scholarly journals Dynamics of productivity of restored vegetation of the Izykhsky surface mine (Terra Modis)

2020 ◽  
Vol 223 ◽  
pp. 03004
Author(s):  
Alexander Zhukov ◽  
Elena Zhukova
Keyword(s):  
Primary Production ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Ecological Monitoring ◽  
Ecological Niches ◽  
Positive Trend ◽  
Surface Mine ◽  
Processing Information ◽  
Terra Modis

The subject of the study is the course of the dynamics of vegetation productivity on the dumps of the surface mine «Izykhsky» (2000-2019), using Terra Modis. The aim is to identify patterns of seasonal and long-term dynamics of restored vegetation as a result of succession according to images. The methodology included processing information on gross primary production and evapotranspiration, as well as identifying the relationship with meteorological data for 2018-2019. There is a positive trend in the long-term dynamics – in terms of total gross primary production from 1.7 to 5.5 kg/m2/8 days and in evapotranspiration from 1142 to 2784 kg/m2/8 days. The evapotranspiration correlated with the productivity. Since 2016, productivity has reached a plateau, which indicates the development of ecological niches by plants. The phytomass of the restored vegetation was in 1.5 times greater, than the mass of the steppe site. Seasonal dynamics in 2019 showed that communities on dumps have higher productivity, in contrast to the steppe, and one peak in the first ten days of July. The sum of temperatures and productivity had a high relationship (R2 = 0.7) in comparison with the steppe (R2 = 0.2). Terra Modis data can be applied in the field of ecological monitoring of vegetation of coal dumps.

scholarly journals Long-term drainage reduces CO<sub>2</sub> uptake and increases CO<sub>2</sub> emission on a Siberian floodplain due to shifts in vegetation community and soil thermal characteristics

2016 ◽  
Vol 13 (14) ◽  
pp. 4219-4235 ◽  
Author(s):  
Min Jung Kwon ◽  
Martin Heimann ◽  
Olaf Kolle ◽  
Kristina A. Luus ◽  
Edward A. G. Schuur ◽  
...  
Keyword(s):  
Primary Production ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Thermal Characteristics ◽  
Growing Season ◽  
Co2 Uptake ◽  
Vegetation Community ◽  
Uptake Rates ◽  
Soil Temperatures

Abstract. With increasing air temperatures and changing precipitation patterns forecast for the Arctic over the coming decades, the thawing of ice-rich permafrost is expected to increasingly alter hydrological conditions by creating mosaics of wetter and drier areas. The objective of this study is to investigate how 10 years of lowered water table depths of wet floodplain ecosystems would affect CO2 fluxes measured using a closed chamber system, focusing on the role of long-term changes in soil thermal characteristics and vegetation community structure. Drainage diminishes the heat capacity and thermal conductivity of organic soil, leading to warmer soil temperatures in shallow layers during the daytime and colder soil temperatures in deeper layers, resulting in a reduction in thaw depths. These soil temperature changes can intensify growing-season heterotrophic respiration by up to 95 %. With decreased autotrophic respiration due to reduced gross primary production under these dry conditions, the differences in ecosystem respiration rates in the present study were 25 %. We also found that a decade-long drainage installation significantly increased shrub abundance, while decreasing Eriophorum angustifolium abundance resulted in Carex sp. dominance. These two changes had opposing influences on gross primary production during the growing season: while the increased abundance of shrubs slightly increased gross primary production, the replacement of E. angustifolium by Carex sp.  significantly decreased it. With the effects of ecosystem respiration and gross primary production combined, net CO2 uptake rates varied between the two years, which can be attributed to Carex-dominated plots' sensitivity to climate. However, underlying processes showed consistent patterns: 10 years of drainage increased soil temperatures in shallow layers and replaced E. angustifolium by Carex sp., which increased CO2 emission and reduced CO2 uptake rates. During the non-growing season, drainage resulted in 4 times more CO2 emissions, with high sporadic fluxes; these fluxes were induced by soil temperatures, E. angustifolium abundance, and air pressure.

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest

2019 ◽  
Vol 11 (14) ◽  
pp. 1688 ◽  
Author(s):  
Christian Dold ◽  
Jerry L. Hatfield ◽  
John H. Prueger ◽  
Tom B. Moorman ◽  
Tom J. Sauer ◽  
...  
Keyword(s):  
Land Use ◽  
Primary Production ◽  
Production Systems ◽  
Ecosystem Respiration ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Corn Yield ◽  
Yield Data ◽  
Carbon Dioxide Fluxes ◽  
Land Use Maps

The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( x ¯ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m−2 for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m−2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ−1 for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m−2 d−1, for corn and soybean, respectively, which is an improvement to the GPPMODIS product (2.44 and 3.30 g C m−2 d−1, respectively). Corn yield, calculated from GPPcalc (12.82 ± 0.65 Mg ha−1), corresponded well to official yield data (13.09 ± 0.09 Mg ha−1), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha−1). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.

scholarly journals P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

2020 ◽  
Vol 13 (3) ◽  
pp. 1545-1581 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Quantum Yield ◽  
Primary Production ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals The influence of local climate change on the productivity of spring cereals in the Kirov region

2021 ◽  
Vol 22 (2) ◽  
pp. 244-253
Author(s):  
I. V. Lyskova ◽  
O. E. Sukhoveeva ◽  
T. V. Lyskova
Keyword(s):  
Air Temperature ◽  
Spring Wheat ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Eastern Region ◽  
Positive Trend ◽  
Local Climate ◽  
Temperature And Precipitation ◽  
Spring Cereals

On the basis of long-term meteorological data and research results in a long-term stationary experiment of 1971-2020 a retrospective analysis of changes in air temperature and precipitation in the eastern region of the central climatic zone of the Kirov region was carried out and the influence of these characteristics on the dynamics of the yield of spring cereals was estimated. It has been established that the average annual air temperature during the research period was 2.4±1.0 °C. At the same time, its stable positive trend was observed at the rate of 0.39 °С /10 years. Two decades from 2001 to 2020 were recorded as the warmest for 50 years, when the temperature was 0.7...2.6 °C above climate normal. Selyaninov hydrothermal coefficient (0.7...2.1) testifies to the contrasting conditions of humidification of the vegetation periods during the research years – from drought to excessively humidified. In a long-term experiment, the yield of spring cereals increased in the row wheat – barley – oats: 2.17±0.86, 3.04±0.61, 3.39±0.65 t/ha, respectively. Strong correlations were marked between the average yield (spring wheat) and weather conditions in June: reverse with air temperature (rр = -0.735) and direct with the amount of precipitation (rр = 0.686). It has been established that the use of phosphorus fertilizers (and their aftereffect) in combination with nitrogen-potassium fertilizers weakened the influence of weather conditions on the productivity of spring wheat: the determination coefficients (R2), which reflect the portion of variability due to weather conditions, were 0.59-0.73 for the variant without fertilizers and decreased to 0.50-0.56 when applying NP3K.

scholarly journals Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

2020 ◽  
Vol 12 (4) ◽  
pp. 2725-2746
Author(s):  
Yi Zheng ◽  
Ruoque Shen ◽  
Yawen Wang ◽  
Xiangqian Li ◽  
Shuguang Liu ◽  
...  
Keyword(s):  
Primary Production ◽  
Environmental Variables ◽  
Gross Primary Production ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Interannual Variations ◽  
Light Use Efficiency ◽  
Long Term Changes ◽  
Use Efficiency

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).

scholarly journals The productivity of agrocoenosis in the steppe zone of Khakasia according to Terra Modis

2020 ◽  
Vol 149 ◽  
pp. 03002
Author(s):  
Elena Zhukova ◽  
Natalya Kutkina ◽  
Alexander Zhukov
Keyword(s):  
Gross Primary Production ◽  
Growing Season ◽  
Steppe Zone ◽  
Perennial Grasses ◽  
Hydrothermal Coefficient ◽  
Arable Fields ◽  
Increase In Productivity ◽  
Terra Modis ◽  
Integral Productivity

The article presents the results of a quantitative assessment of the productivity of agrocoenosis according to Terra MODIS within the steppe zone of Khakasia. The gross primary production of various agrocoenosis during the growing season ranges from 3.42 to 7.41 kg/m2. On average, the integral productivity for the growing season of 2018 was for wheat – 5.01 ± 1.04 kg / m2, oats – 4.86 ± 1.12 kg / m2, barley – 4.52 ± 0.42 kg / m2, buckwheat – 5.25 kg / m2, hayfields – 4.86 ± 0.45 kg / m2, deposits – 4.39 ± 0.40 kg / m2. Least of all phytomass were some arable fields and low-yielding crops of wheat and abandoned lands, and most of all perennial grasses and high-yielding wheat and oats. The GPP indicators are associated with the sum of positive temperatures positively (R2×0.673), to a lesser extent (R2×0.333) with a hydrothermal coefficient for the examined agrocoenosis. The most productive crops and fertile territories were determined based on the seasonal and long-term dynamics of gross productivity. An increase in productivity for some agrocenoses was noted in 2018 according to satellite data.

scholarly journals Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements

2003 ◽  
Vol 17 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tagir G. Gilmanov ◽  
Shashi B. Verma ◽  
Phillip L. Sims ◽  
Tilden P. Meyers ◽  
James A. Bradford ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Light Response ◽  
Co2 Flux ◽  
Southern Plains ◽  
Flux Tower

scholarly journals Remote Sensing Evaluation of CLM4 GPP for the Period 2000–09*

2012 ◽  
Vol 25 (15) ◽  
pp. 5327-5342 ◽  
Author(s):  
Jiafu Mao ◽  
Peter E. Thornton ◽  
Xiaoying Shi ◽  
Maosheng Zhao ◽  
Wilfred M. Post
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Large Scale ◽  
Function Analysis ◽  
Gross Primary Production ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystems ◽  
Northeast Russia ◽  
Community Land Model

Abstract Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments.

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