scholarly journals The effect of environmental drivers on summer spatial variability of water temperature in Polish lowland watercourses

2020 ◽  
Vol 79 (10) ◽  
Author(s):  
Maksym Andrzej Łaszewski
Keyword(s):  
Spatial Variability ◽  
Water Temperature ◽  
Environmental Drivers

Size-fractionated surface phytoplankton in the Kara and Laptev Seas: environmental control and spatial variability

2021 ◽  
Vol 664 ◽  
pp. 59-77
Author(s):  
AB Demidov ◽  
IN Sukhanova ◽  
TA Belevich ◽  
MV Flint ◽  
VI Gagarin ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Primary Production ◽  
Water Temperature ◽  
Environmental Control ◽  
Size Structure ◽  
Total Carbon ◽  
The Arctic ◽  
Size Groups ◽  
Phytoplankton Size ◽  
Phytoplankton Size Structure

Climate-induced variability of phytoplankton size structure influences primary productivity, marine food web dynamics, biosedimentation and exchange of CO2 between the atmosphere and ocean. Investigation of phytoplankton size structure in the Arctic Ocean is important due to rapid changes in its ecosystems related to increasing temperature and declining sea ice cover. We estimated the contribution of surface micro-, nano- and picophytoplankton to the total carbon biomass, chlorophyll a concentration and primary production in the Kara and Laptev Seas and investigated the relationships of these phytoplankton size groups with environmental factors which determine their spatial variability. Additionally, we compared chlorophyll specific carbon fixation rate, specific growth rate and carbon to chlorophyll ratios among different phytoplankton size groups. The investigation was carried out from August to September 2018. Generally, picophytoplankton was dominant in terms of chlorophyll a and primary production in the whole study area. The spatial variability of phytoplankton size classes was influenced by river discharge and relied mainly on water temperature, salinity and dissolved silicon concentration. Microphytoplankton prevailed across the river runoff region under conditions of low salinity and relatively high water temperature, while picophytoplankton was predominant under conditions of high salinity and low water temperature. Our study is the first to characterize size-fractionated phytoplankton abundance in the Kara and Laptev Seas, and provides a baseline for future assessment of the response of Kara and Laptev Sea ecosystems to climate-induced processes using phytoplankton size structure.

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 A systematic approach for comparing modeled biospheric carbon fluxes across regional scales

2011 ◽  
Vol 8 (6) ◽  
pp. 1579-1593 ◽  
Author(s):  
D. N. Huntzinger ◽  
S. M. Gourdji ◽  
K. L. Mueller ◽  
A. M. Michalak
Keyword(s):  
Variable Selection ◽  
Spatial Variability ◽  
Carbon Flux ◽  
Systematic Approach ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Environmental Drivers ◽  
Model Formulation ◽  
Variogram Analysis ◽  
Fire Emissions

Abstract. Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied to systematically compare flux estimates despite the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that best explain the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1° × 1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that explain the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of the differences in spatial patterns of estimated flux can be linked back to differences in model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the systematic approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed, or a detailed sensitivity analysis performed.

Benthic hotspots on the northern Bering and Chukchi continental shelf: Spatial variability in production regimes and environmental drivers

2021 ◽  
Vol 191 ◽  
pp. 102497
Author(s):  
Zhixuan Feng ◽  
Rubao Ji ◽  
Carin Ashjian ◽  
Jinlun Zhang ◽  
Robert Campbell ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Continental Shelf ◽  
Environmental Drivers

scholarly journals A quantitative approach for comparing modeled biospheric carbon flux estimates across regional scales

2010 ◽  
Vol 7 (5) ◽  
pp. 7903-7943
Author(s):  
D. N. Huntzinger ◽  
S. M. Gourdji ◽  
K. L. Mueller ◽  
A. M. Michalak
Keyword(s):  
Variable Selection ◽  
Spatial Variability ◽  
Carbon Flux ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Quantitative Approach ◽  
Environmental Drivers ◽  
Model Formulation ◽  
Variogram Analysis ◽  
Fire Emissions

Abstract. Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied for comparing flux estimates in light of the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that appear to have the greatest control over the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1°×1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that control the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of these factors can be linked back to model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the quantitative approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed or a detailed sensitivity analysis was performed.

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