ED (Ecosystem Demography) gap-oriented models – could reproduce the resilience to moderate disturbance observed in an experimentally manipulated forest (the Forest Accelerated Succession Experiment in northern Michigan, USA, in which 38% of canopy dominants were stem girdled and compared to control plots). Each model was parameterized, spun up, and disturbed following similar protocols and run for 5 years post-disturbance. The models replicated observed declines in aboveground biomass well. Biome-BGC captured the timing and rebound of observed leaf area index (LAI), while ZELIG and ED correctly estimated the magnitude of LAI decline. None of the models fully captured the observed post-disturbance C fluxes, in particular gross primary production or net primary production (NPP). Biome-BGC NPP was correctly resilient but for the wrong reasons, and could not match the absolute observational values. ZELIG and ED, in contrast, exhibited large, unobserved drops in NPP and net ecosystem production. The biological mechanisms proposed to explain the observed rapid resilience of the C cycle are typically not incorporated by these or other models. It is thus an open question whether most ecosystem models will simulate correctly the gradual and less extensive tree mortality characteristic of moderate disturbances.

scholarly journals Small-Scale Forest Structure Influences Spatial Variability of Belowground Carbon Fluxes in a Mature Mediterranean Beech Forest

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 255 ◽  
Author(s):  
Ettore D’Andrea ◽  
Gabriele Guidolotti ◽  
Andrea Scartazza ◽  
Paolo De Angelis ◽  
Giorgio Matteucci
Keyword(s):  
Spatial Variability ◽  
Forest Structure ◽  
Net Primary Production ◽  
Beech Forest ◽  
Environmental Drivers ◽  
Root Production ◽  
Small Scale ◽  
Co2 Efflux ◽  
Area Index ◽  
C Cycle

The tree belowground compartment, especially fine roots, plays a relevant role in the forest ecosystem carbon (C) cycle, contributing largely to soil CO2 efflux (SR) and to net primary production (NPP). Beyond the well-known role of environmental drivers on fine root production (FRP) and SR, other determinants such as forest structure are still poorly understood. We investigated spatial variability of FRP, SR, forest structural traits, and their reciprocal interactions in a mature beech forest in the Mediterranean mountains. In the year of study, FRP resulted in the main component of NPP and explained about 70% of spatial variability of SR. Moreover, FRP was strictly driven by leaf area index (LAI) and soil water content (SWC). These results suggest a framework of close interactions between structural and functional forest features at the local scale to optimize C source–sink relationships under climate variability in a Mediterranean mature beech forest.

scholarly journals Effects of carbon turnover time on terrestrial ecosystem carbon storage

2017 ◽  
Vol 14 (23) ◽  
pp. 5441-5454 ◽  
Author(s):  
Yaner Yan ◽  
Xuhui Zhou ◽  
Lifeng Jiang ◽  
Yiqi Luo
Keyword(s):  
Primary Production ◽  
Spatial Variation ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Soil C ◽  
C Storage ◽  
Key Factor

Abstract. Carbon (C) turnover time is a key factor in determining C storage capacity in various plant and soil pools as well as terrestrial C sink in a changing climate. However, the effects of C turnover time on ecosystem C storage have not been well explored. In this study, we compared mean C turnover times (MTTs) of ecosystem and soil, examined their variability to climate, and then quantified the spatial variation in ecosystem C storage over time from changes in C turnover time and/or net primary production (NPP). Our results showed that mean ecosystem MTT based on gross primary production (GPP; MTTEC_GPP =  Cpool/GPP, 25.0 ± 2.7 years) was shorter than soil MTT (MTTsoil =  Csoil/NPP, 35.5 ± 1.2 years) and NPP-based ecosystem MTT (MTTEC_NPP =  Cpool/NPP, 50.8 ± 3 years; Cpool and Csoil referred to ecosystem or soil C storage, respectively). On the biome scale, temperature is the best predictor for MTTEC (R2 =  0.77, p < 0.001) and MTTsoil (R2 =  0.68, p < 0.001), while the inclusion of precipitation in the model did not improve the performance of MTTEC (R2 =  0.76, p < 0.001). Ecosystem MTT decreased by approximately 4 years from 1901 to 2011 when only temperature was considered, resulting in a large C release from terrestrial ecosystems. The resultant terrestrial C release caused by the decrease in MTT only accounted for about 13.5 % of that due to the change in NPP uptake (159.3 ± 1.45 vs. 1215.4 ± 11.0 Pg C). However, the larger uncertainties in the spatial variation of MTT than temporal changes could lead to a greater impact on ecosystem C storage, which deserves further study in the future.

scholarly journals Importance of succession in estimating biomass loss: Combining remote sensing and individual-based forest models

2020 ◽  
Author(s):  
Ulrike Hiltner ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tree Mortality ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Mortality Rates ◽  
Area Index ◽  
Forest Height ◽  
Biomass Loss

Abstract. Disturbances can have strong impacts on the dynamics and structure of tropical forests. They often lead to increased tree mortality and affect their behaviour as carbon sinks. In the future, the intensity of disturbances, such as extreme weather events, fires, floods, and biotic agents, will probably even increase, with more serious consequences for tropical forests than we have already observed. However, impacts of altering disturbances on rates of forest biomass loss through tree mortality (hereinafter: biomass mortality) have been little described yet. This complicates progress in quantifying the effects of climate change on forests globally. This study aims to analyse the consequences of elevated tree mortality on forest dynamics and to provide a methodology that can reduce uncertainties in estimating biomass mortality rates at local and country level. We achieved this by linking benefits of individual-based forest model-ling, statistical linear regression, and remote sensing. We applied an individual-based forest model to investigate the impact of varying disturbance regimes on the succession dynamic of a humid Terra Firma forest at the Paracou study site in French Guiana. By simulating increased tree mortality rates, we were able to investigate their influence on several forest attributes, namely biomass, leaf area index, forest height, gross primary production, net primary production, and biomass mortality. Based on simulations of leaf area index and forest height, we developed a linear multivariate regression model to project biomass mortality. Our findings demonstrate that severe disturbances altered the succession pattern of the forests in favour of fast-growing species, which changed gross primary production, but net primary production remained stable. We also observed a strong influence on biomass mortality rates as well as observed complex relationships between these rates and single forest attributes (leaf area index, forest height, and biomass). By combining leaf area index and forest height we obtained relationships that allow an estimation of the biomass mortality. Based on these findings, we mapped the biomass mortality for whole French Guiana. We found a nation-wide biomass mortality of 3 % per year (standard deviation = 1.4 % per year). The approach we describe here, provides a novel methodology for quantifying the spatial-temporal distribution of biomass loss, which has recently been identified as particularly critical for monitoring mortality hot spots. Quantifying biomass mortality rates may help reducing uncertainties in the terrestrial component of the global carbon cycle.

scholarly journals Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Shufen Pan ◽  
Hanqin Tian ◽  
Shree R. S. Dangal ◽  
Zhiyun Ouyang ◽  
Bo Tao ◽  
...  
Keyword(s):  
Primary Production ◽  
Model Simulation ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Field Measurements ◽  
Ecosystem Model ◽  
Global Environment ◽  
Ecosystem Models ◽  
Key Indicator

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment.

scholarly journals Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 314
Author(s):  
Andrew Revill ◽  
Vasileios Myrgiotis ◽  
Anna Florence ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Field Experiments ◽  
Process Models ◽  
Agricultural Technologies ◽  
N Application ◽  
Area Index ◽  
Complex Process ◽  
C Cycle ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Honglin He ◽  
Rong Ge ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
Qingqing Chang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Global Climate ◽  
Meteorological Data ◽  
C Sequestration ◽  
Research Network ◽  
Soil Biology ◽  
Area Index ◽  
Ecosystem Research ◽  
Organic C ◽  
C Cycle

AbstractChinese forests cover most of the representative forest types in the Northern Hemisphere and function as a large carbon (C) sink in the global C cycle. The availability of long-term C dynamics observations is key to evaluating and understanding C sequestration of these forests. The Chinese Ecosystem Research Network has conducted normalized and systematic monitoring of the soil-biology-atmosphere-water cycle in Chinese forests since 2000. For the first time, a reference dataset of the decadal C cycle dynamics was produced for 10 typical Chinese forests after strict quality control, including biomass, leaf area index, litterfall, soil organic C, and the corresponding meteorological data. Based on these basic but time-discrete C-cycle elements, an assimilated dataset of key C cycle parameters and time-continuous C sequestration functions was generated via model-data fusion, including C allocation, turnover, and soil, vegetation, and ecosystem C storage. These reference data could be used as a benchmark for model development, evaluation and C cycle research under global climate change for typical forests in the Northern Hemisphere.

scholarly journals Over half of western United States' most abundant tree species in decline

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hunter Stanke ◽  
Andrew O. Finley ◽  
Grant M. Domke ◽  
Aaron S. Weed ◽  
David W. MacFarlane
Keyword(s):  
United States ◽  
Tree Species ◽  
Tree Mortality ◽  
Temperate Forests ◽  
Size Structure ◽  
Forest Disturbance ◽  
Population Decline ◽  
Western United States ◽  
Scale Transformation ◽  
Forest Composition

AbstractChanging forest disturbance regimes and climate are driving accelerated tree mortality across temperate forests. However, it remains unknown if elevated mortality has induced decline of tree populations and the ecological, economic, and social benefits they provide. Here, we develop a standardized forest demographic index and use it to quantify trends in tree population dynamics over the last two decades in the western United States. The rate and pattern of change we observe across species and tree size-distributions is alarming and often undesirable. We observe significant population decline in a majority of species examined, show decline was particularly severe, albeit size-dependent, among subalpine tree species, and provide evidence of widespread shifts in the size-structure of montane forests. Our findings offer a stark warning of changing forest composition and structure across the western US, and suggest that sustained anthropogenic and natural stress will likely result in broad-scale transformation of temperate forests globally.

scholarly journals Estimation of Incident Photosynthetically Active Radiation from GOES Visible Imagery

2008 ◽  
Vol 47 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Tao Zheng ◽  
Shunlin Liang ◽  
Kaicun Wang
Keyword(s):  
Photosynthetically Active Radiation ◽  
Terrestrial Ecosystem ◽  
New Method ◽  
High Temporal Resolution ◽  
Atmospheric Conditions ◽  
Ecosystem Models ◽  
Surface Conditions ◽  
Active Radiation ◽  
Ground Measurement ◽  
Visible Imagery

Abstract Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.

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