scholarly journals Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

2014 ◽  
Vol 11 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.

scholarly journals Spatial scale-dependent land-atmospheric methane exchange in the northern high latitudes from 1993 to 2004

2013 ◽  
Vol 10 (11) ◽  
pp. 18455-18478 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on the land-atmospheric methane (CH4) exchange have not yet been assessed for large regions. Here we used a coupled hydrology-biogeochemistry model to quantify daily CH4 exchange over the pan-Arctic from 1993 to 2004 at two spatial scales (100 km and 5 km). The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for northern high latitudes regions. Our results indicate that 5 km CH4 emissions (38.1–55.4 Tg CH4 yr−1, considering the spatial heterogeneity of WTD) were 42% larger than 100 km CH4 emissions (using grid-cell-mean WTD) and the differences in annual CH4 emissions were due to increased emitting area and enhanced flux density after WTD redistribution. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays a more important role in the 100 km estimates, while the 5 km estimates are more influenced by WTD. This study suggests that previous macro-scale biogeochemical models using grid-cell-mean WTD might have underestimated the regional CH4 budget. The spatial scale-dependent effects of WTD should be considered in future quantifications of regional CH4 emissions.

scholarly journals Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
M. J. Lara ◽  
A. D. McGuire ◽  
E. S. Euskirchen ◽  
H. Genet ◽  
S. Yi ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Spatial Scales ◽  
Regional Scale ◽  
Hierarchical Cluster ◽  
Carbon Dynamics ◽  
Local Scale ◽  
Reduced Representation ◽  
Soil Carbon Dynamics ◽  
Biogeochemical Models ◽  
Northern Alaska

Abstract In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km2–0.5°) at which fine-scale (<1km2) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiaġvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21st-century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (13) ◽  
pp. 3993-4004 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (2) ◽  
pp. 1721-1751 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Optimal configurations of spatial scale for grid cell firing under noise and uncertainty

2014 ◽  
Vol 369 (1635) ◽  
pp. 20130290 ◽  
Author(s):  
Benjamin W. Towse ◽  
Caswell Barry ◽  
Daniel Bush ◽  
Neil Burgess
Keyword(s):  
Spatial Scale ◽  
Spatial Scales ◽  
Grid Cell ◽  
Spatial Uncertainty ◽  
Grid System ◽  
Grid Cells ◽  
Spatial Cues ◽  
Firing Patterns ◽  
Wide Range ◽  
Spatial Noise

We examined the accuracy with which the location of an agent moving within an environment could be decoded from the simulated firing of systems of grid cells. Grid cells were modelled with Poisson spiking dynamics and organized into multiple ‘modules’ of cells, with firing patterns of similar spatial scale within modules and a wide range of spatial scales across modules. The number of grid cells per module, the spatial scaling factor between modules and the size of the environment were varied. Errors in decoded location can take two forms: small errors of precision and larger errors resulting from ambiguity in decoding periodic firing patterns. With enough cells per module (e.g. eight modules of 100 cells each) grid systems are highly robust to ambiguity errors, even over ranges much larger than the largest grid scale (e.g. over a 500 m range when the maximum grid scale is 264 cm). Results did not depend strongly on the precise organization of scales across modules (geometric, co-prime or random). However, independent spatial noise across modules, which would occur if modules receive independent spatial inputs and might increase with spatial uncertainty, dramatically degrades the performance of the grid system. This effect of spatial uncertainty can be mitigated by uniform expansion of grid scales. Thus, in the realistic regimes simulated here, the optimal overall scale for a grid system represents a trade-off between minimizing spatial uncertainty (requiring large scales) and maximizing precision (requiring small scales). Within this view, the temporary expansion of grid scales observed in novel environments may be an optimal response to increased spatial uncertainty induced by the unfamiliarity of the available spatial cues.

scholarly journals Improving a plot-scale methane emission model and its performance at a northeastern Siberian tundra site

2014 ◽  
Vol 11 (14) ◽  
pp. 3985-3999 ◽  
Author(s):  
Y. Mi ◽  
J. van Huissteden ◽  
F. J. W. Parmentier ◽  
A. Gallagher ◽  
A. Budishchev ◽  
...  
Keyword(s):  
Water Table ◽  
Vegetation Type ◽  
Spatial Scales ◽  
Soil Freezing ◽  
Subsurface Water ◽  
Water Evaporation ◽  
Model Parameters ◽  
Emission Model ◽  
Data Set ◽  
Ch4 Emissions

Abstract. In order to better address the feedbacks between climate and wetland methane (CH4) emissions, we tested several mechanistic improvements to the wetland CH4 emission model Peatland-VU with a longer Arctic data set than any other model: (1) inclusion of an improved hydrological module, (2) incorporation of a gross primary productivity (GPP) module, and (3) a more realistic soil-freezing scheme. A long time series of field measurements (2003–2010) from a tundra site in northeastern Siberia is used to validate the model, and the generalized likelihood uncertainty estimation (GLUE) methodology is used to test the sensitivity of model parameters. Peatland-VU is able to capture both the annual magnitude and seasonal variations of the CH4 flux, water table position, and soil thermal properties. However, detailed daily variations are difficult to evaluate due to data limitation. Improvements due to the inclusion of a GPP module are less than anticipated, although this component is likely to become more important at larger spatial scales because the module can accommodate the variations in vegetation traits better than at plot scale. Sensitivity experiments suggest that the methane production rate factor, the methane plant oxidation parameter, the reference temperature for temperature-dependent decomposition, and the methane plant transport rate factor are the most important parameters affecting the data fit, regardless of vegetation type. Both wet and dry vegetation cover are sensitive to the minimum water table level; the former is also sensitive to the runoff threshold and open water correction factor, and the latter to the subsurface water evaporation and evapotranspiration correction factors. These results shed light on model parameterization and future improvement of CH4 modelling. However, high spatial variability of CH4 emissions within similar vegetation/soil units and data quality prove to impose severe limits on model testing and improvement.

scholarly journals Global assessment of how averaging over spatial heterogeneity in precipitation and potential evapotranspiration affects modeled evapotranspiration rates

2020 ◽  
Vol 24 (4) ◽  
pp. 1927-1938 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Ying Fan ◽  
James W. Kirchner
Keyword(s):  
Spatial Heterogeneity ◽  
Land Surface ◽  
Large Scale ◽  
Potential Evapotranspiration ◽  
Spatial Scales ◽  
Grid Cell ◽  
Absolute Magnitude ◽  
Continental Scale ◽  
Heterogeneity Bias ◽  
Biased Estimates

Abstract. Accurately estimating large-scale evapotranspiration (ET) rates is essential to understanding and predicting global change. Evapotranspiration models that are applied at a continental scale typically operate on relatively large spatial grids, with the result that the heterogeneity in land surface properties and processes at smaller spatial scales cannot be explicitly represented. Averaging over this spatial heterogeneity may lead to biased estimates of energy and water fluxes. Here we estimate how averaging over spatial heterogeneity in precipitation (P) and potential evapotranspiration (PET) may affect grid-cell-averaged evapotranspiration rates, as seen from the atmosphere over heterogeneous landscapes across the globe. Our goal is to identify where, under what conditions, and at what scales this “heterogeneity bias” could be most important but not to quantify its absolute magnitude. We use Budyko curves as simple functions that relate ET to precipitation and potential evapotranspiration. Because the relationships driving ET are nonlinear, averaging over subgrid heterogeneity in P and PET will lead to biased estimates of average ET. We examine the global distribution of this bias, its scale dependence, and its sensitivity to variations in P vs. PET. Our analysis shows that this heterogeneity bias is more pronounced in mountainous terrain, in landscapes where spatial variations in P and PET are inversely correlated, and in regions with temperate climates and dry summers. We also show that this heterogeneity bias increases on average, and expands over larger areas, as the grid cell size increases.

scholarly journals Improving a plot-scale methane emission model and its performance at a Northeastern Siberian tundra site

2013 ◽  
Vol 10 (12) ◽  
pp. 20005-20046 ◽  
Author(s):  
Y. Mi ◽  
J. van Huissteden ◽  
F. J. W. Parmentier ◽  
A. Gallagher ◽  
A. Budishchev ◽  
...  
Keyword(s):  
Water Table ◽  
Vegetation Type ◽  
Spatial Scales ◽  
Open Water ◽  
Soil Freezing ◽  
Subsurface Water ◽  
Water Evaporation ◽  
Model Parameters ◽  
Emission Model ◽  
Ch4 Emissions

Abstract. In order to better address the feedbacks between climate and wetland methane (CH4) emissions, we tested several mechanistic improvements to the wetland CH4 emission model Peatland-VU with a longer Arctic dataset than any other model: (1) inclusion of an improved hydrological module; (2) incorporation of a gross primary productivity (GPP) module; (3) a more realistic soil-freezing scheme. A long time series of field measurements (2003–2010) from a tundra site in Northeastern Siberia is used to validate the model, and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology is used to test the sensitivity of model parameters. Peatland-VU is able to capture both the annual magnitude and seasonal variations of the CH4 flux, water table position and soil thermal properties. However, detailed daily variations are difficult to evaluate due to data limitation. Improvements due to the inclusion of a GPP module are less than anticipated, although this component is likely to become more important at larger spatial scales because the module can accommodate the variations in vegetation traits better than at plot-scale. Sensitivity experiments suggest that the methane production rate factor, the methane plant oxidation parameter, the reference temperature for temperature dependent decomposition, and the methane plant transport rate factor are the most important parameters affecting the data fit, regardless of vegetation type. Both wet and dry vegetation cover are sensitive to the minimum water table level, in addition to the runoff threshold and open water correction factor and the subsurface water evaporation and evapotranspiration correction factors, respectively. These results shed light on model parameterization and future improvement of CH4 modelling. However, high spatial variability of CH4 emissions within similar vegetation/soil units and data quality prove to impose severe limits on model testing and improvement.

scholarly journals Global assessment of how averaging over land-surface heterogeneity affects modeled evapotranspiration rates

2019 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Ying Fan ◽  
James W. Kirchner
Keyword(s):  
Spatial Heterogeneity ◽  
Land Surface ◽  
Large Scale ◽  
Potential Evapotranspiration ◽  
Spatial Scales ◽  
Grid Cell ◽  
Heat Fluxes ◽  
Mechanistic Modeling ◽  
Grid Cells ◽  
Heterogeneity Bias

Abstract. The major goal of large-scale Earth System Models (ESMs) is to understand and predict global change. However, computational constraints require ESMs to operate on relatively large spatial grids (typically ~1 degree or ~100 km in size) with the result that the heterogeneity in land surface properties and processes at smaller spatial scales cannot be explicitly represented. Averaging over this spatial heterogeneity may lead to biased estimates of energy and water fluxes in ESMs. For example, evapotranspiration rates and the properties that regulate them are spatially heterogeneous at scales orders of magnitude smaller than typical ESM grid cells. Here we quantify the effects of spatial heterogeneity on grid-cell-averaged evapotranspiration (ET) rates, as seen from the atmosphere over heterogeneous landscapes across the globe. In an earlier study, we used a Budyko framework to functionally relate ET to precipitation (P) and potential evapotranspiration (PET), and used a sub-grid closure relation to quantify the effects of sub-grid heterogeneity on average ET at 1° by 1° grid cells- the scale of typical ESM. We showed that because the relationships driving ET are nonlinear, averaging over sub-grid heterogeneity in P and PET leads to overestimation of average ET. In this study, we extend that work to the globe and examine the global distribution of this bias, its scale dependence, and the underlying mechanisms. Our analysis shows that this heterogeneity bias is more pronounced in mountainous terrain, in landscapes where P is inversely correlated with PET, and in regions with temperate climates and dry summers. We also show that the magnitude of this heterogeneity bias grows on average, and expands over larger areas, as the size of the grid cell increases. Correcting for this overestimation of ET in ESMs is important for modeling the water cycle, as well as for future temperature predictions, since current overestimations of ET rates imply smaller sensible heat fluxes, and potential underestimation of dry and warm conditions in the context of climate change. Our work provides a basis for translating the heterogeneity bias into correction factors in large-scale ESMs, and highlights the regions where more detailed mechanistic modeling is needed.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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