scholarly journals A comprehensive benchmarking system for evaluating global vegetation models

2012 ◽  
Vol 9 (11) ◽  
pp. 15723-15785 ◽  
Author(s):  
D. I. Kelley ◽  
I. Colin Prentice ◽  
S. P. Harrison ◽  
H. Wang ◽  
M. Simard ◽  
...  
Keyword(s):  
Primary Production ◽  
Fire Regime ◽  
Net Primary Production ◽  
Balance Model ◽  
Annual Variability ◽  
Benchmark System ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact ◽  
Benchmarking System

Abstract. We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

scholarly journals A comprehensive benchmarking system for evaluating global vegetation models

2013 ◽  
Vol 10 (5) ◽  
pp. 3313-3340 ◽  
Author(s):  
D. I. Kelley ◽  
I. C. Prentice ◽  
S. P. Harrison ◽  
H. Wang ◽  
M. Simard ◽  
...  
Keyword(s):  
Primary Production ◽  
Seasonal Cycle ◽  
Fire Regime ◽  
Net Primary Production ◽  
Annual Variability ◽  
Benchmark System ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact ◽  
Benchmarking System

Abstract. We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

scholarly journals Degradation of net primary production in a semiarid rangeland

2016 ◽  
Vol 13 (16) ◽  
pp. 4721-4734 ◽  
Author(s):  
Hasan Jackson ◽  
Stephen D. Prince
Keyword(s):  
Primary Production ◽  
Land Degradation ◽  
Land Surface ◽  
Net Primary Production ◽  
Small Areas ◽  
Small Fields ◽  
The Difference ◽  
Vegetation Models ◽  
Annual Reduction ◽  
Global Vegetation

Abstract. Anthropogenic land degradation affects many biogeophysical processes, including reductions of net primary production (NPP). Degradation occurs at scales from small fields to continental and global. While measurement and monitoring of NPP in small areas is routine in some studies, for scales larger than 1 km2, and certainly global, there is no regular monitoring and certainly no attempt to measure degradation. Quantitative and repeatable techniques to assess the extent of deleterious effects and monitor changes are needed to evaluate its effects on, for example, economic yields of primary products such as crops, lumber, and forage, and as a measure of land surface properties which are currently missing from dynamic global vegetation models, assessments of carbon sequestration, and land surface models of heat, water, and carbon exchanges. This study employed the local NPP scaling (LNS) approach to identify patterns of anthropogenic degradation of NPP in the Burdekin Dry Tropics (BDT) region of Queensland, Australia, from 2000 to 2013. The method starts with land classification based on the environmental factors presumed to control (NPP) to group pixels having similar potential NPP. Then, satellite remotely sensing data were used to compare actual NPP with its potential. The difference in units of mass of carbon and percentage loss were the measure of degradation. The entire BDT (7.45  ×  106 km2) was investigated at a spatial resolution of 250  ×  250 m. The average annual reduction in NPP due to anthropogenic land degradation in the entire BDT was −2.14 MgC m−2 yr−1, or 17 % of the non-degraded potential, and the total reduction was −214 MgC yr−1. Extreme average annual losses of 524.8 gC m−2 yr−1 were detected. Approximately 20 % of the BDT was classified as “degraded”. Varying severities and rates of degradation were found among the river basins, of which the Belyando and Suttor were highest. Interannual, negative trends in reductions of NPP occurred in 7 % of the entire region, indicating ongoing degradation. There was evidence of areas that were in a permanently degraded condition. The findings provide strong evidence and quantitative data for reductions in NPP related to anthropogenic land degradation in the BDT.

scholarly journals Degradation of net primary production in a semi-arid rangeland

2016 ◽  
Author(s):  
H. Jackson ◽  
S. D. Prince
Keyword(s):  
Primary Production ◽  
Land Degradation ◽  
Land Surface ◽  
Net Primary Production ◽  
Small Areas ◽  
Small Fields ◽  
The Difference ◽  
Vegetation Models ◽  
Annual Reduction ◽  
Global Vegetation

Abstract. Anthropogenic land degradation affects many biogeophysical processes including reductions of net primary production (NPP). Degradation occurs at scales from small fields to continental and global. While measurement and monitoring of NPP in small areas is routine in some studies, for scales larger than 1 km2, and certainly global, there is no regular monitoring and certainly no attempt to measure degradation. Quantitative and repeatable techniques to assess the extent of deleterious effects and monitor changes are needed to evaluate its effects on, for example, economic yields of primary products such as crops, lumber and forage, and as a measure of land surface properties which are currently missing from dynamic global vegetation models, assessments of carbon sequestration and land surface models of heat, water, and carbon exchanges. This study employed the Local NPP Scaling (LNS) approach to identify patterns of anthropogenic degradation of NPP in the Burdekin Dry Tropics (BDT) region of Queensland, Australia from 2000 to 2013. The method starts with land classification based on the environmental factors presumed to control (NPP) to group pixels having similar potential NPP. Then, satellite remotely sensing data were used to compare actual NPP with its potential. The difference in units of mass of carbon and percentage loss was the measure of degradation. The method is limited spatially only by the capacity to classify the land. The entire BDT (7.45x106 km2) was investigated at a spatial resolution of 250x250 m. The average annual reduction in NPP due to anthropogenic land degradation in the entire BDT was −2.14 MgC m−2 year−1 or 17 % of the non-degraded potential, and the total reduction was −214 MgC year−1. Extreme average annual losses of 524.8 gC m−2 year−1 were detected. Approximately 20 % of the BDT was classified as ‘degraded’. Varying severities and rates of degradation were found among the river basins, of which the Belyando and Suttor were highest. Inter-annual, negative trends in reductions of NPP, occurred in 7 % of the entire region, indicating on-going degradation. There was evidence of areas that were in a permanently degraded condition. The findings provide strong evidence and quantitative data for reductions in NPP related to anthropogenic land degradation in the BDT.

scholarly journals Assessing the representation of the Australian carbon cycle in global vegetation models

2021 ◽  
Author(s):  
Lina Teckentrup ◽  
Martin De Kauwe ◽  
Andy Pitman ◽  
Vladislav Bastrikov ◽  
Daniel Goll ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Residence Time ◽  
Southern Oscillation ◽  
Carbon Accumulation ◽  
Annual Variability ◽  
Australian Continent ◽  
Need To Evaluate ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

<p>Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome productivity (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, leading to marked differences in carbon accumulation in the vegetation on decadal to centennial timescales. We showed that the spread in carbon storage resulted from differences in simulated carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric CO<sub>2</sub> concentration. Notably, models with nutrient cycles did not simulate the smallest response. While our results suggested that changes in the climate forcing do not have a large impact on the carbon cycle on long timescales, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differs strongly between models. In addition, we identified simulated phenology and fire as associated with high model uncertainty, indicating differences in simulated vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and carbon residence time imply different types of vegetation cover across Australia between models, whether prescribed or resulting from model assumptions. Our study highlights the need to evaluate parameter assumptions and key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.</p>

scholarly journals Desertification in Forest, Range and Desert Landuses of Tehran Province, Under the Impact of Climate Change

2016 ◽  
Author(s):  
Hadi Eskandari Dameneh ◽  
Moslem Borji ◽  
Hassan Khosravi ◽  
Ali Salajeghe
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Net Primary Production ◽  
Degradation Process ◽  
Early Warning Systems ◽  
Land Uses ◽  
Emission Scenarios ◽  
Desert Ecosystems ◽  
Tehran Province ◽  
The Impact

Abstract. Persistence of widespread degradation in arid and semi-arid region of Iran necessitates using of monitoring and evaluation systems with appropriate accuracy to determine the degradation process and adoption of early warning systems; because after transition from some thresholds, effective reversible function of ecosystems will not be very easy. This paper tries to monitor the degradation and desertification trends in three land uses including range, forest and desert lands affected by climate change in Tehran province for 2000s and 2030s. For assessing climate changes of Mehrabad synoptic stations the data of two emission scenarios including A2 and B2 were used using statistical downscaling techniques and data generated by SDSM model. The index of net primary production resulting from MODIS satellite images was employed as an indicator of destruction from 2001 to 2010. The results showed that temperature is the most effective driver force which alters the net primary production in rangeland, forest and desert ecosystems of Tehran province. On the basis of monitoring findings under real conditions, in the 2000s, over 60 % of rangelands and 80 % of the forests have been below the average production in the province. On the other hand, the long-term average changes of NPP in rangeland and forests indicated the presence of relatively large areas of these land uses with production rate lower than the desert. The results also showed that, assuming the existence of circumstances of each emission scenarios, the desertification status will not improve significantly in the rangelands and forests of Tehran province.

scholarly journals Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

2011 ◽  
Vol 4 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
F. Maignan ◽  
F.-M. Bréon ◽  
F. Chevallier ◽  
N. Viovy ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Vegetation Dynamic ◽  
Rate Of Increase ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

scholarly journals Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenarios, climate projections, and ecosystem simulations using the ISI-MIP results

2015 ◽  
Vol 6 (2) ◽  
pp. 435-445 ◽  
Author(s):  
K. Nishina ◽  
A. Ito ◽  
P. Falloon ◽  
A. D. Friend ◽  
D. J. Beerling ◽  
...  
Keyword(s):  
Net Primary Production ◽  
Global Climate Models ◽  
Climate Projections ◽  
Emission Scenarios ◽  
Climate Division ◽  
C Dynamics ◽  
C Cycling ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Uncertainty Source

Abstract. We examined the changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVMs) obtained from the Inter-Sectoral Impact Model Intercomparison Project. Simulation results were obtained using five global climate models (GCMs) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (i.e., emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. At the end of the simulation period, changes from the year 2000 in all three variables varied considerably from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC and SOC projections, GVMs are the main influence on uncertainties (60 % and 90 %, respectively) rather than climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in vegetation carbon residence times is dominated by GVM uncertainty, particularly in the latter half of the 21st century. In addition, we found that the contribution of each uncertainty source is spatiotemporally heterogeneous and it differs among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division becomes cooler (from ca. 80 % in the equatorial division to 40 % in the snow division). Our results suggest that to assess climate change impacts on global ecosystem C cycling among each RCP scenario, the long-term C dynamics within the ecosystems (i.e., vegetation turnover and soil decomposition) are more critical factors than photosynthetic processes. The different trends in the contribution of uncertainty sources in each variable among climate divisions indicate that improvement of GVMs based on climate division or biome type will be effective. On the other hand, in dry regions, GCMs are the dominant uncertainty source in climate impact assessments of vegetation and soil C dynamics.

scholarly journals Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

2017 ◽  
Vol 14 (22) ◽  
pp. 5053-5067 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Model Estimate ◽  
Tropical Regions ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Constraint Approach ◽  
The Impact

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

scholarly journals Terrestrial trophic dynamics in the Canadian Arctic

2003 ◽  
Vol 81 (5) ◽  
pp. 827-843 ◽  
Author(s):  
Charles J Krebs ◽  
Kjell Danell ◽  
Anders Angerbjörn ◽  
Jep Agrell ◽  
Dominique Berteaux ◽  
...  
Keyword(s):  
Primary Production ◽  
Standing Crop ◽  
Net Primary Production ◽  
Canadian Arctic ◽  
Faecal Pellet ◽  
Trophic Dynamics ◽  
Balance Model ◽  
Arctic Ecosystems ◽  
Large Herbivore ◽  
Tundra Ecosystems

The Swedish Tundra Northwest Expedition of 1999 visited 17 sites throughout the Canadian Arctic. At 12 sites that were intensively sampled we estimated the standing crop of plants and the densities of herbivores and predators with an array of trapping, visual surveys, and faecal-pellet transects. We developed a trophic-balance model using ECOPATH to integrate these observations and determine the fate of primary and secondary production in these tundra ecosystems, which spanned an 8-fold range of standing crop of plants. We estimated that about 13% of net primary production was consumed by herbivores, while over 70% of small-herbivore production was estimated to flow to predators. Only 9% of large-herbivore production was consumed by predators. Organization of Canadian Arctic ecosystems appears to be more top-down than bottom-up. Net primary production does not seem to be herbivore-limited at any site. This is the first attempt to integrate trophic dynamics over the entire Canadian Arctic.

Simulation of Mediterranean forest carbon pools under expected environmental scenarios

2010 ◽  
Vol 40 (5) ◽  
pp. 850-860 ◽  
Author(s):  
M. Chiesi ◽  
M. Moriondo ◽  
F. Maselli ◽  
L. Gardin ◽  
L. Fibbi ◽  
...  
Keyword(s):  
Primary Production ◽  
Environmental Changes ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Central Italy ◽  
Forest Carbon ◽  
Simulation Approach ◽  
Two Factors ◽  
Vegetation Carbon ◽  
The Impact

Simulating the effects of possible environmental changes on the forest carbon budget requires the use of calibrated and tested models of ecosystem processes. A recently proposed simulation approach based on the use of the BIOME-BGC model was applied to yield estimates of present carbon fluxes and pools in Tuscany forests (central Italy). After the validation of these estimates against existing ground data, the simulation approach was used to assess the impact of plausible climate changes (+2 °C and increased CO2 concentration) on forest carbon dynamics (gross primary production (GPP), net primary production (NPP), and relevant allocations). The results indicate that the temperature change tends to inhibit all production and allocation processes, which are instead enhanced by the CO2 concentration rise. The combination of the two factors leads to a general increase in both GPP and NPP that is higher for deciduous oaks and chestnut (+30% and 24% for GPP and +42% and 31% for NPP, respectively). Additionally, vegetation carbon is slightly increased, while total soil carbon remains almost unchanged with respect to the present conditions. These findings are analyzed with reference to the Tuscany forest situation and previous studies on the subject.

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