Comparing riparian and catchment influences on stream habitat in a forested, montane landscape.

Treesearch

K.M. Burnett; G.H. Reeves

2006-01-01

The goal of this study was to understand relationships between salmon habitat and landscape characteristics, summarized at multiple spatial scales, in a montane basin where forestry is the dominant land use. Specific study objectives were to (1) examine differences among spatial scales for landscape characteristics described with relatively coarse-resolution data, (2)...

Microclimate predicts within-season distribution dynamics of montane forest birds

Treesearch

Sarah J.K. Frey; Adam S. Hadley; Matthew G. Betts; Mark Robertson

2016-01-01

Aim: Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial...

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

The basis of orientation decoding in human primary visual cortex: fine- or coarse-scale biases?

PubMed

Maloney, Ryan T

2015-01-01

Orientation signals in human primary visual cortex (V1) can be reliably decoded from the multivariate pattern of activity as measured with functional magnetic resonance imaging (fMRI). The precise underlying source of these decoded signals (whether by orientation biases at a fine or coarse scale in cortex) remains a matter of some controversy, however. Freeman and colleagues (J Neurosci 33: 19695-19703, 2013) recently showed that the accuracy of decoding of spiral patterns in V1 can be predicted by a voxel's preferred spatial position (the population receptive field) and its coarse orientation preference, suggesting that coarse-scale biases are sufficient for orientation decoding. Whether they are also necessary for decoding remains an open question, and one with implications for the broader interpretation of multivariate decoding results in fMRI studies. Copyright © 2015 the American Physiological Society.

From individual spiking neurons to population behavior: Systematic elimination of short-wavelength spatial modes

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.

2016-02-01

Mean-field models of the brain approximate spiking dynamics by assuming that each neuron responds to its neighbors via a naive spatial average that neglects local fluctuations and correlations in firing activity. In this paper we address this issue by introducing a rigorous formalism to enable spatial coarse-graining of spiking dynamics, scaling from the microscopic level of a single type 1 (integrator) neuron to a macroscopic assembly of spiking neurons that are interconnected by chemical synapses and nearest-neighbor gap junctions. Spiking behavior at the single-neuron scale ℓ ≈10 μ m is described by Wilson's two-variable conductance-based equations [H. R. Wilson, J. Theor. Biol. 200, 375 (1999), 10.1006/jtbi.1999.1002], driven by fields of incoming neural activity from neighboring neurons. We map these equations to a coarser spatial resolution of grid length B ℓ , with B ≫1 being the blocking ratio linking micro and macro scales. Our method systematically eliminates high-frequency (short-wavelength) spatial modes q ⃗ in favor of low-frequency spatial modes Q ⃗ using an adiabatic elimination procedure that has been shown to be equivalent to the path-integral coarse graining applied to renormalization group theory of critical phenomena. This bottom-up neural regridding allows us to track the percolation of synaptic and ion-channel noise from the single neuron up to the scale of macroscopic population-average variables. Anticipated applications of neural regridding include extraction of the current-to-firing-rate transfer function, investigation of fluctuation criticality near phase-transition tipping points, determination of spatial scaling laws for avalanche events, and prediction of the spatial extent of self-organized macrocolumnar structures. As a first-order exemplar of the method, we recover nonlinear corrections for a coarse-grained Wilson spiking neuron embedded in a network of identical diffusively coupled neurons whose chemical synapses have been disabled. Intriguingly, we find that reblocking transforms the original type 1 Wilson integrator into a type 2 resonator whose spike-rate transfer function exhibits abrupt spiking onset with near-vertical takeoff and chaotic dynamics just above threshold.

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

DOE PAGES

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.; ...

2017-09-14

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2017-07-01

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

Bayesian Hierarchical Modeling for Big Data Fusion in Soil Hydrology

NASA Astrophysics Data System (ADS)

Mohanty, B.; Kathuria, D.; Katzfuss, M.

2016-12-01

Soil moisture datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors on the other hand provide observations on a finer spatial scale (meter scale or less) but are sparsely available. Soil moisture is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables. Hydrologic processes usually occur at a scale of 1 km or less and therefore spatially ubiquitous and temporally periodic soil moisture products at this scale are required to aid local decision makers in agriculture, weather prediction and reservoir operations. Past literature has largely focused on downscaling RS soil moisture for a small extent of a field or a watershed and hence the applicability of such products has been limited. The present study employs a spatial Bayesian Hierarchical Model (BHM) to derive soil moisture products at a spatial scale of 1 km for the state of Oklahoma by fusing point scale Mesonet data and coarse scale RS data for soil moisture and its auxiliary covariates such as precipitation, topography, soil texture and vegetation. It is seen that the BHM model handles change of support problems easily while performing accurate uncertainty quantification arising from measurement errors and imperfect retrieval algorithms. The computational challenge arising due to the large number of measurements is tackled by utilizing basis function approaches and likelihood approximations. The BHM model can be considered as a complex Bayesian extension of traditional geostatistical prediction methods (such as Kriging) for large datasets in the presence of uncertainties.

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

Assessing Clark's nutcracker seed-caching flights using maternally inherited mitochondrial DNA of whitebark pine

Treesearch

Bryce A. Richardson; Ned B. Klopfenstein; Steven J. Brunsfeld

2002-01-01

Maternally inherited mitochondrial DNA haplotypes in whitebark pine (Pinus albicaulis Engelm.) were used to examine the maternal genetic structure at three hierarchical spatial scales: fine scale, coarse scale, and interpopulation. These data were used to draw inferences into Clark’s nutcracker (Nucifraga columbiana Wilson)...

Generation, Validation, and Application of Abundance Map Reference Data for Spectral Unmixing

NASA Astrophysics Data System (ADS)

Williams, McKay D.

Reference data ("ground truth") maps traditionally have been used to assess the accuracy of imaging spectrometer classification algorithms. However, these reference data can be prohibitively expensive to produce, often do not include sub-pixel abundance estimates necessary to assess spectral unmixing algorithms, and lack published validation reports. Our research proposes methodologies to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. We generated scene-wide AMRD for three different remote sensing scenes using our remotely sensed reference data (RSRD) technique, which spatially aggregates unmixing results from fine scale imagery (e.g., 1-m Ground Sample Distance (GSD)) to co-located coarse scale imagery (e.g., 10-m GSD or larger). We validated the accuracy of this methodology by estimating AMRD in 51 randomly-selected 10 m x 10 m plots, using seven independent methods and observers, including field surveys by two observers, imagery analysis by two observers, and RSRD using three algorithms. Results indicated statistically-significant differences between all versions of AMRD, suggesting that all forms of reference data need to be validated. Given these significant differences between the independent versions of AMRD, we proposed that the mean of all (MOA) versions of reference data for each plot and class were most likely to represent true abundances. We then compared each version of AMRD to MOA. Best case accuracy was achieved by a version of imagery analysis, which had a mean coverage area error of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving a mean error of 3.0%, with a standard deviation of 6.3%, showing the potential of RSRD-based AMRD generation. Application of validated AMRD to specific coarse scale imagery involved three main parts: 1) spatial alignment of coarse and fine scale imagery, 2) aggregation of fine scale abundances to produce coarse scale imagery-specific AMRD, and 3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point spread function (PSF) aggregation, and found that the PSF approach returned lower error, but that rectangular aggregation more accurately estimated true abundances at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including mean absolute error (MAE) and linear regression (LR). We additionally introduced reference data mean adjusted MAE (MA-MAE), and reference data confidence interval adjusted MAE (CIA-MAE), which account for known error in the reference data itself. MA-MAE analysis indicated that fully constrained linear unmixing of coarse scale imagery across all three scenes returned an error of 10.83% per class and pixel, with regression analysis yielding a slope = 0.85, intercept = 0.04, and R2 = 0.81. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance.

Fine-scale variation of historical fire regimes in sagebrush-steppe and juniper woodland: An example from California, USA

Treesearch

Richard F. Miller; Emily K. Heyerdahl

2008-01-01

Coarse-scale estimates of fire intervals across the mountain big sagebrush (Artemisia tridentata spp. vaseyana (Rydb.) Beetle) alliance range from decades to centuries. However, soil depth and texture can affect the abundance and continuity of fine fuels and vary at fine spatial scales, suggesting fire regimes may vary at similar scales. We explored...

Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington

USGS Publications Warehouse

Klett, Katherine J.C.; Torgersen, Christian E.; Henning, Julie A.; Murray, Christopher J.

2013-01-01

We investigated the spawning patterns of Chinook Salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington, using a unique set of fine- and coarse-scale temporal and spatial data collected during biweekly aerial surveys conducted in 1991–2009 (500 m to 28 km resolution) and 2008–2009 (100–500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held GPS synchronized with in-flight audio recordings. We examined spatial patterns of Chinook Salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook Salmon spawned in the same sections each year with little variation among years. On a coarse scale, 5 years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years. Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations. On a finer temporal scale, we observed that Chinook Salmon spawned in the same sections during the first and last week. Redds were clustered in both 2008 and 2009. Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook Salmon spawning surveys.

On the effects of scale for ecosystem services mapping

USGS Publications Warehouse

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

On the Effects of Scale for Ecosystem Services Mapping

PubMed Central

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability. PMID:25549256

On the effects of scale for ecosystem services mapping.

PubMed

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

Coarse-grained incompressible magnetohydrodynamics: Analyzing the turbulent cascades

DOE PAGES

Aluie, Hussein

2017-02-21

Here, we formulate a coarse-graining approach to the dynamics of magnetohydrodynamic (MHD) fluids at a continuum of length-scales. In this methodology, effective equations are derived for the observable velocity and magnetic fields spatially-averaged at an arbitrary scale of resolution. The microscopic equations for the bare velocity and magnetic fields are renormalized by coarse-graining to yield macroscopic effective equations that contain both a subscale stress and a subscale electromotive force (EMF) generated by nonlinear interaction of eliminated fields and plasma motions. At large coarse-graining length-scales, the direct dissipation of invariants by microscopic mechanisms (such as molecular viscosity and Spitzer resistivity) ismore » shown to be negligible. The balance at large scales is dominated instead by the subscale nonlinear terms, which can transfer invariants across scales, and are interpreted in terms of work concepts for energy and in terms of topological flux-linkage for the two helicities. An important application of this approach is to MHD turbulence, where the coarse-graining length ℓ lies in the inertial cascade range. We show that in the case of sufficiently rough velocity and/or magnetic fields, the nonlinear inter-scale transfer need not vanish and can persist to arbitrarily small scales. Although closed expressions are not available for subscale stress and subscale EMF, we derive rigorous upper bounds on the effective dissipation they produce in terms of scaling exponents of the velocity and magnetic fields. These bounds provide exact constraints on phenomenological theories of MHD turbulence in order to allow the nonlinear cascade of energy and cross-helicity. On the other hand, we show that the forward cascade of magnetic helicity to asymptotically small scales is impossible unless 3rd-order moments of either velocity or magnetic field become infinite.« less

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-03-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shaanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modeled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modeled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI), elevation and aspect have small and additive effects on improving the spatial scaling between these two resolutions.

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-07-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.

Coarse sediment transport dynamics at three spatial scales of bedrock channel bed complexity

NASA Astrophysics Data System (ADS)

Goode, J. R.; Wohl, E.

2007-12-01

Rivers incised into bedrock in fold-dominated terrain display a complex bed topography that strongly interacts with local hydraulics to produce spatial differences in bed sediment flux. We used painted tracer clasts to investigate how this complex bed topography influences coarse sediment transport at three spatial scales (reach, cross- section and grain). The study was conducted along the Ocoee River gorge, Tennessee between the TVA Ocoee #3 dam and the 1996 Olympic whitewater course. The bed topography consists of undulating bedrock ribs, which are formed at a consistent strike to the bedding and cleavage of the metagreywake and phyllite substrate. Ribs vary in their orientation to flow (from parallel to oblique) and amplitude among three study reaches. These bedrock ribs create a rough bed topography that substantially alters the local flow field and influences reach- scale roughness. In each reach, 300 tracer clasts were randomly selected from the existing bed material. Tracer clasts were surveyed and transport distances were calculated after five scheduled summer releases and a suite of slightly larger but sporadic winter releases. Transport distances were examined as a function of rib orientation and amplitude (reach scale), spatial proximity to bedrock ribs and standard deviation of the bed elevation (cross- section scale), and whether clasts were hydraulically shielded by surrounding clasts, incorporated in the armour layer, imbricated, and/or existed in a pothole, in addition to size and angularity. At the reach scale, where ribs are parallel to flow, lower reach-scale roughness leads to greater sediment transport capacity, sediment flux and transport distances because transport is uninhibited in the downstream direction. Preliminary results indicate that cross section scale characteristics of bed topography exert a greater control on transport distances than grain size.

Scaling uncertainties in estimating canopy foliar maintenance respiration for black spruce ecosystems in Alaska

USGS Publications Warehouse

Zhang, X.; McGuire, A.D.; Ruess, Roger W.

2006-01-01

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of carbon exchange between boreal forest ecosystems and the atmosphere. An understanding of the sources of variability of carbon processes at fine scales and how these contribute to uncertainties in estimating carbon fluxes is relevant to representing these processes at coarse scales. To explore some of the challenges and uncertainties in estimating carbon fluxes at fine to coarse scales, we conducted a modeling analysis of canopy foliar maintenance respiration for black spruce ecosystems of Alaska by scaling empirical hourly models of foliar maintenance respiration (Rm) to estimate canopy foliar Rm for individual stands. We used variation in foliar N concentration among stands to develop hourly stand-specific models and then developed an hourly pooled model. An uncertainty analysis identified that the most important parameter affecting estimates of canopy foliar Rm was one that describes R m at 0??C per g N, which explained more than 55% of variance in annual estimates of canopy foliar Rm. The comparison of simulated annual canopy foliar Rm identified significant differences between stand-specific and pooled models for each stand. This result indicates that control over foliar N concentration should be considered in models that estimate canopy foliar Rm of black spruce stands across the landscape. In this study, we also temporally scaled the hourly stand-level models to estimate canopy foliar Rm of black spruce stands using mean monthly temperature data. Comparisons of monthly Rm between the hourly and monthly versions of the models indicated that there was very little difference between the estimates of hourly and monthly models, suggesting that hourly models can be aggregated to use monthly input data with little loss of precision. We conclude that uncertainties in the use of a coarse-scale model for estimating canopy foliar Rm at regional scales depend on uncertainties in representing needle-level respiration and on uncertainties in representing the spatial variability of canopy foliar N across a region. The development of spatial data sets of canopy foliar N represents a major challenge in estimating canopy foliar maintenance respiration at regional scales. ?? Springer 2006.

Powerless fluxes and forces, and change of scale in irreversible thermodynamics

NASA Astrophysics Data System (ADS)

Ostoja-Starzewski, M.; Zubelewicz, A.

2011-08-01

We show that the dissipation function of linear processes in continuum thermomechanics may be treated as the average of the statistically fluctuating dissipation rate on either coarse or small spatial scales. The first case involves thermodynamic orthogonality due to Ziegler, while the second one involves powerless forces in a general solution of the Clausius-Duhem inequality according to Poincaré and Edelen. This formulation is demonstrated using the example of parabolic versus hyperbolic heat conduction. The existence of macroscopic powerless heat fluxes is traced here to the hidden dissipative processes at lower temporal and spatial scales.

Fine resolution probabilistic land cover classification of landscapes in the southeastern United States

Treesearch

Joseph St. Peter; John Hogland; Nathaniel Anderson; Jason Drake; Paul Medley

2018-01-01

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a...

Scaling properties of Arctic sea ice deformation in high-resolution viscous-plastic sea ice models and satellite observations

NASA Astrophysics Data System (ADS)

Hutter, Nils; Losch, Martin; Menemenlis, Dimitris

2017-04-01

Sea ice models with the traditional viscous-plastic (VP) rheology and very high grid resolution can resolve leads and deformation rates that are localised along Linear Kinematic Features (LKF). In a 1-km pan-Arctic sea ice-ocean simulation, the small scale sea-ice deformations in the Central Arctic are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS). A new coupled scaling analysis for data on Eulerian grids determines the spatial and the temporal scaling as well as the coupling between temporal and spatial scales. The spatial scaling of the modelled sea ice deformation implies multi-fractality. The spatial scaling is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling and its coupling to temporal scales with satellite observations and models with the modern elasto-brittle rheology challenges previous results with VP models at coarse resolution where no such scaling was found. The temporal scaling analysis, however, shows that the VP model does not fully resolve the intermittency of sea ice deformation that is observed in satellite data.

The Use of Coarse Resolution Satellite Imagery to Predict Human Puumala Virus Epidemics in Sweden.

DTIC Science & Technology

1992-09-11

the adverse effects on NDVI data quality can occur in both the spatial and temporal dimension. In other words, a specific pixel value recorded in...are compared to the land-oriented systems.22 On the other hand, the very course spatial resolution has the advantage of greatly reducing the volume...necessary on the scale of individual fields, in which case LANDSAT-TM has higher spatial resolution ; and secondly, when specific

Spatial consistency of Chinook salmon redd distribution within and among years in the Cowlitz River, Washington

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klett, Katherine J.; Torgersen, Christian; Henning, Julie

2013-04-28

We investigated the spawning patterns of Chinook salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington (USA) using a unique set of fine- and coarse-scale 35 temporal and spatial data collected during bi-weekly aerial surveys conducted in 1991-2009 (500 m to 28 km resolution) and 2008-2009 (100-500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held global positioning system (GPS) synchronized with in-flight audio recordings. We examined spatial patterns of Chinook salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook salmon spawned in the same sections each yearmore » with little variation among years. On a coarse scale, five years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years resulting in a minimum correlation coefficient of 0.90 (adjusted P = 0.002). Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations (P < 0.001). On a finer temporal scale, we observed that salmon spawned in the same sections during the first and last week (2008: P < 0.02; and 2009: P < 0.001). Redds were clustered in both 2008 and 2009 (P < 0.001). Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook salmon spawning surveys.« less

Downscaling Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi

2014-06-05

Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).

Reconciling transport models across scales: The role of volume exclusion

NASA Astrophysics Data System (ADS)

Taylor, P. R.; Yates, C. A.; Simpson, M. J.; Baker, R. E.

2015-10-01

Diffusive transport is a universal phenomenon, throughout both biological and physical sciences, and models of diffusion are routinely used to interrogate diffusion-driven processes. However, most models neglect to take into account the role of volume exclusion, which can significantly alter diffusive transport, particularly within biological systems where the diffusing particles might occupy a significant fraction of the available space. In this work we use a random walk approach to provide a means to reconcile models that incorporate crowding effects on different spatial scales. Our work demonstrates that coarse-grained models incorporating simplified descriptions of excluded volume can be used in many circumstances, but that care must be taken in pushing the coarse-graining process too far.

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data.

Treesearch

D.J. Hayes; W.B. Cohen

2006-01-01

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse-resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal Moderate...

Elk resource selection at parturition sites, Black Hills, South Dakota

Treesearch

Chadwick P. Lehman; Mark A. Rumble; Christopher T. Rota; Benjamin J. Bird; Dillon T. Fogarty; Joshua J. Millspaugh

2015-01-01

We studied elk (Cervus canadensis nelsoni) parturition sites at coarse (314-km2 and 7-km2) and fine (0.2-ha) scales in the Black Hills, South Dakota, 2011-2013, following a period of population decline and poor calf recruitment. Our objective was to test whether female elk selected parturition sites across spatial scales in association with forage, terrain...

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

A new type of change blindness: smooth, isoluminant color changes are monitored on a coarse spatial scale.

PubMed

Goddard, Erin; Clifford, Colin W G

2013-04-22

Attending selectively to changes in our visual environment may help filter less important, unchanging information within a scene. Here, we demonstrate that color changes can go unnoticed even when they occur throughout an otherwise static image. The novelty of this demonstration is that it does not rely upon masking by a visual disruption or stimulus motion, nor does it require the change to be very gradual and restricted to a small section of the image. Using a two-interval, forced-choice change-detection task and an odd-one-out localization task, we showed that subjects were slowest to respond and least accurate (implying that change was hardest to detect) when the color changes were isoluminant, smoothly varying, and asynchronous with one another. This profound change blindness offers new constraints for theories of visual change detection, implying that, in the absence of transient signals, changes in color are typically monitored at a coarse spatial scale.

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA.

PubMed

Mukerjee, Shaibal; Smith, Luther A; Johnson, Mary M; Neas, Lucas M; Stallings, Casson A

2009-08-01

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.

The Impact of Spatial and Temporal Resolutions in Tropical Summer Rainfall Distribution: Preliminary Results

NASA Astrophysics Data System (ADS)

Liu, Q.; Chiu, L. S.; Hao, X.

2017-10-01

The abundance or lack of rainfall affects peoples' life and activities. As a major component of the global hydrological cycle (Chokngamwong & Chiu, 2007), accurate representations at various spatial and temporal scales are crucial for a lot of decision making processes. Climate models show a warmer and wetter climate due to increases of Greenhouse Gases (GHG). However, the models' resolutions are often too coarse to be directly applicable to local scales that are useful for mitigation purposes. Hence disaggregation (downscaling) procedures are needed to transfer the coarse scale products to higher spatial and temporal resolutions. The aim of this paper is to examine the changes in the statistical parameters of rainfall at various spatial and temporal resolutions. The TRMM Multi-satellite Precipitation Analysis (TMPA) at 0.25 degree, 3 hourly grid rainfall data for a summer is aggregated to 0.5,1.0, 2.0 and 2.5 degree and at 6, 12, 24 hourly, pentad (five days) and monthly resolutions. The probability distributions (PDF) and cumulative distribution functions(CDF) of rain amount at these resolutions are computed and modeled as a mixed distribution. Parameters of the PDFs are compared using the Kolmogrov-Smironov (KS) test, both for the mixed and the marginal distribution. These distributions are shown to be distinct. The marginal distributions are fitted with Lognormal and Gamma distributions and it is found that the Gamma distributions fit much better than the Lognormal.

What it takes to invade grassland ecosystems: traits, introduction history and filtering processes

PubMed Central

Carboni, Marta; Münkemüller, Tamara; Lavergne, Sébastien; Choler, Philippe; Borgy, Benjamin; Violle, Cyrille; Essl, Franz; Roquet, Cristina; Munoz, François; Consortium, DivGrass; Thuiller, Wilfried

2016-01-01

Whether the success of alien species can be explained by their functional or phylogenetic characteristics remains unresolved because of data limitations, scale issues and weak quantifications of success. Using permanent grasslands across France (50,000 vegetation-plots, 2000 species, 130 aliens) and building on the Rabinowitz’ classification to quantify spread, we showed that phylogenetic and functional similarities to natives were the most important correlates of invasion success compared to intrinsic functional characteristics and introduction history. Results contrasted between spatial scales and components of invasion success. Widespread and common aliens were similar to co-occurring natives at coarse scales (indicating environmental filtering), but dissimilar at finer scales (indicating local competition). In contrast, regionally widespread but locally rare aliens showed patterns of competitive exclusion already at coarse scale. Quantifying trait differences between aliens and natives and distinguishing the components of invasion success improved our ability to understand and potentially predict alien spread at multiple scales. PMID:26689431

: “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”

EPA Science Inventory

Global climate models (GCMs) are currently used to obtain information about future changes in the large-scale climate. However, such simulations are typically done at coarse spatial resolutions, with model grid boxes on the order of 100 km on a horizontal side. Therefore, techniq...

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

PubMed

Ho, Hung Chak; Knudby, Anders; Huang, Wei

2015-12-18

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Where Does Wood Most Effectively Enhance Storage? Network-Scale Distribution of Sediment and Organic Matter Stored by Instream Wood

NASA Astrophysics Data System (ADS)

Pfeiffer, Andrew; Wohl, Ellen

2018-01-01

We used 48 reach-scale measurements of large wood and wood-associated sediment and coarse particulate organic matter (CPOM) storage within an 80 km2 catchment to examine spatial patterns of storage relative to stream order. Wood, sediment, and CPOM are not distributed uniformly across the drainage basin. Third- and fourth-order streams (23% of total stream length) disproportionately store wood and coarse and fine sediments: 55% of total wood volume, 78% of coarse sediment, and 49% of fine sediment, respectively. Fourth-order streams store 0.8 m3 of coarse sediment and 0.2 m3 of fine sediment per cubic meter of wood. CPOM storage is highest in first-order streams (60% of storage in 47% of total network stream length). First-order streams can store up to 0.3 m3 of CPOM for each cubic meter of wood. Logjams in third- and fourth-order reaches are primary sediment storage agents, whereas roots in small streams may be more important for storage of CPOM. We propose the large wood particulate storage index to quantify average volume of sediment or CPOM stored by a cubic meter of wood.

Texture coarseness responsive neurons and their mapping in layer 2–3 of the rat barrel cortex in vivo

PubMed Central

Garion, Liora; Dubin, Uri; Rubin, Yoav; Khateb, Mohamed; Schiller, Yitzhak; Azouz, Rony; Schiller, Jackie

2014-01-01

Texture discrimination is a fundamental function of somatosensory systems, yet the manner by which texture is coded and spatially represented in the barrel cortex are largely unknown. Using in vivo two-photon calcium imaging in the rat barrel cortex during artificial whisking against different surface coarseness or controlled passive whisker vibrations simulating different coarseness, we show that layer 2–3 neurons within barrel boundaries differentially respond to specific texture coarsenesses, while only a minority of neurons responded monotonically with increased or decreased surface coarseness. Neurons with similar preferred texture coarseness were spatially clustered. Multi-contact single unit recordings showed a vertical columnar organization of texture coarseness preference in layer 2–3. These findings indicate that layer 2–3 neurons perform high hierarchical processing of tactile information, with surface coarseness embodied by distinct neuronal subpopulations that are spatially mapped onto the barrel cortex. DOI: http://dx.doi.org/10.7554/eLife.03405.001 PMID:25233151

Extended-range high-resolution dynamical downscaling over a continental-scale spatial domain with atmospheric and surface nudging

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

Extended-range high-resolution mesoscale simulations with limited-area atmospheric models when applied to downscale regional analysis fields over large spatial domains can provide valuable information for many applications including the weather-dependent renewable energy industry. Long-term simulations over a continental-scale spatial domain, however, require mechanisms to control the large-scale deviations in the high-resolution simulated fields from the coarse-resolution driving fields. As enforcement of the lateral boundary conditions is insufficient to restrict such deviations, large scales in the simulated high-resolution meteorological fields are therefore spectrally nudged toward the driving fields. Different spectral nudging approaches, including the appropriate nudging length scales as well as the vertical profiles and temporal relaxations for nudging, have been investigated to propose an optimal nudging strategy. Impacts of time-varying nudging and generation of hourly analysis estimates are explored to circumvent problems arising from the coarse temporal resolution of the regional analysis fields. Although controlling the evolution of the atmospheric large scales generally improves the outputs of high-resolution mesoscale simulations within the surface layer, the prognostically evolving surface fields can nevertheless deviate from their expected values leading to significant inaccuracies in the predicted surface layer meteorology. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil moisture, and snow conditions, toward their expected values obtained from a high-resolution offline surface scheme is therefore proposed to limit any considerable deviation. Finally, wind speed and temperature at wind turbine hub height predicted by different spectrally nudged extended-range simulations are compared against observations to demonstrate possible improvements achievable using higher spatiotemporal resolution.

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

DOE PAGES

Pau, G. S. H.; Bisht, G.; Riley, W. J.

2014-09-17

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2, CH 4) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" thatmore » reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.« less

A field study of large-scale oscillation ripples in a very coarse-grained, high-energy marine environment

USGS Publications Warehouse

Hirschaut, D.W.; Dingler, J.R.

1982-01-01

Monastery Beach, Carmel, California is a pocket beach that sits within 200 m of the head of Carmel Submarine Canyon. Coarse to very coarse sand covers both the beach and adjacent shelf; in the latter area incoming waves have shaped the sand into large oscillation ripples. The accessibility of this area and a variable wave climate produce a unique opportunity to study large-scale coarse-grained ripples in a high-energy environment. These ripples, which only occur in very coarse sand, form under the intense, wave-generated currents that exist during storm conditions. Once formed, these ripples do not significantly change under lower energy waves. On three separate occasions scuba divers measured ripples and collected sand samples from ripple crests near fixed reference stakes along three transects. Ripple wavelength and grain size decreased with an increase in water depth. Sediment sorting was best closest to the surf zone and poorest at the rim of Carmel Canyon. Cobbles and gravel observed in ripple troughs represent lag deposits. Carmel Canyon refracts waves approaching Monastery Beach such that wave energy is focused towards the northern and southern portions of the beach, leaving the central part of the beach lower in energy. This energy distribution causes spatial variations in the ripples and grain sizes with the shortest wavelengths and smallest grain sizes being in the central part of the shelf.

An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater

NASA Astrophysics Data System (ADS)

Girotto, M.; De Lannoy, G. J. M.; Reichle, R. H.; Rodell, M.

2015-12-01

The Gravity Recovery And Climate Experiment (GRACE) mission is unique because it provides highly accurate column integrated estimates of terrestrial water storage (TWS) variations. Major limitations of GRACE-based TWS observations are related to their monthly temporal and coarse spatial resolution (around 330 km at the equator), and to the vertical integration of the water storage components. These challenges can be addressed through data assimilation. To date, it is still not obvious how best to assimilate GRACE-TWS observations into a land surface model, in order to improve hydrological variables, and many details have yet to be worked out. This presentation discusses specific recent features of the assimilation of gridded GRACE-TWS data into the NASA Goddard Earth Observing System (GEOS-5) Catchment land surface model to improve soil moisture and shallow groundwater estimates at the continental scale. The major recent advancements introduced by the presented work with respect to earlier systems include: 1) the assimilation of gridded GRACE-TWS data product with scaling factors that are specifically derived for data assimilation purposes only; 2) the assimilation is performed through a 3D assimilation scheme, in which reasonable spatial and temporal error standard deviations and correlations are exploited; 3) the analysis step uses an optimized calculation and application of the analysis increments; 4) a poor-man's adaptive estimation of a spatially variable measurement error. This work shows that even if they are characterized by a coarse spatial and temporal resolution, the observed column integrated GRACE-TWS data have potential for improving our understanding of soil moisture and shallow groundwater variations.

Representing climate, disturbance, and vegetation interactions in landscape models

Treesearch

Robert E. Keane; Donald McKenzie; Donald A. Falk; Erica A.H. Smithwick; Carol Miller; Lara-Karena B. Kellogg

2015-01-01

The prospect of rapidly changing climates over the next century calls for methods to predict their effects on myriad, interactive ecosystem processes. Spatially explicit models that simulate ecosystem dynamics at fine (plant, stand) to coarse (regional, global) scales are indispensable tools for meeting this challenge under a variety of possible futures. A special...

Spatial models reveal the microclimatic buffering capacity of old-growth forests

Treesearch

Sarah J. K. Frey; Adam S. Hadley; Sherri L. Johnson; Mark Schulze; Julia A. Jones; Matthew. G. Betts

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by...

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Validation of non-stationary precipitation series for site-specific impact assessment: Comparison of two statistical downscaling techniques

USDA-ARS?s Scientific Manuscript database

The generation of realistic future precipitation scenarios is crucial for assessing their impacts on a range of environmental and socio-economic impact sectors. A scale mismatch exists, however, between the coarse spatial resolution at which global climate models (GCMs) output future climate scenari...

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

USGS Publications Warehouse

Sleeter, Benjamin M.; Sohl, Terry L.; Bouchard, Michelle A.; Reker, Ryan R.; Soulard, Christopher E.; Acevedo, William; Griffith, Glenn E.; Sleeter, Rachel R.; Auch, Roger F.; Sayler, Kristi L.; Prisley, Stephen; Zhu, Zhi-Liang

2012-01-01

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.

The Relationship Between Fusion, Suppression, and Diplopia in Normal and Amblyopic Vision.

PubMed

Spiegel, Daniel P; Baldwin, Alex S; Hess, Robert F

2016-10-01

Single vision occurs through a combination of fusion and suppression. When neither mechanism takes place, we experience diplopia. Under normal viewing conditions, the perceptual state depends on the spatial scale and interocular disparity. The purpose of this study was to examine the three perceptual states in human participants with normal and amblyopic vision. Participants viewed two dichoptically separated horizontal blurred edges with an opposite tilt (2.35°) and indicated their binocular percept: "one flat edge," "one tilted edge," or "two edges." The edges varied with scale (fine 4 min arc and coarse 32 min arc), disparity, and interocular contrast. We investigated how the binocular interactions vary in amblyopic (visual acuity [VA] > 0.2 logMAR, n = 4) and normal vision (VA ≤ 0 logMAR, n = 4) under interocular variations in stimulus contrast and luminance. In amblyopia, despite the established sensory dominance of the fellow eye, fusion prevails at the coarse scale and small disparities (75%). We also show that increasing the relative contrast to the amblyopic eye enhances the probability of fusion at the fine scale (from 18% to 38%), and leads to a reversal of the sensory dominance at coarse scale. In normal vision we found that interocular luminance imbalances disturbed binocular combination only at the fine scale in a way similar to that seen in amblyopia. Our results build upon the growing evidence that the amblyopic visual system is binocular and further show that the suppressive mechanisms rendering the amblyopic system functionally monocular are scale dependent.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Restricted cross-scale habitat selection by American beavers.

PubMed

Francis, Robert A; Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-12-01

Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection.

Restricted cross-scale habitat selection by American beavers

PubMed Central

Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-01-01

Abstract Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection. PMID:29492032

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

PubMed Central

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-01-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. PMID:25252835

Spatial heterogeneity in ecologically important climate variables at coarse and fine scales in a high-snow mountain landscape.

PubMed

Ford, Kevin R; Ettinger, Ailene K; Lundquist, Jessica D; Raleigh, Mark S; Hille Ris Lambers, Janneke

2013-01-01

Climate plays an important role in determining the geographic ranges of species. With rapid climate change expected in the coming decades, ecologists have predicted that species ranges will shift large distances in elevation and latitude. However, most range shift assessments are based on coarse-scale climate models that ignore fine-scale heterogeneity and could fail to capture important range shift dynamics. Moreover, if climate varies dramatically over short distances, some populations of certain species may only need to migrate tens of meters between microhabitats to track their climate as opposed to hundreds of meters upward or hundreds of kilometers poleward. To address these issues, we measured climate variables that are likely important determinants of plant species distributions and abundances (snow disappearance date and soil temperature) at coarse and fine scales at Mount Rainier National Park in Washington State, USA. Coarse-scale differences across the landscape such as large changes in elevation had expected effects on climatic variables, with later snow disappearance dates and lower temperatures at higher elevations. However, locations separated by small distances (∼20 m), but differing by vegetation structure or topographic position, often experienced differences in snow disappearance date and soil temperature as great as locations separated by large distances (>1 km). Tree canopy gaps and topographic depressions experienced later snow disappearance dates than corresponding locations under intact canopy and on ridges. Additionally, locations under vegetation and on topographic ridges experienced lower maximum and higher minimum soil temperatures. The large differences in climate we observed over small distances will likely lead to complex range shift dynamics and could buffer species from the negative effects of climate change.

Spatial Heterogeneity in Ecologically Important Climate Variables at Coarse and Fine Scales in a High-Snow Mountain Landscape

PubMed Central

Ford, Kevin R.; Ettinger, Ailene K.; Lundquist, Jessica D.; Raleigh, Mark S.; Hille Ris Lambers, Janneke

2013-01-01

Climate plays an important role in determining the geographic ranges of species. With rapid climate change expected in the coming decades, ecologists have predicted that species ranges will shift large distances in elevation and latitude. However, most range shift assessments are based on coarse-scale climate models that ignore fine-scale heterogeneity and could fail to capture important range shift dynamics. Moreover, if climate varies dramatically over short distances, some populations of certain species may only need to migrate tens of meters between microhabitats to track their climate as opposed to hundreds of meters upward or hundreds of kilometers poleward. To address these issues, we measured climate variables that are likely important determinants of plant species distributions and abundances (snow disappearance date and soil temperature) at coarse and fine scales at Mount Rainier National Park in Washington State, USA. Coarse-scale differences across the landscape such as large changes in elevation had expected effects on climatic variables, with later snow disappearance dates and lower temperatures at higher elevations. However, locations separated by small distances (∼20 m), but differing by vegetation structure or topographic position, often experienced differences in snow disappearance date and soil temperature as great as locations separated by large distances (>1 km). Tree canopy gaps and topographic depressions experienced later snow disappearance dates than corresponding locations under intact canopy and on ridges. Additionally, locations under vegetation and on topographic ridges experienced lower maximum and higher minimum soil temperatures. The large differences in climate we observed over small distances will likely lead to complex range shift dynamics and could buffer species from the negative effects of climate change. PMID:23762277

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pau, G. S. H.; Bisht, G.; Riley, W. J.

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2, CH 4) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" thatmore » reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.« less

Modeling spatial variation in avian survival and residency probabilities

USGS Publications Warehouse

Saracco, James F.; Royle, J. Andrew; DeSante, David F.; Gardner, Beth

2010-01-01

The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

The scientific foundation of the LANDFIRE Prototype Project [Chapter 3

Treesearch

Robert E. Keane; Matthew Rollins

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, originated from a recent mapping project that developed a set of coarse-scale spatial data layers for wildland fire management describing fire hazard and ecological status for the conterminous United States (Hardy and others 2001; Schmidt and others 2002; www. fs...

Fanning the flames: climate change stacks odds against fire suppression.

Treesearch

Jonathan Thompson

2005-01-01

There is little question that global warming would increase the risk of wildfires by drying out vegetation and stirring the winds that spread fire. Until recently, however, land managers were unable to formulate appropriate responses because the spatial scales of predictions were far too coarse. Current research being done at the PNW Research Station in Portland,...

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

ProtoMD: A prototyping toolkit for multiscale molecular dynamics

NASA Astrophysics Data System (ADS)

Somogyi, Endre; Mansour, Andrew Abi; Ortoleva, Peter J.

2016-05-01

ProtoMD is a toolkit that facilitates the development of algorithms for multiscale molecular dynamics (MD) simulations. It is designed for multiscale methods which capture the dynamic transfer of information across multiple spatial scales, such as the atomic to the mesoscopic scale, via coevolving microscopic and coarse-grained (CG) variables. ProtoMD can be also be used to calibrate parameters needed in traditional CG-MD methods. The toolkit integrates 'GROMACS wrapper' to initiate MD simulations, and 'MDAnalysis' to analyze and manipulate trajectory files. It facilitates experimentation with a spectrum of coarse-grained variables, prototyping rare events (such as chemical reactions), or simulating nanocharacterization experiments such as terahertz spectroscopy, AFM, nanopore, and time-of-flight mass spectroscopy. ProtoMD is written in python and is freely available under the GNU General Public License from github.com/CTCNano/proto_md.

Gradients estimation from random points with volumetric tensor in turbulence

NASA Astrophysics Data System (ADS)

Watanabe, Tomoaki; Nagata, Koji

2017-12-01

We present an estimation method of fully-resolved/coarse-grained gradients from randomly distributed points in turbulence. The method is based on a linear approximation of spatial gradients expressed with the volumetric tensor, which is a 3 × 3 matrix determined by a geometric distribution of the points. The coarse grained gradient can be considered as a low pass filtered gradient, whose cutoff is estimated with the eigenvalues of the volumetric tensor. The present method, the volumetric tensor approximation, is tested for velocity and passive scalar gradients in incompressible planar jet and mixing layer. Comparison with a finite difference approximation on a Cartesian grid shows that the volumetric tensor approximation computes the coarse grained gradients fairly well at a moderate computational cost under various conditions of spatial distributions of points. We also show that imposing the solenoidal condition improves the accuracy of the present method for solenoidal vectors, such as a velocity vector in incompressible flows, especially when the number of the points is not large. The volumetric tensor approximation with 4 points poorly estimates the gradient because of anisotropic distribution of the points. Increasing the number of points from 4 significantly improves the accuracy. Although the coarse grained gradient changes with the cutoff length, the volumetric tensor approximation yields the coarse grained gradient whose magnitude is close to the one obtained by the finite difference. We also show that the velocity gradient estimated with the present method well captures the turbulence characteristics such as local flow topology, amplification of enstrophy and strain, and energy transfer across scales.

Spatial averaging of a dissipative particle dynamics model for active suspensions

NASA Astrophysics Data System (ADS)

Panchenko, Alexander; Hinz, Denis F.; Fried, Eliot

2018-03-01

Starting from a fine-scale dissipative particle dynamics (DPD) model of self-motile point particles, we derive meso-scale continuum equations by applying a spatial averaging version of the Irving-Kirkwood-Noll procedure. Since the method does not rely on kinetic theory, the derivation is valid for highly concentrated particle systems. Spatial averaging yields stochastic continuum equations similar to those of Toner and Tu. However, our theory also involves a constitutive equation for the average fluctuation force. According to this equation, both the strength and the probability distribution vary with time and position through the effective mass density. The statistics of the fluctuation force also depend on the fine scale dissipative force equation, the physical temperature, and two additional parameters which characterize fluctuation strengths. Although the self-propulsion force entering our DPD model contains no explicit mechanism for aligning the velocities of neighboring particles, our averaged coarse-scale equations include the commonly encountered cubically nonlinear (internal) body force density.

Ground and satellite based assessment of meteorological droughts: The Coello river basin case study

NASA Astrophysics Data System (ADS)

Cruz-Roa, A. F.; Olaya-Marín, E. J.; Barrios, M. I.

2017-10-01

The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998-2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.

Orientation decoding depends on maps, not columns

PubMed Central

Freeman, Jeremy; Brouwer, Gijs Joost; Heeger, David J.; Merriam, Elisha P.

2011-01-01

The representation of orientation in primary visual cortex (V1) has been examined at a fine spatial scale corresponding to the columnar architecture. We present functional magnetic resonance imaging (fMRI) measurements providing evidence for a topographic map of orientation preference in human V1 at a much coarser scale, in register with the angular-position component of the retinotopic map of V1. This coarse-scale orientation map provides a parsimonious explanation for why multivariate pattern analysis methods succeed in decoding stimulus orientation from fMRI measurements, challenging the widely-held assumption that decoding results reflect sampling of spatial irregularities in the fine-scale columnar architecture. Decoding stimulus attributes and cognitive states from fMRI measurements has proven useful for a number of applications, but our results demonstrate that the interpretation cannot assume decoding reflects or exploits columnar organization. PMID:21451017

Application of Geostatistical Simulation to Enhance Satellite Image Products

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer L.; Thirulanambi, Rajkumar; Roy, David

2004-01-01

With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

Role of translational entropy in spatially inhomogeneous, coarse-grained models

NASA Astrophysics Data System (ADS)

Langenberg, Marcel; Jackson, Nicholas E.; de Pablo, Juan J.; Müller, Marcus

2018-03-01

Coarse-grained models of polymer and biomolecular systems have enabled the computational study of cooperative phenomena, e.g., self-assembly, by lumping multiple atomistic degrees of freedom along the backbone of a polymer, lipid, or DNA molecule into one effective coarse-grained interaction center. Such a coarse-graining strategy leaves the number of molecules unaltered. In order to treat the surrounding solvent or counterions on the same coarse-grained level of description, one can also stochastically group several of those small molecules into an effective, coarse-grained solvent bead or "fluid element." Such a procedure reduces the number of molecules, and we discuss how to compensate the concomitant loss of translational entropy by density-dependent interactions in spatially inhomogeneous systems.

Exploring the role of fire, succession, climate, and weather on landscape dynamics using comparative modeling

Treesearch

Robert E. Keane; Geoffrey J. Cary; Mike D. Flannigan; Russell A. Parsons; Ian D. Davies; Karen J. King; Chao Li; Ross A. Bradstock; Malcolm Gill

2013-01-01

An assessment of the relative importance of vegetation change and disturbance as agents of landscape change under current and future climates would (1) provide insight into the controls of landscape dynamics, (2) help inform the design and development of coarse scale spatially explicit ecosystem models such as Dynamic Global Vegetation Models (DGVMs), and (3) guide...

Large-Eddy Simulation of Aeroacoustic Applications

NASA Technical Reports Server (NTRS)

Pruett, C. David; Sochacki, James S.

1999-01-01

This report summarizes work accomplished under a one-year NASA grant from NASA Langley Research Center (LaRC). The effort culminates three years of NASA-supported research under three consecutive one-year grants. The period of support was April 6, 1998, through April 5, 1999. By request, the grant period was extended at no-cost until October 6, 1999. Its predecessors have been directed toward adapting the numerical tool of large-eddy simulation (LES) to aeroacoustic applications, with particular focus on noise suppression in subsonic round jets. In LES, the filtered Navier-Stokes equations are solved numerically on a relatively coarse computational grid. Residual stresses, generated by scales of motion too small to be resolved on the coarse grid, are modeled. Although most LES incorporate spatial filtering, time-domain filtering affords certain conceptual and computational advantages, particularly for aeroacoustic applications. Consequently, this work has focused on the development of subgrid-scale (SGS) models that incorporate time-domain filters.

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change.

PubMed

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-09-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km(2) study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Local elasticity map and plasticity in a model Lennard-Jones glass.

PubMed

Tsamados, Michel; Tanguy, Anne; Goldenberg, Chay; Barrat, Jean-Louis

2009-08-01

In this work we calculate the local elastic moduli in a weakly polydispersed two-dimensional Lennard-Jones glass undergoing a quasistatic shear deformation at zero temperature. The numerical method uses coarse-grained microscopic expressions for the strain, displacement, and stress fields. This method allows us to calculate the local elasticity tensor and to quantify the deviation from linear elasticity (local Hooke's law) at different coarse-graining scales. From the results a clear picture emerges of an amorphous material with strongly spatially heterogeneous elastic moduli that simultaneously satisfies Hooke's law at scales larger than a characteristic length scale of the order of five interatomic distances. At this scale, the glass appears as a composite material composed of a rigid scaffolding and of soft zones. Only recently calculated in nonhomogeneous materials, the local elastic structure plays a crucial role in the elastoplastic response of the amorphous material. For a small macroscopic shear strain, the structures associated with the nonaffine displacement field appear directly related to the spatial structure of the elastic moduli. Moreover, for a larger macroscopic shear strain we show that zones of low shear modulus concentrate most of the strain in the form of plastic rearrangements. The spatiotemporal evolution of this local elasticity map and its connection with long term dynamical heterogeneity as well as with the plasticity in the material is quantified. The possibility to use this local parameter as a predictor of subsequent local plastic activity is also discussed.

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals.

PubMed

Xu, Yinlin; Ma, Qianli D Y; Schmitt, Daniel T; Bernaola-Galván, Pedro; Ivanov, Plamen Ch

2011-11-01

We investigate how various coarse-graining (signal quantization) methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval Δ, this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when Δ < 1, while for Δ > 1 a crossover appears at small scales and moves to intermediate and large scales with increasing Δ. For very rough coarse-graining (Δ > 3) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales; thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry method. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

PubMed Central

Xu, Yinlin; Ma, Qianli D.Y.; Schmitt, Daniel T.; Bernaola-Galván, Pedro; Ivanov, Plamen Ch.

2014-01-01

We investigate how various coarse-graining (signal quantization) methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval Δ, this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when Δ < 1, while for Δ > 1 a crossover appears at small scales and moves to intermediate and large scales with increasing Δ. For very rough coarse-graining (Δ > 3) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales; thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry method. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences. PMID:25392599

Use of expert knowledge to develop fuel maps for wildland fire management [chapter 11

Treesearch

Robert E. Keane; Matt Reeves

2012-01-01

Fuel maps are becoming an essential tool in fire management because they describe, in a spatial context, the one factor that fire managers can control over many scales ­ surface and canopy fuel characteristics. Coarse-resolution fuel maps are useful in global, national, and regional fire danger assessments because they help fire managers effectively plan, allocate, and...

Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling

USGS Publications Warehouse

Dell’Acqua, F.; Gamba, P.; Jaiswal, K.

2012-01-01

This paper discusses spatial aspects of the global exposure dataset and mapping needs for earthquake risk assessment. We discuss this in the context of development of a Global Exposure Database for the Global Earthquake Model (GED4GEM), which requires compilation of a multi-scale inventory of assets at risk, for example, buildings, populations, and economic exposure. After defining the relevant spatial and geographic scales of interest, different procedures are proposed to disaggregate coarse-resolution data, to map them, and if necessary to infer missing data by using proxies. We discuss the advantages and limitations of these methodologies and detail the potentials of utilizing remote-sensing data. The latter is used especially to homogenize an existing coarser dataset and, where possible, replace it with detailed information extracted from remote sensing using the built-up indicators for different environments. Present research shows that the spatial aspects of earthquake risk computation are tightly connected with the availability of datasets of the resolution necessary for producing sufficiently detailed exposure. The global exposure database designed by the GED4GEM project is able to manage datasets and queries of multiple spatial scales.

Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities

USDA-ARS?s Scientific Manuscript database

Low frequency passive microwave remote sensing is a proven technique for soil moisture retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moistur...

Porting LAMMPS to GPUs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, William Michael; Plimpton, Steven James; Wang, Peng

2010-03-01

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers) and solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.

Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

PubMed Central

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P. A.; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

PubMed

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P A; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data.

PubMed

Leyk, Stefan; Runfola, Dan; Nawrotzki, Raphael J; Hunter, Lori M; Riosmena, Fernando

2017-08-01

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on "environmental migration" in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005-2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.

A matter of scale: apparent niche differentiation of diploid and tetraploid plants may depend on extent and grain of analysis.

PubMed

Kirchheimer, Bernhard; Schinkel, Christoph C F; Dellinger, Agnes S; Klatt, Simone; Moser, Dietmar; Winkler, Manuela; Lenoir, Jonathan; Caccianiga, Marco; Guisan, Antoine; Nieto-Lugilde, Diego; Svenning, Jens-Christian; Thuiller, Wilfried; Vittoz, Pascal; Willner, Wolfgang; Zimmermann, Niklaus E; Hörandl, Elvira; Dullinger, Stefan

2016-03-22

Emerging polyploids may depend on environmental niche shifts for successful establishment. Using the alpine plant Ranunculus kuepferi as a model system, we explore the niche shift hypothesis at different spatial resolutions and in contrasting parts of the species range. European Alps. We sampled 12 individuals from each of 102 populations of R. kuepferi across the Alps, determined their ploidy levels, derived coarse-grain (100 × 100 m) environmental descriptors for all sampling sites by downscaling WorldClim maps, and calculated fine-scale environmental descriptors (2 × 2 m) from indicator values of the vegetation accompanying the sampled individuals. Both coarse and fine-scale variables were further computed for 8239 vegetation plots from across the Alps. Subsequently, we compared niche optima and breadths of diploid and tetraploid cytotypes by combining principal components analysis and kernel smoothing procedures. Comparisons were done separately for coarse and fine-grain data sets and for sympatric, allopatric and the total set of populations. All comparisons indicate that the niches of the two cytotypes differ in optima and/or breadths, but results vary in important details. The whole-range analysis suggests differentiation along the temperature gradient to be most important. However, sympatric comparisons indicate that this climatic shift was not a direct response to competition with diploid ancestors. Moreover, fine-grained analyses demonstrate niche contraction of tetraploids, especially in the sympatric range, that goes undetected with coarse-grained data. Although the niche optima of the two cytotypes differ, separation along ecological gradients was probably less decisive for polyploid establishment than a shift towards facultative apomixis, a particularly effective strategy to avoid minority cytotype exclusion. In addition, our results suggest that coarse-grained analyses overestimate niche breadths of widely distributed taxa. Niche comparison analyses should hence be conducted at environmental data resolutions appropriate for the organism and question under study.

Surface fuel treatments in young, regenerating stands affect wildfire severity in a mixed conifer forest, eastside Cascade Range, Washington, USA

Treesearch

Christina Lyons-Tinsley; David L. Peterson

2012-01-01

Previous studies have debated the flammability of young regenerating stands, especially those in a matrix of mature forest, and no consensus has emerged as to whether young stands are inherently prone to high-severity wildfire. This topic has recently been addressed using spatial imagery, and weak inferences were made given the scale mismatch between the coarse...

Computational Investigations of Noise Suppression in Subsonic Round Jets

NASA Technical Reports Server (NTRS)

Pruett, C. David

1997-01-01

NASA Grant NAG1-1802, originally submitted in June 1996 as a two-year proposal, was awarded one-year's funding by NASA LaRC for the period 5 Oct., 1996, through 4 Oct., 1997. Because of the inavailability (from IT at NASA ARC) of sufficient supercomputer time in fiscal 1998 to complete the computational goals of the second year of the original proposal (estimated to be at least 400 Cray C-90 CPU hours), those goals have been appropriately amended, and a new proposal has been submitted to LaRC as a follow-on to NAG1-1802. The current report documents the activities and accomplishments on NAG1-1802 during the one-year period from 5 Oct., 1996, through 4 Oct., 1997. NASA Grant NAG1-1802, and its predecessor, NAG1-1772, have been directed toward adapting the numerical tool of Large-Eddy Simulation (LES) to aeroacoustic applications, with particular focus on noise suppression in subsonic round jets. In LES, the filtered Navier-Stokes equations are solved numerically on a relatively coarse computational grid. Residual stresses, generated by scales of motion too small to be resolved on the coarse grid, are modeled. Although most LES incorporate spatial filtering, time-domain filtering affords certain conceptual and computational advantages, particularly for aeroacoustic applications. Consequently, this work has focused on the development of SubGrid-Scale (SGS) models that incorporate time- domain filters. The author is unaware of any previous attempt at purely time-filtered LES; however, Aldama and Dakhoul and Bedford have considered approaches that combine both spatial and temporal filtering. In our view, filtering in both space and time is redundant, because removal of high frequencies effects the removal of small spatial scales and vice versa.

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Ecological feedbacks. Termite mounds can increase the robustness of dryland ecosystems to climatic change.

PubMed

Bonachela, Juan A; Pringle, Robert M; Sheffer, Efrat; Coverdale, Tyler C; Guyton, Jennifer A; Caylor, Kelly K; Levin, Simon A; Tarnita, Corina E

2015-02-06

Self-organized spatial vegetation patterning is widespread and has been described using models of scale-dependent feedback between plants and water on homogeneous substrates. As rainfall decreases, these models yield a characteristic sequence of patterns with increasingly sparse vegetation, followed by sudden collapse to desert. Thus, the final, spot-like pattern may provide early warning for such catastrophic shifts. In many arid ecosystems, however, termite nests impart substrate heterogeneity by altering soil properties, thereby enhancing plant growth. We show that termite-induced heterogeneity interacts with scale-dependent feedbacks to produce vegetation patterns at different spatial grains. Although the coarse-grained patterning resembles that created by scale-dependent feedback alone, it does not indicate imminent desertification. Rather, mound-field landscapes are more robust to aridity, suggesting that termites may help stabilize ecosystems under global change. Copyright © 2015, American Association for the Advancement of Science.

Resolving Properties of Polymers and Nanoparticle Assembly through Coarse-Grained Computational Studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grest, Gary S.

2017-09-01

Coupled length and time scales determine the dynamic behavior of polymers and polymer nanocomposites and underlie their unique properties. To resolve the properties over large time and length scales it is imperative to develop coarse grained models which retain the atomistic specificity. Here we probe the degree of coarse graining required to simultaneously retain significant atomistic details a nd access large length and time scales. The degree of coarse graining in turn sets the minimum length scale instrumental in defining polymer properties and dynamics. Using polyethylene as a model system, we probe how the coarse - graining scale affects themore » measured dynamics with different number methylene group s per coarse - grained beads. Using these models we simulate polyethylene melts for times over 500 ms to study the viscoelastic properties of well - entangled polymer melts and large nanoparticle assembly as the nanoparticles are driven close enough to form nanostructures.« less

Post-Disturbance Stability of Fish Assemblages Measured at Coarse Taxonomic Resolution Masks Change at Finer Scales.

PubMed

Ceccarelli, Daniela M; Emslie, Michael J; Richards, Zoe T

2016-01-01

Quantifying changes to coral reef fish assemblages in the wake of cyclonic disturbances is challenging due to spatial variability of damage inherent in such events. Often, fish abundance appears stable at one spatial scale (e.g. reef-wide), but exhibits substantial change at finer scales (e.g. site-specific decline or increase). Taxonomic resolution also plays a role; overall stability at coarse taxonomic levels (e.g. family) may mask species-level turnover. Here we document changes to reef fish communities after severe Tropical Cyclone Ita crossed Lizard Island, Great Barrier Reef. Coral and reef fish surveys were conducted concurrently before and after the cyclone at four levels of exposure to the prevailing weather. Coral cover declined across all exposures except sheltered sites, with the largest decline at exposed sites. There was no significant overall reduction in the total density, biomass and species richness of reef fishes between 2011 and 2015, but individual fish taxa (families and species) changed in complex and unpredictable ways. For example, more families increased in density and biomass than decreased following Cyclone Ita, particularly at exposed sites whilst more fish families declined at lagoon sites even though coral cover did not decline. All sites lost biomass of several damselfish species, and at most sites there was an increase in macroinvertivores and grazers. Overall, these results suggest that the degree of change measured at coarse taxonomic levels masked high species-level turnover, although other potential explanations include that there was no impact of the storm, fish assemblages were impacted but underwent rapid recovery or that there is a time lag before the full impacts become apparent. This study confirms that in high-complexity, high diversity ecosystems such as coral reefs, species level analyses are essential to adequately capture the consequences of disturbance events.

Post-Disturbance Stability of Fish Assemblages Measured at Coarse Taxonomic Resolution Masks Change at Finer Scales

PubMed Central

Ceccarelli, Daniela M.

2016-01-01

Quantifying changes to coral reef fish assemblages in the wake of cyclonic disturbances is challenging due to spatial variability of damage inherent in such events. Often, fish abundance appears stable at one spatial scale (e.g. reef-wide), but exhibits substantial change at finer scales (e.g. site-specific decline or increase). Taxonomic resolution also plays a role; overall stability at coarse taxonomic levels (e.g. family) may mask species-level turnover. Here we document changes to reef fish communities after severe Tropical Cyclone Ita crossed Lizard Island, Great Barrier Reef. Coral and reef fish surveys were conducted concurrently before and after the cyclone at four levels of exposure to the prevailing weather. Coral cover declined across all exposures except sheltered sites, with the largest decline at exposed sites. There was no significant overall reduction in the total density, biomass and species richness of reef fishes between 2011 and 2015, but individual fish taxa (families and species) changed in complex and unpredictable ways. For example, more families increased in density and biomass than decreased following Cyclone Ita, particularly at exposed sites whilst more fish families declined at lagoon sites even though coral cover did not decline. All sites lost biomass of several damselfish species, and at most sites there was an increase in macroinvertivores and grazers. Overall, these results suggest that the degree of change measured at coarse taxonomic levels masked high species-level turnover, although other potential explanations include that there was no impact of the storm, fish assemblages were impacted but underwent rapid recovery or that there is a time lag before the full impacts become apparent. This study confirms that in high-complexity, high diversity ecosystems such as coral reefs, species level analyses are essential to adequately capture the consequences of disturbance events. PMID:27285160

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

Resolving Dynamic Properties of Polymers through Coarse-Grained Computational Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salerno, K. Michael; Agrawal, Anupriya; Perahia, Dvora

2016-02-05

Coupled length and time scales determine the dynamic behavior of polymers and underlie their unique viscoelastic properties. To resolve the long-time dynamics it is imperative to determine which time and length scales must be correctly modeled. In this paper, we probe the degree of coarse graining required to simultaneously retain significant atomistic details and access large length and time scales. The degree of coarse graining in turn sets the minimum length scale instrumental in defining polymer properties and dynamics. Using linear polyethylene as a model system, we probe how the coarse-graining scale affects the measured dynamics. Iterative Boltzmann inversion ismore » used to derive coarse-grained potentials with 2–6 methylene groups per coarse-grained bead from a fully atomistic melt simulation. We show that atomistic detail is critical to capturing large-scale dynamics. Finally, using these models we simulate polyethylene melts for times over 500 μs to study the viscoelastic properties of well-entangled polymer melts.« less

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

NASA Astrophysics Data System (ADS)

Mishra, U.; Riley, W. J.

2015-01-01

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.

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

NASA Astrophysics Data System (ADS)

Mishra, U.; Riley, W. J.

2015-07-01

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.

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

DOE PAGES

Mishra, U.; Riley, W. J.

2015-07-02

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 setmore » 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 ( R 2 = 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 ( R 2 ∼ 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.« less

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

DOE PAGES

Mishra, U.; Riley, W. J.

2015-01-01

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 reasonablemore » 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 ( R 2 = 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 ( R 2 ~ 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.« less

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

Passive Sampling to Capture the Spatial Variability of Coarse Particles by Composition in Cleveland, OH

EPA Science Inventory

Passive samplers deployed at 25 sites for three week-long intervals were used to characterize spatial variability in the mass and composition of coarse particulate matter (PM_10-2.5) in Cleveland, OH in summer 2008. The size and composition of individual particles deter...

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States

NASA Astrophysics Data System (ADS)

Peng, Dailiang; Zhang, Xiaoyang; Zhang, Bing; Liu, Liangyun; Liu, Xinjie; Huete, Alfredo R.; Huang, Wenjiang; Wang, Siyuan; Luo, Shezhou; Zhang, Xiao; Zhang, Helin

2017-10-01

Land surface phenology (LSP) has been widely retrieved from satellite data at multiple spatial resolutions, but the spatial scaling effects on LSP detection are poorly understood. In this study, we collected enhanced vegetation index (EVI, 250 m) from collection 6 MOD13Q1 product over the contiguous United States (CONUS) in 2007 and 2008, and generated a set of multiple spatial resolution EVI data by resampling 250 m to 2 × 250 m and 3 × 250 m, 4 × 250 m, …, 35 × 250 m. These EVI time series were then used to detect the start of spring season (SOS) at various spatial resolutions. Further the SOS variation across scales was examined at each coarse resolution grid (35 × 250 m ≈ 8 km, refer to as reference grid) and ecoregion. Finally, the SOS scaling effects were associated with landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation within each reference grid. The results revealed the influences of satellite spatial resolutions on SOS retrievals and the related impact factors. Specifically, SOS significantly varied lineally or logarithmically across scales although the relationship could be either positive or negative. The overall SOS values averaged from spatial resolutions between 250 m and 35 × 250 m at large ecosystem regions were generally similar with a difference less than 5 days, while the SOS values within the reference grid could differ greatly in some local areas. Moreover, the standard deviation of SOS across scales in the reference grid was less than 5 days in more than 70% of area over the CONUS, which was smaller in northeastern than in southern and western regions. The SOS scaling effect was significantly associated with heterogeneity of vegetation properties characterized using land landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation, but the latter was the most important impact factor.

Theory of wavelet-based coarse-graining hierarchies for molecular dynamics.

PubMed

Rinderspacher, Berend Christopher; Bardhan, Jaydeep P; Ismail, Ahmed E

2017-07-01

We present a multiresolution approach to compressing the degrees of freedom and potentials associated with molecular dynamics, such as the bond potentials. The approach suggests a systematic way to accelerate large-scale molecular simulations with more than two levels of coarse graining, particularly applications of polymeric materials. In particular, we derive explicit models for (arbitrarily large) linear (homo)polymers and iterative methods to compute large-scale wavelet decompositions from fragment solutions. This approach does not require explicit preparation of atomistic-to-coarse-grained mappings, but instead uses the theory of diffusion wavelets for graph Laplacians to develop system-specific mappings. Our methodology leads to a hierarchy of system-specific coarse-grained degrees of freedom that provides a conceptually clear and mathematically rigorous framework for modeling chemical systems at relevant model scales. The approach is capable of automatically generating as many coarse-grained model scales as necessary, that is, to go beyond the two scales in conventional coarse-grained strategies; furthermore, the wavelet-based coarse-grained models explicitly link time and length scales. Furthermore, a straightforward method for the reintroduction of omitted degrees of freedom is presented, which plays a major role in maintaining model fidelity in long-time simulations and in capturing emergent behaviors.

Spatial models reveal the microclimatic buffering capacity of old-growth forests

PubMed Central

Frey, Sarah J. K.; Hadley, Adam S.; Johnson, Sherri L.; Schulze, Mark; Jones, Julia A.; Betts, Matthew G.

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming. PMID:27152339

Spatial models reveal the microclimatic buffering capacity of old-growth forests.

PubMed

Frey, Sarah J K; Hadley, Adam S; Johnson, Sherri L; Schulze, Mark; Jones, Julia A; Betts, Matthew G

2016-04-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.

Soil Moisture fusion across scales using a multiscale nonstationary Spatial Hierarchical Model

NASA Astrophysics Data System (ADS)

Kathuria, D.; Mohanty, B.; Katzfuss, M.

2017-12-01

Soil moisture (SM) datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors, on the other hand, provide observations on a finer spatial scale (meter scale or less) but are sparsely available. SM is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables and these interactions change dynamically with footprint scales. Past literature has largely focused on the scale specific effect of these covariates on soil moisture. The present study proposes a robust Multiscale-Nonstationary Spatial Hierarchical Model (MN-SHM) which can assimilate SM from point to RS footprints. The spatial structure of SM across footprints is modeled by a class of scalable covariance functions whose nonstationary depends on atmospheric forcings (such as precipitation) and surface physical controls (such as topography, soil-texture and vegetation). The proposed model is applied to fuse point and airborne ( 1.5 km) SM data obtained during the SMAPVEX12 campaign in the Red River watershed in Southern Manitoba, Canada with SMOS ( 30km) data. It is observed that precipitation, soil-texture and vegetation are the dominant factors which affect the SM distribution across various footprint scales (750 m, 1.5 km, 3 km, 9 km,15 km and 30 km). We conclude that MN-SHM handles the change of support problems easily while retaining reasonable predictive accuracy across multiple spatial resolutions in the presence of surface heterogeneity. The MN-SHM can be considered as a complex non-stationary extension of traditional geostatistical prediction methods (such as Kriging) for fusing multi-platform multi-scale datasets.

Coarse-scale movement patterns of a small-bodied fish inhabiting a desert stream

USGS Publications Warehouse

Dzul, M.C.; Quist, M.C.; Dinsmore, S.J.; Gaines, D.B.; Bower, M.R.

2013-01-01

Located on the floor of Death Valley (CA, USA), Salt Creek harbors a single fish species, the Salt Creek pupfish, Cyprinodon salinus salinus, which has adapted to this extremely harsh environment. Salt Creek is fed by an underground spring and is comprised of numerous pools, runs, and marshes that exhibit substantial variability in temperature, salinity, and dissolved oxygen concentrations. In addition, the wetted area of Salt Creek is reduced throughout the summer months due to high rates of evaporation, with some reaches drying completely. Therefore, it seems logical that short- and long-term movement patterns may play an important role in Salt Creek pupfish population dynamics. The objective of this study was to describe coarse-scale movements of Salt Creek pupfish in Salt Creek during their breeding season from March to May. Sex ratios and length–frequency distributions varied spatially within Salt Creek, suggesting population segregation during the breeding season. Long-distance movements were generally rare, although two fish moved more than 1.2 km. Movement in upstream reaches was rare or absent, in contrast to the greater movement observed in downstream reaches (29% of recaptures). Temporal trends and demographic patterns in movement were not observed. Because the two most downstream habitats dry up in the summer, our results indicate that coarse-scale movements that re-populate downstream reaches likely occur during other times of year. Consequently, the importance of small- and large-scale movements is influenced by season. Further assessment of Salt Creek movement patterns conducted during other times of year may better illuminate long-distance movement patterns and source-sink dynamics.

Multisource Imaging of Seasonal Dynamics in Land Surface Phenology Using Harmonized Landsat and Sentinel-2 Data

NASA Astrophysics Data System (ADS)

Melaas, E. K.; Graesser, J.; Friedl, M. A.

2017-12-01

Land surface phenology, including the timing of phenophase transitions and the entire seasonal cycle of surface reflectance and vegetation indices, is important for a myriad of applications including monitoring the response of terrestrial ecosystems to climate variability and extreme events, and land cover mapping. While methods to monitor and map phenology from coarse spatial resolution instruments such as MODIS are now relatively mature, the spatial resolution of these instruments is inadequate where vegetation properties, land use, and land cover vary at spatial scales of tens of meters. To address this need, algorithms to map phenology at moderate spatial resolution (30 m) using data from Landsat have recently been developed. However, the 16-day repeat cycle of Landsat presents significant challenges in regions where changes are rapid or where cloud cover reduces the frequency of clear-sky views. The European Space Agency's Sentinel-2 satellites, which are designed to provide moderate spatial resolution data at 5-day revisit frequency near the equator and 3 day revisit frequency in the mid-latitudes, will alleviate this constraint in many parts of the world. Here, we use harmonized time series of data from Sentinel-2A and Landsat OLI (HLS) to quantify the timing of land surface phenology metrics across a sample of deciduous forest and grassland-dominated sites, and then compare these estimates with co-located in situ observations. The resulting phenology maps demonstrate the improved information related to landscape-scale features that can be estimated from HLS data relative to comparable metrics from coarse spatial resolution instruments. For example, our results based on HLS data reveal spatial patterns in phenological metrics related to topographic and land cover controls that are not resolved in MODIS data, and show good agreement with transition dates observed from in situ measurements. Our results also show systematic bias toward earlier timing of spring, which is caused by inadequate density of observations that will be mitigated once data from Sentinel-2B are available. Overall, our results highlight the potential for using moderate spatial resolution data from Landsat and Sentinel-2 for developing operational phenology algorithms and products in support of the science community.

Merging fine and coarse resolution remotely sensed data with household-level survey data to evaluate small-scale vulnerability to climate change in West Africa

NASA Astrophysics Data System (ADS)

Grace, K.; Husak, G. J.

2016-12-01

Climate change, in the form of increasingly variable temperatures and rainfall, is anticipated to have potentially dramatic impacts on subsistence agricultural communities throughout the world. Poor people who depend on rainfall to produce food or to produce products to sell to buy food are expected to be particularly vulnerable to the negative impacts associated with climate change. Poor people have extremely limited resources that can be used to cope with weather events and these resources are even more strained when the individuals live in poor countries. While poor and rural producers are most likely to face high levels of vulnerability to food insecurity due to their dependence on rainfall for their agricultural production, annual agricultural censuses are virtually non-existent. Surveying all of the producers in a country each year is extremely costly owing to difficulties in accessing farmers and the costs associated with extensive surveys. The result, however, is very limited information on the spatial and temporal variation in production and the resulting impacts on micro-scale food insecurity and livelihood stability. In this project we use a combination of fine and coarse resolution remotely sensed data ( 1m data, 250m NDVI data and 10km rainfall data, and others) and recently collected survey data from the World Bank to estimate agricultural and land use characteristics at a fine spatial scale in Burkina Faso, Mali and Niger. The analysis will produce estimates of cultivated area that incorporate spatially dynamic climate and vegetation data but that also account for the variation in agricultural practices associated with the different ethnic and religious groups within each country. The survey data will help to calibrate the models and will also serve as a way to validate the statistical models used to estimate on the ground agricultural practices. The models will then be used to evaluate fine-scale agricultural response to climate change in the form of drying and warming.

Effects of spatial resolution and landscape structure on land cover characterization

NASA Astrophysics Data System (ADS)

Yang, Wenli

This dissertation addressed problems in scaling, problems that are among the main challenges in remote sensing. The principal objective of the research was to investigate the effects of changing spatial scale on the representation of land cover. A second objective was to determine the relationship between such effects, characteristics of landscape structure and scaling procedures. Four research issues related to spatial scaling were examined. They included: (1) the upscaling of Normalized Difference Vegetation Index (NDVI); (2) the effects of spatial scale on indices of landscape structure; (3) the representation of land cover databases at different spatial scales; and (4) the relationships between landscape indices and land cover area estimations. The overall bias resulting from non-linearity of NDVI in relation to spatial resolution is generally insignificant as compared to other factors such as influences of aerosols and water vapor. The bias is, however, related to land surface characteristics. Significant errors may be introduced in heterogeneous areas where different land cover types exhibit strong spectral contrast. Spatially upscaled SPOT and TM NDVIs have information content comparable with the AVHRR-derived NDVI. Indices of landscape structure and spatial resolution are generally related, but the exact forms of the relationships are subject to changes in other factors including the basic patch unit constituting a landscape and the proportional area of foreground land cover under consideration. The extent of agreement between spatially aggregated coarse resolution land cover datasets and full resolution datasets changes with the properties of the original datasets, including the pixel size and class definition. There are close relationships between landscape structure and class areas estimated from spatially aggregated land cover databases. The relationships, however, do not permit extension from one area to another. Inversion calibration across different geographic/ecological areas is, therefore, not feasible. Different rules govern the land cover area changes across resolutions when different upscaling methods are used. Special attention should be given to comparison between land cover maps derived using different methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mishra, U.; Riley, W. J.

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 setmore » 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 ( R 2 = 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 ( R 2 ∼ 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.« less

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Bhaskar, A.; Fleming, B.; Hogan, D. M.

2016-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Accounting for small scale heterogeneity in ecohydrologic watershed models

NASA Astrophysics Data System (ADS)

Burke, W.; Tague, C.

2017-12-01

Spatially distributed ecohydrologic models are inherently constrained by the spatial resolution of their smallest units, below which land and processes are assumed to be homogenous. At coarse scales, heterogeneity is often accounted for by computing store and fluxes of interest over a distribution of land cover types (or other sources of heterogeneity) within spatially explicit modeling units. However this approach ignores spatial organization and the lateral transfer of water and materials downslope. The challenge is to account both for the role of flow network topology and fine-scale heterogeneity. We present a new approach that defines two levels of spatial aggregation and that integrates spatially explicit network approach with a flexible representation of finer-scale aspatial heterogeneity. Critically, this solution does not simply increase the resolution of the smallest spatial unit, and so by comparison, results in improved computational efficiency. The approach is demonstrated by adapting Regional Hydro-Ecologic Simulation System (RHESSys), an ecohydrologic model widely used to simulate climate, land use, and land management impacts. We illustrate the utility of our approach by showing how the model can be used to better characterize forest thinning impacts on ecohydrology. Forest thinning is typically done at the scale of individual trees, and yet management responses of interest include impacts on watershed scale hydrology and on downslope riparian vegetation. Our approach allow us to characterize the variability in tree size/carbon reduction and water transfers between neighboring trees while still capturing hillslope to watershed scale effects, Our illustrative example demonstrates that accounting for these fine scale effects can substantially alter model estimates, in some cases shifting the impacts of thinning on downslope water availability from increases to decreases. We conclude by describing other use cases that may benefit from this approach including characterizing urban vegetation and storm water management features and their impact on watershed scale hydrology and biogeochemical cycling.

Multi-scale approaches for high-speed imaging and analysis of large neural populations

PubMed Central

Ahrens, Misha B.; Yuste, Rafael; Peterka, Darcy S.; Paninski, Liam

2017-01-01

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution. PMID:28771570

Scaling range sizes to threats for robust predictions of risks to biodiversity.

PubMed

Keith, David A; Akçakaya, H Resit; Murray, Nicholas J

2018-04-01

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape-scale threats to species and ecosystems. © 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1993-01-01

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

Source identification of coarse particles in the Desert Southwest, USA using Positive Matrix Factorization

EPA Science Inventory

The Desert Southwest Coarse Particulate Matter Study was undertaken to further our understanding of the spatial and temporal variability and sources of fine and coarse particulate matter (PM) in rural, arid, desert environments. Sampling was conducted between February 2009 and Fe...

Contrasting patterns of fine-scale herb layer species composition in temperate forests

NASA Astrophysics Data System (ADS)

Chudomelová, Markéta; Zelený, David; Li, Ching-Feng

2017-04-01

Although being well described at the landscape level, patterns in species composition of forest herb layer are rarely studied at smaller scales. Here, we examined fine-scale environmental determinants and spatial structures of herb layer communities in thermophilous oak- and hornbeam dominated forests of the south-eastern part of the Czech Republic. Species composition of herb layer vegetation and environmental variables were recorded within a fixed grid of 2 × 2 m subplots regularly distributed within 1-ha quadrate plots in three forest stands. For each site, environmental models best explaining species composition were constructed using constrained ordination analysis. Spatial eigenvector mapping was used to model and account for spatial structures in community variation. Mean Ellenberg indicator values calculated for each subplot were used for ecological interpretation of spatially structured residual variation. The amount of variation explained by environmental and spatial models as well as the selection of variables with the best explanatory power differed among sites. As an important environmental factor, relative elevation was common to all three sites, while pH and canopy openness were shared by two sites. Both environmental and community variation was mostly coarse-scaled, as was the spatially structured portion of residual variation. When corrected for bias due to spatial autocorrelation, those environmental factors with already weak explanatory power lost their significance. Only a weak evidence of possibly omitted environmental predictor was found for autocorrelated residuals of site models using mean Ellenberg indicator values. Community structure was determined by different factors at different sites. The relative importance of environmental filtering vs. spatial processes was also site specific, implying that results of fine-scale studies tend to be shaped by local conditions. Contrary to expectations based on other studies, overall dominance of spatial processes at fine scale has not been detected. Ecologists should keep this in mind when making generalizations about community dynamics.

Listening to music primes space: pianists, but not novices, simulate heard actions.

PubMed

Taylor, J Eric T; Witt, Jessica K

2015-03-01

Musicians sometimes report twitching in their fingers or hands while listening to music. This anecdote could be indicative of a tendency for auditory-motor co-representation in musicians. Here, we describe two studies showing that pianists (Experiment 1), but not novices (Experiment 2) automatically generate spatial representations that correspond to learned musical actions while listening to music. Participants made one-handed movements to the left or right from a central location in response to visual stimuli while listening to task-irrelevant auditory stimuli, which were scales played on a piano. These task-irrelevant scales were either ascending (compatible with rightward movements) or descending (compatible with leftward movements). Pianists were faster to respond when the scale direction was compatible with the direction of response movement, whereas novices' movements were unaffected by the scale. These results are in agreement with existing research on action-effect coupling in musicians, which draw heavily on common coding theory. In addition, these results show how intricate auditory stimuli (ascending or descending scales) evoke coarse, domain-general spatial representations.

Brain Regions Involved in the Retrieval of Spatial and Episodic Details Associated with a Familiar Environment: An fMRI Study

ERIC Educational Resources Information Center

Hirshhorn, Marnie; Grady, Cheryl; Rosenbaum, R. Shayna; Winocur, Gordon; Moscovitch, Morris

2012-01-01

Functional magnetic resonance imaging (fMRI) was used to compare brain activity during the retrieval of coarse- and fine-grained spatial details and episodic details associated with a familiar environment. Long-time Toronto residents compared pairs of landmarks based on their absolute geographic locations (requiring either coarse or fine…

The Dynamic General Vegetation Model MC1 over the United States and Canada at a 5-arcminute resolution: model inputs and outputs

Treesearch

Ray Drapek; John B. Kim; Ronald P. Neilson

2015-01-01

Land managers need to include climate change in their decisionmaking, but the climate models that project future climates operate at spatial scales that are too coarse to be of direct use. To create a dataset more useful to managers, soil and historical climate were assembled for the United States and Canada at a 5-arcminute grid resolution. Nine CMIP3 future climate...

Satellite-based Flood Modeling Using TRMM-based Rainfall Products

PubMed Central

Harris, Amanda; Rahman, Sayma; Hossain, Faisal; Yarborough, Lance; Bagtzoglou, Amvrossios C.; Easson, Greg

2007-01-01

Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available at spatial scales ranging from 0.25° to 0.50° and hence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scale flood events. This study assesses the question: what are the hydrologic implications of uncertainty of satellite rainfall data at the coarse scale? We investigated this question on the 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall product assessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real time with a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data can improve application in flood prediction to some extent with the trade-off of more false alarms in peak flow. However, a more rational and regime-based adjustment procedure needs to be identified before the use of satellite data can be institutionalized among flood modelers. PMID:28903302

Upscaling of Hydraulic Conductivity using the Double Constraint Method

NASA Astrophysics Data System (ADS)

El-Rawy, Mustafa; Zijl, Wouter; Batelaan, Okke

2013-04-01

The mathematics and modeling of flow through porous media is playing an increasingly important role for the groundwater supply, subsurface contaminant remediation and petroleum reservoir engineering. In hydrogeology hydraulic conductivity data are often collected at a scale that is smaller than the grid block dimensions of a groundwater model (e.g. MODFLOW). For instance, hydraulic conductivities determined from the field using slug and packer tests are measured in the order of centimeters to meters, whereas numerical groundwater models require conductivities representative of tens to hundreds of meters of grid cell length. Therefore, there is a need for upscaling to decrease the number of grid blocks in a groundwater flow model. Moreover, models with relatively few grid blocks are simpler to apply, especially when the model has to run many times, as is the case when it is used to assimilate time-dependent data. Since the 1960s different methods have been used to transform a detailed description of the spatial variability of hydraulic conductivity to a coarser description. In this work we will investigate a relatively simple, but instructive approach: the Double Constraint Method (DCM) to identify the coarse-scale conductivities to decrease the number of grid blocks. Its main advantages are robustness and easy implementation, enabling to base computations on any standard flow code with some post processing added. The inversion step of the double constraint method is based on a first forward run with all known fluxes on the boundary and in the wells, followed by a second forward run based on the heads measured on the phreatic surface (i.e. measured in shallow observation wells) and in deeper observation wells. Upscaling, in turn is inverse modeling (DCM) to determine conductivities in coarse-scale grid blocks from conductivities in fine-scale grid blocks. In such a way that the head and flux boundary conditions applied to the fine-scale model are also honored at the coarse-scale. Exemplification will be presented for the Kleine Nete catchment, Belgium. As a result we identified coarse-scale conductivities while decreasing the number of grid blocks with the advantage that a model run costs less computation time and requires less memory space. In addition, ranking of models was investigated.

Quantum theory of multiscale coarse-graining.

PubMed

Han, Yining; Jin, Jaehyeok; Wagner, Jacob W; Voth, Gregory A

2018-03-14

Coarse-grained (CG) models serve as a powerful tool to simulate molecular systems at much longer temporal and spatial scales. Previously, CG models and methods have been built upon classical statistical mechanics. The present paper develops a theory and numerical methodology for coarse-graining in quantum statistical mechanics, by generalizing the multiscale coarse-graining (MS-CG) method to quantum Boltzmann statistics. A rigorous derivation of the sufficient thermodynamic consistency condition is first presented via imaginary time Feynman path integrals. It identifies the optimal choice of CG action functional and effective quantum CG (qCG) force field to generate a quantum MS-CG (qMS-CG) description of the equilibrium system that is consistent with the quantum fine-grained model projected onto the CG variables. A variational principle then provides a class of algorithms for optimally approximating the qMS-CG force fields. Specifically, a variational method based on force matching, which was also adopted in the classical MS-CG theory, is generalized to quantum Boltzmann statistics. The qMS-CG numerical algorithms and practical issues in implementing this variational minimization procedure are also discussed. Then, two numerical examples are presented to demonstrate the method. Finally, as an alternative strategy, a quasi-classical approximation for the thermal density matrix expressed in the CG variables is derived. This approach provides an interesting physical picture for coarse-graining in quantum Boltzmann statistical mechanics in which the consistency with the quantum particle delocalization is obviously manifest, and it opens up an avenue for using path integral centroid-based effective classical force fields in a coarse-graining methodology.

Vertical Subsurface Flow Mixing and Horizontal Anisotropy in Coarse Fluvial Aquifers: Structural Aspects

NASA Astrophysics Data System (ADS)

Huggenberger, P.; Huber, E.

2014-12-01

Detailed descriptions of the subsurface heterogeneities in coarse fluvial aquifer gravel often lack in concepts to distinguish between the essence and the noise of a permeability structure and the ability to extrapolate site specific hydraulic information at the tens to several hundred meters scale. At this scale the heterogeneity strongly influences the anisotropies of the flow field and the mixing processes in groundwater. However, in many hydrogeological models the complexity of natural systems is oversimplified. Understanding the link between the dynamics of the surface processes of braided-river systems and the resulting subsurface sedimentary structures is the key to characterizing the complexity of horizontal and vertical mixing processes in groundwater. From the different depositional elements of coarse braided-river systems, the largest permeability contrasts can be observed in the scour-fills. Other elements (e.g. different types of gravel sheets) show much smaller variabilities and could be considered as a kind of matrix. Field experiments on the river Tagliamento (Northeast Italy) based on morphological observation and ground-penetrating radar (GPR) surveys, as well as outcrop analyses of gravel pit exposures (Switzerland) allowed us to define the shape, sizes, spatial distribution and preservation potential of scour-fills. In vertical sections (e.g. 2D GPR data, vertical outcrop), the spatial density of remnant erosional bounding surfaces of scours is an indicator for the dynamics of the braided-river system (lateral mobility of the active floodplain, rate of sediment net deposition and spatial distribution of the confluence scours). In case of combined low aggradation rate and low lateral mobility the deposits may be dominated by a complex overprinting of scour-fills. The delineation of the erosional bounding surfaces, that are coherent over the survey area, is based on the identification of angular discontinuities of the reflectors. Fence diagrams and horizontal time-slices from GPR data are used to construct simplified 3D hydraulic properties distribution models and to derive anisotropy patterns. On the basis of this work, conceptual models could be designed and implemented into numerical models to simulate the flow field and mixing in heterogeneous braided-river deposits.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

PubMed

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

On neutral metacommunity patterns of river basins at different scales of aggregation

NASA Astrophysics Data System (ADS)

Convertino, Matteo; Muneepeerakul, Rachata; Azaele, Sandro; Bertuzzo, Enrico; Rinaldo, Andrea; Rodriguez-Iturbe, Ignacio

2009-08-01

Neutral metacommunity models for spatial biodiversity patterns are implemented on river networks acting as ecological corridors at different resolution. Coarse-graining elevation fields (under the constraint of preserving the basin mean elevation) produce a set of reconfigured drainage networks. The hydrologic assumption made implies uniform runoff production such that each link has the same habitat capacity. Despite the universal scaling properties shown by river basins regardless of size, climate, vegetation, or exposed lithology, we find that species richness at local and regional scales exhibits resolution-dependent behavior. In addition, we investigate species-area relationships and rank-abundance patterns. The slopes of the species-area relationships, which are consistent over coarse-graining resolutions, match those found in real landscapes in the case of long-distance dispersal. The rank-abundance patterns are independent of the resolution over a broad range of dispersal length. Our results confirm that strong interactions occur between network structure and the dispersal of species and that under the assumption of neutral dynamics, these interactions produce resolution-dependent biodiversity patterns that diverge from expectations following from universal geomorphic scaling laws. Both in theoretical and in applied ecology studying how patterns change in resolution is relevant for understanding how ecological dynamics work in fragmented landscape and for sampling and biodiversity management campaigns, especially in consideration of climate change.

NASA Downscaling Project: Final Report

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

NASA Downscaling Project

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.

Model simulations and proxy-based reconstructions for the European region in the past millennium (Invited)

NASA Astrophysics Data System (ADS)

Zorita, E.

2009-12-01

One of the objectives when comparing simulations of past climates to proxy-based climate reconstructions is to asses the skill of climate models to simulate climate change. This comparison may accomplished at large spatial scales, for instance the evolution of simulated and reconstructed Northern Hemisphere annual temperature, or at regional or point scales. In both approaches a 'fair' comparison has to take into account different aspects that affect the inevitable uncertainties and biases in the simulations and in the reconstructions. These efforts face a trade-off: climate models are believed to be more skillful at large hemispheric scales, but climate reconstructions are these scales are burdened by the spatial distribution of available proxies and by methodological issues surrounding the statistical method used to translate the proxy information into large-spatial averages. Furthermore, the internal climatic noise at large hemispheric scales is low, so that the sampling uncertainty tends to be also low. On the other hand, the skill of climate models at regional scales is limited by the coarse spatial resolution, which hinders a faithful representation of aspects important for the regional climate. At small spatial scales, the reconstruction of past climate probably faces less methodological problems if information from different proxies is available. The internal climatic variability at regional scales is, however, high. In this contribution some examples of the different issues faced when comparing simulation and reconstructions at small spatial scales in the past millennium are discussed. These examples comprise reconstructions from dendrochronological data and from historical documentary data in Europe and climate simulations with global and regional models. These examples indicate that the centennial climate variations can offer a reasonable target to assess the skill of global climate models and of proxy-based reconstructions, even at small spatial scales. However, as the focus shifts towards higher frequency variability, decadal or multidecadal, the need for larger simulation ensembles becomes more evident. Nevertheless,the comparison at these time scales may expose some lines of research on the origin of multidecadal regional climate variability.

Effective connectivity in the neural network underlying coarse-to-fine categorization of visual scenes. A dynamic causal modeling study.

PubMed

Kauffmann, Louise; Chauvin, Alan; Pichat, Cédric; Peyrin, Carole

2015-10-01

According to current models of visual perception scenes are processed in terms of spatial frequencies following a predominantly coarse-to-fine processing sequence. Low spatial frequencies (LSF) reach high-order areas rapidly in order to activate plausible interpretations of the visual input. This triggers top-down facilitation that guides subsequent processing of high spatial frequencies (HSF) in lower-level areas such as the inferotemporal and occipital cortices. However, dynamic interactions underlying top-down influences on the occipital cortex have never been systematically investigated. The present fMRI study aimed to further explore the neural bases and effective connectivity underlying coarse-to-fine processing of scenes, particularly the role of the occipital cortex. We used sequences of six filtered scenes as stimuli depicting coarse-to-fine or fine-to-coarse processing of scenes. Participants performed a categorization task on these stimuli (indoor vs. outdoor). Firstly, we showed that coarse-to-fine (compared to fine-to-coarse) sequences elicited stronger activation in the inferior frontal gyrus (in the orbitofrontal cortex), the inferotemporal cortex (in the fusiform and parahippocampal gyri), and the occipital cortex (in the cuneus). Dynamic causal modeling (DCM) was then used to infer effective connectivity between these regions. DCM results revealed that coarse-to-fine processing resulted in increased connectivity from the occipital cortex to the inferior frontal gyrus and from the inferior frontal gyrus to the inferotemporal cortex. Critically, we also observed an increase in connectivity strength from the inferior frontal gyrus to the occipital cortex, suggesting that top-down influences from frontal areas may guide processing of incoming signals. The present results support current models of visual perception and refine them by emphasizing the role of the occipital cortex as a cortical site for feedback projections in the neural network underlying coarse-to-fine processing of scenes. Copyright © 2015 Elsevier Inc. All rights reserved.

Environmental Controls on Multi-Scale Soil Nutrient Variability in the Tropics: the Importance of Land-Cover Change

NASA Astrophysics Data System (ADS)

Holmes, K. W.; Kyriakidis, P. C.; Chadwick, O. A.; Matricardi, E.; Soares, J. V.; Roberts, D. A.

2003-12-01

The natural controls on soil variability and the spatial scales at which correlation exists among soil and environmental variables are critical information for evaluating the effects of deforestation. We detect different spatial scales of variability in soil nutrient levels over a large region (hundreds of thousands of km2) in the Amazon, analyze correlations among soil properties at these different scales, and evaluate scale-specific relationships among soil properties and the factors potentially driving soil development. Statistical relationships among physical drivers of soil formation, namely geology, precipitation, terrain attributes, classified soil types, and land cover derived from remote sensing, were included to determine which factors are related to soil biogeochemistry at each spatial scale. Surface and subsurface soil profile data from a 3000 sample database collected in Rond“nia, Brazil, were used to investigate patterns in pH, phosphorus, nitrogen, organic carbon, effective cation exchange capacity, calcium, magnesium, potassium, aluminum, sand, and clay in this environment grading from closed canopy tropical forest to savanna. We focus on pH in this presentation for simplicity, because pH is the single most important soil characteristic for determining the chemical environment of higher plants and soil microbial activity. We determined four spatial scales which characterize integrated patterns of soil chemistry: less than 3 km; 3 to 10 km; 10 to 68 km; and from 68 to 550 km (extent of study area). Although the finest observable scale was fixed by the field sampling density, the coarser scales were determined from relationships in the data through coregionalization modeling, rather than being imposed by the researcher. Processes which affect soils over short distances, such as land cover and terrain attributes, were good predictors of fine scale spatial components of nutrients; processes which affect soils over very large distances, such as precipitation and geology, were better predictors at coarse spatial scales. However, this result may be affected by the resolution of the available predictor maps. Land-cover change exerted a strong influence on soil chemistry at fine spatial scales, and had progressively less of an effect at coarser scales. It is important to note that land cover, and interactions among land cover and the other predictors, continued to be a significant predictor of soil chemistry at every spatial scale up to hundreds of thousands of kilometers.

Sharpening coarse-to-fine stereo vision by perceptual learning: asymmetric transfer across the spatial frequency spectrum

PubMed Central

Tran, Truyet T.; Craven, Ashley P.; Leung, Tsz-Wing; Chat, Sandy W.; Levi, Dennis M.

2016-01-01

Neurons in the early visual cortex are finely tuned to different low-level visual features, forming a multi-channel system analysing the visual image formed on the retina in a parallel manner. However, little is known about the potential ‘cross-talk’ among these channels. Here, we systematically investigated whether stereoacuity, over a large range of target spatial frequencies, can be enhanced by perceptual learning. Using narrow-band visual stimuli, we found that practice with coarse (low spatial frequency) targets substantially improves performance, and that the improvement spreads from coarse to fine (high spatial frequency) three-dimensional perception, generalizing broadly across untrained spatial frequencies and orientations. Notably, we observed an asymmetric transfer of learning across the spatial frequency spectrum. The bandwidth of transfer was broader when training was at a high spatial frequency than at a low spatial frequency. Stereoacuity training is most beneficial when trained with fine targets. This broad transfer of stereoacuity learning contrasts with the highly specific learning reported for other basic visual functions. We also revealed strategies to boost learning outcomes ‘beyond-the-plateau’. Our investigations contribute to understanding the functional properties of the network subserving stereovision. The ability to generalize may provide a key principle for restoring impaired binocular vision in clinical situations. PMID:26909178

Towards the Development of a More Accurate Monitoring Procedure for Invertebrate Populations, in the Presence of an Unknown Spatial Pattern of Population Distribution in the Field

PubMed Central

Petrovskaya, Natalia B.; Forbes, Emily; Petrovskii, Sergei V.; Walters, Keith F. A.

2018-01-01

Studies addressing many ecological problems require accurate evaluation of the total population size. In this paper, we revisit a sampling procedure used for the evaluation of the abundance of an invertebrate population from assessment data collected on a spatial grid of sampling locations. We first discuss how insufficient information about the spatial population density obtained on a coarse sampling grid may affect the accuracy of an evaluation of total population size. Such information deficit in field data can arise because of inadequate spatial resolution of the population distribution (spatially variable population density) when coarse grids are used, which is especially true when a strongly heterogeneous spatial population density is sampled. We then argue that the average trap count (the quantity routinely used to quantify abundance), if obtained from a sampling grid that is too coarse, is a random variable because of the uncertainty in sampling spatial data. Finally, we show that a probabilistic approach similar to bootstrapping techniques can be an efficient tool to quantify the uncertainty in the evaluation procedure in the presence of a spatial pattern reflecting a patchy distribution of invertebrates within the sampling grid. PMID:29495513

Pattern detection in stream networks: Quantifying spatialvariability in fish distribution

USGS Publications Warehouse

Torgersen, Christian E.; Gresswell, Robert E.; Bateman, Douglas S.

2004-01-01

Biological and physical properties of rivers and streams are inherently difficult to sample and visualize at the resolution and extent necessary to detect fine-scale distributional patterns over large areas. Satellite imagery and broad-scale fish survey methods are effective for quantifying spatial variability in biological and physical variables over a range of scales in marine environments but are often too coarse in resolution to address conservation needs in inland fisheries management. We present methods for sampling and analyzing multiscale, spatially continuous patterns of stream fishes and physical habitat in small- to medium-size watersheds (500–1000 hectares). Geospatial tools, including geographic information system (GIS) software such as ArcInfo dynamic segmentation and ArcScene 3D analyst modules, were used to display complex biological and physical datasets. These tools also provided spatial referencing information (e.g. Cartesian and route-measure coordinates) necessary for conducting geostatistical analyses of spatial patterns (empirical semivariograms and wavelet analysis) in linear stream networks. Graphical depiction of fish distribution along a one-dimensional longitudinal profile and throughout the stream network (superimposed on a 10-metre digital elevation model) provided the spatial context necessary for describing and interpreting the relationship between landscape pattern and the distribution of coastal cutthroat trout (Oncorhynchus clarki clarki) in western Oregon, U.S.A. The distribution of coastal cutthroat trout was highly autocorrelated and exhibited a spherical semivariogram with a defined nugget, sill, and range. Wavelet analysis of the main-stem longitudinal profile revealed periodicity in trout distribution at three nested spatial scales corresponding ostensibly to landscape disturbances and the spacing of tributary junctions.

Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

NASA Astrophysics Data System (ADS)

Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

2017-10-01

Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash-Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

Catching ghosts with a coarse net: use and abuse of spatial sampling data in detecting synchronization

PubMed Central

2017-01-01

Synchronization of population dynamics in different habitats is a frequently observed phenomenon. A common mathematical tool to reveal synchronization is the (cross)correlation coefficient between time courses of values of the population size of a given species where the population size is evaluated from spatial sampling data. The corresponding sampling net or grid is often coarse, i.e. it does not resolve all details of the spatial configuration, and the evaluation error—i.e. the difference between the true value of the population size and its estimated value—can be considerable. We show that this estimation error can make the value of the correlation coefficient very inaccurate or even irrelevant. We consider several population models to show that the value of the correlation coefficient calculated on a coarse sampling grid rarely exceeds 0.5, even if the true value is close to 1, so that the synchronization is effectively lost. We also observe ‘ghost synchronization’ when the correlation coefficient calculated on a coarse sampling grid is close to 1 but in reality the dynamics are not correlated. Finally, we suggest a simple test to check the sampling grid coarseness and hence to distinguish between the true and artifactual values of the correlation coefficient. PMID:28202589

Nonlinear dynamic range transformation in visual communication channels.

PubMed

Alter-Gartenberg, R

1996-01-01

The article evaluates nonlinear dynamic range transformation in the context of the end-to-end continuous-input/discrete processing/continuous-display imaging process. Dynamic range transformation is required when we have the following: (i) the wide dynamic range encountered in nature is compressed into the relatively narrow dynamic range of the display, particularly for spatially varying irradiance (e.g., shadow); (ii) coarse quantization is expanded to the wider dynamic range of the display; and (iii) nonlinear tone scale transformation compensates for the correction in the camera amplifier.

Does the stress-gradient hypothesis hold water? Disentangling spatial and temporal variation in plant effects on soil moisture in dryland systems

USGS Publications Warehouse

Butterfield, Bradley J.; Bradford, John B.; Armas, Cristina; Prieto, Ivan; Pugnaire, Francisco I.

2016-01-01

Taken together, the results of this simulation study suggest that plant effects on soil moisture are predictable based on relatively general relationships between precipitation inputs and differential evaporation and transpiration rates between plant and interspace microsites that are largely driven by temperature. In particular, this study highlights the importance of differentiating between temporal and spatial variation in weather and climate, respectively, in determining plant effects on available soil moisture. Rather than focusing on the somewhat coarse-scale predictions of the SGH, it may be more beneficial to explicitly incorporate plant effects on soil moisture into predictive models of plant-plant interaction outcomes in drylands.

Entropy Production in Field Theories without Time-Reversal Symmetry: Quantifying the Non-Equilibrium Character of Active Matter

NASA Astrophysics Data System (ADS)

Nardini, Cesare; Fodor, Étienne; Tjhung, Elsen; van Wijland, Frédéric; Tailleur, Julien; Cates, Michael E.

2017-04-01

Active-matter systems operate far from equilibrium because of the continuous energy injection at the scale of constituent particles. At larger scales, described by coarse-grained models, the global entropy production rate S quantifies the probability ratio of forward and reversed dynamics and hence the importance of irreversibility at such scales: It vanishes whenever the coarse-grained dynamics of the active system reduces to that of an effective equilibrium model. We evaluate S for a class of scalar stochastic field theories describing the coarse-grained density of self-propelled particles without alignment interactions, capturing such key phenomena as motility-induced phase separation. We show how the entropy production can be decomposed locally (in real space) or spectrally (in Fourier space), allowing detailed examination of the spatial structure and correlations that underly departures from equilibrium. For phase-separated systems, the local entropy production is concentrated mainly on interfaces, with a bulk contribution that tends to zero in the weak-noise limit. In homogeneous states, we find a generalized Harada-Sasa relation that directly expresses the entropy production in terms of the wave-vector-dependent deviation from the fluctuation-dissipation relation between response functions and correlators. We discuss extensions to the case where the particle density is coupled to a momentum-conserving solvent and to situations where the particle current, rather than the density, should be chosen as the dynamical field. We expect the new conceptual tools developed here to be broadly useful in the context of active matter, allowing one to distinguish when and where activity plays an essential role in the dynamics.

Satellite-based Flood Modeling Using TRMM-based Rainfall Products.

PubMed

Harris, Amanda; Rahman, Sayma; Hossain, Faisal; Yarborough, Lance; Bagtzoglou, Amvrossios C; Easson, Greg

2007-12-20

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25 o to 0.50 o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km² Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.

Linking models and data on vegetation structure

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Fisk, J.; Thomas, R. Q.; Dubayah, R.; Moorcroft, P. R.; Shugart, H. H.

2010-06-01

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Sorted bedform pattern evolution: Persistence, destruction and self-organized intermittency

NASA Astrophysics Data System (ADS)

Goldstein, Evan B.; Murray, A. Brad; Coco, Giovanni

2011-12-01

We investigate the long-term evolution of inner continental shelf sorted bedform patterns. Numerical modeling suggests that a range of behaviors are possible, from pattern persistence to spatial-temporal intermittency. Sorted bedform persistence results from a robust sorting feedback that operates when the seabed features a sufficient concentration of coarse material. In the absence of storm events, pattern maturation processes such as defect dynamics and pattern migration tend to cause the burial of coarse material and excavation of fine material, leading to the fining of the active layer. Vertical sorting occurs until a critical state of active layer coarseness is reached. This critical state results in the local cessation of the sorting feedback, leading to a self-organized spatially intermittent pattern, a hallmark of observed sorted bedforms. Bedforms in shallow conditions and those subject to high wave climates may be temporally intermittent features as a result of increased wave orbital velocity during storms. Erosion, or deposition of bimodal sediment, similarly leads to a spatially intermittent pattern, with individual coarse domains exhibiting temporal intermittence. Recurring storm events cause coarsening of the seabed (strengthening the sorting feedback) and the development of large wavelength patterns. Cessation of storm events leads to the superposition of storm (large wavelength) and inter-storm (small wavelength) patterns and spatial heterogeneity of pattern modes.

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

USGS Publications Warehouse

Chaney, Nathaniel W; Wood, Eric F; McBratney, Alexander B; Hempel, Jonathan W; Nauman, Travis; Brungard, Colby W.; Odgers, Nathan P

2016-01-01

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Comparison of Two Grid Refinement Approaches for High Resolution Regional Climate Modeling: MPAS vs WRF

NASA Astrophysics Data System (ADS)

Leung, L.; Hagos, S. M.; Rauscher, S.; Ringler, T.

2012-12-01

This study compares two grid refinement approaches using global variable resolution model and nesting for high-resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales (MPAS), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context with a focus on the spatial and temporal characteristics of tropical precipitation simulated by the models using the same physics package from the Community Atmosphere Model (CAM4). For MPAS, simulations have been performed with a quasi-uniform resolution global domain at coarse (1 degree) and high (0.25 degree) resolution, and a variable resolution domain with a high-resolution region at 0.25 degree configured inside a coarse resolution global domain at 1 degree resolution. Similarly, WRF has been configured to run on a coarse (1 degree) and high (0.25 degree) resolution tropical channel domain as well as a nested domain with a high-resolution region at 0.25 degree nested two-way inside the coarse resolution (1 degree) tropical channel. The variable resolution or nested simulations are compared against the high-resolution simulations that serve as virtual reality. Both MPAS and WRF simulate 20-day Kelvin waves propagating through the high-resolution domains fairly unaffected by the change in resolution. In addition, both models respond to increased resolution with enhanced precipitation. Grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. However, there are important differences between the anomalous patterns in MPAS and WRF due to differences in the grid refinement approaches and sensitivity of model physics to grid resolution. This study highlights the need for "scale aware" parameterizations in variable resolution and nested regional models.

Spatial and Temporal Variability of Outdoor Coarse Particulate Matter Mass Concentrations Measured with a New Coarse Particulate Sampler during the Detroit Exposure and Aerosol Research Study

EPA Science Inventory

The Detroit Exposure and Aerosol Research Study (DEARS) provided data to compare outdoor residential coarse particulate matter (PM_10-2.5) concentrations in six different areas of Detroit with data from a central monitoring site. Daily and seasonal influences on the spa...

Cross-scale interactions affect tree growth and intrinsic water ...

EPA Pesticide Factsheets

1. We investigated the potential of cross-scale interactions to affect the outcome of density reduction in a large-scale silvicultural experiment. 2. We measured tree growth and intrinsic water-use efficiency (iWUE) based on stable carbon isotopes (13C) to investigate the impacts of thinning across a range of progressively finer spatial scales: site, stand, hillslope position, and neighborhood position. In particular, we focused on the influence of thinning beyond the boundaries of thinned stands to include impacts on downslope and neighboring stands across sites varying in soil moisture. 3. Trees at the wet site responded to thinning with increased growth when compared with trees at the dry site. Additionally, trees in thinned stands at the dry site responded with increased iWUE while trees in thinned stands at the wet site showed no difference in iWUE compared to unthinned stands. 4. We hypothesized that water is not the primary limiting factor for growth at our sites, but that thinning released other resources, such as growing space or nutrients to drive the growth response. At progressively finer spatial scales we found that the responses of trees was not driven by hillslope location (i.e., downslope of thinning) but to changes in local neighborhood tree density. 5. The results of this study demonstrated that water can be viewed as an “agent” that allows us to investigate cross-scale interactions as it links coarse to finer spatial scales and vice ver

Methods for the quantification of coarse woody debris and an examination of its spatial patterning: A study from the Tenderfoot Creek Experimental Forest, MT

Treesearch

Paul B. Alaback; Duncan C. Lutes

1997-01-01

Methods for the quantification of coarse woody debris volume and the description of spatial patterning were studied in the Tenderfoot Creek Experimental Forest, Montana. The line transect method was found to be an accurate, unbiased estimator of down debris volume (> 10cm diameter) on 1/4 hectare fixed-area plots, when perpendicular lines were used. The Fischer...

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

Coarse-grained hydrodynamics from correlation functions

NASA Astrophysics Data System (ADS)

Palmer, Bruce

2018-02-01

This paper will describe a formalism for using correlation functions between different grid cells as the basis for determining coarse-grained hydrodynamic equations for modeling the behavior of mesoscopic fluid systems. Configurations from a molecular dynamics simulation or other atomistic simulation are projected onto basis functions representing grid cells in a continuum hydrodynamic simulation. Equilibrium correlation functions between different grid cells are evaluated from the molecular simulation and used to determine the evolution operator for the coarse-grained hydrodynamic system. The formalism is demonstrated on a discrete particle simulation of diffusion with a spatially dependent diffusion coefficient. Correlation functions are calculated from the particle simulation and the spatially varying diffusion coefficient is recovered using a fitting procedure.

Dasymetric high resolution population distribution estimates for improved decision making, with a case study of sea-level rise vulnerability in Boca Raton, Florida

NASA Astrophysics Data System (ADS)

Ziegler, Hannes Moritz

Planners and managers often rely on coarse population distribution data from the census for addressing various social, economic, and environmental problems. In the analysis of physical vulnerabilities to sea-level rise, census units such as blocks or block groups are coarse relative to the required decision-making application. This study explores the benefits offered from integrating image classification and dasymetric mapping at the household level to provide detailed small area population estimates at the scale of residential buildings. In a case study of Boca Raton, FL, a sea-level rise inundation grid based on mapping methods by NOAA is overlaid on the highly detailed population distribution data to identify vulnerable residences and estimate population displacement. The enhanced spatial detail offered through this method has the potential to better guide targeted strategies for future development, mitigation, and adaptation efforts.

Land cover mapping at sub-pixel scales

NASA Astrophysics Data System (ADS)

Makido, Yasuyo Kato

One of the biggest drawbacks of land cover mapping from remotely sensed images relates to spatial resolution, which determines the level of spatial details depicted in an image. Fine spatial resolution images from satellite sensors such as IKONOS and QuickBird are now available. However, these images are not suitable for large-area studies, since a single image is very small and therefore it is costly for large area studies. Much research has focused on attempting to extract land cover types at sub-pixel scale, and little research has been conducted concerning the spatial allocation of land cover types within a pixel. This study is devoted to the development of new algorithms for predicting land cover distribution using remote sensory imagery at sub-pixel level. The "pixel-swapping" optimization algorithm, which was proposed by Atkinson for predicting sub-pixel land cover distribution, is investigated in this study. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation, are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. Two different simulation models were employed to develop spatially autocorrelated binary class maps. In all tested models, Gaussian, Exponential, and IDW, the pixel swapping method improved classification accuracy compared with the initial random allocation of sub-pixels. However the results suggested that equal weight could be used to increase accuracy and sub-pixel spatial autocorrelation instead of using these more complex models of spatial structure. New algorithms for modeling the spatial distribution of multiple land cover classes at sub-pixel scales are developed and evaluated. Three methods are examined: sequential categorical swapping, simultaneous categorical swapping, and simulated annealing. These three methods are applied to classified Landsat ETM+ data that has been resampled to 210 meters. The result suggested that the simultaneous method can be considered as the optimum method in terms of accuracy performance and computation time. The case study employs remote sensing imagery at the following sites: tropical forests in Brazil and temperate multiple land mosaic in East China. Sub-areas for both sites are used to examine how the characteristics of the landscape affect the ability of the optimum technique. Three types of measurement: Moran's I, mean patch size (MPS), and patch size standard deviation (STDEV), are used to characterize the landscape. All results suggested that this technique could increase the classification accuracy more than traditional hard classification. The methods developed in this study can benefit researchers who employ coarse remote sensing imagery but are interested in detailed landscape information. In many cases, the satellite sensor that provides large spatial coverage has insufficient spatial detail to identify landscape patterns. Application of the super-resolution technique described in this dissertation could potentially solve this problem by providing detailed land cover predictions from the coarse resolution satellite sensor imagery.

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

NASA Astrophysics Data System (ADS)

Malbéteau, Yoann; Merlin, Olivier; Molero, Beatriz; Rüdiger, Christoph; Bacon, Stephan

2016-03-01

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40-50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010-2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Multiple Scales of Representation along the Hippocampal Anteroposterior Axis in Humans.

PubMed

Brunec, Iva K; Bellana, Buddhika; Ozubko, Jason D; Man, Vincent; Robin, Jessica; Liu, Zhong-Xu; Grady, Cheryl; Rosenbaum, R Shayna; Winocur, Gordon; Barense, Morgan D; Moscovitch, Morris

2018-06-13

The ability to represent the world accurately relies on simultaneous coarse and fine-grained neural information coding, capturing both gist and detail of an experience. The longitudinal axis of the hippocampus may provide a gradient of representational granularity in spatial and episodic memory in rodents and humans [1-8]. Rodent place cells in the ventral hippocampus exhibit significantly larger place fields and greater autocorrelation than those in the dorsal hippocampus [1, 9-11], which may underlie a coarser and slower changing representation of space [10, 12]. Recent evidence suggests that properties of cellular dynamics in rodents can be captured with fMRI in humans during spatial navigation [13] and conceptual learning [14]. Similarly, mechanisms supporting granularity along the long axis may also be extrapolated to the scale of fMRI signal. Here, we provide the first evidence for separable scales of representation along the human hippocampal anteroposterior axis during navigation and rest by showing (1) greater similarity among voxel time courses and (2) higher temporal autocorrelation in anterior hippocampus (aHPC), relative to posterior hippocampus (pHPC), the human homologs of ventral and dorsal rodent hippocampus. aHPC voxels exhibited more similar activity at each time point and slower signal change over time than voxels in pHPC, consistent with place field organization in rodents. Importantly, similarity between voxels was related to navigational strategy and episodic memory. These findings provide evidence that the human hippocampus supports an anterior-to-posterior gradient of coarse-to-fine spatiotemporal representations, suggesting the existence of a cross-species mechanism, whereby lower neural similarity supports more complex coding of experience. Copyright © 2018 Elsevier Ltd. All rights reserved.

Linking landscape characteristics to mineral site use by band-tailed pigeons in Western Oregon: Coarse-filter conservation with fine-filter tuning

USGS Publications Warehouse

Overton, C.T.; Schmitz, R.A.; Casazza, Michael L.

2006-01-01

Mineral sites are scarce resources of high ion concentration used heavily by the Pacific Coast subpopulation of band-tailed pigeons. Over 20% of all known mineral sites used by band-tailed pigeons in western Oregon, including all hot springs, have been abandoned. Prior investigations have not analyzed stand or landscape level habitat composition in relation to band-tailed pigeon use of mineral sites. We used logistic regression models to evaluate the influence of habitat types, identified from Gap Analysis Program (GAP) products at two spatial scales, on the odds of mineral site use in Oregon (n = 69 currently used and 20 historically used). Our results indicated that the odds of current use were negatively associated with non-forested terrestrial and private land area around mineral sites. Similarly, the odds of current mineral site use were positively associated with forested and special status (GAP stewardship codes 1 and 2) land area. The most important variable associated with the odds of mineral site use was the amount of non-forested land cover at either spatial scale. Our results demonstrate the utility of meso-scale geographic information designed for regional, coarse-filter approaches to conservation in fine-filter investigation of wildlife-habitat relationships. Adjacent landcover and ownership status explain the pattern of use for known mineral sites in western Oregon. In order for conservation and management activities for band-tailed pigeons to be successful, mineral sites need to be addressed as important and vulnerable resources. Management of band-tailed pigeons should incorporate the potential for forest management activities and land ownership patterns to influence the risk of mineral site abandonment.

Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods

NASA Astrophysics Data System (ADS)

Chirico, G. B.; Medina, H.; Romano, N.

2007-02-01

SummarySeveral hydrological applications require the characterisation of the soil hydraulic properties at large spatial scales. Pedotransfer functions (PTFs) are being developed as simplified methods to estimate soil hydraulic properties as an alternative to direct measurements, which are unfeasible for most practical circumstances. The objective of this study is to quantify the uncertainty in PTFs spatial predictions at the hillslope scale as related to the sampling density, due to: (i) the error in estimated soil physico-chemical properties and (ii) PTF model error. The analysis is carried out on a 2-km-long experimental hillslope in South Italy. The method adopted is based on a stochastic generation of patterns of soil variables using sequential Gaussian simulation, conditioned to the observed sample data. The following PTFs are applied: Vereecken's PTF [Vereecken, H., Diels, J., van Orshoven, J., Feyen, J., Bouma, J., 1992. Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties. Soil Sci. Soc. Am. J. 56, 1371-1378] and HYPRES PTF [Wösten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169-185]. The two PTFs estimate reliably the soil water retention characteristic even for a relatively coarse sampling resolution, with prediction uncertainties comparable to the uncertainties in direct laboratory or field measurements. The uncertainty of soil water retention prediction due to the model error is as much as or more significant than the uncertainty associated with the estimated input, even for a relatively coarse sampling resolution. Prediction uncertainties are much more important when PTF are applied to estimate the saturated hydraulic conductivity. In this case model error dominates the overall prediction uncertainties, making negligible the effect of the input error.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Identifying forest patterns from space to explore dynamics across the circumpolar boreal

NASA Astrophysics Data System (ADS)

Montesano, P. M.; Neigh, C. S. R.; Feng, M.; Channan, S.; Sexton, J. O.; Wagner, W.; Wooten, M.; Poulter, B.; Wang, L.

2017-12-01

A variety of forest patterns are the result of interactions between broad-scale climate and local-scale site factors and history across the northernmost portion of the circumpolar boreal. Patterns of forest extent, height, and cover help describe forest structure transitions that influence future and reflect past dynamics. Coarse spaceborne observations lack structural detail at forest transitions, which inhibits understanding of these dynamics. We highlight: (1) the use of sub-meter spaceborne stereogrammetry for deriving structure estimates in boreal forests; (2) its potential to complement other spaceborne estimates of forest structure at critical scales; and (3) the potential of these sub-meter and other Landsat-derived structure estimates for improving understanding of broad-scale boreal dynamics such as carbon flux and albedo, capturing the spatial variability of the boreal-tundra biome boundary, and assessing its potential for change.

a Region-Based Multi-Scale Approach for Object-Based Image Analysis

NASA Astrophysics Data System (ADS)

Kavzoglu, T.; Yildiz Erdemir, M.; Tonbul, H.

2016-06-01

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

Estimating Soil Moisture at High Spatial Resolution with Three Radiometric Satellite Products: A Study from a South-Eastern Australian Catchment

NASA Astrophysics Data System (ADS)

Senanayake, I. P.; Yeo, I. Y.; Tangdamrongsub, N.; Willgoose, G. R.; Hancock, G. R.; Wells, T.; Fang, B.; Lakshmi, V.

2017-12-01

Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions ( several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.

Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

NASA Technical Reports Server (NTRS)

Ott, L.; Putman, B.; Collatz, J.; Gregg, W.

2012-01-01

Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement concepts to create realistic pseudo-datasets. Pseudo-data are averaged over coarse model grid cell areas to better understand the ability of measurements to characterize CO2 distributions and spatial gradients on both short (daily to weekly) and long (monthly to seasonal) time scales

Effect of spatial averaging on multifractal properties of meteorological time series

NASA Astrophysics Data System (ADS)

Hoffmann, Holger; Baranowski, Piotr; Krzyszczak, Jaromir; Zubik, Monika

2016-04-01

Introduction The process-based models for large-scale simulations require input of agro-meteorological quantities that are often in the form of time series of coarse spatial resolution. Therefore, the knowledge about their scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and non-stationarities. Here we assess how spatially aggregating meteorological data to coarser resolutions affects the data's temporal scaling properties. While it is known that spatial aggregation may affect spatial data properties (Hoffmann et al., 2015), it is unknown how it affects temporal data properties. Therefore, the objective of this study was to characterize the aggregation effect (AE) with regard to both temporal and spatial input data properties considering scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological time series through multifractal detrended fluctuation analysis (MFDFA). Materials and Methods Time series coming from years 1982-2011 were spatially averaged from 1 to 10, 25, 50 and 100 km resolution to assess the impact of spatial aggregation. Daily minimum, mean and maximum air temperature (2 m), precipitation, global radiation, wind speed and relative humidity (Zhao et al., 2015) were used. To reveal the multifractal structure of the time series, we used the procedure described in Baranowski et al. (2015). The diversity of the studied multifractals was evaluated by the parameters of time series spectra. In order to analyse differences in multifractal properties to 1 km resolution grids, data of coarser resolutions was disaggregated to 1 km. Results and Conclusions Analysing the spatial averaging on multifractal properties we observed that spatial patterns of the multifractal spectrum (MS) of all meteorological variables differed from 1 km grids and MS-parameters were biased by -29.1 % (precipitation; width of MS) up to >4 % (min. Temperature, Radiation; asymmetry of MS). Also, the spatial variability of MS parameters was strongly affected at the highest aggregation (100 km). Obtained results confirm that spatial data aggregation may strongly affect temporal scaling properties. This should be taken into account when upscaling for large-scale studies. Acknowledgements The study was conducted within FACCE MACSUR. Please see Baranowski et al. (2015) for details on funding. References Baranowski, P., Krzyszczak, J., Sławiński, C. et al. (2015). Climate Research 65, 39-52. Hoffman, H., G. Zhao, L.G.J. Van Bussel et al. (2015). Climate Research 65, 53-69. Zhao, G., Siebert, S., Rezaei E. et al. (2015). Agricultural and Forest Meteorology 200, 156-171.

Transferable Coarse-Grained Models for Ionic Liquids.

PubMed

Wang, Yanting; Feng, Shulu; Voth, Gregory A

2009-04-14

The effective force coarse-graining (EF-CG) method was applied to the imidazolium-based nitrate ionic liquids with various alkyl side-chain lengths. The nonbonded EF-CG forces for the ionic liquid with a short side chain were extended to generate the nonbonded forces for the ionic liquids with longer side chains. The EF-CG force fields for the ionic liquids exhibit very good transferability between different systems at various temperatures and are suitable for investigating the mesoscopic structural properties of this class of ionic liquids. The good additivity and ease of manipulation of the EF-CG force fields can allow for an inverse design methodology of ionic liquids at the coarse-grained level. With the EF-CG force field, the molecular dynamics (MD) simulation at a very large scale has been performed to check the significance of finite size effects on the structural properties. From these MD simulation results, it can be concluded that the finite size effect on the phenomenon of ionic liquid spatial heterogeneity (Wang, Y.; Voth, G. A. J. Am. Chem. Soc. 2005, 127, 12192) is small and that this phenomenon is indeed a nanostructural behavior which leads to the experimentally observed mesoscopic heterogeneous structure of ionic liquids.

Neural mechanisms of coarse-to-fine discrimination in the visual cortex.

PubMed

Purushothaman, Gopathy; Chen, Xin; Yampolsky, Dmitry; Casagrande, Vivien A

2014-12-01

Vision is a dynamic process that refines the spatial scale of analysis over time, as evidenced by a progressive improvement in the ability to detect and discriminate finer details. To understand coarse-to-fine discrimination, we studied the dynamics of spatial frequency (SF) response using reverse correlation in the primary visual cortex (V1) of the primate. In a majority of V1 cells studied, preferred SF either increased monotonically with time (group 1) or changed nonmonotonically, with an initial increase followed by a decrease (group 2). Monotonic shift in preferred SF occurred with or without an early suppression at low SFs. Late suppression at high SFs always accompanied nonmonotonic SF dynamics. Bayesian analysis showed that SF discrimination performance and best discriminable SF frequencies changed with time in different ways in the two groups of neurons. In group 1 neurons, SF discrimination performance peaked on both left and right flanks of the SF tuning curve at about the same time. In group 2 neurons, peak discrimination occurred on the right flank (high SFs) later than on the left flank (low SFs). Group 2 neurons were also better discriminators of high SFs. We examined the relationship between the time at which SF discrimination performance peaked on either flank of the SF tuning curve and the corresponding best discriminable SFs in both neuronal groups. This analysis showed that the population best discriminable SF increased with time in V1. These results suggest neural mechanisms for coarse-to-fine discrimination behavior and that this process originates in V1 or earlier. Copyright © 2014 the American Physiological Society.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

The Effects of Fine-scale Soil Moisture and Canopy Heterogeneities on Energy and Soil Water Fluxes in a Temperate Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

He, L.; Ivanov, V. Y.; Bohrer, G.; Maurer, K.; Vogel, C. S.; Moghaddam, M.

2011-12-01

Vegetation is heterogeneous at different scales, influencing spatially variable energy and water exchanges between land-surface and atmosphere. Current land surface parameterizations of large-scale models consider spatial variability at a scale of a few kilometers and treat vegetation cover as aggregated patches with uniform properties. However, the coupling mechanisms between fine-scale soil moisture, vegetation, and energy fluxes such as evapotranspiration are strongly nonlinear; the aggregation of surface variations may produce biased energy fluxes. This study aims to improve the understanding of the scale impact in atmosphere-biosphere-hydrosphere interactions, which affects predictive capabilities of land surface models. The study uses a high-resolution, physically-based ecohydrological model tRIBS + VEGGIE as a data integration tool to upscale the heterogeneity of canopy distribution resolved at a few meters to the watershed scale. The study was carried out for a spatially heterogeneous, temperate mixed forest environment of Northern Michigan located near the University of Michigan Biological Station (UMBS). Energy and soil water dynamics were simulated at the tree-canopy resolution in the horizontal plane for a small domain (~2 sq. km) located within a footprint of the AmeriFlux tower. A variety of observational data were used to constrain and confirm the model, including a 3-m profile continuous soil moisture dataset and energy flux data (measured at the AmeriFlux tower footprint). A scenario with a spatially uniform canopy, corresponding to the commonly used 'big-leaf' scheme in land surface parameterizations was used to infer the effects of coarse-scale averaging. To gain insights on how heterogeneous canopy and soil moisture interact and contribute to the domain-averaged transpiration, several scenarios of tree-scale leaf area and soil moisture spatial variability were designed. Specifically, for the same mean states, the scenarios of variability of canopy biomass account for the spatial distribution of photosynthesis (and thus the stomatal resistance), the aerodynamic and leaf boundary layer resistances as well as the differential radiation forcing due to tall tree exposure and lateral shading of short trees. The numerical experiments show that by transpiring spatially varying amounts of water, heterogeneous canopies adjust the spatial soil water state to the scaled inverse of the canopy biomass regardless of the initial moisture state. Such a spatial distribution can be further wiped out because of the differential water stress. The aggregation of canopy-scale atmosphere-biosphere-hydrosphere interactions demonstrates non-linear relationship between soil moisture and evapotranspiration, influencing domain-averaged energy fluxes.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

PubMed Central

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID:26402522

3D deblending of simultaneous source data based on 3D multi-scale shaping operator

NASA Astrophysics Data System (ADS)

Zu, Shaohuan; Zhou, Hui; Mao, Weijian; Gong, Fei; Huang, Weilin

2018-04-01

We propose an iterative three-dimensional (3D) deblending scheme using 3D multi-scale shaping operator to separate 3D simultaneous source data. The proposed scheme is based on the property that signal is coherent, whereas interference is incoherent in some domains, e.g., common receiver domain and common midpoint domain. In two-dimensional (2D) blended record, the coherency difference of signal and interference is in only one spatial direction. Compared with 2D deblending, the 3D deblending can take more sparse constraints into consideration to obtain better performance, e.g., in 3D common receiver gather, the coherency difference is in two spatial directions. Furthermore, with different levels of coherency, signal and interference distribute in different scale curvelet domains. In both 2D and 3D blended records, most coherent signal locates in coarse scale curvelet domain, while most incoherent interference distributes in fine scale curvelet domain. The scale difference is larger in 3D deblending, thus, we apply the multi-scale shaping scheme to further improve the 3D deblending performance. We evaluate the performance of 3D and 2D deblending with the multi-scale and global shaping operators, respectively. One synthetic and one field data examples demonstrate the advantage of the 3D deblending with 3D multi-scale shaping operator.

Characterizing and contrasting instream and riparian coarse wood in western Montana basins

Treesearch

Michael K. Young; Ethan A. Mace; Eric T. Ziegler; Elaine K. Sutherland

2006-01-01

The importance of coarse wood to aquatic biota and stream channel structure is widely recognized, yet characterizations of large-scale patterns in coarse wood dimensions and loads are rare. To address these issues, we censused instream coarse wood ( 2 m long and 10 cm minimum diameter) and sampled riparian coarse wood and channel characteristics in and along 13 streams...

Sediment Facies on a Steep Shoreface, Tairua/Pauanui Embayment, New Zealand

NASA Astrophysics Data System (ADS)

Trembanis, A. C.; Hume, T. M.; Gammisch, R. A.; Wright, L. D.; Green, M. O.

2001-05-01

Tairua/Pauanui embayment is a small headland-bound system on the Coromandel Peninsula on the east coast of the North Island of New Zealand. The shoreface in this area is steep ( ~0.85) and concave; however, where the profile is steepest, between 10-15-m water depth, the profile is slightly convex. A sedimentological study of the shoreface was conducted to provide baseline information for a sediment-dynamics study. Detailed swath mapping of the seabed sediment from the beach out to a water depth of ~50 m was conducted using side-scan sonar. Over 200 km of side-scan sonar data were collected by separate surveys in September 2000 and again in February 2001. Ground-truthing of side-scan sonar data was carried out by SCUBA, grab sampling ( ~100 samples) and drop-camera video. A digital terrain model (DTM) of the area was constructed using newly collected bathymetric data along with data from digitized nautical charts. The DTM exposes changes in bathymetry and variation in slope throughout the study area. The acoustic and sedimentologic data were used to identify and map 8 individual facies units. Shoreface facies distribution was found to be patchy and complex. Large-scale ( ~200-m wide x 1600-m long), slightly depressed, mega-rippled coarse-sand/shell-hash units were abruptly truncated by contacts with fine, featureless, continuous sand-cover units. The repeat survey in February indicated stability of the overall shape and location of large-scale facies units, while diver observations indicated that bedforms within units actively migrate. Bedform roughness is highly variable, including patchy reefs/rubble, sand-dollar fields mega-rippled coarse-gravel/sands, ripple scour depressions, and fields of dense tubeworms. The distribution of coarse shell-hash units is consistent with diabathic sediment transport. Three tripods supporting a range of instruments for measuring waves, currents, boundary-layer flows and sediment resuspension and settling were deployed on the shoreface during February 2001, for up to 3 months. Each tripod was situated on a different facies with a view to resolving spatial variability in sediment dynamics and establishing a link between spatially variable bed roughness, sediment mobility and sedimentation patterns. Our ultimate goal is to understand the interactions between substrate and driving flows in this spatially complex setting and how these interactions sculpt the shoreface and possibly control sediment transfers between the inner shelf and beach.

Parameterisation of multi-scale continuum perfusion models from discrete vascular networks.

PubMed

Hyde, Eoin R; Michler, Christian; Lee, Jack; Cookson, Andrew N; Chabiniok, Radek; Nordsletten, David A; Smith, Nicolas P

2013-05-01

Experimental data and advanced imaging techniques are increasingly enabling the extraction of detailed vascular anatomy from biological tissues. Incorporation of anatomical data within perfusion models is non-trivial, due to heterogeneous vessel density and disparate radii scales. Furthermore, previous idealised networks have assumed a spatially repeating motif or periodic canonical cell, thereby allowing for a flow solution via homogenisation. However, such periodicity is not observed throughout anatomical networks. In this study, we apply various spatial averaging methods to discrete vascular geometries in order to parameterise a continuum model of perfusion. Specifically, a multi-compartment Darcy model was used to provide vascular scale separation for the fluid flow. Permeability tensor fields were derived from both synthetic and anatomically realistic networks using (1) porosity-scaled isotropic, (2) Huyghe and Van Campen, and (3) projected-PCA methods. The Darcy pressure fields were compared via a root-mean-square error metric to an averaged Poiseuille pressure solution over the same domain. The method of Huyghe and Van Campen performed better than the other two methods in all simulations, even for relatively coarse networks. Furthermore, inter-compartment volumetric flux fields, determined using the spatially averaged discrete flux per unit pressure difference, were shown to be accurate across a range of pressure boundary conditions. This work justifies the application of continuum flow models to characterise perfusion resulting from flow in an underlying vascular network.

Frontiers in Atmospheric Chemistry Modelling

NASA Astrophysics Data System (ADS)

Colette, Augustin; Bessagnet, Bertrand; Meleux, Frederik; Rouïl, Laurence

2013-04-01

The first pan-European kilometre-scale atmospheric chemistry simulation is introduced. The continental-scale air pollution episode of January 2009 is modelled with the CHIMERE offline chemistry-transport model with a massive grid of 2 million horizontal points, performed on 2000 CPU of a high performance computing system hosted by the Research and Technology Computing Center at the French Alternative Energies and Atomic Energy Commission (CCRT/CEA). Besides the technical challenge, which demonstrated the robustness of the selected air quality model, we discuss the added value in terms of air pollution modelling and decision support. The comparison with in-situ observations shows that model biases are significantly improved despite some spurious added spatial variability attributed to shortcomings in the emission downscaling process and coarse resolution of the meteorological fields. The increased spatial resolution is clearly beneficial for the detection of exceedances and exposure modelling. We reveal small scale air pollution patterns that highlight the contribution of city plumes to background air pollution levels. Up to a factor 5 underestimation of the fraction of population exposed to detrimental levels of pollution can be obtained with a coarse simulation if subgrid scale correction such as urban increments are ignored. This experiment opens new perspectives for environmental decision making. After two decades of efforts to reduce air pollutant emissions across Europe, the challenge is now to find the optimal trade-off between national and local air quality management strategies. While the first approach is based on sectoral strategies and energy policies, the later builds upon new alternatives such as urban development. The strategies, the decision pathways and the involvement of individual citizen differ, and a compromise based on cost and efficiency must be found. We illustrated how high performance computing in atmospheric science can contribute to this aim. Although further developments are still needed to secure the results for routine policy use, the door is now open...

Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes

USGS Publications Warehouse

Mollenhauer, Robert; Mouser, Joshua B.; Brewer, Shannon K.

2018-01-01

Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.

Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis

NASA Astrophysics Data System (ADS)

Mills, D. A.

2017-10-01

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

Coarse-grained description of cosmic structure from Szekeres models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sussman, Roberto A.; Gaspar, I. Delgado; Hidalgo, Juan Carlos, E-mail: sussman@nucleares.unam.mx, E-mail: ismael.delgadog@uaem.edu.mx, E-mail: hidalgo@fis.unam.mx

2016-03-01

We show that the full dynamical freedom of the well known Szekeres models allows for the description of elaborated 3-dimensional networks of cold dark matter structures (over-densities and/or density voids) undergoing ''pancake'' collapse. By reducing Einstein's field equations to a set of evolution equations, which themselves reduce in the linear limit to evolution equations for linear perturbations, we determine the dynamics of such structures, with the spatial comoving location of each structure uniquely specified by standard early Universe initial conditions. By means of a representative example we examine in detail the density contrast, the Hubble flow and peculiar velocities ofmore » structures that evolved, from linear initial data at the last scattering surface, to fully non-linear 10–20 Mpc scale configurations today. To motivate further research, we provide a qualitative discussion on the connection of Szekeres models with linear perturbations and the pancake collapse of the Zeldovich approximation. This type of structure modelling provides a coarse grained—but fully relativistic non-linear and non-perturbative —description of evolving large scale cosmic structures before their virialisation, and as such it has an enormous potential for applications in cosmological research.« less

Potential accuracy of translation estimation between radar and optical images

NASA Astrophysics Data System (ADS)

Uss, M.; Vozel, B.; Lukin, V.; Chehdi, K.

2015-10-01

This paper investigates the potential accuracy achievable for optical to radar image registration by area-based approach. The analysis is carried out mainly based on the Cramér-Rao Lower Bound (CRLB) on translation estimation accuracy previously proposed by the authors and called CRLBfBm. This bound is now modified to take into account radar image speckle noise properties: spatial correlation and signal-dependency. The newly derived theoretical bound is fed with noise and texture parameters estimated for the co-registered pair of optical Landsat 8 and radar SIR-C images. It is found that difficulty of optical to radar image registration stems more from speckle noise influence than from dissimilarity of the considered kinds of images. At finer scales (and higher speckle noise level), probability of finding control fragments (CF) suitable for registration is low (1% or less) but overall number of such fragments is high thanks to image size. Conversely, at the coarse scale, where speckle noise level is reduced, probability of finding CFs suitable for registration can be as high as 40%, but overall number of such CFs is lower. Thus, the study confirms and supports area-based multiresolution approach for optical to radar registration where coarse scales are used for fast registration "lock" and finer scales for reaching higher registration accuracy. The CRLBfBm is found inaccurate for the main scale due to intensive speckle noise influence. For other scales, the validity of the CRLBfBm bound is confirmed by calculating statistical efficiency of area-based registration method based on normalized correlation coefficient (NCC) measure that takes high values of about 25%.

Evacuation simulation using Hybrid Space Discretisation and Application to Large Underground Rail Tunnel Station

NASA Astrophysics Data System (ADS)

Chooramun, N.; Lawrence, P. J.; Galea, E. R.

2017-08-01

In all evacuation simulation tools, the space through which agents navigate and interact is represented by one the following methods, namely Coarse regions, Fine nodes and Continuous regions. Each of the spatial representation methods has its benefits and limitations. For instance, the Coarse approach allows simulations to be processed very rapidly, but is unable to represent the interactions of the agents from an individual perspective; the Continuous approach provides a detailed representation of agent movement and interaction but suffers from relatively poor computational performance. The Fine nodal approach presents a compromise between the Continuous and Coarse approaches such that it allows agent interaction to be modelled while providing good computational performance. Our approach for representing space in an evacuation simulation tool differs such that it allows evacuation simulations to be run using a combination of Coarse regions, Fine nodes and Continuous regions. This approach, which we call Hybrid Spatial Discretisation (HSD), is implemented within the buildingEXODUS evacuation simulation software. The HSD incorporates the benefits of each of the spatial representation methods whilst providing an optimal environment for representing agent movement and interaction. In this work, we demonstrate the effectiveness of the HSD through its application to a moderately large case comprising of an underground rail tunnel station with a population of 2,000 agents.

A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

NASA Astrophysics Data System (ADS)

Sandholt, I.; Nielsen, C.; Stisen, S.

2009-05-01

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

Genetic particle filter application to land surface temperature downscaling

NASA Astrophysics Data System (ADS)

Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz

2014-03-01

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

Correlations between the Signal Complexity of Cerebral and Cardiac Electrical Activity: A Multiscale Entropy Analysis

PubMed Central

Lin, Pei-Feng; Lo, Men-Tzung; Tsao, Jenho; Chang, Yi-Chung; Lin, Chen; Ho, Yi-Lwun

2014-01-01

The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1–58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11–20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6–20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration. PMID:24498375

Constructing Optimal Coarse-Grained Sites of Huge Biomolecules by Fluctuation Maximization.

PubMed

Li, Min; Zhang, John Zenghui; Xia, Fei

2016-04-12

Coarse-grained (CG) models are valuable tools for the study of functions of large biomolecules on large length and time scales. The definition of CG representations for huge biomolecules is always a formidable challenge. In this work, we propose a new method called fluctuation maximization coarse-graining (FM-CG) to construct the CG sites of biomolecules. The defined residual in FM-CG converges to a maximal value as the number of CG sites increases, allowing an optimal CG model to be rigorously defined on the basis of the maximum. More importantly, we developed a robust algorithm called stepwise local iterative optimization (SLIO) to accelerate the process of coarse-graining large biomolecules. By means of the efficient SLIO algorithm, the computational cost of coarse-graining large biomolecules is reduced to within the time scale of seconds, which is far lower than that of conventional simulated annealing. The coarse-graining of two huge systems, chaperonin GroEL and lengsin, indicates that our new methods can coarse-grain huge biomolecular systems with up to 10,000 residues within the time scale of minutes. The further parametrization of CG sites derived from FM-CG allows us to construct the corresponding CG models for studies of the functions of huge biomolecular systems.

An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data.

PubMed

Wang, Fei; Qin, Zhihao; Li, Wenjuan; Song, Caiying; Karnieli, Arnon; Zhao, Shuhe

2014-12-25

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250-500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

NASA Astrophysics Data System (ADS)

Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

The spectral signature of cloud spatial structure in shortwave irradiance

PubMed Central

Song, Shi; Schmidt, K. Sebastian; Pilewskie, Peter; King, Michael D.; Heidinger, Andrew K.; Walther, Andi; Iwabuchi, Hironobu; Wind, Gala; Coddington, Odele M.

2017-01-01

In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields – specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport (H) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε, which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12–19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections. PMID:28824698

The spectral signature of cloud spatial structure in shortwave irradiance.

PubMed

Song, Shi; Schmidt, K Sebastian; Pilewskie, Peter; King, Michael D; Heidinger, Andrew K; Walther, Andi; Iwabuchi, Hironobu; Wind, Gala; Coddington, Odele M

2016-11-08

In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields - specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport ( H ) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε , which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12-19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

PubMed

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

PubMed Central

Stoy, Paul C.; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture.

PubMed

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M; Torres-Rua, Alfonso; McKee, Mac

2017-09-14

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from "AggieAir", an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products.

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture

PubMed Central

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M.; McKee, Mac

2017-01-01

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from “AggieAir”, an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products. PMID:28906428

Coarse-Grain Molecular Dynamics Simulations To Investigate the Bulk Viscosity and Critical Micelle Concentration of the Ionic Surfactant Sodium Dodecyl Sulfate (SDS) in Aqueous Solution.

PubMed

Ruiz-Morales, Yosadara; Romero-Martínez, Ascención

2018-04-12

The first critical micelle concentration (CMC) of the ionic surfactant sodium dodecyl sulfate (SDS) in diluted aqueous solution has been determined at room temperature from the investigation of the bulk viscosity, at several concentrations of SDS, by means of coarse-grain molecular dynamics simulations. The coarse-grained model molecules at the mesoscale level are adopted. The bulk viscosity of SDS was calculated at several millimolar concentrations of SDS in water using the MARTINI force field by means of NVT shear Mesocite molecular dynamics. The definition of each bead in the MARTINI force field is established, as well as their radius, volume, and mass. The effect of the size of the simulation box on the obtained CMC has been investigated, as well as the effect of the number of SDS molecules, in the simulations, on the formation of aggregates. The CMC, which was obtained from a graph of the calculated viscosities versus concentration, is in good agreement with the reported experimental data and does not depend on the size of the box used in the simulation. The formation of a spherical micelle-like aggregate is observed, where the dodecyl sulfate tails point inward and the heads point outward the aggregation micelle, in accordance with experimental observations. The advantage of using coarse-grain molecular dynamics is the possibility of treating explicitly charged beads, applying a shear flow for viscosity calculation, and processing much larger spatial and temporal scales than atomistic molecular dynamics can. Furthermore, the CMC of SDS obtained with the coarse-grained model is in much better agreement with the experimental value than the value obtained with atomistic simulations.

Single Particulate SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

EPA Science Inventory

The physicochemical properties of coarse-mode, iron-containing particles, and their temporal and spatial distributions are poorly understood. Single particle analysis combining x-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively ...

Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks

PubMed Central

Carpentier, Sarah M.; Moreno, Sylvain; McIntosh, Anthony R.

2016-01-01

Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5–7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects. PMID:27243611

Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

PubMed

Carpentier, Sarah M; Moreno, Sylvain; McIntosh, Anthony R

2016-10-01

Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

How well do terrestrial biosphere models simulate coarse-scale runoff in the contiguous United States?

DOE PAGES

Schwalm, C.; Huntzinger, Deborah N.; Cook, Robert B.; ...

2015-03-11

Significant changes in the water cycle are expected under current global environmental change. Robust assessment of present-day water cycle dynamics at continental to global scales is confounded by shortcomings in the observed record. Modeled assessments also yield conflicting results which are linked to differences in model structure and simulation protocol. Here we compare simulated gridded (1 spatial resolution) runoff from six terrestrial biosphere models (TBMs), seven reanalysis products, and one gridded surface station product in the contiguous United States (CONUS) from 2001 to 2005. We evaluate the consistency of these 14 estimates with stream gauge data, both as depleted flowmore » and corrected for net withdrawals (2005 only), at the CONUS and water resource region scale, as well as examining similarity across TBMs and reanalysis products at the grid cell scale. Mean runoff across all simulated products and regions varies widely (range: 71 to 356 mm yr(-1)) relative to observed continental-scale runoff (209 or 280 mm yr(-1) when corrected for net withdrawals). Across all 14 products 8 exhibit Nash-Sutcliffe efficiency values in excess of 0.8 and three are within 10% of the observed value. Region-level mismatch exhibits a weak pattern of overestimation in western and underestimation in eastern regions although two products are systematically biased across all regions and largely scales with water use. Although gridded composite TBM and reanalysis runoff show some regional similarities, individual product values are highly variable. At the coarse scales used here we find that progress in better constraining simulated runoff requires standardized forcing data and the explicit incorporation of human effects (e.g., water withdrawals by source, fire, and land use change). (C) 2015 Elsevier B.V. All rights reserved.« less

Hierarchical coarse-graining strategy for protein-membrane systems to access mesoscopic scales

PubMed Central

Ayton, Gary S.; Lyman, Edward

2014-01-01

An overall multiscale simulation strategy for large scale coarse-grain simulations of membrane protein systems is presented. The protein is modeled as a heterogeneous elastic network, while the lipids are modeled using the hybrid analytic-systematic (HAS) methodology, where in both cases atomistic level information obtained from molecular dynamics simulation is used to parameterize the model. A feature of this approach is that from the outset liposome length scales are employed in the simulation (i.e., on the order of ½ a million lipids plus protein). A route to develop highly coarse-grained models from molecular-scale information is proposed and results for N-BAR domain protein remodeling of a liposome are presented. PMID:20158037

Finite Dimensional Approximations for Continuum Multiscale Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berlyand, Leonid

2017-01-24

The completed research project concerns the development of novel computational techniques for modeling nonlinear multiscale physical and biological phenomena. Specifically, it addresses the theoretical development and applications of the homogenization theory (coarse graining) approach to calculation of the effective properties of highly heterogenous biological and bio-inspired materials with many spatial scales and nonlinear behavior. This theory studies properties of strongly heterogeneous media in problems arising in materials science, geoscience, biology, etc. Modeling of such media raises fundamental mathematical questions, primarily in partial differential equations (PDEs) and calculus of variations, the subject of the PI’s research. The focus of completed researchmore » was on mathematical models of biological and bio-inspired materials with the common theme of multiscale analysis and coarse grain computational techniques. Biological and bio-inspired materials offer the unique ability to create environmentally clean functional materials used for energy conversion and storage. These materials are intrinsically complex, with hierarchical organization occurring on many nested length and time scales. The potential to rationally design and tailor the properties of these materials for broad energy applications has been hampered by the lack of computational techniques, which are able to bridge from the molecular to the macroscopic scale. The project addressed the challenge of computational treatments of such complex materials by the development of a synergistic approach that combines innovative multiscale modeling/analysis techniques with high performance computing.« less

Mapping lightscapes: spatial patterning of artificial lighting in an urban landscape.

PubMed

Hale, James D; Davies, Gemma; Fairbrass, Alison J; Matthews, Thomas J; Rogers, Christopher D F; Sadler, Jon P

2013-01-01

Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city's brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas.

Mapping Lightscapes: Spatial Patterning of Artificial Lighting in an Urban Landscape

PubMed Central

Hale, James D.; Davies, Gemma; Fairbrass, Alison J.; Matthews, Thomas J.; Rogers, Christopher D. F.; Sadler, Jon P.

2013-01-01

Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city’s brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas. PMID:23671566

Alternative spatial configurations to reflect landscape structure in a hydrological model: SUMMA applications to the Reynolds Creek Watershed and the Columbia River Basin

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Clark, Martyn; Mizukami, Naoki; Chegwidden, Oriana

2016-04-01

Most existing hydrological models use a fixed representation of landscape structure. For example, high-resolution, spatially-distributed models may use grid cells that exchange moisture through the saturated subsurface or may divide the landscape into hydrologic response units that only exchange moisture through surface channels. Alternatively, many regional models represent the landscape through coarse elements that do not model any moisture exchange between these model elements. These spatial organizations are often represented at a low-level in the model code and its data structures, which makes it difficult to evaluate different landscape representations using the same hydrological model. Instead, such experimentation requires the use of multiple, different hydrological models, which in turn complicates the analysis, because differences in model outcomes are no longer constrained by differing spatial representations. This inflexibility in the representation of landscape structure also limits a model's capability for scaling local processes to regional outcomes. In this study, we used the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to evaluate different model spatial configurations to represent landscape structure and to evaluate scaling behavior. SUMMA can represent the moisture exchange between arbitrarily shaped landscape elements in a number of different ways, while using the same model parameterizations for vertical fluxes. This allows us to isolate the effects of changes in landscape representations on modeled hydrological fluxes and states. We examine the effects of spatial configuration in Reynolds Creek, Idaho, USA, which is a research watershed with gaged areas from 1-20 km2. We then use the same modeling system to evaluate scaling behavior in simulated hydrological fluxes in the Columbia River Basin, Pacific Northwest, USA. This basin drains more than 500,000 km2 and includes the Reynolds Creek Watershed.

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

The Importance of Biologically Relevant Microclimates in Habitat Suitability Assessments

PubMed Central

Varner, Johanna; Dearing, M. Denise

2014-01-01

Predicting habitat suitability under climate change is vital to conserving biodiversity. However, current species distribution models rely on coarse scale climate data, whereas fine scale microclimate data may be necessary to assess habitat suitability and generate predictive models. Here, we evaluate disparities between temperature data at the coarse scale from weather stations versus fine-scale data measured in microhabitats required for a climate-sensitive mammal, the American pika (Ochotona princeps). We collected two years of temperature data in occupied talus habitats predicted to be suitable (high elevation) and unsuitable (low elevation) by the bioclimatic envelope approach. At low elevations, talus surface and interstitial microclimates drastically differed from ambient temperatures measured on-site and at a nearby weather station. Interstitial talus temperatures were frequently decoupled from high ambient temperatures, resulting in instantaneous disparities of over 30°C between these two measurements. Microhabitat temperatures were also highly heterogeneous, such that temperature measurements within the same patch of talus were not more correlated than measurements at distant patches. An experimental manipulation revealed that vegetation cover may cool the talus surface by up to 10°C during the summer, which may contribute to this spatial heterogeneity. Finally, low elevation microclimates were milder and less variable than typical alpine habitat, suggesting that, counter to species distribution model predictions, these seemingly unsuitable habitats may actually be better refugia for this species under climate change. These results highlight the importance of fine-scale microhabitat data in habitat assessments and underscore the notion that some critical refugia may be counterintuitive. PMID:25115894

The importance of biologically relevant microclimates in habitat suitability assessments.

PubMed

Varner, Johanna; Dearing, M Denise

2014-01-01

Predicting habitat suitability under climate change is vital to conserving biodiversity. However, current species distribution models rely on coarse scale climate data, whereas fine scale microclimate data may be necessary to assess habitat suitability and generate predictive models. Here, we evaluate disparities between temperature data at the coarse scale from weather stations versus fine-scale data measured in microhabitats required for a climate-sensitive mammal, the American pika (Ochotona princeps). We collected two years of temperature data in occupied talus habitats predicted to be suitable (high elevation) and unsuitable (low elevation) by the bioclimatic envelope approach. At low elevations, talus surface and interstitial microclimates drastically differed from ambient temperatures measured on-site and at a nearby weather station. Interstitial talus temperatures were frequently decoupled from high ambient temperatures, resulting in instantaneous disparities of over 30 °C between these two measurements. Microhabitat temperatures were also highly heterogeneous, such that temperature measurements within the same patch of talus were not more correlated than measurements at distant patches. An experimental manipulation revealed that vegetation cover may cool the talus surface by up to 10 °C during the summer, which may contribute to this spatial heterogeneity. Finally, low elevation microclimates were milder and less variable than typical alpine habitat, suggesting that, counter to species distribution model predictions, these seemingly unsuitable habitats may actually be better refugia for this species under climate change. These results highlight the importance of fine-scale microhabitat data in habitat assessments and underscore the notion that some critical refugia may be counterintuitive.

Creation of a Digital Surface Model and Extraction of Coarse Woody Debris from Terrestrial Laser Scans in an Open Eucalypt Woodland

NASA Astrophysics Data System (ADS)

Muir, J.; Phinn, S. R.; Armston, J.; Scarth, P.; Eyre, T.

2014-12-01

Coarse woody debris (CWD) provides important habitat for many species and plays a vital role in nutrient cycling within an ecosystem. In addition, CWD makes an important contribution to forest biomass and fuel loads. Airborne or space based remote sensing instruments typically do not detect CWD beneath the forest canopy. Terrestrial laser scanning (TLS) provides a ground based method for three-dimensional (3-D) reconstruction of surface features and CWD. This research produced a 3-D reconstruction of the ground surface and automatically classified coarse woody debris from registered TLS scans. The outputs will be used to inform the development of a site-based index for the assessment of forest condition, and quantitative assessments of biomass and fuel loads. A survey grade terrestrial laser scanner (Riegl VZ400) was used to scan 13 positions, in an open eucalypt woodland site at Karawatha Forest Park, near Brisbane, Australia. Scans were registered, and a digital surface model (DSM) produced using an intensity threshold and an iterative morphological filter. The DSMs produced from single scans were compared to the registered multi-scan point cloud using standard error metrics including: Root Mean Squared Error (RMSE), Mean Squared Error (MSE), range, absolute error and signed error. In addition the DSM was compared to a Digital Elevation Model (DEM) produced from Airborne Laser Scanning (ALS). Coarse woody debris was subsequently classified from the DSM using laser pulse properties, including: width and amplitude, as well as point spatial relationships (e.g. nearest neighbour slope vectors). Validation of the coarse woody debris classification was completed using true-colour photographs co-registered to the TLS point cloud. The volume and length of the coarse woody debris was calculated from the classified point cloud. A representative network of TLS sites will allow for up-scaling to large area assessment using airborne or space based sensors to monitor forest condition, biomass and fuel loads.

Spatial Downscaling of Alien Species Presences using Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis N.; Katsanevakis, Stelios; Moustakas, Aristides

2017-07-01

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

NASA Astrophysics Data System (ADS)

Bindhu, V. M.; Narasimhan, B.

2015-03-01

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

Exploration of scaling effects on coarse resolution land surface phenology

USDA-ARS?s Scientific Manuscript database

A great number of land surface phenoloy (LSP) data have been produced from various coarse resolution satellite datasets and detection algorithms across regional and global scales. Unlike field- measured phenological events which are quantitatively defined with clear biophysical meaning, current LSP ...

Coarse-Grained Models Reveal Functional Dynamics – II. Molecular Dynamics Simulation at the Coarse-Grained Level – Theories and Biological Applications

PubMed Central

Chng, Choon-Peng; Yang, Lee-Wei

2008-01-01

Molecular dynamics (MD) simulation has remained the most indispensable tool in studying equilibrium/non-equilibrium conformational dynamics since its advent 30 years ago. With advances in spectroscopy accompanying solved biocomplexes in growing sizes, sampling their dynamics that occur at biologically interesting spatial/temporal scales becomes computationally intractable; this motivated the use of coarse-grained (CG) approaches. CG-MD models are used to study folding and conformational transitions in reduced resolution and can employ enlarged time steps due to the absence of some of the fastest motions in the system. The Boltzmann-Inversion technique, heavily used in parameterizing these models, provides a smoothed-out effective potential on which molecular conformation evolves at a faster pace thus stretching simulations into tens of microseconds. As a result, a complete catalytic cycle of HIV-1 protease or the assembly of lipid-protein mixtures could be investigated by CG-MD to gain biological insights. In this review, we survey the theories developed in recent years, which are categorized into Folding-based and Molecular-Mechanics-based. In addition, physical bases in the selection of CG beads/time-step, the choice of effective potentials, representation of solvent, and restoration of molecular representations back to their atomic details are systematically discussed. PMID:19812774

Modeling solute transport in a heterogeneous unsaturated porous medium under dynamic boundary conditions on different spatial scales

NASA Astrophysics Data System (ADS)

Cremer, Clemens; Neuweiler, Insa; Bechtold, Michel

2013-04-01

Understanding transport of solutes/contaminants through unsaturated soil in the shallow subsurface is vital to assess groundwater quality, nutrient cycling or to plan remediation projects. Alternating precipitation and evaporation conditions causing upward and downward flux with differing flow paths, changes in saturation and related structural heterogeneity make the description of transport in the unsaturated zone near the soil-surface a complex problem. Preferential flow paths strongly depend, among other things, on the saturation of a medium. Recent studies (e.g. Bechtold et al., 2011) showed lateral flow and solute transport during evaporation conditions (upward flux) in vertically layered sand columns. Results revealed that during evaporation water and solute are redistributed laterally from coarse to fine media deeper in the soil, and towards zones of lowest hydraulic head near to the soil surface. These zones at the surface can be coarse or fine grained depending on saturation status and evaporation flux. However, if boundary conditions are reversed and precipitation is applied, the flow field is not reversed in the same manner, resulting in entirely different transport patterns for downward and upward flow. Therefore, considering net-flow rates alone is misleading when describing transport in the shallow unsaturated zone. In this contribution, we analyze transport of a solute in the shallow subsurface to assess effects resulting from the superposition of heterogeneous soil structures and dynamic flow conditions on various spatial scales. Two-dimensional numerical simulations of unsaturated flow and transport in heterogeneous porous media under changing boundary conditions are carried out using a finite-volume code coupled to a particle tracking algorithm to quantify solute transport and leaching rates. In order to validate numerical simulations, results are qualitatively compared to those of a physical experiment (Bechtold et al., 2011). Numerical simulations differ in lateral scale reaching from 0.2 m to 1.5 m, while the height of the domain is kept constant to 1.5m. Strong material heterogeneity is realized through vertical layers of coarse and fine sand. Both materials remain permanently under liquid-flow-dominated ('stage1') evaporation conditions. Spatial moments as well as the dilution index (Kitanidis, 1994) are used for quantification of transport behaviour. Results show that, while all simulations led to anomalous transport, infiltration-evaporation cycles lead to faster solute leaching rates than solely infiltration at the same net-infiltration rate in both homogeneous and heterogeneous media. Flow and transport-paths significantly differed between infiltration and evaporation, resulting in lateral water fluxes and hence lateral solute transport. Variation of the width of the model domain shows faster leaching rates for domains with small horizontal extent.

Intermittent particle distribution in synthetic free-surface turbulent flows.

PubMed

Ducasse, Lauris; Pumir, Alain

2008-06-01

Tracer particles on the surface of a turbulent flow have a very intermittent distribution. This preferential concentration effect is studied in a two-dimensional synthetic compressible flow, both in the inertial (self-similar) and in the dissipative (smooth) range of scales, as a function of the compressibility C . The second moment of the concentration coarse grained over a scale r , n_{r};{2} , behaves as a power law in both the inertial and the dissipative ranges of scale, with two different exponents. The shapes of the probability distribution functions of the coarse-grained density n_{r} vary as a function of scale r and of compressibility C through the combination C/r;{kappa} (kappa approximately 0.5) , corresponding to the compressibility, coarse grained over a domain of scale r , averaged over Lagrangian trajectories.

Modelling Soil-Landscapes in Coastal California Hills Using Fine Scale Terrestrial Lidar

NASA Astrophysics Data System (ADS)

Prentice, S.; Bookhagen, B.; Kyriakidis, P. C.; Chadwick, O.

2013-12-01

Digital elevation models (DEMs) are the dominant input to spatially explicit digital soil mapping (DSM) efforts due to their increasing availability and the tight coupling between topography and soil variability. Accurate characterization of this coupling is dependent on DEM spatial resolution and soil sampling density, both of which may limit analyses. For example, DEM resolution may be too coarse to accurately reflect scale-dependent soil properties yet downscaling introduces artifactual uncertainty unrelated to deterministic or stochastic soil processes. We tackle these limitations through a DSM effort that couples moderately high density soil sampling with a very fine scale terrestrial lidar dataset (20 cm) implemented in a semiarid rolling hillslope domain where terrain variables change rapidly but smoothly over short distances. Our guiding hypothesis is that in this diffusion-dominated landscape, soil thickness is readily predicted by continuous terrain attributes coupled with catenary hillslope segmentation. We choose soil thickness as our keystone dependent variable for its geomorphic and hydrologic significance, and its tendency to be a primary input to synthetic ecosystem models. In defining catenary hillslope position we adapt a logical rule-set approach that parses common terrain derivatives of curvature and specific catchment area into discrete landform elements (LE). Variograms and curvature-area plots are used to distill domain-scale terrain thresholds from short range order noise characteristic of very fine-scale spatial data. The revealed spatial thresholds are used to condition LE rule-set inputs, rendering a catenary LE map that leverages the robustness of fine-scale terrain data to create a generalized interpretation of soil geomorphic domains. Preliminary regressions show that continuous terrain variables alone (curvature, specific catchment area) only partially explain soil thickness, and only in a subset of soils. For example, at spatial scales up 20, curvature explains 40% of soil thickness variance among soils <3 m deep, while soils >3 m deep show no clear relation to curvature. To further demonstration our geomorphic segmentation approach, we apply it to DEM domains where diffusion processes are less dominant than in our primary study area. Classified landform map derived from fine scale terrestrial lidar. Color classes depict hydrogeomorphic process domains in zero order watersheds.

Combined point and distributed techniques for multidimensional estimation of spatial groundwater-stream water exchange in a heterogeneous sand bed-stream.

NASA Astrophysics Data System (ADS)

Gaona Garcia, J.; Lewandowski, J.; Bellin, A.

2017-12-01

Groundwater-stream water interactions in rivers determine water balances, but also chemical and biological processes in the streambed at different spatial and temporal scales. Due to the difficult identification and quantification of gaining, neutral and losing conditions, it is necessary to combine techniques with complementary capabilities and scale ranges. We applied this concept to a study site at the River Schlaube, East Brandenburg-Germany, a sand bed stream with intense sediment heterogeneity and complex environmental conditions. In our approach, point techniques such as temperature profiles of the streambed together with vertical hydraulic gradients provide data for the estimation of fluxes between groundwater and surface water with the numerical model 1DTempPro. On behalf of distributed techniques, fiber optic distributed temperature sensing identifies the spatial patterns of neutral, down- and up-welling areas by analysis of the changes in the thermal patterns at the streambed interface under certain flow. The study finally links point and surface temperatures to provide a method for upscaling of fluxes. Point techniques provide point flux estimates with essential depth detail to infer streambed structures while the results hardly represent the spatial distribution of fluxes caused by the heterogeneity of streambed properties. Fiber optics proved capable of providing spatial thermal patterns with enough resolution to observe distinct hyporheic thermal footprints at multiple scales. The relation of thermal footprint patterns and temporal behavior with flux results from point techniques enabled the use of methods for spatial flux estimates. The lack of detailed information of the physical driver's spatial distribution restricts the spatial flux estimation to the application of the T-proxy method, whose highly uncertain results mainly provide coarse spatial flux estimates. The study concludes that the upscaling of groundwater-stream water interactions using thermal measurements with combined point and distributed techniques requires the integration of physical drivers because of the heterogeneity of the flux patterns. Combined experimental and modeling approaches may help to obtain more reliable understanding of groundwater-surface water interactions at multiple scales.

Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error.

PubMed

Chang, Howard H; Peng, Roger D; Dominici, Francesca

2011-10-01

In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.

Quantifying the imprint of mesoscale and synoptic-scale atmospheric transport on total column carbon dioxide measurements

NASA Astrophysics Data System (ADS)

Torres, A. D.; Keppel-Aleks, G.; Doney, S. C.; Feng, S.; Lauvaux, T.; Fendrock, M. A.; Rheuben, J.

2017-12-01

Remote sensing instruments provide an unprecedented density of observations of the atmospheric CO2 column average mole fraction (denoted as XCO2), which can be used to constrain regional scale carbon fluxes. Inferring fluxes from XCO2 observations is challenging, as measurements and inversion methods are sensitive to not only the imprint local and large-scale fluxes, but also mesoscale and synoptic-scale atmospheric transport. Quantifying the fine-scale variability in XCO2 from mesoscale and synoptic-scale atmospheric transport will likely improve overall error estimates from flux inversions by improving estimates of representation errors that occur when XCO2 observations are compared to modeled XCO2 in relatively coarse transport models. Here, we utilize various statistical methods to quantify the imprint of atmospheric transport on XCO2 observations. We compare spatial variations along Orbiting Carbon Observatory (OCO-2) satellite tracks to temporal variations observed by the Total Column Carbon Observing Network (TCCON). We observe a coherent seasonal cycle of both within-day temporal and fine-scale spatial variability (of order 10 km) of XCO2 from these two datasets, suggestive of the imprint of mesoscale systems. To account for other potential sources of error in XCO2 retrieval, we compare observed temporal and spatial variations of XCO2 to high-resolution output from the Weather Research and Forecasting (WRF) model run at 9 km resolution. In both simulations and observations, the Northern hemisphere mid-latitude XCO2 showed peak variability during the growing season when atmospheric gradients are largest. These results are qualitatively consistent with our expectations of seasonal variations of the imprint of synoptic and mesoscale atmospheric transport on XCO2 observations; suggesting that these statistical methods could be sensitive to the imprint of atmospheric transport on XCO2 observations.

Modeling Above-Ground Biomass Across Multiple Circum-Arctic Tundra Sites Using High Spatial Resolution Remote Sensing

NASA Astrophysics Data System (ADS)

Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo

2017-04-01

Biomass is one of the central bio-geophysical variables in Earth observation for tracking plant productivity, and flow of carbon, nutrients, and water. Most of the satellite based biomass mapping exercises in Arctic environments have been performed by using rather coarse spatial resolution data, e.g. Landsat and AVHRR which have spatial resolutions of 30 m and >1 km, respectively. While the coarse resolution images have high temporal resolution, they are incapable of capturing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon exchange patterns. Very high spatial resolution (VHSR, spatial resolution 0.5-2 m) satellite images have the potential to detect environmental variables with an ecologically sound spatial resolution. The usage of VHSR images has, nevertheless, been modest so far in biomass modeling in the Arctic. Our objectives were to use VHSR for predicting above ground biomass in tundra landscapes, evaluate whether a common predictive model can be applied across circum-Arctic tundra and peatland sites having different types of vegetation, and produce knowledge on distribution of plant functional types (PFT) in these sites. Such model development is dependent on ground-based surveys of vegetation with the same spatial resolution and extent with the VHSR images. In this study, we conducted ground-based surveys of vegetation composition and biomass in four different arctic tundra or peatland areas located in Russia, Canada, and Finland. First, we sorted species into PFTs and developed PFT-specific models to predict biomass on the basis of non-destructive measurements (cover, height). Second, we predicted overall biomass on landscape scale by combinations of single bands and vegetation indices of very high resolution satellite images (QuickBird or WorldView-2 images of the eight sites). We compared area-specific empirical regression models and common models that were applied across all sites. We found that NDVI was usually the highest scoring spectral indices in explaining biomass distribution with good explanatory power. Furthermore, models which had more than one explanatory variable had higher explanatory power than models with a single index. The dissimilarity between common and site-specific model estimates was, however, high and data indicates that variation in vegetation properties and its impact on spectral reflectance needs to be acknowledged. Our work produced knowledge on above-ground biomass distribution and contribution of PFTs across circum-Arctic low-growth landscapes and will contribute to developing space-borne vegetation monitoring schemes utilizing VHSR satellite images.

Source identification of coarse particles in the Desert ...

EPA Pesticide Factsheets

The Desert Southwest Coarse Particulate Matter Study was undertaken to further our understanding of the spatial and temporal variability and sources of fine and coarse particulate matter (PM) in rural, arid, desert environments. Sampling was conducted between February 2009 and February 2010 in Pinal County, AZ near the town of Casa Grande where PM concentrations routinely exceed the U.S. National Ambient Air Quality Standards (NAAQS) for both PM10 and PM2.5. In this desert region, exceedances of the PM10 NAAQS are dominated by high coarse particle concentrations, a common occurrence in this region of the United States. This work expands on previously published measurements of PM mass and chemistry by examining the sources of fine and coarse particles and the relative contribution of each to ambient PM mass concentrations using the Positive Matrix Factorization receptor model (Clements et al., 2014). Highlights • Isolation of coarse particles from fine particle sources. • Unique chemical composition of coarse particles. • Role of primary biological particles on aerosol loadings.

Time scale of diffusion in molecular and cellular biology

NASA Astrophysics Data System (ADS)

Holcman, D.; Schuss, Z.

2014-05-01

Diffusion is the driver of critical biological processes in cellular and molecular biology. The diverse temporal scales of cellular function are determined by vastly diverse spatial scales in most biophysical processes. The latter are due, among others, to small binding sites inside or on the cell membrane or to narrow passages between large cellular compartments. The great disparity in scales is at the root of the difficulty in quantifying cell function from molecular dynamics and from simulations. The coarse-grained time scale of cellular function is determined from molecular diffusion by the mean first passage time of molecular Brownian motion to a small targets or through narrow passages. The narrow escape theory (NET) concerns this issue. The NET is ubiquitous in molecular and cellular biology and is manifested, among others, in chemical reactions, in the calculation of the effective diffusion coefficient of receptors diffusing on a neuronal cell membrane strewn with obstacles, in the quantification of the early steps of viral trafficking, in the regulation of diffusion between the mother and daughter cells during cell division, and many other cases. Brownian trajectories can represent the motion of a molecule, a protein, an ion in solution, a receptor in a cell or on its membrane, and many other biochemical processes. The small target can represent a binding site or an ionic channel, a hidden active site embedded in a complex protein structure, a receptor for a neurotransmitter on the membrane of a neuron, and so on. The mean time to attach to a receptor or activator determines diffusion fluxes that are key regulators of cell function. This review describes physical models of various subcellular microdomains, in which the NET coarse-grains the molecular scale to a higher cellular-level, thus clarifying the role of cell geometry in determining subcellular function.

Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-graining Approach

DOE PAGES

Aluie, Hussein; Hecht, Matthew; Vallis, Geoffrey K.

2017-11-14

A coarse-graining framework is implemented to analyze nonlinear processes, measure energy transfer rates and map out the energy pathways from simulated global ocean data. Traditional tools to measure the energy cascade from turbulence theory, such as spectral flux or spectral transfer rely on the assumption of statistical homogeneity, or at least a large separation between the scales of motion and the scales of statistical inhomogeneity. The coarse-graining framework allows for probing the fully nonlinear dynamics simultaneously in scale and in space, and is not restricted by those assumptions. This study describes how the framework can be applied to ocean flows.

Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-graining Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aluie, Hussein; Hecht, Matthew; Vallis, Geoffrey K.

A coarse-graining framework is implemented to analyze nonlinear processes, measure energy transfer rates and map out the energy pathways from simulated global ocean data. Traditional tools to measure the energy cascade from turbulence theory, such as spectral flux or spectral transfer rely on the assumption of statistical homogeneity, or at least a large separation between the scales of motion and the scales of statistical inhomogeneity. The coarse-graining framework allows for probing the fully nonlinear dynamics simultaneously in scale and in space, and is not restricted by those assumptions. This study describes how the framework can be applied to ocean flows.

Regional Management Units for Marine Turtles: A Novel Framework for Prioritizing Conservation and Research across Multiple Scales

PubMed Central

Wallace, Bryan P.; DiMatteo, Andrew D.; Hurley, Brendan J.; Finkbeiner, Elena M.; Bolten, Alan B.; Chaloupka, Milani Y.; Hutchinson, Brian J.; Abreu-Grobois, F. Alberto; Amorocho, Diego; Bjorndal, Karen A.; Bourjea, Jerome; Bowen, Brian W.; Dueñas, Raquel Briseño; Casale, Paolo; Choudhury, B. C.; Costa, Alice; Dutton, Peter H.; Fallabrino, Alejandro; Girard, Alexandre; Girondot, Marc; Godfrey, Matthew H.; Hamann, Mark; López-Mendilaharsu, Milagros; Marcovaldi, Maria Angela; Mortimer, Jeanne A.; Musick, John A.; Nel, Ronel; Pilcher, Nicolas J.; Seminoff, Jeffrey A.; Troëng, Sebastian; Witherington, Blair; Mast, Roderic B.

2010-01-01

Background Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques — including site-based monitoring, genetic analyses, mark-recapture studies and telemetry — can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges. Methodology/Principal Findings To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine- to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally. Conclusions/Significance The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework — including maps and supporting metadata — will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis. PMID:21253007

The modulation of EEG variability between internally- and externally-driven cognitive states varies with maturation and task performance

PubMed Central

Willatt, Stephanie E.; Cortese, Filomeno; Protzner, Andrea B.

2017-01-01

Increasing evidence suggests that brain signal variability is an important measure of brain function reflecting information processing capacity and functional integrity. In this study, we examined how maturation from childhood to adulthood affects the magnitude and spatial extent of state-to-state transitions in brain signal variability, and how this relates to cognitive performance. We looked at variability changes between resting-state and task (a symbol-matching task with three levels of difficulty), and within trial (fixation, post-stimulus, and post-response). We calculated variability with multiscale entropy (MSE), and additionally examined spectral power density (SPD) from electroencephalography (EEG) in children aged 8–14, and in adults aged 18–33. Our results suggest that maturation is characterized by increased local information processing (higher MSE at fine temporal scales) and decreased long-range interactions with other neural populations (lower MSE at coarse temporal scales). Children show MSE changes that are similar in magnitude, but greater in spatial extent when transitioning between internally- and externally-driven brain states. Additionally, we found that in children, greater changes in task difficulty were associated with greater magnitude of modulation in MSE. Our results suggest that the interplay between maturational and state-to-state changes in brain signal variability manifest across different spatial and temporal scales, and influence information processing capacity in the brain. PMID:28750035

A comparative study of two approaches to analyse groundwater recharge, travel times and nitrate storage distribution at a regional scale

NASA Astrophysics Data System (ADS)

Turkeltaub, T.; Ascott, M.; Gooddy, D.; Jia, X.; Shao, M.; Binley, A. M.

2017-12-01

Understanding deep percolation, travel time processes and nitrate storage in the unsaturated zone at a regional scale is crucial for sustainable management of many groundwater systems. Recently, global hydrological models have been developed to quantify the water balance at such scales and beyond. However, the coarse spatial resolution of the global hydrological models can be a limiting factor when analysing regional processes. This study compares simulations of water flow and nitrate storage based on regional and global scale approaches. The first approach was applied over the Loess Plateau of China (LPC) to investigate the water fluxes and nitrate storage and travel time to the LPC groundwater system. Using raster maps of climate variables, land use data and soil parameters enabled us to determine fluxes by employing Richards' equation and the advection - dispersion equation. These calculations were conducted for each cell on the raster map in a multiple 1-D column approach. In the second approach, vadose zone travel times and nitrate storage were estimated by coupling groundwater recharge (PCR-GLOBWB) and nitrate leaching (IMAGE) models with estimates of water table depth and unsaturated zone porosity. The simulation results of the two methods indicate similar spatial groundwater recharge, nitrate storage and travel time distribution. Intensive recharge rates are located mainly at the south central and south west parts of the aquifer's outcrops. Particularly low recharge rates were simulated in the top central area of the outcrops. However, there are significant discrepancies between the simulated absolute recharge values, which might be related to the coarse scale that is used in the PCR-GLOBWB model, leading to smoothing of the recharge estimations. Both models indicated large nitrate inventories in the south central and south west parts of the aquifer's outcrops and the shortest travel times in the vadose zone are in the south central and east parts of the outcrops. Our results suggest that, for the LPC at least, global scale models might be useful for highlighting the locations with higher recharge rates potential and nitrate contamination risk. Global modelling simulations appear ideal as a primary step in recognizing locations which require investigations at the plot, field and local scales.

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters: Theory and application to microfilaments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Tong; Gu, YuanTong, E-mail: yuantong.gu@qut.edu.au

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grainedmore » level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.« less

Coarse-to-Fine Encoding of Spatial Frequency Information into Visual Short-Term Memory for Faces but Impartial Decay

ERIC Educational Resources Information Center

Gao, Zaifeng; Bentin, Shlomo

2011-01-01

Face perception studies investigated how spatial frequencies (SF) are extracted from retinal display while forming a perceptual representation, or their selective use during task-imposed categorization. Here we focused on the order of encoding low-spatial frequencies (LSF) and high-spatial frequencies (HSF) from perceptual representations into…

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

PubMed Central

Selmants, Paul C.; Moreno, Alvaro; Running, Steve W.; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales. PMID:28886187

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

USGS Publications Warehouse

Kimball, Heather L.; Selmants, Paul; Moreno, Alvaro; Running Steve W,; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

PubMed

Kimball, Heather L; Selmants, Paul C; Moreno, Alvaro; Running, Steve W; Giardina, Christian P

2017-01-01

Gross primary production (GPP) is the Earth's largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Meso-scale Computational Investigation of Polyurea Microstructure and Its Role in Shockwave Attenuation/dispersion

DTIC Science & Technology

2015-07-01

grained simulations of the formation of meso-segregated microstructure and its interaction with the shockwave is analyzed in the present work. It is...help identify these phenomena and processes, meso-scale coarse-grained simulations of the formation of meso-segregated microstructure and its...of shockwave-induced hard-domain densification. Keywords: Polyurea; Meso-scale; Coarse-grained simulations ; Shockwave attenuation; shockwave

Semantic, perceptual and number space: relations between category width and spatial processing.

PubMed

Brugger, Peter; Loetscher, Tobias; Graves, Roger E; Knoch, Daria

2007-05-17

Coarse semantic encoding and broad categorization behavior are the hallmarks of the right cerebral hemisphere's contribution to language processing. We correlated 40 healthy subjects' breadth of categorization as assessed with Pettigrew's category width scale with lateral asymmetries in perceptual and representational space. Specifically, we hypothesized broader category width to be associated with larger leftward spatial biases. For the 20 men, but not the 20 women, this hypothesis was confirmed both in a lateralized tachistoscopic task with chimeric faces and a random digit generation task; the higher a male participant's score on category width, the more pronounced were his left-visual field bias in the judgement of chimeric faces and his small-number preference in digit generation ("small" is to the left of "large" in number space). Subjects' category width was unrelated to lateral displacements in a blindfolded tactile-motor rod centering task. These findings indicate that visual-spatial functions of the right hemisphere should not be considered independent of the same hemisphere's contribution to language. Linguistic and spatial cognition may be more tightly interwoven than is currently assumed.

Controls on the variability of net infiltration to desert sandstone

USGS Publications Warehouse

Heilweil, Victor M.; McKinney, Tim S.; Zhdanov, Michael S.; Watt, Dennis E.

2007-01-01

As populations grow in arid climates and desert bedrock aquifers are increasingly targeted for future development, understanding and quantifying the spatial variability of net infiltration becomes critically important for accurately inventorying water resources and mapping contamination vulnerability. This paper presents a conceptual model of net infiltration to desert sandstone and then develops an empirical equation for its spatial quantification at the watershed scale using linear least squares inversion methods for evaluating controlling parameters (independent variables) based on estimated net infiltration rates (dependent variables). Net infiltration rates used for this regression analysis were calculated from environmental tracers in boreholes and more than 3000 linear meters of vadose zone excavations in an upland basin in southwestern Utah underlain by Navajo sandstone. Soil coarseness, distance to upgradient outcrop, and topographic slope were shown to be the primary physical parameters controlling the spatial variability of net infiltration. Although the method should be transferable to other desert sandstone settings for determining the relative spatial distribution of net infiltration, further study is needed to evaluate the effects of other potential parameters such as slope aspect, outcrop parameters, and climate on absolute net infiltration rates.

Regional forest land cover characterisation using medium spatial resolution satellite data

USGS Publications Warehouse

Huang, Chengquan; Homer, Collin G.; Yang, Limin; Wulder, Michael A.; Franklin, Steven E.

2003-01-01

Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and Sturdevant 1993; Fuller et al. 1994b; Skole et al. 1997), and a circa 1990 Landsat image data set covering the entire land area of the globe has also been developed recently (Jones and Smith 2001). An acquisition strategy aimed at acquiring at least one cloud free image per year for the entire land area of the globe has been initiated for Landsat-7 (Arvidson et al. 2001). This will probably ensure the continued dominance of Landsat in the near future.

Effects of turbulent hyporheic mixing on reach-scale solute transport

NASA Astrophysics Data System (ADS)

Roche, K. R.; Li, A.; Packman, A. I.

2017-12-01

Turbulence rapidly mixes solutes and fine particles into coarse-grained streambeds. Both hyporheic exchange rates and spatial variability of hyporheic mixing are known to be controlled by turbulence, but it is unclear how turbulent mixing influences mass transport at the scale of stream reaches. We used a process-based particle-tracking model to simulate local- and reach-scale solute transport for a coarse-bed stream. Two vertical mixing profiles, one with a smooth transition from in-stream to hyporheic transport conditions and a second with enhanced turbulent transport at the sediment-water interface, were fit to steady-state subsurface concentration profiles observed in laboratory experiments. The mixing profile with enhanced interfacial transport better matched the observed concentration profiles and overall mass retention in the streambed. The best-fit mixing profiles were then used to simulate upscaled solute transport in a stream. Enhanced mixing coupled in-stream and hyporheic solute transport, causing solutes exchanged into the shallow subsurface to have travel times similar to the water column. This extended the exponential region of the in-stream solute breakthrough curve, and delayed the onset of the heavy power-law tailing induced by deeper and slower hyporheic porewater velocities. Slopes of observed power-law tails were greater than those predicted from stochastic transport theory, and also changed in time. In addition, rapid hyporheic transport velocities truncated the hyporheic residence time distribution by causing mass to exit the stream reach via subsurface advection, yielding strong exponential tempering in the in-stream breakthrough curves at the timescale of advective hyporheic transport through the reach. These results show that strong turbulent mixing across the sediment-water interface violates the conventional separation of surface and subsurface flows used in current models for solute transport in rivers. Instead, the full distribution of flow and mixing over the surface-subsurface continuum must be explicitly considered to properly interpret solute transport in coarse-bed streams.

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

Spatial heterogeneity in human activities favors the persistence of wolves in agroecosystems.

PubMed

Ahmadi, Mohsen; López-Bao, José Vicente; Kaboli, Mohammad

2014-01-01

As human populations expand, there is increasing demand and pressure for land. Under this scenario, behavioural flexibility and adaptation become important processes leading to the persistence of large carnivores in human-dominated landscapes such as agroecosystems. A growing interest has recently emerged on the outcome of the coexistence between wolves and humans in these systems. It has been suggested that spatial heterogeneity in human activities would be a major environmental factor modulating vulnerability and persistence of this contentious species in agroecosystems. Here, we combined information from 35 den sites detected between 2011 and 2012 in agroecosystems of western Iran (Hamedan province), a set of environmental variables measured at landscape and fine spatial scales, and generalized linear models to identify patterns of den site selection by wolves in a highly-modified agroecosystem. On a landscape level, wolves selected a mixture of rangelands with scattered dry-farms on hillsides (showing a low human use) to locate their dens, avoiding areas with high densities of settlements and primary roads. On a fine spatial scale, wolves primarily excavated dens into the sides of elevated steep-slope hills with availability of water bodies in the vicinity of den sites, and wolves were relegated to dig in places with coarse-soil particles. Our results suggest that vulnerability of wolves in human-dominated landscapes could be compensated by the existence of spatial heterogeneity in human activities. Such heterogeneity would favor wolf persistence in agroecosystems favoring a land sharing model of coexistence between wolves and people.

Spatial Heterogeneity in Human Activities Favors the Persistence of Wolves in Agroecosystems

PubMed Central

Ahmadi, Mohsen; López-Bao, José Vicente; Kaboli, Mohammad

2014-01-01

As human populations expand, there is increasing demand and pressure for land. Under this scenario, behavioural flexibility and adaptation become important processes leading to the persistence of large carnivores in human-dominated landscapes such as agroecosystems. A growing interest has recently emerged on the outcome of the coexistence between wolves and humans in these systems. It has been suggested that spatial heterogeneity in human activities would be a major environmental factor modulating vulnerability and persistence of this contentious species in agroecosystems. Here, we combined information from 35 den sites detected between 2011 and 2012 in agroecosystems of western Iran (Hamedan province), a set of environmental variables measured at landscape and fine spatial scales, and generalized linear models to identify patterns of den site selection by wolves in a highly-modified agroecosystem. On a landscape level, wolves selected a mixture of rangelands with scattered dry-farms on hillsides (showing a low human use) to locate their dens, avoiding areas with high densities of settlements and primary roads. On a fine spatial scale, wolves primarily excavated dens into the sides of elevated steep-slope hills with availability of water bodies in the vicinity of den sites, and wolves were relegated to dig in places with coarse-soil particles. Our results suggest that vulnerability of wolves in human-dominated landscapes could be compensated by the existence of spatial heterogeneity in human activities. Such heterogeneity would favor wolf persistence in agroecosystems favoring a land sharing model of coexistence between wolves and people. PMID:25251567

OpenRBC: Redefining the Frontier of Red Blood Cell Simulations at Protein Resolution

NASA Astrophysics Data System (ADS)

Tang, Yu-Hang; Lu, Lu; Li, He; Grinberg, Leopold; Sachdeva, Vipin; Evangelinos, Constantinos; Karniadakis, George

We present a from-scratch development of OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment - simulating an entire mammal red blood cell lipid bilayer and cytoskeleton modeled by 4 million mesoscopic particles - on a single shared memory node. To achieve this, we invented an adaptive spatial searching algorithm to accelerate the computation of short-range pairwise interactions in an extremely sparse 3D space. The algorithm is based on a Voronoi partitioning of the point cloud of coarse-grained particles, and is continuously updated over the course of the simulation. The algorithm enables the construction of a lattice-free cell list, i.e. the key spatial searching data structure in our code, in O (N) time and space space with cells whose position and shape adapts automatically to the local density and curvature. The code implements NUMA/NUCA-aware OpenMP parallelization and achieves perfect scaling with up to hundreds of hardware threads. The code outperforms a legacy solver by more than 8 times in time-to-solution and more than 20 times in problem size, thus providing a new venue for probing the cytomechanics of red blood cells. This work was supported by the Department of Energy (DOE) Collaboratory on Mathematics for Mesoscopic Model- ing of Materials (CM4). YHT acknowledges partial financial support from an IBM Ph.D. Scholarship Award.

Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

2017-01-01

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Hierarchical coarse-graining model for photosystem II including electron and excitation-energy transfer processes.

PubMed

Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi

2014-03-01

We propose a hierarchical reduction scheme to cope with coupled rate equations that describe the dynamics of multi-time-scale photosynthetic reactions. To numerically solve nonlinear dynamical equations containing a wide temporal range of rate constants, we first study a prototypical three-variable model. Using a separation of the time scale of rate constants combined with identified slow variables as (quasi-)conserved quantities in the fast process, we achieve a coarse-graining of the dynamical equations reduced to those at a slower time scale. By iteratively employing this reduction method, the coarse-graining of broadly multi-scale dynamical equations can be performed in a hierarchical manner. We then apply this scheme to the reaction dynamics analysis of a simplified model for an illuminated photosystem II, which involves many processes of electron and excitation-energy transfers with a wide range of rate constants. We thus confirm a good agreement between the coarse-grained and fully (finely) integrated results for the population dynamics. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Ecology of coarse wood decomposition by the saprotrophic fungus Fomes fomentarius.

PubMed

Větrovský, Tomáš; Voříšková, Jana; Snajdr, Jaroslav; Gabriel, Jiří; Baldrian, Petr

2011-07-01

Saprotrophic wood-inhabiting basidiomycetes are the most important decomposers of lignin and cellulose in dead wood and as such they attracted considerable attention. The aims of this work were to quantify the activity and spatial distribution of extracellular enzymes in coarse wood colonised by the white-rot basidiomycete Fomes fomentarius and in adjacent fruitbodies of the fungus and to analyse the diversity of the fungal and bacterial community in a fungus-colonised wood and its potential effect on enzyme production by F. fomentarius. Fungus-colonised wood and fruitbodies were collected in low management intensity forests in the Czech Republic. There were significant differences in enzyme production by F. fomentarius between Betula pendula and Fagus sylvatica wood, the activity of cellulose and xylan-degrading enzymes was significantly higher in beech wood than in birch wood. Spatial analysis of a sample B. pendula log segment proved that F. fomentarius was the single fungal representative found in the log. There was a high level of spatial variability in the amount of fungal biomass detected, but no effects on enzyme activities were observed. Samples from the fruiting body showed high β-glucosidase and chitinase activities compared to wood samples. Significantly higher levels of xylanase and cellobiohydrolase were found in samples located near the fruitbody (proximal), and higher laccase and Mn-peroxidase activities were found in the distal ones. The microbial community in wood was dominated by the fungus (fungal to bacterial DNA ratio of 62-111). Bacterial abundance composition was lower in proximal than distal parts of wood by a factor of 24. These results show a significant level of spatial heterogeneity in coarse wood. One of the explanations may be the successive colonization of wood by the fungus: due to differential enzyme production, the rates of biodegradation of coarse wood are also spatially inhomogeneous.

An Efficient Approach for Pixel Decomposition to Increase the Spatial Resolution of Land Surface Temperature Images from MODIS Thermal Infrared Band Data

PubMed Central

Wang, Fei; Qin, Zhihao; Li, Wenjuan; Song, Caiying; Karnieli, Arnon; Zhao, Shuhe

2015-01-01

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world. PMID:25609048

The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland.

PubMed

Wang, Ran; Gamon, John A; Cavender-Bares, Jeannine; Townsend, Philip A; Zygielbaum, Arthur I

2018-03-01

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm 2 to 1 m 2 to conventional biodiversity metrics, including species richness, Shannon's index, Simpson's index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon's index and Simpson's index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson's index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm 2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods. ©2018 The Authors Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.

A Fine-Scale Functional Logic to Convergence from Retina to Thalamus.

PubMed

Liang, Liang; Fratzl, Alex; Goldey, Glenn; Ramesh, Rohan N; Sugden, Arthur U; Morgan, Josh L; Chen, Chinfei; Andermann, Mark L

2018-05-31

Numerous well-defined classes of retinal ganglion cells innervate the thalamus to guide image-forming vision, yet the rules governing their convergence and divergence remain unknown. Using two-photon calcium imaging in awake mouse thalamus, we observed a functional arrangement of retinal ganglion cell axonal boutons in which coarse-scale retinotopic ordering gives way to fine-scale organization based on shared preferences for other visual features. Specifically, at the ∼6 μm scale, clusters of boutons from different axons often showed similar preferences for either one or multiple features, including axis and direction of motion, spatial frequency, and changes in luminance. Conversely, individual axons could "de-multiplex" information channels by participating in multiple, functionally distinct bouton clusters. Finally, ultrastructural analyses demonstrated that retinal axonal boutons in a local cluster often target the same dendritic domain. These data suggest that functionally specific convergence and divergence of retinal axons may impart diverse, robust, and often novel feature selectivity to visual thalamus. Copyright © 2018 Elsevier Inc. All rights reserved.

Coarse woody debris assay in northern Arizona mixed-conifer and ponderosa pine forests

Treesearch

Joseph L. Ganey; Scott C. Vojta

2010-01-01

Coarse woody debris (CWD) provides important ecosystem services in forests and affects fire behavior, yet information on amounts and types of CWD typically is limited. To provide such information, we sampled logs and stumps in mixed-conifer and ponderosa pine (Pinus ponderosa) forests in north-central Arizona. Spatial variability was prominent for all CWD parameters....

An engineering closure for heavily under-resolved coarse-grid CFD in large applications

NASA Astrophysics Data System (ADS)

Class, Andreas G.; Yu, Fujiang; Jordan, Thomas

2016-11-01

Even though high performance computation allows very detailed description of a wide range of scales in scientific computations, engineering simulations used for design studies commonly merely resolve the large scales thus speeding up simulation time. The coarse-grid CFD (CGCFD) methodology is developed for flows with repeated flow patterns as often observed in heat exchangers or porous structures. It is proposed to use inviscid Euler equations on a very coarse numerical mesh. This coarse mesh needs not to conform to the geometry in all details. To reinstall physics on all smaller scales cheap subgrid models are employed. Subgrid models are systematically constructed by analyzing well-resolved generic representative simulations. By varying the flow conditions in these simulations correlations are obtained. These comprehend for each individual coarse mesh cell a volume force vector and volume porosity. Moreover, for all vertices, surface porosities are derived. CGCFD is related to the immersed boundary method as both exploit volume forces and non-body conformal meshes. Yet, CGCFD differs with respect to the coarser mesh and the use of Euler equations. We will describe the methodology based on a simple test case and the application of the method to a 127 pin wire-wrap fuel bundle.

Compact cell-centered discretization stencils at fine-coarse block structured grid interfaces

NASA Astrophysics Data System (ADS)

Pletzer, Alexander; Jamroz, Ben; Crockett, Robert; Sides, Scott

2014-03-01

Different strategies for coupling fine-coarse grid patches are explored in the context of the adaptive mesh refinement (AMR) method. We show that applying linear interpolation to fill in the fine grid ghost values can produce a finite volume stencil of comparable accuracy to quadratic interpolation provided the cell volumes are adjusted. The volume of fine cells expands whereas the volume of neighboring coarse cells contracts. The amount by which the cells contract/expand depends on whether the interface is a face, an edge, or a corner. It is shown that quadratic or better interpolation is required when the conductivity is spatially varying, anisotropic, the refinement ratio is other than two, or when the fine-coarse interface is concave.

A new hypothesis and exploratory model for the formation of large-scale inner-shelf sediment sorting and ``rippled scour depressions''

NASA Astrophysics Data System (ADS)

Murray, A. Brad; Thieler, E. Robert

2004-02-01

Recent observations of inner continental shelves in many regions show numerous collections of relatively coarse sediment, which extend kilometers in the cross-shore direction and are on the order of 100 m wide. These "rippled scour depressions" have been interpreted to indicate concentrated cross-shelf currents. However, recent observations strongly suggest that they are associated with sediment transport along-shore rather than cross-shore. A new hypothesis for the origin of these features involves the large wave-generated ripples that form in the coarse material. Wave motions interacting with these large roughness elements generate near-bed turbulence that is greatly enhanced relative to that in other areas. This enhances entrainment and inhibits settling of fine material in an area dominated by coarse sediment. The fine sediment is then carried by mean currents past the coarse accumulations, and deposited where the bed is finer. We hypothesize that these interactions constitute a feedback tending to produce accumulations of fine material separated by self-perpetuating patches of coarse sediments. As with many types of self-organized bedforms, small features would interact as they migrate, leading to a better-organized, larger-scale pattern. As an initial test of this hypothesis, we use a numerical model treating the transport of coarse and fine sediment fractions, treated as functions of the local bed composition—a proxy for the presence of large roughness elements in coarse areas. Large-scale sorted patterns exhibiting the main characteristics of the natural features result robustly in the model, indicating that this new hypothesis offers a plausible explanation for the phenomena.

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval

PubMed Central

Liu, Desheng; Pu, Ruiliang

2008-01-01

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. PMID:27879844

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval.

PubMed

Liu, Desheng; Pu, Ruiliang

2008-04-06

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

Terrestrial habitat selection and strong density-dependent mortality in recently metamorphosed amphibians.

PubMed

Patrick, David A; Harper, Elizabeth B; Hunter, Malcolm L; Calhoun, Aram J K

2008-09-01

To predict the effects of terrestrial habitat change on amphibian populations, we need to know how amphibians respond to habitat heterogeneity, and whether habitat choice remains consistent throughout the life-history cycle. We conducted four experiments to evaluate how the spatial distribution of juvenile wood frogs, Rana sylvatica (including both overall abundance and localized density), was influenced by habitat choice and habitat structure, and how this relationship changed with spatial scale and behavioral phase. The four experiments included (1) habitat manipulation on replicated 10-ha landscapes surrounding breeding pools; (2) short-term experiments with individual frogs emigrating through a manipulated landscape of 1 m wide hexagonal patches; and habitat manipulations in (3) small (4-m2); and (4) large (100-m2) enclosures with multiple individuals to compare behavior both during and following emigration. The spatial distribution of juvenile wood frogs following emigration resulted from differences in the scale at which juvenile amphibians responded to habitat heterogeneity during active vs. settled behavioral phases. During emigration, juvenile wood frogs responded to coarse-scale variation in habitat (selection between 2.2-ha forest treatments) but not to fine-scale variation. After settling, however, animals showed habitat selection at much smaller scales (2-4 m2). This resulted in high densities of animals in small patches of suitable habitat where they experienced rapid mortality. No evidence of density-dependent habitat selection was seen, with juveniles typically choosing to remain at extremely high densities in high-quality habitat, rather than occupying low-quality habitat. These experiments demonstrate how prediction of the terrestrial distribution of juvenile amphibians requires understanding of the complex behavioral responses to habitat heterogeneity. Understanding these patterns is important, given that human alterations to amphibian habitats may generate extremely high densities of animals, resulting in high density-dependent mortality.

Sample-based synthesis of two-scale structures with anisotropy

DOE PAGES

Liu, Xingchen; Shapiro, Vadim

2017-05-19

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

Sample-based synthesis of two-scale structures with anisotropy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Xingchen; Shapiro, Vadim

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

Bridging the scales in atmospheric composition simulations using a nudging technique

NASA Astrophysics Data System (ADS)

D'Isidoro, Massimo; Maurizi, Alberto; Russo, Felicita; Tampieri, Francesco

2010-05-01

Studying the interaction between climate and anthropogenic activities, specifically those concentrated in megacities/hot spots, requires the description of processes in a very wide range of scales from local, where anthropogenic emissions are concentrated to global where we are interested to study the impact of these sources. The description of all the processes at all scales within the same numerical implementation is not feasible because of limited computer resources. Therefore, different phenomena are studied by means of different numerical models that can cover different range of scales. The exchange of information from small to large scale is highly non-trivial though of high interest. In fact uncertainties in large scale simulations are expected to receive large contribution from the most polluted areas where the highly inhomogeneous distribution of sources connected to the intrinsic non-linearity of the processes involved can generate non negligible departures between coarse and fine scale simulations. In this work a new method is proposed and investigated in a case study (August 2009) using the BOLCHEM model. Monthly simulations at coarse (0.5° European domain, run A) and fine (0.1° Central Mediterranean domain, run B) horizontal resolution are performed using the coarse resolution as boundary condition for the fine one. Then another coarse resolution run (run C) is performed, in which the high resolution fields remapped on to the coarse grid are used to nudge the concentrations on the Po Valley area. The nudging is applied to all gas and aerosol species of BOLCHEM. Averaged concentrations and variances over Po Valley and other selected areas for O3 and PM are computed. It is observed that although the variance of run B is markedly larger than that of run A, the variance of run C is smaller because the remapping procedure removes large portion of variance from run B fields. Mean concentrations show some differences depending on species: in general mean values of run C lie between run A and run B. A propagation of the signal outside the nudging region is observed, and is evaluated in terms of differences between coarse resolution (with and without nudging) and fine resolution simulations.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

Terrestrial Environmental Variables Derived From EOS Platform Sensors

NASA Technical Reports Server (NTRS)

Stadler, Stephen J.; Czajkowski, Kevin P.; Goward, Samuel N.; Xue, Yongkang

2001-01-01

The three main objectives of the overall project were: 1. Adaptation of environmental constraint methods to take advantage of EOS sensors, specifically, MODIS, ASTER, and Landsat-7, in addition to the PM AVHRR observations 2. Refinement of environmental constraint methods based on fundamental scientific knowledge. 3. Assessment of spatial scaling patterns in environmental constraint measurements to evaluate the potential biases and errors that occur when estimating regional and global-scale NPP patterns with moderate to coarse satellite observations. These goals were modified because, on one hand, MODIS data did not become available until after the first year of the project and because of project staffing issues at the University of Maryland., The OSU portion of the project contained a modest amount of funding and responsibility compared to the University of Maryland and the University of Toledo.

Integration of soil moisture and geophysical datasets for improved water resource management in irrigated systems

NASA Astrophysics Data System (ADS)

Finkenbiner, Catherine; Franz, Trenton E.; Avery, William Alexander; Heeren, Derek M.

2016-04-01

Global trends in consumptive water use indicate a growing and unsustainable reliance on water resources. Approximately 40% of total food production originates from irrigated agriculture. With increasing crop yield demands, water use efficiency must increase to maintain a stable food and water trade. This work aims to increase our understanding of soil hydrologic fluxes at intermediate spatial scales. Fixed and roving cosmic-ray neutron probes were combined in order to characterize the spatial and temporal patterns of soil moisture at three study sites across an East-West precipitation gradient in the state of Nebraska, USA. A coarse scale map was generated for the entire domain (122 km2) at each study site. We used a simplistic data merging technique to produce a statistical daily soil moisture product at a range of key spatial scales in support of current irrigation technologies: the individual sprinkler (˜102m2) for variable rate irrigation, the individual wedge (˜103m2) for variable speed irrigation, and the quarter section (0.82 km2) for uniform rate irrigation. Additionally, we were able to generate a daily soil moisture product over the entire study area at various key modeling and remote sensing scales 12, 32, and 122 km2. Our soil moisture products and derived soil properties were then compared against spatial datasets (i.e. field capacity and wilting point) from the US Department of Agriculture Web Soil Survey. The results show that our "observed" field capacity was higher compared to the Web Soil Survey products. We hypothesize that our results, when provided to irrigators, will decrease water losses due to runoff and deep percolation as sprinkler managers can better estimate irrigation application depth and times in relation to soil moisture depletion below field capacity and above maximum allowable depletion. The incorporation of this non-contact and pragmatic geophysical method into current irrigation practices across the state and globe has the potential to greatly increase agricultural water use efficiency at scale.

Modelling soil properties in a crop field located in Croatia

NASA Astrophysics Data System (ADS)

Bogunovic, Igor; Pereira, Paulo; Millan, Mesic; Percin, Aleksandra; Zgorelec, Zeljka

2016-04-01

Development of tillage activities had negative effects on soil quality as destruction of soil horizons, compacting and aggregates destruction, increasing soil erosion and loss of organic matter. For a better management in order to mitigate the effects of intensive soil management in land degradation it is fundamental to map the spatial distribution of soil properties (Brevik et al., 2016). The understanding the distribution of the variables in space is very important for a sustainable management, in order to identify areas that need a potential intervention and decrease the economic losses (Galiati et al., 2016). The objective of this work is study the spatial distribution of some topsoil properties as clay, fine silt, coarse silt, fine sand, coarse sand, penetration resistance, moisture and organic matter in a crop field located in Croatia. A grid with 275x25 (625 m2) was designed and a total of 48 samples were collected. Previous to data modelling, data normality was checked using the Shapiro wilk-test. As in previous cases (Pereira et al., 2015), data did not followed the normal distribution, even after a logarithmic (Log), square-root, and box cox transformation. Thus, for modeling proposes, we used the log transformed data, since was the closest to the normality. In order to identify groups among the variables we applied a principal component analysis (PCA), based on the correlation matrix. On average clay content was 15.47% (±3.23), fine silt 24.24% (±4.08), coarse silt 35.34% (±3.12), fine sand 20.93% (±4.68), coarse sand 4.02% (±1.69), penetration resistance 0.66 MPa (±0.28), organic matter 1.51% (±0.25) and soil moisture 32.04% (±3.27). The results showed that the PCA identified three factors explained at least one of the variables. The first factor had high positive loadings in soil clay, fine silt and organic matter and a high negative loading in fine sand. The second factor had high positive loadings in coarse sand and moisture and a high negative loading in coarse silt. Finally, the factor 3 had a positive loading in penetration resistance. The loadings of these three factors were mapped using ordinary kriging method. The analysis of incremental spatial correlation identified that the highest spatial correlation in the factor 1 was identified at 41.87 m, in factor 2 at 75.61 m and factor 3 at 143.9 m. In the case of factor 2, the maximum peak of spatial autocorrelation was significant at a p<0.05. This showed that the variable has a random distribution, as confirmed with the Moran's I spatial correlation analysis. In relation to the other factors the maximum peaks were significantly clustered at a p<0.001. These results suggested that the each factor has a different spatial pattern and the studied soil properties explained by each factor had a different spatial distribution. References Breivik, E., Baumgarten, A., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Jordán, A. Soil mapping, classification, and modelling: history and future directions. Geoderma, 264, Part B, 256-274. Galiati, A., Gristina, L., Crescimanno, Barone, E., Novara, A. (2016) Towards more efficient incentives for agri-environment measures in degraded and eroded vineyards. Land Degradation and Development, DOI: 10.1002/ldr.2389 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2015) Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, 26, 180-192.

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant.

PubMed

Rascher, U; Alonso, L; Burkart, A; Cilia, C; Cogliati, S; Colombo, R; Damm, A; Drusch, M; Guanter, L; Hanus, J; Hyvärinen, T; Julitta, T; Jussila, J; Kataja, K; Kokkalis, P; Kraft, S; Kraska, T; Matveeva, M; Moreno, J; Muller, O; Panigada, C; Pikl, M; Pinto, F; Prey, L; Pude, R; Rossini, M; Schickling, A; Schurr, U; Schüttemeyer, D; Verrelst, J; Zemek, F

2015-12-01

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress. © 2015 John Wiley & Sons Ltd.

Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale

NASA Astrophysics Data System (ADS)

von Ruette, J.; Lehmann, P.; Or, D.

2014-10-01

The occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall-induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based "Catchment-scale Hydromechanical Landslide Triggering" (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60,000 m3 for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small-scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.

Thermal Characteristics of Urban Landscapes

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Quattrochi, Dale A.

1998-01-01

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., less than 15 m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for Huntsville, AL.

Assessing and mapping spatial associations among oral cancer mortality rates, concentrations of heavy metals in soil, and land use types based on multiple scale data.

PubMed

Lin, Wei-Chih; Lin, Yu-Pin; Wang, Yung-Chieh; Chang, Tsun-Kuo; Chiang, Li-Chi

2014-02-21

In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate data for Taiwan from the district scale to a 1 km × 1 km grid scale. Local cluster analysis (LCA) of OC mortality rates was then performed to identify OC mortality rate hot spots based on the downscaled and the p-field-simulated OC mortality maps. The relationship between OC mortality and land use was studied by overlapping the maps of the downscaled OC mortality, the LCA results, and the land uses. One thousand simulations were performed to quantify local and spatial uncertainties in the LCA to identify OC mortality hot spots. The scatter plots and Spearman's rank correlation yielded the relationship between OC mortality and concentrations of the seven metals in the 1 km cell grid. The correlation analysis results for the 1 km scale revealed a weak correlation between OC mortality rate and concentrations of the seven studied heavy metals in soil. Accordingly, the heavy metal concentrations in soil are not major determinants of OC mortality rates at the 1 km scale at which soils were sampled. The LCA statistical results for local indicator of spatial association (LISA) revealed that the sites with high probability of high-high (high value surrounded by high values) OC mortality at the 1 km grid scale were clustered in southern, eastern, and mid-western Taiwan. The number of such sites was also significantly higher on agricultural land and in urban regions than on land with other uses. The proposed approach can be used to downscale and evaluate uncertainty in mortality data from a coarse scale to a fine scale at which useful additional information can be obtained for assessing and managing land use and risk.

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over Valencia Anchor Station by Using Downscaling Technique

NASA Astrophysics Data System (ADS)

Ansari Amoli, Abdolreza; Lopez-Baeza, Ernesto; Mahmoudi, Ali; Mahmoodi, Ali

2016-07-01

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over the Valencia Anchor Station by Using a Downscaling Technique Ansari Amoli, A.(1),Mahmoodi, A.(2) and Lopez-Baeza, E.(3) (1) Department of Earth Physics and Thermodynamics, University of Valencia, Spain (2) Centre d'Etudes Spatiales de la BIOsphère (CESBIO), France (3) Department of Earth Physics and Thermodynamics, University of Valencia, Spain Soil moisture products from active sensors are not operationally available. Passive remote sensors return more accurate estimates, but their resolution is much coarser. One solution to overcome this problem is the synergy between radar and radiometric data by using disaggregation (downscaling) techniques. Few studies have been conducted to merge high resolution radar and coarse resolution radiometer measurements in order to obtain an intermediate resolution product. In this paper we present an algorithm using combined available SMAP (Soil Moisture Active and Passive) radar and SMOS (Soil Moisture and Ocean Salinity) radiometer measurements to estimate surface soil moisture over the Valencia Anchor Station (VAS), Valencia, Spain. The goal is to combine the respective attributes of the radar and radiometer observations to estimate soil moisture at a resolution of 3 km. The algorithm disaggregates the coarse resolution SMOS (15 km) radiometer brightness temperature product based on the spatial variation of the high resolution SMAP (3 km) radar backscatter. The disaggregation of the radiometer brightness temperature uses the radar backscatter spatial patterns within the radiometer footprint that are inferred from the radar measurements. For this reason the radar measurements within the radiometer footprint are scaled by parameters that are derived from the temporal fluctuations in the radar and radiometer measurements.

Evaluation of coarse scale land surface remote sensing albedo product over rugged terrain

NASA Astrophysics Data System (ADS)

Wen, J.; Xinwen, L.; You, D.; Dou, B.

2017-12-01

Satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. The accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. And more literatures investigated the validation methods about the albedo validation in a flat or homogenous surface. However, the albedo performance over rugged terrain is still unknow due to the validation method limited. A multi-validation strategy is implemented to give a comprehensive albedo validation, which will involve the high resolution albedo processing, high resolution albedo validation based on in situ albedo, and the method to upscale the high resolution albedo to a coarse scale albedo. Among them, the high resolution albedo generation and the upscale method is the core step for the coarse scale albedo validation. In this paper, the high resolution albedo is generated by Angular Bin algorithm. And a albedo upscale method over rugged terrain is developed to obtain the coarse scale albedo truth. The in situ albedo located 40 sites in mountain area are selected globally to validate the high resolution albedo, and then upscaled to the coarse scale albedo by the upscale method. This paper takes MODIS and GLASS albedo product as a example, and the prelimarily results show the RMSE of MODIS and GLASS albedo product over rugged terrain are 0.047 and 0.057, respectively under the RMSE with 0.036 of high resolution albedo.

Carbon nanotube growth density control

NASA Technical Reports Server (NTRS)

Delzeit, Lance D. (Inventor); Schipper, John F. (Inventor)

2010-01-01

Method and system for combined coarse scale control and fine scale control of growth density of a carbon nanotube (CNT) array on a substrate, using a selected electrical field adjacent to a substrate surface for coarse scale density control (by one or more orders of magnitude) and a selected CNT growth temperature range for fine scale density control (by multiplicative factors of less than an order of magnitude) of CNT growth density. Two spaced apart regions on a substrate may have different CNT growth densities and/or may use different feed gases for CNT growth.

Coarse-scale population structure of pathogenic Armillaria species in a mixed-conifer forest in the Blue Mountains of northeast Oregon.

Treesearch

B.A. Ferguson; T.A. Dreisbach; C.G. Parks; G.M. Filip; C.L. Schmitt

2003-01-01

The coarse-scale population structure of pathogenic Armillaria (Fr.) Staude species was determined on approximately 16 100 ha Of relatively dry, mixed-conifer forest in the Blue Mountains of northeast Oregon. Sampling of recently dead or live, symptomatic conifers produced 112 isolates of Armillaria from six tree species.

A machine learning approach for efficient uncertainty quantification using multiscale methods

NASA Astrophysics Data System (ADS)

Chan, Shing; Elsheikh, Ahmed H.

2018-02-01

Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

Coarse Grid Modeling of Turbine Film Cooling Flows Using Volumetric Source Terms

NASA Technical Reports Server (NTRS)

Heidmann, James D.; Hunter, Scott D.

2001-01-01

The recent trend in numerical modeling of turbine film cooling flows has been toward higher fidelity grids and more complex geometries. This trend has been enabled by the rapid increase in computing power available to researchers. However, the turbine design community requires fast turnaround time in its design computations, rendering these comprehensive simulations ineffective in the design cycle. The present study describes a methodology for implementing a volumetric source term distribution in a coarse grid calculation that can model the small-scale and three-dimensional effects present in turbine film cooling flows. This model could be implemented in turbine design codes or in multistage turbomachinery codes such as APNASA, where the computational grid size may be larger than the film hole size. Detailed computations of a single row of 35 deg round holes on a flat plate have been obtained for blowing ratios of 0.5, 0.8, and 1.0, and density ratios of 1.0 and 2.0 using a multiblock grid system to resolve the flows on both sides of the plate as well as inside the hole itself. These detailed flow fields were spatially averaged to generate a field of volumetric source terms for each conservative flow variable. Solutions were also obtained using three coarse grids having streamwise and spanwise grid spacings of 3d, 1d, and d/3. These coarse grid solutions used the integrated hole exit mass, momentum, energy, and turbulence quantities from the detailed solutions as volumetric source terms. It is shown that a uniform source term addition over a distance from the wall on the order of the hole diameter is able to predict adiabatic film effectiveness better than a near-wall source term model, while strictly enforcing correct values of integrated boundary layer quantities.

Fine-Grained Parcellation of Brain Connectivity Improves Differentiation of States of Consciousness During Graded Propofol Sedation.

PubMed

Liu, Xiaolin; Lauer, Kathryn K; Ward, B Douglas; Roberts, Christopher J; Liu, Suyan; Gollapudy, Suneeta; Rohloff, Robert; Gross, William; Xu, Zhan; Chen, Guangyu; Binder, Jeffrey R; Li, Shi-Jiang; Hudetz, Anthony G

2017-08-01

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.

Multi-scale modeling of diffusion-controlled reactions in polymers: renormalisation of reactivity parameters.

PubMed

Everaers, Ralf; Rosa, Angelo

2012-01-07

The quantitative description of polymeric systems requires hierarchical modeling schemes, which bridge the gap between the atomic scale, relevant to chemical or biomolecular reactions, and the macromolecular scale, where the longest relaxation modes occur. Here, we use the formalism for diffusion-controlled reactions in polymers developed by Wilemski, Fixman, and Doi to discuss the renormalisation of the reactivity parameters in polymer models with varying spatial resolution. In particular, we show that the adjustments are independent of chain length. As a consequence, it is possible to match reactions times between descriptions with different resolution for relatively short reference chains and to use the coarse-grained model to make quantitative predictions for longer chains. We illustrate our results by a detailed discussion of the classical problem of chain cyclization in the Rouse model, which offers the simplest example of a multi-scale descriptions, if we consider differently discretized Rouse models for the same physical system. Moreover, we are able to explore different combinations of compact and non-compact diffusion in the local and large-scale dynamics by varying the embedding dimension.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

Efficient coarse simulation of a growing avascular tumor

PubMed Central

Kavousanakis, Michail E.; Liu, Ping; Boudouvis, Andreas G.; Lowengrub, John; Kevrekidis, Ioannis G.

2013-01-01

The subject of this work is the development and implementation of algorithms which accelerate the simulation of early stage tumor growth models. Among the different computational approaches used for the simulation of tumor progression, discrete stochastic models (e.g., cellular automata) have been widely used to describe processes occurring at the cell and subcell scales (e.g., cell-cell interactions and signaling processes). To describe macroscopic characteristics (e.g., morphology) of growing tumors, large numbers of interacting cells must be simulated. However, the high computational demands of stochastic models make the simulation of large-scale systems impractical. Alternatively, continuum models, which can describe behavior at the tumor scale, often rely on phenomenological assumptions in place of rigorous upscaling of microscopic models. This limits their predictive power. In this work, we circumvent the derivation of closed macroscopic equations for the growing cancer cell populations; instead, we construct, based on the so-called “equation-free” framework, a computational superstructure, which wraps around the individual-based cell-level simulator and accelerates the computations required for the study of the long-time behavior of systems involving many interacting cells. The microscopic model, e.g., a cellular automaton, which simulates the evolution of cancer cell populations, is executed for relatively short time intervals, at the end of which coarse-scale information is obtained. These coarse variables evolve on slower time scales than each individual cell in the population, enabling the application of forward projection schemes, which extrapolate their values at later times. This technique is referred to as coarse projective integration. Increasing the ratio of projection times to microscopic simulator execution times enhances the computational savings. Crucial accuracy issues arising for growing tumors with radial symmetry are addressed by applying the coarse projective integration scheme in a cotraveling (cogrowing) frame. As a proof of principle, we demonstrate that the application of this scheme yields highly accurate solutions, while preserving the computational savings of coarse projective integration. PMID:22587128

Recent Advances in Transferable Coarse-Grained Modeling of Proteins

PubMed Central

Kar, Parimal; Feig, Michael

2017-01-01

Computer simulations are indispensable tools for studying the structure and dynamics of biological macromolecules. Biochemical processes occur on different scales of length and time. Atomistic simulations cannot cover the relevant spatiotemporal scales at which the cellular processes occur. To address this challenge, coarse-grained (CG) modeling of the biological systems are employed. Over the last few years, many CG models for proteins continue to be developed. However, many of them are not transferable with respect to different systems and different environments. In this review, we discuss those CG protein models that are transferable and that retain chemical specificity. We restrict ourselves to CG models of soluble proteins only. We also briefly review recent progress made in the multi-scale hybrid all-atom/coarse-grained simulations of proteins. PMID:25443957

Coarse graining atomistic simulations of plastically deforming amorphous solids

NASA Astrophysics Data System (ADS)

Hinkle, Adam R.; Rycroft, Chris H.; Shields, Michael D.; Falk, Michael L.

2017-05-01

The primary mode of failure in disordered solids results from the formation and persistence of highly localized regions of large plastic strains known as shear bands. Continuum-level field theories capable of predicting this mechanical response rely upon an accurate representation of the initial and evolving states of the amorphous structure. We perform molecular dynamics simulations of a metallic glass and propose a methodology for coarse graining discrete, atomistic quantities, such as the potential energies of the elemental constituents. A strain criterion is established and used to distinguish the coarse-grained degrees-of-freedom inside the emerging shear band from those of the surrounding material. A signal-to-noise ratio provides a means of evaluating the strength of the signal of the shear band as a function of the coarse graining. Finally, we investigate the effect of different coarse graining length scales by comparing a two-dimensional, numerical implementation of the effective-temperature description in the shear transformation zone (STZ) theory with direct molecular dynamics simulations. These comparisons indicate the coarse graining length scale has a lower bound, above which there is a high level of agreement between the atomistics and the STZ theory, and below which the concept of effective temperature breaks down.

Multi-criteria Decision Analysis to Model Ixodes ricinus Habitat Suitability.

PubMed

Rousseau, Raphaël; McGrath, Guy; McMahon, Barry J; Vanwambeke, Sophie O

2017-09-01

Tick-borne diseases present a major threat to both human and livestock health throughout Europe. The risk of infection is directly related to the presence of its vector. Thereby it is important to know their distribution, which is strongly associated with environmental factors: the presence and availability of a suitable habitat, of a suitable climate and of hosts. The present study models the habitat suitability for Ixodes ricinus in Ireland, where data on tick distribution are scarce. Tick habitat suitability was estimated at a coarse scale (10 km) with a multi-criteria decision analysis (MCDA) method according to four different scenarios (depending on the variables used and on the weights granted to each of them). The western part of Ireland and the Wicklow mountains in the East were estimated to be the most suitable areas for I. ricinus in the island. There was a good level of agreement between results from the MCDA and recorded tick presence. The different scenarios did not affect the spatial outputs substantially. The current study suggests that tick habitat suitability can be mapped accurately at a coarse scale in a data-scarce context using knowledge-based methods. It can serve as a guideline for future countrywide sampling that would help to determine local risk of tick presence and refining knowledge on tick habitat suitability in Ireland.

Effects of biotic and abiotic factors on the distribution and abundance of larval two-lined salamanders (Eurycea bislineata) across spatial scales.

PubMed

Barr, Garrett E; Babbitt, Kimberly J

2002-10-01

We sampled eight streams in the White Mountain National Forest, New Hampshire, throughout their elevational reach for larval salamanders and predatory fish to examine the effects of abiotic factors and predation on the distribution and abundance of larval salamanders. Eurycea bislineata (two-lined salamander) and Salvelinus fontinalis (brook trout) abundance varied among and within streams. Eurycea bislineata showed a negative association with S. fontinalis across spatial scales (micro-scale, among quadrats; meso-scale, among pool/riffle pairs; macro-scale, among streams). At the smallest scale, the average density of larval E. bislineata was greatest in microhabitats with relatively high boulder cover and low sand and bare rock cover only in the presence of S. fontinalis; no such relationship was observed in the absence of S. fontinalis. In a mesocosm experiment, larval salamander survival was higher in enclosures containing cobbles than enclosures containing a gravel mix, illustrating the advantage of coarse substrates with interstitial spaces that are inaccessible to predatory fish. At the meso-scale, E. bislineata larvae were less abundant in stream sections with S. fontinalis than those without. Among streams, those with many S. fontinalis had fewer E. bislineata. Of the abiotic parameters measured, water temperature and pH were positively related to E. bislineata presence, and elevation, water temperature, pH, canopy cover, and gradient were positively related to E. bislineata abundance. Larval Plethodontid salamanders can reach high densities and appear to have strong interactions with stream biota, thus their functional role in stream communities deserves further attention.

A new hypothesis and exploratory model for the formation of large-scale inner-shelf sediment sorting and "rippled scour depressions"

USGS Publications Warehouse

Murray, A.B.; Thieler, E.R.

2004-01-01

Recent observations of inner continental shelves in many regions show numerous collections of relatively coarse sediment, which extend kilometers in the cross-shore direction and are on the order of 100m wide. These "rippled scour depressions" have been interpreted to indicate concentrated cross-shelf currents. However, recent observations strongly suggest that they are associated with sediment transport along-shore rather than cross-shore. A new hypothesis for the origin of these features involves the large wave-generated ripples that form in the coarse material. Wave motions interacting with these large roughness elements generate near-bed turbulence that is greatly enhanced relative to that in other areas. This enhances entrainment and inhibits settling of fine material in an area dominated by coarse sediment. The fine sediment is then carried by mean currents past the coarse accumulations, and deposited where the bed is finer. We hypothesize that these interactions constitute a feedback tending to produce accumulations of fine material separated by self-perpetuating patches of coarse sediments. As with many types of self-organized bedforms, small features would interact as they migrate, leading to a better-organized, larger-scale pattern. As an initial test of this hypothesis, we use a numerical model treating the transport of coarse and fine sediment fractions, treated as functions of the local bed composition - a proxy for the presence of large roughness elements in coarse areas. Large-scale sorted patterns exhibiting the main characteristics of the natural features result robustly in the model, indicating that this new hypothesis offers a plausible explanation for the phenomena. ?? 2003 Elsevier Ltd. All rights reserved.

Operator Spreading in Random Unitary Circuits

NASA Astrophysics Data System (ADS)

Nahum, Adam; Vijay, Sagar; Haah, Jeongwan

2018-04-01

Random quantum circuits yield minimally structured models for chaotic quantum dynamics, which are able to capture, for example, universal properties of entanglement growth. We provide exact results and coarse-grained models for the spreading of operators by quantum circuits made of Haar-random unitaries. We study both 1 +1 D and higher dimensions and argue that the coarse-grained pictures carry over to operator spreading in generic many-body systems. In 1 +1 D , we demonstrate that the out-of-time-order correlator (OTOC) satisfies a biased diffusion equation, which gives exact results for the spatial profile of the OTOC and determines the butterfly speed vB. We find that in 1 +1 D , the "front" of the OTOC broadens diffusively, with a width scaling in time as t1 /2. We address fluctuations in the OTOC between different realizations of the random circuit, arguing that they are negligible in comparison to the broadening of the front within a realization. Turning to higher dimensions, we show that the averaged OTOC can be understood exactly via a remarkable correspondence with a purely classical droplet growth problem. This implies that the width of the front of the averaged OTOC scales as t1 /3 in 2 +1 D and as t0.240 in 3 +1 D (exponents of the Kardar-Parisi-Zhang universality class). We support our analytic argument with simulations in 2 +1 D . We point out that, in two or higher spatial dimensions, the shape of the spreading operator at late times is affected by underlying lattice symmetries and, in general, is not spherical. However, when full spatial rotational symmetry is present in 2 +1 D , our mapping implies an exact asymptotic form for the OTOC, in terms of the Tracy-Widom distribution. For an alternative perspective on the OTOC in 1 +1 D , we map it to the partition function of an Ising-like statistical mechanics model. As a result of special structure arising from unitarity, this partition function reduces to a random walk calculation which can be performed exactly. We also use this mapping to give exact results for entanglement growth in 1 +1 D circuits.

Coarse graining for synchronization in directed networks

NASA Astrophysics Data System (ADS)

Zeng, An; Lü, Linyuan

2011-05-01

Coarse-graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve statistical properties as well as the dynamic behaviors of the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse-graining directed networks lacks of consideration. In this paper we proposed a path-based coarse-graining (PCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree networks and variants of Barabási-Albert networks, Watts-Strogatz networks, and Erdös-Rényi networks, we find our method can effectively preserve the network synchronizability.

Predictive coarse-graining

NASA Astrophysics Data System (ADS)

Schöberl, Markus; Zabaras, Nicholas; Koutsourelakis, Phaedon-Stelios

2017-03-01

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method [1] and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo - Expectation-Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

Predictive coarse-graining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schöberl, Markus, E-mail: m.schoeberl@tum.de; Zabaras, Nicholas; Department of Aerospace and Mechanical Engineering, University of Notre Dame, 365 Fitzpatrick Hall, Notre Dame, IN 46556

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extendedmore » to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.« less

Coarse-grained stochastic processes and kinetic Monte Carlo simulators for the diffusion of interacting particles

NASA Astrophysics Data System (ADS)

Katsoulakis, Markos A.; Vlachos, Dionisios G.

2003-11-01

We derive a hierarchy of successively coarse-grained stochastic processes and associated coarse-grained Monte Carlo (CGMC) algorithms directly from the microscopic processes as approximations in larger length scales for the case of diffusion of interacting particles on a lattice. This hierarchy of models spans length scales between microscopic and mesoscopic, satisfies a detailed balance, and gives self-consistent fluctuation mechanisms whose noise is asymptotically identical to the microscopic MC. Rigorous, detailed asymptotics justify and clarify these connections. Gradient continuous time microscopic MC and CGMC simulations are compared under far from equilibrium conditions to illustrate the validity of our theory and delineate the errors obtained by rigorous asymptotics. Information theory estimates are employed for the first time to provide rigorous error estimates between the solutions of microscopic MC and CGMC, describing the loss of information during the coarse-graining process. Simulations under periodic boundary conditions are used to verify the information theory error estimates. It is shown that coarse-graining in space leads also to coarse-graining in time by q2, where q is the level of coarse-graining, and overcomes in part the hydrodynamic slowdown. Operation counting and CGMC simulations demonstrate significant CPU savings in continuous time MC simulations that vary from q3 for short potentials to q4 for long potentials. Finally, connections of the new coarse-grained stochastic processes to stochastic mesoscopic and Cahn-Hilliard-Cook models are made.

Mimicking coarse-grained simulations without coarse-graining: enhanced sampling by damping short-range interactions.

PubMed

Wei, Dongshan; Wang, Feng

2010-08-28

The damped-short-range-interaction (DSRI) method is proposed to mimic coarse-grained simulations by propagating an atomistic scale system on a smoothed potential energy surface. The DSRI method has the benefit of enhanced sampling provided by a typical coarse-grained simulation without the need to perform coarse-graining. Our method was used to simulate liquid water, alanine dipeptide folding, and the self-assembly of dimyristoylphosphatidylcholine lipid. In each case, our method appreciably accelerated the dynamics without significantly changing the free energy surface. Additional insights from DSRI simulations and the promise of coupling our DSRI method with Hamiltonian replica-exchange molecular dynamics are discussed.

Mimicking coarse-grained simulations without coarse-graining: Enhanced sampling by damping short-range interactions

NASA Astrophysics Data System (ADS)

Wei, Dongshan; Wang, Feng

2010-08-01

The damped-short-range-interaction (DSRI) method is proposed to mimic coarse-grained simulations by propagating an atomistic scale system on a smoothed potential energy surface. The DSRI method has the benefit of enhanced sampling provided by a typical coarse-grained simulation without the need to perform coarse-graining. Our method was used to simulate liquid water, alanine dipeptide folding, and the self-assembly of dimyristoylphosphatidylcholine lipid. In each case, our method appreciably accelerated the dynamics without significantly changing the free energy surface. Additional insights from DSRI simulations and the promise of coupling our DSRI method with Hamiltonian replica-exchange molecular dynamics are discussed.

Preferential binding effects on protein structure and dynamics revealed by coarse-grained Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Pandey, R. B.; Jacobs, D. J.; Farmer, B. L.

2017-05-01

The effect of preferential binding of solute molecules within an aqueous solution on the structure and dynamics of the histone H3.1 protein is examined by a coarse-grained Monte Carlo simulation. The knowledge-based residue-residue and hydropathy-index-based residue-solvent interactions are used as input to analyze a number of local and global physical quantities as a function of the residue-solvent interaction strength (f). Results from simulations that treat the aqueous solution as a homogeneous effective solvent medium are compared to when positional fluctuations of the solute molecules are explicitly considered. While the radius of gyration (Rg) of the protein exhibits a non-monotonic dependence on solvent interaction over a wide range of f within an effective medium, an abrupt collapse in Rg occurs in a narrow range of f when solute molecules rapidly bind to a preferential set of sites on the protein. The structure factor S(q) of the protein with wave vector (q) becomes oscillatory in the collapsed state, which reflects segmental correlations caused by spatial fluctuations in solute-protein binding. Spatial fluctuations in solute binding also modify the effective dimension (D) of the protein in fibrous (D ˜ 1.3), random-coil (D ˜ 1.75), and globular (D ˜ 3) conformational ensembles as the interaction strength increases, which differ from an effective medium with respect to the magnitude of D and the length scale.

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.

PubMed

Wen, Ying; Hou, Lili; He, Lianghua; Peterson, Bradley S; Xu, Dongrong

2015-05-01

Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

2016-04-01

Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

Interlaced coarse-graining for the dynamical cluster approximation

NASA Astrophysics Data System (ADS)

Haehner, Urs; Staar, Peter; Jiang, Mi; Maier, Thomas; Schulthess, Thomas

The negative sign problem remains a challenging limiting factor in quantum Monte Carlo simulations of strongly correlated fermionic many-body systems. The dynamical cluster approximation (DCA) makes this problem less severe by coarse-graining the momentum space to map the bulk lattice to a cluster embedded in a dynamical mean-field host. Here, we introduce a new form of an interlaced coarse-graining and compare it with the traditional coarse-graining. We show that it leads to more controlled results with weaker cluster shape and smoother cluster size dependence, which with increasing cluster size converge to the results obtained using the standard coarse-graining. In addition, the new coarse-graining reduces the severity of the fermionic sign problem. Therefore, it enables calculations on much larger clusters and can allow the evaluation of the exact infinite cluster size result via finite size scaling. To demonstrate this, we study the hole-doped two-dimensional Hubbard model and show that the interlaced coarse-graining in combination with the DCA+ algorithm permits the determination of the superconducting Tc on cluster sizes, for which the results can be fitted with the Kosterlitz-Thouless scaling law. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) awarded by the INCITE program, and of the Swiss National Supercomputing Center. OLCF is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

Influence of canopy closure and shrub coverage on travel along coarse woody debris by Eastern chipmunks (Tamias striatus)

Treesearch

Patrick A. Zollner; Kevin J. Crane

2003-01-01

We investigated relationships between canopy closure, shrub cover and the use of coarse woody debris as a travel path by eastern chipmunks (Tamias striatus) in the north central United States. Fine scale movements of chipmunks were followed with tracking spools and the percentage of each movement path directly along coarse woody debris was recorded...

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

The neural bases of spatial frequency processing during scene perception

PubMed Central

Kauffmann, Louise; Ramanoël, Stephen; Peyrin, Carole

2014-01-01

Theories on visual perception agree that scenes are processed in terms of spatial frequencies. Low spatial frequencies (LSF) carry coarse information whereas high spatial frequencies (HSF) carry fine details of the scene. However, how and where spatial frequencies are processed within the brain remain unresolved questions. The present review addresses these issues and aims to identify the cerebral regions differentially involved in low and high spatial frequency processing, and to clarify their attributes during scene perception. Results from a number of behavioral and neuroimaging studies suggest that spatial frequency processing is lateralized in both hemispheres, with the right and left hemispheres predominantly involved in the categorization of LSF and HSF scenes, respectively. There is also evidence that spatial frequency processing is retinotopically mapped in the visual cortex. HSF scenes (as opposed to LSF) activate occipital areas in relation to foveal representations, while categorization of LSF scenes (as opposed to HSF) activates occipital areas in relation to more peripheral representations. Concomitantly, a number of studies have demonstrated that LSF information may reach high-order areas rapidly, allowing an initial coarse parsing of the visual scene, which could then be sent back through feedback into the occipito-temporal cortex to guide finer HSF-based analysis. Finally, the review addresses spatial frequency processing within scene-selective regions areas of the occipito-temporal cortex. PMID:24847226

Towards a High-Resolution Global Inundation Delineation Dataset

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.

2011-12-01

Although their importance for biodiversity, flow regulation and ecosystem service provision is widely recognized, wetlands and temporarily inundated landscapes remain poorly mapped globally because of their inherent elusive nature. Inventorying of wetland resources has been identified in international agreements as an essential component of appropriate conservation efforts and management initiatives of these threatened ecosystems. However, despite recent advances in remote sensing surface water monitoring, current inventories of surface water variations remain incomplete at the regional-to-global scale due to methodological limitations restricting truly global application. Remote sensing wetland applications such as SAR L-band are particularly constrained by image availability and heterogeneity of acquisition dates, while coarse resolution passive microwave and multi-sensor methods cannot discriminate distinct surface water bodies. As a result, the most popular global wetland dataset remains to this day the Global Lake & Wetland Database (Lehner and Doll, 2004) a spatially inconsistent database assembled from various existing data sources. The approach taken in this project circumvents the limitations of current global wetland monitoring methods by combining globally available topographic and hydrographic data to downscale coarse resolution global inundation data (Prigent et al., 2007) and thus create a superior inundation delineation map product. The developed procedure downscales inundation data from the coarse resolution (~27km) of current passive microwave sensors to the finer spatial resolution (~500m) of the topographic and hydrographic layers of HydroSHEDS' data suite (Lehner et al., 2006), while retaining the high temporal resolution of the multi-sensor inundation dataset. From the downscaling process emerges new information on the specific location of inundation, but also on its frequency and duration. The downscaling algorithm employs a decision tree classifier trained on regional remote sensing wetland maps, to derive inundation probability followed by a seeded region growing segmentation process to redistribute the inundated area at the finer resolution. Assessment of the algorithm's performance is accomplished by evaluating the level of agreement between its outputted downscaled inundation maps and existing regional remote sensing inundation delineation. Upon completion, this project's will offer a dynamic globally seamless inundation map at an unprecedented spatial and temporal scale, which will provide the baseline inventory long requested by the research community, and will open the door to a wide array of possible conservation and hydrological modeling applications which were until now data-restricted. Literature Lehner, B., K. Verdin, and A. Jarvis. 2008. New global hydrography derived from spaceborne elevation data. Eos 89, no. 10. Lehner, B, and P Doll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, no. 1-4: 1-22. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews. 2007. Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research 112, no. D12: 1-13.

A Hybrid Coarse-graining Approach for Lipid Bilayers at Large Length and Time Scales

PubMed Central

Ayton, Gary S.; Voth, Gregory A.

2009-01-01

A hybrid analytic-systematic (HAS) coarse-grained (CG) lipid model is developed and employed in a large-scale simulation of a liposome. The methodology is termed hybrid analyticsystematic as one component of the interaction between CG sites is variationally determined from the multiscale coarse-graining (MS-CG) methodology, while the remaining component utilizes an analytic potential. The systematic component models the in-plane center of mass interaction of the lipids as determined from an atomistic-level MD simulation of a bilayer. The analytic component is based on the well known Gay-Berne ellipsoid of revolution liquid crystal model, and is designed to model the highly anisotropic interactions at a highly coarse-grained level. The HAS CG approach is the first step in an “aggressive” CG methodology designed to model multi-component biological membranes at very large length and timescales. PMID:19281167

Spatial analysis of sunshine duration by combination of satellite and station data

NASA Astrophysics Data System (ADS)

Frei, C.; Stöckli, R.; Dürr, B.

2009-09-01

Sunshine duration can exhibit rich fine scale patterns associated with special meteorological phenomena, such as fog layers and topographically triggered clouds. Networks of climate stations are mostly too coarse and poorly representative to resolve these patterns explicitly. We present a method which combines station observations with satellite-derived cloud-cover data to produce km-scale fields of sunshine duration. The method is not relying on contemporous satellite information, hence it can be applied over climatological time scales. We apply and evaluate the combination method over the territory of Switzerland. The combination method is based on Universal Kriging. First, the satellite data (a Heliosat clear sky index from MSG, extending over a 5 year preiod) is subjected to a S-mode Principal Component (PC) Analysis. Second, a set of leading PC loadings (seasonally stratified) is introduced as external drift covariates and their optimal linear combination is estimated from the station data (70 stations). Finally, the stochastic component is an autocorrelated field with an exponential variogram, estimated climatologically for each calendar month. For Switzerland the leading PCs of the clear sky index depict familiar patterns of cloud variability which are inhereted in the combination process. The resulting sunshine duration fields exhibit fine-scale structures that are physically plausible, linked to the topography and characteristic of the regional climate. These patterns could not be inferred from station data and/or topographic predictors alone. A cross-validation reveals that the combination method explains between 80-90% of the spatial variance in winter and autumn months. In spring and summer the relative performance is lower (60-75% explained spatial variance) but absolute errors are smaller. Our presentation will also discuss some results from a climatology of the derived sunshine duration fields.

Coarse-graining using the relative entropy and simplex-based optimization methods in VOTCA

NASA Astrophysics Data System (ADS)

Rühle, Victor; Jochum, Mara; Koschke, Konstantin; Aluru, N. R.; Kremer, Kurt; Mashayak, S. Y.; Junghans, Christoph

2014-03-01

Coarse-grained (CG) simulations are an important tool to investigate systems on larger time and length scales. Several methods for systematic coarse-graining were developed, varying in complexity and the property of interest. Thus, the question arises which method best suits a specific class of system and desired application. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides a uniform platform for coarse-graining methods and allows for their direct comparison. We present recent advances of VOTCA, namely the implementation of the relative entropy method and downhill simplex optimization for coarse-graining. The methods are illustrated by coarse-graining SPC/E bulk water and a water-methanol mixture. Both CG models reproduce the pair distributions accurately. SYM is supported by AFOSR under grant 11157642 and by NSF under grant 1264282. CJ was supported in part by the NSF PHY11-25915 at KITP. K. Koschke acknowledges funding by the Nestle Research Center.

The dimension of attractors underlying periodic turbulent Poiseuille flow

NASA Technical Reports Server (NTRS)

Keefe, Laurence; Moin, Parviz; Kim, John

1992-01-01

A lower bound on the Liapunov dimenison, D-lambda, of the attractor underlying turbulent, periodic Poiseuille flow at a pressure-gradient Reynolds number of 3200 is calculated, on the basis of a coarse-grained (16x33x8) numerical solution, to be approximately 352. Comparison of Liapunov exponent spectra from this and a higher-resolution (16x33x16) simulation on the same spatial domain shows these spectra to have a universal shape when properly scaled. On the basis of these scaling properties, and a partial exponent spectrum from a still higher-resolution (32x33x32) simulation, it is argued that the actual dimension of the attractor underlying motion of the given computational domain is approximately 780. It is suggested that this periodic turbulent shear flow is deterministic chaos, and that a strange attractor does underly solutions to the Navier-Stokes equations in such flows.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

Spatial and temporal patterns of beetles associated with coarse woody debris in managed bottomland hardwood forests

Treesearch

Michael D. Ulyshen; James L. Hanula; Scott Horn; John C. Kilgo; Christopher E. Moorman

2004-01-01

Malaise traps were used to sample beetles in artificial canopy gaps of different size (0.13 ha, 0.26 ha, and 0.50 ha) and age in a South Carolina bottomland hardwood forest. Traps were placed at the center, edge, and in the surrounding forest of each gap. Young gaps (~1 year) had large amounts of coarse woody debris compared to the surrounding forest, while older gaps...

Global response of the growing season to soil moisture and topography

NASA Astrophysics Data System (ADS)

Guevara, M.; Arroyo, C.; Warner, D. L.; Equihua, J.; Lule, A. V.; Schwartz, A.; Taufer, M.; Vargas, R.

2017-12-01

Soil moisture has a direct influence in plant productivity. Plant productivity and its greenness can be inferred by remote sensing with higher spatial detail than soil moisture. The objective was to improve the coarse scale of currently available satellite soil moisture estimates and identify areas of strong coupling between the interannual variability soil moisture and the maximum greenness vegetation fraction (MGVF) at the global scale. We modeled, cross-validated and downscaled remotely sensed soil moisture using machine learning and digital terrain analysis across 23 years (1991-2013) of available data. Improving the accuracy (0.69-0.87 % of cross-validated explained variance) and the spatial detail (from 27 to 15km) of satellite soil moisture, we filled temporal gaps of information across vegetated areas where satellite soil moisture does not work properly. We found that 7.57% of global vegetated area shows strong correlation with our downscaled product (R2>0.5, Fig. 1). We found a dominant positive response of vegetation greenness to topography-based soil moisture across water limited environments, however, the tropics and temperate environments of higher latitudes showed a sparse negative response. We conclude that topography can be used to effectively improve the spatial detail of globally available remotely sensed soil moisture, which is convenient to generate unbiased comparisons with global vegetation dynamics, and better inform land and crop modeling efforts.

Coarse-scale restoration planning and design in Interior Columbia River Basin ecosystems: An example for restoring declining whitebark pine forests

Treesearch

Robert E. Keane; James P. Menakis; Wendel J. Hann

1996-01-01

During the last 2 years, many people from numerous government agencies and private institutions compiled a scientific assessment of the natural and human resources of the Interior Columbia River Basin (Jensen and Bourgeron 1993). This assessment is meant to guide the development of a coarse-scale Environmental Impact Statement for all 82 million hectares comprising the...

Geographical Pattern and Environmental Correlates of Regional-Scale General Flowering in Peninsular Malaysia

PubMed Central

Numata, Shinya; Yasuda, Masatoshi; Suzuki, Ryo O.; Hosaka, Tetsuro; Noor, Nur Supardi Md.; Fletcher, Christine D.; Hashim, Mazlan

2013-01-01

In South-East Asian dipterocarp forests, many trees synchronize their reproduction at the community level, but irregularly, in a phenomenon known as general flowering (GF). Several proximate cues have been proposed as triggers for the synchronization of Southeast Asian GF, but the debate continues, as many studies have not considered geographical variation in climate and flora. We hypothesized that the spatial pattern of GF forests is explained by previously proposed climatic cues if there are common cues for GF among regions. During the study, GF episodes occurred every year, but the spatial occurrence varied considerably from just a few forests to the whole of Peninsular Malaysia. In 2001, 2002 and 2005, minor and major GF occurred widely throughout Peninsular Malaysia (GF2001, GF2002, and GF2005), and the geographical patterns of GF varied between the episodes. In the three regional-scale GF episodes, most major events occurred in regions where prolonged drought (PD) had been recorded prior, and significant associations between GF scores and PD were found in GF2001 and GF2002. However, the frequency of PD was higher than that of GF throughout the peninsula. In contrast, low temperature (LT) was observed during the study period only before GF2002 and GF2005, but there was no clear spatial relationship between GF and LT in the regional-scale episodes. There was also no evidence that last GF condition influenced the magnitude of GF. Thus, our results suggest that PD would be essential to trigger regional-scale GF in the peninsula, but also that PD does not fully explain the spatial and temporal patterns of GF. The coarse relationships between GF and the proposed climatic cues may be due to the geographical variation in proximate cues for GF, and the climatic and floristic geographical variations should be considered to understand the proximate factors of GF. PMID:24260159

Geographical pattern and environmental correlates of regional-scale general flowering in Peninsular Malaysia.

PubMed

Numata, Shinya; Yasuda, Masatoshi; Suzuki, Ryo O; Hosaka, Tetsuro; Noor, Nur Supardi Md; Fletcher, Christine D; Hashim, Mazlan

2013-01-01

In South-East Asian dipterocarp forests, many trees synchronize their reproduction at the community level, but irregularly, in a phenomenon known as general flowering (GF). Several proximate cues have been proposed as triggers for the synchronization of Southeast Asian GF, but the debate continues, as many studies have not considered geographical variation in climate and flora. We hypothesized that the spatial pattern of GF forests is explained by previously proposed climatic cues if there are common cues for GF among regions. During the study, GF episodes occurred every year, but the spatial occurrence varied considerably from just a few forests to the whole of Peninsular Malaysia. In 2001, 2002 and 2005, minor and major GF occurred widely throughout Peninsular Malaysia (GF2001, GF2002, and GF2005), and the geographical patterns of GF varied between the episodes. In the three regional-scale GF episodes, most major events occurred in regions where prolonged drought (PD) had been recorded prior, and significant associations between GF scores and PD were found in GF2001 and GF2002. However, the frequency of PD was higher than that of GF throughout the peninsula. In contrast, low temperature (LT) was observed during the study period only before GF2002 and GF2005, but there was no clear spatial relationship between GF and LT in the regional-scale episodes. There was also no evidence that last GF condition influenced the magnitude of GF. Thus, our results suggest that PD would be essential to trigger regional-scale GF in the peninsula, but also that PD does not fully explain the spatial and temporal patterns of GF. The coarse relationships between GF and the proposed climatic cues may be due to the geographical variation in proximate cues for GF, and the climatic and floristic geographical variations should be considered to understand the proximate factors of GF.

Problems in evaluating regional and local trends in temperature: An example from eastern Colorado, USA

USGS Publications Warehouse

Pielke, R.A.; Stohlgren, T.; Schell, L.; Parton, W.; Doesken, N.; Redmond, K.; Moeny, J.; McKee, T.; Kittel, T.G.F.

2002-01-01

We evaluated long-term trends in average maximum and minimum temperatures, threshold temperatures, and growing season in eastern Colorado, USA, to explore the potential shortcomings of many climate-change studies that either: (1) generalize regional patterns from single stations, single seasons, or a few parameters over short duration from averaging dissimilar stations: or (2) generalize an average regional pattern from coarse-scale general circulation models. Based on 11 weather stations, some trends were weakly regionally consistent with previous studies of night-time temperature warming. Long-term (80 + years) mean minimum temperatures increased significantly (P < 0.2) in about half the stations in winter, spring, and autumn and six stations had significant decreases in the number of days per year with temperatures ??? - 17.8 ??C (???0??F). However, spatial and temporal variation in the direction of change was enormous for all the other weather parameters tested, and, in the majority of tests, few stations showed significant trends (even at P < 0.2). In summer, four stations had significant increases and three stations had significant decreases in minimum temperatures, producing a strongly mixed regional signal. Trends in maximum temperature varied seasonally and geographically, as did trends in threshold temperature days ???32.2??C (???90??F) or days ???37.8??C (???100??F). There was evidence of a subregional cooling in autumn's maximum temperatures, with five stations showing significant decreasing trends. There were many geographic anomalies where neighbouring weather stations differed greatly in the magnitude of change or where they had significant and opposite trends. We conclude that sub-regional spatial and seasonal variation cannot be ignored when evaluating the direction and magnitude of climate change. It is unlikely that one or a few weather stations are representative of regional climate trends, and equally unlikely that regionally projected climate change from coarse-scale general circulation models will accurately portray trends at sub-regional scales. However, the assessment of a group of stations for consistent more qualitative trends (such as the number of days less than - 17.8??C, such as we found) provides a reasonably robust procedure to evaluate climate trends and variability. Copyright ?? 2002 Royal Meteorological Society.

Lennard-Jones type pair-potential method for coarse-grained lipid bilayer membrane simulations in LAMMPS

NASA Astrophysics Data System (ADS)

Fu, S.-P.; Peng, Z.; Yuan, H.; Kfoury, R.; Young, Y.-N.

2017-01-01

Lipid bilayer membranes have been extensively studied by coarse-grained molecular dynamics simulations. Numerical efficiencies have been reported in the cases of aggressive coarse-graining, where several lipids are coarse-grained into a particle of size 4 ∼ 6 nm so that there is only one particle in the thickness direction. Yuan et al. proposed a pair-potential between these one-particle-thick coarse-grained lipid particles to capture the mechanical properties of a lipid bilayer membrane, such as gel-fluid-gas phase transitions of lipids, diffusion, and bending rigidity Yuan et al. (2010). In this work we implement such an interaction potential in LAMMPS to simulate large-scale lipid systems such as a giant unilamellar vesicle (GUV) and red blood cells (RBCs). We also consider the effect of cytoskeleton on the lipid membrane dynamics as a model for RBC dynamics, and incorporate coarse-grained water molecules to account for hydrodynamic interactions. The interaction between the coarse-grained water molecules (explicit solvent molecules) is modeled as a Lennard-Jones (L-J) potential. To demonstrate that the proposed methods do capture the observed dynamics of vesicles and RBCs, we focus on two sets of LAMMPS simulations: 1. Vesicle shape transitions with enclosed volume; 2. RBC shape transitions with different enclosed volume. Finally utilizing the parallel computing capability in LAMMPS, we provide some timing results for parallel coarse-grained simulations to illustrate that it is possible to use LAMMPS to simulate large-scale realistic complex biological membranes for more than 1 ms.

Testing for entanglement with periodic coarse graining

NASA Astrophysics Data System (ADS)

Tasca, D. S.; Rudnicki, Łukasz; Aspden, R. S.; Padgett, M. J.; Souto Ribeiro, P. H.; Walborn, S. P.

2018-04-01

Continuous-variable systems find valuable applications in quantum information processing. To deal with an infinite-dimensional Hilbert space, one in general has to handle large numbers of discretized measurements in tasks such as entanglement detection. Here we employ the continuous transverse spatial variables of photon pairs to experimentally demonstrate entanglement criteria based on a periodic structure of coarse-grained measurements. The periodization of the measurements allows an efficient evaluation of entanglement using spatial masks acting as mode analyzers over the entire transverse field distribution of the photons and without the need to reconstruct the probability densities of the conjugate continuous variables. Our experimental results demonstrate the utility of the derived criteria with a success rate in entanglement detection of ˜60 % relative to 7344 studied cases.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

Bridging the gap between small and large scale sediment budgets? - A scaling challenge in the Upper Rhone Basin, Switzerland

NASA Astrophysics Data System (ADS)

Schoch, Anna; Blöthe, Jan; Hoffmann, Thomas; Schrott, Lothar

2016-04-01

A large number of sediment budgets have been compiled on different temporal and spatial scales in alpine regions. Detailed sediment budgets based on the quantification of a number of sediment storages (e.g. talus cones, moraine deposits) exist only for a few small scale drainage basins (up to 10² km²). In contrast, large scale sediment budgets (> 10³ km²) consider only long term sediment sinks such as valley fills and lakes. Until now, these studies often neglect small scale sediment storages in the headwaters. However, the significance of these sediment storages have been reported. A quantitative verification whether headwaters function as sediment source regions is lacking. Despite substantial transport energy in mountain environments due to steep gradients and high relief, sediment flux in large river systems is frequently disconnected from alpine headwaters. This leads to significant storage of coarse-grained sediment along the flow path from rockwall source regions to large sedimentary sinks in major alpine valleys. To improve the knowledge on sediment budgets in large scale alpine catchments and to bridge the gap between small and large scale sediment budgets, we apply a multi-method approach comprising investigations on different spatial scales in the Upper Rhone Basin (URB). The URB is the largest inneralpine basin in the European Alps with a size of > 5400 km². It is a closed system with Lake Geneva acting as an ultimate sediment sink for suspended and clastic sediment. We examine the spatial pattern and volumes of sediment storages as well as the morphometry on the local and catchment-wide scale. We mapped sediment storages and bedrock in five sub-regions of the study area (Goms, Lötschen valley, Val d'Illiez, Vallée de la Liène, Turtmann valley) in the field and from high-resolution remote sensing imagery to investigate the spatial distribution of different sediment storage types (e.g. talus deposits, debris flow cones, alluvial fans). These sub-regions cover all three litho-tectonic units of the URB (Helvetic nappes, Penninic nappes, External massifs) and different catchment sizes to capture the inherent variability. Different parameters characterizing topography, surface characteristics, and vegetation cover are analyzed for each storage type. The data is then used in geostatistical models (PCA, stepwise logistic regression) to predict the spatial distribution of sediment storage for the whole URB. We further conduct morphometric analyses of the URB to gain information on the varying degree of glacial imprint and postglacial landscape evolution and their control on the spatial distribution of sediment storage in a large scale drainage basin. Geophysical methods (ground penetrating radar and electrical resistivity tomography) are applied on different sediment storage types on the local scale to estimate mean thicknesses. Additional data from published studies are used to complement our dataset. We integrate the local data in the statistical model on the spatial distribution of sediment storages for the whole URB. Hence, we can extrapolate the stored sediment volumes to the regional scale in order to bridge the gap between small and large scale studies.

Spatial and Temporal Characteristics of Insulator Contaminations Revealed by Daily Observations of Equivalent Salt Deposit Density

PubMed Central

Ruan, Ling; Han, Ge; Zhu, Zhongmin; Zhang, Miao; Gong, Wei

2015-01-01

The accurate estimation of deposits adhering on insulators is of great significance to prevent pollution flashovers which cause huge costs worldwide. Researchers have developed sensors using different technologies to monitor insulator contamination on a fine time scale. However, there is lack of analysis of these data to reveal spatial and temporal characteristics of insulator contamination, and as a result the scheduling of periodical maintenance of power facilities is highly dependent on personal experience. Owing to the deployment of novel sensors, daily Equivalent Salt Deposit Density (ESDD) observations of over two years were collected and analyzed for the first time. Results from 16 sites distributed in four regions of Hubei demonstrated that spatial heterogeneity can be seen at both the fine and coarse geographical scales, suggesting that current polluted area maps are necessary but are not sufficient conditions to guide the maintenance of power facilities. Both the local emission and the regional air pollution condition exert evident influences on deposit accumulation. A relationship between ESDD and PM10 was revealed by using regression analysis, proving that air pollution exerts influence on pollution accumulations on insulators. Moreover, the seasonality of ESDD was discovered for the first time by means of time series analysis, which could help engineers select appropriate times to clean the contamination. Besides, the trend component shows that the ESDD increases in a negative exponential fashion with the accumulation date (ESDD = a − b × exp(−time)) at a long time scale in real environments. PMID:25643058

The Use of the Airborne Thermal/Visible Land Application Sensor (ATLAS) to Determine the Thermal Response Numbers for Urban Areas

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Rickman, Doug; Quattroch, Dale; Estes. Maury

2007-01-01

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., < 15m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number)(Luvall and Holbo 1989) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for several cities in the United States.

Geospatial assessment of ecological functions and flood-related risks on floodplains along major rivers in the Puget Sound Basin, Washington

USGS Publications Warehouse

Konrad, Christopher P.

2015-01-01

Ecological functions and flood-related risks were assessed for floodplains along the 17 major rivers flowing into Puget Sound Basin, Washington. The assessment addresses five ecological functions, five components of flood-related risks at two spatial resolutions—fine and coarse. The fine-resolution assessment compiled spatial attributes of floodplains from existing, publically available sources and integrated the attributes into 10-meter rasters for each function, hazard, or exposure. The raster values generally represent different types of floodplains with regard to each function, hazard, or exposure rather than the degree of function, hazard, or exposure. The coarse-resolution assessment tabulates attributes from the fine-resolution assessment for larger floodplain units, which are floodplains associated with 0.1 to 21-kilometer long segments of major rivers. The coarse-resolution assessment also derives indices that can be used to compare function or risk among different floodplain units and to develop normative (based on observed distributions) standards. The products of the assessment are available online as geospatial datasets (Konrad, 2015; http://dx.doi.org/10.5066/F7DR2SJC).

Coarse grained modeling of directed assembly to form functional nanoporous films

NASA Astrophysics Data System (ADS)

Al Khatib, Amir

A coarse-grained (CG) simulation of polyethylene glycol (PEG) and Polymethylsilsesquixane nanoparticle (PMSSQ) referred to as (NP) at different sizes and concentrations were done using the Martini coarse-grained (CG) force field. The interactions between CG PEG and CG NP were parameterized from the chemical compound of each molecule and based on Martini force field. NP particles migrates to the surface of the substrate in an agreement with the experimental output at high temperature of 800K. This demonstration of nanoparticles-polymer film to direct it to self-assemble a systematically spatial pattern using the substrate surface energy as the key gating parameter. Validation of the model comparing molecular dynamics simulations with experimental data collected from previous study. NP interaction with the substrate at low interactions energy using Lennard-Johns potential were able to direct the NP to self-assemble in a hexagonal shape up to 4 layers above the substrate. This thesis established that substrate surface energy is a key gating parameter to direct the collective behavior of functional nanoparticles to form thin nanoporous films with spatially predetermined optical/dielectric constants.

Spatial Frequency Priming of Scene Perception in Adolescents with and without ASD

ERIC Educational Resources Information Center

Vanmarcke, Steven; Noens, Ilse; Steyaert, Jean; Wagemans, Johan

2017-01-01

While most typically developing (TD) participants have a coarse-to-fine processing style, people with autism spectrum disorder (ASD) seem to be less globally and more locally biased when processing visual information. The stimulus-specific spatial frequency content might be directly relevant to determine this temporal hierarchy of visual…

Area-to-point regression kriging for pan-sharpening

NASA Astrophysics Data System (ADS)

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.

2016-04-01

Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

NASA Astrophysics Data System (ADS)

Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

2015-12-01

Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we contend that creating believable soil carbon predictions requires a robust, transparent, and community-available benchmarking framework. I will present an ILAMB evaluation of several of the above-mentioned approaches in ACME, and attempt to motivate community adoption of this evaluation approach.

Integrating satellite actual evapotranspiration patterns into distributed model parametrization and evaluation for a mesoscale catchment

NASA Astrophysics Data System (ADS)

Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.

2016-12-01

Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.

Predicting future spatial distribution of SOC across entire France

NASA Astrophysics Data System (ADS)

Meersmans, Jeroen; Van Rompaey, Anton; Quine, Tim; Martin, Manuel; Pagé, Christian; Arrouays, Dominique

2013-04-01

Soil organic carbon (SOC) is widely recognized as a key factor controlling soil quality and as a crucial and active component of the global C-cycle. Hence, there exists a growing interest in monitoring and modeling the spatial and temporal behavior of this pool. So far, a large attempt has been made to map SOC at national scales for current and/or past situations. Despite some coarse predictions, detailed spatial SOC predictions for the future are still lacking. In this study we aim to predict future spatial evolution of SOC driven by climate and land use change for France up to the year 2100. Therefore, we combined 1) an existing model, predicting SOC as a function of soil type, climate, land use and management (Meersmans et al 2012), with 2) eight different IPCC spatial explicit climate change predictions (conducted by CERFACS) and 3) Land use change scenario predictions. We created business-as-usual land use change scenarios by extrapolating observed trends and calibrating logistic regression models, incorporating a large set of physical and socio-economic factors, at the regional level in combination with a multi-objective land allocation (MOLA) procedure. The resultant detailed projections of future SOC evolution across all regions of France, allow us to identify regions that are most likely to be characterized by a significant gain or loss of SOC and the degree to which land use decisions/outcomes control the scale of loss and gain. Therefore, this methodology and resulting maps can be considered as powerful tools to aid decision making concerning appropriate soil management, in order to enlarge SOC storage possibilities and reduce soil related CO2 fluxes.

Fast coarse-fine locating method for φ-OTDR.

PubMed

Mei, Xuanwei; Pang, Fufei; Liu, Huanhuan; Yu, Guoqin; Shao, Yuying; Qian, Tianyu; Mou, Chengbo; Lv, Longbao; Wang, Tingyun

2018-02-05

We proposed and demonstrated a coarse-fine method to achieve fast locating of external vibration for the phase-sensitive optical time-domain reflectometer (φ-OTDR) sensing system. Firstly, the acquired backscattered traces from heterodyne coherent φ-OTDR systems are spatially divided into a few segments along a sensing fiber for coarse locating, and most of the acquired data can be excluded by comparing the phase difference between the endpoints in adjacent segments. Secondly, the amplitude-based locating is implemented within the target segments for fine locating. By using the proposed coarse-fine locating method, we have numerically and experimentally investigated a distributed vibration sensor based on the heterodyne coherent φ-OTDR system with a 50-km-long sensing fiber. We find that the computation cost of signal processing for locating is significantly reduced in the long-haul sensing fiber, showing a potential application in real-time locating of external vibration.

Preparation of Entangled Polymer Melts of Various Architecture for Coarse-Grained Models

DTIC Science & Technology

2011-09-01

Simulator ( LAMMPS ). This report presents a theory overview and a manual how to use the method. 15. SUBJECT TERMS Ammunition, coarse-grained model...polymer builder, LAMMPS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 26 19a. NAME OF RESPONSIBLE PERSON...scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ). Gel is an in house written C program of coarse- grained polymer builder, and LAMMPS is

Coarse gaining of molecular crystals: limitations imposed by molecular flexibility

NASA Astrophysics Data System (ADS)

Picu, Catalin; Pal, Anirban

Molecular crystals include molecular electronics, energetic materials, pharmaceuticals and some food components. In many of these applications the small scale mechanical behavior of the crystal is important such as for example in energetic materials where detonation is induced by the formation of hot spots which are induced thermomechanically, and in pharmaceuticals where phase stability is critical for the biochemical activity of the drug. Accurate modeling of these processes requires resolving the atomistic scale details of the material. However, the cost of these models is very large due to the complexity of the molecules forming the crystal, and some form of coarse graning is necessary. In this study we identify the limitations imposed by the need to accurately capture molecular flexibility on the development of coarse grained models for the energetic molecular crystal RDX. We define guidelines for the definition of coarse grained models that target elastic and plastic crystal scale properties such as elastic constants, thermal expansion, compressibility, the critical stress for the motion of dislocations (Peierls stress) and the stacking fault energy This work was supported by the ARO through Grant W911NF-09-1-0330 and AFRL through Grant FA8651-16-1-0004.

Mid-21st century air quality at the urban scale under the influence of changed climate and emissions - case studies for Paris and Stockholm

NASA Astrophysics Data System (ADS)

Markakis, Konstantinos; Valari, Myrto; Engardt, Magnuz; Lacressonniere, Gwendoline; Vautard, Robert; Andersson, Camilla

2016-02-01

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modelled at 4 and 1 km horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine-resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional-scale emission projections by comparing modelled pollutant concentrations between the fine- and coarse-scale simulations over the study areas. We show that over urban areas with major regional contribution (e.g. the city of Stockholm) the bias related to coarse-scale projections may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modelling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily mean and maximum) is up to -5 % for Paris and -2 % for Stockholm city. The climate benefit on PM2.5 and PM10 in Paris is between -5 and -10 %, while for Stockholm we estimate mixed trends of up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily mean and maximum ozone and 20 % for PM. Through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily mean ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting from a health impact perspective.

Extended-Range High-Resolution Dynamical Downscaling over a Continental-Scale Domain

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

High-resolution mesoscale simulations, when applied for downscaling meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically downscale coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields resulting from the proposed downscaling strategy have significantly improved spatiotemporal variance compared to those from the operational forecasts, and any time series generated from the downscaled fields do not suffer from discontinuities due to switching between the consecutive forecasts.

Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany.

PubMed

Kauhl, B; Maier, W; Schweikart, J; Keste, A; Moskwyn, M

2018-01-10

Hypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost. To visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics. The sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45-64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations. Area deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.

Eco-evolutionary spatial dynamics in the Glanville fritillary butterfly.

PubMed

Hanski, Ilkka A

2011-08-30

Demographic population dynamics, gene flow, and local adaptation may influence each other and lead to coupling of ecological and evolutionary dynamics, especially in species inhabiting fragmented heterogeneous environments. Here, I review long-term research on eco-evolutionary spatial dynamics in the Glanville fritillary butterfly inhabiting a large network of approximately 4,000 meadows in Finland. The metapopulation persists in a balance between frequent local extinctions and recolonizations. The genetic spatial structure as defined by neutral markers is much more coarse-grained than the demographic spatial structure determined by the fragmented habitat, yet small-scale spatial structure has important consequences for the dynamics. I discuss three examples of eco-evolutionary spatial dynamics. (i) Extinction-colonization metapopulation dynamics influence allele frequency changes in the phosphoglucose isomerase (Pgi) gene, which leads to strong associations between genetic variation in Pgi and dispersal, recolonization, and local population dynamics. (ii) Inbreeding in local populations increases their risk for extinction, whereas reciprocal effects between inbreeding, population size, and emigration represent likely eco-evolutionary feedbacks. (iii) Genetically determined female oviposition preference for two host plant species exhibits a cline paralleling a gradient in host plant relative abundances, and host plant preference of dispersing females in relation to the host plant composition of habitat patches influences immigration (gene flow) and recolonization (founder events). Eco-evolutionary spatial dynamics in heterogeneous environments may not lead to directional evolutionary changes unless the environment itself changes, but eco-evolutionary dynamics may contribute to the maintenance of genetic variation attributable to fluctuating selection in space and time.

Numerical Upscaling of Solute Transport in Fractured Porous Media Based on Flow Aligned Blocks

NASA Astrophysics Data System (ADS)

Leube, P.; Nowak, W.; Sanchez-Vila, X.

2013-12-01

High-contrast or fractured-porous media (FPM) pose one of the largest unresolved challenges for simulating large hydrogeological systems. The high contrast in advective transport between fast conduits and low-permeability rock matrix, including complex mass transfer processes, leads to the typical complex characteristics of early bulk arrivals and long tailings. Adequate direct representation of FPM requires enormous numerical resolutions. For large scales, e.g. the catchment scale, and when allowing for uncertainty in the fracture network architecture or in matrix properties, computational costs quickly reach an intractable level. In such cases, multi-scale simulation techniques have become useful tools. They allow decreasing the complexity of models by aggregating and transferring their parameters to coarser scales and so drastically reduce the computational costs. However, these advantages come at a loss of detail and accuracy. In this work, we develop and test a new multi-scale or upscaled modeling approach based on block upscaling. The novelty is that individual blocks are defined by and aligned with the local flow coordinates. We choose a multi-rate mass transfer (MRMT) model to represent the remaining sub-block non-Fickian behavior within these blocks on the coarse scale. To make the scale transition simple and to save computational costs, we capture sub-block features by temporal moments (TM) of block-wise particle arrival times to be matched with the MRMT model. By predicting spatial mass distributions of injected tracers in a synthetic test scenario, our coarse-scale solution matches reasonably well with the corresponding fine-scale reference solution. For predicting higher TM-orders (such as arrival time and effective dispersion), the prediction accuracy steadily decreases. This is compensated to some extent by the MRMT model. If the MRMT model becomes too complex, it loses its effect. We also found that prediction accuracy is sensitive to the choice of the effective dispersion coefficients and on the block resolution. A key advantage of the flow-aligned blocks is that the small-scale velocity field is reproduced quite accurately on the block-scale through their flow alignment. Thus, the block-scale transverse dispersivities remain in the similar magnitude as local ones, and they do not have to represent macroscopic uncertainty. Also, the flow-aligned blocks minimize numerical dispersion when solving the large-scale transport problem.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Drawing from Memory: Hand-Eye Coordination at Multiple Scales

PubMed Central

Spivey, Michael J.

2013-01-01

Eyes move to gather visual information for the purpose of guiding behavior. This guidance takes the form of perceptual-motor interactions on short timescales for behaviors like locomotion and hand-eye coordination. More complex behaviors require perceptual-motor interactions on longer timescales mediated by memory, such as navigation, or designing and building artifacts. In the present study, the task of sketching images of natural scenes from memory was used to examine and compare perceptual-motor interactions on shorter and longer timescales. Eye and pen trajectories were found to be coordinated in time on shorter timescales during drawing, and also on longer timescales spanning study and drawing periods. The latter type of coordination was found by developing a purely spatial analysis that yielded measures of similarity between images, eye trajectories, and pen trajectories. These results challenge the notion that coordination only unfolds on short timescales. Rather, the task of drawing from memory evokes perceptual-motor encodings of visual images that preserve coarse-grained spatial information over relatively long timescales as well. PMID:23554894

Application of GRACE for Monitoring Groundwater in Data Scarce Regions

NASA Technical Reports Server (NTRS)

Rodell, Matt; Li, Bailing; Famiglietti, Jay; Zaitchik, Ben

2012-01-01

In the United States, groundwater storage is somewhat well monitored (spatial and temporal data gaps notwithstanding) and abundant data are freely and easily accessible. Outside of the U.S., groundwater often is not monitored systematically and where it is the data are rarely centralized and made available. Since 2002 the Gravity Recovery and Climate Experiment (GRACE) satellite mission has delivered gravity field observations which have been used to infer variations in total terrestrial water storage, including groundwater, at regional to continental scales. Challenges to using GRACE for groundwater monitoring include its relatively coarse spatial and temporal resolutions, its inability to differentiate groundwater from other types of water on and under the land surface, and typical 2-3 month data latency. Data assimilation can be used to overcome these challenges, but uncertainty in the results remains and is difficult to quantify without independent observations. Nevertheless, the results are preferable to the alternative - no data at all- and GRACE has already revealed groundwater variability and trends in regions where only anecdotal evidence existed previously.

Spatial Transport of Magnetic Flux Surfaces in Strongly Anisotropic Turbulence

NASA Astrophysics Data System (ADS)

Matthaeus, W. H.; Servidio, S.; Wan, M.; Ruffolo, D. J.; Rappazzo, A. F.; Oughton, S.

2013-12-01

Magnetic flux surfaces afford familiar descriptions of spatial structure, dynamics, and connectivity of magnetic fields, with particular relevance in contexts such as solar coronal flux tubes, magnetic field connectivity in the interplanetary and interstellar medium, as well as in laboratory plasmas and dynamo problems [1-4]. Typical models assume that field-lines are orderly, and flux tubes remain identifiable over macroscopic distances; however, a previous study has shown that flux tubes shred in the presence of fluctuations, typically losing identity after several correlation scales [5]. Here, the structure of magnetic flux surfaces is numerically investigated in a reduced magnetohydrodynamic (RMHD) model of homogeneous turbulence. Short and long-wavelength behavior is studied statistically by propagating magnetic surfaces along the mean field. At small scales magnetic surfaces become complex, experiencing an exponential thinning. At large scales, instead, the magnetic flux undergoes a diffusive behavior. The link between the diffusion of the coarse-grained flux and field-line random walk is established by means of a multiple scale analysis. Both large and small scales limits are controlled by the Kubo number. These results have consequences for understanding and interpreting processes such as magnetic reconnection and field-line diffusion in plasmas [6]. [1] E. N. Parker, Cosmical Magnetic Fields (Oxford Univ. Press, New York, 1979). [2] J. R. Jokipii and E. N. Parker, Phys. Rev. Lett. 21, 44 (1968). [3] R. Bruno et al., Planet. Space Sci. 49, 1201 (2001). [4] M. N. Rosenbluth et al., Nuclear Fusion 6, 297 (1966). [5] W. H. Matthaeus et al., Phys. Rev. Lett. 75, 2136 (1995). [6] S. Servidio et al., submitted (2013).

Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

NASA Astrophysics Data System (ADS)

Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.

2017-10-01

Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS estimates for improved data assimilation results.

Modeling nutrient in-stream processes at the watershed scale using Nutrient Spiralling metrics

NASA Astrophysics Data System (ADS)

Marcé, R.; Armengol, J.

2009-01-01

One of the fundamental problems of using large-scale biogeochemical models is the uncertainty involved in aggregating the components of fine-scale deterministic models in watershed applications, and in extrapolating the results of field-scale measurements to larger spatial scales. Although spatial or temporal lumping may reduce the problem, information obtained during fine-scale research may not apply to lumped categories. Thus, the use of knowledge gained through fine-scale studies to predict coarse-scale phenomena is not straightforward. In this study, we used the nutrient uptake metrics defined in the Nutrient Spiralling concept to formulate the equations governing total phosphorus in-stream fate in a watershed-scale biogeochemical model. The rationale of this approach relies on the fact that the working unit for the nutrient in-stream processes of most watershed-scale models is the reach, the same unit used in field research based on the Nutrient Spiralling concept. Automatic calibration of the model using data from the study watershed confirmed that the Nutrient Spiralling formulation is a convenient simplification of the biogeochemical transformations involved in total phosphorus in-stream fate. Following calibration, the model was used as a heuristic tool in two ways. First, we compared the Nutrient Spiralling metrics obtained during calibration with results obtained during field-based research in the study watershed. The simulated and measured metrics were similar, suggesting that information collected at the reach scale during research based on the Nutrient Spiralling concept can be directly incorporated into models, without the problems associated with upscaling results from fine-scale studies. Second, we used results from our model to examine some patterns observed in several reports on Nutrient Spiralling metrics measured in impaired streams. Although these two exercises involve circular reasoning and, consequently, cannot validate any hypothesis, this is a powerful example of how models can work as heuristic tools to compare hypotheses and stimulate research in ecology.

Coarse-graining errors and numerical optimization using a relative entropy framework

NASA Astrophysics Data System (ADS)

Chaimovich, Aviel; Shell, M. Scott

2011-03-01

The ability to generate accurate coarse-grained models from reference fully atomic (or otherwise "first-principles") ones has become an important component in modeling the behavior of complex molecular systems with large length and time scales. We recently proposed a novel coarse-graining approach based upon variational minimization of a configuration-space functional called the relative entropy, Srel, that measures the information lost upon coarse-graining. Here, we develop a broad theoretical framework for this methodology and numerical strategies for its use in practical coarse-graining settings. In particular, we show that the relative entropy offers tight control over the errors due to coarse-graining in arbitrary microscopic properties, and suggests a systematic approach to reducing them. We also describe fundamental connections between this optimization methodology and other coarse-graining strategies like inverse Monte Carlo, force matching, energy matching, and variational mean-field theory. We suggest several new numerical approaches to its minimization that provide new coarse-graining strategies. Finally, we demonstrate the application of these theoretical considerations and algorithms to a simple, instructive system and characterize convergence and errors within the relative entropy framework.

Distributed Soil Moisture Estimation in a Mountainous Semiarid Basin: Constraining Soil Parameter Uncertainty through Field Studies

NASA Astrophysics Data System (ADS)

Yatheendradas, S.; Vivoni, E.

2007-12-01

A common practice in distributed hydrological modeling is to assign soil hydraulic properties based on coarse textural datasets. For semiarid regions with poor soil information, the performance of a model can be severely constrained due to the high model sensitivity to near-surface soil characteristics. Neglecting the uncertainty in soil hydraulic properties, their spatial variation and their naturally-occurring horizonation can potentially affect the modeled hydrological response. In this study, we investigate such effects using the TIN-based Real-time Integrated Basin Simulator (tRIBS) applied to the mid-sized (100 km2) Sierra Los Locos watershed in northern Sonora, Mexico. The Sierra Los Locos basin is characterized by complex mountainous terrain leading to topographic organization of soil characteristics and ecosystem distributions. We focus on simulations during the 2004 North American Monsoon Experiment (NAME) when intensive soil moisture measurements and aircraft- based soil moisture retrievals are available in the basin. Our experiments focus on soil moisture comparisons at the point, topographic transect and basin scales using a range of different soil characterizations. We compare the distributed soil moisture estimates obtained using (1) a deterministic simulation based on soil texture from coarse soil maps, (2) a set of ensemble simulations that capture soil parameter uncertainty and their spatial distribution, and (3) a set of simulations that conditions the ensemble on recent soil profile measurements. Uncertainties considered in near-surface soil characterization provide insights into their influence on the modeled uncertainty, into the value of soil profile observations, and into effective use of on-going field observations for constraining the soil moisture response uncertainty.

A Multi-Scale, Integrated Approach to Representing Watershed Systems

NASA Astrophysics Data System (ADS)

Ivanov, Valeriy; Kim, Jongho; Fatichi, Simone; Katopodes, Nikolaos

2014-05-01

Understanding and predicting process dynamics across a range of scales are fundamental challenges for basic hydrologic research and practical applications. This is particularly true when larger-spatial-scale processes, such as surface-subsurface flow and precipitation, need to be translated to fine space-time scale dynamics of processes, such as channel hydraulics and sediment transport, that are often of primary interest. Inferring characteristics of fine-scale processes from uncertain coarse-scale climate projection information poses additional challenges. We have developed an integrated model simulating hydrological processes, flow dynamics, erosion, and sediment transport, tRIBS+VEGGIE-FEaST. The model targets to take the advantage of the current generation of wealth of data representing watershed topography, vegetation, soil, and landuse, as well as to explore the hydrological effects of physical factors and their feedback mechanisms over a range of scales. We illustrate how the modeling system connects precipitation-hydrologic runoff partition process to the dynamics of flow, erosion, and sedimentation, and how the soil's substrate condition can impact the latter processes, resulting in a non-unique response. We further illustrate an approach to using downscaled climate change information with a process-based model to infer the moments of hydrologic variables in future climate conditions and explore the impact of climate information uncertainty.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle.

PubMed

Ferrari, Renata; Marzinelli, Ezequiel M; Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F; Byrne, Maria; Malcolm, Hamish A; Williams, Stefan B; Steinberg, Peter D

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate 'no-take' and 'general-use' (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5-10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle

PubMed Central

Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F.; Byrne, Maria; Malcolm, Hamish A.; Williams, Stefan B.; Steinberg, Peter D.

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate ‘no-take’ and ‘general-use’ (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5–10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales. PMID:29547656

Mid-21st century air quality at the urban scale under the influence of changed climate and emissions: case studies for Paris and Stockholm

NASA Astrophysics Data System (ADS)

Markakis, K.; Valari, M.; Engardt, M.; Lacressonnière, G.; Vautard, R.; Andersson, C.

2015-10-01

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 \\unit{km} horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5 % for Paris and -2 % for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5 % while for Stockholm we observe mixed trends up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily average and maximum ozone and 20 % for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective.

Regional climate model sensitivity to domain size

NASA Astrophysics Data System (ADS)

Leduc, Martin; Laprise, René

2009-05-01

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.

Drivers of small scale variability in soil-atmosphere fluxes of CH4, N2O and CO2 in a forest soil

NASA Astrophysics Data System (ADS)

Maier, Martin; Nicolai, Clara; Wheeler, Denis; Lang, Friedeike; Paulus, Sinikka

2016-04-01

Soil-atmosphere fluxes of CH4, N2O and CO2 can vary on different spatial scales, on large scales between ecosystems but also within apparently homogenous sites. While CO2 and CH4 consumption is rather evenly distibuted in well aerated soils, the production of N2O and CH4 seems to occur at hot spots that can be associated with anoxic or suboxic conditions. Small-scale variability in soil properties is well-known from field soil assesment, affecting also soil aeration and thus theoretically, greenhouse gas fluxes. In many cases different plant species are associated with different soil conditions and vegetation mapping should therefor combined with soil mapping. Our research objective was explaining the small scale variability of greenhouse gas fluxes in an apparently homogeneous 50 years old Scots Pine stand in a former riparian flood plain.We combined greenhouse gas measurements and soil physical lab measurments with field soil assessment and vegetation mapping. Measurements were conducted with at 60 points at a plot of 30 X 30 m at the Hartheim monitoring site (SW Germany). For greenhouse gas measurements a non-steady state chamber system and laser analyser, and a photoacoustic analyser were used. Our study shows that the well aerated site was a substantial sink for atmospheric CH4 (-2.4 nmol/m² s) and also a for N2O (-0.4 nmol/m² s), but less pronounced, whereas CO2 production was a magnitude larger (2.6 μmol/m² s). The spatial variability of the CH4 consumption of the soils could be explained by the variability of the soil gas diffusivity (measured in situ + using soil cores). Deviations of this clear trend were only observed at points where decomposing woody debris was directly under the litter layer. Soil texture ranged from gravel, coarse sand, fine sand to pure silt, with coarser texture having higher soil gas diffusivity. Changes in texture were rather abrupt at some positions or gradual at other positions, and were well reflected in the vegetation structure. On patches of gravel and coarse sand there was hardly any ground vegatation, and a shrublayer was found only at silty patches Our results indicate that a stratification and regionalisation approach based on vegetation structure and soil texture represents a promising tool for the adjustment of sampling designs for soil gas flux measurement. Acknowledgement This research was financially supported by the project DFG (MA 5826/2-1).

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.

PubMed

Michael, Edwin; Singh, Brajendra K; Mayala, Benjamin K; Smith, Morgan E; Hampton, Scott; Nabrzyski, Jaroslaw

2017-09-27

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries. We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Human activity spaces and plague risks in three contrasting landscapes in Lushoto District, Tanzania.

PubMed

Hieronimo, Proches; Gulinck, Hubert; Kimaro, Didas N; Mulungu, Loth S; Kihupi, Nganga I; Msanya, Balthazar M; Leirs, Herwig; Deckers, Jozef A

2014-07-01

Since 1980 plague has been a human threat in the Western Usambara Mountains in Tanzania. However, the spatial-temporal pattern of plague occurrence remains poorly understood. The main objective of this study was to gain understanding of human activity patterns in relation to spatial distribution of fleas in Lushoto District. Data were collected in three landscapes differing in plague incidence. Field survey coupled with Geographic Information System (GIS) and physical sample collections were used to collect data in wet (April to June 2012) and dry (August to October 2012) seasons. Data analysis was done using GIS, one-way ANOVA and nonparametric statistical tools. The degree of spatial co-occurrence of potential disease vectors (fleas) and humans in Lushoto focus differs significantly (p ≤ 0.05) among the selected landscapes, and in both seasons. This trend gives a coarse indication of the possible association of the plague outbreaks and the human frequencies of contacting environments with fleas. The study suggests that plague surveillance and control programmes at landscape scale should consider the existence of plague vector contagion risk gradient from high to low incidence landscapes due to human presence and intensity of activities.

Towards a more consistent picture of isopycnal mixing in climate models

NASA Astrophysics Data System (ADS)

Gnanadesikan, A.; Pradal, M. A. S.; Koszalka, I.; Abernathey, R. P.

2014-12-01

The stirring of tracers by mesoscale eddies along isopycnal surfaces is often represented in coarse-resolution models by the Redi diffusion parameter ARedi. Theoretical treatments of ARedi often assume it should scale as the eddy energy or the growth rate of mesoscale eddies,. producing a picture where it is high in boundary currents and low )of order a few hundred m2/s) in the gyre interiors. However, observational estimates suggest that ARedi should be very large (of order thousands of m2/s) in the gyre interior. We present results of recent simulations comparing a range of spatially constant values ARedi (with values of 400, 800, 1200 and 2400 m2/s) to a spatially resolved estimate based on altimetry and a zonally averaged version of the same estimate. In general, increasing the ARedi coefficient destratifies and warms the high latitudes. Relative to our control simulation, the spatially dependent coefficient is lower in the Southern Ocean, but high in the North Pacific, and so the temperature changes mirror this. We also examine the response of ocean hypoxia to these changes. In general, the zonally averaged version of the altimetry-based estimate of ARedi does not capture the full 2d representation.

Coarse-grained molecular dynamics simulations for giant protein-DNA complexes

NASA Astrophysics Data System (ADS)

Takada, Shoji

Biomolecules are highly hierarchic and intrinsically flexible. Thus, computational modeling calls for multi-scale methodologies. We have been developing a coarse-grained biomolecular model where on-average 10-20 atoms are grouped into one coarse-grained (CG) particle. Interactions among CG particles are tuned based on atomistic interactions and the fluctuation matching algorithm. CG molecular dynamics methods enable us to simulate much longer time scale motions of much larger molecular systems than fully atomistic models. After broad sampling of structures with CG models, we can easily reconstruct atomistic models, from which one can continue conventional molecular dynamics simulations if desired. Here, we describe our CG modeling methodology for protein-DNA complexes, together with various biological applications, such as the DNA duplication initiation complex, model chromatins, and transcription factor dynamics on chromatin-like environment.

Investigating the influence of lithologic heterogeneity on gas hydrate formation and methane recycling at the base of the gas hydrate stability zone in channelized systems

NASA Astrophysics Data System (ADS)

Daigle, H.; Nole, M.; Cook, A.; Malinverno, A.

2017-12-01

In marine environments, gas hydrate preferentially accumulates in coarse-grained sediments. At the meso- to micro-scale, however, hydrate distribution in these coarse-grained units is often heterogeneous. We employ a methane hydrate reservoir simulator coupling heat and mass transfer as well as capillary effects to investigate how capillary controls on methane solubility affect gas and hydrate accumulations in reservoirs characterized by graded bedding and alternating sequences of coarse-grained sands and fine-grained silt and clay. Simulations bury a channelized reservoir unit encased in homogeneous, fine-grained material characterized by small pores (150 nm) and low permeability ( 1 md in the absence of hydrate). Pore sizes within each reservoir bed between vary between coarse sand and fine silt. Sands have a median pore size of 35 microns and a lognormal pore size distribution. We also investigate how the amount of labile organic carbon (LOC) affects hydrate growth due to microbial methanogenesis within the sediments. In a diffusion-dominated system, methane movies into reservoir layers along spatial gradients in dissolved methane concentration. Hydrate grows in such a way as to minimize these concentration gradients by accumulating slower in finer-grained reservoir layers and faster in coarser-grained layers. Channelized, fining-upwards sediment bodies accumulate hydrate first along their outer surfaces and thence inward from top to bottom. If LOC is present in thin beds within the channel, higher saturations of hydrate will be distributed more homogeneously throughout the unit. When buried beneath the GHSZ, gas recycling can occur only if enough hydrate is present to form a connected gas phase upon dissociation. Simulations indicate that this is difficult to achieve for diffusion-dominated systems, especially those with thick GHSZs and/or small amounts of LOC. However, capillary-driven fracturing behavior may be more prevalent in settings with thick GHSZs.

Investigating the influence of lithologic heterogeneity on gas hydrate formation and methane recycling at the base of the gas hydrate stability zone in channelized systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daigle, Hugh; Nole, Michael; Cook, Ann

In marine environments, gas hydrate preferentially accumulates in coarse-grained sediments. At the meso- to micro-scale, however, hydrate distribution in these coarse-grained units is often heterogeneous. We employ a methane hydrate reservoir simulator coupling heat and mass transfer as well as capillary effects to investigate how capillary controls on methane solubility affect gas and hydrate accumulations in reservoirs characterized by graded bedding and alternating sequences of coarse-grained sands and fine-grained silt and clay. Simulations bury a channelized reservoir unit encased in homogeneous, fine-grained material characterized by small pores (150 nm) and low permeability (~1 md in the absence of hydrate). Poremore » sizes within each reservoir bed between vary between coarse sand and fine silt. Sands have a median pore size of 35 microns and a lognormal pore size distribution. We also investigate how the amount of labile organic carbon (LOC) affects hydrate growth due to microbial methanogenesis within the sediments. In a diffusion-dominated system, methane movies into reservoir layers along spatial gradients in dissolved methane concentration. Hydrate grows in such a way as to minimize these concentration gradients by accumulating slower in finer-grained reservoir layers and faster in coarser-grained layers. Channelized, fining-upwards sediment bodies accumulate hydrate first along their outer surfaces and thence inward from top to bottom. If LOC is present in thin beds within the channel, higher saturations of hydrate will be distributed more homogeneously throughout the unit. When buried beneath the GHSZ, gas recycling can occur only if enough hydrate is present to form a connected gas phase upon dissociation. Simulations indicate that this is difficult to achieve for diffusion-dominated systems, especially those with thick GHSZs and/or small amounts of LOC. However, capillary-driven fracturing behavior may be more prevalent in settings with thick GHSZs.« less

Effect of spatial resolution on remote sensing estimation of total evaporation in the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Shoko, Cletah; Clark, David; Mengistu, Michael; Dube, Timothy; Bulcock, Hartley

2015-01-01

This study evaluated the effect of two readily available multispectral sensors: the newly launched 30 m spatial resolution Landsat 8 and the long-serving 1000 m moderate resolution imaging spectroradiometer (MODIS) datasets in the spatial representation of total evaporation in the heterogeneous uMngeni catchment, South Africa, using the surface energy balance system model. The results showed that sensor spatial resolution plays a critical role in the accurate estimation of energy fluxes and total evaporation across a heterogeneous catchment. Landsat 8 estimates showed better spatial representation of the biophysical parameters and total evaporation for different land cover types, due to the relatively higher spatial resolution compared to the coarse spatial resolution MODIS sensor. Moreover, MODIS failed to capture the spatial variations of total evaporation estimates across the catchment. Analysis of variance (ANOVA) results showed that MODIS-based total evaporation estimates did not show any significant differences across different land cover types (one-way ANOVA; F1.924=1.412, p=0.186). However, Landsat 8 images yielded significantly different estimates between different land cover types (one-way ANOVA; F1.993=5.185, p<0.001). The validation results showed that Landsat 8 estimates were more comparable to eddy covariance (EC) measurements than the MODIS-based total evaporation estimates. EC measurement on May 23, 2013, was 3.8 mm/day, whereas the Landsat 8 estimate on the same day was 3.6 mm/day, with MODIS showing significantly lower estimates of 2.3 mm/day. The findings of this study underscore the importance of spatial resolution in estimating spatial variations of total evaporation at the catchment scale, thus, they provide critical information on the relevance of the readily available remote sensing products in water resources management in data-scarce environments.

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

NASA Astrophysics Data System (ADS)

Mehran, Ali; Clark, Elizabeth A.; Lettenmaier, Dennis P.

2017-11-01

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

Modelling spatial patterns of urban growth in Africa

PubMed Central

Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius

2013-01-01

The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

NASA Astrophysics Data System (ADS)

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-05-01

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructs high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss-Lobatto-Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Kai; Fu, Shubin; Chung, Eric T.

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

DOE PAGES

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-02-04

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

Detection and extraction of orientation-and-scale-dependent information from two-dimensional GPR data with tuneable directional wavelet filters

NASA Astrophysics Data System (ADS)

Tzanis, Andreas

2013-02-01

The Ground Probing Radar (GPR) is a valuable tool for near surface geological, geotechnical, engineering, environmental, archaeological and other work. GPR images of the subsurface frequently contain geometric information (constant or variable-dip reflections) from various structures such as bedding, cracks, fractures, etc. Such features are frequently the target of the survey; however, they are usually not good reflectors and they are highly localized in time and in space. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is very sensitive to broadband noise from buried small objects, electromagnetic anthropogenic activity and systemic factors, which frequently blurs the reflections from such targets. This paper introduces a method to de-noise GPR data and extract geometric information from scale-and-dip dependent structural features, based on one-dimensional B-Spline Wavelets, two-dimensional directional B-Spline Wavelet (BSW) Filters and two-dimensional Gabor Filters. A directional BSW Filter is built by sidewise arranging s identical one-dimensional wavelets of length L, tapering the s-parallel direction (span) with a suitable window function and rotating the resulting matrix to the desired orientation. The length L of the wavelet defines the temporal and spatial scale to be isolated and the span determines the length over which to smooth (spatial resolution). The Gabor Filter is generated by multiplying an elliptical Gaussian by a complex plane wave; at any orientation the temporal or spatial scale(s) to be isolated are determined by the wavelength. λ of the plane wave and the spatial resolution by the spatial aspect ratio γ, which specifies the ellipticity of the support of the Gabor function. At any orientation, both types of filter may be tuned at any frequency or spatial wavenumber by varying the length or the wavelength respectively. The filters can be applied directly to two-dimensional radargrams, in which case they abstract information about given scales at given orientations. Alternatively, they can be rotated to different orientations under adaptive control, so that they remain tuned at a given frequency or wavenumber and the resulting images can be stacked in the LS sense, so as to obtain a complete representation of the input data at a given temporal or spatial scale. In addition to isolating geometrical information for further scrutiny, the proposed filtering methods can be used to enhance the S/N ratio in a manner particularly suitable for GPR data, because the frequency response of the filters mimics the frequency characteristics of the source wavelet. Finally, signal attenuation and temporal localization are closely associated: low attenuation interfaces tend to produce reflections rich in high frequencies and fine-scale localization as a function of time. Conversely, high attenuation interfaces will produce reflections rich in low frequencies and broad localization. Accordingly, the temporal localization characteristics of the filters may be exploited to investigate the characteristics of signal propagation (hence material properties). The method is shown to be very effective in extracting fine to coarse scale information from noisy data and is demonstrated with applications to noisy GPR data from archaeometric and geotechnical surveys.

Pine invasions in treeless environments: dispersal overruns microsite heterogeneity.

PubMed

Pauchard, Aníbal; Escudero, Adrián; García, Rafael A; de la Cruz, Marcelino; Langdon, Bárbara; Cavieres, Lohengrin A; Esquivel, Jocelyn

2016-01-01

Understanding biological invasions patterns and mechanisms is highly needed for forecasting and managing these processes and their negative impacts. At small scales, ecological processes driving plant invasions are expected to produce a spatially explicit pattern driven by propagule pressure and local ground heterogeneity. Our aim was to determine the interplay between the intensity of seed rain, using distance to a mature plantation as a proxy, and microsite heterogeneity in the spreading of Pinus contorta in the treeless Patagonian steppe. Three one-hectare plots were located under different degrees of P. contorta invasion (Coyhaique Alto, 45° 30'S and 71° 42'W). We fitted three types of inhomogeneous Poisson models to each pine plot in an attempt for describing the observed pattern as accurately as possible: the "dispersal" models, "local ground heterogeneity" models, and "combined" models, using both types of covariates. To include the temporal axis in the invasion process, we analyzed both the pattern of young and old recruits and also of all recruits together. As hypothesized, the spatial patterns of recruited pines showed coarse scale heterogeneity. Early pine invasion spatial patterns in our Patagonian steppe site is not different from expectations of inhomogeneous Poisson processes taking into consideration a linear and negative dependency of pine recruit intensity on the distance to afforestations. Models including ground-cover predictors were able to describe the point pattern process only in a couple of cases but never better than dispersal models. This finding concurs with the idea that early invasions depend more on seed pressure than on the biotic and abiotic relationships seed and seedlings establish at the microsite scale. Our results show that without a timely and active management, P. contorta will invade the Patagonian steppe independently of the local ground-cover conditions.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.

PubMed

Hanigan, Ivan C; Williamson, Grant J; Knibbs, Luke D; Horsley, Joshua; Rolfe, Margaret I; Cope, Martin; Barnett, Adrian G; Cowie, Christine T; Heyworth, Jane S; Serre, Marc L; Jalaludin, Bin; Morgan, Geoffrey G

2017-11-07

Exposure to traffic related nitrogen dioxide (NO 2 ) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO 2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO 2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO 2 . The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

Mapping and predictive variations of soil bacterial richness across France

PubMed Central

Dequietd, Samuel; Saby, Nicolas P. A.; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition. PMID:29059218

Mapping and predictive variations of soil bacterial richness across France.

PubMed

Terrat, Sébastien; Horrigue, Walid; Dequiedt, Samuel; Saby, Nicolas P A; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

Rainbow trout movement behavior and habitat occupancy are influenced by sex and Pacific salmon presence in an Alaska river system

USGS Publications Warehouse

Fraley, Kevin M.; Falke, Jeffrey A.; McPhee, Megan V.; Prakash, Anupma

2018-01-01

We used spatially continuous field-measured and remotely-sensed aquatic habitat characteristics paired with weekly ground-based telemetry tracking and snorkel surveys to describe movements and habitat occupancy of adult rainbow trout (N = 82) in a runoff-fed, salmon-influenced southcentral Alaska river system. We found that during the ice-free feeding season (June through September) rainbow trout occurrence was associated more with fine-scale (channel unit) characteristics relative to coarse-scale (stream reach) variables. The presence of Pacific salmon (which provide an important seasonal food subsidy), and habitat size were particularly useful predictors. Weekly movement distance differed between pre- and post- spawning salmon arrival, but did not vary by sex. Habitat quality, season, and the arrival of spawning salmon influenced the likelihood of rainbow trout movement, and fish moved farther to seek out higher quality habitats. Because rainbow trout respond to habitat factors at multiple scales and seek out salmon-derived subsidies, it will be important to take a multiscale approach in protecting trout and salmon populations and managing the associated fisheries.

Spatial Patterns of Road-Induced Backwater Sediment Storage Across A Rural to Urban Gradient

NASA Astrophysics Data System (ADS)

Copeland, M.; Bain, D.

2017-12-01

Road networks dominate many landscapes and often interact with stream networks to alter basin sediment dynamics. Currently, conceptual models of catchment-scale sediment fluxes remain at a coarse scale (i.e., the entire catchment) and are unable to resolve important human-driven sediment storage processes. The spatio-temporal complexity of the interactions between road networks and streams has made it challenging to infer the fine-scale impacts of road crossings on fluvial systems. Here, road crossings in multiple drainage networks and the associated backwater sediment accumulations are examined along a rural to urban gradient around Pittsburgh, PA. Preliminary results indicate that upstream drainage area, channel slope, and human activities control stream crossing type and therefore drive associated sediment accumulation, particularly in urban headwater channels. The data indicate that the combination of land use intensity and infrastructure age influences the volume of sediment trapped in road-induced backwaters. Clarification of the coupled human, road-building, and natural stream adjustments will allow for more effective treatments of fluvial impacts, such as the "urban stream syndrome."

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

Coarse-Grained Models for Protein-Cell Membrane Interactions

PubMed Central

Bradley, Ryan; Radhakrishnan, Ravi

2015-01-01

The physiological properties of biological soft matter are the product of collective interactions, which span many time and length scales. Recent computational modeling efforts have helped illuminate experiments that characterize the ways in which proteins modulate membrane physics. Linking these models across time and length scales in a multiscale model explains how atomistic information propagates to larger scales. This paper reviews continuum modeling and coarse-grained molecular dynamics methods, which connect atomistic simulations and single-molecule experiments with the observed microscopic or mesoscale properties of soft-matter systems essential to our understanding of cells, particularly those involved in sculpting and remodeling cell membranes. PMID:26613047

Coarse-graining errors and numerical optimization using a relative entropy framework.

PubMed

Chaimovich, Aviel; Shell, M Scott

2011-03-07

The ability to generate accurate coarse-grained models from reference fully atomic (or otherwise "first-principles") ones has become an important component in modeling the behavior of complex molecular systems with large length and time scales. We recently proposed a novel coarse-graining approach based upon variational minimization of a configuration-space functional called the relative entropy, S(rel), that measures the information lost upon coarse-graining. Here, we develop a broad theoretical framework for this methodology and numerical strategies for its use in practical coarse-graining settings. In particular, we show that the relative entropy offers tight control over the errors due to coarse-graining in arbitrary microscopic properties, and suggests a systematic approach to reducing them. We also describe fundamental connections between this optimization methodology and other coarse-graining strategies like inverse Monte Carlo, force matching, energy matching, and variational mean-field theory. We suggest several new numerical approaches to its minimization that provide new coarse-graining strategies. Finally, we demonstrate the application of these theoretical considerations and algorithms to a simple, instructive system and characterize convergence and errors within the relative entropy framework. © 2011 American Institute of Physics.

Spatial and temporal dynamics of commercial reef-fish fisheries on the West Florida Shelf: Understanding drivers of fleet behavior and the implications for future management

NASA Astrophysics Data System (ADS)

Cockrell, M.; Murawski, S. A.; Sanchirico, J. N.; O'Farrell, S.; Strelcheck, A.

2016-02-01

Spatial and temporal patterns of fishing activity have historically been described over relatively coarse scales or with limited datasets. However, new and innovative approaches for fisheries management will require an understanding of both species population dynamics and fleet behavior at finer spatial and temporal resolution. In this study we describe the spatial and temporal patterns of commercial reef-fish fisheries on the West Florida Shelf (WFS) from 2006-14, using a combination of on-board observer, catch logbook, and vessel satellite tracking data. The satellite tracking data is both high resolution (ie, records from each vessel at least once every hour for the duration of a trip), and required of all federally-permitted reef fish vessels in the Gulf of Mexico, making this a uniquely rich and powerful dataset. Along with spatial and temporal fishery dynamics, we quantified concomitant patterns in fishery economics and catch metrics, such as total landings and catch composition. Fishery patterns were correlated to a number of variables across the vessel, trip, and whole fleet scales, including vessel size, distance from home port, number of days at sea, and days available to fish. Notably, changes in management structure during the years examined (eg, establishment of a seasonal closed area in 2009 and implementation of an individual fishing quota system for Grouper-Tilefish in 2010), as well as emergency spatial closures during the Deepwater Horizon oil spill in 2010, enabled us to examine the impacts of specific management frameworks on the WFS reef-fish fishery. This research highlights the need to better understand the biological, economic, and social impacts within fisheries when managing for conservation and fisheries sustainability. We discuss our results in the context of a changing policy and management landscape for marine and coastal resources in the Gulf of Mexico.

Natural habitats matter: Determinants of spatial pattern in the composition of animal assemblages of the Czech Republic

NASA Astrophysics Data System (ADS)

Divíšek, Jan; Zelený, David; Culek, Martin; Št'astný, Karel

2014-08-01

Studies that explore species-environment relationships at a broad scale are usually limited by the availability of sufficient habitat description, which is often too coarse to differentiate natural habitat patches. Therefore, it is not well understood how the distribution of natural habitats affects broad-scale patterns in the distribution of animal species. In this study, we evaluate the role of field-mapped natural habitats, land-cover types derived from remote sensing and climate on the composition of assemblages of five distinct animal groups, namely non-volant mammals, birds, reptiles, amphibians and butterflies native to the Czech Republic. First, we used variation partitioning based on redundancy analysis to evaluate the extent to which the environmental variables and their spatial structure might underlie the observed spatial patterns in the composition of animal assemblages. Second, we partitioned variations explained by climate, natural habitats and land-cover to compare their relative importance. Finally, we tested the independent effects of each variable in order to evaluate the significance of their contributions to the environmental model. Our results showed that spatial patterns in the composition of assemblages of almost all the considered animal groups may be ascribed mostly to variations in the environment. Although the shared effects of climatic variables, natural habitats and land-cover types explained the largest proportion of variation in each animal group, the variation explained purely by natural habitats was always higher than the variation explained purely by climate or land-cover. We conclude that most spatial variation in the composition of assemblages of almost all animal groups probably arises from biological processes operating within a spatially structured environment and suggest that natural habitats are important to explain observed patterns because they often perform better than habitat descriptions based on remote sensing. This underlines the value of using appropriate habitat data, for which high-resolution and large-area field-mapping projects are necessary.

Coarse-grained Simulations of Conformational Changes in Multidrug Resistance Transporters

NASA Astrophysics Data System (ADS)

Jewel, S. M. Yead; Dutta, Prashanta; Liu, Jin

2016-11-01

The overexpression of multidrug resistance (MDR) systems on the gram negative bacteria causes serious problems for treatment of bacterial infectious diseases. The system effectively pumps the antibiotic drugs out of the bacterial cells. During the pumping process one of the MDR components, AcrB undergoes a series of large-scale conformational changes which are responsible for drug recognition, binding and expelling. All-atom simulations are unable to capture those conformational changes because of computational cost. Here, we implement a hybrid coarse-grained force field that couples the united-atom protein models with the coarse-grained MARTINI water/lipid, to investigate the proton-dependent conformational changes of AcrB. The simulation results in early stage ( 100 ns) of proton-dependent conformational changes agree with all-atom simulations, validating the coarse-grained model. The coarse-grained force field allows us to explore the process in microsecond simulations. Starting from the crystal structures of Access(A)/Binding(B)/Extrusion(E) monomers in AcrB, we find that deprotonation of Asp407 and Asp408 in monomer E causes a series of large-scale conformational changes from ABE to AAA in absence of drug molecules, which is consistent with experimental findings. This work is supported by NIH Grant: 1R01GM122081-01.

SST Variation Due to Interactive Convective-Radiative Processes

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Shie, C.-L.; Johnson, D.; Simpson, J.; Li, X.; Sui, C.-H.

2000-01-01

The recent linking of Cloud-Resolving Models (CRMs) to Ocean-Mixed Layer (OML) models has provided a powerful new means of quantifying the role of cloud systems in ocean-atmosphere coupling. This is due to the fact that the CRM can better resolve clouds and cloud systems and allow for explicit cloud-radiation interaction. For example, Anderson (1997) applied an atmospheric forcing associated with a CRM simulated squall line to a 3-D OML model (one way or passive interaction). His results suggested that the spatial variability resulting from the squall forcing can last at least 24 hours when forced with otherwise spatially uniform fluxes. In addition, the sea surface salinity (SSS) variability continuously decreased following the forcing, while some of the SST variability remained when a diurnal mixed layer capped off the surface structure. The forcing used in the OML model, however, focused on shorter time (8 h) and smaller spatial scales (100-120 km). In this study, the 3-D Goddard Cumulus Ensemble Model (GCE; 512 x 512 x 23 cu km, 2-km horizontal resolution) is used to simulate convective active episodes occurring in the Western Pacific warm pool and Eastern Atlantic regions. The model is integrated for seven days, and the simulated results are coupled to an OML model to better understand the impact of precipitation and changes in the planetary boundary layer upon SST variation. We will specifically examine and compare the results of linking the OML model with various spatially-averaged outputs from GCE simulations (i.e., 2 km vs. 10-50 km horizontal resolutions), in order to help understand the SST sensitivity to multi-scale influences. This will allow us to assess the importance of explicitly simulated deep and shallow clouds, as well as the subgrid-scale effects (in coarse-model runs) upon SST variation. Results using both 1-D and 2-D OML models will be evaluated to assess the effects of horizontal advection.

Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data

NASA Astrophysics Data System (ADS)

Shang, Barry Zhongqi

The development of multiscale methods for computational simulation of biophysical systems represents a significant challenge. Effective computational models that bridge physical insights obtained from atomistic simulations and experimental findings are lacking. An accurate passing of information between these scales would enable: (1) an improved physical understanding of structure-function relationships, and (2) enhanced rational strategies for molecular engineering and materials design. Two approaches are described in this dissertation to facilitate these multiscale goals. In Part I, we develop a lattice kinetic Monte Carlo model to simulate cellulose decomposition by cellulase enzymes and to understand the effects of spatial confinement on enzyme kinetics. An enhanced mechanistic understanding of this reaction system could enhance the design of cellulose bioconversion technologies for renewable and sustainable energy. Using our model, we simulate the reaction up to experimental conversion times of days, while simultaneously capturing the microscopic kinetic behaviors. Therefore, the influence of molecular-scale kinetics on the macroscopic conversion rate is made transparent. The inclusion of spatial constraints in the kinetic model represents a significant advance over classical mass-action models commonly used to describe this reaction system. We find that restrictions due to enzyme jamming and substrate heterogeneity at the molecular level play a dominate role in limiting cellulose conversion. We identify that the key rate limitations are the slow rates of enzyme complexation with glucan chains and the competition between enzyme processivity and jamming. We show that the kinetics of complexation, which involves extraction of a glucan chain end from the cellulose surface and threading through the enzyme active site, occurs slowly on the order of hours, while intrinsic hydrolytic bond cleavage occurs on the order of seconds. We also elucidate the subtle trade-off between processivity and jamming. Highly processive enzymes cleave a large fraction of a glucan chain during each processive run but are prone to jamming at obstacles. Less processive enzymes avoid jamming but cleave only a small fraction of a chain. Optimizing this trade-off maximizes the cellulose conversion rate. We also elucidate the molecular-scale kinetic origins for synergy among cellulases in enzyme mixtures. In contrast to the currently accepted theory, we show that the ability of an endoglucanase to increase the concentration of chain ends for exoglucanases is insufficient for synergy to occur. Rather, endoglucanases must enhance the rate of complexation between exoglucanases and the newly created chain ends. This enhancement occurs when the endoglucanase is able to partially decrystallize the cellulose surface. We show generally that the driving forces for complexation and jamming, which govern the kinetics of pure exoglucanases, also control the degree of synergy in endo-exo mixtures. In Part II, we focus our attention on a different multiscale problem. This challenge is the development of coarse-grained models from atomistic models to access larger length- and time-scales in a simulation. This problem is difficult because it requires a delicate balance between maintaining (1) physical simplicity in the coarse-grained model and (2) physical consistency with the atomistic model. To achieve these goals, we develop a scheme to coarse-grain an atomistic fluid model into a fluctuating hydrodynamics (FHD) model. The FHD model describes the solvent as a field of fluctuating mass, momentum, and energy densities. The dynamics of the fluid are governed by continuum balance equations and fluctuation-dissipation relations based on the constitutive transport laws. The incorporation of both macroscopic transport and microscopic fluctuation phenomena could provide richer physical insight into the behaviors of biophysical systems driven by hydrodynamic fluctuations, such as hydrophobic assembly and crystal nucleation. We further extend our coarse-graining method by developing an interfacial FHD model using information obtained from simulations of an atomistic liquid-vapor interface. We illustrate that a phenomenological Ginzburg-Landau free energy employed in the FHD model can effectively represent the attractive molecular interactions of the atomistic model, which give rise to phase separation. For argon and water, we show that the interfacial FHD model can reproduce the compressibility, surface tension, and capillary wave spectrum of the atomistic model. Via this approach, simulations that explore the coupling between hydrodynamic fluctuations and phase equilibria with molecular-scale consistency are now possible. In both Parts I and II, the emerging theme is that the combination of bottom-up coarse graining and top-down phenomenology is essential for enabling a multiscale approach to remain physically consistent with molecular-scale interactions while simultaneously capturing the collective macroscopic behaviors. This hybrid strategy enables the resulting computational models to be both physically insightful and practically meaningful. (Abstract shortened by UMI.).

Improvements to a global-scale groundwater model to estimate the water table across New Zealand

NASA Astrophysics Data System (ADS)

Westerhoff, Rogier; Miguez-Macho, Gonzalo; White, Paul

2017-04-01

Groundwater models at the global scale have become increasingly important in recent years to assess the effects of climate change and groundwater depletion. However, these global-scale models are typically not used for studies at the catchment scale, because they are simplified and too spatially coarse. In this study, we improved the global-scale Equilibrium Water Table (EWT) model, so it could better assess water table depth and water table elevation at the national scale for New Zealand. The resulting National Water Table (NWT) model used improved input data (i.e., national input data of terrain, geology, and recharge) and model equations (e.g., a hydraulic conductivity - depth relation). The NWT model produced maps of the water table that identified the main alluvial aquifers with fine spatial detail. Two regional case studies at the catchment scale demonstrated excellent correlation between the water table elevation and observations of hydraulic head. The NWT water tables are an improved water table estimation over the EWT model. In two case studies the NWT model provided a better approximation to observed water table for deep aquifers and the improved resolution of the model provided the capability to fill the gaps in data-sparse areas. This national model calculated water table depth and elevation across regional jurisdictions. Therefore, the model is relevant where trans-boundary issues, such as source protection and catchment boundary definition, occur. The NWT model also has the potential to constrain the uncertainty of catchment-scale models, particularly where data are sparse. Shortcomings of the NWT model are caused by the inaccuracy of input data and the simplified model properties. Future research should focus on improved estimation of input data (e.g., hydraulic conductivity and terrain). However, more advanced catchment-scale groundwater models should be used where groundwater flow is dominated by confining layers and fractures.

An analytical coarse-graining method which preserves the free energy, structural correlations, and thermodynamic state of polymer melts from the atomistic to the mesoscale.

PubMed

McCarty, J; Clark, A J; Copperman, J; Guenza, M G

2014-05-28

Structural and thermodynamic consistency of coarse-graining models across multiple length scales is essential for the predictive role of multi-scale modeling and molecular dynamic simulations that use mesoscale descriptions. Our approach is a coarse-grained model based on integral equation theory, which can represent polymer chains at variable levels of chemical details. The model is analytical and depends on molecular and thermodynamic parameters of the system under study, as well as on the direct correlation function in the k → 0 limit, c0. A numerical solution to the PRISM integral equations is used to determine c0, by adjusting the value of the effective hard sphere diameter, dHS, to agree with the predicted equation of state. This single quantity parameterizes the coarse-grained potential, which is used to perform mesoscale simulations that are directly compared with atomistic-level simulations of the same system. We test our coarse-graining formalism by comparing structural correlations, isothermal compressibility, equation of state, Helmholtz and Gibbs free energies, and potential energy and entropy using both united atom and coarse-grained descriptions. We find quantitative agreement between the analytical formalism for the thermodynamic properties, and the results of Molecular Dynamics simulations, independent of the chosen level of representation. In the mesoscale description, the potential energy of the soft-particle interaction becomes a free energy in the coarse-grained coordinates which preserves the excess free energy from an ideal gas across all levels of description. The structural consistency between the united-atom and mesoscale descriptions means the relative entropy between descriptions has been minimized without any variational optimization parameters. The approach is general and applicable to any polymeric system in different thermodynamic conditions.

Consequences of least tern (Sternula antillarum) microhabitat nest-site selection on natural and mechanically constructed sandbars in the Missouri River

USGS Publications Warehouse

Stucker, Jennifer H.; Buhl, Deborah A.; Sherfy, Mark H.

2013-01-01

Nest-habitat selection in colonial species has rarely been assessed at multiple spatial scales to evaluate its fitness consequences. Management for the federally endangered U.S. Interior population of Least Terns (Sternula antillarum) has focused on maintenance of breeding habitats, including mechanical construction of sandbars from dredged material. Least Terns are attracted to large areas of unvegetated substrate, yet small-scale habitat features are thought to trigger selection for nesting. We evaluated nest-scale habitat selection to determine (1) whether selection differs between constructed and natural sandbars and (2) the subsequent consequences of habitat selection on nest success. During 2006–2008, we examined 869 Least Tern nest sites on constructed and natural sandbars in the Missouri River for evidence of microhabitat selection at the nest in relation to habitat within the surrounding 3-m area. Least Tern nest sites had coarser and larger substrate materials at the nest, more debris, and less vegetation than the surrounding area. Nests in constructed habitats had a greater percentage of coarse substrates and less vegetation or debris than nests in naturally created habitats. Apparent nest success was 1.8× greater on constructed than on natural sandbars. Nest success was best predicted by models with two spatial scales of predictors, including substrates (nest) and vegetation and debris (nest or surrounding area). Our results indicate that Least Terns select nest microhabitat characteristics that are associated with wind- and water-scoured habitats, and that nest success increases when these habitats are selected.

Downscaling climate model output for water resources impacts assessment (Invited)

NASA Astrophysics Data System (ADS)

Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

2013-12-01

Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Spectral Upscaling for Graph Laplacian Problems with Application to Reservoir Simulation

DOE PAGES

Barker, Andrew T.; Lee, Chak S.; Vassilevski, Panayot S.

2017-10-26

Here, we consider coarsening procedures for graph Laplacian problems written in a mixed saddle-point form. In that form, in addition to the original (vertex) degrees of freedom (dofs), we also have edge degrees of freedom. We extend previously developed aggregation-based coarsening procedures applied to both sets of dofs to now allow more than one coarse vertex dof per aggregate. Those dofs are selected as certain eigenvectors of local graph Laplacians associated with each aggregate. Additionally, we coarsen the edge dofs by using traces of the discrete gradients of the already constructed coarse vertex dofs. These traces are defined on themore » interface edges that connect any two adjacent aggregates. The overall procedure is a modification of the spectral upscaling procedure developed in for the mixed finite element discretization of diffusion type PDEs which has the important property of maintaining inf-sup stability on coarse levels and having provable approximation properties. We consider applications to partitioning a general graph and to a finite volume discretization interpreted as a graph Laplacian, developing consistent and accurate coarse-scale models of a fine-scale problem.« less

Eco-evolutionary spatial dynamics in the Glanville fritillary butterfly

PubMed Central

Hanski, Ilkka A.

2011-01-01

Demographic population dynamics, gene flow, and local adaptation may influence each other and lead to coupling of ecological and evolutionary dynamics, especially in species inhabiting fragmented heterogeneous environments. Here, I review long-term research on eco-evolutionary spatial dynamics in the Glanville fritillary butterfly inhabiting a large network of approximately 4,000 meadows in Finland. The metapopulation persists in a balance between frequent local extinctions and recolonizations. The genetic spatial structure as defined by neutral markers is much more coarse-grained than the demographic spatial structure determined by the fragmented habitat, yet small-scale spatial structure has important consequences for the dynamics. I discuss three examples of eco-evolutionary spatial dynamics. (i) Extinction-colonization metapopulation dynamics influence allele frequency changes in the phosphoglucose isomerase (Pgi) gene, which leads to strong associations between genetic variation in Pgi and dispersal, recolonization, and local population dynamics. (ii) Inbreeding in local populations increases their risk for extinction, whereas reciprocal effects between inbreeding, population size, and emigration represent likely eco-evolutionary feedbacks. (iii) Genetically determined female oviposition preference for two host plant species exhibits a cline paralleling a gradient in host plant relative abundances, and host plant preference of dispersing females in relation to the host plant composition of habitat patches influences immigration (gene flow) and recolonization (founder events). Eco-evolutionary spatial dynamics in heterogeneous environments may not lead to directional evolutionary changes unless the environment itself changes, but eco-evolutionary dynamics may contribute to the maintenance of genetic variation attributable to fluctuating selection in space and time. PMID:21788506

Coarse-Grained Models for Automated Fragmentation and Parametrization of Molecular Databases.

PubMed

Fraaije, Johannes G E M; van Male, Jan; Becherer, Paul; Serral Gracià, Rubèn

2016-12-27

We calibrate coarse-grained interaction potentials suitable for screening large data sets in top-down fashion. Three new algorithms are introduced: (i) automated decomposition of molecules into coarse-grained units (fragmentation); (ii) Coarse-Grained Reference Interaction Site Model-Hypernetted Chain (CG RISM-HNC) as an intermediate proxy for dissipative particle dynamics (DPD); and (iii) a simple top-down coarse-grained interaction potential/model based on activity coefficient theories from engineering (using COSMO-RS). We find that the fragment distribution follows Zipf and Heaps scaling laws. The accuracy in Gibbs energy of mixing calculations is a few tenths of a kilocalorie per mole. As a final proof of principle, we use full coarse-grained sampling through DPD thermodynamics integration to calculate log P OW for 4627 compounds with an average error of 0.84 log unit. The computational speeds per calculation are a few seconds for CG RISM-HNC and a few minutes for DPD thermodynamic integration.

Cumulative impacts on seabed habitats: an indicator for assessments of good environmental status.

PubMed

Korpinen, Samuli; Meidinger, Manuel; Laamanen, Maria

2013-09-15

The European seas are under anthropogenic pressures impacting the state of water quality, benthic habitats and species. The EU Marine Strategy Framework Directive (MSFD) requires the Member States to assess the impacts of pressures and make a programme of measures leading to good environmental status (GES) by 2020. This study presents a method for assessing the quantity and distribution of anthropogenic impacts on benthic habitats in the Baltic Sea by using spatial data of human pressures and benthic habitats. The southern sub-basins were more extensively impacted than the northern sub-basins. Over the entire sea area, deep sea habitats were more impacted than shallower infralittoral and circalittoral habitats. Sand and coarse sediments were the seabed types relatively most impacted in the Baltic Sea scale. A comparison against tentative thresholds for GES showed that in the sub-basin scale only one third of the habitat types was in GES. Copyright © 2013 Elsevier Ltd. All rights reserved.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Ballard, Christopher C.; Esty, C. Clark; Egolf, David A.

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Influence of spatial variation of phenomenological parameters on the modeling of boundary conditions for flows with dynamic wetting

NASA Astrophysics Data System (ADS)

Hizumi, Yuka; Omori, Takeshi; Yamaguchi, Yasutaka; Kajisima, Takeo

2014-11-01

For reliable prediction of multiphase flows in micro- and nano-scales, continuum models are expected to account for small scale physics near the contact line (CL) region. Some existing works (for example the series of papers by the group of Qian and Ren) have been successful in deriving continuum models and corresponding boundary conditions which reproduce well the molecular dynamics (MD) simulation results. Their studies, however, did not fully address the issue of adsorption layer especially in the CL region, and it is still not clear if general conclusion can be deduced from their results. In the present study we investigate in detail the local viscosity and the corresponding stress tensor formulation in the solid-liquid interface and in the CL region of immiscible two-phase Couette flows by means of MD simulation. The application limit of the generalized Navier boundary condition and the continuum model with uniform viscosity is addressed by systematic coarse-graining of sampling bins.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation.

PubMed

Ballard, Christopher C; Esty, C Clark; Egolf, David A

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Progressive simplification and transmission of building polygons based on triangle meshes

NASA Astrophysics Data System (ADS)

Li, Hongsheng; Wang, Yingjie; Guo, Qingsheng; Han, Jiafu

2010-11-01

Digital earth is a virtual representation of our planet and a data integration platform which aims at harnessing multisource, multi-resolution, multi-format spatial data. This paper introduces a research framework integrating progressive cartographic generalization and transmission of vector data. The progressive cartographic generalization provides multiple resolution data from coarse to fine as key scales and increments between them which is not available in traditional generalization framework. Based on the progressive simplification algorithm, the building polygons are triangulated into meshes and encoded according to the simplification sequence of two basic operations, edge collapse and vertex split. The map data at key scales and encoded increments between them are stored in a multi-resolution file. As the client submits requests to the server, the coarsest map is transmitted first and then the increments. After data decoding and mesh refinement the building polygons with more details will be visualized. Progressive generalization and transmission of building polygons is demonstrated in the paper.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

NASA Astrophysics Data System (ADS)

Lange, Stefan

2018-05-01

Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

The DRAGON scale concept and results for remote sensing of aerosol properties

NASA Astrophysics Data System (ADS)

Holben, B. N.; Eck, T. F.; Schafer, J.; Giles, D. M.; Kim, J.; Sano, I.; Mukai, S.; Kim, Y. J.; Reid, J. S.; Pickering, K. E.; Crawford, J. H.; Smirnov, A.; Sinyuk, A.; Slutsker, I.; Sorokin, M.; Rodriguez, J.; Liew, S.; Trevino, N.; Lim, H.; Lefer, B. L.; Nadkarni, R.; Macke, A.; Kinne, S. A.; Anderson, B. E.; Russell, P. B.; Maring, H. B.; Welton, E. J.; da Silva, A.; Toon, O. B.; Redemann, J.

2013-12-01

Aerosol processes occur at microscales but are typically observed and reported at continental to global scales. Often observable aerosol processes that have significant anthropogenic impact occur on spatial scales of tens to a few hundred km, representative of convective cloud processing, urban/megacity sources, anthropogenic burning and natural wildfires, dry lakebed dust sources etc. Historically remote sensing of aerosols has relied on relatively coarse temporal and spatial resolution satellite observations or high temporal resolution point observations from ground-based monitoring sites from networks such as AERONET, SKYNET, MPLNET and many other surface observation platforms. Airborne remote and in situ observations combined with assimilation models were/are to be the mesoscale link between the ground- and space-based RS scales. However clearly the in situ and ground-based RS characterizations of aerosols require a convergence of thought, parameterization and actual scale measurements in order to advance this goal. This has been served by periodic multidisciplinary field campaigns yet only recently has a concerted effort been made to establish these ground-based networks in an effort to capture the mesoscale processes through measurement programs such as DISCOVER AQ and NASA AERONET's effort to foster such measurements and analysis through the Distributed Regional Aerosol Gridded Observation Networks (DRAGON), short term meso-networks, with partners in Asia and Europe and N. America. This talk will review the historical need for such networks and discuss some of the results and in some cases unexpected findings from the eight DRAGON campaigns conducted the last several years. Emphasis will be placed on the most recent DISCOVER AQ campaign conducted in Houston TX and the synergism with a regional to global network plan through the SEAC4RS US campaign.

Scaling between reanalyses and high-resolution land-surface modelling in mountainous areas - enabling better application and testing of reanalyses in heterogeneous environments

NASA Astrophysics Data System (ADS)

Gruber, S.; Fiddes, J.

2013-12-01

In mountainous topography, the difference in scale between atmospheric reanalyses (typically tens of kilometres) and relevant processes and phenomena near the Earth surface, such as permafrost or snow cover (meters to tens of meters) is most obvious. This contrast of scales is one of the major obstacles to using reanalysis data for the simulation of surface phenomena and to confronting reanalyses with independent observation. At the example of modelling permafrost in mountain areas (but simple to generalise to other phenomena and heterogeneous environments), we present and test methods against measurements for (A) scaling atmospheric data from the reanalysis to the ground level and (B) smart sampling of the heterogeneous landscape in order to set up a lumped model simulation that represents the high-resolution land surface. TopoSCALE (Part A, see http://dx.doi.org/10.5194/gmdd-6-3381-2013) is a scheme, which scales coarse-grid climate fields to fine-grid topography using pressure level data. In addition, it applies necessary topographic corrections e.g. those variables required for computation of radiation fields. This provides the necessary driving fields to the LSM. Tested against independent ground data, this scheme has been shown to improve the scaling and distribution of meteorological parameters in complex terrain, as compared to conventional methods, e.g. lapse rate based approaches. TopoSUB (Part B, see http://dx.doi.org/10.5194/gmd-5-1245-2012) is a surface pre-processor designed to sample a fine-grid domain (defined by a digital elevation model) along important topographical (or other) dimensions through a clustering scheme. This allows constructing a lumped model representing the main sources of fine-grid variability and applying a 1D LSM efficiently over large areas. Results can processed to derive (i) summary statistics at coarse-scale re-analysis grid resolution, (ii) high-resolution data fields spatialized to e.g., the fine-scale digital elevation model grid, or (iii) validation products for locations at which measurements exist, only. The ability of TopoSUB to approximate results simulated by a 2D distributed numerical LSM at a factor of ~10,000 less computations is demonstrated by comparison of 2D and lumped simulations. Successful application of the combined scheme in the European Alps is reported and based on its results, open issues for future research are outlined.

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale

PubMed Central

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2018-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3–5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage. PMID:29888751

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.

PubMed

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.

Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield and production is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to provide temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (10-30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat yield assessment at regional scale. For the former, we adapt a previously developed approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument at 250 m resolution that allows automatic mapping of winter crops taking into account a priori knowledge on crop calendar. For the latter, we use a generalized winter wheat yield forecasting model that is based on estimation of the peak Normalized Difference Vegetation Index (NDVI) from MODIS image time-series, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A improves both winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times compared to using a single satellite.

Shape-driven 3D segmentation using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2006-01-01

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

Residential indoor and outdoor coarse particles and associated endotoxin exposures

NASA Astrophysics Data System (ADS)

Wheeler, Amanda J.; Dobbin, Nina A.; Lyrette, Ninon; Wallace, Lance; Foto, Mark; Mallick, Ranjeeta; Kearney, Jill; Van Ryswyk, Keith; Gilbert, Nicolas L.; Harrison, Ian; Rispler, Kathleen; Héroux, Marie-Eve

2011-12-01

There is a growing body of evidence demonstrating that coarse particles (PM 10-2.5) have detrimental impacts upon health, especially for respiratory effects. There are limited data available for indoor residential exposures. Some data exist regarding the composition of this PM size fraction with emphasis on crustal elements and biological components. This study includes data from 146 homes sampled in Regina, Saskatchewan (SK) where 5-day integrated concurrent monitoring of indoor and outdoor coarse particles was conducted during the winter and summer of 2007. The coarse particle filters were subsequently analysed for endotoxin content to determine the contribution of this compound. Winter indoor geometric mean concentrations of coarse particles exceeded outdoor concentrations (3.73 μg m -3 vs 2.49 μg m -3; paired t-test p < 0.0001); however the reverse was found in summer (4.34 μg m -3 vs 8.82 μg m -3; paired t-test p < 0.0001). Linear regression indicated that winter predictors of indoor coarse particles were outdoor coarse particles, ventilation and presence of at least two or more occupants. During the summer, increased use of central air conditioning was associated with reduced coarse particles, while smoking and the presence of two or more occupants resulted in increased coarse particles. Endotoxin concentrations (EU μg -1) were lower indoors than outdoors in both seasons. Spatial variability of ambient coarse particles was assessed to determine the suitability of using a single monitoring station within a city to estimate exposure. The coefficients of variation between homes sampled simultaneously and the central monitoring station were calculated (median COV in summer = 15% and winter = 24%) and showed significant variability by week, especially during the summer months, suggesting a single site may be insufficient for characterizing exposure. Future studies should consider daily measurements per home to understand shorter term exposures and day to day variability of these pollutants.

Edgeworth streaming model for redshift space distortions

NASA Astrophysics Data System (ADS)

Uhlemann, Cora; Kopp, Michael; Haugg, Thomas

2015-09-01

We derive the Edgeworth streaming model (ESM) for the redshift space correlation function starting from an arbitrary distribution function for biased tracers of dark matter by considering its two-point statistics and show that it reduces to the Gaussian streaming model (GSM) when neglecting non-Gaussianities. We test the accuracy of the GSM and ESM independent of perturbation theory using the Horizon Run 2 N -body halo catalog. While the monopole of the redshift space halo correlation function is well described by the GSM, higher multipoles improve upon including the leading order non-Gaussian correction in the ESM: the GSM quadrupole breaks down on scales below 30 Mpc /h whereas the ESM stays accurate to 2% within statistical errors down to 10 Mpc /h . To predict the scale-dependent functions entering the streaming model we employ convolution Lagrangian perturbation theory (CLPT) based on the dust model and local Lagrangian bias. Since dark matter halos carry an intrinsic length scale given by their Lagrangian radius, we extend CLPT to the coarse-grained dust model and consider two different smoothing approaches operating in Eulerian and Lagrangian space, respectively. The coarse graining in Eulerian space features modified fluid dynamics different from dust while the coarse graining in Lagrangian space is performed in the initial conditions with subsequent single-streaming dust dynamics, implemented by smoothing the initial power spectrum in the spirit of the truncated Zel'dovich approximation. Finally, we compare the predictions of the different coarse-grained models for the streaming model ingredients to N -body measurements and comment on the proper choice of both the tracer distribution function and the smoothing scale. Since the perturbative methods we considered are not yet accurate enough on small scales, the GSM is sufficient when applied to perturbation theory.

Dual-scale Galerkin methods for Darcy flow

NASA Astrophysics Data System (ADS)

Wang, Guoyin; Scovazzi, Guglielmo; Nouveau, Léo; Kees, Christopher E.; Rossi, Simone; Colomés, Oriol; Main, Alex

2018-02-01

The discontinuous Galerkin (DG) method has found widespread application in elliptic problems with rough coefficients, of which the Darcy flow equations are a prototypical example. One of the long-standing issues of DG approximations is the overall computational cost, and many different strategies have been proposed, such as the variational multiscale DG method, the hybridizable DG method, the multiscale DG method, the embedded DG method, and the Enriched Galerkin method. In this work, we propose a mixed dual-scale Galerkin method, in which the degrees-of-freedom of a less computationally expensive coarse-scale approximation are linked to the degrees-of-freedom of a base DG approximation. We show that the proposed approach has always similar or improved accuracy with respect to the base DG method, with a considerable reduction in computational cost. For the specific definition of the coarse-scale space, we consider Raviart-Thomas finite elements for the mass flux and piecewise-linear continuous finite elements for the pressure. We provide a complete analysis of stability and convergence of the proposed method, in addition to a study on its conservation and consistency properties. We also present a battery of numerical tests to verify the results of the analysis, and evaluate a number of possible variations, such as using piecewise-linear continuous finite elements for the coarse-scale mass fluxes.

Radial Bias Is Not Necessary For Orientation Decoding

PubMed Central

Pratte, Michael S.; Sy, Jocelyn L.; Swisher, Jascha D.; Tong, Frank

2015-01-01

Multivariate pattern analysis can be used to decode the orientation of a viewed grating from fMRI signals in early visual areas. Although some studies have reported identifying multiple sources of the orientation information that make decoding possible, a recent study argued that orientation decoding is only possible because of a single source: a coarse-scale retinotopically organized preference for radial orientations. Here we aim to resolve these discrepant findings. We show that there were subtle, but critical, experimental design choices that led to the erroneous conclusion that a radial bias is the only source of orientation information in fMRI signals. In particular, we show that the reliance on a fast temporal-encoding paradigm for spatial mapping can be problematic, as effects of space and time become conflated and lead to distorted estimates of a voxel’s orientation or retinotopic preference. When we implement minor changes to the temporal paradigm or to the visual stimulus itself, by slowing the periodic rotation of the stimulus or by smoothing its contrast-energy profile, we find significant evidence of orientation information that does not originate from radial bias. In an additional block-paradigm experiment where space and time were not conflated, we apply a formal model comparison approach and find that many voxels exhibit more complex tuning properties than predicted by radial bias alone or in combination with other known coarse-scale biases. Our findings support the conclusion that radial bias is not necessary for orientation decoding. In addition, our study highlights potential limitations of using temporal phase-encoded fMRI designs for characterizing voxel tuning properties. PMID:26666900

Factors Impacting Spatial Patterns of Snow Distribution in a Small Catchment near Nome, AK

NASA Astrophysics Data System (ADS)

Chen, M.; Wilson, C. J.; Charsley-Groffman, L.; Busey, R.; Bolton, W. R.

2017-12-01

Snow cover plays an important role in the climate, hydrology and ecological systems of the Arctic due to its influence on the water balance, thermal regimes, vegetation and carbon flux. Thus, snow depth and coverage have been key components in all the earth system models but are often poorly represented for arctic regions, where fine scale snow distribution data is sparse. The snow data currently used in the models is at coarse resolution, which in turn leads to high uncertainty in model predictions. Through the DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic, high resolution snow distribution data is being developed and applied in catchment scale models to ultimately improve representation of snow and its interactions with other model components in the earth system models . To improve these models, it is important to identify key factors that control snow distribution and quantify the impacts of those factors on snow distribution. In this study, two intensive snow depth surveys (1 to 10 meters scale) were conducted for a 2.3 km2 catchment on the Teller road, near Nome, AK in the winter of 2016 and 2017. We used a statistical model to quantify the impacts of vegetation types, macro-topography, micro-topography, and meteorological parameters on measured snow depth. The results show that snow spatial distribution was similar between 2016 and 2017, snow depth was spatially auto correlated over small distance (2-5 meters), but not spatially auto correlated over larger distance (more than 2-5 meters). The coefficients of variation of snow depth was above 0.3 for all the snow survey transects (500-800 meters long). Variation of snow depth is governed by vegetation height, aspect, slope, surface curvature, elevation and wind speed and direction. We expect that this empirical statistical model can be used to estimate end of winter snow depth for the whole watershed and will further develop the model using data from other arctic regions to estimate seasonally dynamic snow coverage and properties for use in catchment scale to pan-Arctic models.

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

An initial assessment of a SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

NASA Astrophysics Data System (ADS)

Mishra, Vikalp; Ellenburg, W. Lee; Griffin, Robert E.; Mecikalski, John R.; Cruise, James F.; Hain, Christopher R.; Anderson, Martha C.

2018-06-01

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of -0.022 and -0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E.

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Treesearch

A. M. S. Smith; N. A. Drake; M. J. Wooster; A. T. Hudak; Z. A. Holden; C. J. Gibbons

2007-01-01

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution...

Parameterizing the Morse Potential for Coarse-Grained Modeling of Blood Plasma

PubMed Central

Zhang, Na; Zhang, Peng; Kang, Wei; Bluestein, Danny; Deng, Yuefan

2014-01-01

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters are systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately. PMID:24910470

High-Resolution Subtropical Summer Precipitation Derived from Dynamical Downscaling of the NCEP-DOE Reanalysis: How Much Small-Scale Information Is Added by a Regional Model?

NASA Technical Reports Server (NTRS)

Lim, Young-Kwon; Stefanova, Lydia B.; Chan, Steven C.; Schubert, Siegfried D.; OBrien, James J.

2010-01-01

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5deg latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (19902005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93 (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92 (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.

Understanding bulk behavior of particulate materials from particle scale simulations

NASA Astrophysics Data System (ADS)

Deng, Xiaoliang

Particulate materials play an increasingly significant role in various industries, such as pharmaceutical manufacturing, food, mining, and civil engineering. The objective of this research is to better understand bulk behaviors of particulate materials from particle scale simulations. Packing properties of assembly of particles are investigated first, focusing on the effects of particle size, surface energy, and aspect ratio on the coordination number, porosity, and packing structures. The simulation results show that particle sizes, surface energy, and aspect ratio all influence the porosity of packing to various degrees. The heterogeneous force networks within particle assembly under external compressive loading are investigated as well. The results show that coarse-coarse contacts dominate the strong network and coarse-fine contacts dominate the total network. Next, DEM models are developed to simulate the particle dynamics inside a conical screen mill (comil) and magnetically assisted impaction mixer (MAIM), both are important particle processing devices. For comil, the mean residence time (MRT), spatial distribution of particles, along with the collision dynamics between particles as well as particle and vessel geometries are examined as a function of the various operating parameters such as impeller speed, screen hole size, open area, and feed rate. The simulation results can help better understand dry coating experimental results using comil. For MAIM system, the magnetic force is incorporated into the contact model, allowing to describe the interactions between magnets. The simulation results reveal the connections between homogeneity of mixture and particle scale variables such as size of magnets and surface energy of non-magnets. In particular, at the fixed mass ratio of magnets to non-magnets and surface energy the smaller magnets lead to better homogeneity of mixing, which is in good agreement with previously published experimental results. Last but not least, numerical simulations, along with theoretical analysis, are performed to investigate the interparticle force of dry coated particles. A model is derived and can be used to predict the probabilities of hose-host (HH), host-guest (HG), and guest-guest (GG) contacts. The results indicate that there are three different regions dominated by HH, HG, and GG contacts, respectively. Moreover, the critical SAC for the transition of HG to GG contacts is lower than previously estimated value. In summary, particle packing, particle dynamics associated with various particle processing devices, and interparticle force of dry coated particles are investigated in this thesis. The results show that particle scale information such as coordination number, collision dynamics, and contact force between particles from simulation results can help better understand bulk properties of assembly of individual particles.

Multi-Scale Fractal Analysis of Image Texture and Pattern

NASA Technical Reports Server (NTRS)

Emerson, Charles W.

1998-01-01

Fractals embody important ideas of self-similarity, in which the spatial behavior or appearance of a system is largely independent of scale. Self-similarity is defined as a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. An ideal fractal (or monofractal) curve or surface has a constant dimension over all scales, although it may not be an integer value. This is in contrast to Euclidean or topological dimensions, where discrete one, two, and three dimensions describe curves, planes, and volumes. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution. However, most geographical phenomena are not strictly self-similar at all scales, but they can often be modeled by a stochastic fractal in which the scaling and self-similarity properties of the fractal have inexact patterns that can be described by statistics. Stochastic fractal sets relax the monofractal self-similarity assumption and measure many scales and resolutions in order to represent the varying form of a phenomenon as a function of local variables across space. In image interpretation, pattern is defined as the overall spatial form of related features, and the repetition of certain forms is a characteristic pattern found in many cultural objects and some natural features. Texture is the visual impression of coarseness or smoothness caused by the variability or uniformity of image tone or color. A potential use of fractals concerns the analysis of image texture. In these situations it is commonly observed that the degree of roughness or inexactness in an image or surface is a function of scale and not of experimental technique. The fractal dimension of remote sensing data could yield quantitative insight on the spatial complexity and information content contained within these data. A software package known as the Image Characterization and Modeling System (ICAMS) was used to explore how fractal dimension is related to surface texture and pattern. The ICAMS software was verified using simulated images of ideal fractal surfaces with specified dimensions. The fractal dimension for areas of homogeneous land cover in the vicinity of Huntsville, Alabama was measured to investigate the relationship between texture and resolution for different land covers.

Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI.

PubMed

Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Berenfeld, Omer; Snyder, Brett; Boyers, Pamela; Gold, Jeffrey

2014-02-01

We present a comprehensive validation analysis to assess the geometric impact of using coarsely-sliced short-axis images to reconstruct patient-specific cardiac geometry. The methods utilize high-resolution diffusion tensor MRI (DTMRI) datasets as reference geometries from which synthesized coarsely-sliced datasets simulating in vivo MRI were produced. 3D models are reconstructed from the coarse data using variational implicit surfaces through a commonly used modeling tool, CardioViz3D. The resulting geometries were then compared to the reference DTMRI models from which they were derived to analyze how well the synthesized geometries approximate the reference anatomy. Averaged over seven hearts, 95% spatial overlap, less than 3% volume variability, and normal-to-surface distance of 0.32 mm was observed between the synthesized myocardial geometries reconstructed from 8 mm sliced images and the reference data. The results provide strong supportive evidence to validate the hypothesis that coarsely-sliced MRI may be used to accurately reconstruct geometric ventricular models. Furthermore, the use of DTMRI for validation of in vivo MRI presents a novel benchmark procedure for studies which aim to substantiate their modeling and simulation methods using coarsely-sliced cardiac data. In addition, the paper outlines a suggested original procedure for deriving image-based ventricular models using the CardioViz3D software. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The relative influence of geographic location and reach-scale habitat on benthic invertebrate assemblages in six ecoregions

USGS Publications Warehouse

Munn, M.D.; Waite, I.R.; Larsen, D.P.; Herlihy, A.T.

2009-01-01

The objective of this study was to determine the relative influence of reach-specific habitat variables and geographic location on benthic invertebrate assemblages within six ecoregions across the Western USA. This study included 417 sites from six ecoregions. A total of 301 taxa were collected with the highest richness associated with ecoregions dominated by streams with coarse substrate (19-29 taxa per site). Lowest richness (seven to eight taxa per site) was associated with ecoregions dominated by fine-grain substrate. Principle component analysis (PCA) on reach-scale habitat separated the six ecoregions into those in high-gradient mountainous areas (Coast Range, Cascades, and Southern Rockies) and those in lower-gradient ecoregions (Central Great Plains and Central California Valley). Nonmetric multidimensional scaling (NMS) models performed best in ecoregions dominated by coarse-grain substrate and high taxa richness, along with coarse-grain substrates sites combined from multiple ecoregions regardless of location. In contrast, ecoregions or site combinations dominated by fine-grain substrate had poor model performance (high stress). Four NMS models showed that geographic location (i.e. latitude and longitude) was important for: (1) all ecoregions combined, (2) all sites dominated by coarse-grain sub strate combined, (3) Cascades Ecoregion, and (4) Columbia Ecoregion. Local factors (i.e. substrate or water temperature) seem to be overriding factors controlling invertebrate composition across the West, regardless of geographic location. ?? The Author(s) 2008.

Thematic mapper flight model preshipment review data package. Volume 4: Appendix. Part G: Miscellaneous system data

NASA Technical Reports Server (NTRS)

1982-01-01

Functional and design data from various thematic mapper subsystems are presented. Coarse focus, modulation transfer function, and shim requirements are addressed along with spectral matching and spatial coverage tests.

Erosion and deposition by supercritical density flows during channel avulsion and backfilling: Field examples from coarse-grained deepwater channel-levée complexes (Sandino Forearc Basin, southern Central America)

NASA Astrophysics Data System (ADS)

Lang, Jörg; Brandes, Christian; Winsemann, Jutta

2017-03-01

Erosion and deposition by supercritical density flows can strongly impact the facies distribution and architecture of submarine fans. Field examples from coarse-grained channel-levée complexes from the Sandino Forearc Basin (southern Central America) show that cyclic-step and antidune deposits represent common sedimentary facies of these depositional systems and relate to the different stages of avulsion, bypass, levée construction and channel backfilling. During channel avulsion, large-scale scour-fill complexes (18 to 29 m deep, 18 to 25 m wide, 60 to > 120 m long) were incised by supercritical density flows. The multi-storey infill of the large-scale scour-fill complexes comprises amalgamated massive, normally coarse-tail graded or widely spaced subhorizontally stratified conglomerates and pebbly sandstones, interpreted as deposits of the hydraulic-jump zone of cyclic steps. The large-scale scour-fill complexes can be distinguished from small-scale channel fills based on the preservation of a steep upper margin and a coarse-grained infill comprising mainly amalgamated hydraulic-jump zone deposits. Channel fills include repeated successions deposited by cyclic steps with superimposed antidunes. The deposits of the hydraulic-jump zone of cyclic steps comprise regularly spaced scours (0.2 to 2.6 m deep, 0.8 to 23 m long) infilled by intraclast-rich conglomerates or pebbly sandstones, displaying normal coarse-tail grading or backsets. These deposits are laterally and vertically associated with subhorizontally stratified, low-angle cross-stratified or sinusoidally stratified sandstones and pebbly sandstones, which were deposited by antidunes on the stoss side of the cyclic steps during flow re-acceleration. The field examples indicate that so-called spaced stratified deposits may commonly represent antidune deposits with varying stratification styles controlled by the aggradation rate, grain-size distribution and amalgamation. The deposits of small-scale cyclic steps with superimposed antidunes form fining-upwards successions with decreasing antidune wavelengths, indicating waning flows. Such cyclic step-antidune successions form the characteristic basal infill of mid-fan channels, and are inferred to be related to successive supercritical high-density turbidity flows triggered by retrogressive slope failures.

Controls on patterns of coarse organic particle retention in headwater streams

Treesearch

E. N. Jack Brookshire; Kathleen A. Dwire

2003-01-01

Organic matter retention is an integral ecosystem process affecting C and nutrient dynamics and biota in streams. Influences of discharge (Q), reach-scale channel form, and riparian vegetation on coarse particulate organic matter (CPOM) retention were analyzed in 2 headwater streams in northeastern Oregon. Ginkgo biloba leaves were released in coniferous forest reaches...

Brain regions involved in the retrieval of spatial and episodic details associated with a familiar environment: an fMRI study.

PubMed

Hirshhorn, Marnie; Grady, Cheryl; Rosenbaum, R Shayna; Winocur, Gordon; Moscovitch, Morris

2012-11-01

Functional magnetic resonance imaging (fMRI) was used to compare brain activity during the retrieval of coarse- and fine-grained spatial details and episodic details associated with a familiar environment. Long-time Toronto residents compared pairs of landmarks based on their absolute geographic locations (requiring either coarse or fine discriminations) or based on previous visits to those landmarks (requiring episodic details). An ROI analysis of the hippocampus showed that all three conditions activated the hippocampus bilaterally. Fine-grained spatial judgments recruited an additional region of the right posterior hippocampus, while episodic judgments recruited an additional region of the right anterior hippocampus, and a more extensive region along the length of the left hippocampus. To examine whole-brain patterns of activity, Partial Least Squares (PLS) analysis was used to identify sets of brain regions whose activity covaried with the three conditions. All three comparison judgments recruited the default mode network including the posterior cingulate/retrosplenial cortex, middle frontal gyrus, hippocampus, and precuneus. Fine-grained spatial judgments also recruited additional regions of the precuneus, parahippocampal cortex and the supramarginal gyrus. Episodic judgments recruited the posterior cingulate and medial frontal lobes as well as the angular gyrus. These results are discussed in terms of their implications for theories of hippocampal function and spatial and episodic memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multiscale equation-free algorithms for molecular dynamics

NASA Astrophysics Data System (ADS)

Abi Mansour, Andrew

Molecular dynamics is a physics-based computational tool that has been widely employed to study the dynamics and structure of macromolecules and their assemblies at the atomic scale. However, the efficiency of molecular dynamics simulation is limited because of the broad spectrum of timescales involved. To overcome this limitation, an equation-free algorithm is presented for simulating these systems using a multiscale model cast in terms of atomistic and coarse-grained variables. Both variables are evolved in time in such a way that the cross-talk between short and long scales is preserved. In this way, the coarse-grained variables guide the evolution of the atom-resolved states, while the latter provide the Newtonian physics for the former. While the atomistic variables are evolved using short molecular dynamics runs, time advancement at the coarse-grained level is achieved with a scheme that uses information from past and future states of the system while accounting for both the stochastic and deterministic features of the coarse-grained dynamics. To complete the multiscale cycle, an atom-resolved state consistent with the updated coarse-grained variables is recovered using algorithms from mathematical optimization. This multiscale paradigm is extended to nanofluidics using concepts from hydrodynamics, and it is demonstrated for macromolecular and nanofluidic systems. A toolkit is developed for prototyping these algorithms, which are then implemented within the GROMACS simulation package and released as an open source multiscale simulator.

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing

2012-01-01

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth.

PubMed

de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-12-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth.

Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

NASA Astrophysics Data System (ADS)

de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

2017-12-01

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth.

Acoustic estimates of zooplankton and micronekton biomass in cyclones and anticyclones of the northeastern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Ressler, Patrick Henry

2001-12-01

In the Gulf of Mexico (GOM), coarse to mesoscale eddies can enhance the supply of limiting nutrients into the euphotic zone, elevating primary production. This leads to 'oases' of enriched standing stocks of zooplankton and micronekton in otherwise oligotrophic deepwater (>200 m bottom depth). A combination of acoustic volume backscattering (Sv) measurements with an acoustic Doppler current profiler (ADCP) and concurrent net sampling of zooplankton and micronekton biomass in GOM eddy fields between October 1996 and November 1998 confirmed that cyclones and flow confluences were areas of locally enhanced Sv and standing stock biomass. Net samples were used both to 'sea-truth' the acoustic measurements and to assess the influence of taxonomic composition on measured Sv. During October 1996 and August 1997, a mesoscale (200--300 km diameter) cyclone-anticyclone pair in the northeastern GOM was surveyed as part of a cetacean (whale and dolphin) and seabird habitat, study. Acoustic estimates of biomass in the upper 10--50 m of the water column showed that the cyclone and flow confluence were enriched relative to anticyclonic Loop Current Eddies during both years. Cetacean and seabird survey results reported by other project researchers imply that these eddies provide preferential habitat because they foster locally higher concentrations of higher-trophic-level prey. Sv measurements in November 1997 and 1998 showed that coarse scale eddies (30--150 km diameter) probably enhanced nutrients and S, in the deepwater GOM within 100 km of the Mississippi delta, an area suspected to be important habitat for cetaceans and seabirds. Finally, Sv, data collected during November-December 1997 and October-December 1998 from a mooring at the head of DeSoto Canyon in the northeastern GOM revealed temporal variability at a single location: characteristic temporal decorrelation scales were 1 day (diel vertical migration of zooplankton and micronekton) and 5 days (advective processes). A combination of acoustic and net sampling is a useful way to survey temporal and spatial patterns in zooplankton and micronekton biomass in coarse to mesoscale eddies. Further research should employ such a combination of methods to investigate plankton patterns in eddies and their implications for cetacean and seabird habitat.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasts in a Rainfall-Runoff Dominated Basin

NASA Astrophysics Data System (ADS)

Sinha, T.; Arumugam, S.

2012-12-01

Seasonal streamflow forecasts contingent on climate forecasts can be effectively utilized in updating water management plans and optimize generation of hydroelectric power. Streamflow in the rainfall-runoff dominated basins critically depend on forecasted precipitation in contrast to snow dominated basins, where initial hydrological conditions (IHCs) are more important. Since precipitation forecasts from Atmosphere-Ocean-General Circulation Models are available at coarse scale (~2.8° by 2.8°), spatial and temporal downscaling of such forecasts are required to implement land surface models, which typically runs on finer spatial and temporal scales. Consequently, multiple sources are introduced at various stages in predicting seasonal streamflow. Therefore, in this study, we addresses the following science questions: 1) How do we attribute the errors in monthly streamflow forecasts to various sources - (i) model errors, (ii) spatio-temporal downscaling, (iii) imprecise initial conditions, iv) no forecasts, and (iv) imprecise forecasts? and 2) How does monthly streamflow forecast errors propagate with different lead time over various seasons? In this study, the Variable Infiltration Capacity (VIC) model is calibrated over Apalachicola River at Chattahoochee, FL in the southeastern US and implemented with observed 1/8° daily forcings to estimate reference streamflow during 1981 to 2010. The VIC model is then forced with different schemes under updated IHCs prior to forecasting period to estimate relative mean square errors due to: a) temporally disaggregation, b) spatial downscaling, c) Reverse Ensemble Streamflow Prediction (imprecise IHCs), d) ESP (no forecasts), and e) ECHAM4.5 precipitation forecasts. Finally, error propagation under different schemes are analyzed with different lead time over different seasons.

A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant.

PubMed

Wainwright, Haruko M; Seki, Akiyuki; Chen, Jinsong; Saito, Kimiaki

2017-02-01

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps

PubMed Central

Nieto-Lugilde, Diego; Lenoir, Jonathan; Abdulhak, Sylvain; Aeschimann, David; Dullinger, Stefan; Gégout, Jean-Claude; Guisan, Antoine; Pauli, Harald; Renaud, Julien; Theurillat, Jean-Paul; Thuiller, Wilfried; Van Es, Jérémie; Vittoz, Pascal; Willner, Wolfgang; Wohlgemuth, Thomas; Zimmermann, Niklaus E.; Svenning, Jens-Christian

2015-01-01

The role of competition for light among plants has long been recognised at local scales, but its importance for plant species distributions at larger spatial scales has generally been ignored. Tree cover modifies the local abiotic conditions below the canopy, notably by reducing light availability, and thus, also the performance of species that are not adapted to low-light conditions. However, this local effect may propagate to coarser spatial grains, by affecting colonisation probabilities and local extinction risks of herbs and shrubs. To assess the effect of tree cover at both the plot- and landscape-grain sizes (approximately 10-m and 1-km), we fit Generalised Linear Models (GLMs) for the plot-level distributions of 960 species of herbs and shrubs using 6,935 vegetation plots across the European Alps. We ran four models with different combinations of variables (climate, soil and tree cover) at both spatial grains for each species. We used partial regressions to evaluate the independent effects of plot- and landscape-grain tree cover on plot-level plant communities. Finally, the effects on species-specific elevational range limits were assessed by simulating a removal experiment comparing the species distributions under high and low tree cover. Accounting for tree cover improved the model performance, with the probability of the presence of shade-tolerant species increasing with increasing tree cover, whereas shade-intolerant species showed the opposite pattern. The tree cover effect occurred consistently at both the plot and landscape spatial grains, albeit most strongly at the former. Importantly, tree cover at the two grain sizes had partially independent effects on plot-level plant communities. With high tree cover, shade-intolerant species exhibited narrower elevational ranges than with low tree cover whereas shade-tolerant species showed wider elevational ranges at both limits. These findings suggest that forecasts of climate-related range shifts for herb and shrub species may be modified by tree cover dynamics. PMID:26290621

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

PubMed

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Duong, Nguyen Dinh; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment

PubMed Central

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Dinh Duong, Nguyen; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. PMID:24465714

Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA

NASA Astrophysics Data System (ADS)

Deo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Andersen, Hans-Erik

2018-05-01

Aboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat and MODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000 m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (~1 km wide) predicted via LiDAR metrics at 30 m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R 2 > 0.66), although the R 2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000 m provide acceptable accuracy for continental scale analysis of AGB.

Coarse woody debris metrics in a California oak woodland

Treesearch

William D. Tietje; Michael A. Hardy; Christopher C. Yim

2015-01-01

Little information is available on the metrics of coarse woody debris (CWD) in California oak woodland, most notably at the scale of the stand and woodland type. In a remote part of the National Guard Post, Camp Roberts, that has not burned in over a half century, we tallied 314 pieces of CWD in a blue oak (Quercus douglasii)-coast live oak (

A Synopsis of Global Mapping of Freshwater Habitats and Biodiversity: Implications for Conservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

McManamay, Ryan A.; Griffiths, Natalie A.; DeRolph, Christopher R.

Accurately mapping freshwater habitats and biodiversity at high-resolutions across the globe is essential for assessing the vulnerability and threats to freshwater organisms and prioritizing conservation efforts. Since the 2000s, extensive efforts have been devoted to mapping global freshwater habitats (rivers, lakes, and wetlands), the spatial representation of which has changed dramatically over time with new geospatial data products and improved remote sensing technologies. Some of these mapping efforts, however, are still coarse representations of actual conditions. Likewise, the resolution and scope of global freshwater biodiversity compilation efforts have also increased, but are yet to mirror the spatial resolution and fidelitymore » of mapped freshwater environments. In our synopsis, we find that efforts to map freshwater habitats have been conducted independently of those for freshwater biodiversity; subsequently, there is little congruence in the spatial representation and resolution of the two efforts. We suggest that global species distribution models are needed to fill this information gap; however, limiting data on habitat characteristics at scales that complement freshwater habitats has prohibited global high-resolution biogeography efforts. Emerging research trends, such as mapping habitat alteration in freshwater ecosystems and trait biogeography, show great promise in mechanistically linking global anthropogenic stressors to freshwater biodiversity decline and extinction risk.« less

Assessment of near-source air pollution at a fine spatial scale ...

EPA Pesticide Factsheets

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle, an all-electric vehicle measuring real-time concentrations of particulate and gaseous pollutants, was utilized to map air pollution trends near the Port of Charleston in South Carolina. High-resolution monitoring was performed along driving routes near several port terminals and rail yard facilities, recording geospatial coordinates and measurements of pollutants including black carbon, size-resolved particle count ranging from ultrafine to coarse (6 nm to 20 µm), carbon monoxide, carbon dioxide, and nitrogen dioxide. Additionally, a portable meteorological station was used to characterize local meteorology. Port activity data was provided by the Port Authority of Charleston and includes counts of ships and trucks, and port service operations such as cranes and forklifts during the sampling time periods. Measurements are supplemented with modeling performed with AERMOD and RLINE in order to characterize the impact of the various terminals at the Port of Charleston on local air quality. Specifically, the data are used to determine the magnitude of the increase in local, near-port pollutant concentrations as well as the spatial extent to which concentration is elevated above background. These effects are studied in relation to a number of potentially significant factors such

Grid-based mapping: A method for rapidly determining the spatial distributions of small features over very large areas

NASA Astrophysics Data System (ADS)

Ramsdale, Jason D.; Balme, Matthew R.; Conway, Susan J.; Gallagher, Colman; van Gasselt, Stephan A.; Hauber, Ernst; Orgel, Csilla; Séjourné, Antoine; Skinner, James A.; Costard, Francois; Johnsson, Andreas; Losiak, Anna; Reiss, Dennis; Swirad, Zuzanna M.; Kereszturi, Akos; Smith, Isaac B.; Platz, Thomas

2017-06-01

The increased volume, spatial resolution, and areal coverage of high-resolution images of Mars over the past 15 years have led to an increased quantity and variety of small-scale landform identifications. Though many such landforms are too small to represent individually on regional-scale maps, determining their presence or absence across large areas helps form the observational basis for developing hypotheses on the geological nature and environmental history of a study area. The combination of improved spatial resolution and near-continuous coverage significantly increases the time required to analyse the data. This becomes problematic when attempting regional or global-scale studies of metre and decametre-scale landforms. Here, we describe an approach for mapping small features (from decimetre to kilometre scale) across large areas, formulated for a project to study the northern plains of Mars, and provide context on how this method was developed and how it can be implemented. Rather than ;mapping; with points and polygons, grid-based mapping uses a ;tick box; approach to efficiently record the locations of specific landforms (we use an example suite of glacial landforms; including viscous flow features, the latitude dependant mantle and polygonised ground). A grid of squares (e.g. 20 km by 20 km) is created over the mapping area. Then the basemap data are systematically examined, grid-square by grid-square at full resolution, in order to identify the landforms while recording the presence or absence of selected landforms in each grid-square to determine spatial distributions. The result is a series of grids recording the distribution of all the mapped landforms across the study area. In some ways, these are equivalent to raster images, as they show a continuous distribution-field of the various landforms across a defined (rectangular, in most cases) area. When overlain on context maps, these form a coarse, digital landform map. We find that grid-based mapping provides an efficient solution to the problems of mapping small landforms over large areas, by providing a consistent and standardised approach to spatial data collection. The simplicity of the grid-based mapping approach makes it extremely scalable and workable for group efforts, requiring minimal user experience and producing consistent and repeatable results. The discrete nature of the datasets, simplicity of approach, and divisibility of tasks, open up the possibility for citizen science in which crowdsourcing large grid-based mapping areas could be applied.

Nonlocal and Mixed-Locality Multiscale Finite Element Methods

DOE PAGES

Costa, Timothy B.; Bond, Stephen D.; Littlewood, David J.

2018-03-27

In many applications the resolution of small-scale heterogeneities remains a significant hurdle to robust and reliable predictive simulations. In particular, while material variability at the mesoscale plays a fundamental role in processes such as material failure, the resolution required to capture mechanisms at this scale is often computationally intractable. Multiscale methods aim to overcome this difficulty through judicious choice of a subscale problem and a robust manner of passing information between scales. One promising approach is the multiscale finite element method, which increases the fidelity of macroscale simulations by solving lower-scale problems that produce enriched multiscale basis functions. Here, inmore » this study, we present the first work toward application of the multiscale finite element method to the nonlocal peridynamic theory of solid mechanics. This is achieved within the context of a discontinuous Galerkin framework that facilitates the description of material discontinuities and does not assume the existence of spatial derivatives. Analysis of the resulting nonlocal multiscale finite element method is achieved using the ambulant Galerkin method, developed here with sufficient generality to allow for application to multiscale finite element methods for both local and nonlocal models that satisfy minimal assumptions. Finally, we conclude with preliminary results on a mixed-locality multiscale finite element method in which a nonlocal model is applied at the fine scale and a local model at the coarse scale.« less

Metallicity fluctuation statistics in the interstellar medium and young stars - I. Variance and correlation

NASA Astrophysics Data System (ADS)

Krumholz, Mark R.; Ting, Yuan-Sen

2018-04-01

The distributions of a galaxy's gas and stars in chemical space encode a tremendous amount of information about that galaxy's physical properties and assembly history. However, present methods for extracting information from chemical distributions are based either on coarse averages measured over galactic scales (e.g. metallicity gradients) or on searching for clusters in chemical space that can be identified with individual star clusters or gas clouds on ˜1 pc scales. These approaches discard most of the information, because in galaxies gas and young stars are observed to be distributed fractally, with correlations on all scales, and the same is likely to be true of metals. In this paper we introduce a first theoretical model, based on stochastically forced diffusion, capable of predicting the multiscale statistics of metal fields. We derive the variance, correlation function, and power spectrum of the metal distribution from first principles, and determine how these quantities depend on elements' astrophysical origin sites and on the large-scale properties of galaxies. Among other results, we explain for the first time why the typical abundance scatter observed in the interstellar media of nearby galaxies is ≈0.1 dex, and we predict that this scatter will be correlated on spatial scales of ˜0.5-1 kpc, and over time-scales of ˜100-300 Myr. We discuss the implications of our results for future chemical tagging studies.

Nonlocal and Mixed-Locality Multiscale Finite Element Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Costa, Timothy B.; Bond, Stephen D.; Littlewood, David J.

In many applications the resolution of small-scale heterogeneities remains a significant hurdle to robust and reliable predictive simulations. In particular, while material variability at the mesoscale plays a fundamental role in processes such as material failure, the resolution required to capture mechanisms at this scale is often computationally intractable. Multiscale methods aim to overcome this difficulty through judicious choice of a subscale problem and a robust manner of passing information between scales. One promising approach is the multiscale finite element method, which increases the fidelity of macroscale simulations by solving lower-scale problems that produce enriched multiscale basis functions. Here, inmore » this study, we present the first work toward application of the multiscale finite element method to the nonlocal peridynamic theory of solid mechanics. This is achieved within the context of a discontinuous Galerkin framework that facilitates the description of material discontinuities and does not assume the existence of spatial derivatives. Analysis of the resulting nonlocal multiscale finite element method is achieved using the ambulant Galerkin method, developed here with sufficient generality to allow for application to multiscale finite element methods for both local and nonlocal models that satisfy minimal assumptions. Finally, we conclude with preliminary results on a mixed-locality multiscale finite element method in which a nonlocal model is applied at the fine scale and a local model at the coarse scale.« less

Characterization of coarse particulate matter in school gyms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Branis, Martin, E-mail: branis@natur.cuni.cz; Safranek, Jiri

2011-05-15

We investigated the mass concentration, mineral composition and morphology of particles resuspended by children during scheduled physical education in urban, suburban and rural elementary school gyms in Prague (Czech Republic). Cascade impactors were deployed to sample the particulate matter. Two fractions of coarse particulate matter (PM{sub 10-2.5} and PM{sub 2.5-1.0}) were characterized by gravimetry, energy dispersive X-ray spectrometry and scanning electron microscopy. Two indicators of human activity, the number of exercising children and the number of physical education hours, were also recorded. Lower mass concentrations of coarse particulate matter were recorded outdoors (average PM{sub 10-2.5} 4.1-7.4 {mu}g m{sup -3} andmore » PM{sub 2.5-1.0} 2.0-3.3 {mu}g m{sup -3}) than indoors (average PM{sub 10-2.5} 13.6-26.7 {mu}g m{sup -3} and PM{sub 2.5-1.0} 3.7-7.4 {mu}g m{sup -3}). The indoor concentrations of coarse aerosol were elevated during days with scheduled physical education with an average indoor-outdoor (I/O) ratio of 2.5-16.3 for the PM{sub 10-2.5} and 1.4-4.8 for the PM{sub 2.5-1.0} values. Under extreme conditions, the I/O ratios reached 180 (PM{sub 10-2.5}) and 19.1 (PM{sub 2.5-1.0}). The multiple regression analysis based on the number of students and outdoor coarse PM as independent variables showed that the main predictor of the indoor coarse PM concentrations is the number of students in the gym. The effect of outdoor coarse PM was weak and inconsistent. The regression models for the three schools explained 60-70% of the particular dataset variability. X-ray spectrometry revealed 6 main groups of minerals contributing to resuspended indoor dust. The most abundant particles were those of crustal origin composed of Si, Al, O and Ca. Scanning electron microscopy showed that, in addition to numerous inorganic particles, various types of fibers and particularly skin scales make up the main part of the resuspended dust in the gyms. In conclusion, school gyms were found to be indoor microenvironments with high concentrations of coarse particulate matter, which can contribute to increased short-term inhalation exposure of exercising children. - Highlights: {yields} We studied concentration, composition and morphology of coarse particles in gyms. {yields} Indoor concentration of coarse particles was high during days with pupils activity. {yields} Effect of outdoor coarse dust on indoor levels was weak and inconsistent. {yields} Six main groups of minerals contributing to indoor resuspended dust were determined. {yields} The most abundant coarse particles were human skin scales.« less

Spatial autocorrelation of radiation measured by the Earth Radiation Budget Experiment: Scene inhomogeneity and reciprocity violation

NASA Technical Reports Server (NTRS)

Davies, Roger

1994-01-01

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles less than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.

Kilometric Scale Modeling of the North West European Shelf Seas: Exploring the Spatial and Temporal Variability of Internal Tides

NASA Astrophysics Data System (ADS)

Guihou, K.; Polton, J.; Harle, J.; Wakelin, S.; O'Dea, E.; Holt, J.

2018-01-01

The North West European Shelf break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the fine-scale processes over the whole region. In this context, under the FASTNEt program, a new NEMO configuration of the North West European Shelf and Atlantic Margin at 1/60° (˜1.8 km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and fine-resolution temperature profiles is assessed against a basin-scale (1/12°, ˜9 km) configuration and a standard regional configuration (7 km resolution). The seasonal cycle is well reproduced in all configurations though the fine-resolution allows the simulation of smaller scale processes. Time series of temperature at various locations on the shelf show the presence of internal waves with a strong spatiotemporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semidiurnal, inertial, and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2 m for 30% of the stratified domain. With sufficiently fine resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern.

Global-scale Ionospheric Outflow: Major Processes and Unresolved Problems

NASA Astrophysics Data System (ADS)

Liemohn, M. W.; Welling, D. T.; Ilie, R.; Khazanov, G. V.; Jahn, J. M.; Zou, S.; Ganushkina, N. Y.; Valek, P. W.; Elliott, H. A.; Gilchrist, B. E.; Hoegy, W. R.; Glocer, A.

2016-12-01

Outflow from the ionosphere is a major source of plasma to the magnetosphere. Its presence, especially that of ions heavier than He+, mass loads the magnetosphere and changes reconnection rates, current system configurations, plasma wave excitation and wave-particle interactions. It even impacts the propagation of information. We present a brief overview of the major processes and scientific history of this field. There are still major gaps, however, in our understanding of the global-scale nature of ionospheric outflow. We discuss these unresolved problems highlighting the leading questions still outstanding on this topic. First and foremost, since the measurements of ionospheric outflow have largely come from individual satellites and sounding rockets, the processes are best known on the local level, while the spatial distribution of outflow has never been simultaneously measured on more global scales. The spatial coherence and correlation of outflow across time and space have not been quantified. Furthermore, the composition of the outflow is often only measured at a coarse level of H+, He+, and O+, neglecting other species such as N+ or moleculars. However, resolving O+ from N+, as is customary in planetary research, aids in revealing the physics and altitude dependence of the energization processes in the ionosphere. Similarly, fine-resolution velocity space measurements of ionospheric outflow have been limited, yet such observations can also reveal energization processes driving the outflow. A final unresolved issue to mention is magnetically conjugate outflow and the full extent of hemispherically asymmetric outflow fluxes or fluence. Each of these open questions have substantial ramifications for magnetospheric physics; their resolution could yield sweeping changes in our understanding of nonlinear feedback and cross-scale physical interactions, magnetosphere-ionosphere coupling, and geospace system-level science.

Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change.

PubMed

Carroll, Carlos; Roberts, David R; Michalak, Julia L; Lawler, Joshua J; Nielsen, Scott E; Stralberg, Diana; Hamann, Andreas; Mcrae, Brad H; Wang, Tongli

2017-11-01

As most regions of the earth transition to altered climatic conditions, new methods are needed to identify refugia and other areas whose conservation would facilitate persistence of biodiversity under climate change. We compared several common approaches to conservation planning focused on climate resilience over a broad range of ecological settings across North America and evaluated how commonalities in the priority areas identified by different methods varied with regional context and spatial scale. Our results indicate that priority areas based on different environmental diversity metrics differed substantially from each other and from priorities based on spatiotemporal metrics such as climatic velocity. Refugia identified by diversity or velocity metrics were not strongly associated with the current protected area system, suggesting the need for additional conservation measures including protection of refugia. Despite the inherent uncertainties in predicting future climate, we found that variation among climatic velocities derived from different general circulation models and emissions pathways was less than the variation among the suite of environmental diversity metrics. To address uncertainty created by this variation, planners can combine priorities identified by alternative metrics at a single resolution and downweight areas of high variation between metrics. Alternately, coarse-resolution velocity metrics can be combined with fine-resolution diversity metrics in order to leverage the respective strengths of the two groups of metrics as tools for identification of potential macro- and microrefugia that in combination maximize both transient and long-term resilience to climate change. Planners should compare and integrate approaches that span a range of model complexity and spatial scale to match the range of ecological and physical processes influencing persistence of biodiversity and identify a conservation network resilient to threats operating at multiple scales. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Forced Gravity Waves and the Tropospheric Response to Convection

NASA Astrophysics Data System (ADS)

Halliday, O. J.; Griffiths, S. D.; Parker, D. J.; Stirling, A.

2017-12-01

It has been known for some time that gravity waves facilitate atmospheric adjustment to convective heating. Further, convectively forced gravity waves condition the neighboring atmosphere for the initiation and / or suppression of convection. Despite this, the radiation of gravity waves in macro-scale models (which are typically forced at the grid-scale, by existing parameterization schemes) is not well understood. We present here theoretical and numerical work directed toward improving our understanding of convectively forced gravity wave effects at the mesoscale. Using the linear hydrostatic equations of motion for an incompressible (but non-Boussinesq) fluid with vertically varying buoyancy frequency, we find a radiating solution to prescribed sensible heating. We then interrogate the spatial and temporal sensitivity of the vertical velocity and potential temperature response to different heating functions, considering the remote and near-field forced response both to steady and pulsed heating. We find that the meso-scale tropospheric response to convection is significantly dependent on the upward radiation characteristics of the gravity waves, which are in turn dependent upon the temporal and spatial structure of the source, and stratification of the domain. Moving from a trapped to upwardly-radiating solution there is a 50% reduction in tropospherically averaged vertical velocity, but significant perturbations persist for up to 4 hours in the far-field. We find the tropospheric adjustment to be sensitive to the horizontal length scale which characterizes the heating, observing a 20% reduction in vertical velocity when comparing the response from a 10 km to a 100 km heat source. We assess the implications for parameterization of convection in coarse-grained models in the light of these findings. We show that an idealized `full-physics' nonlinear simulation of deep convection in the UK Met Office Unified Model is qualitatively described by the linear solution: departures are quantified and explored.

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

1976-01-01

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Multiple time-scales and the developmental dynamics of social systems.

PubMed

Flack, Jessica C

2012-07-05

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the 'coarseness' of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.

Coarse-graining to the meso and continuum scales with molecular-dynamics-like models

NASA Astrophysics Data System (ADS)

Plimpton, Steve

Many engineering-scale problems that industry or the national labs try to address with particle-based simulations occur at length and time scales well beyond the most optimistic hopes of traditional coarse-graining methods for molecular dynamics (MD), which typically start at the atomic scale and build upward. However classical MD can be viewed as an engine for simulating particles at literally any length or time scale, depending on the models used for individual particles and their interactions. To illustrate I'll highlight several coarse-grained (CG) materials models, some of which are likely familiar to molecular-scale modelers, but others probably not. These include models for water droplet freezing on surfaces, dissipative particle dynamics (DPD) models of explosives where particles have internal state, CG models of nano or colloidal particles in solution, models for aspherical particles, Peridynamics models for fracture, and models of granular materials at the scale of industrial processing. All of these can be implemented as MD-style models for either soft or hard materials; in fact they are all part of our LAMMPS MD package, added either by our group or contributed by collaborators. Unlike most all-atom MD simulations, CG simulations at these scales often involve highly non-uniform particle densities. So I'll also discuss a load-balancing method we've implemented for these kinds of models, which can improve parallel efficiencies. From the physics point-of-view, these models may be viewed as non-traditional or ad hoc. But because they are MD-style simulations, there's an opportunity for physicists to add statistical mechanics rigor to individual models. Or, in keeping with a theme of this session, to devise methods that more accurately bridge models from one scale to the next.

A hierarchical framework for investigating epiphyte assemblages: networks, meta-communities, and scale.

PubMed

Burns, K C; Zotz, G

2010-02-01

Epiphytes are an important component of many forested ecosystems, yet our understanding of epiphyte communities lags far behind that of terrestrial-based plant communities. This discrepancy is exacerbated by the lack of a theoretical context to assess patterns in epiphyte community structure. We attempt to fill this gap by developing an analytical framework to investigate epiphyte assemblages, which we then apply to a data set on epiphyte distributions in a Panamanian rain forest. On a coarse scale, interactions between epiphyte species and host tree species can be viewed as bipartite networks, similar to pollination and seed dispersal networks. On a finer scale, epiphyte communities on individual host trees can be viewed as meta-communities, or suites of local epiphyte communities connected by dispersal. Similar analytical tools are typically employed to investigate species interaction networks and meta-communities, thus providing a unified analytical framework to investigate coarse-scale (network) and fine-scale (meta-community) patterns in epiphyte distributions. Coarse-scale analysis of the Panamanian data set showed that most epiphyte species interacted with fewer host species than expected by chance. Fine-scale analyses showed that epiphyte species richness on individual trees was lower than null model expectations. Therefore, epiphyte distributions were clumped at both scales, perhaps as a result of dispersal limitations. Scale-dependent patterns in epiphyte species composition were observed. Epiphyte-host networks showed evidence of negative co-occurrence patterns, which could arise from adaptations among epiphyte species to avoid competition for host species, while most epiphyte meta-communities were distributed at random. Application of our "meta-network" analytical framework in other locales may help to identify general patterns in the structure of epiphyte assemblages and their variation in space and time.

On the applicability of density dependent effective interactions in cluster-forming systems

NASA Astrophysics Data System (ADS)

Montes-Saralegui, Marta; Kahl, Gerhard; Nikoubashman, Arash

2017-02-01

We systematically studied the validity and transferability of the force-matching algorithm for computing effective pair potentials in a system of dendritic polymers, i.e., a particular class of ultrasoft colloids. We focused on amphiphilic dendrimers, macromolecules which can aggregate into clusters of overlapping particles to minimize the contact area with the surrounding implicit solvent. Simulations were performed for both the monomeric and coarse-grained models in the liquid phase at densities ranging from infinite dilution up to values close to the freezing point. The effective pair potentials for the coarse-grained simulations were computed from the monomeric simulations both in the zero-density limit (Φeff0) and at each investigated finite density (Φeff). Conducting the coarse-grained simulations with Φeff0 at higher densities is not appropriate as they failed at reproducing the structural properties of the monomeric simulations. In contrast, we found excellent agreement between the spatial dendrimer distributions obtained from the coarse-grained simulations with Φeff and the microscopically detailed simulations at low densities, where the macromolecules were distributed homogeneously in the system. However, the reliability of the coarse-grained simulations deteriorated significantly as the density was increased further and the cluster occupation became more polydisperse. Under these conditions, the effective pair potential of the coarse-grained model can no longer be computed by averaging over the whole system, but the local density needs to be taken into account instead.

Shape-Driven 3D Segmentation Using Spherical Wavelets

PubMed Central

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2013-01-01

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details. PMID:17354875

On system behaviour using complex networks of a compression algorithm

NASA Astrophysics Data System (ADS)

Walker, David M.; Correa, Debora C.; Small, Michael

2018-01-01

We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.

Emergent space-time via a geometric renormalization method

NASA Astrophysics Data System (ADS)

Rastgoo, Saeed; Requardt, Manfred

2016-12-01

We present a purely geometric renormalization scheme for metric spaces (including uncolored graphs), which consists of a coarse graining and a rescaling operation on such spaces. The coarse graining is based on the concept of quasi-isometry, which yields a sequence of discrete coarse grained spaces each having a continuum limit under the rescaling operation. We provide criteria under which such sequences do converge within a superspace of metric spaces, or may constitute the basin of attraction of a common continuum limit, which hopefully may represent our space-time continuum. We discuss some of the properties of these coarse grained spaces as well as their continuum limits, such as scale invariance and metric similarity, and show that different layers of space-time can carry different distance functions while being homeomorphic. Important tools in this analysis are the Gromov-Hausdorff distance functional for general metric spaces and the growth degree of graphs or networks. The whole construction is in the spirit of the Wilsonian renormalization group (RG). Furthermore, we introduce a physically relevant notion of dimension on the spaces of interest in our analysis, which, e.g., for regular lattices reduces to the ordinary lattice dimension. We show that this dimension is stable under the proposed coarse graining procedure as long as the latter is sufficiently local, i.e., quasi-isometric, and discuss the conditions under which this dimension is an integer. We comment on the possibility that the limit space may turn out to be fractal in case the dimension is noninteger. At the end of the paper we briefly mention the possibility that our network carries a translocal far order that leads to the concept of wormhole spaces and a scale dependent dimension if the coarse graining procedure is no longer local.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Environmental analysis of groundwater in Mecosta County, Michigan.

PubMed

Steinman, Alan D; Biddanda, Bopi; Chu, Xuefeng; Thompson, Kurt; Rediske, Rick

2007-11-01

Groundwater withdrawal has major economic, social, and environmental implications. In Michigan, recent legislative activity has begun to address the issue of groundwater sustainability. However, more hydrologic data are needed to help inform policy and legislation. A study was conducted in Mecosta County, Michigan to: (1) determine if a relationship could be established between land use/land cover and groundwater quality; and (2) develop a conceptual model for the shallow groundwater system of the study region. In general, groundwater quality was good, with below detection levels of E. coli, low total bacterial counts, and relatively low nutrient concentrations. No statistically significant associations were found between the bacterial numbers and either land use or the physical/chemical attributes measured, which may be because the scale of our spatial analysis was too coarse to detect patterns. Finer-scale, localized processes may have a greater influence on microorganism growth and abundance than coarser-scale, regional processes in this area. Our groundwater analysis suggested that shallow groundwater flow paths are generally consistent with regional surface water flow networks, and that shallow groundwater levels in most of the region have fluctuated within 1-2 m over the past 30 years, with no obvious increasing or decreasing trend.

Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

PubMed

Wang, Hongye; McIntosh, Anthony R; Kovacevic, Natasa; Karachalios, Maria; Protzner, Andrea B

2016-07-01

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Nanoposition sensors with superior linear response to position and unlimited travel ranges

NASA Astrophysics Data System (ADS)

Lee, Sheng-Chiang; Peters, Randall D.

2009-04-01

With the advancement in nanotechnology, the ability of positioning/measuring at subnanometer scale has been one of the most critical issues for the nanofabrication industry and researchers using scanning probe microscopy. Commercial nanopositioners have achieved direct measurements at the scale of 0.01 nm with capacitive sensing metrology. However, the commercial sensors have small dynamic ranges (up to only a few hundred micrometers) and are relatively large in size (centimeters in the transverse directions to the motion), which is necessary for healthy signal detections but making it difficult to use on smaller devices. This limits applications in which large materials (on the scale of centimeters or greater) are handled with needs of subnanometer resolutions. What has been done in the past is to combine the fine and coarse translation stages with different dynamic ranges to simultaneously achieve long travel range and high spatial resolution. In this paper, we present a novel capacitive position sensing metrology with ultrawide dynamic range from subnanometer to literally any practically desired length for a translation stage. This sensor will greatly simplify the task and enhance the performance of direct metrology in a hybrid translational stage covering translation tasks from subnanometer to centimeters.

A coarse-grained model for synergistic action of multiple enzymes on cellulose

DOE PAGES

Asztalos, Andrea; Daniels, Marcus; Sethi, Anurag; ...

2012-08-01

In this study, degradation of cellulose to glucose requires the cooperative action of three classes of enzymes, collectively known as cellulases. Endoglucanases randomly bind to cellulose surfaces and generate new chain ends by hydrolyzing -1,4-D-glycosidic bonds. Exoglucanases bind to free chain ends and hydrolyze glycosidic bonds in a processive manner releasing cellobiose units. Then, -glucosidases hydrolyze soluble cellobiose to glucose. Optimal synergistic action of these enzymes is essential for efficient digestion of cellulose. Experiments show that as hydrolysis proceeds and the cellulose substrate becomes more heterogeneous, the overall degradation slows down. As catalysis occurs on the surface of crystalline cellulose,more » several factors affect the overall hydrolysis. Therefore, spatial models of cellulose degradation must capture effects such as enzyme crowding and surface heterogeneity, which have been shown to lead to a reduction in hydrolysis rates. As a result, we present a coarse-grained stochastic model for capturing the key events associated with the enzymatic degradation of cellulose at the mesoscopic level. This functional model accounts for the mobility and action of a single cellulase enzyme as well as the synergy of multiple endo- and exo-cellulases on a cellulose surface. The quantitative description of cellulose degradation is calculated on a spatial model by including free and bound states of both endo- and exo-cellulases with explicit reactive surface terms (e.g., hydrogen bond breaking, covalent bond cleavages) and corresponding reaction rates. The dynamical evolution of the system is simulated by including physical interactions between cellulases and cellulose. In conclusion, our coarse-grained model reproduces the qualitative behavior of endoglucanases and exoglucanases by accounting for the spatial heterogeneity of the cellulose surface as well as other spatial factors such as enzyme crowding. Importantly, it captures the endo-exo synergism of cellulase enzyme cocktails. This model constitutes a critical step towards testing hypotheses and understanding approaches for maximizing synergy and substrate properties with a goal of cost effective enzymatic hydrolysis.« less

Coarse-grained forms for equations describing the microscopic motion of particles in a fluid.

PubMed

Das, Shankar P; Yoshimori, Akira

2013-10-01

Exact equations of motion for the microscopically defined collective density ρ(x,t) and the momentum density ĝ(x,t) of a fluid have been obtained in the past starting from the corresponding Langevin equations representing the dynamics of the fluid particles. In the present work we average these exact equations of microscopic dynamics over the local equilibrium distribution to obtain stochastic partial differential equations for the coarse-grained densities with smooth spatial and temporal dependence. In particular, we consider Dean's exact balance equation for the microscopic density of a system of interacting Brownian particles to obtain the basic equation of the dynamic density functional theory with noise. Our analysis demonstrates that on thermal averaging the dependence of the exact equations on the bare interaction potential is converted to dependence on the corresponding thermodynamic direct correlation functions in the coarse-grained equations.

Coarse-grained simulations of polyelectrolyte complexes: MARTINI models for poly(styrene sulfonate) and poly(diallyldimethylammonium)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vögele, Martin; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt a. M.; Holm, Christian

2015-12-28

We present simulations of aqueous polyelectrolyte complexes with new MARTINI models for the charged polymers poly(styrene sulfonate) and poly(diallyldimethylammonium). Our coarse-grained polyelectrolyte models allow us to study large length and long time scales with regard to chemical details and thermodynamic properties. The results are compared to the outcomes of previous atomistic molecular dynamics simulations and verify that electrostatic properties are reproduced by our MARTINI coarse-grained approach with reasonable accuracy. Structural similarity between the atomistic and the coarse-grained results is indicated by a comparison between the pair radial distribution functions and the cumulative number of surrounding particles. Our coarse-grained models aremore » able to quantitatively reproduce previous findings like the correct charge compensation mechanism and a reduced dielectric constant of water. These results can be interpreted as the underlying reason for the stability of polyelectrolyte multilayers and complexes and validate the robustness of the proposed models.« less

Fine-scale topography in sensory systems: insights from Drosophila and vertebrates

PubMed Central

Kaneko, Takuya; Ye, Bing

2015-01-01

To encode the positions of sensory stimuli, sensory circuits form topographic maps in the central nervous system through specific point-to-point connections between pre- and post-synaptic neurons. In vertebrate visual systems, the establishment of topographic maps involves the formation of a coarse topography followed by that of fine-scale topography that distinguishes the axon terminals of neighboring neurons. It is known that intrinsic differences in the form of broad gradients of guidance molecules instruct coarse topography while neuronal activity is required for fine-scale topography. On the other hand, studies in the Drosophila visual system have shown that intrinsic differences in cell adhesion among the axon terminals of neighboring neurons instruct the fine-scale topography. Recent studies on activity-dependent topography in the Drosophila somatosensory system have revealed a role of neuronal activity in creating molecular differences among sensory neurons for establishing fine-scale topography, implicating a conserved principle. Here we review the findings in both Drosophila and vertebrates and propose an integrated model for fine-scale topography. PMID:26091779

Fine-scale topography in sensory systems: insights from Drosophila and vertebrates.

PubMed

Kaneko, Takuya; Ye, Bing

2015-09-01

To encode the positions of sensory stimuli, sensory circuits form topographic maps in the central nervous system through specific point-to-point connections between pre- and postsynaptic neurons. In vertebrate visual systems, the establishment of topographic maps involves the formation of a coarse topography followed by that of fine-scale topography that distinguishes the axon terminals of neighboring neurons. It is known that intrinsic differences in the form of broad gradients of guidance molecules instruct coarse topography while neuronal activity is required for fine-scale topography. On the other hand, studies in the Drosophila visual system have shown that intrinsic differences in cell adhesion among the axon terminals of neighboring neurons instruct the fine-scale topography. Recent studies on activity-dependent topography in the Drosophila somatosensory system have revealed a role of neuronal activity in creating molecular differences among sensory neurons for establishing fine-scale topography, implicating a conserved principle. Here we review the findings in both Drosophila and vertebrates and propose an integrated model for fine-scale topography.

Coarse woody type: A new method for analyzing coarse woody debris and forest change

Treesearch

C. W. Woodall; L. M. Nagel

2006-01-01

The species composition of both standing live and down dead trees has been used separately to determine forest stand dynamics in large-scale forest ecosystem assessments. The species composition of standing live trees has been used to indicate forest stand diversity while the species composition of down dead trees has been used to indicate wildlife habitat. To assess...

Estimating resource acquisition and at-sea body condition of a marine predator

PubMed Central

Schick, Robert S; New, Leslie F; Thomas, Len; Costa, Daniel P; Hindell, Mark A; McMahon, Clive R; Robinson, Patrick W; Simmons, Samantha E; Thums, Michele; Harwood, John; Clark, James S

2013-01-01

Body condition plays a fundamental role in many ecological and evolutionary processes at a variety of scales and across a broad range of animal taxa. An understanding of how body condition changes at fine spatial and temporal scales as a result of interaction with the environment provides necessary information about how animals acquire resources. However, comparatively little is known about intra- and interindividual variation of condition in marine systems. Where condition has been studied, changes typically are recorded at relatively coarse time-scales. By quantifying how fine-scale interaction with the environment influences condition, we can broaden our understanding of how animals acquire resources and allocate them to body stores. Here we used a hierarchical Bayesian state-space model to estimate the body condition as measured by the size of an animal's lipid store in two closely related species of marine predator that occupy different hemispheres: northern elephant seals (Mirounga angustirostris) and southern elephant seals (Mirounga leonina). The observation model linked drift dives to lipid stores. The process model quantified daily changes in lipid stores as a function of the physiological condition of the seal (lipid:lean tissue ratio, departure lipid and departure mass), its foraging location, two measures of behaviour and environmental covariates. We found that physiological condition significantly impacted lipid gain at two time-scales – daily and at departure from the colony – that foraging location was significantly associated with lipid gain in both species of elephant seals and that long-term behavioural phase was associated with positive lipid gain in northern and southern elephant seals. In northern elephant seals, the occurrence of short-term behavioural states assumed to represent foraging were correlated with lipid gain. Lipid gain was a function of covariates in both species. Southern elephant seals performed fewer drift dives than northern elephant seals and gained lipids at a lower rate. We have demonstrated a new way to obtain time series of body condition estimates for a marine predator at fine spatial and temporal scales. This modelling approach accounts for uncertainty at many levels and has the potential to integrate physiological and movement ecology of top predators. The observation model we used was specific to elephant seals, but the process model can readily be applied to other species, providing an opportunity to understand how animals respond to their environment at a fine spatial scale. PMID:23869551

Time-marching multi-grid seismic tomography

NASA Astrophysics Data System (ADS)

Tong, P.; Yang, D.; Liu, Q.

2016-12-01

From the classic ray-based traveltime tomography to the state-of-the-art full waveform inversion, because of the nonlinearity of seismic inverse problems, a good starting model is essential for preventing the convergence of the objective function toward local minima. With a focus on building high-accuracy starting models, we propose the so-called time-marching multi-grid seismic tomography method in this study. The new seismic tomography scheme consists of a temporal time-marching approach and a spatial multi-grid strategy. We first divide the recording period of seismic data into a series of time windows. Sequentially, the subsurface properties in each time window are iteratively updated starting from the final model of the previous time window. There are at least two advantages of the time-marching approach: (1) the information included in the seismic data of previous time windows has been explored to build the starting models of later time windows; (2) seismic data of later time windows could provide extra information to refine the subsurface images. Within each time window, we use a multi-grid method to decompose the scale of the inverse problem. Specifically, the unknowns of the inverse problem are sampled on a coarse mesh to capture the macro-scale structure of the subsurface at the beginning. Because of the low dimensionality, it is much easier to reach the global minimum on a coarse mesh. After that, finer meshes are introduced to recover the micro-scale properties. That is to say, the subsurface model is iteratively updated on multi-grid in every time window. We expect that high-accuracy starting models should be generated for the second and later time windows. We will test this time-marching multi-grid method by using our newly developed eikonal-based traveltime tomography software package tomoQuake. Real application results in the 2016 Kumamoto earthquake (Mw 7.0) region in Japan will be demonstrated.

Quantification of Impervious Surfaces Along the Wasatch Front, Utah: AN Object-Based Image Analysis Approach to Identifying AN Indicator for Wetland Stress

NASA Astrophysics Data System (ADS)

Leydsman-McGinty, E. I.; Ramsey, R. D.; McGinty, C.

2013-12-01

The Remote Sensing/GIS Laboratory at Utah State University, in cooperation with the United States Environmental Protection Agency, is quantifying impervious surfaces for three watershed sub-basins in Utah. The primary objective of developing watershed-scale quantifications of impervious surfaces is to provide an indicator of potential impacts to wetlands that occur within the Wasatch Front and along the Great Salt Lake. A geospatial layer of impervious surfaces can assist state agencies involved with Utah's Wetlands Program Plan (WPP) in understanding the impacts of impervious surfaces on wetlands, as well as support them in carrying out goals and actions identified in the WPP. The three watershed sub-basins, Lower Bear-Malad, Lower Weber, and Jordan, span the highly urbanized Wasatch Front and are consistent with focal areas in need of wetland monitoring and assessment as identified in Utah's WPP. Geospatial layers of impervious surface currently exist in the form of national and regional land cover datasets; however, these datasets are too coarse to be utilized in fine-scale analyses. In addition, the pixel-based image processing techniques used to develop these coarse datasets have proven insufficient in smaller scale or detailed studies, particularly when applied to high-resolution satellite imagery or aerial photography. Therefore, object-based image analysis techniques are being implemented to develop the geospatial layer of impervious surfaces. Object-based image analysis techniques employ a combination of both geospatial and image processing methods to extract meaningful information from high-resolution imagery. Spectral, spatial, textural, and contextual information is used to group pixels into image objects and then subsequently used to develop rule sets for image classification. eCognition, an object-based image analysis software program, is being utilized in conjunction with one-meter resolution National Agriculture Imagery Program (NAIP) aerial photography from 2011.

Integrating Green and Blue Water Management Tools for Land and Water Resources Planning

NASA Astrophysics Data System (ADS)

Jewitt, G. P. W.

2009-04-01

The role of land use and land use change on the hydrological cycle is well known. However, the impacts of large scale land use change are poorly considered in water resources planning, unless they require direct abstraction of water resources and associated development of infrastructure e.g. Irrigation Schemes. However, large scale deforestation for the supply of raw materials, expansion of the areas of plantation forestry, increasing areas under food production and major plans for cultivation of biofuels in many developing countries are likely to result in extensive land use change. Given the spatial extent and temporal longevity of these proposed developments, major impacts on water resources are inevitable. It is imperative that managers and planners consider the consequences for downstream ecosystems and users in such developments. However, many popular tools, such as the vitual water approach, provide only coarse scale "order of magnitude" type estimates with poor consideration of, and limited usefulness, for land use planning. In this paper, a framework for the consideration of the impacts of large scale land use change on water resources at a range of temporal and spatial scales is presented. Drawing on experiences from South Africa, where the establishment of exotic commercial forest plantations is only permitted once a water use license has been granted, the framework adopts the "green water concept" for the identification of potential high impact areas of land use change and provides for integration with traditional "blue water" water resources planning tools for more detailed planning. Appropriate tools, ranging from simple spreadsheet solutions to more sophisticated remote sensing and hydrological models are described, and the application of the framework for consideration of water resources impacts associated with the establishment of large scale tectona grandis, sugar cane and jatropha curcas plantations is illustrated through examples in Mozambique and South Africa. Keywords: Land use change, water resources, green water, blue water, biofuels, developing countries

Impacts of Agricultural Decision Making and Adaptive Management on Food Security in Africa

NASA Astrophysics Data System (ADS)

Caylor, K. K.; Evans, T. P.; Estes, L. D.; Sheffield, J.; Plale, B. A.; Attari, S.

2014-12-01

Despite massive investments in food aid, agricultural extension, and seed/fertilizer subsidies, nearly 1 billion people in the developing world are food insecure and vulnerable to climate variability. Sub-Saharan Africa is most vulnerable, as approximately 25% of its people are undernourished (FAO/FAOSTAT 2013) and 96% of its cropland is rainfed (FAO 2002). The ability of subsistence farmers to respond to changes in water availability involves both inter-and intra-seasonal adaptation. Adaptive capacity diminishes over the season as decisions are made, resources are used, and the set of possible futures becomes restricted. Assessing the intra-seasonal adaptive capacity of smallholders requires integrating physical models of hydrological and agricultural dynamics with farmer decision-making at fine temporal (e.g. weekly) and spatial (e.g. crop field) scales. However, there is an intrinsic challenge to modeling the dynamics of these sociohydrologic systems, because important and uncharacterized spatial and temporal scale mismatches exist between the level at which the water resource is best understood and the level at which human dynamics are more predictable. For example, the skill of current process-based land surface models is primarily confined to short-term (daily to weekly), national- to regional-scale assessments, and reliable agricultural yield estimates and forecasts for small-scale farming systems remain elusive. In contrast, process-based social science modeling has focused on agent-based approaches that generate fine-scale (individual to community) dynamics over rather coarse time scales (yearly to decadal). A major obstacle to addressing this mismatch is the fundamental fact that the highest skill domain of one framework is essentially unpredictable in the other. We present a coupled sociohydrological observation framework designed to addressing this gap, and demonstrate its utility to understand relationships between climate variability, decision making, and crop production for subsistence agriculturalists in Kenya and Zambia.

Plant-based plume-scale mapping of tritium contamination in desert soils

USGS Publications Warehouse

Andraski, Brian J.; Stonestrom, David A.; Michel, R.L.; Halford, K.J.; Radyk, J.C.

2005-01-01

Plant-based techniques were tested for field-scale evaluation of tritium contamination adjacent to a low-level radioactive waste (LLRW) facility in the Amargosa Desert, Nevada. Objectives were to (i) characterize and map the spatial variability of tritium in plant water, (ii) develop empirical relations to predict and map subsurface contamination from plant-water concentrations, and (iii) gain insight into tritium migration pathways and processes. Plant sampling [creosote bush, Larrea tridentata (Sessé & Moc. ex DC.) Coville] required one-fifth the time of soil water vapor sampling. Plant concentrations were spatially correlated to a separation distance of 380 m; measurement uncertainty accounted for <0.1% of the total variability in the data. Regression equations based on plant tritium explained 96 and 90% of the variation in root-zone and sub-root-zone soil water vapor concentrations, respectively. The equations were combined with kriged plant-water concentrations to map subsurface contamination. Mapping showed preferential lateral movement of tritium through a dry, coarse-textured layer beneath the root zone, with concurrent upward movement through the root zone. Analysis of subsurface fluxes along a transect perpendicular to the LLRW facility showed that upward diffusive-vapor transport dominates other transport modes beneath native vegetation. Downward advective-liquid transport dominates at one endpoint of the transect, beneath a devegetated road immediately adjacent to the facility. To our knowledge, this study is the first to document large-scale subsurface vapor-phase tritium migration from a LLRW facility. Plant-based methods provide a noninvasive, cost-effective approach to mapping subsurface tritium migration in desert areas.

Large-scale mapping and predictive modeling of submerged aquatic vegetation in a shallow eutrophic lake.

PubMed

Havens, Karl E; Harwell, Matthew C; Brady, Mark A; Sharfstein, Bruce; East, Therese L; Rodusky, Andrew J; Anson, Daniel; Maki, Ryan P

2002-04-09

A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in August-September 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

Assessing uncertainty in high-resolution spatial climate data across the US Northeast.

PubMed

Bishop, Daniel A; Beier, Colin M

2013-01-01

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980-2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products.

What Data to Use for Forest Conservation Planning? A Comparison of Coarse Open and Detailed Proprietary Forest Inventory Data in Finland

PubMed Central

Lehtomäki, Joona; Tuominen, Sakari; Toivonen, Tuuli; Leinonen, Antti

2015-01-01

The boreal region is facing intensifying resource extraction pressure, but the lack of comprehensive biodiversity data makes operative forest conservation planning difficult. Many countries have implemented forest inventory schemes and are making extensive and up-to-date forest databases increasingly available. Some of the more detailed inventory databases, however, remain proprietary and unavailable for conservation planning. Here, we investigate how well different open and proprietary forest inventory data sets suit the purpose of conservation prioritization in Finland. We also explore how much priorities are affected by using the less accurate but open data. First, we construct a set of indices for forest conservation value based on quantitative information commonly found in forest inventories. These include the maturity of the trees, tree species composition, and site fertility. Secondly, using these data and accounting for connectivity between forest types, we investigate the patterns in conservation priority. For prioritization, we use Zonation, a method and software for spatial conservation prioritization. We then validate the prioritizations by comparing them to known areas of high conservation value. We show that the overall priority patterns are relatively consistent across different data sources and analysis options. However, the coarse data cannot be used to accurately identify the high-priority areas as it misses much of the fine-scale variation in forest structures. We conclude that, while inventory data collected for forestry purposes may be useful for forest conservation purposes, it needs to be detailed enough to be able to account for more fine-scaled features of high conservation value. These results underline the importance of making detailed inventory data publicly available. Finally, we discuss how the prioritization methodology we used could be integrated into operative forest management, especially in countries in the boreal zone. PMID:26317227

What Data to Use for Forest Conservation Planning? A Comparison of Coarse Open and Detailed Proprietary Forest Inventory Data in Finland.

PubMed

Lehtomäki, Joona; Tuominen, Sakari; Toivonen, Tuuli; Leinonen, Antti

2015-01-01

The boreal region is facing intensifying resource extraction pressure, but the lack of comprehensive biodiversity data makes operative forest conservation planning difficult. Many countries have implemented forest inventory schemes and are making extensive and up-to-date forest databases increasingly available. Some of the more detailed inventory databases, however, remain proprietary and unavailable for conservation planning. Here, we investigate how well different open and proprietary forest inventory data sets suit the purpose of conservation prioritization in Finland. We also explore how much priorities are affected by using the less accurate but open data. First, we construct a set of indices for forest conservation value based on quantitative information commonly found in forest inventories. These include the maturity of the trees, tree species composition, and site fertility. Secondly, using these data and accounting for connectivity between forest types, we investigate the patterns in conservation priority. For prioritization, we use Zonation, a method and software for spatial conservation prioritization. We then validate the prioritizations by comparing them to known areas of high conservation value. We show that the overall priority patterns are relatively consistent across different data sources and analysis options. However, the coarse data cannot be used to accurately identify the high-priority areas as it misses much of the fine-scale variation in forest structures. We conclude that, while inventory data collected for forestry purposes may be useful for forest conservation purposes, it needs to be detailed enough to be able to account for more fine-scaled features of high conservation value. These results underline the importance of making detailed inventory data publicly available. Finally, we discuss how the prioritization methodology we used could be integrated into operative forest management, especially in countries in the boreal zone.

Predictability of the California Current System

NASA Technical Reports Server (NTRS)

Miller, Arthur J.; Chereskin, T.; Cornuelle, B. D.; Niiler, P. P.; Moisan, J. R.; Lindstrom, Eric (Technical Monitor)

2001-01-01

The physical and biological oceanography of the Southern California Bight (SCB), a highly productive subregion of the California Current System (CCS) that extends from Point Conception, California, south to Ensenada, Mexico, continues to be extensively studied. For example, the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has sampled this region for over 50 years, providing an unparalleled time series of physical and biological data. However, our understanding of what physical processes control the large-scale and mesoscale variations in these properties is incomplete. In particular, the non-synoptic and relatively coarse spatial sampling (70km) of the hydrographic grid does not completely resolve the mesoscale eddy field (Figure 1a). Moreover, these unresolved physical variations exert a dominant influence on the evolution of the ecosystem. In recent years, additional datasets that partially sample the SCB have become available. Acoustic Doppler Current Profiler (ADCP) measurements, which now sample upper-ocean velocity between stations, and sea level observations along TOPEX tracks give a more complete picture of the mesoscale variability. However, both TOPEX and ADCP are well-sampled only along the cruise or orbit tracks and coarsely sampled in time and between tracks. Surface Lagrangian drifters also sample the region, although irregularly in time and space. SeaWiFS provides estimates of upper-ocean chlorophyll-a (chl-alpha), usually giving nearly complete coverage for week-long intervals, depending on cloud coverage. Historical ocean color data from the Coastal Zone Color Scanner (CZCS) has been used extensively to determine phytoplankton patterns and variability, characterize the primary production across the SCB coastal fronts, and describe the seasonal and interannual variability in pigment concentrations. As in CalCOFI, these studies described much of the observed structures and their variability over relatively large space and time scales.

A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

PubMed Central

Philips, Ryan T.; Chakravarthy, V. Srinivasa

2017-01-01

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation (rc). The rc between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps. PMID:28111542

A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

PubMed

Philips, Ryan T; Chakravarthy, V Srinivasa

2016-01-01

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation ( r c ). The r c between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps.

Cyclic steps and superimposed antidune deposits: important elements of coarse-grained deepwater channel-levée complexes

NASA Astrophysics Data System (ADS)

Lang, Joerg; Brandes, Christian; Winsemann, Jutta

2017-04-01

The facies distribution and architecture of submarine fans can be strongly impacted by erosion and deposition by supercritical density flows. We present field examples from the Sandino Forearc Basin (southern Central America), where cyclic-step and antidune deposits represent important sedimentary facies of coarse-grained channel-levée complexes. These bedforms occur in all sub-environments of the depositional systems and relate to the different stages of avulsion, bypass, levée construction and channel backfilling. Large-scale scours (18 to 29 m deep, 18 to 25 m wide, 60 to >120 m long) with an amalgamated infill, comprising massive, normally coarse-tail graded or spaced subhorizontally stratified conglomerates and pebbly sandstones, are interpreted as deposits of the hydraulic-jump zone of cyclic steps. These cyclic steps probably formed during avulsion, when high-density flows were routed into the evolving channel. The large-scale scour fills can be distinguished from small-scale channel fills based on the preservation of a steep upper margin and a coarse-grained infill comprising mainly amalgamated hydraulic-jump deposits. Channel fills include repetitive successions deposited by cyclic steps with superimposed antidunes. The hydraulic-jump zone of cyclic-step deposits comprises regularly spaced scours (0.2 to 2.6 m deep, 0.8 to 23 m wide), which are infilled by intraclast-rich conglomerates or pebbly sandstones and display normal coarse-tail grading or backsets. Laterally and vertically these deposits are associated with subhorizontally stratified, low-angle cross-stratified or sinusoidal stratified pebbly sandstones and sandstones (wavelength 0.5 to 18 m), interpreted as representing antidune deposits formed on the stoss-side of the cyclic steps during flow re-acceleration. The field examples indicate that so-called crudely or spaced stratified deposits may commonly represent antidune deposits with varying stratification styles controlled by the aggradation rate, grain-size distribution and amalgamation. The deposits of small-scale cyclic steps with superimposed antidunes form fining upwards successions with decreasing antidune wavelengths. Such cyclic step-antidune successions are the characteristic basal infill of channels, probably related to supercritical high-density turbidity flows triggered by retrogressive slope failures.

A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration

PubMed Central

Chang, Xueli; Du, Siliang; Li, Yingying; Fang, Shenghui

2018-01-01

Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. PMID:29702589

A coarse-to-fine approach for pericardial effusion localization and segmentation in chest CT scans

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chellamuthu, Karthik; Lu, Le; Bagheri, Mohammadhadi; Summers, Ronald M.

2018-02-01

Pericardial effusion on CT scans demonstrates very high shape and volume variability and very low contrast to adjacent structures. This inhibits traditional automated segmentation methods from achieving high accuracies. Deep neural networks have been widely used for image segmentation in CT scans. In this work, we present a two-stage method for pericardial effusion localization and segmentation. For the first step, we localize the pericardial area from the entire CT volume, providing a reliable bounding box for the more refined segmentation step. A coarse-scaled holistically-nested convolutional networks (HNN) model is trained on entire CT volume. The resulting HNN per-pixel probability maps are then threshold to produce a bounding box covering the pericardial area. For the second step, a fine-scaled HNN model is trained only on the bounding box region for effusion segmentation to reduce the background distraction. Quantitative evaluation is performed on a dataset of 25 CT scans of patient (1206 images) with pericardial effusion. The segmentation accuracy of our two-stage method, measured by Dice Similarity Coefficient (DSC), is 75.59+/-12.04%, which is significantly better than the segmentation accuracy (62.74+/-15.20%) of only using the coarse-scaled HNN model.

Potential of SENTINEL-2 images for predicting common topsoil properties over Temperate and Mediterranean agroecosystems

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gomez, Cécile; Fouad, Youssef; Gilliot, Jean-Marc; Lagacherie, Philippe

2017-04-01

This study aimed at exploring the potential of SENTINEL-2 (S2A) multispectral satellite images for predicting several topsoil properties in two contrasted environments: a temperate region marked by intensive annual crop cultivation and soils derived from either loess or colluvium and/or marine limestone or chalk for one part (Versailles Plain, 221 km2), and a Mediterranean region marked by vineyard cultivation and soils derived from either lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (La Peyne catchment, 48 km2) for the other part. Two S2A images (acquired in mid-March 2016 over each site) were atmospherically corrected. Then NDVI was computed and thresholded (0.35) in order to extract bare soils. Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations in the Versailles Plain and La Peyne catchment, respectively. Ten soil properties were investigated in both regions: pH, cation exchange capacity (CEC), five texture fractions (clay, coarse silt, fine silt, coarse sand and fine sand), iron, calcium carbonate and soil organic carbon (SOC) in the tilled horizon. Predictive abilities were studied according to R_cv2 and ratio of performance to deviation (RPD). Intermediate to near intermediate performances of prediction (R_cv2 and RPD between 0.28-0.70 and 1.19-1.85 respectively) were obtained for 6 topsoil properties: clay, iron, SOC, CEC, pH, coarse silt. In the Versailles Plain, 5 out of these properties could be predicted (by decreasing performance, CEC, SOC, pH, clay, coarse silt), while there were 4 predictable properties for La Peyne catchment (Iron, clay, CEC, coarse silt). The amount in coarse fragment content appeared to impact prediction error for iron content over La Peyne, while it influenced prediction error for SOC content over the Versailles Plain along with calcium carbonate content. A spatial structure of the estimated soil properties for bare soils pixels was highlighted, which promises further improvements in spatial prediction models for these properties. This work was carried out in the framework of both the TOSCA-CES "Cartographie Numérique des sols" and the PLEIADES-CO projects of the French Space Agency (CNES).

Effect of Downscaled Forcings and Soil Texture Properties on Hyperresolution Hydrologic Simulations in a Regional Basin in Northwest Mexico

NASA Astrophysics Data System (ADS)

Ko, A.; Mascaro, G.; Vivoni, E. R.

2017-12-01

Hyper-resolution (< 1 km) hydrological modeling is expected to support a range of studies related to the terrestrial water cycle. A critical need for increasing the utility of hyper-resolution modeling is the availability of meteorological forcings and land surface characteristics at high spatial resolution. Unfortunately, in many areas these datasets are only available at coarse (> 10 km) scales. In this study, we address some of the challenges by applying a parallel version of the Triangulated Irregular Network (TIN)-based Real Time Integrated Basin Simulator (tRIBS) to the Rio Sonora Basin (RSB) in northwest Mexico. The RSB is a large, semiarid watershed ( 21,000 km2) characterized by complex topography and a strong seasonality in vegetation conditions, due to the North American monsoon. We conducted simulations at an average spatial resolution of 88 m over a decadal (2004-2013) period using spatially-distributed forcings from remotely-sensed and reanalysis products. Meteorological forcings were derived from the North American Land Data Assimilation System (NLDAS) at the original resolution of 12 km and were downscaled at 1 km with techniques accounting for terrain effects. Two grids of soil properties were created from different sources, including: (i) CONABIO (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad) at 6 km resolution; and (ii) ISRIC (International Soil Reference Information Centre) at 250 m. Time-varying vegetation parameters were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) composite products. The model was first calibrated and validated through distributed soil moisture data from a network of 20 soil moisture stations during the monsoon season. Next, hydrologic simulations were conducted with five different combinations of coarse and downscaled forcings and soil properties. Outputs in the different configurations were then compared with independent observations of soil moisture, and with estimates of land surface temperature (1 km, daily) and evapotranspiration (1 km, monthly) from MODIS. This study is expected to support the community involved in hyper-resolution hydrologic modeling by identifying the crucial factors that, if available at higher resolution, lead to the largest improvement of the simulation prognostic capability.

Brownian dynamics simulations of lipid bilayer membrane with hydrodynamic interactions in LAMMPS

NASA Astrophysics Data System (ADS)

Fu, Szu-Pei; Young, Yuan-Nan; Peng, Zhangli; Yuan, Hongyan

2016-11-01

Lipid bilayer membranes have been extensively studied by coarse-grained molecular dynamics simulations. Numerical efficiencies have been reported in the cases of aggressive coarse-graining, where several lipids are coarse-grained into a particle of size 4 6 nm so that there is only one particle in the thickness direction. Yuan et al. proposed a pair-potential between these one-particle-thick coarse-grained lipid particles to capture the mechanical properties of a lipid bilayer membrane (such as gel-fluid-gas phase transitions of lipids, diffusion, and bending rigidity). In this work we implement such interaction potential in LAMMPS to simulate large-scale lipid systems such as vesicles and red blood cells (RBCs). We also consider the effect of cytoskeleton on the lipid membrane dynamics as a model for red blood cell (RBC) dynamics, and incorporate coarse-grained water molecules to account for hydrodynamic interactions. The interaction between the coarse-grained water molecules (explicit solvent molecules) is modeled as a Lennard-Jones (L-J) potential. We focus on two sets of LAMMPS simulations: 1. Vesicle shape transitions with varying enclosed volume; 2. RBC shape transitions with different enclosed volume. This work is funded by NSF under Grant DMS-1222550.

Brownian dynamics simulations of lipid bilayer membrane with hydrodynamic interactions in LAMMPS

NASA Astrophysics Data System (ADS)

Fu, Szu-Pei; Young, Yuan-Nan; Peng, Zhangli; Yuan, Hongyan

Lipid bilayer membranes have been extensively studied by coarse-grained molecular dynamics simulations. Numerical efficiency has been reported in the cases of aggressive coarse-graining, where several lipids are coarse-grained into a particle of size 4 6 nm so that there is only one particle in the thickness direction. Yuan et al. proposed a pair-potential between these one-particle-thick coarse-grained lipid particles to capture the mechanical properties of a lipid bilayer membrane (such as gel-fluid-gas phase transitions of lipids, diffusion, and bending rigidity). In this work we implement such interaction potential in LAMMPS to simulate large-scale lipid systems such as vesicles and red blood cells (RBCs). We also consider the effect of cytoskeleton on the lipid membrane dynamics as a model for red blood cell (RBC) dynamics, and incorporate coarse-grained water molecules to account for hydrodynamic interactions. The interaction between the coarse-grained water molecules (explicit solvent molecules) is modeled as a Lennard-Jones (L-J) potential. We focus on two sets of LAMMPS simulations: 1. Vesicle shape transitions with varying enclosed volume; 2. RBC shape transitions with different enclosed volume.

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale. Representative temporal factors are being developed to capture crop-specific NH3 emission variability by combining knowledge of local crop management practices with high resolution cropland and soil maps. This improved spatially and temporally dependent NH3 emission inventory for agricultural fertilization is being prepared as a direct input to a state of the art air quality model to evaluate the effects of agricultural fertilization on regional air quality and atmospheric deposition of reactive nitrogen species.

Deploying digital health data to optimize influenza surveillance at national and local scales

PubMed Central

Arab, Ali; Viboud, Cécile; Grenfell, Bryan T.; Bansal, Shweta

2018-01-01

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries. PMID:29513661

Spatio-temporal conditional inference and hypothesis tests for neural ensemble spiking precision

PubMed Central

Harrison, Matthew T.; Amarasingham, Asohan; Truccolo, Wilson

2014-01-01

The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339

Bird Communities and Environmental Correlates in Southern Oregon and Northern California, USA.

PubMed

Stephens, Jaime L; Dinger, Eric C; Alexander, John D; Mohren, Sean R; Ralph, C John; Sarr, Daniel A

2016-01-01

We examined avian community ecology in the Klamath Ecoregion and determined that individual bird species co-exist spatially to form 29 statistically distinguishable bird groups. We identified climate, geography, and vegetation metrics that are correlated with these 29 bird groups at three scales: Klamath Ecoregion, vegetation formation (agriculture, conifer, mixed conifer/hardwood, shrubland), and National Park Service unit. Two climate variables (breeding season mean temperature and temperature range) and one geography variable (elevation) were correlated at all scales, suggesting that for some vegetation formations and park units there is sufficient variation in climate and geography to be an important driver of bird communities, a level of variation we expected only at the broader scale. We found vegetation to be important at all scales, with coarse metrics (environmental site potential and existing vegetation formation) meaningful across all scales and structural vegetation patterns (e.g. succession, disturbance) important only at the scale of vegetation formation or park unit. Additionally, we examined how well six National Park Service units represent bird communities in the broader Klamath Ecoregion. Park units are inclusive of most bird communities with the exception of the oak woodland community; mature conifer forests are well represented, primarily associated with conifer canopy and lacking multi-layered structure. Identifying environmental factors that shape bird communities at three scales within this region is important; such insights can inform local and regional land management decisions necessary to ensure bird conservation in this globally significant region.

Bird Communities and Environmental Correlates in Southern Oregon and Northern California, USA

PubMed Central

Dinger, Eric C.; Alexander, John D.; Mohren, Sean R.; Ralph, C. John; Sarr, Daniel A.

2016-01-01

We examined avian community ecology in the Klamath Ecoregion and determined that individual bird species co-exist spatially to form 29 statistically distinguishable bird groups. We identified climate, geography, and vegetation metrics that are correlated with these 29 bird groups at three scales: Klamath Ecoregion, vegetation formation (agriculture, conifer, mixed conifer/hardwood, shrubland), and National Park Service unit. Two climate variables (breeding season mean temperature and temperature range) and one geography variable (elevation) were correlated at all scales, suggesting that for some vegetation formations and park units there is sufficient variation in climate and geography to be an important driver of bird communities, a level of variation we expected only at the broader scale. We found vegetation to be important at all scales, with coarse metrics (environmental site potential and existing vegetation formation) meaningful across all scales and structural vegetation patterns (e.g. succession, disturbance) important only at the scale of vegetation formation or park unit. Additionally, we examined how well six National Park Service units represent bird communities in the broader Klamath Ecoregion. Park units are inclusive of most bird communities with the exception of the oak woodland community; mature conifer forests are well represented, primarily associated with conifer canopy and lacking multi-layered structure. Identifying environmental factors that shape bird communities at three scales within this region is important; such insights can inform local and regional land management decisions necessary to ensure bird conservation in this globally significant region. PMID:27732625

Tests of dynamic Lagrangian eddy viscosity models in Large Eddy Simulations of flow over three-dimensional bluff bodies

NASA Astrophysics Data System (ADS)

Tseng, Yu-Heng; Meneveau, Charles; Parlange, Marc B.

2004-11-01

Large Eddy Simulations (LES) of atmospheric boundary-layer air movement in urban environments are especially challenging due to complex ground topography. Typically in such applications, fairly coarse grids must be used where the subgrid-scale (SGS) model is expected to play a crucial role. A LES code using pseudo-spectral discretization in horizontal planes and second-order differencing in the vertical is implemented in conjunction with the immersed boundary method to incorporate complex ground topography, with the classic equilibrium log-law boundary condition in the new-wall region, and with several versions of the eddy-viscosity model: (1) the constant-coefficient Smagorinsky model, (2) the dynamic, scale-invariant Lagrangian model, and (3) the dynamic, scale-dependent Lagrangian model. Other planar-averaged type dynamic models are not suitable because spatial averaging is not possible without directions of statistical homogeneity. These SGS models are tested in LES of flow around a square cylinder and of flow over surface-mounted cubes. Effects on the mean flow are documented and found not to be major. Dynamic Lagrangian models give a physically more realistic SGS viscosity field, and in general, the scale-dependent Lagrangian model produces larger Smagorinsky coefficient than the scale-invariant one, leading to reduced distributions of resolved rms velocities especially in the boundary layers near the bluff bodies.

Memory matters: influence from a cognitive map on animal space use.

PubMed

Gautestad, Arild O

2011-10-21

A vertebrate individual's cognitive map provides a capacity for site fidelity and long-distance returns to favorable patches. Fractal-geometrical analysis of individual space use based on collection of telemetry fixes makes it possible to verify the influence of a cognitive map on the spatial scatter of habitat use and also to what extent space use has been of a scale-specific versus a scale-free kind. This approach rests on a statistical mechanical level of system abstraction, where micro-scale details of behavioral interactions are coarse-grained to macro-scale observables like the fractal dimension of space use. In this manner, the magnitude of the fractal dimension becomes a proxy variable for distinguishing between main classes of habitat exploration and site fidelity, like memory-less (Markovian) Brownian motion and Levy walk and memory-enhanced space use like Multi-scaled Random Walk (MRW). In this paper previous analyses are extended by exploring MRW simulations under three scenarios: (1) central place foraging, (2) behavioral adaptation to resource depletion (avoidance of latest visited locations) and (3) transition from MRW towards Levy walk by narrowing memory capacity to a trailing time window. A generalized statistical-mechanical theory with the power to model cognitive map influence on individual space use will be important for statistical analyses of animal habitat preferences and the mechanics behind site fidelity and home ranges. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bias-correction and Spatial Disaggregation for Climate Change Impact Assessments at a basin scale

NASA Astrophysics Data System (ADS)

Nyunt, Cho; Koike, Toshio; Yamamoto, Akio; Nemoto, Toshihoro; Kitsuregawa, Masaru

2013-04-01

Basin-scale climate change impact studies mainly rely on general circulation models (GCMs) comprising the related emission scenarios. Realistic and reliable data from GCM is crucial for national scale or basin scale impact and vulnerability assessments to build safety society under climate change. However, GCM fail to simulate regional climate features due to the imprecise parameterization schemes in atmospheric physics and coarse resolution scale. This study describes how to exclude some unsatisfactory GCMs with respect to focused basin, how to minimize the biases of GCM precipitation through statistical bias correction and how to cover spatial disaggregation scheme, a kind of downscaling, within in a basin. GCMs rejection is based on the regional climate features of seasonal evolution as a bench mark and mainly depends on spatial correlation and root mean square error of precipitation and atmospheric variables over the target region. Global Precipitation Climatology Project (GPCP) and Japanese 25-uear Reanalysis Project (JRA-25) are specified as references in figuring spatial pattern and error of GCM. Statistical bias-correction scheme comprises improvements of three main flaws of GCM precipitation such as low intensity drizzled rain days with no dry day, underestimation of heavy rainfall and inter-annual variability of local climate. Biases of heavy rainfall are conducted by generalized Pareto distribution (GPD) fitting over a peak over threshold series. Frequency of rain day error is fixed by rank order statistics and seasonal variation problem is solved by using a gamma distribution fitting in each month against insi-tu stations vs. corresponding GCM grids. By implementing the proposed bias-correction technique to all insi-tu stations and their respective GCM grid, an easy and effective downscaling process for impact studies at the basin scale is accomplished. The proposed method have been examined its applicability to some of the basins in various climate regions all over the world. The biases are controlled very well by using this scheme in all applied basins. After that, bias-corrected and downscaled GCM precipitation are ready to use for simulating the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) to analyse the stream flow change or water availability of a target basin under the climate change in near future. Furthermore, it can be investigated any inter-disciplinary studies such as drought, flood, food, health and so on.In summary, an effective and comprehensive statistical bias-correction method was established to fulfil the generative applicability of GCM scale to basin scale without difficulty. This gap filling also promotes the sound decision of river management in the basin with more reliable information to build the resilience society.

A PIXEL COMPOSITION-BASED REFERENCE DATA SET FOR THEMATIC ACCURACY ASSESSMENT

EPA Science Inventory

Developing reference data sets for accuracy assessment of land-cover classifications derived from coarse spatial resolution sensors such as MODIS can be difficult due to the large resolution differences between the image data and available reference data sources. Ideally, the spa...

Weakening of Indian Summer Monsoon Rainfall due to Changes in Land Use Land Cover

PubMed Central

Paul, Supantha; Ghosh, Subimal; Oglesby, Robert; Pathak, Amey; Chandrasekharan, Anita; Ramsankaran, RAAJ

2016-01-01

Weakening of Indian summer monsoon rainfall (ISMR) is traditionally linked with large-scale perturbations and circulations. However, the impacts of local changes in land use and land cover (LULC) on ISMR have yet to be explored. Here, we analyzed this topic using the regional Weather Research and Forecasting model with European Center for Medium range Weather Forecast (ECMWF) reanalysis data for the years 2000–2010 as a boundary condition and with LULC data from 1987 and 2005. The differences in LULC between 1987 and 2005 showed deforestation with conversion of forest land to crop land, though the magnitude of such conversion is uncertain because of the coarse resolution of satellite images and use of differential sources and methods for data extraction. We performed a sensitivity analysis to understand the impacts of large-scale deforestation in India on monsoon precipitation and found such impacts are similar to the observed changes in terms of spatial patterns and magnitude. We found that deforestation results in weakening of the ISMR because of the decrease in evapotranspiration and subsequent decrease in the recycled component of precipitation. PMID:27553384

Modified Light Use Efficiency Model for Assessment of Carbon Sequestration in Grasslands of Kazakhstan: Combining Ground Biomass Data and Remote-sensing

NASA Technical Reports Server (NTRS)

Propastin, Pavel A.; Kappas, Martin W.; Herrmann, Stefanie M.; Tucker, Compton J.

2012-01-01

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (en) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of en suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.

A High-Resolution Aerosol Retrieval Method for Urban Areas Using MISR Data

NASA Astrophysics Data System (ADS)

Moon, T.; Wang, Y.; Liu, Y.; Yu, B.

2012-12-01

Satellite-retrieved Aerosol Optical Depth (AOD) can provide a cost-effective way to monitor particulate air pollution without using expensive ground measurement sensors. One of the current state-of-the-art AOD retrieval method is NASA's Multi-angle Imaging SpectroRadiometer (MISR) operational algorithm, which has the spatial resolution of 17.6 km x 17.6 km. While the MISR baseline scheme already leads to exciting research opportunities to study particle compositions at regional scale, its spatial resolution is too coarse for analyzing urban areas where the AOD level has stronger spatial variations. We develop a novel high-resolution AOD retrieval algorithm that still uses MISR's radiance observations but has the resolution of 4.4km x 4.4km. We achieve the high resolution AOD retrieval by implementing a hierarchical Bayesian model and Monte-Carlo Markov Chain (MCMC) inference method. Our algorithm not only improves the spatial resolution, but also extends the coverage of AOD retrieval and provides with additional composition information of aerosol components that contribute to the AOD. We validate our method using the recent NASA's DISCOVER-AQ mission data, which contains the ground measured AOD values for Washington DC and Baltimore area. The validation result shows that, compared to the operational MISR retrievals, our scheme has 41.1% more AOD retrieval coverage for the DISCOVER-AQ data points and 24.2% improvement in mean-squared error (MSE) with respect to the AERONET ground measurements.

Size of Coarse Woody Debris 5 Years After Girdling and Removal Treatments in 50-Year-Old Loblolly Pine Plantations

Treesearch

M. Boyd Edwards

2004-01-01

In 1996, a study began at Savannah River Site to investigate large-scale replicated forest areas to control coarse woody debris for integrated biodiversity objectives. Research design was a randomized complete block with four treatments replicated in four blocks, resulting in 16 plots. The treatments applied to 50-year-old loblolly pine stands were (1) control, (2)...