scholarly journals Pan-Arctic modelling of net ecosystem exchange of CO 2

2013 ◽  
Vol 368 (1624) ◽  
pp. 20120485 ◽  
Author(s):  
G. R. Shaver ◽  
E. B. Rastetter ◽  
V. Salmon ◽  
L. E. Street ◽  
M. J. van de Weg ◽  
...  
Keyword(s):  
Leaf Area ◽  
Air Temperature ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
The Arctic ◽  
Model Parameters ◽  
Arctic Ecosystems ◽  
Area Index ◽  
Wide Range ◽  
Single Regression

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

scholarly journals Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images

2017 ◽  
Vol 12 (9) ◽  
pp. 095002 ◽  
Author(s):  
Sari Juutinen ◽  
Tarmo Virtanen ◽  
Vladimir Kondratyev ◽  
Tuomas Laurila ◽  
Maiju Linkosalmi ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Dynamics ◽  
Satellite Images ◽  
Arctic Tundra ◽  
The Arctic ◽  
Area Index

scholarly journals High NDVI and Potential Canopy Photosynthesis of South American Subtropical Forests despite Seasonal Changes in Leaf Area Index and Air Temperature

Forests ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 287-308 ◽  
Author(s):  
Piedad Cristiano ◽  
Nora Madanes ◽  
Paula Campanello ◽  
Débora di Francescantonio ◽  
Sabrina Rodríguez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Air Temperature ◽  
Seasonal Changes ◽  
South American ◽  
Canopy Photosynthesis ◽  
Area Index ◽  
Subtropical Forests

scholarly journals LaiPen LP 100 – a new device for estimating forest ecosystem leaf area index compared to the etalon: A methodologic case study

2018 ◽  
Vol 64 (No. 11) ◽  
pp. 455-468
Author(s):  
Jakub Černý ◽  
Jan Krejza ◽  
Radek Pokorný ◽  
Pavel Bednář
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Methods ◽  
Ecological Studies ◽  
Area Index ◽  
New Device ◽  
Area Efficiency ◽  
Wide Range ◽  
World Standard

Fast and precise leaf area index (LAI) estimation of a forest stand is frequently needed for a wide range of ecological studies. In the presented study, we compared side-by-side two instruments for performing LAI estimation (i.e. LaiPen LP 100 as a “newly developed device” and LAI-2200 PCA as the “world standard”), both based on indirect optical methods for performing LAI estimation in pure Norway spruce (Picea abies (Linnaeus) H. Karsten) stands under different thinning treatments. LAI values estimated by LaiPen LP 100 were approximate 5.8% lower compared to those measured by LAI-2200 PCA when averaging all collected data regardless of the thinning type. Nevertheless, when we considered the differences among LAI values at each measurement point within a regular grid, LaiPen LP 100 overestimated LAI values compared to those from LAI-2200 PCA on average by 1.4%. Therefore, both instruments are comparable. Similar LAI values between thinning from above (A) and thinning from below (B) approaches were indirectly detected by both instruments. The highest values of canopy production index and leaf area efficiency were observed within the stand thinned from above (plot A).

Enriched nutrient availability strengthens the net C uptake of the northernmost ecosystem station in Greenland

2020 ◽  
Author(s):  
Efrén López-Blanco ◽  
Marcin Jackowicz-Korczynski ◽  
Mikhail Mastepanov ◽  
Kirstine Skov ◽  
Andreas Westergaard-Nielsen ◽  
...  
Keyword(s):  
The Arctic ◽  
Leaf Mass Per Area ◽  
Sink Strength ◽  
Sampling Point ◽  
Arctic Ecosystems ◽  
Ancillary Data ◽  
Area Index ◽  
Leaf Mass ◽  
C Cycle ◽  
C Stocks

&lt;p&gt;Although the Arctic tundra is an essential contributor to the global carbon (C) cycle, there is a lack of reference sites from where full C exchange dynamics can be characterized under harsh conditions and remoteness. The Greenland Ecosystem Monitoring (GEM) programme efforts have envisioned integrated and long-term activities to contribute to the basic scientific understanding of the Arctic and their responses to climate changes. Here we present 20+ years across the 2008-2018 period of C flux and ancillary data from two twin ecosystem stations in Greenland: Zackenberg (74&amp;#176;N) and Kobbefjord (64&amp;#176;N). In this project we show that Zackenberg fen has a significant higher C sink strength in a higher latitude during regularly shorter growing seasons compared to Kobbefjord fen. This ecosystem acted as a sink of CO&lt;sub&gt;2&lt;/sub&gt; uptaking on average -50 g C m&lt;sup&gt;-2&lt;/sup&gt; (range of +21 to -90 g C m&lt;sup&gt;-2&lt;/sup&gt;), more than twice compared to Kobbefjord (-18 g C m&lt;sup&gt;-2 &lt;/sup&gt;as average and range of +41 to -41 g C m&lt;sup&gt;-2&lt;/sup&gt;). We found that Zackenberg is a nutrient richer fen - the increased C uptake strength is associated with 3 times higher levels in soils of dissolved organic carbon and 5 times more plant nutrients, including dissolved organic nitrogen, nitrates. Additional evidences from in-situ sampling point to higher leaf area index (140%), foliar nitrogen (71%), and leaf mass per area (5%) in the northernmost site supporting the nutrient richer hypothesis. To test this overarching hypothesis, we further used the Soil-Plant-Atmosphere (SPA) model. We can explain ~68%, ~80% and ~67% of the variability of daily net ecosystem exchange of CO&lt;sub&gt;2&lt;/sub&gt;, photosynthesis and respiration respectively applying the model parameterization previously used in Kobbefjord but with increases in initial C stocks, leaf mass per area, N content and Q&lt;sub&gt;10 &lt;/sub&gt;of foliar and root respiration rates. Therefore, we conclude that the limitations of plant phenology timing in Zackenberg regarding net C uptake have not only been counterbalanced but also intensified due to richer compositions of nutrients and minerals. &lt;span&gt;More high-temporal monitoring activities in Arctic ecosystems are needed not only to allow straightforward comparisons of key biogeochemical processes but also to help us understand the underlying differences in sensitive and rapidly changing ecosystems. &lt;/span&gt;&lt;/p&gt;

The Effect of Palm Age and Planting Density on the Partitioning of Assimilates in Oil Palm (Elaeis guineensis)

1988 ◽  
Vol 24 (1) ◽  
pp. 53-66 ◽  
Author(s):  
C. J. Breure
Keyword(s):  
Leaf Area ◽  
Oil Palm ◽  
Light Transmission ◽  
Elaeis Guineensis ◽  
Light Response ◽  
Planting Density ◽  
Maintenance Respiration ◽  
Area Index ◽  
Growth Respiration ◽  
Photosynthetic Assimilation

SUMMARYYield and growth records from an oil palm planting density experiment, comparing 56, 110, 148 and 186 palms ha−1, and a progeny experiment, planted at 115 and 143 palms ha−1, were used to estimate the partitioning of assimilates into those used for structural dry matter (DM) production, and those used for growth and maintenance respiration.Gross photosynthetic assimilation (A) for closed canopies was estimated from absorbed photosynthetically active radiation (PAR), derived from actual sunshine hours, and the assimilation-light response curve, to be 128 t CH2O ha−1 year−1. A for non-closed canopies was calculated by correcting for the degree of light transmission, which in turn was estimated from recorded leaf area index values (L), i.e. the total leaf area per unit ground area.Forty-eight percent of gross assimilation was used for DM production, about half of this being lost in growth respiration. The remaining 52% was lost in maintenance respiration. These losses appeared to level off before crown expansion was completed, and since trunk biomass continued to increase, maintenance respiration per unit biomass (R) decreased with age.An increase in planting density reduced the assimilates available for bunch DM, had little effect on those for vegetative growth, but strongly reduced maintenance respiration and, since biomass was little affected, reduced R. Assimilates for bunch DM ha−1 reached a maximum at L = 5.6.The observed trends in R as a function of palm age and planting density merit further study.

scholarly journals Moist moss tundra on Kapp Linne, Svalbard is a net source of CO<sub>2</sub> and CH<sub>4</sub> to the atmosphere

2021 ◽  
Author(s):  
Anders Lindroth ◽  
Norbert Pirk ◽  
Ingibjörg S. Jónsdóttir ◽  
Christian Stiegler ◽  
Leif Klemedtsson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Growing Season ◽  
Mean Value ◽  
The Arctic ◽  
Spatial And Temporal Variability ◽  
Annual Nee ◽  
Greenness Index

Abstract. We measured CO2 and CH4 fluxes using chambers and eddy covariance (only CO2) from a moist moss tundra in Svalbard. The average net ecosystem exchange (NEE) during the summer (June–August) was −0.40 g C m−2 day−1 or −37 g C m−2 for the whole summer. Including spring and autumn periods the NEE was reduced to −6.8 g C m−2 and the annual NEE became positive, 24.7 gC m−2 due to the losses during the winter. The CH4 flux during the summer period showed a large spatial and temporal variability. The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m−2s−1 which corresponds to a growing season estimate of 0.04 to 0.16 g CH4 m−2. We find that this moss tundra emits about 94–100 g CO2-equivalents m−2 yr−1 of which CH4 is responsible for 3.5–9.3 % using GWP100 of 27.9 respectively GWP20. Air temperature, soil moisture and greenness index contributed significantly to explain the variation in ecosystem respiration (Reco) while active layer depth, soil moisture and greenness index were the variables that best explained CH4 emissions. Estimate of temperature sensitivity of Reco and gross primary productivity showed that a modest increase in air temperature of 1 degree did not significantly change the NEE during the growing season but that the annual NEE would be even more positive adding another 8.5 g C m−2 to the atmosphere. We tentatively suggest that the warming of the Arctic that has already taken place is partly responsible for the fact that the moist moss tundra now is a source of CO2 to the atmosphere.

scholarly journals Pollution in the Arctic Ocean: An overview of multiple pressures and implications for ecosystem services

AMBIO ◽  
2021 ◽  
Author(s):  
Bryony L. Townhill ◽  
Efstathios Reppas-Chrysovitsinos ◽  
Roxana Sühring ◽  
Crispin J. Halsall ◽  
Elena Mengo ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Human Activities ◽  
Complex Mixture ◽  
The Arctic ◽  
Arctic Ecosystems ◽  
Anthropogenic Pressures ◽  
Fishing Activity ◽  
International Policies ◽  
Wide Range ◽  
Regulatory Decisions

AbstractThe Arctic is undergoing unprecedented change. Observations and models demonstrate significant perturbations to the physical and biological systems. Arctic species and ecosystems, particularly in the marine environment, are subject to a wide range of pressures from human activities, including exposure to a complex mixture of pollutants, climate change and fishing activity. These pressures affect the ecosystem services that the Arctic provides. Current international policies are attempting to support sustainable exploitation of Arctic resources with a view to balancing human wellbeing and environmental protection. However, assessments of the potential combined impacts of human activities are limited by data, particularly related to pollutants, a limited understanding of physical and biological processes, and single policies that are limited to ecosystem-level actions. This manuscript considers how, when combined, a suite of existing tools can be used to assess the impacts of pollutants in combination with other anthropogenic pressures on Arctic ecosystems, and on the services that these ecosystems provide. Recommendations are made for the advancement of targeted Arctic research to inform environmental practices and regulatory decisions.

scholarly journals Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

2022 ◽  
Vol 3 ◽  
Author(s):  
Azbina Rahman ◽  
Xinxuan Zhang ◽  
Paul Houser ◽  
Timothy Sauer ◽  
Viviana Maggioni
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Random Errors ◽  
Area Index ◽  
Leaf Mass ◽  
The Impact

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

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