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 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 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 Using Terrestrial Laser Scanning to Characterize Peatland Microtopography and Assess Tree Growth Responses to Elevated Temperature and CO2

2020 ◽  
Author(s):  
Jake D. Graham
Keyword(s):  
Elevated Temperature ◽  
Environmental Change ◽  
Primary Production ◽  
Tree Growth ◽  
Land Surface ◽  
Net Primary Production ◽  
Growth Responses ◽  
Aboveground Net Primary Production ◽  
Response To Temperature ◽  
Northern Peatlands

Northern peatlands are a major terrestrial carbon (C) store, with an annual sink of 0.1 Pg C yr-1 and a total storage estimate of 547 Pg C. Northern peatlands are also major contributors of atmospheric methane, a potent greenhouse gas. The microtopography of peatlands helps modulate peatland carbon fluxes; however, there is a lack of quantitative characterizations of microtopography in the literature. The lack of formalized schemes to characterize microtopography makes comparisons between studies difficult. Further, many land surface models do not accurately simulate peatland C emissions, in part because they do not adequately represent peatland microtopography and hydrology. The C balance of peatlands is determined by differences in C influxes and effluxes, with the largest being net primary production and heterotrophic respiration, respectively. Tree net primary production at a treed bog in northern Minnesota represented about 13% of C inputs to the peatland, and marks tree aboveground net primary production (ANPP) as an important pathway for C to enter peatlands. Tree species Picea mariana (Black spruce) and Larix Laricina (Tamarack) are typically found in wooded peatlands in North America, and are widely distributed in the North American boreal zone. Therefore, understanding how these species will respond to environmental change is needed to make predictions of peatland C budgets in the future. As the climate warms, peatlands are expected to increase C release to the atmosphere, resulting in a positive feedback loop. Further, climate warming is expected to occur faster in northern latitudes compared to the rest of the globe. The Spruce and Peatland Responses Under Changing Environments (SPRUCE; https://mnspruce.ornl.gov/) manipulates temperature and CO2 concentrations to evaluate the in-situ response of a peatland to environmental change and is located in Minnesota, USA. In this dissertation, I documented surface roughness metrics for peatland microtopography in SPRUCE plots and developed three explicit methods for classifying frequently used microtopographic classes (microforms) for different scientific applications. Subsequently I used one of these characterizations to perform a sensitivity analysis and improve the parameterization of microtopography in a land surface model that was calibrated at the SPRUCE site. The modeled outputs of C from the analyses ranged from 0.8-34.8% when microtopographical parameters were allowed to vary within observed ranges. Further, C related outputs when using our data-driven parameterization differed from outputs when using the default parameterization by -7.9 - 12.2%. Finally, I utilized TLS point clouds to assess the effect elevated temperature and CO2 concentrations had on P. mariana and L. laricina after the first four years of SPRUCE treatments. I observed that P. mariana growth (aboveground net primary production) had a negative response to temperature initially, but the relationship became less pronounced through time. Conversely, L. laricina had no growth response to temperature initially, but developed a positive relationship through time. The divergent growth responses of P. mariana and L. laricina resulted in no detectable change in aboveground net primary production at the community level. Results from this dissertation help improve how peatland microtopography is represented, and improves understanding of how peatland tree growth will respond to environmental change in the future.

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 Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

2021 ◽  
Vol 18 (1) ◽  
pp. 95-112
Author(s):  
Peter Horvath ◽  
Hui Tang ◽  
Rune Halvorsen ◽  
Frode Stordal ◽  
Lena Merete Tallaksen ◽  
...  
Keyword(s):  
High Latitude ◽  
Vegetation Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Radiative Properties ◽  
Community Land Model ◽  
Precipitation Seasonality ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin

2003 ◽  
Vol 33 (10) ◽  
pp. 2007-2018 ◽  
Author(s):  
S N Burrows ◽  
S T Gower ◽  
J M Norman ◽  
G Diak ◽  
D S Mackay ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Vegetation Cover ◽  
Land Surface ◽  
Net Primary Production ◽  
Mixed Forest ◽  
Forested Wetlands ◽  
Area Index

Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPPA) for a land surface in northern Wisconsin, examine the spatial patterns of NPPA and its components, and correlate NPPA with vegetation cover types and leaf area index. Mean NPPA for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha–1·year–1, respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPPA (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPPA differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPPA was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPPA for the 3 km × 2 km site was 5.8 t dry mass·ha–1·year–1.

scholarly journals Multiple steady-states in the terrestrial atmosphere-biosphere system: a result of a discrete vegetation classification?

2007 ◽  
Vol 4 (5) ◽  
pp. 707-714 ◽  
Author(s):  
A. Kleidon ◽  
K. Fraedrich ◽  
C. Low
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Steady States ◽  
Multiple Steady States ◽  
Atmospheric Conditions ◽  
Vegetation Productivity ◽  
Positive Feedbacks ◽  
Terrestrial Atmosphere ◽  
The Difference ◽  
Global Vegetation

Abstract. Multiple steady states in the atmosphere-biosphere system can arise as a consequence of interactions and positive feedbacks. While atmospheric conditions affect vegetation productivity in terms of available light, water, and heat, different levels of vegetation productivity can result in differing energy- and water partitioning at the land surface, thereby leading to different atmospheric conditions. Here we investigate the emergence of multiple steady states in the terrestrial atmosphere-biosphere system and focus on the role of how vegetation is represented in the model: (i) in terms of a few, discrete vegetation classes, or (ii) a continuous representation. We then conduct sensitivity simulations with respect to initial conditions and to the number of discrete vegetation classes in order to investigate the emergence of multiple steady states. We find that multiple steady states occur in our model only if vegetation is represented by a few vegetation classes. With an increased number of classes, the difference between the number of multiple steady states diminishes, and disappears completely in our model when vegetation is represented by 8 classes or more. Despite the convergence of the multiple steady states into a single one, the resulting climate-vegetation state is nevertheless less productive when compared to the emerging state associated with the continuous vegetation parameterization. We conclude from these results that the representation of vegetation in terms of a few, discrete vegetation classes can result (a) in an artificial emergence of multiple steady states and (b) in a underestimation of vegetation productivity. Both of these aspects are important limitations to be considered when global vegetation-atmosphere models are to be applied to topics of global change.

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