A dynamic aerodynamic resistance approach to calculate high resolution sensible heat fluxes in urban areas

NASA Astrophysics Data System (ADS)

Crawford, Ben; Grimmond, Sue; Kent, Christoph; Gabey, Andrew; Ward, Helen; Sun, Ting; Morrison, William

2017-04-01

Remotely sensed data from satellites have potential to enable high-resolution, automated calculation of urban surface energy balance terms and inform decisions about urban adaptations to environmental change. However, aerodynamic resistance methods to estimate sensible heat flux (QH) in cities using satellite-derived observations of surface temperature are difficult in part due to spatial and temporal variability of the thermal aerodynamic resistance term (rah). In this work, we extend an empirical function to estimate rah using observational data from several cities with a broad range of surface vegetation land cover properties. We then use this function to calculate spatially and temporally variable rah in London based on high-resolution (100 m) land cover datasets and in situ meteorological observations. In order to calculate high-resolution QH based on satellite-observed land surface temperatures, we also develop and employ novel methods to i) apply source area-weighted averaging of surface and meteorological variables across the study spatial domain, ii) calculate spatially variable, high-resolution meteorological variables (wind speed, friction velocity, and Obukhov length), iii) incorporate spatially interpolated urban air temperatures from a distributed sensor network, and iv) apply a modified Monte Carlo approach to assess uncertainties with our results, methods, and input variables. Modeled QH using the aerodynamic resistance method is then compared to in situ observations in central London from a unique network of scintillometers and eddy-covariance measurements.

Constraints on Vesta's elemental composition: Fast neutron measurements by Dawn's gamma ray and neutron detector

PubMed Central

Lawrence, David J; Peplowski, Patrick N; Prettyman, Thomas H; Feldman, William C; Bazell, David; Mittlefehldt, David W; Reedy, Robert C; Yamashita, Naoyuki

2013-01-01

Surface composition information from Vesta is reported using fast neutron data collected by the gamma ray and neutron detector on the Dawn spacecraft. After correcting for variations due to hydrogen, fast neutrons show a compositional dynamic range and spatial variability that is consistent with variations in average atomic mass from howardite, eucrite, and diogenite (HED) meteorites. These data provide additional compositional evidence that Vesta is the parent body to HED meteorites. A subset of fast neutron data having lower statistical precision show spatial variations that are consistent with a 400 ppm variability in hydrogen concentrations across Vesta and supports the idea that Vesta's hydrogen is due to long-term delivery of carbonaceous chondrite material. PMID:26074718

Analysis of shifts in the spatial distribution of vegetation due to climate change

NASA Astrophysics Data System (ADS)

del Jesus, Manuel; Díez-Sierra, Javier; Rinaldo, Andrea; Rodríguez-Iturbe, Ignacio

2017-04-01

Climate change will modify the statistical regime of most climatological variables, inducing changes on average values and in the natural variability of environmental variables. These environmental variables may be used to explain the spatial distribution of functional types of vegetation in arid and semiarid watersheds through the use of plant optimization theories. Therefore, plant optimization theories may be used to approximate the response of the spatial distribution of vegetation to a changing climate. Predicting changes in these spatial distributions is important to understand how climate change may affect vegetated ecosystems, but it is also important for hydrological engineering applications where climate change effects on water availability are assessed. In this work, Maximum Entropy Production (MEP) is used as the plant optimization theory that describes the spatial distribution of functional types of vegetation. Current climatological conditions are obtained from direct observations from meteorological stations. Climate change effects are evaluated for different temporal horizons and different climate change scenarios using numerical model outputs from the CMIP5. Rainfall estimates are downscaled by means of a stochastic point process used to model rainfall. The study is carried out for the Rio Salado watershed, located within the Sevilleta LTER site, in New Mexico (USA). Results show the expected changes in the spatial distribution of vegetation and allow to evaluate the expected variability of the changes. The updated spatial distributions allow to evaluate the vegetated ecosystem health and its updated resilience. These results can then be used to inform the hydrological modeling part of climate change assessments analyzing water availability in arid and semiarid watersheds.

Improving our process understanding of methane emissions from a mid-latitude reservoir by combining eddy covariance monitoring with spatial surveys

EPA Science Inventory

Reservoirs are a globally important source of methane (CH4) to the atmosphere, but measuring CH4 emission rates from reservoirs is difficult due to the spatial and temporal variability of the various emission pathways, including ebullition and diffusion. We used the eddy covarian...

Global Variability and Changes in Ocean Total Alkalinity from Aquarius Satellite

NASA Astrophysics Data System (ADS)

Fine, R. A.; Willey, D. A.; Millero, F. J., Jr.

2016-02-01

To document effects of ocean acidification it is important to have an understanding of the processes and parameters that influence alkalinity. Alkalinity is a gauge on the ability of seawater to neutralize acids. We use Aquarius satellite data, which allow unprecedented global mapping of surface total alkalinity as it correlates strongly with salinity and to a lesser extent with temperature. Spatial variability in total alkalinity and salinity exceed temporal variability, the latter includes seasonal and differences compared to climatological data. The northern hemisphere has more spatial and monthly variability in total alkalinity and salinity, while less variability in Southern Ocean alkalinity is due to less salinity variability and upwelling of waters enriched in alkalinity. Satellite alkalinity data are providing a global baseline that can be used for comparing with future carbon data, and for evaluating spatial and temporal variability and past trends. For the first time it is shown that recent satellite derived total alkalinity in the subtropics have increased as compared with climatological data; this is reflective of large scale changes in the global water cycle. Total alkalinity increases imply increased dissolution of calcareous minerals and difficulty for calcifying organisms to make their shells.

An 'Observational Large Ensemble' to compare observed and modeled temperature trend uncertainty due to internal variability.

NASA Astrophysics Data System (ADS)

Poppick, A. N.; McKinnon, K. A.; Dunn-Sigouin, E.; Deser, C.

2017-12-01

Initial condition climate model ensembles suggest that regional temperature trends can be highly variable on decadal timescales due to characteristics of internal climate variability. Accounting for trend uncertainty due to internal variability is therefore necessary to contextualize recent observed temperature changes. However, while the variability of trends in a climate model ensemble can be evaluated directly (as the spread across ensemble members), internal variability simulated by a climate model may be inconsistent with observations. Observation-based methods for assessing the role of internal variability on trend uncertainty are therefore required. Here, we use a statistical resampling approach to assess trend uncertainty due to internal variability in historical 50-year (1966-2015) winter near-surface air temperature trends over North America. We compare this estimate of trend uncertainty to simulated trend variability in the NCAR CESM1 Large Ensemble (LENS), finding that uncertainty in wintertime temperature trends over North America due to internal variability is largely overestimated by CESM1, on average by a factor of 32%. Our observation-based resampling approach is combined with the forced signal from LENS to produce an 'Observational Large Ensemble' (OLENS). The members of OLENS indicate a range of spatially coherent fields of temperature trends resulting from different sequences of internal variability consistent with observations. The smaller trend variability in OLENS suggests that uncertainty in the historical climate change signal in observations due to internal variability is less than suggested by LENS.

Does spatial arrangement of 3D plants affect light transmission and extinction coefficient within maize crops?

USDA-ARS?s Scientific Manuscript database

Row spacing effects on light interception and extinction coefficient have been inconsistent for maize (Zea mays L.) when calculated with field measurements. To avoid inconsistencies due to variable light conditions and variable leaf canopies, we used a model to describe three-dimensional (3D) shoot ...

Mars ozone: Mariner 9 revisited

NASA Technical Reports Server (NTRS)

Lindner, Bernhard Lee

1994-01-01

The efficacy of the UV spectroscopy technique used by Mariner 9 to remotely measure ozone abundance at Mars is discussed. Previously-inferred ozone abundances could be underestimated by as much as a factor of 3, and much of the observed variability in the ozone abundance could be due to temporal and spatial variability in cloud and dust amount.

Spatial variability of methane production and methanogen communities within a eutrophic reservoir: evaluating the importance of organic matter source and quantity

EPA Science Inventory

Freshwater reservoirs are an important source of the greenhouse gas methane (CH4) to the atmosphere, but there is a wide range of estimates of global emissions, due in part to variability of methane emissions rates within reservoirs. While morphological characteristics, including...

Temporal and spatial variation in pharmaceutical concentrations in an urban river system

USGS Publications Warehouse

Burns, Emily E.; Carter, Laura J.; Kolpin, Dana W.; Thomas-Oates, Jane; Boxall, Alistair B.A.

2018-01-01

Many studies have quantified pharmaceuticals in the environment, few however, have incorporated detailed temporal and spatial variability due to associated costs in terms of time and materials. Here, we target 33 physico-chemically diverse pharmaceuticals in a spatiotemporal exposure study into the occurrence of pharmaceuticals in the wastewater system and the Rivers Ouse and Foss (two diverse river systems) in the city of York, UK. Removal rates in two of the WWTPs sampled (a conventional activated sludge (CAS) and trickling filter plant) ranged from not eliminated (carbamazepine) to >99% (paracetamol). Data comparisons indicate that pharmaceutical exposures in river systems are highly variable regionally, in part due to variability in prescribing practices, hydrology, wastewater management, and urbanisation and that select annual median pharmaceutical concentrations observed in this study were higher than those previously observed in the European Union and Asia thus far. Significant spatial variability was found between all sites in both river systems, while seasonal variability was significant for 86% and 50% of compounds in the River Foss and Ouse, respectively. Seasonal variations in flow, in-stream attenuation, usage and septic effluent releases are suspected drivers behind some of the observed temporal exposure variability. When the data were used to evaluate a simple environmental exposure model for pharmaceuticals, mean ratios of predicted environmental concentrations (PECs), obtained using the model, to measured environmental concentrations (MECs) were 0.51 and 0.04 for the River Foss and River Ouse, respectively. Such PEC/MEC ratios indicate that the model underestimates actual concentrations in both river systems, but to a much greater extent in the larger River Ouse.

Spatial response surface modelling in the presence of data paucity for the evaluation of potential human health risk due to the contamination of potable water resources.

PubMed

Liu, Shen; McGree, James; Hayes, John F; Goonetilleke, Ashantha

2016-10-01

Potential human health risk from waterborne diseases arising from unsatisfactory performance of on-site wastewater treatment systems is driven by landscape factors such as topography, soil characteristics, depth to water table, drainage characteristics and the presence of surface water bodies. These factors are present as random variables which are spatially distributed across a region. A methodological framework is presented that can be applied to model and evaluate the influence of various factors on waterborne disease potential. This framework is informed by spatial data and expert knowledge. For prediction at unsampled sites, interpolation methods were used to derive a spatially smoothed surface of disease potential which takes into account the uncertainty due to spatial variation at any pre-determined level of significance. This surface was constructed by accounting for the influence of multiple variables which appear to contribute to disease potential. The framework developed in this work strengthens the understanding of the characteristics of disease potential and provides predictions of this potential across a region. The study outcomes presented constitutes an innovative approach to environmental monitoring and management in the face of data paucity. Copyright © 2016 Elsevier B.V. All rights reserved.

Sea-level rise impacts on the temporal and spatial variability of extreme water levels: A case study for St. Peter-Ording, Germany

NASA Astrophysics Data System (ADS)

Santamaria-Aguilar, S.; Arns, A.; Vafeidis, A. T.

2017-04-01

Both the temporal and spatial variability of storm surge water level (WL) curves are usually not taken into account in flood risk assessments as observational data are often scarce. In addition, sea-level rise (SLR) can further affect the variability of WLs. We analyze the temporal and spatial variability of the WL curve of 75 historical storm surge events that have been numerically simulated for St. Peter-Ording at the German North Sea coast, considering the effects induced by three SLR scenarios (RCP 4.5, RCP 8.5, and a RCP 8.5 high end scenario). We assess potential impacts of these scenarios on two parameters related to flooding: overflow volumes and fullness. Our results indicate that due to both the temporal and spatial variability of those events the resulting overflow volume can be two or even three times greater. We observe a steepening of the WL curve with an increase of the tidal range under the three SLR scenarios, although SLR induced effects are relatively higher for the RCP 4.5. The steepening of the WL curve with SLR produces a reduction of the fullness, but the changes in overflow volumes also depend on the magnitude of the storm surge event.

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.

Spatial-temporal variability in GHG fluxes and their functional interpretation in RusFluxNet

NASA Astrophysics Data System (ADS)

Vasenev, Ivan; Meshalkina, Julia; Sarzhanov, Dmitriy; Mazirov, Ilia; Yaroslavtsev, Alex; Komarova, Tatiana; Tikhonova, Maria

2016-04-01

High spatial and temporal variability is mutual feature for most modern boreal landscapes in the European Territory of Russia. This variability is result of their relatively young natural and land-use age with very complicated development stories. RusFluxNet includes a functionally-zonal set of representative natural, agricultural and urban ecosystems from the Central Forest Reserve in the north till the Central Chernozemic Reserve in the south (more than 1000 km distance). Especial attention has been traditionally given to their soil cover and land-use detailed variability, morphogenetic and functional dynamics. Central Forest Biosphere Reserve (360 km to North-West from Moscow) is the principal southern-taiga one in the European territory of Russia with long history of mature spruce ecosystem structure and dynamics investigation. Our studies (in frame of RF Governmental projects #11.G34.31.0079 and #14.120.14.4266) have been concentrated on the soil carbon stocks and GHG fluxes spatial variability and dynamics due to dominated there windthrow and fallow-forest successions. In Moscow RTSAU campus gives a good possibility to develop the ecosystem and soil monitoring of GHG fluxes in the comparable sites of urban forest, field crops and lawn ecosystems taking especial attention on their meso- and micro-relief, soil cover patterns and subsoil, vegetation and land-use technologies, temperature and moisture spatial and temporal variability. In the Central Chernozemic Biosphere Reserve and adjacent areas we do the comparative analysis of GHG fluxes and balances in the virgin and mowed meadow-steppe, forest, pasture, cropland and three types of urban ecosystems with similar subsoil and relief conditions. The carried out researches have shown not only sharp (in 2-5 times) changes in GHG ecosystem and soil fluxes and balances due to seasonal and daily microclimate variation, vegetation and crop development but their essential (in 2-4 times) spatial variability due to different meso- or micro-relief forms, natural or man-made succession studies, topsoil texture or organic matter state, subsoil or perched groundwater features. Zonal, seasonal and functional subdividing the monitoring data allows essentially increase the regression links between GHG fluxes and air or soil temperature and moisture (to 0.75-0.87) that is very important for their modeling and prediction. In taiga and mix-forest zones usually there is stronger effect on GHG fluxes by air temperature than soil one due to comparatively thin (from 3 till 10 cm) layer of principal soil organic and/or humus-accumulative horizons with maximum biological activity that usually determines the total rate of GHG soil fluxes. Unfavorable seasonal conditions (dry season or low temperature) determine essential (in 1.5-2 times) decreasing not only in soil GHG fluxes but in level of their spatial variability, intraseasonal and daily dynamics too. These trends are most obvious in case of more open and sensitive to the external factors ecosystems, for example in case of industrial area lawns or at the first stages of the windthrow or fallow-forest successions. Understanding the principal regional and land-use-determined regularities of spatial and temporal changes in ecosystem and soil GHG fluxes help better modeling them in the process of spatial intra- and extrapolations, seasonal and interseasonal predictions, taking into attention basic and current principal ecological factors limiting GHG fluxes and balances. Their introduction in the ecological or agroecological models and land-use decision support systems allows improve the quality of environmental/agroecological monitoring and control not only for GHG emission but also for soil organic matter conservation, manure and nitrogen fertilizer application that is often crucially important for sustainable rural development and profitable farming.

Spectral and spatial variability of undisturbed and disturbed grass under different view and illumination directions

NASA Astrophysics Data System (ADS)

Borel-Donohue, Christoph C.; Shivers, Sarah Wells; Conover, Damon

2017-05-01

It is well known that disturbed grass covered surfaces show variability with view and illumination conditions. A good example is a grass field in a soccer stadium that shows stripes indicating in which direction the grass was mowed. These spatial variations are due to a complex interplay of spectral characteristics of grass blades, density, their length and orientations. Viewing a grass surface from nadir or near horizontal directions results in observing different components. Views from a vertical direction show more variations due to reflections from the randomly oriented grass blades and their shadows. Views from near horizontal show a mixture of reflected and transmitted light from grass blades. An experiment was performed on a mowed grass surface which had paths of simulated heavy foot traffic laid down in different directions. High spatial resolution hyperspectral data cubes were taken by an imaging spectrometer covering the visible through near infrared over a period of time covering several hours. Ground truth grass reflectance spectra with a hand held spectrometer were obtained of undisturbed and disturbed areas. Close range images were taken of selected areas with a hand held camera which were then used to reconstruct the 3D geometry of the grass using structure-from-motion algorithms. Computer graphics rendering using raytracing of reconstructed and procedurally created grass surfaces were used to compute BRDF models. In this paper, we discuss differences between observed and simulated spectral and spatial variability. Based on the measurements and/or simulations, we derive simple spectral index methods to detect spatial disturbances and apply scattering models.

Thaw depth spatial and temporal variability at the Limnopolar Lake CALM-S site, Byers Peninsula, Livingston Island, Antarctica.

PubMed

de Pablo, M A; Ramos, M; Molina, A; Prieto, M

2018-02-15

A new Circumpolar Active Layer Monitoring (CALM) site was established in 2009 at the Limnopolar Lake watershed in Byers Peninsula, Livingston Island, Antarctica, to provide a node in the western Antarctic Peninsula, one of the regions that recorded the highest air temperature increase in the planet during the last decades. The first detailed analysis of the temporal and spatial evolution of the thaw depth at the Limnopolar Lake CALM-S site is presented here, after eight years of monitoring. The average values range between 48 and 29cm, decreasing at a ratio of 16cm/decade. The annual thaw depth observations in the 100×100 m CALM grid are variable (Variability Index of 34 to 51%), although both the Variance Coefficient and the Climate Matrix Analysis Residual point to the internal consistency of the data. Those differences could be explained then by the terrain complexity and node-specific variability due to the ground properties. The interannual variability was about 60% during 2009-2012, increasing to 124% due to the presence of snow in 2013, 2015 and 2016. The snow has been proposed here as one of the most important factors controlling the spatial variability of ground thaw depth, since its values correlate with the snow thickness but also with the ground surface temperature and unconfined compression resistance, as measured in 2010. The topography explains the thaw depth spatial distribution pattern, being related to snowmelt water and its accumulation in low-elevation areas (downslope-flow). Patterned grounds and other surface features correlate well with high thaw depth patterns as well. The edaphic factor (E=0.05842m 2 /°C·day; R 2 =0.63) is in agreement with other permafrost environments, since frozen index (F>0.67) and MAAT (<-2°C) denote a continuous permafrost existence in the area. All these characteristics provided the basis for further comparative analyses between others nearby CALM sites. Copyright © 2017 Elsevier B.V. All rights reserved.

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

NASA Technical Reports Server (NTRS)

Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

2014-01-01

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

A Generalized Subsurface Flow Parameterization Considering Subgrid Spatial Variability of Recharge and Topography

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

Huang, Maoyi; Liang, Xu; Leung, Lai R.

2008-12-05

Subsurface flow is an important hydrologic process and a key component of the water budget, especially in humid regions. In this study, a new subsurface flow formulation is developed that incorporates spatial variability of both topography and recharge. It is shown through theoretical derivation and case studies that the power law and exponential subsurface flow parameterizations and the parameterization proposed by Woods et al.[1997] are all special cases of the new formulation. The subsurface flows calculated using the new formulation compare well with values derived from observations at the Tulpehocken Creek and Walnut Creek watersheds. Sensitivity studies show that whenmore » the spatial variability of topography or recharge, or both is increased, the subsurface flows increase at the two aforementioned sites and the Maimai hillslope. This is likely due to enhancement of interactions between the groundwater table and the land surface that reduce the flow path. An important conclusion of this study is that the spatial variability of recharge alone, and/or in combination with the spatial variability of topography can substantially alter the behaviors of subsurface flows. This suggests that in macroscale hydrologic models or land surface models, subgrid variations of recharge and topography can make significant contributions to the grid mean subsurface flow and must be accounted for in regions with large surface heterogeneity. This is particularly true for regions with humid climate and relatively shallow groundwater table where the combined impacts of spatial variability of recharge and topography are shown to be more important. For regions with arid climate and relatively deep groundwater table, simpler formulations, especially the power law, for subsurface flow can work well, and the impacts of subgrid variations of recharge and topography may be ignored.« less

Separating the effects of environment and space on tree species distribution: from population to community.

PubMed

Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang

2013-01-01

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

Electrically tunable spatially variable switching in ferroelectric liquid crystal/water system

NASA Astrophysics Data System (ADS)

Choudhary, A.; Coondoo, I.; Prakash, J.; Sreenivas, K.; Biradar, A. M.

2009-04-01

An unusual switching phenomenon in the region outside conducting patterned area in ferroelectric liquid crystal (FLC) containing about 1-2 wt % of water has been observed. The presence of water in the studied heterogeneous system was confirmed by Fourier transform infrared spectroscopy. The observed optical studies have been emphasized on the "spatially variable switching" phenomenon of the molecules in the nonconducting region of the cell. The observed phenomenon is due to diffusion of water between the smectic layers of the FLC and the interaction of the curved electric field lines with the FLC molecules in the nonconducting region.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Estimating water retention curves for sandy soils at the Doñana National Park, SW Spain

USDA-ARS?s Scientific Manuscript database

The determination of soil water retention curves (SWRC) in the laboratory is a slow and tedious task, which is especially challenging for sandy soils due to their low water retention capacity and large water content changes for small pressure head differences. Due to spatial variability within larg...

Impacts of climate variability and change on crop yield in sub-Sahara Africa

NASA Astrophysics Data System (ADS)

Pan, S.; Zhang, J.; Yang, J.; Chen, G.; Xu, R.; Zhang, B.; Lou, Y.

2017-12-01

Much concern has been raised about the impacts of climate change and climate extremes on Africa's food security. The impact of climate change on Africa's agriculture is likely to be severe compared to other continents due to high rain-fed agricultural dependence, and limited ability to mitigate and adapt to climate change. In recent decades, warming in Africa is more pronounced and faster than the global average and this trend is likely to continue in the future. However, quantitative assessment on impacts of climate extremes and climate change on crop yield has not been well investigated yet. By using an improved agricultural module of the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed impacts of historical climate variability and future climate change on food crop yield across the sub-Sahara Africa during1980-2016 and the rest of the 21st century (2017-2099). Our simulated results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Droughts have largely reduced crop yield in the most vulnerable regions of Sub-Sahara Africa. Future projections with DLEM-AG2 show that food crop production in Sub-Sahara Africa would be favored with limiting end-of-century warming to below 1.50 C.

The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.

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.

Temporal and spatial variation in pharmaceutical concentrations in an urban river system.

PubMed

Burns, Emily E; Carter, Laura J; Kolpin, Dana W; Thomas-Oates, Jane; Boxall, Alistair B A

2018-06-15

Many studies have quantified pharmaceuticals in the environment, few however, have incorporated detailed temporal and spatial variability due to associated costs in terms of time and materials. Here, we target 33 physico-chemically diverse pharmaceuticals in a spatiotemporal exposure study into the occurrence of pharmaceuticals in the wastewater system and the Rivers Ouse and Foss (two diverse river systems) in the city of York, UK. Removal rates in two of the WWTPs sampled (a conventional activated sludge (CAS) and trickling filter plant) ranged from not eliminated (carbamazepine) to >99% (paracetamol). Data comparisons indicate that pharmaceutical exposures in river systems are highly variable regionally, in part due to variability in prescribing practices, hydrology, wastewater management, and urbanisation and that select annual median pharmaceutical concentrations observed in this study were higher than those previously observed in the European Union and Asia thus far. Significant spatial variability was found between all sites in both river systems, while seasonal variability was significant for 86% and 50% of compounds in the River Foss and Ouse, respectively. Seasonal variations in flow, in-stream attenuation, usage and septic effluent releases are suspected drivers behind some of the observed temporal exposure variability. When the data were used to evaluate a simple environmental exposure model for pharmaceuticals, mean ratios of predicted environmental concentrations (PECs), obtained using the model, to measured environmental concentrations (MECs) were 0.51 and 0.04 for the River Foss and River Ouse, respectively. Such PEC/MEC ratios indicate that the model underestimates actual concentrations in both river systems, but to a much greater extent in the larger River Ouse. Copyright © 2018 Elsevier Ltd. All rights reserved.

Can basin land use effects on physical characteristics of streams be determined at broad geographic scales?

USGS Publications Warehouse

Goldstein, R.M.; Carlisle, D.M.; Meador, M.R.; Short, T.M.

2007-01-01

The environmental setting (e.g., climate, topography, geology) and land use affect stream physical characteristics singly and cumulatively. At broad geographic scales, we determined the importance of environmental setting and land use in explaining variation in stream physical characteristics. We hypothesized that as the spatial scale decreased from national to regional, land use would explain more of the variation in stream physical characteristics because environmental settings become more homogeneous. At a national scale, stepwise linear regression indicated that environmental setting was more important in explaining variability in stream physical characteristics. Although statistically discernible, the amount of variation explained by land use was not remarkable due to low partial correlations. At level II ecoregion spatial scales (southeastern USA plains, central USA plains, and a combination of the western Cordillera and the western interior basins and ranges), environmental setting variables were again more important predictors of stream physical characteristics, however, as the spatial scale decreased from national to regional, the portion of variability in stream physical characteristics explained by basin land use increased. Development of stream habitat indicators of land use will depend upon an understanding of relations between stream physical characteristics and environmental factors at multiple spatial scales. Smaller spatial scales will be necessary to reduce the confounding effects of variable environmental settings before the effects of land use can be reliably assessed. ?? Springer Science+Business Media B.V. 2006.

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

Ghosh, Subimal; Das, Debasish; Kao, Shih-Chieh

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century, as well as on whether the changes observed are due to global warming or regional urbanization. Although a uniform and consistent decrease in moderate rainfall has been reported, a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability.We show that when generalizedmore » extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.« less

Effect of Variable Spatial Scales on USLE-GIS Computations

NASA Astrophysics Data System (ADS)

Patil, R. J.; Sharma, S. K.

2017-12-01

Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

Influence of impulsivity-reflexivity when testing dynamic spatial ability: sex and g differences.

PubMed

Quiroga, M Angeles; Hernández, José Manuel; Rubio, Victor; Shih, Pei Chun; Santacreu, José

2007-11-01

This work analyzes the possibility that the differences in the performance of men and women in dynamic spatial tasks such as the Spatial Orientation Dynamic Test-Revised (SODT-R; Santacreu & Rubio, 1998), obtained in previous works, are due to cognitive style (Reflexivity-Impulsivity) or to the speed-accuracy tradeoff (SATO) that the participants implement. If these differences are due to cognitive style, they would be independent of intelligence, whereas if they are due to SATO, they may be associated with intelligence. In this work, 1652 participants, 984 men and 668 women, ages between 18 and 55 years, were assessed. In addition to the SODT-R, the "Test de Razonamiento Analitico, Secuencial e Inductivo" (TRASI [Analytical, Sequential, and Inductive Reasoning Test]; Rubio & Santacreu, 2003) was administered as a measure of general intelligence. Impulsivity scores (Zi) of Salkind and Wright (1977) were used to analyze reflexivity-impulsivity and SATO. The results obtained indicate that (a) four performance groups can be identified: Fast-accurate, Slow-inaccurate, Impulsive, and Reflexive. The first two groups solve the task as a function of a competence variable and the last two as a function of a personality variable; (b) performance differences should be attributed to SATO; (c) SATO differs depending on sex and intelligence level.

New species of Eunotia from small isolated wetlands in Florida

EPA Science Inventory

Diatom species composition of small wetlands is diverse and unique due to a plethora of spatial and temporal variables. Diatoms from small wetlands can contribute greatly to better understanding microbial biodiversity, distribution, dispersal and populations.

Changes In The Heating Degree-days In Norway Due Toglobal Warming

NASA Astrophysics Data System (ADS)

Skaugen, T. E.; Tveito, O. E.; Hanssen-Bauer, I.

A continuous spatial representation of temperature improves the possibility topro- duce maps of temperature-dependent variables. A temperature scenario for the period 2021-2050 is obtained for Norway from the Max-Planck-Institute? AOGCM, GSDIO ECHAM4/OPEC 3. This is done by an ?empirical downscaling method? which in- volves the use of empirical links between large-scale fields and local variables to de- duce estimates of the local variables. The analysis is obtained at forty-six sites in Norway. Spatial representation of the anomalies of temperature in the scenario period compared to the normal period (1961-1990) is obtained with the use of spatial interpo- lation in a GIS. The temperature scenario indicates that we will have a warmer climate in Norway in the future, especially during the winter season. The heating degree-days (HDD) is defined as the accumulated Celsius degrees be- tween the daily mean temperature and a threshold temperature. For Scandinavian countries, this threshold temperature is 17 Celsius degrees. The HDD is found to be a good estimate of accumulated cold. It is therefore a useful index for heating energy consumption within the heating season, and thus to power production planning. As a consequence of the increasing temperatures, the length of the heating season and the HDD within this season will decrease in Norway in the future. The calculations of the heating season and the HDD is estimated at grid level with the use of a GIS. The spatial representation of the heating season and the HDD can then easily be plotted. Local information of the variables being analysed can be withdrawn from the spatial grid in a GIS. The variable is prepared for further spatial analysis. It may also be used as an input to decision making systems.

The Variability of the Horizontal Circulation in the Troposphere and Stratosphere: A Comparison

NASA Technical Reports Server (NTRS)

Perlwitz, Judith; Graf, Hans-F.; Hansem, James E. (Technical Monitor)

2001-01-01

The variability of the horizontal circulation in the stratosphere and troposphere of the Northern Hemisphere (NH) is compared by using various approaches. Spatial degrees of freedom (dof) on different time scales were derived. Modes of variability were computed in geopotential height fields at the tropospheric and stratospheric pressure levels by applying multivariate statistical approaches. Features of the spatial and temporal variability of the winterly zonal wind were studied with the help of recurrence and persistence analyses. The geopotential height and zonally-averaged zonal wind at the 50-, 500- and 1000-hPa level are used to investigate the behavior of the horizontal circulation in the lower stratosphere, mid-troposphere and at the near surface level, respectively. It is illustrated that the features of the variability of the horizontal circulation are very similar in the mid-troposphere and at the near surface level. Due to the filtering of tropospheric disturbances by the stratospheric and upper tropospheric zonal mean flow, the variability of the stratospheric circulation exhibits less spatial complexity than the circulation at tropospheric pressure levels. There exist enormous differences in the number of degrees of freedom (or free variability modes) between both atmospheric layers. Results of the analyses clearly show that the concept of a zonally symmetric AO with a simple structure in the troposphere similar to the one in the stratosphere is not valid. It is concluded that the spatially filtered climate change signal can be detected earlier in the stratosphere than in the mid-troposphere or at the near surface level.

Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

NASA Astrophysics Data System (ADS)

Marshall, Hans-Peter

The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution on a slope. The ability to accurately characterize snowpack properties at much higher resolutions and spatial extent than previously possible will hopefully help lead to a more complete understanding of spatial variability, its effect on remote sensing measurements and snow slope stability, and result in improvements in avalanche prediction and accuracy of SWE estimates from space.

Environmental characteristics drive variation in Amazonian understorey bird assemblages

PubMed Central

Magnusson, William E.; Anderson, Marti J.; Schlegel, Martin; Pe’er, Guy; Henle, Klaus

2017-01-01

Tropical bird assemblages display patterns of high alpha and beta diversity and, as tropical birds exhibit strong habitat specificity, their spatial distributions are generally assumed to be driven primarily by environmental heterogeneity and interspecific interactions. However, spatial distributions of some Amazonian forest birds are also often restricted by large rivers and other large-scale topographic features, suggesting that dispersal limitation may also play a role in driving species’ turnover. In this study, we evaluated the effects of environmental characteristics, topographic and spatial variables on variation in local assemblage structure and diversity of birds in an old-growth forest in central Amazonia. Birds were mist-netted in 72 plots distributed systematically across a 10,000 ha reserve in each of three years. Alpha diversity remained stable through time, but species composition changed. Spatial variation in bird-assemblage structure was significantly related to environmental and topographic variables but not strongly related to spatial variables. At a broad scale, we found bird assemblages to be significantly distinct between two watersheds that are divided by a central ridgeline. We did not detect an effect of the ridgeline per se in driving these patterns, indicating that most birds are able to fly across it, and that differences in assemblage structure between watersheds may be due to unmeasured environmental variables or unique combinations of measured variables. Our study indicates that complex geography and landscape features can act together with environmental variables to drive changes in the diversity and composition of tropical bird assemblages at local scales, but highlights that we still know very little about what makes different parts of tropical forest suitable for different species. PMID:28225774

Aspect-related Vegetation Differences Amplify Soil Moisture Variability in Semiarid Landscapes

NASA Astrophysics Data System (ADS)

Yetemen, O.; Srivastava, A.; Kumari, N.; Saco, P. M.

2017-12-01

Soil moisture variability (SMV) in semiarid landscapes is affected by vegetation, soil texture, climate, aspect, and topography. The heterogeneity in vegetation cover that results from the effects of microclimate, terrain attributes (slope gradient, aspect, drainage area etc.), soil properties, and spatial variability in precipitation have been reported to act as the dominant factors modulating SMV in semiarid ecosystems. However, the role of hillslope aspect in SMV, though reported in many field studies, has not received the same degree of attention probably due to the lack of extensive large datasets. Numerical simulations can then be used to elucidate the contribution of aspect-driven vegetation patterns to this variability. In this work, we perform a sensitivity analysis to study on variables driving SMV using the CHILD landscape evolution model equipped with a spatially-distributed solar-radiation component that couples vegetation dynamics and surface hydrology. To explore how aspect-driven vegetation heterogeneity contributes to the SMV, CHILD was run using a range of parameters selected to reflect different scenarios (from uniform to heterogeneous vegetation cover). Throughout the simulations, the spatial distribution of soil moisture and vegetation cover are computed to estimate the corresponding coefficients of variation. Under the uniform spatial precipitation forcing and uniform soil properties, the factors affecting the spatial distribution of solar insolation are found to play a key role in the SMV through the emergence of aspect-driven vegetation patterns. Hence, factors such as catchment gradient, aspect, and latitude, define water stress and vegetation growth, and in turn affect the available soil moisture content. Interestingly, changes in soil properties (porosity, root depth, and pore-size distribution) over the domain are not as effective as the other factors. These findings show that the factors associated to aspect-related vegetation differences amplify the soil moisture variability of semi-arid landscapes.

Understanding the spatial complexity of surface hoar from slope to range scale

NASA Astrophysics Data System (ADS)

Hendrikx, J.

2015-12-01

Surface hoar, once buried, is a common weak layer type in avalanche accidents in continental and intermountain snowpacks around the World. Despite this, there is still limited understanding of the spatial variability in both the formation of, and eventual burial of, surface hoar at spatial scales which are of critical importance to avalanche forecasters. While it is relatively well understood that aspect plays an important role in the spatial location of the formation, and burial of these grain forms, due to the unequal distribution of incoming radiation, this factor alone does not explain the complex and often confusing spatial pattern of these grains forms throughout the landscape at different spatial scales. In this paper we present additional data from a unique data set including over two hundred days of manual observations of surface hoar at sixteen locations on Pioneer Mountain at the Yellowstone Club in southwestern Montana. Using this wealth of observational data located on different aspects, elevations and exposures, coupled with detailed meteorological observations, and detailed slope scale observation, we examine the spatial variability of surface hoar at this scale, and examine the factors that control its spatial distribution. Our results further supports our preliminary work, which shows that small-scale slope conditions, meteorological differences, and local scale lapse rates, can greatly influence the spatial variability of surface hoar, over and above that which aspect alone can explain. These results highlight our incomplete understanding of the processes at both the slope and range scale, and are likely to have implications for both regional and local scale avalanche forecasting in environments where surface hoar cause ongoing instabilities.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

The near-infrared counterpart of a variable galactic plane radio source

NASA Technical Reports Server (NTRS)

Margon, Bruce; Phillips, Andrew C.; Ciardullo, Robin; Jacoby, George H.

1992-01-01

A near-infrared counterpart to the highly variable, unresolved galactic plane radio source GT 0116 + 622 is identified. This source is of particular interest, as it has been previously suggested to be the counterpart of the gamma-ray source Cas gamma-l. The present NIR and red images detect a faint, spatially extended (3 arcsec FWHM), very red object coincident with the radio position. There is complex spatial structure which may be due in part to an unrelated superposed foreground object. Observations on multiple nights show no evidence for flux variability, despite the high amplitude variability on a time-scale of days reported for the radio source. The data are consistent with an interpretation of GT 0116 + 622 as an unusually variable, obscured active galaxy at a distance of several hundred megaparsecs, although more exotic, and in particular galactic, interpretations cannot yet be ruled out. If the object is extragalactic, the previously suggested identification with the gamma-ray source would seem unlikely.

Screening variability and change of soil moisture under wide-ranging climate conditions: Snow dynamics effects.

PubMed

Verrot, Lucile; Destouni, Georgia

2015-01-01

Soil moisture influences and is influenced by water, climate, and ecosystem conditions, affecting associated ecosystem services in the landscape. This paper couples snow storage-melting dynamics with an analytical modeling approach to screening basin-scale, long-term soil moisture variability and change in a changing climate. This coupling enables assessment of both spatial differences and temporal changes across a wide range of hydro-climatic conditions. Model application is exemplified for two major Swedish hydrological basins, Norrström and Piteälven. These are located along a steep temperature gradient and have experienced different hydro-climatic changes over the time period of study, 1950-2009. Spatially, average intra-annual variability of soil moisture differs considerably between the basins due to their temperature-related differences in snow dynamics. With regard to temporal change, the long-term average state and intra-annual variability of soil moisture have not changed much, while inter-annual variability has changed considerably in response to hydro-climatic changes experienced so far in each basin.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Spatial uncertainty analysis: Propagation of interpolation errors in spatially distributed models

USGS Publications Warehouse

Phillips, D.L.; Marks, D.G.

1996-01-01

In simulation modelling, it is desirable to quantify model uncertainties and provide not only point estimates for output variables but confidence intervals as well. Spatially distributed physical and ecological process models are becoming widely used, with runs being made over a grid of points that represent the landscape. This requires input values at each grid point, which often have to be interpolated from irregularly scattered measurement sites, e.g., weather stations. Interpolation introduces spatially varying errors which propagate through the model We extended established uncertainty analysis methods to a spatial domain for quantifying spatial patterns of input variable interpolation errors and how they propagate through a model to affect the uncertainty of the model output. We applied this to a model of potential evapotranspiration (PET) as a demonstration. We modelled PET for three time periods in 1990 as a function of temperature, humidity, and wind on a 10-km grid across the U.S. portion of the Columbia River Basin. Temperature, humidity, and wind speed were interpolated using kriging from 700- 1000 supporting data points. Kriging standard deviations (SD) were used to quantify the spatially varying interpolation uncertainties. For each of 5693 grid points, 100 Monte Carlo simulations were done, using the kriged values of temperature, humidity, and wind, plus random error terms determined by the kriging SDs and the correlations of interpolation errors among the three variables. For the spring season example, kriging SDs averaged 2.6??C for temperature, 8.7% for relative humidity, and 0.38 m s-1 for wind. The resultant PET estimates had coefficients of variation (CVs) ranging from 14% to 27% for the 10-km grid cells. Maps of PET means and CVs showed the spatial patterns of PET with a measure of its uncertainty due to interpolation of the input variables. This methodology should be applicable to a variety of spatially distributed models using interpolated inputs.

Effects of Spatial Variability of Soil Properties on the Triggering of Rainfall-Induced Shallow Landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2015-04-01

Naturally-occurring spatial variations in soil properties (e.g., soil depth, moisture, and texture) affect key hydrological processes and potentially the mechanical response of soil to hydromechanical loading (relative to the commonly-assumed uniform soil mantle). We quantified the effects of soil spatial variability on the triggering of rainfall-induced shallow landslides at the hillslope- and catchment-scales, using a physically-based landslide triggering model that considers interacting soil columns with mechanical strength thresholds (represented by the Fiber Bundle Model). The spatial variations in soil properties are represented as Gaussian random distributions and the level of variation is characterized by the coefficient of variation and correlation lengths of soil properties (i.e., soil depth, soil texture and initial water content in this study). The impacts of these spatial variations on landslide triggering characteristics were measured by comparing the times to triggering and landslide volumes for heterogeneous soil properties and homogeneous cases. Results at hillslope scale indicate that for spatial variations of an individual property (without cross correlation), the increasing of coefficient of variation introduces weak spots where mechanical damage is accelerated and leads to earlier onset of landslide triggering and smaller volumes. Increasing spatial correlation length of soil texture and initial water content also induces early landslide triggering and small released volumes due to the transition of failure mode from brittle to ductile failure. In contrast, increasing spatial correlation length of soil depth "reduces" local steepness and postpones landslide triggering. Cross-correlated soil properties generally promote landslide initiation, but depending on the internal structure of spatial distribution of each soil property, landslide triggering may be reduced. The effects of cross-correlation between initial water content and soil texture were investigated in detail at the catchment scale by incorporating correlations of both variables with topography. Results indicate that the internal structure of the spatial distribution of each soil property together with their interplays determine the overall performance of the coupled spatial variability. This study emphasizes the importance of both the randomness and spatial structure of soil properties on landslide triggering and characteristics.

The Spatial and Temporal Variability of Meltwater Flow Paths: Insights From a Grid of Over 100 Snow Lysimeters

NASA Astrophysics Data System (ADS)

Webb, R. W.; Williams, M. W.; Erickson, T. A.

2018-02-01

Snowmelt is an important part of the hydrologic cycle and ecosystem dynamics for headwater systems. However, the physical process of water flow through snow is a poorly understood aspect of snow hydrology as meltwater flow paths tend to be highly complex. Meltwater flow paths diverge and converge as percolating meltwater reaches stratigraphic layer interfaces creating high spatial variability. Additionally, a snowpack is temporally heterogeneous due to rapid localized metamorphism that occurs during melt. This study uses a snowmelt lysimeter array at tree line in the Niwot Ridge study area of northern Colorado. The array is designed to address the issue of spatial and temporal variability of basal discharge at 105 locations over an area of 1,300 m2. Observed coefficients of variation ranged from 0 to almost 10 indicating more variability than previously observed, though this variability decreased throughout each melt season. Snowmelt basal discharge also significantly increases as snow depth decreases displaying a cluster pattern that peaks during weeks 3-5 of the snowmelt season. These results are explained by the flow of meltwater along snow layer interfaces. As the snowpack becomes less stratified through the melt season, the pattern transforms from preferential flow paths to uniform matrix flow. Correlation ranges of the observed basal discharge correspond to a mean representative elementary area of 100 m2, or a characteristic length of 10 m. Snowmelt models representing processes at scales less than this will need to explicitly incorporate the spatial variability of snowmelt discharge and meltwater flow paths through snow between model pixels.

The energy balance of an urban area: Examining temporal and spatial variability through measurements, remote sensing and modeling

NASA Astrophysics Data System (ADS)

Offerle, Brian

Urban environmental problems related to air quality, thermal stress, issues of water demand and quality, all of which are linked directly or indirectly to urban climate, are emerging as major environmental concerns at the start of the 21st century. Thus there are compelling social, political and economic, and scientific reasons that make the study and understanding of the fundamental causes of urban climates critically important. This research addresses these topics through an intensive study of the surface energy balance of Lodz, Poland. The research examines the temporal variability in long-term measurements of urban surface-atmosphere exchange at a downtown location and the spatial variability of this exchange over distinctly different neighborhoods using shorter-term observations. These observations provide the basis for an evaluation of surface energy balance models. Monthly patterns in energy exchange are consistent from year-to-year with variability determined by net radiation and the timing and amount of precipitation. Spatial variability can be determined from plan area fractions of vegetation and impervious surface, though heat storage exerts a strong control on shorter term variability of energy exchange, within and between locations in an urban area. Anthropogenic heat fluxes provide most of the energy driving surface-atmosphere exchange in winter, From a modeling perspective, sensible heat fluxes can be reliably determined from radiometrically sensed surface temperatures and spatially representative surface-atmosphere exchange in an urban area can be determined from satellite remote sensing products. Models of the urban surface energy balance showed good agreement with mean values of energy exchange and under most conditions represented the temporal variability due to synoptic and shorter time scale forcing well.

Spatial Durbin model analysis macroeconomic loss due to natural disasters

NASA Astrophysics Data System (ADS)

Kusrini, D. E.; Mukhtasor

2015-03-01

Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.

Introduction of digital soil mapping techniques for the nationwide regionalization of soil condition in Hungary; the first results of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Takács, Katalin; Bakacsi, Zsófia; Szabó, József; Dobos, Endre

2014-05-01

Due to the former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project (DOSoReMI.hu; Digital, Optimized, Soil Related Maps and Information in Hungary) we would like to significantly extend the potential, how countrywide soil information requirements could be satisfied in Hungary. We started to compile digital soil related maps which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. The spatial resolution of the targeted countrywide, digital, thematic maps is at least 1:50.000 (approx. 50-100 meter raster resolution). DOSoReMI.hu results are also planned to contribute to the European part of GSM.net products. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. In our paper we will present the first results. - Regression kriging (RK) has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matter content. In the course of RK-based mapping spatially segmented categorical information provided by the SMUs of Digital Kreybig Soil Information System (DKSIS) has been also used in the form of indicator variables. - Classification and regression trees (CART) were used to improve the spatial resolution of category-type soil maps (thematic downscaling), like genetic soil type and soil productivity maps. The approach was justified by the fact that certain thematic soil maps are not available in the required scale. Decision trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identified and expressed in decision rules made the creation of spatially refined maps possible with the aid of high resolution environmental auxiliary variables. Among these co-variables, a special role was played by larger scale spatial soil information with diverse attributes. As a next step, the testing of random forests for the same purposes has been started. - Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil (related) map. This suggests the opportunity of optimization. For the creation of an object specific soil (related) map with predefined parameters (resolution, accuracy, reliability etc.) one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). The first findings on the inclusion and joint usage of spatial soil data as well as on the consistency of various evaluations of the result maps will be also presented. Acknowledgement: Our work has been supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Major influencing factors of indoor radon concentrations in Switzerland.

PubMed

Kropat, Georg; Bochud, Francois; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios, Martha; Baechler, Sébastien

2014-03-01

In Switzerland, nationwide large-scale radon surveys have been conducted since the early 1980s to establish the distribution of indoor radon concentrations (IRC). The aim of this work was to study the factors influencing IRC in Switzerland using univariate analyses that take into account biases caused by spatial irregularities of sampling. About 212,000 IRC measurements carried out in more than 136,000 dwellings were available for this study. A probability map to assess risk of exceeding an IRC of 300 Bq/m(3) was produced using basic geostatistical techniques. Univariate analyses of IRC for different variables, namely the type of radon detector, various building characteristics such as foundation type, year of construction and building type, as well as the altitude, the average outdoor temperature during measurement and the lithology, were performed comparing 95% confidence intervals among classes of each variable. Furthermore, a map showing the spatial aggregation of the number of measurements was generated for each class of variable in order to assess biases due to spatially irregular sampling. IRC measurements carried out with electret detectors were 35% higher than measurements performed with track detectors. Regarding building characteristics, the IRC of apartments are significantly lower than individual houses. Furthermore, buildings with concrete foundations have the lowest IRC. A significant decrease in IRC was found in buildings constructed after 1900 and again after 1970. Moreover, IRC decreases at higher outdoor temperatures. There is also a tendency to have higher IRC with altitude. Regarding lithology, carbonate rock in the Jura Mountains produces significantly higher IRC, almost by a factor of 2, than carbonate rock in the Alps. Sedimentary rock and sediment produce the lowest IRC while carbonate rock from the Jura Mountains and igneous rock produce the highest IRC. Potential biases due to spatially unbalanced sampling of measurements were identified for several influencing factors. Significant associations were found between IRC and all variables under study. However, we showed that the spatial distribution of samples strongly affected the relevance of those associations. Therefore, future methods to estimate local radon hazards should take the multidimensionality of the process of IRC into account. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Methane Seeps in the Gulf of Mexico: repeat acoustic surveying shows highly temporally and spatially variable venting

NASA Astrophysics Data System (ADS)

Beaumont, B. C.; Raineault, N.

2016-02-01

Scientists have recognized that natural seeps account for a large amount of methane emissions. Despite their widespread occurrence in areas like the Gulf of Mexico, little is known about the temporal variability and site-scale spatial variability of venting over time. We used repeat acoustic surveys to compare multiple days of seep activity and determine the changes in the locus of methane emission and plume height. The Sleeping Dragon site was surveyed with an EM302 multibeam sonar on three consecutive days in 2014 and 4 days within one week in 2015. The data revealed three distinctive plume regions. The locus of venting varied by 10-60 meters at each site. The plume that exhibited the least spatial variability in venting, was also the most temporally variable. This seep was present in one-third of survey dates in 2014 and three quarters of survey dates in 2015, showing high day-to-day variability. The plume height was very consistent for this plume, whereas the other plumes were more consistent temporally, but varied in maximum plume height detection by 25-85 m. The single locus of emission at the site that had high day-to-day variability may be due to a single conduit for methane release, which is sometimes closed off by carbonate or clathrate hydrate formation. In addition to day-to-day temporal variability, the locus of emission at one site was observed to shift from a point-source in 2014 to a diffuse source in 2015 at a nearby location. ROV observations showed that one of the seep sites that closed off temporarily, experienced an explosive breakthrough of gas, releasing confined methane and blowing out rock. The mechanism that causes on/off behavior of certain plumes, combined with the spatial variability of the locus of methane release shown in this study may point to carbonate or hydrate formation in the seep plumbing system and should be further investigated.

Characterizing Variability in Long Period Horizontal Tilt Noise Through Coherence Analysis

NASA Astrophysics Data System (ADS)

Rohde, M. D.; Ringler, A. T.; Hutt, C. R.; Wilson, D.; Holland, A. A.

2016-12-01

Tilt induced horizontal noise fundamentally limits a wide variety of seismological studies. This noise source is not well characterized or understood and the spatial variability has yet to be well constrained. Long-period (i.e., greater than 100 seconds period) horizontal seismic noise is generally known to be of greater magnitude than long-period vertical seismic noise due to tilt noise. As a result, many studies only make use of the vertical seismic wavefield as opposed to all three axes. The main source of long-period horizontal seismic noise is hypothesized to be tilt due to atmospheric pressure variation. Reducing horizontal tilt noise could lead to improved resolution of torsional earth modes and other long-period horizontal seismic signals that are often dominated by tilt noise, as well as better construction of seismic isolation systems for sensitive scientific experiments. We looked at a number of small aperture array configurations. For each array we installed eight Streckeisen STS-2 broadband seismometers in the Albuquerque Seismological Laboratory (ASL) underground vault. The data from these array configurations was used to characterize the long period horizontal tilt noise over a spatially small scale. Sensors were installed approximately 1 to 10 meters apart depending on the array configuration. Coherence as a function of frequency was calculated between sensors, of which we examine the frequency band between 10 and 500 seconds. We observed complexity in the pair-wise coherence with respect to frequency, seismometer axis, and time, even for spatially close sensors. We present some possible explanations for the large variability in our coherence observations and demonstrate how these results can be applied to find potentially low horizontal noise locations over small spatial scales, such as in stations with multiple co-located sensors within the Global Seismographic Network.

Disentangling Global Warming, Multidecadal Variability, and El Niño in Pacific Temperatures

NASA Astrophysics Data System (ADS)

Wills, Robert C.; Schneider, Tapio; Wallace, John M.; Battisti, David S.; Hartmann, Dennis L.

2018-03-01

A key challenge in climate science is to separate observed temperature changes into components due to internal variability and responses to external forcing. Extended integrations of forced and unforced climate models are often used for this purpose. Here we demonstrate a novel method to separate modes of internal variability from global warming based on differences in time scale and spatial pattern, without relying on climate models. We identify uncorrelated components of Pacific sea surface temperature variability due to global warming, the Pacific Decadal Oscillation (PDO), and the El Niño-Southern Oscillation (ENSO). Our results give statistical representations of PDO and ENSO that are consistent with their being separate processes, operating on different time scales, but are otherwise consistent with canonical definitions. We isolate the multidecadal variability of the PDO and find that it is confined to midlatitudes; tropical sea surface temperatures and their teleconnections mix in higher-frequency variability. This implies that midlatitude PDO anomalies are more persistent than previously thought.

Influence of throughfall spatial and temporal patterns on soil moisture variability under Downy oak and Scots pine stands in Mediterranean conditions

NASA Astrophysics Data System (ADS)

Llorens, Pilar; Garcia-Estringana, Pablo; Cayuela, Carles; Latron, Jérôme; Molina, Antonio; Gallart, Francesc

2015-04-01

Temporal and spatial variability of throughfall and stemflow patterns, due to differences in forest structure and seasonality of Mediterranean climate, may lead to significant changes in the volume of water that locally reaches the soil, with a potential effect on groundwater recharge and on hydrological response of forested hillslopes. Two forest stands in Mediterranean climatic conditions were studied to explore the role of vegetation on the temporal and spatial redistribution of rainfall. One is a Downy oak forest (Quercus pubescens) and the other is a Scots pine forest (Pinus sylvestris), both located in the Vallcebre research catchments (NE Spain, 42° 12'N, 1° 49'E). These plots are representative of Mediterranean mountain areas with spontaneous afforestation by Scots pine as a consequence of the abandonment of agricultural terraces, formerly covered by Downy oaks. The monitoring design of each plot consists of 20 automatic rain recorders to measuring throughfall, 7 stemflow rings connected to tipping-buckets and 40 automatic soil moisture probes. All data were recorded each 5 min. Bulk rainfall and meteorological conditions above both forest covers were also recorded, and canopy cover and biometric characteristics of the plots were measured. Results indicate a marked temporal stability of throughfall in both stands, and a lower persistence of spatial patterns in the leafless period than in the leafed one in the oaks stand. Moreover, in the oaks plot the ranks of gauges in the leafed and leafless periods were not significantly correlated, indicating different wet and dry hotspots in each season. The spatial distribution of throughfall varied significantly depending on rainfall volume, with small events having larger variability, whereas large events tended to homogenize the relative differences in point throughfall. Soil water content spatial variability increased with increasing soil water content, but direct dependence of soil water content variability on throughfall patterns is difficult to establish.

Multi-scale approach to the environmental factors effects on spatio-temporal variability of Chironomus salinarius (Diptera: Chironomidae) in a French coastal lagoon

NASA Astrophysics Data System (ADS)

Cartier, V.; Claret, C.; Garnier, R.; Fayolle, S.; Franquet, E.

2010-03-01

The complexity of the relationships between environmental factors and organisms can be revealed by sampling designs which consider the contribution to variability of different temporal and spatial scales, compared to total variability. From a management perspective, a multi-scale approach can lead to time-saving. Identifying environmental patterns that help maintain patchy distribution is fundamental in studying coastal lagoons, transition zones between continental and marine waters characterised by great environmental variability on spatial and temporal scales. They often present organic enrichment inducing decreased species richness and increased densities of opportunist species like C hironomus salinarius, a common species that tends to swarm and thus constitutes a nuisance for human populations. This species is dominant in the Bolmon lagoon, a French Mediterranean coastal lagoon under eutrophication. Our objective was to quantify variability due to both spatial and temporal scales and identify the contribution of different environmental factors to this variability. The population of C. salinarius was sampled from June 2007 to June 2008 every two months at 12 sites located in two areas of the Bolmon lagoon, at two different depths, with three sites per area-depth combination. Environmental factors (temperature, dissolved oxygen both in sediment and under water surface, sediment organic matter content and grain size) and microbial activities (i.e. hydrolase activities) were also considered as explanatory factors of chironomid densities and distribution. ANOVA analysis reveals significant spatial differences regarding the distribution of chironomid larvae for the area and the depth scales and their interaction. The spatial effect is also revealed for dissolved oxygen (water), salinity and fine particles (area scale), and for water column depth. All factors but water column depth show a temporal effect. Spearman's correlations highlight the seasonal effect (temperature, dissolved oxygen in sediment and water) as well as the effect of microbial activities on chironomid larvae. Our results show that a multi-scale approach identifies patchy distribution, even when there is relative environmental homogeneity.

Post-fire reconstructions of fire intensity from fire severity data: quantifying the role of spatial variability of fire intensity on forest dynamics

NASA Astrophysics Data System (ADS)

Baker, Patrick; Oborne, Lisa

2015-04-01

Large, high-intensity fires have direct and long-lasting effects on forest ecosystems and present a serious threat to human life and property. However, even within the most catastrophic fires there is important variability in local-scale intensity that has important ramifications for forest mortality and regeneration. Quantifying this variability is difficult due to the rarity of catastrophic fire events, the extreme conditions at the time of the fires, and their large spatial extent. Instead fire severity is typically measured or estimated from observed patterns of vegetation mortality; however, differences in species- and size-specific responses to fires often makes fire severity a poor proxy for fire intensity. We developed a statistical method using simple, plot-based measurements of individual tree mortality to simultaneously estimate plot-level fire intensity and species-specific mortality patterns as a function of tree size. We applied our approach to an area of forest burned in the catastrophic Black Saturday fires that occurred near Melbourne, Australia, in February 2009. Despite being the most devastating fire in the past 70 years and our plots being located in the area that experienced some of the most intense fires in the 350,000 ha fire complex, we found that the estimated fire intensity was highly variable at multiple spatial scales. All eight tree species in our study differed in their susceptibility to fire-induced mortality, particularly among the largest size classes. We also found that seedling height and species richness of the post-fire seedling communities were both positively correlated with fire intensity. Spatial variability in disturbance intensity has important, but poorly understood, consequences for the short- and long-term dynamics of forests in the wake of catastrophic wildfires. Our study provides a tool to estimate fire intensity after a fire has passed, allowing new opportunities for linking spatial variability in fire intensity to forest ecosystem dynamics.

Spatial Scale Variability of NH3 and Impacts to interpolated Concentration Grids

EPA Science Inventory

Over the past decade, reduced nitrogen (NH3, NH4) has become an important component of atmospheric nitrogen deposition due to increases in agricultural activities and reductions in oxidized sulfur and nitrogen emissions from the power sector and mobile sources. Reduced nitrogen i...

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.

Variability of streambed hydraulic conductivity in an intermittent stream reach regulated by Vented Dams: A case study

NASA Astrophysics Data System (ADS)

Naganna, Sujay Raghavendra; Deka, Paresh Chandra

2018-07-01

The hydro-geological properties of streambed together with the hydraulic gradients determine the fluxes of water, energy and solutes between the stream and underlying aquifer system. Dam induced sedimentation affects hyporheic processes and alters substrate pore space geometries in the course of progressive stabilization of the sediment layers. Uncertainty in stream-aquifer interactions arises from the inherent complex-nested flow paths and spatio-temporal variability of streambed hydraulic properties. A detailed field investigation of streambed hydraulic conductivity (Ks) using Guelph Permeameter was carried out in an intermittent stream reach of the Pavanje river basin located in the mountainous, forested tract of western ghats of India. The present study reports the spatial and temporal variability of streambed hydraulic conductivity along the stream reach obstructed by two Vented Dams in sequence. Statistical tests such as Levene's and Welch's t-tests were employed to check for various variability measures. The strength of spatial dependence and the presence of spatial autocorrelation among the streambed Ks samples were tested by using Moran's I statistic. The measures of central tendency and dispersion pointed out reasonable spatial variability in Ks distribution throughout the study reach during two consecutive years 2016 and 2017. The streambed was heterogeneous with regard to hydraulic conductivity distribution with high-Ks zones near the backwater areas of the vented dam and low-Ks zones particularly at the tail water section of vented dams. Dam operational strategies were responsible for seasonal fluctuations in sedimentation and modifications to streambed substrate characteristics (such as porosity, grain size, packing etc.), resulting in heterogeneous streambed Ks profiles. The channel downstream of vented dams contained significantly more cohesive deposits of fine sediment due to the overflow of surplus suspended sediment-laden water at low velocity and pressure head. The statistical test results accept the hypothesis of significant spatial variability of streambed Ks but refuse to accept the temporal variations. The deterministic and geo-statistical approaches of spatial interpolation provided virtuous surface maps of streambed Ks distribution.

Spatial pattern dynamics due to the fitness gradient flux in evolutionary games.

PubMed

deForest, Russ; Belmonte, Andrew

2013-06-01

We introduce a nondiffusive spatial coupling term into the replicator equation of evolutionary game theory. The spatial flux is based on motion due to local gradients in the relative fitness of each strategy, providing a game-dependent alternative to diffusive coupling. We study numerically the development of patterns in one dimension (1D) for two-strategy games including the coordination game and the prisoner's dilemma, and in two dimensions (2D) for the rock-paper-scissors game. In 1D we observe modified traveling wave solutions in the presence of diffusion, and asymptotic attracting states under a frozen-strategy assumption without diffusion. In 2D we observe spiral formation and breakup in the frozen-strategy rock-paper-scissors game without diffusion. A change of variables appropriate to replicator dynamics is shown to correctly capture the 1D asymptotic steady state via a nonlinear diffusion equation.

Spatial pattern dynamics due to the fitness gradient flux in evolutionary games

NASA Astrophysics Data System (ADS)

deForest, Russ; Belmonte, Andrew

2013-06-01

We introduce a nondiffusive spatial coupling term into the replicator equation of evolutionary game theory. The spatial flux is based on motion due to local gradients in the relative fitness of each strategy, providing a game-dependent alternative to diffusive coupling. We study numerically the development of patterns in one dimension (1D) for two-strategy games including the coordination game and the prisoner's dilemma, and in two dimensions (2D) for the rock-paper-scissors game. In 1D we observe modified traveling wave solutions in the presence of diffusion, and asymptotic attracting states under a frozen-strategy assumption without diffusion. In 2D we observe spiral formation and breakup in the frozen-strategy rock-paper-scissors game without diffusion. A change of variables appropriate to replicator dynamics is shown to correctly capture the 1D asymptotic steady state via a nonlinear diffusion equation.

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

Quantifying the residence time and flushing characteristics of a shallow, back-barrier estuary: Application of hydrodynamic and particle tracking models

USGS Publications Warehouse

Defne, Zafer; Ganju, Neil K.

2015-01-01

Estuarine residence time is a major driver of eutrophication and water quality. Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, is a lagoonal back-barrier estuary that is subject to anthropogenic pressures including nutrient loading, eutrophication, and subsequent declines in water quality. A combination of hydrodynamic and particle tracking modeling was used to identify the mechanisms controlling flushing, residence time, and spatial variability of particle retention. The models demonstrated a pronounced northward subtidal flow from Little Egg Inlet in the south to Pt. Pleasant Canal in the north due to frictional effects in the inlets, leading to better flushing of the southern half of the estuary and particle retention in the northern estuary. Mean residence time for BB-LEH was 13 days but spatial variability was between ∼0 and 30 days depending on the initial particle location. Mean residence time with tidal forcing alone was 24 days (spatial variability between ∼0 and 50 days); the tides were relatively inefficient in flushing the northern end of the Bay. Scenarios with successive exclusion of physical processes from the models revealed that meteorological and remote offshore forcing were stronger drivers of exchange than riverine inflow. Investigations of water quality and eutrophication should take into account spatial variability in hydrodynamics and residence time in order to better quantify the roles of nutrient loading, production, and flushing.

Fluctuation of Indoor Radon and VOC Concentrations Due to Seasonal Variations

EPA Science Inventory

This research was conducted to better characterize the spatial and temporal variability of vapor intrusion by collecting a full year’s dataset of weekly measurements of subslab soil gas, external soil gas, and indoor air, on a single house that is impacted by vapor intrusion of r...

Spatio-temporal patterns of soil water storage under dryland agriculture at the watershed scale

USDA-ARS?s Scientific Manuscript database

Soil water patterns vary significantly due to precipitation, soil properties, topographic features, and land use. We used empirical orthogonal function (EOF) analysis to characterize the spatial variability of soil water across a 37-ha field of the Washington State University Cook Agronomy Farm near...

Distribution of Gull Specific Molecular Marker in Coastal Areas of Lake Ontario

EPA Science Inventory

Gulls have been implicated as primary sources of fecal contamination in the Great Lakes, a fact that may have health implications due to the potential spread of microbial pathogens by waterfowl. To better understand the spatial variability of gull fecal contamination, a gull-spe...

Concentration and biochemical gradients of seston in Lake Ontario

USGS Publications Warehouse

Kelly, Patrick T.; Weidel, Brian C.; Paufve, Matthew R.; O'Malley, Brian P.; Watkins, James M.; Rudstam, Lars G.; Jones, Stuart E.

2017-01-01

Spatial variability in resource quantity and quality may have important implications for the distribution and productivity of primary consumers. In Lake Ontario, ecosystem characteristics suggest the potential for significant spatial heterogeneity in seston quantity and quality, particularly due to the potential for nearshore-offshore gradients in allochthonous nutrient supply, and the formation of a deep chlorophyll layer (DCL) in July. We assessed total and zooplankton food particle size-fractionated chlorophyll a concentrations, as well as carbon-to-phosphorus stoichiometry and essential fatty acid composition of seston across a distance-from-shore and depth transect. We observed time, sampling depth, and distance from shore to be the best predictors of chlorophyll a concentration. Resource quality was much more homogenous in space, but there were strong patterns through time, as both stoichiometric and fatty acid qualities in general were greatest in May, and lowest in July/August. We did observe a peak in essential fatty acid concentration near the DCL in during time of formation, possibly due to differences in phytoplankton community composition between the DCL and epilimnion. These results suggest the potential for a spatially and temporally dynamic resource base for consumers in Lake Ontario, which may be important in developing a broader understanding of variable consumer productivity.

Soil internal drainage: temporal stability and spatial variability in succession bean-black oat

NASA Astrophysics Data System (ADS)

Salvador, M. M. S.; Libardi, P. L.; Moreira, N. B.; Sousa, H. H. F.; Neiverth, C. A.

2012-04-01

There are a variety of studies considering the soil water content, but those who consider the flow of water, which are translated by deep drainage and capillary rise are scarce, especially those who assess their spatio-temporal variability, due to its laborious obtaining. Large areas have been considered homogeneous, but show considerable spatial variability inherent in the soil, causing the appearance of zones of distinct physical properties. In deep, sandy soils where the groundwater level is far below the root zone of interference, internal drainage is one of the factors limiting the supply of water to the soil surface, and possibly one of the biggest factors that determines what kinds satisfactory development of plants present in a given landscape. The forms of relief may also be indicators of changes in soil properties, because this variability is caused by small changes that affect the slope of the pedogenetic processes and the transport and storage of water in the soil profile, i.e., the different trajectories of water flow in different forms of the landscape, is the cause of variability. The objectives of this research were: i) evaluate the spatial and temporal stability of internal soil water drainage in a place near and another distant from the root system in a bean-black-oat succession and ii) verify their spatial variability in relation to relief. With the hydraulic conductivity obtained by the instantaneous profile method and the total potential gradient obtained from the difference in readings of tensiometers installed at depths of 0.35 and 0.45 and 0.75 and 0.85 m in 60 sampling points totaling 1680 and 1200 observations during the cultivation of beans and oats, respectively, was obtained so the internal drainage / capillary rise through the Darcy-Buckingham equation. To evaluate the temporal stability the method used was the relative difference and Spearman correlation test and the spatial variability was analyzed as geostatistical methodology. During the period when the water flow in soil is higher, there is strong temporal stability in the depth of 0.40 m, which is the opposite for the periods of drying. The lowest relative difference and standard deviation for the internal drainage obtained during the cultivation of beans and depth of 0.40 m confirm the hypothesis that the research carried out during periods of soil water recharge have less variability than those in the drying period. Temporal stability was due to the topographic position of selected points, since the points chosen for the depth of 0.40 m in both growing seasons, are located on the lower portion of the relief, and the nominees for the depth of 0,80 m, the highest portion. There were differences in the spatial pattern of water flow in the soil along the crop succession, i.e. the seasonal demand for water by plants and evaporation from the soil at the time of drying, changed their distribution model with internal drainage phases and stages capillary rise.

Relative spatial soil geochemical variability along two transects across the United States and Canada

USGS Publications Warehouse

Garrett, Robert G.

2009-01-01

The patterns of relative variability differ by transect and horizon. The N–S transect A-horizon soils show significant between-40-km scale variability for 29 elements, with only 4 elements (Ca, Mg, Pb and Sr) showing in excess of 50% of their variability at the within-40-km and ‘at-site’ scales. In contrast, the C-horizon data demonstrate significant between-40-km scale variability for 26 elements, with 21 having in excess of 50% of their variability at the within-40-km and ‘at-site’ scales. In 36 instances, the ‘at-site’ variability is statistically significant in terms of the sample preparation and analysis variability. It is postulated that this contrast between the A- and C- horizons along the N–S transect, that is dominated by agricultural land uses, is due to the local homogenization of Ap-horizon soils by tillage reducing the ‘at-site’ variability. The spatial variability is distributed similarly between scales for the A- and C-horizon soils of the E–W transect. For all elements, there is significant variability at the within-40-km scale. Notwithstanding this, there is significant between-40-km variability for 28 and 20 of the elements in the A- and C-horizon data, respectively. The differences between the two transects are attributed to (1) geology, the N–S transect runs generally parallel to regional strikes, whereas the E–W transect runs across regional structures and lithologies; and (2) land use, with agricultural tillage dominating along the N–S transect. The spatial analysis of the transect data indicates that continental-scale maps demonstrating statistically significant patterns of geochemical variability may be prepared for many elements from data on soil samples collected on a 40 x 40 km grid or similar sampling designs resulting in a sample density of 1 site per 1600 km2.

Characterization of the spatial distribution of porosity in the eogenetic karst Miami Limestone using ground penetrating radar

NASA Astrophysics Data System (ADS)

Mount, G. J.; Comas, X.; Wright, W. J.; McClellan, M. D.

2014-12-01

Hydrogeologic characterization of karst limestone aquifers is difficult due to the variability in the spatial distribution of porosity and dissolution features. Typical methods for aquifer investigation, such as drilling and pump testing, are limited by the scale or spatial extent of the measurement. Hydrogeophysical techniques such as ground penetrating radar (GPR) can provide indirect measurements of aquifer properties and be expanded spatially beyond typical point measures. This investigation used a multiscale approach to identify and quantify porosity distribution in the Miami Limestone, the lithostratigraphic unit that composes the uppermost portions of the Biscayne Aquifer in Miami Dade County, Florida. At the meter scale, laboratory measures of porosity and dielectric permittivity were made on blocks of Miami Limestone using zero offset GPR, laboratory and digital image techniques. Results show good correspondence between GPR and analytical porosity estimates and show variability between 22 and 66 %. GPR measurements at the field scale 10-1000 m investigated the bulk porosity of the limestone based on the assumption that a directly measured water table would remain at a consistent depth in the GPR reflection record. Porosity variability determined from the changes in the depth to water table resulted in porosity values that ranged from 33 to 61 %, with the greatest porosity variability being attributed to the presence of dissolution features. At the larger field scales, 100 - 1000 m, fitting of hyperbolic diffractions in GPR common offsets determined the vertical and horizontal variability of porosity in the saturated subsurface. Results indicate that porosity can vary between 23 and 41 %, and delineate potential areas of enhanced recharge or groundwater / surface water interactions. This study shows porosity variability in the Miami Limestone can range from 22 to 66 % within 1.5 m distances, with areas of high macroporosity or karst dissolution features occupying the higher end of the range. Spatial variability in porosity distribution may affect ground water recharge, allowing zones of high porosity and thus enhanced infiltration to concentrate contaminants into the aquifer and may play a role in small and regional scale aquifer models.

Analysis of the historical precipitation in the South East Iberian Peninsula at different spatio-temporal scale. Study of the meteorological drought

NASA Astrophysics Data System (ADS)

Fernández-Chacón, Francisca; Pulido-Velazquez, David; Jiménez-Sánchez, Jorge; Luque-Espinar, Juan Antonio

2017-04-01

Precipitation is a fundamental climate variable that has a pronounced spatial and temporal variability on a global scale, as well as at regional and sub-regional scales. Due to its orographic complexity and its latitude the Iberian Peninsula (IP), located to the west of the Mediterranean Basin between the Atlantic Ocean and the Mediterranean Sea, has a complex climate. Over the peninsula there are strong north-south and east-west gradients, as a consequence of the different low-frequency atmospheric patterns, and he overlap of these over the year will be determinants in the variability of climatic variables. In the southeast of the Iberian Peninsula dominates a dry Mediterranean climate, the precipitation is characterized as being an intermittent and discontinuous variable. In this research information coming from the Spain02 v4 database was used to study the South East (SE) IP for the 1971-2010 period with a spatial resolution of 0.11 x 0.11. We analysed precipitation at different time scale (daily, monthly, seasonal, annual,…) to study the spatial distribution and temporal tendencies. The high spatial, intra-annual and inter-annual climatic variability observed makes it necessary to propose a climatic regionalization. In addition, for the identified areas and subareas of homogeneous climate we have analysed the evolution of the meteorological drought for the same period at different time scales. The standardized precipitation index has been used at 12, 24 and 48 month temporal scale. The climatic complexity of the area determines a high variability in the drought characteristics, duration, intensity and frequency in the different climatic areas. This research has been supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 project for the data provided for this study.

The test-retest reliability and minimal detectable change of spatial and temporal gait variability during usual over-ground walking for younger and older adults.

PubMed

Almarwani, Maha; Perera, Subashan; VanSwearingen, Jessie M; Sparto, Patrick J; Brach, Jennifer S

2016-02-01

Gait variability is a marker of gait performance and future mobility status in older adults. Reliability of gait variability has been examined mainly in community dwelling older adults who are likely to fluctuate over time. The purpose of this study was to compare test-retest reliability and determine minimal detectable change (MDC) of spatial and temporal gait variability in younger and older adults. Forty younger (mean age=26.6 ± 6.0 years) and 46 older adults (mean age=78.1 ± 6.2 years) were included in the study. Gait characteristics were measured twice, approximately 1 week apart, using a computerized walkway (GaitMat II). Participants completed 4 passes on the GaitMat II at their self-selected walking speed. Test-retest reliability was calculated using Intra-class correlation coefficients (ICCs(2,1)), 95% limits of agreement (95% LoA) in conjunction with Bland-Altman plots, relative limits of agreement (LoA%) and standard error of measurement (SEM). The MDC at 90% and 95% level were also calculated. ICCs of gait variability ranged 0.26-0.65 in younger and 0.28-0.74 in older adults. The LoA% and SEM were consistently higher (i.e. less reliable) for all gait variables in older compared to younger adults except SEM for step width. The MDC was consistently larger for all gait variables in older compared to younger adults except step width. ICCs were of limited utility due to restricted ranges in younger adults. Based on absolute reliability measures and MDC, younger had greater test-retest reliability and smaller MDC of spatial and temporal gait variability compared to older adults. Copyright © 2015 Elsevier B.V. All rights reserved.

A method for estimating spatially variable seepage and hydrualic conductivity in channels with very mild slopes

USGS Publications Warehouse

Shanafield, Margaret; Niswonger, Richard G.; Prudic, David E.; Pohll, Greg; Susfalk, Richard; Panday, Sorab

2014-01-01

Infiltration along ephemeral channels plays an important role in groundwater recharge in arid regions. A model is presented for estimating spatial variability of seepage due to streambed heterogeneity along channels based on measurements of streamflow-front velocities in initially dry channels. The diffusion-wave approximation to the Saint-Venant equations, coupled with Philip's equation for infiltration, is connected to the groundwater model MODFLOW and is calibrated by adjusting the saturated hydraulic conductivity of the channel bed. The model is applied to portions of two large water delivery canals, which serve as proxies for natural ephemeral streams. Estimated seepage rates compare well with previously published values. Possible sources of error stem from uncertainty in Manning's roughness coefficients, soil hydraulic properties and channel geometry. Model performance would be most improved through more frequent longitudinal estimates of channel geometry and thalweg elevation, and with measurements of stream stage over time to constrain wave timing and shape. This model is a potentially valuable tool for estimating spatial variability in longitudinal seepage along intermittent and ephemeral channels over a wide range of bed slopes and the influence of seepage rates on groundwater levels.

Controls on Soil Organic Matter in Blue Carbon Ecosystems along the South Florida Coast

NASA Astrophysics Data System (ADS)

Smoak, J. M.; Rosenheim, B. E.; Moyer, R. P.; Radabaugh, K.; Chambers, L. G.; Lagomasino, D.; Lynch, J.; Cahoon, D. R.

2017-12-01

Coastal wetlands store disproportionately large amounts of carbon due to high rates of net primary productivity and slow microbial degradation of organic matter in water-saturated soils. Wide spatial and temporal variability in plant communities and soil biogeochemistry necessitate location-specific quantification of carbon stocks to improve current wetland carbon inventories and future projections. We apply field measurements, remote sensing technology, and spatiotemporal models to quantify regional carbon storage and to model future spatial variability of carbon stocks in mangroves and coastal marshes in Southwest Florida. We examine soil carbon accumulation and accretion rates on time scales ranging from decadal to millennial to project responses to climate change, including variations in inundation and salinity. Once freshwater and oligohaline wetlands are exposed to increased duration and spatial extent of inundation and salinity from seawater, soil redox potential, soil respiration, and the intensification of osmotic stress to vegetation and the soil microbial community can affect the soil C balance potentially increasing rates of mineralization.

Spatial variability of the Black Sea surface temperature from high resolution modeling and satellite measurements

NASA Astrophysics Data System (ADS)

Mizyuk, Artem; Senderov, Maxim; Korotaev, Gennady

2016-04-01

Large number of numerical ocean models were implemented for the Black Sea basin during last two decades. They reproduce rather similar structure of synoptical variability of the circulation. Since 00-s numerical studies of the mesoscale structure are carried out using high performance computing (HPC). With the growing capacity of computing resources it is now possible to reconstruct the Black Sea currents with spatial resolution of several hundreds meters. However, how realistic these results can be? In the proposed study an attempt is made to understand which spatial scales are reproduced by ocean model in the Black Sea. Simulations are made using parallel version of NEMO (Nucleus for European Modelling of the Ocean). A two regional configurations with spatial resolutions 5 km and 2.5 km are described. Comparison of the SST from simulations with two spatial resolutions shows rather qualitative difference of the spatial structures. Results of high resolution simulation are compared also with satellite observations and observation-based products from Copernicus using spatial correlation and spectral analysis. Spatial scales of correlations functions for simulated and observed SST are rather close and differs much from satellite SST reanalysis. Evolution of spectral density for modelled SST and reanalysis showed agreed time periods of small scales intensification. Using of the spectral analysis for satellite measurements is complicated due to gaps. The research leading to this results has received funding from Russian Science Foundation (project № 15-17-20020)

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

NASA Astrophysics Data System (ADS)

Shoaib, Syed Abu; Marshall, Lucy; Sharma, Ashish

2018-06-01

Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

Efficient Reformulation of HOTFGM: Heat Conduction with Variable Thermal Conductivity

NASA Technical Reports Server (NTRS)

Zhong, Yi; Pindera, Marek-Jerzy; Arnold, Steven M. (Technical Monitor)

2002-01-01

Functionally graded materials (FGMs) have become one of the major research topics in the mechanics of materials community during the past fifteen years. FGMs are heterogeneous materials, characterized by spatially variable microstructure, and thus spatially variable macroscopic properties, introduced to enhance material or structural performance. The spatially variable material properties make FGMs challenging to analyze. The review of the various techniques employed to analyze the thermodynamical response of FGMs reveals two distinct and fundamentally different computational strategies, called uncoupled macromechanical and coupled micromechanical approaches by some investigators. The uncoupled macromechanical approaches ignore the effect of microstructural gradation by employing specific spatial variations of material properties, which are either assumed or obtained by local homogenization, thereby resulting in erroneous results under certain circumstances. In contrast, the coupled approaches explicitly account for the micro-macrostructural interaction, albeit at a significantly higher computational cost. The higher-order theory for functionally graded materials (HOTFGM) developed by Aboudi et al. is representative of the coupled approach. However, despite its demonstrated utility in applications where micro-macrostructural coupling effects are important, the theory's full potential is yet to be realized because the original formulation of HOTFGM is computationally intensive. This, in turn, limits the size of problems that can be solved due to the large number of equations required to mimic realistic material microstructures. Therefore, a basis for an efficient reformulation of HOTFGM, referred to as user-friendly formulation, is developed herein, and subsequently employed in the construction of the efficient reformulation using the local/global conductivity matrix approach. In order to extend HOTFGM's range of applicability, spatially variable thermal conductivity capability at the local level is incorporated into the efficient reformulation. Analytical solutions to validate both the user-friendly and efficient reformulations am also developed. Volume discretization sensitivity and validation studies, as well as a practical application of the developed efficient reformulation are subsequently carried out. The presented results illustrate the accuracy and implementability of both the user-friendly formulation and the efficient reformulation of HOTFGM.

Spatial variability in plant species composition and peatland carbon exchange

NASA Astrophysics Data System (ADS)

Goud, E.; Moore, T. R.; Roulet, N. T.

2015-12-01

Plant species shifts in response to global change will have significant impacts on ecosystem carbon (C) exchange and storage arising from changes in hydrology. Spatial variation in peatland C fluxes have largely been attributed to the spatial distribution of microhabitats that arise from variation in surface topography and water table depth, but little is known about how plant species composition impacts peatland C cycling or how these impacts will be influenced by changing environmental conditions. We quantified the effect of species composition and environmental variables on carbon dioxide (CO2) and methane (CH4) fluxes over 2 years in a temperate peatland for four plant communities situated along a water table gradient from ombrotrophic bog to beaver pond. We hypothesized that (i) spatial heterogeneity in species composition would drive predictable spatial heterogeneity in C fluxes due to variation in plant traits and ecological tolerances, and (ii) increases in peat temperature would increase C fluxes. Species had different effects on C fluxes primarily due to differences in leaf traits. Differences in ecological tolerances among communities resulted in different rates of CO2 exchange in response to changes in water table depth. There was an overall reduction in ecosystem respiration (ER), gross primary productivity (GPP) and CH4 flux in response to colder peat temperatures in the second year, and the additive effects of a deeper water table in the bog margin and pond sites further reduced flux rates in these areas. These results demonstrate that different plant species can increase or decrease the flux of C into and out of peatlands based on differences in leaf traits and ecological tolerances, and that CO2 and CH4 fluxes are sensitive to changes in soil temperature, especially when coupled with changes in moisture availability.

Microscale spatial distribution and health assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) at nine communities in Xi'an, China.

PubMed

Xu, Hongmei; Ho, Steven Sai Hang; Gao, Meiling; Cao, Junji; Guinot, Benjamin; Ho, Kin Fai; Long, Xin; Wang, Jingzhi; Shen, Zhenxing; Liu, Suixin; Zheng, Chunli; Zhang, Qian

2016-11-01

Spatial variability of polycyclic aromatic hydrocarbons (PAHs) associated with fine particulate matter (PM 2.5 ) was investigated in Xi'an, China, in summer of 2013. Sixteen priority PAHs were quantified in 24-h integrated air samples collected simultaneously at nine urban and suburban communities. The total quantified PAHs mass concentrations ranged from 32.4 to 104.7 ng m -3 , with an average value of 57.1 ± 23.0 ng m -3 . PAHs were observed higher concentrations at suburban communities (average: 86.3 ng m -3 ) than at urban ones (average: 48.8 ng m -3 ) due to a better enforcement of the pollution control policies at the urban scale, and meanwhile the disorganized management of motor vehicles and massive building constructions in the suburbs. Elevated PAH levels were observed in the industrialized regions (west and northwest of Xi'an) from Kriging interpolation analysis. Satellite-based visual interpretations of land use were also applied for the supporting the spatial distribution of PAHs among the communities. The average benzo[a]pyrene-equivalent toxicity (Σ[BaP] eq ) at the nine communities was 6.9 ± 2.2 ng m -3 during the sampling period, showing a generally similar spatial distribution to PAHs levels. On average, the excess inhalation lifetime cancer risk derived from Σ[BaP] eq indicated that eight persons per million of community residents would develop cancer due to PM 2.5 -bound PAHs exposure in Xi'an. The great in-city spatial variability of PAHs confirmed the importance of multiple points sampling to conduct exposure health risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inferring Processes from Spatial Patterns: The Role of Directional and Non–Directional Forces in Shaping Fish Larvae Distribution in a Freshwater Lake System

PubMed Central

Bertolo, Andrea; Blanchet, F. Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

2012-01-01

Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models. PMID:23185585

Dual Nitrate Isotopes in Dry Deposition: Utility for Partitioning Nox Source Contributions to Landscape Nitrogen Deposition

EPA Science Inventory

Dry deposition is a major component of total nitrogen deposition and thus an important source of bioavailable nitrogen to ecosystems. However, relative to wet deposition, less is known regarding the sources and spatial variability of dry deposition. This is in part due to diffi...

Response surface models of subsoil K concentration for loess over till soils in Missouri

USDA-ARS?s Scientific Manuscript database

Crop uptake of potassium (K) has demonstrated sensitivity to subsoil variation in K content. This fact has not been sufficiently considered in K management strategies in part due to logistical difficulties in sampling spatially variable subsoil K. We propose a simplified soil factorial model, a resp...

Spatial variation related to hydroloigc patterns and vegetation in greenhouse gas fluxes from the Mississippi Delta agricultural region

USDA-ARS?s Scientific Manuscript database

Methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) fluxes from agricultural landscapes may contribute significantly to regional greenhouse gas budgets due to stimulation of soil microbial activity through fertilizer application and variable soil moisture effects. In this study, measuremen...

Spatial and temporal distribution of trunk-injected 14C-Imidacloprid in Fraxinus trees

Treesearch

Sara R. Tanis; Bert M. Cregg; David Mota-Sanchez; Deborah G. McCullough; Therese M. Poland

2012-01-01

BACKGROUND: Since the discovery of Agrilus planipennis Fairmaire (emerald ash borer) in 2002, researchers have tested several methods of chemical control. Soil drench or trunk injection products containing imidacloprid are commonly used to control adults. However, efficacy can be highly variable andmay be due to uneven translocation of systemic...

Windthrow and fallow-forest successions impacts in soil carbon stocks and GHG fluxes spatial variability and dynamics in the Central Russia' reserve spruce ecosystems

NASA Astrophysics Data System (ADS)

Vasenev, Ivan; Ivanov, Alexey; Komarova, Tatyana; Valentini, Riccardo

2015-04-01

High spatial and temporal variability is mutual feature for most forest soils that is especially obvious in case of their carbon stocks and GHG fluxes. This phenomenon is generally well-known but not so often becomes the object of special precision investigation in detail and small scales so there are still serious gaps in its principal factors understanding due to their high bioclimatic, regional, landscape, tree species and temporal variability. Southern taiga is one of the most environmentally important world zonal forest ecosystems due to its still comparatively intensive carbon biogeochemical cycle and huge area in the northern Eurasia with strong anthropogenic impacts by Western & Central European and Southern & Eastern Asian regions. Central Forest Biospheric Reserve (Tver region, 360 km to North-West from Moscow) is the principal southern-taiga reserve in the European territory of Russia. Since start of its research activity in 1939 the reserve became the regional center of mature spruce ecosystem structure and dynamics investigation. In 1970-1980-s there have been done complex investigations of windthrow soil patterns and fallow-forest successions. Since middle of 1990-s the ecosystem-level GHG fluxes have been observed by eddy covariance method. Since 2012 the detailed year-round monitoring is running in the southern-taiga zonal station of the regional system RusFluxNet with especial attention on the soil carbon stocks and GHG fluxes spatial variability and dynamics due to windthrow and fallow-forest successions (in frame of RF Governmental projects #11.G34.31.0079 and #14.120.14.4266). Soil carbon dynamics is investigated in decades-hundred-year chronosequences of dominated parcels and different-size windthrow soil cover patterns, including direct investigation during last 33 years with detailed mapping, soil profile morphometrics and bulk density, morphogenetic and statistical analysis of mass data. Morphogenetic analysis of microrelief, soil profile and cover have been accompanied by researches of soil regimes (temperature, moisture, pH, oxidation-reduction potential, microbiological activity) and transformations of representative topsoil materials at the different stages of windthrow soil successions. Since 2012 soil CO2 fluxes have been analyzed every ten days in situ by method of exposition chambers with infra red gas analyzer (Li-Cor 820). At the same periods soil gas fluxes have been sampled from the exposition chambers into vials with the following CH4 and N2O analysis by gas chromatograph. The carried out researches have shown sharp increase of rates of typomorphic soil forming processes within windthrow hole and mound soil successions: (a) lateral input of organic matter in soils of fresh holes - up to 2-3 kg m-2y-1; (b) fulvic acid formation - up to 100-200 g m-2y-1 in soils of young holes and mounds; (c) Al-Fe-humus migration - up to 0.7-1.2 kg cm m-2y-1; (d) humus-accumulated and eluvial horizon development - up to 1-2 mm y-1. The conducted researches have shown high temporal and spatial variability of CO2 fluxes due to soil cover and windthrow complex patterns, windthrow or fallow-forest succession stage and age, air and soil temperature (up to R = 0.64 for taiga, and R = 0.75 for fallow), soil moisture (up to R = -0.65/0.66 both for taiga and fallow) and some other characteristics of the studied objects. Soil CO2 emission is essentially decreased with fallow-forest age. Maximum CO2 fluxes have been observed between 12:00 and 16:00. Within fallow-forest succession the maximum CH4 emission has been fixed in first (grass) stage, and N2O fluxes increase due to temperature rise and moisture decreasing. Usually there is stronger effect on GHG fluxes by air temperature than soil one due to comparatively thin layer of soil organic and/or humus-accumulative subhorizons with maximum biological activity that usually determines the total rate of GHG principal soil fluxes. Unfavorable seasonal climatic conditions (dry season or low temperature) determine essential (in 1.5-2 times) decreasing not only in soil GHG fluxes but in level of their spatial variability, seasonal and daily dynamics too. These trends are most obvious in case of more open ecosystems at the first stages of the fallow-forest succession. Understanding the principal regularities of spatial and temporal changes in soil GHG fluxes help better modelling them in the process of spatial intra- and extrapolations, seasonal and interseasonal predictions, taking into attention basic and current principal factors limiting GHG fluxes.

Wind-driven Ocean Circulation and the Spatial-temporal Variability of Dissolved Inorganic Carbon in the Gulf of Tehuantepec, North Eastern Tropical Pacific

NASA Astrophysics Data System (ADS)

Chapa, C.; Beier, E.; Durazo, R.; Martin Hernandez-Ayon, J. M.; Alin, S. R.; Lopez-Perez, A.

2016-12-01

The relationship between the surface enrichment of dissolved inorganic carbon (DIC) and wind variability and circulation in the Gulf of Tehuantepec (GT) was examined from satellite images and in situ data from three cruises (June 2010; April and November 2013). Monthly mean wind climatologies (and derived variables), sea surface temperature and sea surface height anomaly fields were analyzed in the GT and part of the NETP. Signal decomposition according to circulation scales (seasonal, inter-annual, mesoscale) was performed using harmonic analysis for the seasonal components, and empirical orthogonal functions for the residuals, applied to satellite sea-level anomaly data. The results show that wind is the main driving force of the variability in the GT. Mesoscale is the variable with the highest percent of local variance (25-75%), due mainly to mesoscale eddies, followed by seasonality (20-55%), and finally the inter-annual signal (10-30%), dominated by ENSO. Mesoscale and seasonality prevailed during the samplings. The changes in circulation led to variations in the concentration of surface DIC ranging between 100 and 300 µmol kg-1 (436 µatm) due to Ekman pumping. The largest enrichment occurred in November 2013 after a strong northerly wind event. However, the predominance of mesoscale events suggests that changes in dissolved inorganic carbon resulting from mesoscale- derived Ekman pumping may become important in the long term and with a larger spatial and temporal coverage. The results suggest that the seasonal cycle of dissolved inorganic carbon may be linked to wind seasonality.

Biogeochemical typology and temporal variability of lagoon waters in a coral reef ecosystem subject to terrigeneous and anthropogenic inputs (New Caledonia).

PubMed

Fichez, R; Chifflet, S; Douillet, P; Gérard, P; Gutierrez, F; Jouon, A; Ouillon, S; Grenz, C

2010-01-01

Considering the growing concern about the impact of anthropogenic inputs on coral reefs and coral reef lagoons, surprisingly little attention has been given to the relationship between those inputs and the trophic status of lagoon waters. The present paper describes the distribution of biogeochemical parameters in the coral reef lagoon of New Caledonia where environmental conditions allegedly range from pristine oligotrophic to anthropogenically influenced. The study objectives were to: (i) identify terrigeneous and anthropogenic inputs and propose a typology of lagoon waters, (ii) determine temporal variability of water biogeochemical parameters at time-scales ranging from hours to seasons. Combined ACP-cluster analyses revealed that over the 2000 km(2) lagoon area around the city of Nouméa, "natural" terrigeneous versus oceanic influences affecting all stations only accounted for less than 20% of the spatial variability whereas 60% of that spatial variability could be attributed to significant eutrophication of a limited number of inshore stations. ACP analysis allowed to unambiguously discriminating between the natural trophic enrichment along the offshore-inshore gradient and anthropogenically induced eutrophication. High temporal variability in dissolved inorganic nutrients concentrations strongly hindered their use as indicators of environmental status. Due to longer turn over time, particulate organic material and more specifically chlorophyll a appeared as more reliable nonconservative tracer of trophic status. Results further provided evidence that ENSO occurrences might temporarily lower the trophic status of the New Caledonia lagoon. It is concluded that, due to such high frequency temporal variability, the use of biogeochemical parameters in environmental surveys require adapted sampling strategies, data management and environmental alert methods. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Investigatigating inter-/intra-annual variability of surface hydrology at northern high latitude from spaceborne measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2014-12-01

Lakes encompass a large part of the surface cover in the northern boreal and tundra areas of northern Canada and are therefore a significant component of the terrestrial hydrological system. To understand the hydrologic cycle over subarctic and arctic landscapes, estimating surface parameters such as surface net radiation, soil moisture, and surface albedo is important. Although ground-based field measurements provide a good temporal resolution, these data provide a limited spatial representation and are often restricted to the summer period (from June to August), and few surface-based stations are located in high-latitude regions. In this respect, spaceborne remote sensing provides the means to monitor surface hydrology and to estimate components of the surface energy balance with reasonable spatial and temporal resolutions required for hydrological investigations, as well as for providing more spatially representative lake-relevant information than available from in situ measurements. The primary objective of this study is to quantify the sources of temporal and spatial variability in surface albedo over subarctic wetland from satellite derived albedo measurements in the Hudson Bay Lowlands near Churchill, Manitoba. The spatial variability in albedo within each land-cover type is investigated through optical satellite imagery from Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper Plus, and Landsat-8 Operational Land Imager obtained in different seasons from spring into fall (April and October) over a 30-year period (1984-2013). These data allowed for an examination of the spatial variability of surface albedo under relatively dry and wet summer conditions (i.e. 1984, 1998 versus 1991, 2005). A detailed analysis of Landsat-derived surface albedo (ranging from 0.09 to 0.15) conducted in the Churchill region for August is inversely related to surface water fraction calculated from Landsat images. Preliminary analysis of surface albedo observed between July and August are 0.10 to 0.15, and vary due to differences in meteorological parameters such as rainfall, surface moisture and surface air temperature. Overall, spaceborne optical data are an invaluable source for investigating changes and variability in surface albedo in relation to surface hydrology over subarctic regions.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Bark beetle-induced tree mortality alters stand energy budgets due to water budget changes

NASA Astrophysics Data System (ADS)

Reed, David E.; Ewers, Brent E.; Pendall, Elise; Frank, John; Kelly, Robert

2018-01-01

Insect outbreaks are major disturbances that affect a land area similar to that of forest fires across North America. The recent mountain pine bark beetle ( D endroctonus ponderosae) outbreak and its associated blue stain fungi ( Grosmannia clavigera) are impacting water partitioning processes of forests in the Rocky Mountain region as the spatially heterogeneous disturbance spreads across the landscape. Water cycling may dramatically change due to increasing spatial heterogeneity from uneven mortality. Water and energy storage within trees and soils may also decrease, due to hydraulic failure and mortality caused by blue stain fungi followed by shifts in the water budget. This forest disturbance was unique in comparison to fire or timber harvesting because water fluxes were altered before significant structural change occurred to the canopy. We investigated the impacts of bark beetles on lodgepole pine ( Pinus contorta) stand and ecosystem level hydrologic processes and the resulting vertical and horizontal spatial variability in energy storage. Bark beetle-impacted stands had on average 57 % higher soil moisture, 1.5 °C higher soil temperature, and 0.8 °C higher tree bole temperature over four growing seasons compared to unimpacted stands. Seasonal latent heat flux was highly correlated with soil moisture. Thus, high mortality levels led to an increase in ecosystem level Bowen ratio as sensible heat fluxes increased yearly and latent heat fluxes varied with soil moisture levels. Decline in canopy biomass (leaf, stem, and branch) was not seen, but ground-to-atmosphere longwave radiation flux increased, as the ground surface was a larger component of the longwave radiation. Variability in soil, latent, and sensible heat flux and radiation measurements increased during the disturbance. Accounting for stand level variability in water and energy fluxes will provide a method to quantify potential drivers of ecosystem processes and services as well as lead to greater confidence in measurements for all dynamic disturbances.

Impact of spatial variation in snow water equivalent and snow ablation on spring snowcover depletion over an alpine ridge

NASA Astrophysics Data System (ADS)

Schirmer, Michael; Harder, Phillip; Pomeroy, John

2016-04-01

The spatial and temporal dynamics of mountain snowmelt are controlled by the spatial distribution of snow accumulation and redistribution and the pattern of melt energy applied to this snowcover. In order to better quantify the spatial variations of accumulation and ablation, Structure-from-Motion techniques were applied to sequential aerial photographs of an alpine ridge in the Canadian Rocky Mountains taken from an Unmanned Aerial Vehicle (UAV). Seven spatial maps of snow depth and changes to depth during late melt (May-July) were generated at very high resolutions covering an area of 800 x 600 m. The accuracy was assessed with over 100 GPS measurements and RMSE were found to be less than 10 cm. Low resolution manual measurements of density permitted calculation of snow water equivalent (SWE) and change in SWE (ablation rate). The results indicate a highly variable initial SWE distribution, which was five times more variable than the spatial variation in ablation rate. Spatial variation in ablation rate was still substantial, with a factor of two difference between north and south aspects and small scale variations due to local dust deposition. However, the impact of spatial variations in ablation rate on the snowcover depletion curve could not be discerned. The reason for this is that only a weak spatial correlation developed between SWE and ablation rate. These findings suggest that despite substantial variations in ablation rate, snowcover depletion curve calculations should emphasize the spatial variation of initial SWE rather than the variation in ablation rate. While there is scientific evidence from other field studies that support this, there are also studies that suggest that spatial variations in ablation rate can influence snowcover depletion curves in complex terrain, particularly in early melt. The development of UAV photogrammetry has provided an opportunity for further detailed measurement of ablation rates, SWE and snowcover depletion over complex terrain and UAV field studies are recommended to clarify the relative importance of SWE and melt variability on snowcover depletion in various environmental conditions.

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Spatial variability of soil total and DTPA-extractable cadmium caused by long-term application of phosphate fertilizers, crop rotation, and soil characteristics.

PubMed

Jafarnejadi, A R; Sayyad, Gh; Homaee, M; Davamei, A H

2013-05-01

Increasing cadmium (Cd) accumulation in agricultural soils is undesirable due to its hazardous influences on human health. Thus, having more information on spatial variability of Cd and factors effective to increase its content on the cultivated soils is very important. Phosphate fertilizers are main contamination source of cadmium (Cd) in cultivated soils. Also, crop rotation is a critical management practice which can alter soil Cd content. This study was conducted to evaluate the effects of long-term consumption of the phosphate fertilizers, crop rotations, and soil characteristics on spatial variability of two soil Cd species (i.e., total and diethylene triamine pentaacetic acid (DTPA) extractable) in agricultural soils. The study was conducted in wheat farms of Khuzestan Province, Iran. Long-term (27-year period (1980 to 2006)) data including the rate and the type of phosphate fertilizers application, the respective area, and the rotation type of different regions were used. Afterwards, soil Cd content (total or DTPA extractable) and its spatial variability in study area (400,000 ha) were determined by sampling from soils of 255 fields. The results showed that the consumption rate of di-ammonium phosphate fertilizer have been varied enormously in the period study. The application rate of phosphorus fertilizers was very high in some subregions with have extensive agricultural activities (more than 95 kg/ha). The average and maximum contents of total Cd in the study region were obtained as 1.47 and 2.19 mg/kg and DTPA-extractable Cd as 0.084 and 0.35 mg/kg, respectively. The spatial variability of Cd indicated that total and DTPA-extractable Cd contents were over 0.8 and 0.1 mg/kg in 95 and 25 % of samples, respectively. The spherical model enjoys the best fitting and lowest error rate to appraise the Cd content. Comparing the phosphate fertilizer consumption rate with spatial variability of the soil cadmium (both total and DTPA extractable) revealed the high correlation between the consumption rate of P fertilizers and soil Cd content. Rotation type was likely the main effective factor on variations of the soil DTPA-extractable Cd contents in some parts (eastern part of study region) and could explain some Cd variation. Total Cd concentrations had significant correlation with the total neutralizing value (p < 0.01), available P (p < 0.01), cation exchange capacity (p < 0.05), and organic carbon (p < 0.05) variables. The DTPA-extractable Cd had significant correlation with OC (p < 0.01), pH, and clay content (p < 0.05). Therefore, consumption rate of the phosphate fertilizers and crop rotation are important factors on solubility and hence spatial variability of Cd content in agricultural soils.

Processes of 30-90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

NASA Astrophysics Data System (ADS)

Vialard, J.; Jayakumar, A.; Gnanaseelan, C.; Lengaigne, M.; Sengupta, D.; Goswami, B. N.

2012-05-01

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30-90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against ~0.25 for wind stress) and in observations (0.8 regression coefficient); ~60% of the heat flux variation is due do shortwave radiation and ~40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70-100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our ~100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

Mushroom biomass and diversity are driven by different spatio-temporal scales along Mediterranean elevation gradients

NASA Astrophysics Data System (ADS)

Alday, Josu G.; Martínez de Aragón, Juan; de-Miguel, Sergio; Bonet, José Antonio

2017-04-01

Mushrooms are important non-wood-forest-products in many Mediterranean ecosystems, being highly vulnerable to climate change. However, the ecological scales of variation of mushroom productivity and diversity, and climate dependence has been usually overlooked due to a lack of available data. We determined the spatio-temporal variability of epigeous sporocarps and the climatic factors driving their fruiting to plan future sustainable management of wild mushrooms production. We collected fruiting bodies in Pinus sylvestris stands along an elevation gradient for 8 consecutive years. Overall, sporocarp biomass was mainly dependent on inter-annual variations, whereas richness was more spatial-scale dependent. Elevation was not significant, but there were clear elevational differences in biomass and richness patterns between ectomycorrhizal and saprotrophic guilds. The main driver of variation was late-summer-early-autumn precipitation. Thus, different scale processes (inter-annual vs. spatial-scale) drive sporocarp biomass and diversity patterns; temporal effects for biomass and ectomycorrhizal fungi vs. spatial scale for diversity and saprotrophic fungi. The significant role of precipitation across fungal guilds and spatio-temporal scales indicates that it is a limiting resource controlling sporocarp production and diversity in Mediterranean regions. The high spatial and temporal variability of mushrooms emphasize the need for long-term datasets of multiple spatial points to effectively characterize fungal fruiting patterns.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

Landscape patterns and soil organic carbon stocks in agricultural bocage landscapes

NASA Astrophysics Data System (ADS)

Viaud, Valérie; Lacoste, Marine; Michot, Didier; Walter, Christian

2014-05-01

Soil organic carbon (SOC) has a crucial impact on global carbon storage at world scale. SOC spatial variability is controlled by the landscape patterns resulting from the continuous interactions between the physical environment and the society. Natural and anthropogenic processes occurring and interplaying at the landscape scale, such as soil redistribution in the lateral and vertical dimensions by tillage and water erosion processes or spatial differentiation of land-use and land-management practices, strongly affect SOC dynamics. Inventories of SOC stocks, reflecting their spatial distribution, are thus key elements to develop relevant management strategies to improving carbon sequestration and mitigating climate change and soil degradation. This study aims to quantify SOC stocks and their spatial distribution in a 1,000-ha agricultural bocage landscape with dairy production as dominant farming system (Zone Atelier Armorique, LTER Europe, NW France). The site is characterized by high heterogeneity on short distance due to a high diversity of soils with varying waterlogging, soil parent material, topography, land-use and hedgerow density. SOC content and stocks were measured up to 105-cm depth in 200 sampling locations selected using conditioned Latin hypercube sampling. Additive sampling was designed to specifically explore SOC distribution near to hedges: 112 points were sampled at fixed distance on 14 transects perpendicular from hedges. We illustrate the heterogeneity of spatial and vertical distribution of SOC stocks at landscape scale, and quantify SOC stocks in the various landscape components. Using multivariate statistics, we discuss the variability and co-variability of existing spatial organization of cropping systems, environmental factors, and SOM stocks, over landscape. Ultimately, our results may contribute to improving regional or national digital soil mapping approaches, by considering the distribution of SOC stocks within each modeling unit and by accounting for the impact of sensitive ecosystems.

Graffiti for science - erosion painting reveals spatially variable erosivity of sediment-laden flows

NASA Astrophysics Data System (ADS)

Beer, Alexander R.; Kirchner, James W.; Turowski, Jens M.

2016-12-01

Spatially distributed detection of bedrock erosion is a long-standing challenge. Here we show how the spatial distribution of surface erosion can be visualized and analysed by observing the erosion of paint from natural bedrock surfaces. If the paint is evenly applied, it creates a surface with relatively uniform erodibility, such that spatial variability in the erosion of the paint reflects variations in the erosivity of the flow and its entrained sediment. In a proof-of-concept study, this approach provided direct visual verification that sediment impacts were focused on upstream-facing surfaces in a natural bedrock gorge. Further, erosion painting demonstrated strong cross-stream variations in bedrock erosion, even in the relatively narrow (5 m wide) gorge that we studied. The left side of the gorge experienced high sediment throughput with abundant lateral erosion on the painted wall up to 80 cm above the bed, but the right side of the gorge only showed a narrow erosion band 15-40 cm above the bed, likely due to deposited sediment shielding the lower part of the wall. This erosion pattern therefore reveals spatial stream bed aggradation that occurs during flood events in this channel. The erosion painting method provides a simple technique for mapping sediment impact intensities and qualitatively observing spatially distributed erosion in bedrock stream reaches. It can potentially find wide application in both laboratory and field studies.

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

PubMed Central

Mukerjee, Shaibal; Smith, Luther; Neas, Lucas; Norris, Gary

2012-01-01

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult. PMID:23226985

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Effects of biotic and abiotic factors on resistance versus resilience of Douglas fir to drought.

PubMed

Carnwath, Gunnar; Nelson, Cara

2017-01-01

Significant increases in tree mortality due to drought-induced physiological stress have been documented worldwide. This trend is likely to continue with increased frequency and severity of extreme drought events in the future. Therefore, understanding the factors that influence variability in drought responses among trees will be critical to predicting ecosystem responses to climate change and developing effective management actions. In this study, we used hierarchical mixed-effects models to analyze drought responses of Pseudotsuga menziesii in 20 unmanaged forests stands across a broad range of environmental conditions in northeastern Washington, USA. We aimed to 1) identify the biotic and abiotic attributes most closely associated with the responses of individual trees to drought and 2) quantify the variability in drought responses at different spatial scales. We found that growth rates and competition for resources significantly affected resistance to a severe drought event in 2001: slow-growing trees and trees growing in subordinate canopy positions and/or with more neighbors suffered greater declines in radial growth during the drought event. In contrast, the ability of a tree to return to normal growth when climatic conditions improved (resilience) was unaffected by competition or relative growth rates. Drought responses were significantly influenced by tree age: older trees were more resistant but less resilient than younger trees. Finally, we found differences between resistance and resilience in spatial scale: a significant proportion (approximately 50%) of the variability in drought resistance across the study area was at broad spatial scales (i.e. among different forest types), most likely due to differences in the total amount of precipitation received at different elevations; in contrast, variation in resilience was overwhelmingly (82%) at the level of individual trees within stands and there was no difference in drought resilience among forest types. Our results suggest that for Pseudotsuga menziesii resistance and resilience to drought are driven by different factors and vary at different spatial scales.

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.

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2016-01-01

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Effects of biotic and abiotic factors on resistance versus resilience of Douglas fir to drought

PubMed Central

Nelson, Cara

2017-01-01

Significant increases in tree mortality due to drought-induced physiological stress have been documented worldwide. This trend is likely to continue with increased frequency and severity of extreme drought events in the future. Therefore, understanding the factors that influence variability in drought responses among trees will be critical to predicting ecosystem responses to climate change and developing effective management actions. In this study, we used hierarchical mixed-effects models to analyze drought responses of Pseudotsuga menziesii in 20 unmanaged forests stands across a broad range of environmental conditions in northeastern Washington, USA. We aimed to 1) identify the biotic and abiotic attributes most closely associated with the responses of individual trees to drought and 2) quantify the variability in drought responses at different spatial scales. We found that growth rates and competition for resources significantly affected resistance to a severe drought event in 2001: slow-growing trees and trees growing in subordinate canopy positions and/or with more neighbors suffered greater declines in radial growth during the drought event. In contrast, the ability of a tree to return to normal growth when climatic conditions improved (resilience) was unaffected by competition or relative growth rates. Drought responses were significantly influenced by tree age: older trees were more resistant but less resilient than younger trees. Finally, we found differences between resistance and resilience in spatial scale: a significant proportion (approximately 50%) of the variability in drought resistance across the study area was at broad spatial scales (i.e. among different forest types), most likely due to differences in the total amount of precipitation received at different elevations; in contrast, variation in resilience was overwhelmingly (82%) at the level of individual trees within stands and there was no difference in drought resilience among forest types. Our results suggest that for Pseudotsuga menziesii resistance and resilience to drought are driven by different factors and vary at different spatial scales. PMID:28973008

Spatial Variability of Snowpack Properties On Small Slopes

NASA Astrophysics Data System (ADS)

Pielmeier, C.; Kronholm, K.; Schneebeli, M.; Schweizer, J.

The spatial variability of alpine snowpacks is created by a variety of parameters like deposition, wind erosion, sublimation, melting, temperature, radiation and metamor- phism of the snow. Spatial variability is thought to strongly control the avalanche initi- ation and failure propagation processes. Local snowpack measurements are currently the basis for avalanche warning services and there exist contradicting hypotheses about the spatial continuity of avalanche active snow layers and interfaces. Very little about the spatial variability of the snowpack is known so far, therefore we have devel- oped a systematic and objective method to measure the spatial variability of snowpack properties, layering and its relation to stability. For a complete coverage, the analysis of the spatial variability has to entail all scales from mm to km. In this study the small to medium scale spatial variability is investigated, i.e. the range from centimeters to tenths of meters. During the winter 2000/2001 we took systematic measurements in lines and grids on a flat snow test field with grid distances from 5 cm to 0.5 m. Fur- thermore, we measured systematic grids with grid distances between 0.5 m and 2 m in undisturbed flat fields and on small slopes above the tree line at the Choerbschhorn, in the region of Davos, Switzerland. On 13 days we measured the spatial pattern of the snowpack stratigraphy with more than 110 snow micro penetrometer measure- ments at slopes and flat fields. Within this measuring grid we placed 1 rutschblock and 12 stuffblock tests to measure the stability of the snowpack. With the large num- ber of measurements we are able to use geostatistical methods to analyse the spatial variability of the snowpack. Typical correlation lengths are calculated from semivari- ograms. Discerning the systematic trends from random spatial variability is analysed using statistical models. Scale dependencies are shown and recurring scaling patterns are outlined. The importance of the small and medium scale spatial variability for the larger (kilometer) scale spatial variability as well as for the avalanche formation are discussed. Finally, an outlook on spatial models for the snowpack variability is given.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

Capturing temporal and spatial variability in the chemistry of shallow permafrost ponds

NASA Astrophysics Data System (ADS)

Morison, Matthew Q.; Macrae, Merrin L.; Petrone, Richard M.; Fishback, LeeAnn

2017-12-01

Across the circumpolar north, the fate of small freshwater ponds and lakes (< 1 km2) has been the subject of scientific interest due to their ubiquity in the landscape, capacity to exchange carbon and energy with the atmosphere, and their potential to inform researchers about past climates through sediment records. A changing climate has implications for the capacity of ponds and lakes to support organisms and store carbon, which in turn has important feedbacks to climate change. Thus, an improved understanding of pond biogeochemistry is needed. To characterize spatial and temporal patterns in water column chemistry, a suite of tundra ponds were examined to answer the following research questions: (1) does temporal variability exceed spatial variability? (2) If temporal variability exists, do all ponds (or groups of ponds) behave in a similar temporal pattern, linked to seasonal hydrologic drivers or precipitation events? Six shallow ponds located in the Hudson Bay Lowlands region were monitored between May and October 2015 (inclusive, spanning the entire open-water period). The ponds span a range of biophysical conditions including pond area, perimeter, depth, and shoreline development. Water samples were collected regularly, both bimonthly over the ice-free season and intensively during and following a large summer storm event. Samples were analysed for nitrogen speciation (NO3-, NH4+, dissolved organic nitrogen) and major ions (Cl-, SO42-, K+, Ca2+, Mg2+, Na+). Across all ponds, temporal variability (across the season and within a single rain event) exceeded spatial variability (variation among ponds) in concentrations of several major species (Cl-, SO42-, K+, Ca2+, Na+). Evapoconcentration and dilution of pond water with precipitation and runoff inputs were the dominant processes influencing a set of chemical species which are hydrologically driven (Cl-, Na+, K+, Mg2+, dissolved organic nitrogen), whereas the dissolved inorganic nitrogen species were likely mediated by processes within ponds. This work demonstrates the importance of understanding hydrologically driven chemodynamics in permafrost ponds on multiple scales (seasonal and event scale).

Efficiently enforcing artisanal fisheries to protect estuarine biodiversity.

PubMed

Duarte de Paula Costa, Micheli; Mills, Morena; Richardson, Anthony J; Fuller, Richard A; Muelbert, José H; Possingham, Hugh P

2018-06-26

Artisanal fisheries support millions of livelihoods worldwide, yet ineffective enforcement can allow for continued environmental degradation due to overexploitation. Here, we use spatial planning to design an enforcement strategy for a pre-existing spatial closure for artisanal fisheries considering climate variability, existing seasonal fishing closures, representative conservation targets and enforcement costs. We calculated enforcement cost in three ways, based on different assumptions about who could be responsible for monitoring the fishery. We applied this approach in the Patos Lagoon estuary (Brazil), where we found three important results. First, spatial priorities for enforcement were similar under different climate scenarios. Second, we found that the cost and percentage of area enforced varied among scenarios tested by the conservation planning analysis, with only a modest increase in budget needed to incorporate climate variability. Third, we found that spatial priorities for enforcement depend on whether enforcement is carried out by a central authority or by the community itself. Here, we demonstrated a method that can be used to efficiently design enforcement plans, resulting in the conservation of biodiversity and estuarine resources. Also, cost of enforcement can be potentially reduced when fishers are empowered to enforce management within their fishing grounds. © 2018 by the Ecological Society of America.

Do lower income areas have more pedestrian casualties?

PubMed

Noland, Robert B; Klein, Nicholas J; Tulach, Nicholas K

2013-10-01

Pedestrian and motor vehicle casualties are analyzed for the State of New Jersey with the objective of determining how the income of an area may be associated with casualties. We develop a maximum-likelihood negative binomial model to examine how various spatially defined variables, including road, income, and vehicle ownership, may be associated with casualties using census block-group level data. Due to suspected spatial correlation in the data we also employ a conditional autoregressive Bayesian model using Markov Chain Monte Carlo simulation, implemented with Crimestat software. Results suggest that spatial correlation is an issue as some variables are not statistically significant in the spatial model. We find that both pedestrian and motor vehicle casualties are greater in lower income block groups. Both are also associated with less household vehicle ownership, which is not surprising for pedestrian casualties, but is a surprising result for motor vehicle casualties. Controls for various road categories provide expected relationships. Individual level data is further examined to determine relationships between the location of a crash victim and their residence zip code, and this largely confirms a residual effect associated with both lower income individuals and lower income areas. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prospects and pitfalls of occupational hazard mapping: 'between these lines there be dragons'.

PubMed

Koehler, Kirsten A; Volckens, John

2011-10-01

Hazard data mapping is a promising new technique that can enhance the process of occupational exposure assessment and risk communication. Hazard maps have the potential to improve worker health by providing key input for the design of hazard intervention and control strategies. Hazard maps are developed with aid from direct-reading instruments, which can collect highly spatially and temporally resolved data in a relatively short period of time. However, quantifying spatial-temporal variability in the occupational environment is not a straightforward process, and our lack of understanding of how to ascertain and model spatial and temporal variability is a limiting factor in the use and interpretation of workplace hazard maps. We provide an example of how sources of and exposures to workplace hazards may be mischaracterized in a hazard map due to a lack of completeness and representativeness of collected measurement data. Based on this example, we believe that a major priority for research in this emerging area should focus on the development of a statistical framework to quantify uncertainty in spatially and temporally varying data. In conjunction with this need is one for the development of guidelines and procedures for the proper sampling, generation, and evaluation of workplace hazard maps.

Continuous Variable Cluster State Generation over the Optical Spatial Mode Comb

DOE PAGES

Pooser, Raphael C.; Jing, Jietai

2014-10-20

One way quantum computing uses single qubit projective measurements performed on a cluster state (a highly entangled state of multiple qubits) in order to enact quantum gates. The model is promising due to its potential scalability; the cluster state may be produced at the beginning of the computation and operated on over time. Continuous variables (CV) offer another potential benefit in the form of deterministic entanglement generation. This determinism can lead to robust cluster states and scalable quantum computation. Recent demonstrations of CV cluster states have made great strides on the path to scalability utilizing either time or frequency multiplexingmore » in optical parametric oscillators (OPO) both above and below threshold. The techniques relied on a combination of entangling operators and beam splitter transformations. Here we show that an analogous transformation exists for amplifiers with Gaussian inputs states operating on multiple spatial modes. By judicious selection of local oscillators (LOs), the spatial mode distribution is analogous to the optical frequency comb consisting of axial modes in an OPO cavity. We outline an experimental system that generates cluster states across the spatial frequency comb which can also scale the amount of quantum noise reduction to potentially larger than in other systems.« less

Land agroecological quality assessment in conditions of high spatial soil cover variability at the Pereslavskoye Opolye.

NASA Astrophysics Data System (ADS)

Morev, Dmitriy; Vasenev, Ivan

2015-04-01

The essential spatial variability is mutual feature for most natural and man-changed soils at the Central region of European territory of Russia. The original spatial heterogeneity of forest soils has been further complicated by a specific land-use history and human impacts. For demand-driven land-use planning and decision making the quantitative analysis and agroecological interpretation of representative soil cover spatial variability is an important and challenging task that receives increasing attention from private companies, governmental and environmental bodies. Pereslavskoye Opolye is traditionally actively used in agriculture due to dominated high-quality cultivated soddy-podzoluvisols which are relatively reached in organic matter (especially for conditions of the North part at the European territory of Russia). However, the soil cover patterns are often very complicated even within the field that significantly influences on crop yield variability and have to be considered in farming system development and land agroecological quality evaluation. The detailed investigations of soil regimes and mapping of the winter rye yield have been carried in conditions of two representative fields with slopes sharply contrasted both in aspects and degrees. Rye biological productivity and weed infestation have been measured in elementary plots of 0.25 m2 with the following analysis the quality of the yield. In the same plot soil temperature and moisture have been measured by portable devices. Soil sampling was provided from three upper layers by drilling. The results of ray yield detailed mapping shown high differences both in average values and within-field variability on different slopes. In case of low-gradient slope (field 1) there is variability of ray yield from 39.4 to 44.8 dt/ha. In case of expressed slope (field 2) the same species of winter rye grown with the same technology has essentially lower yield and within-field variability from 20 to 29.6 dt/ha. The variability in crop yield between two fields is determined by their differences in mesorelief, A-horizon average thickness and slightly changes in soil temperature. The within-field crop yield variability is determined by microrelief and connected differences in soil moisture. Higher soil cover variability reflects in higher variability of winter ray yield and its quality that could be predicted and planed in conditions of concrete field and year according to principal limiting factors evaluation.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050.

PubMed

Song, Yongze; Ge, Yong; Wang, Jinfeng; Ren, Zhoupeng; Liao, Yilan; Peng, Junhuan

2016-07-07

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Cotunneling and polaronic effect in granular systems

NASA Astrophysics Data System (ADS)

Ioselevich, A. S.; Sivak, V. V.

2017-06-01

We theoretically study the conductivity in arrays of metallic grains due to the variable-range multiple cotunneling of electrons with short-range (screened) Coulomb interaction. The system is supposed to be coupled to random stray charges in the dielectric matrix that are only loosely bounded to their spatial positions by elastic forces. The flexibility of the stray charges gives rise to a polaronic effect, which leads to the onset of Arrhenius-type conductivity behavior at low temperatures, replacing conventional Mott variable-range hopping. The effective activation energy logarithmically depends on temperature due to fluctuations of the polaron barrier heights. We present the unified theory that covers both weak and strong polaron effect regimes of hopping in granular metals and describes the crossover from elastic to inelastic cotunneling.

Mesoscale density variability in the mesosphere and thermosphere: Effects of vertical flow accelerations

NASA Technical Reports Server (NTRS)

Revelle, D. O.

1987-01-01

A mechanistic one dimensional numerical (iteration) model was developed which can be used to simulate specific types of mesoscale atmospheric density (and pressure) variability in the mesosphere and the thermosphere, namely those due to waves and those due to vertical flow accelerations. The model was developed with the idea that it could be used as a supplement to the TGCMs (thermospheric general circulation models) since such models have a very limited ability to model phenomena on small spatial scales. The simplest case to consider was the integration upward through a time averaged, height independent, horizontally divergent flow field. Vertical winds were initialized at the lower boundary using the Ekman pumping theory over flat terrain. The results of the computations are summarized.

Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions

NASA Astrophysics Data System (ADS)

Oaida, C. M.; Andreadis, K.; Reager, J. T., II; Famiglietti, J. S.; Levoe, S.

2017-12-01

Accurately estimating how much snow water equivalent (SWE) is stored in mountainous regions characterized by complex terrain and snowmelt-driven hydrologic cycles is not only greatly desirable, but also a big challenge. Mountain snowpack exhibits high spatial variability across a broad range of spatial and temporal scales due to a multitude of physical and climatic factors, making it difficult to observe or estimate in its entirety. Combing remotely sensed data and high resolution hydrologic modeling through data assimilation (DA) has the potential to provide a spatially and temporally continuous SWE dataset at horizontal scales that capture sub-grid snow spatial variability and are also relevant to stakeholders such as water resource managers. Here, we present the evaluation of a new snow DA approach that uses a Local Ensemble Transform Kalman Filter (LETKF) in tandem with the Variable Infiltration Capacity macro-scale hydrologic model across the Western United States, at a daily temporal resolution, and a horizontal resolution of 1.75 km x 1.75 km. The LETKF is chosen for its relative simplicity, ease of implementation, and computational efficiency and scalability. The modeling/DA system assimilates daily MODIS Snow Covered Area and Grain Size (MODSCAG) fractional snow cover over, and has been developed to efficiently calculate SWE estimates over extended periods of time and covering large regional-scale areas at relatively high spatial resolution, ultimately producing a snow reanalysis-type dataset. Here we focus on the assessment of SWE produced by the DA scheme over several basins in California's Sierra Nevada Mountain range where Airborne Snow Observatory data is available, during the last five water years (2013-2017), which include both one of the driest and one of the wettest years. Comparison against such a spatially distributed SWE observational product provides a greater understanding of the model's ability to estimate SWE and SWE spatial variability, and highlights under which conditions snow cover DA can add value in estimating SWE.

Field measurements and modeling to resolve m2 to km2 CH4 emissions for a complex urban source: An Indiana landfill study

USDA-ARS?s Scientific Manuscript database

Large uncertainties for landfill CH4 emissions due to spatial and temporal variabilities remain unresolved by short-term field campaigns and historic GHG inventory models. Using four field methods (aircraft-based mass balance, tracer correlation, vertical radial plume mapping, and static chambers) ...

Using Moran's I and GIS to study spatial pattern of forest litter carbon density in typical subtropical region, China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2013-12-01

The spatial variation of forest litter carbon (FLC) density in the typical subtropical forests in southeast China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (South-North) × 6 km (East-West) grid system in Zhejiang Province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using Local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas. While Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns in distribution map were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS could be used to study spatial patterns of environmental variables related to forest ecosystem.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

PubMed Central

Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

2016-01-01

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

PubMed

Ghosh, Subimal; Vittal, H; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K S; Dhanesh, Y; Sudheer, K P; Gunthe, S S

2016-01-01

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Spatial variations in δ13C and δ15N values of primary consumers in a coastal lagoon

NASA Astrophysics Data System (ADS)

Como, S.; Magni, P.; Van Der Velde, G.; Blok, F. S.; Van De Steeg, M. F. M.

2012-12-01

The analysis of the contribution of a food source to a consumer's diet or the trophic position of a consumer is highly sensitive to the variability of the isotopic values used as input data. However, little is known in coastal lagoons about the spatial variations in the isotopic values of primary consumers considered 'end members' in the isotope mixing models for quantifying the diet of secondary consumers or as a baseline for estimating the trophic position of consumers higher up in the food web. We studied the spatial variations in the δ13C and δ15N values of primary consumers and sedimentary organic matter (SOM) within a selected area of the Cabras lagoon (Sardinia, Italy). Our aim was to assess how much of the spatial variation in isotopic values of primary consumers was due to the spatial variability between sites and how much was due to differences in short distances from the shore. Samples were collected at four stations (50-100 m apart) selected randomly at two sites (1.5-2 km apart) chosen randomly at two distances from the shore (i.e. in proximity of the shore -Nearshore - and about 200 m away from the shore -Offshore). The sampling was repeated in March, May and August 2006 using new sites at the two chosen distances from the shore on each date. The isotopic values of size-fractionated seston and macrophytes were also analyzed as a complementary characterization of the study area. While δ15N did not show any spatial variations, the δ13C values of deposit feeders, Alitta (=Neanthes) succinea, Lekanesphaera hookeri, Hydrobia acuta and Gammarus aequicauda, were more depleted Offshore than Nearshore. For these species, there were significant effects of distance or distance × dates in the mean δ13C values, irrespective of the intrinsic variation between sites. SOM showed similar spatial variations in δ13C values, with Nearshore-Offshore differences up to 6‰. This indicates that the spatial isotopic changes are transferred from the food sources to the deposit feeders studied. In contrast, δ13C and δ15N values of suspension feeders, Ficopomatus enigmaticus and Amphibalanus amphitrite, did not show major variations, either between sites, or between Nearshore and Offshore. These different patterns between deposit feeders and suspension feeders are probably due to a weaker trophic link of the latter with SOM. We suggest that the Nearshore-Offshore gradient might be an important source of isotopic variation that needs to be considered in future web studies in coastal lagoons.

Ammonia and Methane Dairy Emission Plumes in the San Joaquin Valley of California from Individual Feedlot to Regional Scales

NASA Technical Reports Server (NTRS)

Miller, David J.; Sun, Kang; Pan, Da; Zondlo, Mark A.; Nowak, John B.; Liu, Zhen; Diskin, Glenn; Sachse, Glen; Beyersdorf, Andreas; Ferrare, Richard;

Environmental condition assessment of US military installations using GIS based spatial multi-criteria decision analysis.

PubMed

Singer, Steve; Wang, Guangxing; Howard, Heidi; Anderson, Alan

2012-08-01

Environment functions in various aspects including soil and water conservation, biodiversity and habitats, and landscape aesthetics. Comprehensive assessment of environmental condition is thus a great challenge. The issues include how to assess individual environmental components such as landscape aesthetics and integrate them into an indicator that can comprehensively quantify environmental condition. In this study, a geographic information systems based spatial multi-criteria decision analysis was used to integrate environmental variables and create the indicator. This approach was applied to Fort Riley Military installation in which land condition and its dynamics due to military training activities were assessed. The indicator was derived by integrating soil erosion, water quality, landscape fragmentation, landscape aesthetics, and noise based on the weights from the experts by assessing and ranking the environmental variables in terms of their importance. The results showed that landscape level indicator well quantified the overall environmental condition and its dynamics, while the indicator at level of patch that is defined as a homogeneous area that is different from its surroundings detailed the spatiotemporal variability of environmental condition. The environmental condition was mostly determined by soil erosion, then landscape fragmentation, water quality, landscape aesthetics, and noise. Overall, environmental condition at both landscape and patch levels greatly varied depending on the degree of ground and canopy disturbance and their spatial patterns due to military training activities and being related to slope. It was also determined the environment itself could be recovered quickly once military training was halt or reduced. Thus, this study provided an effective tool for the army land managers to monitor environmental dynamics and plan military training activities. Its limitation lies at that the obtained values of the indicator vary and are subjective to the experts' knowledge and experience. Thus, further advancing this approach is needed by developing a scientific method to derive the weights of environmental variables.

Ammonia and methane dairy emissions in the San Joaquin Valley of California from individual feedlot to regional scale

DOE PAGES

Miller, David J.; Sun, Kang; Tao, Lei; ...

2015-09-27

Agricultural ammonia (NH 3) emissions are highly uncertain, with high spatiotemporal variability and a lack of widespread in situ measurements. Regional NH 3 emission estimates using mass balance or emission ratio approaches are uncertain due to variable NH 3 sources and sinks as well as unknown plume correlations with other dairy source tracers. We characterize the spatial distributions of NH 3 and methane (CH 4) dairy plumes using in situ surface and airborne measurements in the Tulare dairy feedlot region of the San Joaquin Valley, California, during the NASA Deriving Information on Surface conditions from Column and Vertically Resolved Observationsmore » Relevant to Air Quality 2013 field campaign. Surface NH 3 and CH 4 mixing ratios exhibit large variability with maxima localized downwind of individual dairy feedlots. The geometric mean NH 3:CH 4 enhancement ratio derived from surface measurements is 0.15 ± 0.03 ppmv ppmv –1. Individual dairy feedlots with spatially distinct NH 3 and CH 4 source pathways led to statistically significant correlations between NH 3 and CH 4 in 68% of the 69 downwind plumes sampled. At longer sampling distances, the NH 3:CH 4 enhancement ratio decreases 20–30%, suggesting the potential for NH 3 deposition as a loss term for plumes within a few kilometers downwind of feedlots. Aircraft boundary layer transect measurements directly above surface mobile measurements in the dairy region show comparable gradients and geometric mean enhancement ratios within measurement uncertainties, even when including NH 3 partitioning to submicron particles. Individual NH 3 and CH 4 plumes sampled at close proximity where losses are minimal are not necessarily correlated due to lack of mixing and distinct source pathways. As a result, our analyses have important implications for constraining NH 3 sink and plume variability influences on regional NH 3 emission estimates and for improving NH 3 emission inventory spatial allocations.« less

Spatial and temporal variability of throughfall and soil moisture in a deciduous forest in the low mountain ranges (Hesse, Germany)

NASA Astrophysics Data System (ADS)

Chifflard, Peter; Weishaupt, Philipp; Reiss, Martin

2017-04-01

Spatial and temporal patterns of throughfall can affect the heterogeneity of ecological, biogeochemical and hydrological processes at a forest floor and further the underlying soil. Previous research suggests different factors controlling the spatial and temporal patterns of throughfall, but most studies focus on coniferous forest, where the vegetation coverage is more or less constant over time. In deciduous forests the leaf area index varies due to the leaf fall in autumn which implicates a specific spatial and temporal variability of throughfall and furthermore of the soil moisture. Therefore, in the present study, the measurements of throughfall and soil moisture in a deciduous forest in the low mountain ranges focused especially on the period of leaf fall. The aims of this study were: 1) to detect the spatial and temporal variability of both the throughfall and the soil moisture, 2) to examine the temporal stability of the spatial patterns of the throughfall and soil moisture and 3) relate the soil moisture patterns to the throughfall patterns and further to the canopy characteristics. The study was carried out in a small catchment on middle Hesse (Germany) which is covered by beech forest. Annual mean air temperature is 9.4°C (48.9˚F) and annual mean precipitation is 650 mm. Base materials for soil genesis is greywacke and clay shale from Devonian deposits. The soil type at the study plot is a shallow cambisol. The study plot covers an area of about 150 m2 where 77 throughfall samplers where installed. The throughfall and the soil moisture (FDR-method, 20 cm depth) was measured immediately after every rainfall event at the 77 measurement points. During the period of October to December 2015 altogether 7 events were investigated. The geostatistical method kriging was used to interpolate between the measurements points to visualize the spatial patterns of each investigated parameter. Time-stability-plots were applied to examine temporal scatters of each investigated parameter. The spearmen and pearson correlation coefficients were applied to detect the relationship between the different investigated parameters. First results show that the spatial variability of throughfall decreases if the total amount of the throughfall increases. The soil moisture shows a similar behavior. It`s spatial variability decreases if higher soil moisture values were measured. Concerning the temporal stability of throughfall it can be shown that it is very high during the leaf-free period, although the rainfall events have different total througfall amounts. The soil moisture patterns consists of a low temporal stability and additionally only during one event a significant correlations between throughfall and soil moisture patterns exists. This implies that other factors than the throughfall patterns control the spatial patterns of soil moisture.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts

PubMed Central

Lee, Hyung Joo; Gent, Janneane F.; Leaderer, Brian P.; Koutrakis, Petros

2011-01-01

To protect public health from PM2.5 air pollution, it is critical to identify the source types of PM2.5 mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM2.5 source types and quantify the source contributions to PM2.5 in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM2.5 mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM2.5. Due to sparse ground-level PM2.5 monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM2.5 monitors is more reliable than using data from the nearest central monitor. PMID:21429560

Improved Cloud and Snow Screening in MAIAC Aerosol Retrievals Using Spectral and Spatial Analysis

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Laszlo, I.; Kokrkin, S.

2012-01-01

An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2014-04-01

Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south-north) × 6 km (east-west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

ISM DUST GRAINS AND N-BAND SPECTRAL VARIABILITY IN THE SPATIALLY RESOLVED SUBARCSECOND BINARY UY Aur

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

Skemer, Andrew J.; Close, Laird M.; Hinz, Philip M.

2010-03-10

The 10 {mu}m silicate feature is an essential diagnostic of dust-grain growth and planet formation in young circumstellar disks. The Spitzer Space Telescope has revolutionized the study of this feature, but due to its small (85 cm) aperture, it cannot spatially resolve small/medium-separation binaries ({approx}<3''; {approx}< 420 AU) at the distances of the nearest star-forming regions ({approx}140 pc). Large, 6-10 m ground-based telescopes with mid-infrared instruments can resolve these systems. In this paper, we spatially resolve the 0.''88 binary, UY Aur, with MMTAO/BLINC-MIRAC4 mid-infrared spectroscopy. We then compare our spectra to Spitzer/IRS (unresolved) spectroscopy, and resolved images from IRTF/MIRAC2, Keck/OSCIR,more » and Gemini/Michelle, which were taken over the past decade. We find that UY Aur A has extremely pristine, interstellar medium (ISM)-like grains and that UY Aur B has an unusually shaped silicate feature, which is probably the result of blended emission and absorption from foreground extinction in its disk. We also find evidence for variability in both UY Aur A and UY Aur B by comparing synthetic photometry from our spectra with resolved imaging from previous epochs. The photometric variability of UY Aur A could be an indication that the silicate emission itself is variable, as was recently found in EX Lupi. Otherwise, the thermal continuum is variable, and either the ISM-like dust has never evolved, or it is being replenished, perhaps by UY Aur's circumbinary disk.« less

Variability, trends, and drivers of regional fluctuations in Australian fire activity

NASA Astrophysics Data System (ADS)

Earl, Nick; Simmonds, Ian

2017-07-01

Throughout the world fire regimes are determined by climate, vegetation, and anthropogenic factors, and they have great spatial and temporal variability. The availability of high-quality satellite data has revolutionized fire monitoring, allowing for a more consistent and comprehensive evaluation of temporal and spatial patterns. Here we utilize a satellite based "active fire" (AF) product to statistically analyze 2001-2015 variability and trends in Australian fire activity and link this to precipitation and large-scale atmospheric structures (namely, the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD)) known to have potential for predicting fire activity in different regions. It is found that Australian fire activity is decreasing (during summer (December-February)) or stable, with high temporal and spatial variability. Eastern New South Wales (NSW) has the strongest decreasing trend (to the 1% confidence level), especially during the winter (JJA) season. Other significantly decreasing areas are Victoria/NSW, Tasmania, and South-east Queensland. These decreasing fire regions are relatively highly populated, so we suggest that the declining trends are due to improved fire management, reducing the size and duration of bush fires. Almost half of all Australian AFs occur during spring (September-November). We show that there is considerable potential throughout Australia for a skillful forecast for future season fire activity based on current and previous precipitation activity, ENSO phase, and to a lesser degree, the IOD phase. This is highly variable, depending on location, e.g., the IOD phase is for more indicative of fire activity in southwest Western Australia than for Queensland.

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

Xiong, Wei; Balkovic, Juraj; van der Velde, M.

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER)more » and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.« less

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin.

PubMed

Mishra, Ashok; Singh, R; Raghuwanshi, N S; Chatterjee, C; Froebrich, Jochen

2013-12-01

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB. Copyright © 2013 Elsevier B.V. All rights reserved.

Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Wang, J.; Cai, X.

2007-12-01

A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.

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.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Sources of Uncertainty in the Prediction of LAI / fPAR from MODIS

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Ganapol, Barry D.; Brass, James A. (Technical Monitor)

2002-01-01

To explicate the sources of uncertainty in the prediction of biophysical variables over space, consider the general equation: where z is a variable with values on some nominal, ordinal, interval or ratio scale; y is a vector of input variables; u is the spatial support of y and z ; x and u are the spatial locations of y and z , respectively; f is a model and B is the vector of the parameters of this model. Any y or z has a value and a spatial extent which is called its support. Viewed in this way, categories of uncertainty are from variable (e.g. measurement), parameter, positional. support and model (e.g. structural) sources. The prediction of Leaf Area Index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) are examples of z variables predicted using model(s) as a function of y variables and spatially constant parameters. The MOD15 algorithm is an example of f, called f(sub 1), with parameters including those defined by one of six biome types and solar and view angles. The Leaf Canopy Model (LCM)2, a nested model that combines leaf radiative transfer with a full canopy reflectance model through the phase function, is a simpler though similar radiative transfer approach to f(sub 1). In a previous study, MOD15 and LCM2 gave similar results for the broadleaf forest biome. Differences between these two models can be used to consider the structural uncertainty in prediction results. In an effort to quantify each of the five sources of uncertainty and rank their relative importance for the LAI/fPAR prediction problem, we used recent data for an EOS Core Validation Site in the broadleaf biome with coincident surface reflectance, vegetation index, fPAR and LAI products from the Moderate Resolution Imaging Spectrometer (MODIS). Uncertainty due to support on the input reflectance variable was characterized using Landsat ETM+ data. Input uncertainties were propagated through the LCM2 model and compared with published uncertainties from the MOD15 algorithm.

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

NASA Astrophysics Data System (ADS)

Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

2010-01-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

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

Cafferty, Kara G.; Searcy, Erin M.; Nguyen, Long

To meet Energy Independence and Security Act (EISA) cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels and access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver on-spec biomass feedstocks at preprocessing “depots”, which densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The harvesting, preprocessing, and logistics (HPL) of biomass commodity supply chains thus could introduce spatially variable environmental impacts into the biofuel life cyclemore » due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG) emissions of corn stover logisticsHPL within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. Monte Carlo simulation was used to estimate the spatial uncertainty in the HPL gate-to-gate sequence. The results show that the transport of densified biomass introduces the highest variability and contribution to the carbon footprint of the logistics HPL supply chain (0.2-13 g CO 2e/MJ). Moreover, depending upon the biomass availability and its spatial density and surrounding transportation infrastructure (road and rail), logistics HPL processes can increase the variability in life cycle environmental impacts for lignocellulosic biofuels. Within Kansas, life cycle GHG emissions could range from 24 to 41 g CO 2e/MJ depending upon the location, size and number of preprocessing depots constructed. However, this range can be minimized through optimizing the siting of preprocessing depots where ample rail infrastructure exists to supply biomass commodity to a regional biorefinery supply system« less

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

DOE PAGES

Cafferty, Kara G.; Searcy, Erin M.; Nguyen, Long; ...

2014-11-04

To meet Energy Independence and Security Act (EISA) cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels and access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver on-spec biomass feedstocks at preprocessing “depots”, which densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The harvesting, preprocessing, and logistics (HPL) of biomass commodity supply chains thus could introduce spatially variable environmental impacts into the biofuel life cyclemore » due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG) emissions of corn stover logisticsHPL within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. Monte Carlo simulation was used to estimate the spatial uncertainty in the HPL gate-to-gate sequence. The results show that the transport of densified biomass introduces the highest variability and contribution to the carbon footprint of the logistics HPL supply chain (0.2-13 g CO 2e/MJ). Moreover, depending upon the biomass availability and its spatial density and surrounding transportation infrastructure (road and rail), logistics HPL processes can increase the variability in life cycle environmental impacts for lignocellulosic biofuels. Within Kansas, life cycle GHG emissions could range from 24 to 41 g CO 2e/MJ depending upon the location, size and number of preprocessing depots constructed. However, this range can be minimized through optimizing the siting of preprocessing depots where ample rail infrastructure exists to supply biomass commodity to a regional biorefinery supply system« less

The role of storm scale, position and movement in controlling urban flood response

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James

2018-01-01

The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

Along-track calibration of SWIR push-broom hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Jemec, Jurij; Pernuš, Franjo; Likar, Boštjan; Bürmen, Miran

2016-05-01

Push-broom hyperspectral imaging systems are increasingly used for various medical, agricultural and military purposes. The acquired images contain spectral information in every pixel of the imaged scene collecting additional information about the imaged scene compared to the classical RGB color imaging. Due to the misalignment and imperfections in the optical components comprising the push-broom hyperspectral imaging system, variable spectral and spatial misalignments and blur are present in the acquired images. To capture these distortions, a spatially and spectrally variant response function must be identified at each spatial and spectral position. In this study, we propose a procedure to characterize the variant response function of Short-Wavelength Infrared (SWIR) push-broom hyperspectral imaging systems in the across-track and along-track direction and remove its effect from the acquired images. A custom laser-machined spatial calibration targets are used for the characterization. The spatial and spectral variability of the response function in the across-track and along-track direction is modeled by a parametrized basis function. Finally, the characterization results are used to restore the distorted hyperspectral images in the across-track and along-track direction by a Richardson-Lucy deconvolution-based algorithm. The proposed calibration method in the across-track and along-track direction is thoroughly evaluated on images of targets with well-defined geometric properties. The results suggest that the proposed procedure is well suited for fast and accurate spatial calibration of push-broom hyperspectral imaging systems.

Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field

NASA Technical Reports Server (NTRS)

Franklin, Rima B.; Mills, Aaron L.

2003-01-01

To better understand the distribution of soil microbial communities at multiple spatial scales, a survey was conducted to examine the spatial organization of community structure in a wheat field in eastern Virginia (USA). Nearly 200 soil samples were collected at a variety of separation distances ranging from 2.5 cm to 11 m. Whole-community DNA was extracted from each sample, and community structure was compared using amplified fragment length polymorphism (AFLP) DNA fingerprinting. Relative similarity was calculated between each pair of samples and compared using geostatistical variogram analysis to study autocorrelation as a function of separation distance. Spatial autocorrelation was found at scales ranging from 30 cm to more than 6 m, depending on the sampling extent considered. In some locations, up to four different correlation length scales were detected. The presence of nested scales of variability suggests that the environmental factors regulating the development of the communities in this soil may operate at different scales. Kriging was used to generate maps of the spatial organization of communities across the plot, and the results demonstrated that bacterial distributions can be highly structured, even within a habitat that appears relatively homogeneous at the plot and field scale. Different subsets of the microbial community were distributed differently across the plot, and this is thought to be due to the variable response of individual populations to spatial heterogeneity associated with soil properties. c2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Wildland fire emissions, carbon, and climate: U.S. emissions inventories

Treesearch

Narasimhan K. Larkin; Sean M. Raffuse; Tara M. Strand

2014-01-01

Emissions from wildland fire are both highly variable and highly uncertain over a wide range of temporal and spatial scales. Wildland fire emissions change considerably due to fluctuations from year to year with overall fire season severity, from season to season as different regions pass in and out of wildfire and prescribed fire periods, and from day to day as...

Is lodgepole pine mortality due to mountain pine beetle linked to the North American Monsoon?

Treesearch

Sara A. Goeking; Greg C. Liknes

2012-01-01

Regional precipitation patterns may have influenced the spatial variability of tree mortality during the recent mountain pine beetle (Dendroctonus ponderosa) (MPB) outbreak in the western United States. Data from the Forest Inventory and Analysis (FIA) Program show that the outbreak was especially severe in the state of Colorado where over 10 million lodgepole pines (...

Spatial and Temporal Variability of Zooplankton Thin Layers: The Effects of Composition and Orientation on Acoustic Detection of Layers

DTIC Science & Technology

2008-01-01

whereas the pump collected more rare taxa (cladocerans and siphonophores ). These differences could be due to the volume sampled. ZOOVIS-SC imaged...Larvaceans 3016 1.3x 4102 3.5x Siphonophores 85 0.2x 215 0.14x 11 IMPACT/APPLICATIONS The image data from ZOOVIS-SC can

Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data

Treesearch

L. Monika Moskal; Diane M. Styers; Meghan Halabisky

2011-01-01

Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes....

Variability of Upper-Tropospheric Precipitable from Satellite and Model Reanalysis Datasets

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Iwai, Hisaki

1999-01-01

Numerous datasets have been used to quantify water vapor and its variability in the upper-troposphere from satellite and model reanalysis data. These investigations have shown some usefulness in monitoring seasonal and inter-annual variations in moisture either globally, with polar orbiting satellite data or global model output analysis, or regionally, with the higher spatial and temporal resolution geostationary measurements. The datasets are not without limitations, however, due to coverage or limited temporal sampling, and may also contain bias in their representation of moisture processes. The research presented in this conference paper inter-compares the NVAP, NCEP/NCAR and DAO reanalysis models, and GOES satellite measurements of upper-tropospheric,precipitable water for the period from 1988-1994. This period captures several dramatic swings in climate events associated with ENSO events. The data are evaluated for temporal and spatial continuity, inter-compared to assess reliability and potential bias, and analyzed in light of expected trends due to changes in precipitation and synoptic-scale weather features. This work is the follow-on to previous research which evaluated total precipitable water over the same period. The relationship between total and upper-level precipitable water in the datasets will be discussed as well.

Scaling water and energy fluxes in climate systems - Three land-atmospheric modeling experiments

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Lakshmi, Venkataraman

1993-01-01

Three numerical experiments that investigate the scaling of land-surface processes - either of the inputs or parameters - are reported, and the aggregated processes are compared to the spatially variable case. The first is the aggregation of the hydrologic response in a catchment due to rainfall during a storm event and due to evaporative demands during interstorm periods. The second is the spatial and temporal aggregation of latent heat fluxes, as calculated from SiB. The third is the aggregation of remotely sensed land vegetation and latent and sensible heat fluxes using TM data from the FIFE experiment of 1987 in Kansas. In all three experiments it was found that the surface fluxes and land characteristics can be scaled, and that macroscale models based on effective parameters are sufficient to account for the small-scale heterogeneities investigated.

Increased variability of watershed areas in patients with high-grade carotid stenosis.

PubMed

Kaczmarz, Stephan; Griese, Vanessa; Preibisch, Christine; Kallmayer, Michael; Helle, Michael; Wustrow, Isabel; Petersen, Esben Thade; Eckstein, Hans-Henning; Zimmer, Claus; Sorg, Christian; Göttler, Jens

2018-03-01

Watershed areas (WSAs) of the brain are most susceptible to acute hypoperfusion due to their peripheral location between vascular territories. Additionally, chronic WSA-related vascular processes underlie cognitive decline especially in patients with cerebral hemodynamic compromise. Despite of high relevance for both clinical diagnostics and research, individual in vivo WSA definition is fairly limited to date. Thus, this study proposes a standardized segmentation approach to delineate individual WSAs by use of time-to-peak (TTP) maps and investigates spatial variability of individual WSAs. We defined individual watershed masks based on relative TTP increases in 30 healthy elderly persons and 28 patients with unilateral, high-grade carotid stenosis, being at risk for watershed-related hemodynamic impairment. Determined WSA location was confirmed by an arterial transit time atlas and individual super-selective arterial spin labeling. We compared spatial variability of WSA probability maps between groups and assessed TTP differences between hemispheres in individual and group-average watershed locations. Patients showed significantly higher spatial variability of WSAs than healthy controls. Perfusion on the side of the stenosis was delayed within individual watershed masks as compared to a watershed template derived from controls, being independent from the grade of the stenosis and collateralization status of the circle of Willis. Results demonstrate feasibility of individual WSA delineation by TTP maps in healthy elderly and carotid stenosis patients. Data indicate necessity of individual segmentation approaches especially in patients with hemodynamic compromise to detect critical regions of impaired hemodynamics.

Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution.

PubMed

Alcala-Canto, Yazmin; Figueroa-Castillo, Juan Antonio; Ibarra-Velarde, Froylán; Vera-Montenegro, Yolanda; Cervantes-Valencia, María Eugenia; Salem, Abdelfattah Z M; Cuéllar-Ordaz, Jorge Alfredo

2018-05-07

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks' distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

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.

Influence of UV illumination on the cold temperature operation of a LiNbO(3) Q-switched Nd:YAG laser.

PubMed

Cole, Brian; Goldberg, Lew; King, Vernon; Leach, Jeff

2010-04-26

UV illumination of a lithium niobate Q-switch was demonstrated as an effective means to eliminate a loss in hold-off and associated prelasing that occurs under cold temperature operation of Q-switched lasers. This degradation occurs due to the pyroelectric effect, where an accumulation of charge on crystal faces results in a reduction in the Q-switch hold-off and a spatially variable loss of the Q-switch in its high-transmission state, both resulting in lowering of the maximum Q-switched pulse energy. With UV illumination, the resulting creation of photo-generated carriers was shown to be effective in eliminating both of these effects. A Q-switched Nd:YAG laser utilizing UV-illuminated LiNbO(3) was shown to operate under cold temperatures without prelasing or spatially variable loss.

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

NASA Technical Reports Server (NTRS)

Vogelmann, Andrew M.; McFarquhar, Greg M.; Ogren, John A.; Turner, David D.; Comstock, Jennifer M.; Feingold, Graham; Long, Charles N.; Jonsson, Haflidi H.; Bucholtz, Anthony; Collins, Don R.;

Spatial and layer-controlled variability in fracture networks

NASA Astrophysics Data System (ADS)

Procter, Andrew; Sanderson, David J.

2018-03-01

Topological sampling, based on 1) node counting and 2) circular sampling areas, is used to measure fracture intensity in surface exposures of a layered limestone/shale sequence in north Somerset, UK. This method provides similar levels of precision as more traditional line samples, but is about 10 times quicker and allows characterization of the network topology. Georeferencing of photographs of the sample sites allows later analysis of trace lengths and orientations, and identification of joint set development. ANOVA tests support a complex interaction of within-layer, between-layer and between-location variability in fracture intensity, with the different layers showing anomalous intensity at different locations. This variation is not simply due to bed thickness, nor can it be related to any obvious compositional or textural variation between the limestone beds. These results are used to assess approaches to the spatial mapping of fracture intensity.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Spatiotemporal variability of stream habitat and movement of three species of fish

USGS Publications Warehouse

Roberts, J.H.; Angermeier, P.L.

2007-01-01

Relationships between environmental variability and movement are poorly understood, due to both their complexity and the limited ecological scope of most movement studies. We studied movements of fantail (Etheostoma flabellare), riverweed (E. podostemone), and Roanoke darters (Percina roanoka) through two stream systems during two summers. We then related movement to variability in measured habitat attributes using logistic regression and exploratory data plots. We indexed habitat conditions at both microhabitat (i.e., patches of uniform depth, velocity, and substrate) and mesohabitat (i.e., riffle and pool channel units) spatial scales, and determined how local habitat conditions were affected by landscape spatial (i.e., longitudinal position, land use) and temporal contexts. Most spatial variability in habitat conditions and fish movement was unexplained by a site's location on the landscape. Exceptions were microhabitat diversity, which was greater in the less-disturbed watershed, and riffle isolation and predator density in pools, which were greater at more-downstream sites. Habitat conditions and movement also exhibited only minor temporal variability, but the relative influences of habitat attributes on movement were quite variable over time. During the first year, movements of fantail and riverweed darters were triggered predominantly by loss of shallow microhabitats; whereas, during the second year, microhabitat diversity was more strongly related (though in opposite directions) to movement of these two species. Roanoke darters did not move in response to microhabitat-scale variables, presumably because of the species' preference for deeper microhabitats that changed little over time. Conversely, movement of all species appeared to be constrained by riffle isolation and predator density in pools, two mesohabitat-scale attributes. Relationships between environmental variability and movement depended on both the spatiotemporal scale of consideration and the ecology of the species. Future studies that integrate across scales, taxa, and life-histories are likely to provide greater insight into movement ecology than will traditional, single-season, single-species approaches. ?? 2006 Springer-Verlag.

Multivariate spatial analysis of a heavy rain event in a densely populated delta city

NASA Astrophysics Data System (ADS)

Gaitan, Santiago; ten Veldhuis, Marie-claire; Bruni, Guenda; van de Giesen, Nick

2014-05-01

Delta cities account for half of the world's population and host key infrastructure and services for the global economic growth. Due to the characteristic geography of delta areas, these cities face high vulnerability to extreme weather and pluvial flooding risks, that are expected to increase as climate change drives heavier rain events. Besides, delta cities are subjected to fast urban densification processes that progressively make them more vulnerable to pluvial flooding. Delta cities need to be adapted to better cope with this threat. The mechanism leading to damage after heavy rains is not completely understood. For instance, current research has shown that rain intensities and volumes can only partially explain the occurrence and localization of rain-related insurance claims (Spekkers et al., 2013). The goal of this paper is to provide further insights into spatial characteristics of the urban environment that can significantly be linked to pluvial-related flooding impacts. To that end, a study-case has been selected: on October 12 to 14 2013, a heavy rain event triggered pluvial floods in Rotterdam, a densely populated city which is undergoing multiple climate adaptation efforts and is located in the Meuse river Delta. While the average yearly precipitation in this city is around 800 mm, local rain gauge measurements ranged from aprox. 60 to 130 mm just during these three days. More than 600 citizens' telephonic complaints reported impacts related to rainfall. The registry of those complaints, which comprises around 300 calls made to the municipality and another 300 to the fire brigade, was made available for research. Other accessible information about this city includes a series of rainfall measurements with up to 1 min time-step at 7 different locations around the city, ground-based radar rainfall data (1 Km^2 spatial resolution and 5 min time-step), a digital elevation model (50 cm of horizontal resolution), a model of overland-flow paths, cadastral maps describing individual location and types of buildings, and maps on categorical socioeconomic statistics (1 Ha of spatial resolution). On the basis of the quality and availability of the mentioned information, spatial and temporal units of analysis will be discussed and defined. Aggregation of single occurrences for binary variables will be performed, while simple interpolations or averages will be used in case of continuous or categorical data. To determine spatial clustering within each variable, Nearest Neighbor Distance and Spatial Autocorrelation tests will be carried out. When appropriate, the Getis-Ord Gi* test will be used to identify single variable clusters. Finally, with the purpose of inferring possible associations between the available spatially distributed variables, a Mantel test will be applied to variables with a probed non-random spatial pattern. The results of this paper will allow to determine if the environmental characteristics described by the available data can provide additional explanation of the variability of rain-related damage in a delta city which is willing to become climate-proof.

Analysis of the spatial and temporal distribution of malaria in an area of Northern Guatemala with seasonal malaria transmission.

PubMed

Malvisi, Lucio; Troisi, Catherine L; Selwyn, Beatrice J

2018-06-23

The risk of malaria infection displays spatial and temporal variability that is likely due to interaction between the physical environment and the human population. In this study, we performed a spatial analysis at three different time points, corresponding to three cross-sectional surveys conducted as part of an insecticide-treated bed nets efficacy study, to reveal patterns of malaria incidence distribution in an area of Northern Guatemala characterized by low malaria endemicity. A thorough understanding of the spatial and temporal patterns of malaria distribution is essential for targeted malaria control programs. Two methods, the local Moran's I and the Getis-Ord G * (d), were used for the analysis, providing two different statistical approaches and allowing for a comparison of results. A distance band of 3.5 km was considered to be the most appropriate distance for the analysis of data based on epidemiological and entomological factors. Incidence rates were higher at the first cross-sectional survey conducted prior to the intervention compared to the following two surveys. Clusters or hot spots of malaria incidence exhibited high spatial and temporal variations. Findings from the two statistics were similar, though the G * (d) detected cold spots using a higher distance band (5.5 km). The high spatial and temporal variability in the distribution of clusters of high malaria incidence seems to be consistent with an area of unstable malaria transmission. In such a context, a strong surveillance system and the use of spatial analysis may be crucial for targeted malaria control activities.

Remote sensing of the Canadian Arctic: Modelling biophysical variables

NASA Astrophysics Data System (ADS)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data.

Life history and spatial traits predict extinction risk due to climate change

NASA Astrophysics Data System (ADS)

Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

2014-03-01

There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data

NASA Astrophysics Data System (ADS)

Glover, David M.; Doney, Scott C.; Oestreich, William K.; Tullo, Alisdair W.

2018-01-01

Mesoscale (10-300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998-2010) and 8 year MODIS/Aqua (2003-2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.

Applications of Geostatistics in Plant Nematology

PubMed Central

Wallace, M. K.; Hawkins, D. M.

1994-01-01

The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the Ap horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical semi-variogram model, with little random variability associated with soil data and large inherent variability for nematode data. Block kriging of soil and nematode data provided useful contour maps of the data. Change of snpport calculations indicated that most of the random variation in nematode data was due to short-range spatial variability in the nematode population densities. PMID:19279938

Applications of geostatistics in plant nematology.

PubMed

Wallace, M K; Hawkins, D M

1994-12-01

The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the A(p) horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical semi-variogram model, with little random variability associated with soil data and large inherent variability for nematode data. Block kriging of soil and nematode data provided useful contour maps of the data. Change of snpport calculations indicated that most of the random variation in nematode data was due to short-range spatial variability in the nematode population densities.

SU-G-IeP4-13: PET Image Noise Variability and Its Consequences for Quantifying Tumor Hypoxia

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

Kueng, R; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario; Manser, P

Purpose: The values in a PET image which represent activity concentrations of a radioactive tracer are influenced by a large number of parameters including patient conditions as well as image acquisition and reconstruction. This work investigates noise characteristics in PET images for various image acquisition and image reconstruction parameters. Methods: Different phantoms with homogeneous activity distributions were scanned using several acquisition parameters and reconstructed with numerous sets of reconstruction parameters. Images from six PET scanners from different vendors were analyzed and compared with respect to quantitative noise characteristics. Local noise metrics, which give rise to a threshold value defining themore » metric of hypoxic fraction, as well as global noise measures in terms of noise power spectra (NPS) were computed. In addition to variability due to different reconstruction parameters, spatial variability of activity distribution and its noise metrics were investigated. Patient data from clinical trials were mapped onto phantom scans to explore the impact of the scanner’s intrinsic noise variability on quantitative clinical analysis. Results: Local noise metrics showed substantial variability up to an order of magnitude for different reconstruction parameters. Investigations of corresponding NPS revealed reconstruction dependent structural noise characteristics. For the acquisition parameters, noise metrics were guided by Poisson statistics. Large spatial non-uniformity of the noise was observed in both axial and radial direction of a PET image. In addition, activity concentrations in PET images of homogeneous phantom scans showed intriguing spatial fluctuations for most scanners. The clinical metric of the hypoxic fraction was shown to be considerably influenced by the PET scanner’s spatial noise characteristics. Conclusion: We showed that a hypoxic fraction metric based on noise characteristics requires careful consideration of the various dependencies in order to justify its quantitative validity. This work may result in recommendations for harmonizing QA of PET imaging for multi-institutional clinical trials.« less

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

NASA Technical Reports Server (NTRS)

Li, Zhen; Adamec, David; Takahashi, Taro; Sutherland, Stewart C.

2004-01-01

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Dissecting the multi-scale spatial relationship of earthworm assemblages with soil environmental variability.

PubMed

Jiménez, Juan J; Decaëns, Thibaud; Lavelle, Patrick; Rossi, Jean-Pierre

2014-12-05

Studying the drivers and determinants of species, population and community spatial patterns is central to ecology. The observed structure of community assemblages is the result of deterministic abiotic (environmental constraints) and biotic factors (positive and negative species interactions), as well as stochastic colonization events (historical contingency). We analyzed the role of multi-scale spatial component of soil environmental variability in structuring earthworm assemblages in a gallery forest from the Colombian "Llanos". We aimed to disentangle the spatial scales at which species assemblages are structured and determine whether these scales matched those expressed by soil environmental variables. We also tested the hypothesis of the "single tree effect" by exploring the spatial relationships between root-related variables and soil nutrient and physical variables in structuring earthworm assemblages. Multivariate ordination techniques and spatially explicit tools were used, namely cross-correlograms, Principal Coordinates of Neighbor Matrices (PCNM) and variation partitioning analyses. The relationship between the spatial organization of earthworm assemblages and soil environmental parameters revealed explicitly multi-scale responses. The soil environmental variables that explained nested population structures across the multi-spatial scale gradient differed for earthworms and assemblages at the very-fine- (<10 m) to medium-scale (10-20 m). The root traits were correlated with areas of high soil nutrient contents at a depth of 0-5 cm. Information on the scales of PCNM variables was obtained using variogram modeling. Based on the size of the plot, the PCNM variables were arbitrarily allocated to medium (>30 m), fine (10-20 m) and very fine scales (<10 m). Variation partitioning analysis revealed that the soil environmental variability explained from less than 1% to as much as 48% of the observed earthworm spatial variation. A large proportion of the spatial variation did not depend on the soil environmental variability for certain species. This finding could indicate the influence of contagious biotic interactions, stochastic factors, or unmeasured relevant soil environmental variables.

Estimating ocean production from satellite-derived chlorophyll - Insights from the EASTROPAC data set

NASA Technical Reports Server (NTRS)

Eppley, R. W.; Stewart, E.; Abbott, M. R.; Owen, R. W.

1985-01-01

The EASTROPAC expedition took place in 1967-68 in the eastern tropical Pacific Ocean. Primary production was related to near-surface chlorophyll in these data. Much of the variability in the relation was due to the light-history of the phytoplankton and its photoadaptive state. This was due to changes in the depth of mixing of the surface waters more than changes in insolation. Accurate estimates of production from satellite chlorophyll measurements may require knowledge of the temporal and spatial variation in mixing of this region.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Assessing the resolution-dependent utility of tomograms for geostatistics

USGS Publications Warehouse

Day-Lewis, F. D.; Lane, J.W.

2004-01-01

Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

Statistical and Spatial Analysis of Bathymetric Data for the St. Clair River, 1971-2007

USGS Publications Warehouse

Bennion, David

2009-01-01

To address questions concerning ongoing geomorphic processes in the St. Clair River, selected bathymetric datasets spanning 36 years were analyzed. Comparisons of recent high-resolution datasets covering the upper river indicate a highly variable, active environment. Although statistical and spatial comparisons of the datasets show that some changes to the channel size and shape have taken place during the study period, uncertainty associated with various survey methods and interpolation processes limit the statistically certain results. The methods used to spatially compare the datasets are sensitive to small variations in position and depth that are within the range of uncertainty associated with the datasets. Characteristics of the data, such as the density of measured points and the range of values surveyed, can also influence the results of spatial comparison. With due consideration of these limitations, apparently active and ongoing areas of elevation change in the river are mapped and discussed.

Simulation of crop yield variability by improved root-soil-interaction modelling

NASA Astrophysics Data System (ADS)

Duan, X.; Gayler, S.; Priesack, E.

2009-04-01

Understanding the processes and factors that govern the within-field variability in crop yield has attached great importance due to applications in precision agriculture. Crop response to environment at field scale is a complex dynamic process involving the interactions of soil characteristics, weather conditions and crop management. The numerous static factors combined with temporal variations make it very difficult to identify and manage the variability pattern. Therefore, crop simulation models are considered to be useful tools in analyzing separately the effects of change in soil or weather conditions on the spatial variability, in order to identify the cause of yield variability and to quantify the spatial and temporal variation. However, tests showed that usual crop models such as CERES-Wheat and CERES-Maize were not able to quantify the observed within-field yield variability, while their performance on crop growth simulation under more homogeneous and mainly non-limiting conditions was sufficent to simulate average yields at the field-scale. On a study site in South Germany, within-field variability in crop growth has been documented since years. After detailed analysis and classification of the soil patterns, two site specific factors, the plant-available-water and the O2 deficiency, were considered as the main causes of the crop growth variability in this field. Based on our measurement of root distribution in the soil profile, we hypothesize that in our case the insufficiency of the applied crop models to simulate the yield variability can be due to the oversimplification of the involved root models which fail to be sensitive to different soil conditions. In this study, the root growth model described by Jones et al. (1991) was adapted by using data of root distributions in the field and linking the adapted root model to the CERES crop model. The ability of the new root model to increase the sensitivity of the CERES crop models to different enviromental conditions was then evaluated by means of comparison of the simualtion results with measured data and by scenario calculations.

Characterizing Vineyard Water Status Variability in a Premium Winegrape Vineyard

NASA Astrophysics Data System (ADS)

Smart, David; Carvahlo, Angela

2017-04-01

One of the biggest challenges in viticulture and winemaking is managing and optimizing yield and quality across vineyard blocks that show high spatial variability. Studies have shown that zonal management of vine water status can contribute significantly to improving overall fruit quality and improving uniformity. Vine water status is a major parameter for vine management because it affects both wine quality and yield. In order to optimize vineyard management and harvesting practices, it is necessary to characterize vineyard variability in terms of water status. Establishing a targeted irrigation program first requires spatially characterizing the variability in vine water status of a vineyard. In California, due to the low or no rainfall during the active growing season, the majority of vineyards implement some type of irrigation management program. As water supplies continue to decrease as a consequence of persistent drought, establishing efficient and targeted water use programs is of growing importance in California. The aim of this work was to characterize the spatial variability of plant-water relations across a non-uniform 4 ha block in Napa Valley with the primary objective of establishing vineyard irrigation management zones. The study plot was divided into three sections, designated the North, Middle and South sections, each at about 1.3 hectares. Stem (Ψstem) and midday (Ψl) leaf water potential and predawn (ΨPD) water potential were measured at 36 locations within the block at 14 (Ψl), 10 (ΨPD) and 2 (Ψstem) points in time throughout the growing season. Of the three techniques utilized to evaluate water status, ΨPD and Ψstem were the most sensitive indicators of water stress conditions. An integrated overview of water use efficiency over the growing season was assessed by measuring the leaf carbon isotope ratio of δ13C. Fully mature leaves were sampled from 280 vines and results show, similarly to ΨPD and Ψstem, that the North section (-28.05%) was significantly different than the South (at -28.31) and Middle (at -28.33) sections. Interblock variability can be reduced by managing water supply to the North section independently of the South and Middle sections. For Napa due to foggy mornings and overcast skies, Ψl provided the least discriminatory water status measurements.

Patterns in coupled water and energy cycle: Modeling, synthesis with observations, and assessing the subsurface-landsurface interactions

NASA Astrophysics Data System (ADS)

Rahman, A.; Kollet, S. J.; Sulis, M.

2013-12-01

In the terrestrial hydrological cycle, the atmosphere and the free groundwater table act as the upper and lower boundary condition, respectively, in the non-linear two-way exchange of mass and energy across the land surface. Identifying and quantifying the interactions among various atmospheric-subsurface-landsurface processes is complicated due to the diverse spatiotemporal scales associated with these processes. In this study, the coupled subsurface-landsurface model ParFlow.CLM was applied over a ~28,000 km2 model domain encompassing the Rur catchment, Germany, to simulate the fluxes of the coupled water and energy cycle. The model was forced by hourly atmospheric data from the COSMO-DE model (numerical weather prediction system of the German Weather Service) over one year. Following a spinup period, the model results were synthesized with observed river discharge, soil moisture, groundwater table depth, temperature, and landsurface energy flux data at different sites in the Rur catchment. It was shown that the model is able to reproduce reasonably the dynamics and also absolute values in observed fluxes and state variables without calibration. The spatiotemporal patterns in simulated water and energy fluxes as well as the interactions were studied using statistical, geostatistical and wavelet transform methods. While spatial patterns in the mass and energy fluxes can be predicted from atmospheric forcing and power law scaling in the transition and winter months, it appears that, in the summer months, the spatial patterns are determined by the spatially correlated variability in groundwater table depth. Continuous wavelet transform techniques were applied to study the variability of the catchment average mass and energy fluxes at varying time scales. From this analysis, the time scales associated with significant interactions among different mass and energy balance components were identified. The memory of precipitation variability in subsurface hydrodynamics acts at the 20-30 day time scale, while the groundwater contribution to sustain the long-term variability patterns in evapotranspiration acts at the 40-60 day scale. Diurnal patterns in connection with subsurface hydrodynamics were also detected. Thus, it appears that the subsurface hydrodynamics respond to the temporal patterns in land surface fluxes due to the variability in atmospheric forcing across multiple space and time scales.

China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.

PubMed

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-09-18

Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

Long-wave Irradiance Measurement and Modeling during Snowmelt, a Case Study in the Yukon Territory, Canada

NASA Astrophysics Data System (ADS)

Sicart, J.; Essery, R.; Pomeroy, J.

2004-12-01

At high latitudes, long-wave radiation emitted by the atmosphere and solar radiation can provide similar amounts of energy for snowmelt due to the low solar elevation and the high albedo of snow. This paper investigates temporal and spatial variations of long-wave irradiance at the snow surface in an open sub-Arctic environment. Measurements were conducted in the Wolf Creek Research Basin, Yukon Territory, Canada (60°36'N, 134°57'W) during the springs of 2002, 2003 and 2004. The main causes of temporal variability are air temperature and cloud cover, especially in the beginning of the melting period when the atmosphere is still cold. Spatial variability was investigated through a sensitivity study to sky view factors and to temperatures of surrounding terrain. The formula of Brutsaert gives a useful estimation of the clear-sky irradiance at hourly time steps. Emission by clouds was parameterized at the daily time scale from the atmospheric attenuation of solar radiation. The inclusion of air temperature variability does not much improve the calculation of cloud emission.

Synergetic use of Sentinel-1 and 2 to improve agro-hydrological modeling. Results of groundwater pumping estimates in south-India and nitrogen excess in south-west of France

NASA Astrophysics Data System (ADS)

Ferrant, S.; Le Page, M.; Kerr, Y. H.; Selles, A.; Mermoz, S.; Al-Bitar, A.; Muddu, S.; Gascoin, S.; Marechal, J. C.; Durand, P.; Salmon-Monviola, J.; Ceschia, E.; Bustillo, V.

2016-12-01

Nitrogen transfers at agricultural catchment level are intricately linked to water transfers. Agro-hydrological modeling approaches aim at integrating spatial heterogeneity of catchment physical properties together with agricultural practices to spatially estimate the water and nitrogen cycles. As in hydrology, the calibration schemes are designed to optimize the performance of the temporal dynamics and biases in model simulations, while ignoring the simulated spatial pattern. Yet, crop uses, i.e. transpiration and nitrogen exported by harvest, are the main fluxes at the catchment scale, highly variable in space and time. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to reset soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model. This study takes two agro-hydrological contexts as demonstrators: 1-spatial nitrogen excess estimation in south-west of France, and 2-groundwater extraction for rice irrigation in south-India. Spatio-temporal patterns are involved in respectively surface water contamination due to over-fertilization and local groundwater shortages due to over-pumping for above rice inundation. Optimized Leaf Area Index profiles are simulated at the satellite images pixel level using an agro-hydrological model to reproduce spatial and temporal crop growth dynamics in south-west of France, improving the in-stream nitrogen fluxes by 12%. Accurate detection of irrigated area extents are obtained with the thresholding method based on optical indices, with a kappa of 0.81 for the dry season 2016. The actual monsoon season is monitored and will be presented. These extents drive the groundwater pumping and are highly variable in time (from 2 to 8% of the total area).

Tropical Warm Pool Surface Heat Budgets and Temperature: Contrasts Between 1997-98 El Nino and 1998-99 La Nina

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Chou, Ming-Dah; Chan, Pui-King; Lin, Po-Hsiung; Wang, Kung-Hwa

2003-01-01

Seasonal and interannual variations of the net surface heating F(sub NET) and sea surface temperature tendency (T(sub s)/dt) in the tropical eastern Indian and western Pacific Oceans are studied. The surface heat fluxes are derived from the Special Sensor Microwave/Imager and Japanese Geostationary Meteorological Satellite radiance measurements for the period October 1997-September 2000. It is found that the magnitude of solar heating is lager than that of evaporative cooling, but the spatial variation of the latter is significantly large than the former. As a result, the spatial variations of seasonal and interannual variability of F(sub NET), follow closely that of evaporative cooling. Seasonal variations of F(sub NET) and T(sub s)/dt are significantly correlated, except for the equatorial western Pacific. The high correlation is primarily attributable to high correlation between seasonal cycles of solar heating and T(sub s)/dt. The change of F(sub NET) between 1997-98 El Nino and 1998-99 La Nina is significantly larger in the tropical eastern Indian Ocean than tropical western Pacific. For the former region, the reduced evaporative cooling arising from weakened winds during the El Nino is generally associated with enhanced solar heating due to decreased cloudiness, and thus increases the interannual variability of F(sub NET). For the latter region, the reduced evaporative cooling due to weakened winds is generally associated with but exceeds the reduced solar heating arising from increased cloudiness, and vise versa. Thus the interannual variability of F(sub NET) is reduced due to this offsetting effect. Interannual variations of F(sub NET) and T(sub s)/dt have very low correlation. This is most likely related to interannual variability of ocean dynamics, which includes the variations of solar radiation penetrating through oceanic mixed layer, upwelling of cold thermocline water, Indonesian throughflow for transporting heat from the Pacific to Indian Ocean, and interhemispheric transport in the Indian Ocean.

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.

Modeling tree crown dynamics with 3D partial differential equations.

PubMed

Beyer, Robert; Letort, Véronique; Cournède, Paul-Henry

2014-01-01

We characterize a tree's spatial foliage distribution by the local leaf area density. Considering this spatially continuous variable allows to describe the spatiotemporal evolution of the tree crown by means of 3D partial differential equations. These offer a framework to rigorously take locally and adaptively acting effects into account, notably the growth toward light. Biomass production through photosynthesis and the allocation to foliage and wood are readily included in this model framework. The system of equations stands out due to its inherent dynamic property of self-organization and spontaneous adaptation, generating complex behavior from even only a few parameters. The density-based approach yields spatially structured tree crowns without relying on detailed geometry. We present the methodological fundamentals of such a modeling approach and discuss further prospects and applications.

Evaluation of the spatial variability of soil water content at the spatial resolution of SMAP data products : case studies in Italy and Morocco

NASA Astrophysics Data System (ADS)

Menenti, Massimo; Akdim, Nadia; Alfieri, Silvia Maria; Labbassi, Kamal; De Lorenzi, Francesca; Bonfante, Antonello; Basile, Angelo

2014-05-01

Frequent and contiguous observations of soil water content such as the ones to be provided by SMAP are potentially useful to improve distributed models of soil water balance. This requires matching of observations and model estimates provided both sample spatial patterns consistently. The spatial resolution of SMAP soil water content data products ranges from 3 km X 3 km to 40 km X 40 km. Even the highest spatial resolution may not be sufficient to capture the spatial variability due to terrain, soil properties and precipitation. We have evaluated the SMAP spatial resolution against spatial variability of soil water content in two Mediterranean landscapes: a hilly area dominated by vineyards and olive orchards in Central Italy and a large irrigation schemes (Doukkala) in Morocco. The "Valle Telesina" is a 20,000 ha complex landscape located in South Italy in the Campania region, which has a complex geology and geomorphology and it is characterised by an E-W elongated graben where the Calore river flows. The main crops are grapevine (6,448 ha) and olive (3,390 ha). Soil information was mainly derived from an existing soil map at 1:50 000 scale (Terribile et al., 1996). The area includes 47 SMUs (Soil Mapping Units) and about 60 soil typological units (STUs). (Bonfante et al., 2011). In Doukkala, the soil water retention and unsaturated capillary conductivity were estimated from grain size distribution of a number of samples (22 pilot points, each one sampled in 3 horizons of 20cm), and combined with a soil map. The land use classification was carried out using a NDVI time series at high spatial resolution (Landsat TM and SPOT HRV). We have calculated soil water content for each soil unit in each area in response to several climate cases generating daily maps of soil water content at different depths. To reproduce spatial sampling by SMAP we have filtered these spatial patterns by calculating box averages with grid sizes of 1 km X 1 km and 5 km X 5 km. We have repeated this procedure for soil water content in the 0 to 5 cm and 0 to 10 cm depths. For each case we have compared the variance of filtered soil water content with the expected accuracy of SMAP soil water content. The two areas are very different as regards morphology and soil formation. The Valle Telesina is characterized by a very significant variability of soil hydrological properties leading to complex patterns in soil water content. Contrariwise, the soil properties estimated for all soil mapping units in the Dhoukkala collapse into just two pairs of water retention and hydraulic conductivity characteristics, leading to smoother patterns of soil water content.

Comparing interpolation techniques for annual temperature mapping across Xinjiang region

NASA Astrophysics Data System (ADS)

Ren-ping, Zhang; Jing, Guo; Tian-gang, Liang; Qi-sheng, Feng; Aimaiti, Yusupujiang

2016-11-01

Interpolating climatic variables such as temperature is challenging due to the highly variable nature of meteorological processes and the difficulty in establishing a representative network of stations. In this paper, based on the monthly temperature data which obtained from the 154 official meteorological stations in the Xinjiang region and surrounding areas, we compared five spatial interpolation techniques: Inverse distance weighting (IDW), Ordinary kriging, Cokriging, thin-plate smoothing splines (ANUSPLIN) and Empirical Bayesian kriging(EBK). Error metrics were used to validate interpolations against independent data. Results indicated that, the ANUSPLIN performed best than the other four interpolation methods.

The influence of prescribed fire and burn interval on fuel loads in four North Carolina forest ecosystems

Treesearch

M.J. Gavazzi; S.G. McNulty

2014-01-01

Prescribed fire is an important management tool in southern US forests, with more acres burned in the South than any other region of the US. Research from prescribed fire studies shows high temporal and spatial variability in available fuel loads due to physiographic, edaphic, meteorological and biological factors. In an effort to account for parts of this variation...

Tree regeneration spatial patterns in ponderosa pine forests following stand-replacing fire: Influence of topography and neighbors

Treesearch

Justin P. Ziegler; Chad M. Hoffman; Paula J. Fornwalt; Carolyn H. Sieg; Michael A. Battaglia; Marin E. Chambers; Jose M. Iniguez

2017-01-01

Shifting fire regimes alter forest structure assembly in ponderosa pine forests and may produce structural heterogeneity following stand-replacing fire due, in part, to fine-scale variability in growing environments. We mapped tree regeneration in eighteen plots 11 to 15 years after stand-replacing fire in Colorado and South Dakota, USA. We used point pattern analyses...

Spatial and Temporal Variability of Zooplankton Thin Layers: The Effects of Composition and Orientation on Acoustic Detection of Layers

DTIC Science & Technology

2010-09-30

abundant taxa (copepods and larvaceans), whereas the pump collected more rare taxa (cladocerans and siphonophores ). These differences could be due...4.2x 14848 6.8x Cladocerans 199 0.3x 195 0.88x Larvaceans 3016 1.3x 4102 3.5x Siphonophores 85 0.2x 215 0.14x

Water yield responses to climate change and variability across the North–South Transect of Eastern China (NSTEC)

Treesearch

Nan Lu; Ge Sun; Xiaoming Feng; Bojie Fu

2013-01-01

China is facing a growing water crisis due to climate and land use change, and rise in human water demand across this rapidly developing country. Understanding the spatial and temporal ecohydrologic responses to climate change is critical to sustainable water resource management. We investigated water yield (WY) responses to historical (1981–2000) and projected...

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

Dynamic hydro-climatic networks in pristine and regulated rivers

NASA Astrophysics Data System (ADS)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A < 103 km2) are usually mild enough to guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes involved in the river food web (e.g. biofilm and riparian vegetation dynamics), the study of rivers as dynamic networks provides important clues to water management strategies and freshwater ecosystem studies.

Spatial Surface PM2.5 Concentration Estimates for Wildfire Smoke Plumes in the Western U.S. Using Satellite Retrievals and Data Assimilation Techniques

NASA Astrophysics Data System (ADS)

Loria Salazar, S. M.; Holmes, H.

2015-12-01

Health effects studies of aerosol pollution have been extended spatially using data assimilation techniques that combine surface PM2.5 concentrations and Aerosol Optical Depth (AOD) from satellite retrievals. While most of these models were developed for the dark-vegetated eastern U.S. they are being used in the semi-arid western U.S. to remotely sense atmospheric aerosol concentrations. These models are helpful to understand the spatial variability of surface PM2.5concentrations in the western U.S. because of the sparse network of surface monitoring stations. However, the models developed for the eastern U.S. are not robust in the western U.S. due to different aerosol formation mechanisms, transport phenomena, and optical properties. This region is a challenge because of complex terrain, anthropogenic and biogenic emissions, secondary organic aerosol formation, smoke from wildfires, and low background aerosol concentrations. This research concentrates on the use and evaluation of satellite remote sensing to estimate surface PM2.5 concentrations from AOD satellite retrievals over California and Nevada during the summer months of 2012 and 2013. The aim of this investigation is to incorporate a spatial statistical model that uses AOD from AERONET as well as MODIS, surface PM2.5 concentrations, and land-use regression to characterize spatial surface PM2.5 concentrations. The land use regression model uses traditional inputs (e.g. meteorology, population density, terrain) and non-traditional variables (e.g. FIre Inventory from NCAR (FINN) emissions and MODIS albedo product) to account for variability related to smoke plume trajectories and land use. The results will be used in a spatially resolved health study to determine the association between wildfire smoke exposure and cardiorespiratory health endpoints. This relationship can be used with future projections of wildfire emissions related to climate change and droughts to quantify the expected health impact.

Local Hotspots In The Gulf Of Maine: Spatial Overlap Between Dynamic Aggregations Of Primary Productivity And Fish Abundance

NASA Astrophysics Data System (ADS)

Ribera, M.

2016-02-01

Identification of biological hotspots may be a necessary step toward ecosystem-based management goals, as these often signal underlying processes that aggregate or stimulate resources in a particular location. However, previously used metrics to locate these hotspots are not easily adapted to local marine datasets, in part due to the high spatial and temporal variability of phytoplankton populations. While most fish species in temperate regions are well adapted to the seasonal variability of phytoplankton abundance, it is the variability beyond this predictable pattern (i.e. anomalies) that may heavily impact the abundance and spatial distribution of organisms higher up the food chain. The objective of this study was to identify local-scale biological hotspots in a region in the western side of the Gulf of Maine using remote sensing chlorophyll-a data (from MERIS sensor), and to study the spatial overlap between these hotspots and high concentrations of fish abundance (derived from VTR dataset). For this reason, we defined a new hotspot metric that identified as a hotspot any area that consistently exhibited high-magnitude anomalies through time, a sign of highly dynamic communities. We improved on previous indices by minimizing the effect that different means and variances across space may have on the results, a situation that often occurs when comparing coastal and offshore systems. Results show a significant spatial correlation between pelagic fish abundance and aggregations of primary productivity. Spatial correlations were also significant between benthic fish abundance and primary productivity hotspots, but only during spring months. We argue that this new hotspot index compliments existing global measures as it helps managers understand the dynamic characteristics of a complex marine system. It also provides a unique metric that is easily compared across space and between different trophic levels, which may facilitate future ecosystem-wide studies.

Local Hotspots In The Gulf Of Maine: Spatial Overlap Between Dynamic Aggregations Of Primary Productivity And Fish Abundance

NASA Astrophysics Data System (ADS)

Ribera, M.

2016-12-01

Identification of biological hotspots may be a necessary step toward ecosystem-based management goals, as these often signal underlying processes that aggregate or stimulate resources in a particular location. However, previously used metrics to locate these hotspots are not easily adapted to local marine datasets, in part due to the high spatial and temporal variability of phytoplankton populations. While most fish species in temperate regions are well adapted to the seasonal variability of phytoplankton abundance, it is the variability beyond this predictable pattern (i.e. anomalies) that may heavily impact the abundance and spatial distribution of organisms higher up the food chain. The objective of this study was to identify local-scale biological hotspots in a region in the western side of the Gulf of Maine using remote sensing chlorophyll-a data (from MERIS sensor), and to study the spatial overlap between these hotspots and high concentrations of fish abundance (derived from VTR dataset). For this reason, we defined a new hotspot metric that identified as a hotspot any area that consistently exhibited high-magnitude anomalies through time, a sign of highly dynamic communities. We improved on previous indices by minimizing the effect that different means and variances across space may have on the results, a situation that often occurs when comparing coastal and offshore systems. Results show a significant spatial correlation between pelagic fish abundance and aggregations of primary productivity. Spatial correlations were also significant between benthic fish abundance and primary productivity hotspots, but only during spring months. We argue that this new hotspot index compliments existing global measures as it helps managers understand the dynamic characteristics of a complex marine system. It also provides a unique metric that is easily compared across space and between different trophic levels, which may facilitate future ecosystem-wide studies.

Capability of Hyperspectral data in Spatial Variability Distribution of Chlorophyll and Water Stress in Rice Agriculture System

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2016-12-01

Abstract : The mapping and analysis of spatial variability within the field is a challenging task. However, field variability of a single vegetation cover does not give satisfactory results mainly due to low spectral resolution and non-availability of remote sensing data. From the NASA Earth Observing-1 (EO-1) satellite data, spatial distribution of biophysical parameters like chlorophyll and relative water content in a rice agriculture system is carried out in the present study. Hyperion L1R product composed of 242 spectral bands with 30m spatial resolution was acquired for Assam, India. This high dimensional data is allowed for pre-processing to get an atmospherically corrected imagery. Moreover, ground based hyperspectral measurements are collected from experimental rice fields from the study site using hand held ASD spectroradiometer (350-1050 nm). Published indices specifically designed for chlorophyll (OASVI, mSR, and MTCI indices) and water content (WI and WBI indices) are selected based on stastical performance of the in-situ hyperspectral data. Index models are established for the respective biophysical parameters and observed that the aforementioned indices followed different linear and nonlinear relationships which are completely different from the published indices. By employing the presently developed relationships, spatial variation of total chlorophyll and water stress are mapped for a rice agriculture system from Hyperion imagery. The findings showed that, the variation of chlorophyll and water content ranged from 1.77-10.61mg/g and 40-90% respectively for the studied rice agriculture system. The spatial distribution of these parameters resulted from presently developed index models are well captured from Hyperion imagery and they have good agreement with observed field based chlorophyll (1.14-7.26 mg/g) and water content (60-95%) of paddy crop. This study can be useful in providing essential information to assess the paddy field heterogeneity in an agriculture system. Keywords: Paddy crop, vegetation index, hyperspectral data, chlorophyll, water content

Biomass and the Climatic Space from historical to future scenarios of a Seasonally Dry Tropical Forest - Caatinga

NASA Astrophysics Data System (ADS)

Castanho, A. D. D. A.; Coe, M. T.; Maia Andrade, E.; Walker, W.; Baccini, A.; Brando, P. M.; Farina, M.

2017-12-01

The Caatinga found in the semiarid region in northeastern Brazil is the largest continuous seasonally dry tropical forest in South America. The region has for centuries been subject to anthropogenic activities of land conversion, abandonment, and regrowth. The region also has a large spatial variability of edaphic-climatic properties. These effects together contribute to a wide variability of plant physiognomies and biomass concentration. In addition to land use change due to anthropogenic activities the region is exposed in the near and long term to dryer conditions. The main goal of this work was to validate a high spatial resolution (30 m) map of above ground biomass, understand the climatic role in the biomass spatial variability in the present, and the potential threat to vegetation for future climatic shifts. Satellite-derived biomass products are advanced tools that can address spatial changes in forest structure for an extended region. Here we combine a compilation of published field phytosociological observations across the region with a new 30-meter spatial resolution satellite biomass product. Climate data used for this analyses were based on the CRU (Climate Research Unit, UEA) for the historical time period and for the future a mean and 25-75% quantiles of the CMIP Global Climate model estimates for the RCP scenarios of 4.5 and 8.5 W/m2. The high heterogeneity in the biomass and physiognomy distribution across the Caatinga region is mostly explained by the climatic space defined by the precipitation and dryness index. The Caatinga region has historically already been exposed to shift in its climatic properties, driving all the physiognomies, to a dryer climatic space within the last decade. Future climate intensify the observed trends. This study provides a clearer understanding of the spatial distribution of Caatinga vegetation, its biomass, and relationships to climate, which are essential for strategic development planning, preservation of the biome functions, human services, and biodiversity, face future climate scenarios.

Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N-ICE2015) expedition

NASA Astrophysics Data System (ADS)

Gallet, Jean-Charles; Merkouriadi, Ioanna; Liston, Glen E.; Polashenski, Chris; Hudson, Stephen; Rösel, Anja; Gerland, Sebastian

2017-10-01

Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N-ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second-year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first-year ice. The average density was 350-400 kg m-3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-07-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Spatial and temporal variability of thermohaline properties in the Bay of Koper (northern Adriatic Sea)

NASA Astrophysics Data System (ADS)

Soczka Mandac, Rok; Žagar, Dušan; Faganeli, Jadran

2013-04-01

In this study influence of fresh water discharge on the spatial and temporal variability of thermohaline (TH) conditions is explored for the Bay of Koper (Bay). The Bay is subject to different driving agents: wind stress (bora, sirocco), tidal and seiches effect, buoyancy fluxes, general circulation of the Adriatic Sea and discharge of the Rizana and Badaševica rivers. These rivers have torrential characteristics that are hard to forecast in relation to meteorological events (precipitation). Therefore, during episodic events the spatial and temporal variability of TH properties in the Bay is difficult to determine [1]. Measurements of temperature, salinity and turbidity were conducted monthly on 35 sampling points in the period: June 2011 - December 2012. The data were processed and spatial interpolated with an objective analysis method. Furthermore, empirical orthogonal function analysis (EOF) [2] was applied to investigate spatial and temporal TH variations. Strong horizontal and vertical stratification was observed in the beginning of June 2011 due to high fresh water discharge of the Rizana (31 m3/s) and Badaševica (2 m3/s) rivers. The horizontal gradient (ΔT = 6°C) was noticed near the mouth of the Rizana river. Similar pattern was identified for salinity field on the boundary of the front where the gradient was ΔS = 20 PSU. Vertical temperature gradient was ΔT = 4°C while salinity gradient was ΔS = 18 PSU in the subsurface layer at depth of 3 m. Spatial analysis of the first principal component (86% of the total variance) shows uniform temperature distribution in the surface layer (1m) during the studied period. Furthermore, temporal variability of temperature shows seasonal variation with a minimum in February and maximum in August. This confirms that episodic events have a negligible effect on spatial and temporal variation of temperature in the subsurface layer. Further analysis will include application of EOF on the salinity, density and total suspended matter. Additionally, we will investigate the cross correlations between the above mentioned parameters with singular value decomposition method. Reference: 1. Faganeli, J., Planinc, R., Pezdic, J., Smodis, B., Stegnar, P., and Ogorelec, B. 1991. Marine geology of Gulf of Trieste (northern Adriatic): Geochemical aspects. Marine Geology, 99: 93-108. 2. Glover, M., Jenkins, J., and Doney, S. C. 2011. Modeling methods for marine science. Cambridge University Press, 571 p.

Spatial variability of shortwave radiative fluxes in the context of snowmelt

NASA Astrophysics Data System (ADS)

Pinker, Rachel T.; Ma, Yingtao; Hinkelman, Laura; Lundquist, Jessica

2014-05-01

Snow-covered mountain ranges are a major source of water supply for run-off and groundwater recharge. Snowmelt supplies as much as 75% of surface water in basins of the western United States. Factors that affect the rate of snow melt include incoming shortwave and longwave radiation, surface albedo, snow emissivity, snow surface temperature, sensible and latent heat fluxes, ground heat flux, and energy transferred to the snowpack from deposited snow or rain. The net radiation generally makes up about 80% of the energy balance and is dominated by the shortwave radiation. Complex terrain poses a great challenge for obtaining the needed information on radiative fluxes from satellites due to elevation issues, spatially-variable cloud cover, rapidly changing surface conditions during snow fall and snow melt, lack of high quality ground truth for evaluation of the satellite based estimates, as well as scale issues between the ground observations and the satellite footprint. In this study we utilize observations of high spatial resolution (5-km) as available from the Moderate Resolution Imaging Spectro-radiometer (MODIS) to derive surface shortwave radiative fluxes in complex terrain, with attention to the impact of slopes on the amount of radiation received. The methodology developed has been applied to several water years (January to July during 2003, 2004, 2005 and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability in the shortwave fluxes. It is planned to apply the findings from this study for testing improvements in Snow Water Equivalent (SWE) estimates.

Confronting weather and climate models with observational data from soil moisture networks over the United States

PubMed Central

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal D.; Balsamo, Gianpaolo; Lawrence, David M.

2018-01-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison. PMID:29645013

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

NASA Technical Reports Server (NTRS)

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A., Jr.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal Dean;

Assessing spatial and temporal variability of acid-extractable organics in oil sands process-affected waters.

PubMed

Frank, Richard A; Milestone, Craig B; Rowland, Steve J; Headley, John V; Kavanagh, Richard J; Lengger, Sabine K; Scarlett, Alan G; West, Charles E; Peru, Kerry M; Hewitt, L Mark

2016-10-01

The acid-extractable organic compounds (AEOs), including naphthenic acids (NAs), present within oil sands process-affected water (OSPW) receive great attention due to their known toxicity. While recent progress in advanced separation and analytical methodologies for AEOs has improved our understanding of the composition of these mixtures, little is known regarding any variability (i.e., spatial, temporal) inherent within, or between, tailings ponds. In this study, 5 samples were collected from the same location of one tailings pond over a 2-week period. In addition, 5 samples were collected simultaneously from different locations within a tailings pond from a different mine site, as well as its associated recycling pond. In both cases, the AEOs were analyzed using SFS, ESI-MS, HRMS, GC×GC-ToF/MS, and GC- & LC-QToF/MS (GC analyses following conversion to methyl esters). Principal component analysis of HRMS data was able to distinguish the ponds from each other, while data from GC×GC-ToF/MS, and LC- and GC-QToF/MS were used to differentiate samples from within the temporal and spatial sample sets, with the greater variability associated with the latter. Spatial differences could be attributed to pond dynamics, including differences in inputs of tailings and surface run-off. Application of novel chemometric data analyses of unknown compounds detected by LC- and GC-QToF/MS allowed further differentiation of samples both within and between data sets, providing an innovative approach for future fingerprinting studies. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Confronting weather and climate models with observational data from soil moisture networks over the United States.

PubMed

Dirmeyer, Paul A; Wu, Jiexia; Norton, Holly E; Dorigo, Wouter A; Quiring, Steven M; Ford, Trenton W; Santanello, Joseph A; Bosilovich, Michael G; Ek, Michael B; Koster, Randal D; Balsamo, Gianpaolo; Lawrence, David M

2016-04-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

Estimating under-five mortality in space and time in a developing world context.

PubMed

Wakefield, Jon; Fuglstad, Geir-Arne; Riebler, Andrea; Godwin, Jessica; Wilson, Katie; Clark, Samuel J

2018-01-01

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

ISM Dust Grains and N-band Spectral Variability in the Spatially Resolved Subarcsecond Binary UY Aur

NASA Astrophysics Data System (ADS)

Skemer, Andrew J.; Close, Laird M.; Hinz, Philip M.; Hoffmann, William F.; Greene, Thomas P.; Males, Jared R.; Beck, Tracy L.

2010-03-01

The 10 μm silicate feature is an essential diagnostic of dust-grain growth and planet formation in young circumstellar disks. The Spitzer Space Telescope has revolutionized the study of this feature, but due to its small (85 cm) aperture, it cannot spatially resolve small/medium-separation binaries (lsim3''; <~ 420 AU) at the distances of the nearest star-forming regions (~140 pc). Large, 6-10 m ground-based telescopes with mid-infrared instruments can resolve these systems. In this paper, we spatially resolve the 0farcs88 binary, UY Aur, with MMTAO/BLINC-MIRAC4 mid-infrared spectroscopy. We then compare our spectra to Spitzer/IRS (unresolved) spectroscopy, and resolved images from IRTF/MIRAC2, Keck/OSCIR, and Gemini/Michelle, which were taken over the past decade. We find that UY Aur A has extremely pristine, interstellar medium (ISM)-like grains and that UY Aur B has an unusually shaped silicate feature, which is probably the result of blended emission and absorption from foreground extinction in its disk. We also find evidence for variability in both UY Aur A and UY Aur B by comparing synthetic photometry from our spectra with resolved imaging from previous epochs. The photometric variability of UY Aur A could be an indication that the silicate emission itself is variable, as was recently found in EX Lupi. Otherwise, the thermal continuum is variable, and either the ISM-like dust has never evolved, or it is being replenished, perhaps by UY Aur's circumbinary disk. The observations reported here were partially obtained at the Infrared Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement no. NCC 5-538 with the National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program.

Impacts of cattle grazing on spatio-temporal variability of soil moisture and above-ground live plant biomass in mixed grasslands

NASA Astrophysics Data System (ADS)

Virk, Ravinder

Areas with relatively high spatial heterogeneity generally have more biodiversity than spatially homogeneous areas due to increased potential habitat. Management practices such as controlled grazing also affect the biodiversity in grasslands, but the nature of this impact is not well understood. Therefore this thesis studies the impacts of variation in grazing on soil moisture and biomass heterogeneity. These are not only important in terms of management of protected grasslands, but also for designing an effective grazing system from a livestock management point of view. This research is a part of the cattle grazing experiment underway in Grasslands National Park (GNP) of Canada since 2006, as part of the adaptive management process for restoring ecological integrity of the northern mixed-grass prairie region. An experimental approach using field measurements and remote sensing (Landsat) was combined with modelling (CENTURY) to examine and predict the impacts of grazing intensity on the spatial heterogeneity and patterns of above-ground live plant biomass (ALB) in experimental pastures in a mixed grassland ecosystem. The field-based research quantified the temporal patterns and spatial variability in both soil moisture (SM) and ALB, and the influence of local intra-seasonal weather variability and slope location on the spatio-temporal variability of SM and ALB at field plot scales. Significant impacts of intra-seasonal weather variability, slope position and grazing pressure on SM and ALB across a range of scales (plot and local (within pasture)) were found. Grazing intensity significantly affected the ALB even after controlling for the effect of slope position. Satellite-based analysis extended the scale of interest to full pastures and the surrounding region to assess the effects of grazing intensity on the spatio-temporal pattern of ALB in mixed grasslands. Overall, low to moderate grazing intensity showed increase in ALB heterogeneity whereas no change in ALB heterogeneity over time was observed for heavy grazing intensity. All grazing intensities showed decrease in spatial range (patch size) over time indicating that grazing is a patchy process. The study demonstrates that cattle grazing with variable intensity can maintain and change the spatial patterns of vegetation in the studied region. Using a modelling approach, the relative degrees to which grazing intensity and soil properties affect grassland productivity and carbon dynamics at longer time-periods were investigated. Both grass productivity and carbon dynamics are sensitive to variability in soil texture and grazing intensity. Moderate grazing is predicted to be the best option in terms of maintaining sufficient heterogeneity to support species diversity, as well as for carbon management in the mixed grassland ecosystem.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts.

PubMed

Lee, Hyung Joo; Gent, Janneane F; Leaderer, Brian P; Koutrakis, Petros

2011-05-01

To protect public health from PM(2.5) air pollution, it is critical to identify the source types of PM(2.5) mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM(2.5) source types and quantify the source contributions to PM(2.5) in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM(2.5) mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM(2.5). Due to sparse ground-level PM(2.5) monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM(2.5) monitors is more reliable than using data from the nearest central monitor. Copyright © 2011 Elsevier B.V. All rights reserved.

Stochastic transport in the presence of spatial disorder: Fluctuation-induced corrections to homogenization

NASA Astrophysics Data System (ADS)

Russell, Matthew J.; Jensen, Oliver E.; Galla, Tobias

2016-10-01

Motivated by uncertainty quantification in natural transport systems, we investigate an individual-based transport process involving particles undergoing a random walk along a line of point sinks whose strengths are themselves independent random variables. We assume particles are removed from the system via first-order kinetics. We analyze the system using a hierarchy of approaches when the sinks are sparsely distributed, including a stochastic homogenization approximation that yields explicit predictions for the extrinsic disorder in the stationary state due to sink strength fluctuations. The extrinsic noise induces long-range spatial correlations in the particle concentration, unlike fluctuations due to the intrinsic noise alone. Additionally, the mean concentration profile, averaged over both intrinsic and extrinsic noise, is elevated compared with the corresponding profile from a uniform sink distribution, showing that the classical homogenization approximation can be a biased estimator of the true mean.

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

Albedo Spatial Variability and Causes on the Western Greenland Ice Sheet Percolation Zone

NASA Astrophysics Data System (ADS)

Lewis, G.; Osterberg, E. C.; Hawley, R. L.; Koffman, B. G.; Marshall, H. P.; Birkel, S. D.; Dibb, J. E.

2016-12-01

Many recent studies have concluded that Greenland Ice Sheet (GIS) mass loss has been accelerating over recent decades, but spatial and temporal variations in GIS mass balance remain poorly understood due to a complex relationship among precipitation and temperature changes, increasing melt and runoff, ice discharge, and surface albedo. Satellite measurements from MODerate resolution Imaging Spectroradiometer (MODIS) indicate that albedo has been declining over the past decade, but the cause and extent of GIS albedo change remains poorly constrained by field data. As fresh snow (albedo > 0.85) warms and melts, its albedo decreases due to snow grain growth, promoting solar absorption, higher snowpack temperatures and further melt. However, dark impurities like soot and dust can also significantly reduce snow albedo, even in the dry snow zone. While many regional climate models (e.g. the Regional Atmospheric Climate MOdel - RACMO2) calculate albedo spatial resolutions on the order of 10-30 km, and MODIS averages albedo over 500 m, surface features like sastrugi can affect albedo on much smaller scales. Here we assess the relative importance of grain size and shape vs. impurity concentrations on albedo in the western GIS percolation zone. We collected broadband albedo measurements (300-2500 nm at 3-8 nm resolution) at 35 locations using an ASD FieldSpec4 spectroradiometer to simultaneously quantify radiative fluxes and spectral reflectance. Measurements were collected on 10 x 10 m, 1 x 1 km, 5 x 5 km, and 10 x 10 km grids to determine the spatial variability of albedo as part of the 850-km Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) traverse from Raven/Dye 2 to Summit. Additionally, we collected shallow (0-50 cm) snow pit samples every 5 cm at ASD measurement sites to quantify black carbon and mineral dust concentrations and size distributions using a Single Particle Soot Photometer and Coulter Counter, respectively. Preliminary results indicate larger albedo variability in the infrared than visible and near infrared. We compare our in situ field measurements with co-located albedo data from airplanes, satellites, and climate models, and discuss implications for GIS surface mass balance.

Modeling the Impacts of Long-Term Warming Trends on Gross Primary Productivity Across North America

NASA Astrophysics Data System (ADS)

Mekonnen, Z. A.; Grant, R. F.

2014-12-01

There is evidence of warming over recent decades in most regions of North America (NA) that affects ecosystem productivity and the past decade has been the warmest since instrumental records of global surface temperatures began. In this study, we examined the spatial and temporal variability and trends of warming across NA using climate data from the North America Regional Reanalysis (NARR) from 1979 to 2010 with a 3-hourly time-step and 0.250 x 0.250 spatial resolution as part of the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A comprehensive mathematical process model, ecosys was used to simulate impacts of this variability in warming on gross primary productivity (GPP). In a test of model results, annual GPP modeled for pixels which corresponded to the locations of 25 eddy covariance towers correlated well (R2=0.76) with annual GPP derived from the flux towers in 2005. At the continental scale long-term (2000 - 2010) annual average modeled GPP for NA correlated well (geographically weighed regression R2 = 0.8) with MODIS GPP, demonstrating close similarities in spatial patterns. Results from the NARR indicated that most areas of NA, particularly high latitude regions, have experienced warming but changes in precipitation vary spatially over the last three decades. GPP modeled in most areas with lower mean annual air temperature (Ta), such as those in boreal climate zones, increased due to early spring and late autumn warming observed in NARR. However modeled GPP declined in most southwestern regions of NA, due to water stress from rising Ta and declining precipitation. Overall, GPP modeled across NA had a positive trend of +0.025 P g C yr-1 with a range of -1.16 to 0.87 P g C yr-1 from the long-term mean. Interannual variability of GPP was the greatest in southwest of US and part of the Great Plains, which could be as a result of frequent El Niño-Southern Oscillation' (ENSO) events that led to major droughts.

Spatio-Temporal Variability of Groundwater Storage in India

NASA Technical Reports Server (NTRS)

Bhanja, Soumendra; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2016-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Ground water storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent).In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatio-temporal variability of groundwater storage in India.

PubMed

Bhanja, Soumendra N; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2017-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

The influence of bed friction variability due to land cover on storm-driven barrier island morphodynamics

USGS Publications Warehouse

Passeri, Davina L.; Long, Joseph W.; Plant, Nathaniel G.; Bilskie, Matthew V.; Hagen, Scott C.

2018-01-01

Variations in bed friction due to land cover type have the potential to influence morphologic change during storm events; the importance of these variations can be studied through numerical simulation and experimentation at locations with sufficient observational data to initialize realistic scenarios, evaluate model accuracy and guide interpretations. Two-dimensional in the horizontal plane (2DH) morphodynamic (XBeach) simulations were conducted to assess morphodynamic sensitivity to spatially varying bed friction at Dauphin Island, AL using hurricanes Ivan (2004) and Katrina (2005) as experimental test cases. For each storm, three bed friction scenarios were simulated: (1) a constant Chezy coefficient across land and water, (2) a constant Chezy coefficient across land and depth-dependent Chezy coefficients across water, and (3) spatially varying Chezy coefficients across land based on land use/land cover (LULC) data and depth-dependent Chezy coefficients across water. Modeled post-storm bed elevations were compared qualitatively and quantitatively with post-storm lidar data. Results showed that implementing spatially varying bed friction influenced the ability of XBeach to accurately simulate morphologic change during both storms. Accounting for frictional effects due to large-scale variations in vegetation and development reduced cross-barrier sediment transport and captured overwash and breaching more accurately. Model output from the spatially varying friction scenarios was used to examine the need for an existing sediment transport limiter, the influence of pre-storm topography and the effects of water level gradients on storm-driven morphodynamics.

China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

PubMed Central

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-01-01

Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables. PMID:28927016

Shifts of environmental and phytoplankton variables in a regulated river: A spatial-driven analysis.

PubMed

Sabater-Liesa, Laia; Ginebreda, Antoni; Barceló, Damià

2018-06-18

The longitudinal structure of the environmental and phytoplankton variables was investigated in the Ebro River (NE Spain), which is heavily affected by water abstraction and regulation. A first exploration indicated that the phytoplankton community did not resist the impact of reservoirs and barely recovered downstream of them. The spatial analysis showed that the responses of the phytoplankton and environmental variables were not uniform. The two set of variables revealed spatial variability discontinuities and river fragmentation upstream and downstream from the reservoirs. Reservoirs caused the replacement of spatially heterogeneous habitats by homogeneous spatially distributed water bodies, these new environmental conditions downstream benefiting the opportunist and cosmopolitan algal taxa. The application of a spatial auto-regression model to algal biomass (chlorophyll-a) permitted to capture the relevance and contribution of extra-local influences in the river ecosystem. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

SPATIAL AND TEMPORAL VARIABILITY AND DRIVERS OF NET ECOSYSTEM METABOLISM IN WESTERN GULF OF MEXICO ESTUARIES

EPA Science Inventory

Net ecosystem metabolism (NEM) is becoming a commonly used ecological indicator of estuarine ecosystem metabolic rates. Estuarine ecosystem processes are spatially and temporally variable, but the corresponding variability in NEM has not been properly assessed. Spatial and temp...

Variability of phase and amplitude fronts due to horizontal refraction in shallow water.

PubMed

Katsnelson, Boris G; Grigorev, Valery A; Lynch, James F

2018-01-01

The variability of the interference pattern of a narrow-band sound signal in a shallow water waveguide in the horizontal plane in the presence of horizontal stratification, in particular due to linear internal waves, is studied. It is shown that lines of constant phase (a phase front) and lines of constant amplitude/envelope (an amplitude front) for each waveguide mode may have different directions in the spatial vicinity of the point of reception. The angle between them depends on the waveguide's parameters, the mode number, and the sound frequency. Theoretical estimates and data processing methodology for obtaining these angles from experimental data recorded by a horizontal line array are proposed. The behavior of the angles, which are obtained for two episodes from the Shallow Water 2006 (SW06) experiment, show agreement with the theory presented.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

PubMed

Tran, Phoebe; Waller, Lance

2015-01-01

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

Spatial pattern analysis of Cu, Zn and Ni and their interpretation in the Campania region (Italy)

NASA Astrophysics Data System (ADS)

Petrik, Attila; Albanese, Stefano; Jordan, Gyozo; Rolandi, Roberto; De Vivo, Benedetto

2017-04-01

The uniquely abundant Campanian topsoil dataset enabled us to perform a spatial pattern analysis on 3 potentially toxic elements of Cu, Zn and Ni. This study is focusing on revealing the spatial texture and distribution of these elements by spatial point pattern and image processing analysis such as lineament density and spatial variability index calculation. The application of these methods on geochemical data provides a new and efficient tool to understand the spatial variation of concentrations and their background/baseline values. The determination and quantification of spatial variability is crucial to understand how fast the change in concentration is in a certain area and what processes might govern the variation. The spatial variability index calculation and image processing analysis including lineament density enables us to delineate homogenous areas and analyse them with respect to lithology and land use. Identification of spatial outliers and their patterns were also investigated by local spatial autocorrelation and image processing analysis including the determination of local minima and maxima points and singularity index analysis. The spatial variability of Cu and Zn reveals the highest zone (Cu: 0.5 MAD, Zn: 0.8-0.9 MAD, Median Deviation Index) along the coast between Campi Flegrei and the Sorrento Peninsula with the vast majority of statistically identified outliers and high-high spatial clustered points. The background/baseline maps of Cu and Zn reveals a moderate to high variability (Cu: 0.3 MAD, Zn: 0.4-0.5 MAD) NW-SE oriented zone including disrupted patches from Bisaccia to Mignano following the alluvial plains of Appenine's rivers. This zone has high abundance of anomaly concentrations identified using singularity analysis and it also has a high density of lineaments. The spatial variability of Ni shows the highest variability zone (0.6-0.7 MAD) around Campi Flegrei where the majority of low outliers are concentrated. The variability of background/baseline map of Ni reveals a shift to the east in case of highest variability zones coinciding with limestone outcrops. The high segmented area between Mignano and Bisaccia partially follows the alluvial plains of Appenine's rivers which seem to be playing a crucial role in the distribution and redistribution pattern of Cu, Zn and Ni in Campania. The high spatial variability zones of the later elements are located in topsoils on volcanoclastic rocks and are mostly related to cultivation and urbanised areas.

Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region

NASA Astrophysics Data System (ADS)

Gao, Hongkai; Hrachowitz, Markus; Sriwongsitanon, Nutchanart; Fenicia, Fabrizio; Gharari, Shervan; Savenije, Hubert H. G.

2016-10-01

Understanding which catchment characteristics dominate hydrologic response and how to take them into account remains a challenge in hydrological modeling, particularly in ungauged basins. This is even more so in nontemperate and nonhumid catchments, where—due to the combination of seasonality and the occurrence of dry spells—threshold processes are more prominent in rainfall runoff behavior. An example is the tropical savannah, the second largest climatic zone, characterized by pronounced dry and wet seasons and high evaporative demand. In this study, we investigated the importance of landscape variability on the spatial variability of stream flow in tropical savannah basins. We applied a stepwise modeling approach to 23 subcatchments of the Upper Ping River in Thailand, where gradually more information on landscape was incorporated. The benchmark is represented by a classical lumped model (FLEXL), which does not account for spatial variability. We then tested the effect of accounting for vegetation information within the lumped model (FLEXLM), and subsequently two semidistributed models: one accounting for the spatial variability of topography-based landscape features alone (FLEXT), and another accounting for both topographic features and vegetation (FLEXTM). In cross validation, each model was calibrated on one catchment, and then transferred with its fitted parameters to the remaining catchments. We found that when transferring model parameters in space, the semidistributed models accounting for vegetation and topographic heterogeneity clearly outperformed the lumped model. This suggests that landscape controls a considerable part of the hydrological function and explicit consideration of its heterogeneity can be highly beneficial for prediction in ungauged basins in tropical savannah.

PBSM3D: A finite volume, scalar-transport blowing snow model for use with variable resolution meshes

NASA Astrophysics Data System (ADS)

Marsh, C.; Wayand, N. E.; Pomeroy, J. W.; Wheater, H. S.; Spiteri, R. J.

2017-12-01

Blowing snow redistribution results in heterogeneous snowcovers that are ubiquitous in cold, windswept environments. Capturing this spatial and temporal variability is important for melt and runoff simulations. Point scale blowing snow transport models are difficult to apply in fully distributed hydrological models due to landscape heterogeneity and complex wind fields. Many existing distributed snow transport models have empirical wind flow and/or simplified wind direction algorithms that perform poorly in calculating snow redistribution where there are divergent wind flows, sharp topography, and over large spatial extents. Herein, a steady-state scalar transport model is discretized using the finite volume method (FVM), using parameterizations from the Prairie Blowing Snow Model (PBSM). PBSM has been applied in hydrological response units and grids to prairie, arctic, glacier, and alpine terrain and shows a good capability to represent snow redistribution over complex terrain. The FVM discretization takes advantage of the variable resolution mesh in the Canadian Hydrological Model (CHM) to ensure efficient calculations over small and large spatial extents. Variable resolution unstructured meshes preserve surface heterogeneity but result in fewer computational elements versus high-resolution structured (raster) grids. Snowpack, soil moisture, and streamflow observations were used to evaluate CHM-modelled outputs in a sub-arctic and an alpine basin. Newly developed remotely sensed snowcover indices allowed for validation over large basins. CHM simulations of snow hydrology were improved by inclusion of the blowing snow model. The results demonstrate the key role of snow transport processes in creating pre-melt snowcover heterogeneity and therefore governing post-melt soil moisture and runoff generation dynamics.

The brain map of gait variability in aging, cognitive impairment and dementia. A systematic review

PubMed Central

Tian, Qu; Chastan, Nathalie; Bair, Woei-Nan; Resnick, Susan M.; Ferrucci, Luigi; Studenski, Stephanie A.

2017-01-01

While gait variability may reflect subtle changes due to aging or cognitive impairment (CI), associated brain characteristics remain unclear. We summarize structural and functional neuroimaging findings associated with gait variability in older adults with and without CI and dementia. We identified 17 eligible studies; all were cross-sectional; few examined multiple brain areas. In older adults, temporal gait variability was associated with structural differences in medial areas important for lower limb coordination and balance. Both temporal and spatial gait variability were associated with structural and functional differences in hippocampus and primary sensorimotor cortex and structural differences in anterior cingulate cortex, basal ganglia, association tracts, and posterior thalamic radiation. In CI or dementia, some associations were found in primary motor cortex, hippocampus, prefrontal cortex and basal ganglia. In older adults, gait variability may be associated with areas important for sensorimotor integration and coordination. To comprehend the neural basis of gait variability with aging and CI, longitudinal studies of multiple brain areas are needed. PMID:28115194

The potential of using Landsat time-series to extract tropical dry forest phenology

NASA Astrophysics Data System (ADS)

Zhu, X.; Helmer, E.

2016-12-01

Vegetation phenology is the timing of seasonal developmental stages in plant life cycles. Due to the persistent cloud cover in tropical regions, current studies often use satellite data with high frequency, such as AVHRR and MODIS, to detect vegetation phenology. However, the spatial resolution of these data is from 250 m to 1 km, which does not have enough spatial details and it is difficult to relate to field observations. To produce maps of phenology at a finer spatial resolution, this study explores the feasibility of using Landsat images to detect tropical forest phenology through reconstructing a high-quality, seasonal time-series of images, and tested it in Mona Island, Puerto Rico. First, an automatic method was applied to detect cloud and cloud shadow, and a spatial interpolator was use to retrieve pixels covered by clouds, shadows, and SLC-off gaps. Second, enhanced vegetation index time-series derived from the reconstructed Landsat images were used to detect 11 phenology variables. Detected phenology is consistent with field investigations, and its spatial pattern is consistent with the rainfall distribution on this island. In addition, we may expect that phenology should correlate with forest biophysical attributes, so 47 plots with field measurement of biophysical attributes were used to indirectly validate the phenology product. Results show that phenology variables can explain a lot of variations in biophysical attributes. This study suggests that Landsat time-series has great potential to detect phenology in tropical areas.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Simulating maize yield and biomass with spatial variability of soil field capacity

USDA-ARS?s Scientific Manuscript database

Spatial variability in field soil water and other properties is a challenge for system modelers who use only representative values for model inputs, rather than their distributions. In this study, we compared simulation results from a calibrated model with spatial variability of soil field capacity ...

The interacting effect of cognitive and motor task demands on performance of gait, balance and cognition in young adults.

PubMed

Szturm, Tony; Maharjan, Pramila; Marotta, Jonathan J; Shay, Barbara; Shrestha, Shiva; Sakhalkar, Vedant

2013-09-01

Mobility limitations and cognitive impairments, each common with aging, reduce levels of physical and mental activity, are prognostic of future adverse health events, and are associated with an increased fall risk. The purpose of this study was to examine whether divided attention during walking at a constant speed would decrease locomotor rhythm, stability, and cognitive performance. Young healthy participants (n=20) performed a visuo-spatial cognitive task in sitting and while treadmill walking at 2 speeds (0.7 and 1.0 m/s).Treadmill speed had a significant effect on temporal gait variables and ML-COP excursion. Cognitive load did not have a significant effect on average temporal gait variables or COP excursion, but variation of gait variables increased during dual-task walking. ML and AP trunk motion was found to decrease during dual-task walking. There was a significant decrease in cognitive performance (success rate, response time and movement time) while walking, but no effect due to treadmill speed. In conclusion walking speed is an important variable to be controlled in studies that are designed to examine effects of concurrent cognitive tasks on locomotor rhythm, pacing and stability. Divided attention during walking at a constant speed did result in decreased performance of a visuo-spatial cognitive task and an increased variability in locomotor rhythm. Copyright © 2013 Elsevier B.V. All rights reserved.

Observed and simulated changes in Antarctic sea ice and sea level pressure: anthropogenic or natural variability? (Invited)

NASA Astrophysics Data System (ADS)

Hobbs, W. R.

2013-12-01

Statistically-significant changes in Antarctic sea ice cover and the overlying atmosphere have been observed over the last 30 years, but there is an open question of whether these changes are due to multi-decadal natural variability or an anthropogenically-forced response. A number of recent papers have shown that the slight increase in total sea ice cover is within the bounds of internal variability exhibited by coupled climate models in the CMIP5 suite. Modelled changes for the same time period generally show a decrease, but again with a magnitude that is within internal variability. However, in contrast to the Arctic, sea ice tends in the Antarctic are spatially highly heterogeneous, and consideration of the total ice cover may mask important regional signals. In this work, a robust ';fingerprinting' approach is used to show that the observed spatial pattern of sea ice trends is in fact outside simulated natural variability in west Antarctic, and furthermore that the CMIP5 models consistently show decreased ice cover in the Ross and Weddell Seas, sectors which in fact have an observed increase in cover. As a first step towards understanding the disagreement between models and observations, modelled sea level pressure trends are analysed using and optimal fingerprinting approach, to identify whether atmospheric deficiencies in the models can explain the model-observation discrepancy.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Synoptic Storms in the North Atlantic in the Atmospheric Reanalysis and Scatterometer-Based Wind Products

NASA Astrophysics Data System (ADS)

Dukhovskoy, D. S.; Bourassa, M. A.

2016-12-01

The study compares and analyses the characteristics of synoptic storms in the Subpolar North Atlantic over the time period from 2000 through 2009 derived from reanalysis data sets and scatterometer-based gridded wind products. The analysis is performed for ocean 10-m winds derived from the following wind data sets: NCEP/DOE AMIP-II reanalysis (NCEPR2), NCAR/CFSR, Arctic System Reanalysis (ASR) version 1, Cross-Calibrated Multi-Platform (CCMP) wind product versions 1.1 and recently released version 2.0 prepared by the Remote Sensing Systems, and QuikSCAT. A cyclone tracking algorithm employed in this study for storm identification is based on average vorticity fields derived from the wind data. The study discusses storm characteristics such as storm counts, trajectories, intensity, integrated kinetic energy, spatial scale. Interannal variability of these characteristics in the data sets is compared. The analyses demonstrates general agreement among the wind data products on the characteristics of the storms, their spatial distribution and trajectories. On average, the NCEPR2 storms are more energetic mostly due to large spatial scales and stronger winds. There is noticeable interannual variability in the storm characteristics, yet no obvious trend in storms is observed in the data sets.

Benchmarking variable-density flow in saturated and unsaturated porous media

NASA Astrophysics Data System (ADS)

Guevara Morel, Carlos Roberto; Cremer, Clemens; Graf, Thomas

2015-04-01

In natural environments, fluid density and viscosity can be affected by spatial and temporal variations of solute concentration and/or temperature. These variations can occur, for example, due to salt water intrusion in coastal aquifers, leachate infiltration from waste disposal sites and upconing of saline water from deep aquifers. As a consequence, potentially unstable situations may exist in which a dense fluid overlies a less dense fluid. This situation can produce instabilities that manifest as dense plume fingers that move vertically downwards counterbalanced by vertical upwards flow of the less dense fluid. Resulting free convection increases solute transport rates over large distances and times relative to constant-density flow. Therefore, the understanding of free convection is relevant for the protection of freshwater aquifer systems. The results from a laboratory experiment of saturated and unsaturated variable-density flow and solute transport (Simmons et al., Transp. Porous Medium, 2002) are used as the physical basis to define a mathematical benchmark. The HydroGeoSphere code coupled with PEST are used to estimate the optimal parameter set capable of reproducing the physical model. A grid convergency analysis (in space and time) is also undertaken in order to obtain the adequate spatial and temporal discretizations. The new mathematical benchmark is useful for model comparison and testing of variable-density variably saturated flow in porous media.

Temporally variable environments maintain more beta-diversity in Mediterranean landscapes

NASA Astrophysics Data System (ADS)

Martin, Beatriz; Ferrer, Miguel

2015-10-01

We examined the relationships between different environmental factors and the alpha and beta-diversity of terrestrial vertebrates (birds, mammals, amphibians and reptiles) in a Mediterranean region at the landscape level. We investigated whether the mechanisms underlying alpha and beta-diversity patterns are influenced by energy availability, habitat heterogeneity and temporal variability and if the drivers of the diversity patterns differed between both components of diversity. We defined alpha-diversity as synonym of species richness whereas beta-diversity was measured as distinctiveness. We evaluated a total of 13 different predictors using generalized linear mixed model (GLMM) analysis. Habitat spatial heterogeneity increased alpha-diversity, but contrastingly, it did not significantly affect beta-diversity among sites. Disturbed landscapes may show higher habitat spatial variation and higher alpha-diversity due to the contribution of highly generalist species that are wide-distributed and do not differ in composition (beta-diversity) among different sites within the region. Contrastingly, higher beta-diversity levels were negatively related to more stable sites in terms of temporal environmental variation. This negative relationship between environmental stability and beta-diversity levels is explained in terms of species adaptation to the local environmental conditions. Our study highlights the importance of temporal environmental variability in maintaining beta-diversity patterns under highly variable environmental conditions.

Validation and Temporal Analysis of Lai and Fapar Products Derived from Medium Resolution Sensor

NASA Astrophysics Data System (ADS)

Claverie, M.; Vermote, E. F.; Baret, F.; Weiss, M.; Hagolle, O.; Demarez, V.

2012-12-01

Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been defined as Essential Climate Variables. Many Earth surface monitoring applications are based on global estimation combined with a relatively high frequency. The medium spatial resolution sensors (MRS), such as SPOT-VGT, MODIS or MERIS, have been widely used to provide land surface products (mainly LAI and FAPAR) to the scientific community. These products require quality assessment and consistency. However, due to consistency of the ground measurements spatial sampling, the medium resolution is not appropriate for direct validation with in situ measurements sampling. It is thus more adequate to use high spatial resolution sensors which can integrate the spatial variability. The recent availability of combined high spatial (8 m) and temporal resolutions (daily) Formosat-2 data allows to evaluate the accuracy and the temporal consistency of medium resolution sensors products. In this study, we proposed to validate MRS products over a cropland area and to analyze their spatial and temporal consistency. As a matter of fact, this study belongs to the Stage 2 of the validation, as defined by the Land Product Validation sub-group of the Earth Observation Satellites. Reference maps, derived from the aggregation of Formosat-2 data (acquired during the 2006-2010 period over croplands in southwest of France), were compared with (i) two existing global biophysical variables products (GEOV1/VGT and MODIS-15 coll. 5), and (ii) a new product (MODdaily) derived from the inversion of PROSAIL radiative transfer model (EMMAH, INRA Avignon) applied on MODIS BRDF-corrected daily reflectance. Their uncertainty was calculated with 105 LAI and FAPAR reference maps, which uncertainties (22 % for LAI and 12% for FAPAR) were evaluated with in situ measurements performed over maize, sunflower and soybean. Inter-comparison of coarse resolution (0.05°) products showed that LAI and FAPAR have consistent phenology (Figure). The GEOLAND-2 showed the smoothest time series due to a 30-day composite, while MODdaily noise was satisfactory (<12%). The RMSE of LAI calculated for the period 2006-2010 were 0.46 for GEOV1/VGT, 0.19 for MODIS-15 and 0.16 for MODdaily. A significant overestimation (bias=0.43) of the LAI peak were observed for GEOV1/VGT products, while MOD-15 showed a small underestimation (bias=-0.14) of highest LAI. Finally, over a larger area (a quarter of France) covered by cropland, grassland and forest, the products displayed a good spatial consistency.; LAI 2006-2010 time-series of a coarse resolution pixel of cropland (extent in upper-left corner). Products are compared to Formosat-2 reference maps.

Spatial Patterns of Forest Cover Loss in the Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Molinario, G.; Hansen, M.; Potapov, P.; Justice, C. O.

2013-12-01

Three groups of metrics of spatial patterns of forest cover loss were calculated for the Democratic Republic of Congo (DRC). While other studies had previously assessed landscape patterns in the Congo Basin, they had done so for small areas due to data limitations. The input data for this study, the Forets d;Afrique Central Evaluee par Teledetection(FACET), allowed the analysis to be performed at the national level. FACET is a landsat-scale dataset giving an unprecedented synoptic view of forest cover and forest cover loss for the DRC for three time periods: 2000, 2005 and 2010. The three groups of metrics evaluated the following spatial characteristics of forest cover loss for the same standard 1.5km unit of area: proportions of typologies of forest lost, forest fragmentation and proximity of forest loss patches from other land cover types. Results indicate that there are several different typologies of forest cover loss in the DRC, and offer quantitative explanations of these differences, providing a valuable locally-relevant tool for land use planning, available at the national level. Spatial patterns of forest cover loss highlight differences between areas of high primary forest loss due to agriculture conversion in frontier deforestation, such as in the east of the country, areas of equivalent primary and secondary forest loss emanating from the rural complex and areas of variable proportions of primary and secondary forest loss but important ecological repercussions of forest fragmentation due to isolated, but systematic forest perforations. Typologies of spatial patterns of forest cover loss are presented as well as their correlated drivers, and ecological, conservation and land use planning considerations are discussed.

Influence of tillage in soil penetration resistance variability in an olive orchard

NASA Astrophysics Data System (ADS)

López de Herrera, Juan; Herrero Tejedor, Tomas; Saa-Requejo, Antonio; Tarquis, Ana M.

2015-04-01

Soil attributes usually present a high degree of spatial variation due to a combination of physical, chemical, biological or climatic processes operating at different scales. The quantification and interpretation of such variability is a key issue for site-specific soil management (Brouder et al., 2001). The usual geostatistical approach studies soil variability by means of the semi-variograms. However, recently a multiscaling approach has been applied on the determination of the scaling data properties (Kravechenko et al., 1999; Caniego et al., 2005; Tarquis et al., 2008). This work focus in the multifractal analysis as a way to characterize the variability of field data in a case study of soil penetrometer resistance (SPR) in two olive orchards, one applying tillage for 20 years and the other one non. The field measurements and soil data were obtained at the village of Puebla de Almenara (Cuenca, Spain) (39o 47'42.37'N, 2o 49'29.23'W) with 869 m of elevation approximately. The characteristic of the soil at the surface is classified as clay loam texture according to Guidelines for soil description of FAO. The soil consists of clays and red silts with some clusters of limestone's and sands. Two transect data were collected from 128 points between the squared of the olive tree, tillage and no tillage area, for SPR readings with a sampling interval of 50 cm. In each sampling, readings were obtained from 0 cm till 20 cm of depth, with an interval of 5 cm. The multifractal spectrum for each area and depth was estimated showing a characteristic pattern and differentiating both treatments. References Brouder, S., Hofmann, B., Reetz, H.F., 2001. Evaluating spatial variability of soil parameters for input management. Better Crops 85, 8-11. Kravchenko, A.N., Boast, C.W., Bullock, D.G., 1999. Multifractal analysis of soil spatial variability. Agron. J. 91, 1033-1041. Caniego, F.J., R. Espejo, M.A. Martín, F. San José, 2005. Multifractal scaling of soil spatial variability. Ecological Modelling, 182, 291-303. Tarquis, A.M., N. Bird, M.C. Cartagena, A. Whitmore and Y. Pachepsky, 2008. Multiscale entropy-based analyses of soil transect data. Vadose Zone Journal, 7(2), 563-569.

Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

NASA Astrophysics Data System (ADS)

Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

2018-03-01

In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

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.

Spatial patterns of throughfall isotopic composition at the event and seasonal timescales

Treesearch

Scott T. Allen; Richard F. Keim; Jeffrey J. McDonnell

2015-01-01

Spatial variability of throughfall isotopic composition in forests is indicative of complex processes occurring in the canopy and remains insufficiently understood to properly characterize precipitation inputs to the catchment water balance. Here we investigate variability of throughfall isotopic composition with the objectives: (1) to quantify the spatial variability...

Closing the water balance with cosmic-ray soil moisture measurements and assessing their spatial variability within two semiarid watersheds

NASA Astrophysics Data System (ADS)

Schreiner-McGraw, A. P.; Vivoni, E. R.; Mascaro, G.; Franz, T. E.

2015-06-01

Soil moisture dynamics reflect the complex interactions of meteorological conditions with soil, vegetation and terrain properties. In this study, intermediate scale soil moisture estimates from the cosmic-ray sensing (CRS) method are evaluated for two semiarid ecosystems in the southwestern United States: a mesquite savanna at the Santa Rita Experimental Range (SRER) and a mixed shrubland at the Jornada Experimental Range (JER). Evaluations of the CRS method are performed for small watersheds instrumented with a distributed sensor network consisting of soil moisture sensor profiles, an eddy covariance tower and runoff flumes used to close the water balance. We found an excellent agreement between the CRS method and the distributed sensor network (RMSE of 0.009 and 0.013 m3 m-3 at SRER and JER) at the hourly time scale over the 19-month study period, primarily due to the inclusion of 5 cm observations of shallow soil moisture. Good agreement was obtained in soil moisture changes estimated from the CRS and watershed water balance methods (RMSE = 0.001 and 0.038 m3 m-3 at SRER and JER), with deviations due to bypassing of the CRS measurement depth during large rainfall events. This limitation, however, was used to show that drier-than-average conditions at SRER promoted plant water uptake from deeper layers, while the wetter-than-average period at JER resulted in leakage towards deeper soils. Using the distributed sensor network, we quantified the spatial variability of soil moisture in the CRS footprint and the relation between evapotranspiration and soil moisture, in both cases finding similar predictive relations at both sites that are applicable to other semiarid ecosystems in the southwestern US. Furthermore, soil moisture spatial variability was related to evapotranspiration in a manner consistent with analytical relations derived using the CRS method, opening up new possibilities for understanding land-atmosphere interactions.

Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset.

Lens-Aided Multi-Angle Spectroscopy (LAMAS) Reveals Small-Scale Outflow Structure in Quasars

NASA Astrophysics Data System (ADS)

Green, Paul J.

2006-06-01

Spectral differences between lensed quasar image components are common. Since lensing is intrinsically achromatic, these differences are typically explained as the effect of either microlensing, or as light path time delays sampling intrinsic quasar spectral variability. Here we advance a novel third hypothesis: some spectral differences are due to small line-of-sight differences through quasar disk wind outflows. In particular, we propose that variable spectral differences seen only in component A of the widest separation lens SDSS J1004+4112 are due to differential absorption along the sight lines. The absorber properties required by this hypothesis are akin to known broad absorption line (BAL) outflows but must have a broader, smoother velocity profile. We interpret the observed C IV emission-line variability as further evidence for spatial fine structure transverse to the line of sight. Since outflows are likely to be rotating, such absorber fine structure can consistently explain some of the UV and X-ray variability seen in AGNs. The implications are many: (1) Spectroscopic differences in other lensed objects may be due to this ``lens-aided multi-angle spectroscopy'' (LAMAS). (2) Outflows have fine structure on size scales of arcseconds, as seen from the nucleus. (3) Assuming either broad absorption line region sizes proposed in recent wind models, or typically assumed continuum emission region sizes, LAMAS and/or variability provide broadly consistent absorber size scale estimates of ~1015 cm. (4) Very broad smooth absorption may be ubiquitous in quasar spectra, even when no obvious troughs are seen.

Temporal and spatial variation of hyperiid amphipod assemblages in response to hydrographic processes in the Panama Bight, eastern tropical Pacific

NASA Astrophysics Data System (ADS)

Valencia, Bellineth; Lavaniegos, Bertha; Giraldo, Alan; Rodríguez-Rubio, Efraín

2013-03-01

The variability of hyperiid amphipod assemblages was analyzed based on seasonal (dry vs. wet) and spatial differences (neritic vs. oceanic) in the Panama Bight. Four oceanographic cruises were carried out during 2007-2008, two during the dry season (Feb. 2007 and Mar. 2008) and two during the wet season (Sep. 2007 and Sep. 2008). Cooler (<20 °C) and saltier waters (>34 psu) at 30 m were observed during the dry season, while the opposite pattern occurred during the wet season (>25 °C,<33 psu). Seventy hyperiids species were found, with Hyperioides sibaginis and Lestrigonus bengalensis being the most abundant (76%). The structure of the hyperiids assemblages was similar between seasons; this may be due to the interannual variability associated with two ENSO processes (El Niño: Feb. 2007; La Niña: Sep. 2007 and Mar. 2008). Spatially, the structure of the hyperiids assemblages changed during the wet season, as a consequence of the strong neritic-oceanic contrast in salinity, while it was spatially similar during the dry season, as a consequence of species penetration toward the coast promoted by colder and saltier waters. The abundance of hyperiids was significantly correlated with the abundance of gelatinous zooplankton (siphonophores, ctenophores, doliolids and salps), which are commonly used as hosts by this group.

Cloudy Windows: What GCM Ensembles, Reanalyses and Observations Tell Us About Uncertainty in Greenland's Future Climate and Surface Melting

NASA Astrophysics Data System (ADS)

Reusch, D. B.

2016-12-01

Any analysis that wants to use a GCM-based scenario of future climate benefits from knowing how much uncertainty the GCM's inherent variability adds to the development of climate change predictions. This is extra relevant in the polar regions due to the potential of global impacts (e.g., sea level rise) from local (ice sheet) climate changes such as more frequent/intense surface melting. High-resolution, regional-scale models using GCMs for boundary/initial conditions in future scenarios inherit a measure of GCM-derived externally-driven uncertainty. We investigate these uncertainties for the Greenland ice sheet using the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Recent simulations are skill-tested against the ERA-Interim reanalysis and AWS observations with results informing future scenarios. We focus on key variables influencing surface melting through decadal climatologies, nonlinear analysis of variability with self-organizing maps (SOMs), regional-scale modeling (Polar WRF), and simple melt models. Relative to the ensemble average, spatially averaged climatological July temperature anomalies over a Greenland ice-sheet/ocean domain are mostly between +/- 0.2 °C. The spatial average hides larger local anomalies of up to +/- 2 °C. The ensemble average itself is 2 °C cooler than ERA-Interim. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. For CESMLE, the SOM patterns summarize the variability of multiple realizations of climate. Changes in pattern frequency by ensemble member show the influence of initial conditions. For example, basic statistical analysis of pattern frequency yields interquartile ranges of 2-4% for individual patterns across the ensemble. In climate terms, this tells us about climate state variability through the range of the ensemble, a potentially significant source of melt-prediction uncertainty. SOMs can also capture the different trajectories of climate due to intramodel variability over time. Polar WRF provides higher resolution regional modeling with improved, polar-centric model physics. Simple melt models allow us to characterize impacts of the upstream uncertainties on estimates of surface melting.

Unleashing spatially distributed ecohydrology modeling using Big Data tools

NASA Astrophysics Data System (ADS)

Miles, B.; Idaszak, R.

2015-12-01

Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well as point time series of arbitrary variables at arbitrary points in space within a watershed or river basin. By treating ecohydrology modeling as a Big Data problem, we hope to provide a platform for answering transformative science and management questions related to water quantity and quality in a world of non-stationary climate.

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.

How does complex terrain influence responses of carbon and water cycle processes to climate variability and climate change? (Invited)

NASA Astrophysics Data System (ADS)

Bond, B. J.; Peterson, K.; McKane, R.; Lajtha, K.; Quandt, D. J.; Allen, S. T.; Sell, S.; Daly, C.; Harmon, M. E.; Johnson, S. L.; Spies, T.; Sollins, P.; Abdelnour, A. G.; Stieglitz, M.

2010-12-01

We are pursuing the ambitious goal of understanding how complex terrain influences the responses of carbon and water cycle processes to climate variability and climate change. Our studies take place in H.J. Andrews Experimental Forest, an LTER (Long Term Ecological Research) site situated in Oregon’s central-western Cascade Range. Decades of long-term measurements and intensive research have revealed influences of topography on vegetation patterns, disturbance history, and hydrology. More recent research has shown surprising interactions between microclimates and synoptic weather patterns due to cold air drainage and pooling in mountain valleys. Using these data and insights, in addition to a recent LiDAR (Light Detection and Ranging) reconnaissance and a small sensor network, we are employing process-based models, including “SPA” (Soil-Plant-Atmosphere, developed by Mathew Williams of the University of Edinburgh), and “VELMA” (Visualizing Ecosystems for Land Management Alternatives, developed by Marc Stieglitz and colleagues of the Georgia Institute of Technology) to focus on two important features of mountainous landscapes: heterogeneity (both spatial and temporal) and connectivity (atmosphere-canopy-hillslope-stream). Our research questions include: 1) Do fine-scale spatial and temporal heterogeneity result in emergent properties at the basin scale, and if so, what are they? 2) How does connectivity across ecosystem components affect system responses to climate variability and change? Initial results show that for environmental drivers that elicit non-linear ecosystem responses on the plot scale, such as solar radiation, soil depth and soil water content, fine-scale spatial heterogeneity may produce unexpected emergent properties at larger scales. The results from such modeling experiments are necessarily a function of the supporting algorithms. However, comparisons based on models such as SPA and VELMA that operate at much different spatial scales (plots vs. hillslopes) and levels of biophysical organization (individual plants vs. aggregate plant biomass) can help us to understand how and why mountainous ecosystems may have distinctive responses to climate variability and climate change.

Factors Influencing Spatial and Annual Variability in Eelgrass (Zostera marina L.) Meadows in Willapa Bay, Washington, and Coos Bay, Oregon, Estuaries

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

Thom, Ronald M.; Borde, Amy B.; Rumrill, Steven

2003-08-01

Environmental factors that influence annual variability and spatial differences in eelgrass meadows (Zostera marina L.) were examined within Willapa Bay, WA, and Coos Bay, OR, over a period of 4 years (1998-2001). A suite of eelgrass metrics were recorded annually at field sites that spanned the estuarine gradient from the marine-dominated to mesohaline regions. Growth of eelgrass plants was also monitored on a monthly basis within Sequim Bay, WA. Both the spatial cover and density of Z. marina were positively correlated with estuarine salinity and inversely correlated with temperature of the tideflat sediment. Experimental evidence verified that optimal eelgrass growthmore » occurred at highest salinities and relatively low temperatures. Eelgrass density, biomass, and the incident of flowering plants all increased substantially in Willapa Bay, and less so in Coos Bay, over the duration of the study. Warmer winters and cooler summers associated with the transition from El Ni?o to La Ni?a ocean conditions during the study period were correlated with the increase in eelgrass abundance and flowering. Anthropogenic factors (e.g., disturbance and erosion by vessel wakes and recreational shellfishing activities) may have contributed to spatial variability. Our findings indicate that large-scale changes in climate and nearshore ocean conditions can exert a strong regional influence on eelgrass abundance, which can vary annually by as much as 700% in Willapa Bay. Lower levels of variability observed in Coos Bay may be due to the stronger and more direct influence of the nearshore Pacific Ocean. We conclude that climate variation may have profound effects on the abundance and distribution of eelgrass meadows throughout the Pacific Northwest, and we anticipate that ocean conditions will emerge as a primary driving force for living estuarine resources and ecological processes that are associated with Z. marina beds within the landscape of these estuarine tidal basins.« less

Spatiotemporal correlation structure of the Earth's surface temperature

NASA Astrophysics Data System (ADS)

Fredriksen, Hege-Beate; Rypdal, Kristoffer; Rypdal, Martin

2015-04-01

We investigate the spatiotemporal temperature variability for several gridded instrumental and climate model data sets. The temporal variability is analysed by estimating the power spectral density and studying the differences between local and global temperatures, land and sea, and among local temperature records at different locations. The spatiotemporal correlation structure is analysed through cross-spectra that allow us to compute frequency-dependent spatial autocorrelation functions (ACFs). Our results are then compared to theoretical spectra and frequency-dependent spatial ACFs derived from a fractional stochastic-diffusive energy balance model (FEBM). From the FEBM we expect both local and global temperatures to have a long-range persistent temporal behaviour, and the spectral exponent (β) is expected to increase by a factor of two when going from local to global scales. Our comparison of the average local spectrum and the global spectrum shows good agreement with this model, although the FEBM has so far only been studied for a pure land planet and a pure ocean planet, respectively, with no seasonal forcing. Hence it cannot capture the substantial variability among the local spectra, in particular between the spectra for land and sea, and for equatorial and non-equatorial temperatures. Both models and observation data show that land temperatures in general have a low persistence, while sea surface temperatures show a higher, and also more variable degree of persistence. Near the equator the spectra deviate from the power-law shape expected from the FEBM. Instead we observe large variability at time scales of a few years due to ENSO, and a flat spectrum at longer time scales, making the spectrum more reminiscent of that of a red noise process. From the frequency-dependent spatial ACFs we observe that the spatial correlation length increases with increasing time scale, which is also consistent with the FEBM. One consequence of this is that longer-lasting structures must also be wider in space. The spatial correlation length is also observed to be longer for land than for sea. The climate model simulations studied are mainly CMIP5 control runs of length 500-1000 yr. On time scales up to several centuries we do not observe that the difference between the local and global spectral exponents vanish. This also follows from the FEBM and shows that the dynamics is spatiotemporal (not just temporal) even on these time scales.

Seismologic applications of GRACE time-variable gravity measurements

NASA Astrophysics Data System (ADS)

Li, Jin; Chen, Jianli; Zhang, Zizhan

2014-04-01

The Gravity Recovery and Climate Experiment (GRACE) has been measuring temporal and spatial variations of mass redistribution within the Earth system since 2002. As large earthquakes cause significant mass changes on and under the Earth's surface, GRACE provides a new means from space to observe mass redistribution due to earthquake deformations. GRACE serves as a good complement to other earthquake measurements because of its extensive spatial coverage and being free from terrestrial restriction. During its over 10 years mission, GRACE has successfully detected seismic gravitational changes of several giant earthquakes, which include the 2004 Sumatra-Andaman earthquake, 2010 Maule (Chile) earthquake, and 2011 Tohoku-Oki (Japan) earthquake. In this review, we describe by examples how to process GRACE time-variable gravity data to retrieve seismic signals, and summarize the results of recent studies that apply GRACE observations to detect co- and post-seismic signals and constrain fault slip models and viscous lithospheric structures. We also discuss major problems and give an outlook in this field of GRACE application.

The Trapping Index: How to integrate the Eulerian and the Lagrangian approach for the computation of the transport time scales of semi-enclosed basins.

PubMed

Cucco, Andrea; Umgiesser, Georg

2015-09-15

In this work, we investigated if the Eulerian and the Lagrangian approaches for the computation of the Transport Time Scales (TTS) of semi-enclosed water bodies can be used univocally to define the spatial variability of basin flushing features. The Eulerian and Lagrangian TTS were computed for both simplified test cases and a realistic domain: the Venice Lagoon. The results confirmed the two approaches cannot be adopted univocally and that the spatial variability of the water renewal capacity can be investigated only through the computation of both the TTS. A specific analysis, based on the computation of a so-called Trapping Index, was then suggested to integrate the information provided by the two different approaches. The obtained results proved the Trapping Index to be useful to avoid any misleading interpretation due to the evaluation of the basin renewal features just from an Eulerian only or from a Lagrangian only perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Uncertainty analysis for the steady-state flows in a dual throat nozzle

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

Chen, Q.-Y.; Gottlieb, David; Hesthaven, Jan S.

2005-03-20

It is well known that the steady state of an isentropic flow in a dual-throat nozzle with equal throat areas is not unique. In particular there is a possibility that the flow contains a shock wave, whose location is determined solely by the initial condition. In this paper, we consider cases with uncertainty in this initial condition and use generalized polynomial chaos methods to study the steady-state solutions for stochastic initial conditions. Special interest is given to the statistics of the shock location. The polynomial chaos (PC) expansion modes are shown to be smooth functions of the spatial variable x,more » although each solution realization is discontinuous in the spatial variable x. When the variance of the initial condition is small, the probability density function of the shock location is computed with high accuracy. Otherwise, many terms are needed in the PC expansion to produce reasonable results due to the slow convergence of the PC expansion, caused by non-smoothness in random space.« less

Feasibility of automated dropsize distributions from holographic data using digital image processing techniques. [particle diameter measurement technique

NASA Technical Reports Server (NTRS)

Feinstein, S. P.; Girard, M. A.

1979-01-01

An automated technique for measuring particle diameters and their spatial coordinates from holographic reconstructions is being developed. Preliminary tests on actual cold-flow holograms of impinging jets indicate that a suitable discriminant algorithm consists of a Fourier-Gaussian noise filter and a contour thresholding technique. This process identifies circular as well as noncircular objects. The desired objects (in this case, circular or possibly ellipsoidal) are then selected automatically from the above set and stored with their parametric representations. From this data, dropsize distributions as a function of spatial coordinates can be generated and combustion effects due to hardware and/or physical variables studied.

A new gridded on-road CO2 emissions inventory for the United States, 1980-2011

NASA Astrophysics Data System (ADS)

Gately, C.; Hutyra, L.; Sue Wing, I.

2013-12-01

On-road transportation is responsible for 28% of all U.S. fossil fuel CO2 emissions. However, mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories have used spatial proxies such as population and road density to downscale national or state level data, which may introduce errors where the proxy variables and actual emissions are weakly correlated. We have developed a national on-road emissions inventory product based on roadway-level traffic data obtained from the Highway Performance Monitoring System. We produce annual estimates of on-road CO2 emissions at a 1km spatial resolution for the contiguous United States for the years 1980 through 2011. For the year 2011 we also produce an hourly emissions product at the 1km scale using hourly traffic volumes from hundreds of automated traffic counters across the country. National on-road emissions rose at roughly 2% per year from 1980 to 2006, with emissions peaking at 1.71 Tg CO2 in 2007. However, while national emissions have declined 6% since the peak, we observe considerable regional variation in emissions trends post-2007. While many states show stable or declining on-road emissions, several states and metropolitan areas in the Midwest, mountain west and south had emissions increases of 3-10% from 2008 to 2011. Our emissions estimates are consistent with state-reported totals of gasoline and diesel fuel consumption. This is in contrast to on-road CO2 emissions estimated by the Emissions Database of Global Atmospheric Research (EDGAR), which we show to be inconsistent in matching on-road emissions to published fuel consumption at the scale of U.S. states, due to the non-linear relationships between emissions and EDGAR's chosen spatial proxies at these scales. Since our emissions estimates were generated independent of population density and other demographic data, we were able to conduct a panel regression analysis to estimate the relationship between these variables and on-road CO2 at various spatial scales. In the case of Massachusetts we find a non-linear relationship between emissions and population density indicating that increasing density resulted in increased emissions when density is less than 2000 persons-km-2. These results highlight the value of using an emissions inventory with high spatial and temporal resolution. At coarser spatial scales, much of the variation in population density and on-road emissions between towns is lost due to aggregation. The high spatial resolution and broad temporal scope of our CO2 estimates provides a basis for analyses to support emissions monitoring, verification and mitigation policies at regional, state and local scale.

Using generalized additive mixed models to assess spatial, temporal, and hydrologic controls on bacteria and nitrate in a vulnerable agricultural aquifer.

PubMed

Mellor, Andrea F P; Cey, Edwin E

2015-11-01

The Abbotsford-Sumas aquifer (ASA) has a history of nitrate contamination from agricultural land use and manure application to soils, yet little is known about its microbial groundwater quality. The goal of this study was to investigate the spatiotemporal distribution of pathogen indicators (Escherichia coli [E. coli] and total coliform [TC]) and nitrate in groundwater, and their potential relation to hydrologic drivers. Sampling of 46 wells over an 11-month period confirmed elevated nitrate concentrations, with more than 50% of samples exceeding 10 mg-N/L. E. coli detections in groundwater were infrequent (4 of 385 total samples) and attributed mainly to surface water-groundwater connections along Fishtrap Creek, which tested positive for E. coli in every sampling event. TC was detected frequently in groundwater (70% of samples) across the ASA. Generalized additive mixed models (GAMMs) yielded valuable insights into relationships between TC or nitrate and a range of spatial, temporal, and hydrologic explanatory variables. Increased TC values over the wetter fall and winter period were most strongly related to groundwater temperatures and levels, while precipitation and well location were weaker (but still significant) predictors. In contrast, the moderate temporal variability in nitrate concentrations was not significantly related to hydrologic forcings. TC was relatively widespread across the ASA and spatial patterns could not be attributed solely to surface water connectivity. Varying nitrate concentrations across the ASA were significantly related to both well location and depth, likely due to spatially variable nitrogen loading and localized geochemical attenuation (i.e., denitrification). Vulnerability of the ASA to bacteria was clearly linked to hydrologic conditions, and was distinct from nitrate, such that a groundwater management strategy specifically for bacterial contaminants is warranted. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterizing the nature and variability of avalanche hazard in western Canada

NASA Astrophysics Data System (ADS)

Shandro, Bret; Haegeli, Pascal

2018-04-01

The snow and avalanche climate types maritime, continental and transitional are well established and have been used extensively to characterize the general nature of avalanche hazard at a location, study inter-seasonal and large-scale spatial variabilities and provide context for the design of avalanche safety operations. While researchers and practitioners have an experience-based understanding of the avalanche hazard associated with the three climate types, no studies have described the hazard character of an avalanche climate in detail. Since the 2009/2010 winter, the consistent use of Statham et al. (2017) conceptual model of avalanche hazard in public avalanche bulletins in Canada has created a new quantitative record of avalanche hazard that offers novel opportunities for addressing this knowledge gap. We identified typical daily avalanche hazard situations using self-organizing maps (SOMs) and then calculated seasonal prevalence values of these situations. This approach produces a concise characterization that is conducive to statistical analyses, but still provides a comprehensive picture that is informative for avalanche risk management due to its link to avalanche problem types. Hazard situation prevalence values for individual seasons, elevations bands and forecast regions provide unprecedented insight into the inter-seasonal and spatial variability of avalanche hazard in western Canada.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Climate-based archetypes for the environmental fate assessment of chemicals.

PubMed

Ciuffo, Biagio; Sala, Serenella

2013-11-15

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Spatial Variability of Streambed Hydraulic Conductivity of a Lowland River

NASA Astrophysics Data System (ADS)

Schneidewind, Uwe; Thornton, Steven; Van De Vijver, Ellen; Joris, Ingeborg; Seuntjens, Piet

2015-04-01

Streambed hydraulic conductivity K is a key physical parameter, which describes flow processes in the hyporheic zone (HZ), i.e. the dynamic interface between aquifers and streams or rivers. Knowledge of the spatial variability of K is also important for the interpretation of contaminant transport processes in the HZ. Streambed K can vary over several magnitudes at small spatial scales. It depends mostly on streambed sediment characteristics (e.g. effective porosity, grain size, packing), streambed processes (e.g. sedimentation, colmation and erosion) and the development of stream channel geometry and streambed morphology (e.g. dunes, anti-dunes, pool-riffle sequences, etc.). Although heterogeneous in natural streambeds, streambed K is often considered to be a constant parameter due to a lack of information on its spatial distribution. Here we show the spatial variability of streambed K for a small section of the River Tern, a lowland river in the UK. Streambed K was determined for more than 120 vertically and horizontally distributed locations from grain size analyses using four empirical approaches (Hazen, Beyer, Kozeny and the USBR model). Additionally, streambed K was estimated from falling head tests in 36 piezometers installed into the streambed on a 4 m by 16 m grid, by applying the Springer-Gelhar Model. For both methods streambed K followed a log-normal distribution. Variogram analysis was used to deduce the spatial variability of the streambed K values within several streambed profiles parallel and perpendicular to the main flow direction in the stream. Hydraulic conductivity Kg estimated from grain size analyses varied between 1 m/d and 155 m/d with standard deviations of 79% to 99% depending on the empirical approach used. Kh estimated from falling head tests varied between 1 m/d and 22 m/d with a standard deviation of about 50%, depending on the degree of anisotropy assumed. After rescaling the data to obtain a common sample support, Pearson correlation coefficients r were calculated between Kg and Kh. Overall, a relatively weak correlation (r < 0.3) was found between both parameters. This is most probably a result from soil coring that destroys the original sediment structure and any anisotropy within it. Analysis of streambed K improved our understanding of the flow behavior in the HZ on a local scale. This will be of importance for the subsequent assessment of nitrate transport and attenuation in the river section.

Spatial heterogeneity in the carrying capacity of sika deer in Japan.

PubMed

Iijima, Hayato; Ueno, Mayumi

2016-06-09

Carrying capacity is 1 driver of wildlife population dynamics. Although in previous studies carrying capacity was considered to be a fixed entity, it may differ among locations due to environmental variation. The factors underlying variability in carrying capacity, however, have rarely been examined. Here, we investigated spatial heterogeneity in the carrying capacity of Japanese sika deer ( Cervus nippon ) from 2005 to 2014 in Yamanashi Prefecture, central Japan (mesh with grid cells of 5.5×4.6 km) by state-space modeling. Both carrying capacity and density dependence differed greatly among cells. Estimated carrying capacities ranged from 1.34 to 98.4 deer/km 2 . According to estimated population dynamics, grid cells with larger proportions of artificial grassland and deciduous forest were subject to lower density dependence and higher carrying capacity. We conclude that population dynamics of ungulates may vary spatially through spatial variation in carrying capacity and that the density level for controlling ungulate abundance should be based on the current density level relative to the carrying capacity for each area.

High-speed limnology: using advanced sensors to investigate spatial variability in biogeochemistry and hydrology.

PubMed

Crawford, John T; Loken, Luke C; Casson, Nora J; Smith, Colin; Stone, Amanda G; Winslow, Luke A

2015-01-06

Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h(-1)) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial-aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.

High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology

USGS Publications Warehouse

Crawford, John T.; Loken, Luke C.; Casson, Nora J.; Smith, Collin; Stone, Amanda G.; Winslow, Luke A.

2015-01-01

Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h–1) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial–aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.

Trends and Variability of Global Fire Emissions Due To Historical Anthropogenic Activities

NASA Astrophysics Data System (ADS)

Ward, Daniel S.; Shevliakova, Elena; Malyshev, Sergey; Rabin, Sam

2018-01-01

Globally, fires are a major source of carbon from the terrestrial biosphere to the atmosphere, occurring on a seasonal cycle and with substantial interannual variability. To understand past trends and variability in sources and sinks of terrestrial carbon, we need quantitative estimates of global fire distributions. Here we introduce an updated version of the Fire Including Natural and Agricultural Lands model, version 2 (FINAL.2), modified to include multiday burning and enhanced fire spread rate in forest crowns. We demonstrate that the improved model reproduces the interannual variability and spatial distribution of fire emissions reported in present-day remotely sensed inventories. We use FINAL.2 to simulate historical (post-1700) fires and attribute past fire trends and variability to individual drivers: land use and land cover change, population growth, and lightning variability. Global fire emissions of carbon increase by about 10% between 1700 and 1900, reaching a maximum of 3.4 Pg C yr-1 in the 1910s, followed by a decrease to about 5% below year 1700 levels by 2010. The decrease in emissions from the 1910s to the present day is driven mainly by land use change, with a smaller contribution from increased fire suppression due to increased human population and is largest in Sub-Saharan Africa and South Asia. Interannual variability of global fire emissions is similar in the present day as in the early historical period, but present-day wildfires would be more variable in the absence of land use change.

Local energy decay for linear wave equations with variable coefficients

NASA Astrophysics Data System (ADS)

Ikehata, Ryo

2005-06-01

A uniform local energy decay result is derived to the linear wave equation with spatial variable coefficients. We deal with this equation in an exterior domain with a star-shaped complement. Our advantage is that we do not assume any compactness of the support on the initial data, and its proof is quite simple. This generalizes a previous famous result due to Morawetz [The decay of solutions of the exterior initial-boundary value problem for the wave equation, Comm. Pure Appl. Math. 14 (1961) 561-568]. In order to prove local energy decay, we mainly apply two types of ideas due to Ikehata-Matsuyama [L2-behaviour of solutions to the linear heat and wave equations in exterior domains, Sci. Math. Japon. 55 (2002) 33-42] and Todorova-Yordanov [Critical exponent for a nonlinear wave equation with damping, J. Differential Equations 174 (2001) 464-489].

Greenland ice sheet albedo variability and feedback: 2000-2015

NASA Astrophysics Data System (ADS)

Box, J. E.; van As, D.; Fausto, R. S.; Mottram, R.; Langen, P. P.; Steffen, K.

2015-12-01

Absorbed solar irradiance represents the dominant source of surface melt energy for Greenland ice. Surface melting has increased as part of a positive feedback amplifier due to surface darkening. The 16 most recent summers of observations from the NASA MODIS sensor indicate a darkening exceeding 6% in July when most melting occurs. Without the darkening, the increase in surface melting would be roughly half as large. A minority of the albedo decline signal may be from sensor degradation. So, in this study, MOD10A1 and MCD43 albedo products from MODIS are evaluated for sensor degradation and anisotropic reflectance errors. Errors are minimized through calibration to GC-Net and PROMICE Greenland snow and ice ground control data. The seasonal and spatial variability in Greenland snow and ice albedo over a 16 year period is presented, including quantifying changing absorbed solar irradiance and melt enhancement due to albedo feedback using the DMI HIRHAM5 5 km model.

Coupled economic-coastline modeling with suckers and free riders

NASA Astrophysics Data System (ADS)

Williams, Zachary C.; McNamara, Dylan E.; Smith, Martin D.; Murray, A. Brad.; Gopalakrishnan, Sathya

2013-06-01

erosion is a natural trend along most sandy coastlines. Humans often respond to shoreline erosion with beach nourishment to maintain coastal property values. Locally extending the shoreline through nourishment alters alongshore sediment transport and changes shoreline dynamics in adjacent coastal regions. If left unmanaged, sandy coastlines can have spatially complex or simple patterns of erosion due to the relationship of large-scale morphology and the local wave climate. Using a numerical model that simulates spatially decentralized and locally optimal nourishment decisions characteristic of much of U.S. East Coast beach management, we find that human erosion intervention does not simply reflect the alongshore erosion pattern. Spatial interactions generate feedbacks in economic and physical variables that lead to widespread emergence of "free riders" and "suckers" with subsequent inequality in the alongshore distribution of property value. Along cuspate coastlines, such as those found along the U.S. Southeast Coast, these long-term property value differences span an order of magnitude. Results imply that spatially decentralized management of nourishment can lead to property values that are divorced from spatial erosion signals; this management approach is unlikely to be optimal.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Sea Level Budget along the East Coast of North America

NASA Astrophysics Data System (ADS)

Pease, A. M.; Davis, J. L.; Vinogradova, N. T.

2016-12-01

We analyzed tide gauge data, taken from 1955 to 2015, from 29 locations along the east coast of North America. A well-documented period of sea-level acceleration began around 1990. The sea level rate (referenced to epoch 1985.0) and acceleration (post-1990) are spatially and temporally variable, due to various physical processes, each of which is also spatially and temporally variable. To determine the sea-level budgets for rate and acceleration, we considered a number of major contributors to sea level change: ocean density and dynamics, glacial isostatic adjustment (GIA), the inverted barometer effect, and mass change associated with the Greenland Ice Sheet (GIS) and the Antarctic Ice Sheet (AIS). The geographic variability in the budgets for sea-level rate is dominated by GIA. At some sites, GIA is the largest contributor to the rate. The geographic variability in the budgets for sea-level acceleration is dominated by ocean dynamics and density and GIS mass loss. The figure below shows budgets for sea-level rate (left) and acceleration (right) for Key West, Fla., (top) and The Battery in New York City (bottom). The blue represents values (with error bar shown) estimated from tide gauge observations, and the yellow represents the total values estimated from the individual model contributions (each in red, green, cyan, pink, and black). The estimated totals for rate and acceleration are good matches to the tide-gauge inferences. To achieve a reasonable fit, a scaling factor (admittance) for the combined contribution of ocean dynamics and density was estimated; this admittance may reflect the low spatial sampling of the GECCO2 model we used, or other problems in modeling coastal sea-level. The significant contributions of mass loss to the acceleration enable us to predict that, if such mass-loss continues or increases, the character of sea-level change on the North American east coast will change in the next 50-100 years. In particular, whereas GIA presently dominates the spatial variability of sea-level change, mass loss from Greenland and Antarctica will dominate it by 2050-2100. However, the long-term contribution of ocean dynamics and density remain more of a question.

Spatial Variability of Trace Gases During DISCOVER-AQ: Planning for Geostationary Observations of Atmospheric Composition

NASA Technical Reports Server (NTRS)

Follette-Cook, Melanie B.; Pickering, K.; Crawford, J.; Appel, W.; Diskin, G.; Fried, A.; Loughner, C.; Pfister, G.; Weinheimer, A.

2015-01-01

Results from an in-depth analysis of trace gas variability in MD indicated that the variability in this region was large enough to be observable by a TEMPO-like instrument. The variability observed in MD is relatively similar to the other three campaigns with a few exceptions: CO variability in CA was much higher than in the other regions; HCHO variability in CA and CO was much lower; MD showed the lowest variability in NO2All model simulations do a reasonable job simulating O3 variability. For CO, the CACO simulations largely under over estimate the variability in the observations. The variability in HCHO is underestimated for every campaign. NO2 variability is slightly overestimated in MD, more so in CO. The TX simulation underestimates the variability in each trace gas. This is most likely due to missing emissions sources (C. Loughner, manuscript in preparation).Future Work: Where reasonable, we will use these model outputs to further explore the resolvability from space of these key trace gases using analyses of tropospheric column amounts relative to satellite precision requirements, similar to Follette-Cook et al. (2015).

Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products.

PubMed

Hu, Kexiang; Awange, Joseph L; Khandu; Forootan, Ehsan; Goncalves, Rodrigo Mikosz; Fleming, Kevin

2017-12-01

For Brazil, a country frequented by droughts and whose rural inhabitants largely depend on groundwater, reliance on isotope for its monitoring, though accurate, is expensive and limited in spatial coverage. We exploit total water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellites to analyse spatial-temporal groundwater changes in relation to geological characteristics. Large-scale groundwater changes are estimated using GRACE-derived TWS and altimetry observations in addition to GLDAS and WGHM model outputs. Additionally, TRMM precipitation data are used to infer impacts of climate variability on groundwater fluctuations. The results indicate that climate variability mainly controls groundwater change trends while geological properties control change rates, spatial distribution, and storage capacity. Granular rocks in the Amazon and Guarani aquifers are found to influence larger storage capability, higher permeability (>10 -4 m/s) and faster response to rainfall (1 to 3months' lag) compared to fractured rocks (permeability <10 -7 m/s and lags > 3months) found only in Bambui aquifer. Groundwater in the Amazon region is found to rely not only on precipitation but also on inflow from other regions. Areas beyond the northern and southern Amazon basin depict a 'dam-like' pattern, with high inflow and slow outflow rates (recharge slope > 0.75, discharge slope < 0.45). This is due to two impermeable rock layer-like 'walls' (permeability <10 -8 m/s) along the northern and southern Alter do Chão aquifer that help retain groundwater. The largest groundwater storage capacity in Brazil is the Amazon aquifer (with annual amplitudes of > 30cm). Amazon's groundwater declined between 2002 and 2008 due to below normal precipitation (wet seasons lasted for about 36 to 47% of the time). The Guarani aquifer and adjacent coastline areas rank second in terms of storage capacity, while the northeast and southeast coastal regions indicate the smallest storage capacity due to lack of rainfall (annual average is rainfall <10cm). Copyright © 2017 Elsevier B.V. All rights reserved.

Interactions Between Ice Thickness, Bottom Ice Algae, and Transmitted Spectral Irradiance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Arntsen, A. E.; Perovich, D. K.; Polashenski, C.; Stwertka, C.

2015-12-01

The amount of light that penetrates the Arctic sea ice cover impacts sea-ice mass balance as well as ecological processes in the upper ocean. The seasonally evolving macro and micro spatial variability of transmitted spectral irradiance observed in the Chukchi Sea from May 18 to June 17, 2014 can be primarily attributed to variations in snow depth, ice thickness, and bottom ice algae concentrations. This study characterizes the interactions among these dominant variables using observed optical properties at each sampling site. We employ a normalized difference index to compute estimates of Chlorophyll a concentrations and analyze the increased attenuation of incident irradiance due to absorption by biomass. On a kilometer spatial scale, the presence of bottom ice algae reduced the maximum transmitted irradiance by about 1.5 orders of magnitude when comparing floes of similar snow and ice thicknesses. On a meter spatial scale, the combined effects of disparities in the depth and distribution of the overlying snow cover along with algae concentrations caused maximum transmittances to vary between 0.0577 and 0.282 at a single site. Temporal variability was also observed as the average integrated transmitted photosynthetically active radiation increased by one order of magnitude to 3.4% for the last eight measurement days compared to the first nine. Results provide insight on how interrelated physical and ecological parameters of sea ice in varying time and space may impact new trends in Arctic sea ice extent and the progression of melt.

Investigation of Influential Factors for Bicycle Crashes Using a Spatiotemporal Model

NASA Astrophysics Data System (ADS)

Gill, G.; Sakrani, T.; Cheng, W.; Zhou, J.

2017-09-01

Despite the numerous potential advantages of indulging in bicycling, such as elevation of health and environment along with mitigation of congestion, the cyclists are a vulnerable group of commuters which is exposed to safety risks. This study aims to investigate the explanatory variables at transportation planning level which have a significant impact on the bicycle crashes. To account for the serial changes around the built environment, the linear time trend as well as time-varying coefficients are utilized for the covariates. These model modifications help account for the variations in the environment which may escape the incorporated variables due to lack of robustness in data. Also, to incorporate the interaction of roadway, demographic, and socioeconomic features within a Traffic Analysis Zone (TAZ), with the bicycle crashes of that area, a spatial correlation is integrated. This spatial correlation accounts for the spatially structured random effects which capture the unobserved heterogeneity and add towards building more comprehensive model with relatively precise estimates. Two different age groups, the student population in the TAZs, the presence of arterial roads and bike lanes, were observed to be statistically significant variables related with bicycle crashes. These observations will guide the transportation planning organizations which focus on the entity of TAZ while developing policies. The results of the current study establish a quantifies relationship between the significant factors and the crash count which will enable the planners to choose the most cost-efficient, yet most productive, factors which needs to be addressed for mitigation of crashes.

Generalised synthesis of space-time variability in flood response: Dynamics of flood event types

NASA Astrophysics Data System (ADS)

Viglione, Alberto; Battista Chirico, Giovanni; Komma, Jürgen; Woods, Ross; Borga, Marco; Blöschl, Günter

2010-05-01

A analytical framework is used to characterise five flood events of different type in the Kamp area in Austria: one long-rain event, two short-rain events, one rain-on-snow event and one snowmelt event. Specifically, the framework quantifies the contributions of the space-time variability of rainfall/snowmelt, runoff coefficient, hillslope and channel routing to the flood runoff volume and the delay and spread of the resulting hydrograph. The results indicate that the components obtained by the framework clearly reflect the individual processes which characterise the event types. For the short-rain events, temporal, spatial and movement components can all be important in runoff generation and routing, which would be expected because of their local nature in time and, particularly, in space. For the long-rain event, the temporal components tend to be more important for runoff generation, because of the more uniform spatial coverage of rainfall, while for routing the spatial distribution of the produced runoff, which is not uniform, is also important. For the rain-on-snow and snowmelt events, the spatio-temporal variability terms typically do not play much role in runoff generation and the spread of the hydrograph is mainly due to the duration of the event. As an outcome of the framework, a dimensionless response number is proposed that represents the joint effect of runoff coefficient and hydrograph peakedness and captures the absolute magnitudes of the observed flood peaks.

Quantifying space-time dynamics of flood event types

NASA Astrophysics Data System (ADS)

Viglione, Alberto; Chirico, Giovanni Battista; Komma, Jürgen; Woods, Ross; Borga, Marco; Blöschl, Günter

2010-11-01

SummaryA generalised framework of space-time variability in flood response is used to characterise five flood events of different type in the Kamp area in Austria: one long-rain event, two short-rain events, one rain-on-snow event and one snowmelt event. Specifically, the framework quantifies the contributions of the space-time variability of rainfall/snowmelt, runoff coefficient, hillslope and channel routing to the flood runoff volume and the delay and spread of the resulting hydrograph. The results indicate that the components obtained by the framework clearly reflect the individual processes which characterise the event types. For the short-rain events, temporal, spatial and movement components can all be important in runoff generation and routing, which would be expected because of their local nature in time and, particularly, in space. For the long-rain event, the temporal components tend to be more important for runoff generation, because of the more uniform spatial coverage of rainfall, while for routing the spatial distribution of the produced runoff, which is not uniform, is also important. For the rain-on-snow and snowmelt events, the spatio-temporal variability terms typically do not play much role in runoff generation and the spread of the hydrograph is mainly due to the duration of the event. As an outcome of the framework, a dimensionless response number is proposed that represents the joint effect of runoff coefficient and hydrograph peakedness and captures the absolute magnitudes of the observed flood peaks.

Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States

PubMed Central

Liu, Zhihua; Wimberly, Michael C.

2015-01-01

An improved understanding of the relative influences of climatic and landscape controls on multiple fire regime components is needed to enhance our understanding of modern fire regimes and how they will respond to future environmental change. To address this need, we analyzed the spatio-temporal patterns of fire occurrence, size, and severity of large fires (> 405 ha) in the western United States from 1984–2010. We assessed the associations of these fire regime components with environmental variables, including short-term climate anomalies, vegetation type, topography, and human influences, using boosted regression tree analysis. Results showed that large fire occurrence, size, and severity each exhibited distinctive spatial and spatio-temporal patterns, which were controlled by different sets of climate and landscape factors. Antecedent climate anomalies had the strongest influences on fire occurrence, resulting in the highest spatial synchrony. In contrast, climatic variability had weaker influences on fire size and severity and vegetation types were the most important environmental determinants of these fire regime components. Topography had moderately strong effects on both fire occurrence and severity, and human influence variables were most strongly associated with fire size. These results suggest a potential for the emergence of novel fire regimes due to the responses of fire regime components to multiple drivers at different spatial and temporal scales. Next-generation approaches for projecting future fire regimes should incorporate indirect climate effects on vegetation type changes as well as other landscape effects on multiple components of fire regimes. PMID:26465959

Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean Basins

NASA Technical Reports Server (NTRS)

Liu, Jianbo; Kummerow, Christian D.; Elsaesser, Gregory S.

2016-01-01

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.

Spatial Variability of Dissolved Organic Carbon in Headwater Wetlands in Central Pennsylvania

NASA Astrophysics Data System (ADS)

Reichert-Eberhardt, A. J.; Wardrop, D.; Boyer, E. W.

2011-12-01

Dissolved organic carbon (DOC) is known to be of an important factor in many microbially mediated biochemical processes, such as denitrification, that occur in wetlands. The spatial variability of DOC within a wetland could impact the microbes that fuel these processes, which in turn can affect the ecosystem services provided by wetlands. However, the amount of spatial variability of DOC in wetlands is generally unknown. Furthermore, it is unknown how disturbance to wetlands can affect spatial variability of DOC. Previous research in central Pennsylvania headwater wetland soils has shown that wetlands with increased human disturbance had decreased heterogeneity in soil biochemistry. To address groundwater chemical variability 20 monitoring wells were installed in a random pattern in a 400 meter squared plot in a low-disturbance headwater wetland and a high-disturbance headwater wetland in central Pennsylvania. Water samples from these wells will be analyzed for DOC, dissolved inorganic carbon, nitrate, ammonia, and sulfate concentrations, as well as pH, conductivity, and temperature on a seasonal basis. It is hypothesized that there will be greater spatial variability of groundwater chemistry in the low disturbance wetland than the high disturbance wetland. This poster will present the initial data concerning DOC spatial variability in both the low and high impact headwater wetlands.

Integration of Hydrogeophysical Datasets for Improved Water Resource Management in Irrigated Systems

NASA Astrophysics Data System (ADS)

Finkenbiner, C. E.; Franz, T. E.; Heeren, D.; Gibson, J. P.; Russell, M. V.

2016-12-01

With an average irrigation water use efficiency of approximately 45% in the United States, improvements in water management can be made within agricultural systems. Advancements in precision irrigation technologies allow application rates and times to vary within a field. Current limitations in applying these technologies are often attributed to the quantification of soil spatial variability. This work aims to increase our understanding of soil hydrologic fluxes at intermediate spatial scales. Field capacity and wilting point values for a field near Sutherland, NE were downloaded from the USDA SSURGO database. Stationary and roving cosmic-ray neutron probes (CRNP) (sensor measurement volume of 300 m radius sphere and 30 cm vertical soil depth) were combined in order to characterize the spatial and temporal patterns of soil moisture at the site. We used a 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 ( 102 m2) for variable rate irrigation, the individual wedge ( 103 m2) for variable speed irrigation, and the quarter section (0.82 km2) for uniform rate irrigation. The results show our CRNP "observed" field capacity was higher compared to the SSURGO products. The measured hydraulic properties from sixty-two soil cores collected from the field correlate well with our "observed" CRNP values. 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 depths and times in relation to soil moisture depletion below field capacity and above maximum allowable depletion. The incorporation of the CRNP into current irrigation practices has the potential to greatly increase agricultural water use efficiency. Moreover, the defined soil hydraulic properties at various spatial scales offers additional valuable datasets for the land surface modeling community.

Utilizing NASA DISCOVER-AQ Data to Examine Spatial Gradients in Complex Emission Environments

NASA Astrophysics Data System (ADS)

Buzanowicz, M. E.; Moore, W.; Crawford, J. H.; Schroeder, J.

2017-12-01

Although many regulations have been enacted with the goal of improving air quality, many parts of the US are still classified as `non-attainment areas' because they frequently violate federal air quality standards. Adequately monitoring the spatial distribution of pollutants both within and outside of non-attainment areas has been an ongoing challenge for regulators. Observations of near-surface pollution from space-based platforms would provide an unprecedented view of the spatial distribution of pollution, but this goal has not yet been realized due to fundamental limitations of satellites, specifically because the footprint size of satellite measurements may not be sufficiently small enough to capture true gradients in pollution, and rather represents an average over a large area. NASA's DISCOVER-AQ was a multi-year field campaign aimed at improving our understanding of the role that remote sensing, including satellite-based remote sensing, could play in air quality monitoring systems. DISCOVER-AQ data will be utilized to create a metric to examine spatial gradients and how satellites can capture those gradients in areas with complex emission environments. Examining horizontal variability within a vertical column is critical to understanding mixing within the atmosphere. Aircraft spirals conducted during DISCOVER-AQ were divided into octants, and averages of a given a species were calculated, with certain points receiving a flag. These flags were determined by calculating gradients between subsequent octants. Initial calculations have shown that over areas with large point source emissions, such as Platteville and Denver-La Casa in Colorado, and Essex, Maryland, satellite retrievals may not adequately capture spatial variability in the atmosphere, thus complicating satellite inversion techniques and limiting our ability to understand human exposure on sub-grid scales. Further calculations at other locations and for other trace gases are necessary to determine the effects of vertical variability within the atmosphere.

Spatio-temporal representativeness of ground-based downward solar radiation measurements

NASA Astrophysics Data System (ADS)

Schwarz, Matthias; Wild, Martin; Folini, Doris

2017-04-01

Surface solar radiation (SSR) is most directly observed with ground based pyranometer measurements. Besides measurement uncertainties, which arise from the pyranometer instrument itself, also errors attributed to the limited spatial representativeness of observations from single sites for their large-scale surrounding have to be taken into account when using such measurements for energy balance studies. In this study the spatial representativeness of 157 homogeneous European downward surface solar radiation time series from the Global Energy Balance Archive (GEBA) and the Baseline Surface Radiation Network (BSRN) were examined for the period 1983-2015 by using the high resolution (0.05°) surface solar radiation data set from the Satellite Application Facility on Climate Monitoring (CM-SAF SARAH) as a proxy for the spatiotemporal variability of SSR. By correlating deseasonalized monthly SSR time series form surface observations against single collocated satellite derived SSR time series, a mean spatial correlation pattern was calculated and validated against purely observational based patterns. Generally decreasing correlations with increasing distance from station, with high correlations (R2 = 0.7) in proximity to the observational sites (±0.5°), was found. When correlating surface observations against time series from spatially averaged satellite derived SSR data (and thereby simulating coarser and coarser grids), very high correspondence between sites and the collocated pixels has been found for pixel sizes up to several degrees. Moreover, special focus was put on the quantification of errors which arise in conjunction to spatial sampling when estimating the temporal variability and trends for a larger region from a single surface observation site. For 15-year trends on a 1° grid, errors due to spatial sampling in the order of half of the measurement uncertainty for monthly mean values were found.

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.

Organic carbon stock modelling for the quantification of the carbon sinks in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Durante, Pilar; Algeet, Nur; Oyonarte, Cecilio

2017-04-01

Given the recent environmental policies derived from the serious threats caused by global change, practical measures to decrease net CO2 emissions have to be put in place. Regarding this, carbon sequestration is a major measure to reduce atmospheric CO2 concentrations within a short and medium term, where terrestrial ecosystems play a basic role as carbon sinks. Development of tools for quantification, assessment and management of organic carbon in ecosystems at different scales and management scenarios, it is essential to achieve these commitments. The aim of this study is to establish a methodological framework for the modeling of this tool, applied to a sustainable land use planning and management at spatial and temporal scale. The methodology for carbon stock estimation in ecosystems is based on merger techniques between carbon stored in soils and aerial biomass. For this purpose, both spatial variability map of soil organic carbon (SOC) and algorithms for calculation of forest species biomass will be created. For the modelling of the SOC spatial distribution at different map scales, it is necessary to fit in and screen the available information of soil database legacy. Subsequently, SOC modelling will be based on the SCORPAN model, a quantitative model use to assess the correlation among soil-forming factors measured at the same site location. These factors will be selected from both static (terrain morphometric variables) and dynamic variables (climatic variables and vegetation indexes -NDVI-), providing to the model the spatio-temporal characteristic. After the predictive model, spatial inference techniques will be used to achieve the final map and to extrapolate the data to unavailable information areas (automated random forest regression kriging). The estimated uncertainty will be calculated to assess the model performance at different scale approaches. Organic carbon modelling of aerial biomass will be estimate using LiDAR (Light Detection And Ranging) algorithms. The available LiDAR databases will be used. LiDAR statistics (which describe the LiDAR cloud point data to calculate forest stand parameters) will be correlated with different canopy cover variables. The regression models applied to the total area will produce a continuous geo-information map to each canopy variable. The CO2 estimation will be calculated by dry-mass conversion factors for each forest species (C kg-CO2 kg equivalent). The result is the organic carbon modelling at spatio-temporal scale with different levels of uncertainty associated to the predictive models and diverse detailed scales. However, one of the main expected problems is due to the heterogeneous spatial distribution of the soil information, which influences on the prediction of the models at different spatial scales and, consequently, at SOC map scale. Besides this, the variability and mixture of the forest species of the aerial biomass decrease the accuracy assessment of the organic carbon.

Spatiotemporal variation in heat-related out-of-hospital cardiac arrest during the summer in Japan.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2017-04-01

Although several studies have reported the impacts of extremely high temperature on cardiovascular diseases, few studies have investigated the spatiotemporal variation in the incidence of out-of-hospital cardiac arrest (OHCA) due to extremely high temperature in Japan. Daily OHCA data from 2005 to 2014 were acquired from all 47 prefectures of Japan. We used time-series Poisson regression analysis combined with a distributed lag non-linear model to assess the temporal variability in the effects of extremely high temperature on OHCA incidence in each prefecture, adjusted for time trends. Spatial variability in the relationships between extremely high temperature and OHCA between prefectures was estimated using a multivariate random-effects meta-analysis. We analyzed 166,496 OHCA cases of presumed cardiac origin occurring during the summer (June to September) that met the inclusion criteria. The minimum morbidity percentile (MMP) was the 51st percentile of temperature during the summer in Japan. The overall cumulative relative risk at the 99th percentile vs. the MMP over lags 0-10days was 1.21 (95% CI: 1.12-1.31). There was also a strong low temperature effect during the summer periods. No substantial difference in spatial or temporal variability was observed over the study period. Our study demonstrated spatiotemporal homogeneity in the risk of OHCA during periods of extremely high temperature between 2005 and 2014 in Japan. Our findings suggest that public health strategies for OHCA due to extremely high temperatures should be finely adjusted and should particularly account for the unchanging risk during the summer. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii

Treesearch

Lisa M. Ellsworth; Creighton M. Litton; Andrew D. Taylor; J. Boone Kauffman

2013-01-01

Frequent wildfires in tropical landscapes dominated by non-native invasive grasses threaten surrounding ecosystems and developed areas. To better manage fire, accurate estimates of the spatial and temporal variability in fuels are urgently needed. We quantified the spatial variability in live and dead fine fuel loads and moistures at four guinea grass (...

Evaluating shrub-associated spatial patterns of soil properties in a shrub-steppe ecosystem using multiple-variable geostatistics

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

Halvorson, J.J.; Smith, J.L.; Bolton, H. Jr.

1995-09-01

Geostatistics are often calculated for a single variable at a time, even though many natural phenomena are functions of several variables. The objective of this work was to demonstrate a nonparametric approach for assessing the spatial characteristics of multiple-variable phenomena. Specifically, we analyzed the spatial characteristics of resource islands in the soil under big sagebrush (Artemisia tridentala Nutt.), a dominant shrub in the intermountain western USA. For our example, we defined resource islands as a function of six soil variables representing concentrations of soil resources, populations of microorganisms, and soil microbial physiological variables. By collectively evaluating the indicator transformations ofmore » these individual variables, we created a new data set, termed a multiple-variable indicator transform or MVIT. Alternate MVITs were obtained by varying the selection criteria. Each MVIT was analyzed with variography to characterize spatial continuity, and with indicator kriging to predict the combined probability of their occurrence at unsampled locations in the landscape. Simple graphical analysis and variography demonstrated spatial dependence for all individual soil variables. Maps derived from ordinary kriging of MVITs suggested that the combined probabilities for encountering zones of above-median resources were greatest near big sagebrush. 51 refs., 5 figs., 1 tab.« less

Floodplain complexity and surface metrics: influences of scale and geomorphology

USGS Publications Warehouse

Scown, Murray W.; Thoms, Martin C.; DeJager, Nathan R.

2015-01-01

Many studies of fluvial geomorphology and landscape ecology examine a single river or landscape, thus lack generality, making it difficult to develop a general understanding of the linkages between landscape patterns and larger-scale driving variables. We examined the spatial complexity of eight floodplain surfaces in widely different geographic settings and determined how patterns measured at different scales relate to different environmental drivers. Floodplain surface complexity is defined as having highly variable surface conditions that are also highly organised in space. These two components of floodplain surface complexity were measured across multiple sampling scales from LiDAR-derived DEMs. The surface character and variability of each floodplain were measured using four surface metrics; namely, standard deviation, skewness, coefficient of variation, and standard deviation of curvature from a series of moving window analyses ranging from 50 to 1000 m in radius. The spatial organisation of each floodplain surface was measured using spatial correlograms of the four surface metrics. Surface character, variability, and spatial organisation differed among the eight floodplains; and random, fragmented, highly patchy, and simple gradient spatial patterns were exhibited, depending upon the metric and window size. Differences in surface character and variability among the floodplains became statistically stronger with increasing sampling scale (window size), as did their associations with environmental variables. Sediment yield was consistently associated with differences in surface character and variability, as were flow discharge and variability at smaller sampling scales. Floodplain width was associated with differences in the spatial organization of surface conditions at smaller sampling scales, while valley slope was weakly associated with differences in spatial organisation at larger scales. A comparison of floodplain landscape patterns measured at different scales would improve our understanding of the role that different environmental variables play at different scales and in different geomorphic settings.

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

Assessing the Importance of Incorporating Spatial and Temporal Variability of Soil and Plant Parameters into Local Water Balance Models for Precision Agriculture: Investigations within a California Vineyard

NASA Astrophysics Data System (ADS)

Hubbard, S.; Pierce, L.; Grote, K.; Rubin, Y.

2003-12-01

Due Due to the high cash crop nature of premium winegrapes, recent research has focused on developing a better understanding of the factors that influence winegrape spatial and temporal variability. Precision grapevine irrigation schemes require consideration of the factors that regulate vineyard water use such as (1) plant parameters, (2) climatic conditions, and (3) water availability in the soil as a function of soil texture. The inability to sample soil and plant parameters accurately, at a dense enough resolution, and over large enough areas has limited previous investigations focused on understanding the influences of soil water and vegetation on water balance at the local field scale. We have acquired several novel field data sets to describe the small scale (decimeters to a hundred meters) spatial variability of soil and plant parameters within a 4 acre field study site at the Robert Mondavi Winery in Napa County, California. At this site, we investigated the potential of ground penetrating radar data (GPR) for providing estimates of near surface water content. Calibration of grids of 900 MHz GPR groundwave data with conventional soil moisture measurements revealed that the GPR volumetric water content estimation approach was valid to within 1 percent accuracy, and that the data grids provided unparalleled density of soil water content over the field site as a function of season. High-resolution airborne multispectral remote sensing data was also collected at the study site, which was converted to normalized difference vegetation index (NDVI) and correlated to leaf area index (LAI) using plant-based measurements within a parallel study. Meteorological information was available from a weather station of the California Irrigation management Information System, located less than a mile from our study area. The measurements were used within a 2-D Vineyard Soil Irrigation Model (VSIM), which can incorporate the spatially variable, high-resolution soil and plant-based information. VSIM, which is based on the concept that equilibrium exists between climate, soils, and LAI, was used to simulate vine water stress, water use, and irrigation requirements during a single year for the site. Using the simple water-balance model with the dense characterization data, we will discuss: (1) the ability to predict vineyard soil water content at the small scales of soil heterogeneity that are observed in nature at the local-scale, (2) the relative importance of plant, climate, and soil information to predictions of the soil water balance at the site, (3) the influence of crop cover in the water balance predictions.

Instrumenting an upland research catchment in Canterbury, New Zealand to study controls on variability of soil moisture, shallow groundwater and streamflow

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Srinivasan, Ms

2015-04-01

Hydrologists recognise the importance of vertical drainage and deep flow paths in runoff generation, even in headwater catchments. Both soil and groundwater stores are highly variable over multiple scales, and the distribution of water has a strong control on flow rates and timing. In this study, we instrumented an upland headwater catchment in New Zealand to measure the temporal and spatial variation in unsaturated and saturated-zone responses. In NZ, upland catchments are the source of much of the water used in lowland agriculture, but the hydrology of such catchments and their role in water partitioning, storage and transport is poorly understood. The study area is the Langs Gully catchment in the North Branch of the Waipara River, Canterbury: this catchment was chosen to be representative of the foothills environment, with lightly managed dryland pasture and native Matagouri shrub vegetation cover. Over a period of 16 months we measured continuous soil moisture at 32 locations and near-surface water table (< 2 m) at 14 locations, as well as measuring flow at 3 stream gauges. The distributed measurement sites were located to allow comparisons between North and South facing locations, near-stream versus hillslope locations, and convergent versus divergent hillslopes. We found that temporal variability is strongly controlled by the climatic seasonal cycle, for both soil moisture and water table, and for both the mean and extremes of their distributions. Groundwater is a larger water storage component than soil moisture, and the difference increases with catchment wetness. The spatial standard deviation of both soil moisture and groundwater is larger in winter than in summer. It peaks during rainfall events due to partial saturation of the catchment, and also rises in spring as different locations dry out at different rates. The most important controls on spatial variability are aspect and distance from stream. South-facing and near-stream locations have higher water tables and more, larger soil moisture wetting events. Typical hydrological models do not explicitly account for aspect, but our results suggest that it is an important factor in hillslope runoff generation. Co-measurement of soil moisture and water table level allowed us to identify interrelationships between the two. Locations where water tables peaked closest to the surface had consistently wetter soils and higher water tables. These wetter sites were the same across seasons. However, temporary patterns of strong soil moisture response to summer storms did not correspond to the wetter sites. Total catchment spatial variability is composed of multiple variability sources, and the dominant type is sensitive to those stores that are close to a threshold such as field capacity or saturation. Therefore, we classified spatial variability as 'summer mode' or 'winter mode'. In summer mode, variability is controlled by shallow processes e.g. interactions of water with soils and vegetation. In winter mode, variability is controlled by deeper processes e.g. groundwater movement and bypass flow. Double flow peaks observed during some events show the direct impact of groundwater variability on runoff generation. Our results suggest that emergent catchment behaviour depends on the combination of these multiple, time varying components of variability.

Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals.

PubMed

Nijhof, Carl O P; Huijbregts, Mark A J; Golsteijn, Laura; van Zelm, Rosalie

2016-04-01

We compared the influence of spatial variability in environmental characteristics and the uncertainty in measured substance properties of seven chemicals on freshwater fate factors (FFs), representing the residence time in the freshwater environment, and on exposure factors (XFs), representing the dissolved fraction of a chemical. The influence of spatial variability was quantified using the SimpleBox model in which Europe was divided in 100 × 100 km regions, nested in a regional (300 × 300 km) and supra-regional (500 × 500 km) scale. Uncertainty in substance properties was quantified by means of probabilistic modelling. Spatial variability and parameter uncertainty were expressed by the ratio k of the 95%ile and 5%ile of the FF and XF. Our analysis shows that spatial variability ranges in FFs of persistent chemicals that partition predominantly into one environmental compartment was up to 2 orders of magnitude larger compared to uncertainty. For the other (less persistent) chemicals, uncertainty in the FF was up to 1 order of magnitude larger than spatial variability. Variability and uncertainty in freshwater XFs of the seven chemicals was negligible (k < 1.5). We found that, depending on the chemical and emission scenario, accounting for region-specific environmental characteristics in multimedia fate modelling, as well as accounting for parameter uncertainty, can have a significant influence on freshwater fate factor predictions. Therefore, we conclude that it is important that fate factors should not only account for parameter uncertainty, but for spatial variability as well, as this further increases the reliability of ecotoxicological impacts in LCA. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

PubMed

Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

2018-03-01

There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

Global linkages between teleconnection patterns and the terrestrial biosphere

NASA Astrophysics Data System (ADS)

Dahlin, Kyla M.; Ault, Toby R.

2018-07-01

Interannual variability in the global carbon cycle is largely due to variations in carbon uptake by terrestrial ecosystems, yet linkages between climate variability and variability in the terrestrial carbon cycle are not well understood at the global scale. Using a 30-year satellite record of semi-monthly leaf area index (LAI), we show that four modes of climate variability - El Niño/Southern Oscillation, the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Indian Ocean Dipole Mode - strongly impact interannual vegetation growth patterns, with 68% of the land surface impacted by at least one of these teleconnection patterns, yet the spatial distribution of these impacts is heterogeneous. Considering the patterns' impacts by biome, none has an exclusively positive or negative relationship with LAI. Our findings imply that future changes in the frequency and/or magnitude of teleconnection patterns will lead to diverse changes to the terrestrial biosphere and the global carbon cycle.

Local oceanographic variability influences the performance of juvenile abalone under climate change.

PubMed

Boch, C A; Micheli, F; AlNajjar, M; Monismith, S G; Beers, J M; Bonilla, J C; Espinoza, A M; Vazquez-Vera, L; Woodson, C B

2018-04-03

Climate change is causing warming, deoxygenation, and acidification of the global ocean. However, manifestation of climate change may vary at local scales due to oceanographic conditions. Variation in stressors, such as high temperature and low oxygen, at local scales may lead to variable biological responses and spatial refuges from climate impacts. We conducted outplant experiments at two locations separated by ~2.5 km and two sites at each location separated by ~200 m in the nearshore of Isla Natividad, Mexico to assess how local ocean conditions (warming and hypoxia) may affect juvenile abalone performance. Here, we show that abalone growth and mortality mapped to variability in stress exposure across sites and locations. These insights indicate that management decisions aimed at maintaining and recovering valuable marine species in the face of climate change need to be informed by local variability in environmental conditions.

Ionospheric responses during equinox and solstice periods over Turkey

NASA Astrophysics Data System (ADS)

Karatay, Secil; Cinar, Ali; Arikan, Feza

2017-11-01

Ionospheric electron density is the determining variable for investigation of the spatial and temporal variations in the ionosphere. Total Electron Content (TEC) is the integral of the electron density along a ray path that indicates the total variability through the ionosphere. Global Positioning System (GPS) recordings can be utilized to estimate the TEC, thus GPS proves itself as a useful tool in monitoring the total variability of electron distribution within the ionosphere. This study focuses on the analysis of the variations of ionosphere over Turkey that can be grouped into anomalies during equinox and solstice periods using TEC estimates obtained by a regional GPS network. It is observed that noon time depletions in TEC distributions predominantly occur in winter for minimum Sun Spots Numbers (SSN) in the central regions of Turkey which also exhibit high variability due to midlatitude winter anomaly. TEC values and ionospheric variations at solstice periods demonstrate significant enhancements compared to those at equinox periods.

Disentangling environmental correlates of vascular plant biodiversity in a Mediterranean hotspot.

PubMed

Molina-Venegas, Rafael; Aparicio, Abelardo; Pina, Francisco José; Valdés, Benito; Arroyo, Juan

2013-10-01

We determined the environmental correlates of vascular plant biodiversity in the Baetic-Rifan region, a plant biodiversity hotspot in the western Mediterranean. A catalog of the whole flora of Andalusia and northern Morocco, the region that includes most of the Baetic-Rifan complex, was compiled using recent comprehensive floristic catalogs. Hierarchical cluster analysis (HCA) and detrended correspondence analysis (DCA) of the different ecoregions of Andalusia and northern Morocco were conducted to determine their floristic affinities. Diversity patterns were studied further by focusing on regional endemic taxa. Endemic and nonendemic alpha diversities were regressed to several environmental variables. Finally, semi-partial regressions on distance matrices were conducted to extract the respective contributions of climatic, altitudinal, lithological, and geographical distance matrices to beta diversity in endemic and nonendemic taxa. We found that West Rifan plant assemblages had more similarities with Andalusian ecoregions than with other nearby northern Morocco ecoregions. The endemic alpha diversity was explained relatively well by the environmental variables related to summer drought and extreme temperature values. Of all the variables, geographical distance contributed by far the most to spatial turnover in species diversity in the Baetic-Rifan hotspot. In the Baetic range, elevation was the most significant driver of nonendemic species beta diversity, while lithology and elevation were the main drivers of endemic beta diversity. Despite the fact that Andalusia and northern Morocco are presently separated by the Atlantic Ocean and the Mediterranean Sea, the Baetic and Rifan mountain ranges have many floristic similarities - especially in their western ranges - due to past migration of species across the Strait of Gibraltar. Climatic variables could be shaping the spatial distribution of endemic species richness throughout the Baetic-Rifan hotspot. Determinants of spatial turnover in biodiversity in the Baetic-Rifan hotspot vary in importance between endemic and nonendemic species.

Detecting changes resulting from human pressure in a naturally quick-changing and heterogeneous environment: Spatial and temporal scales of variability in coastal lagoons

NASA Astrophysics Data System (ADS)

Pérez-Ruzafa, A.; Marcos, C.; Pérez-Ruzafa, I. M.; Barcala, E.; Hegazi, M. I.; Quispe, J.

2007-10-01

To detect changes in ecosystems due to human impact, experimental designs must include replicates at the appropriate scale to avoid pseudoreplication. Although coastal lagoons, with their highly variable environmental factors and biological assemblages, are relatively well-studied systems, very little is known about their natural scales of variation. In this study, we investigate the spatio-temporal scales of variability in the Mar Menor coastal lagoon (SE Spain) using structured hierarchical sampling designs, mixed and permutational multi-variate analyses of variance, and ordination multi-variate analyses applied to hydrographical parameters, nutrients, chlorophyll a and ichthyoplankton in the water column, and to macrophyte and fish benthic assemblages. Lagoon processes in the Mar Menor show heterogeneous patterns at different temporal and spatial scales. The water column characteristics (including nutrient concentration) showed small-scale spatio-temporal variability, from 10 0 to 10 1 km and from fortnightly to seasonally. Biological features (chlorophyll a concentration and ichthyoplankton assemblage descriptors) showed monthly changes and spatial patterns at the scale of 10 0 (chlorophyll a) - 10 1 km (ichthyoplankton). Benthic assemblages (macrophytes and fishes) showed significant differences between types of substrates in the same locality and between localities, according to horizontal gradients related with confinement in the lagoon, at the scale of 10 0-10 1 km. The vertical zonation of macrophyte assemblages (at scales of 10 1-10 2 cm) overlaps changes in substrata and horizontal gradients. Seasonal patterns in vegetation biomass were not significant, but the significant interaction between Locality and Season indicated that the seasons of maximum and minimum biomass depend on local environmental conditions. Benthic fish assemblages showed no significant patterns at the monthly scale but did show seasonal patterns.

Statistical and Conceptual Model Testing Geomorphic Principles through Quantification in the Middle Rio Grande River, NM.

NASA Astrophysics Data System (ADS)

Posner, A. J.

2017-12-01

The Middle Rio Grande River (MRG) traverses New Mexico from Cochiti to Elephant Butte reservoirs. Since the 1100s, cultivating and inhabiting the valley of this alluvial river has required various river training works. The mid-20th century saw a concerted effort to tame the river through channelization, Jetty Jacks, and dam construction. A challenge for river managers is to better understand the interactions between a river training works, dam construction, and the geomorphic adjustments of a desert river driven by spring snowmelt and summer thunderstorms carrying water and large sediment inputs from upstream and ephemeral tributaries. Due to its importance to the region, a vast wealth of data exists for conditions along the MRG. The investigation presented herein builds upon previous efforts by combining hydraulic model results, digitized planforms, and stream gage records in various statistical and conceptual models in order to test our understanding of this complex system. Spatially continuous variables were clipped by a set of river cross section data that is collected at decadal intervals since the early 1960s, creating a spatially homogenous database upon which various statistical testing was implemented. Conceptual models relate forcing variables and response variables to estimate river planform changes. The developed database, represents a unique opportunity to quantify and test geomorphic conceptual models in the unique characteristics of the MRG. The results of this investigation provides a spatially distributed characterization of planform variable changes, permitting managers to predict planform at a much higher resolution than previously available, and a better understanding of the relationship between flow regime and planform changes such as changes to longitudinal slope, sinuosity, and width. Lastly, data analysis and model interpretation led to the development of a new conceptual model for the impact of ephemeral tributaries in alluvial rivers.

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data*

PubMed Central

Peng, Dai-liang; Huang, Jing-feng; Huete, Alfredo R.; Yang, Tai-ming; Gao, Ping; Chen, Yan-chun; Chen, Hui; Li, Jun; Liu, Zhan-yu

2010-01-01

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP. PMID:20349524

Mapping canopy gap fraction and leaf area index at continent-scale from satellite lidar

NASA Astrophysics Data System (ADS)

Mahoney, C.; Hopkinson, C.; Held, A. A.

2015-12-01

Information on canopy cover is essential for understanding spatial and temporal variability in vegetation biomass, local meteorological processes and hydrological transfers within vegetated environments. Gap fraction (GF), an index of canopy cover, is often derived over large areas (100's km2) via airborne laser scanning (ALS), estimates of which are reasonably well understood. However, obtaining country-wide estimates is challenging due to the lack of spatially distributed point cloud data. The Geoscience Laser Altimeter System (GLAS) removes spatial limitations, however, its large footprint nature and continuous waveform data measurements make derivations of GF challenging. ALS data from 3 Australian sites are used as a basis to scale-up GF estimates to GLAS footprint data by the use of a physically-based Weibull function. Spaceborne estimates of GF are employed in conjunction with supplementary predictor variables in the predictive Random Forest algorithm to yield country-wide estimates at a 250 m spatial resolution; country-wide estimates are accompanied with uncertainties at the pixel level. Preliminary estimates of effective Leaf Area Index (eLAI) are also presented by converting GF via the Beer-Lambert law, where an extinction coefficient of 0.5 is employed; deemed acceptable at such spatial scales. The need for such wide-scale quantification of GF and eLAI are key in the assessment and modification of current forest management strategies across Australia. Such work also assists Australia's Terrestrial Ecosystem Research Network (TERN), a key asset to policy makers with regards to the management of the national ecosystem, in fulfilling their government issued mandates.

High Resolution Mapping of Wetland Ecosystems SPOT-5 Take 5 for Evaluation of Sentinel-2

NASA Astrophysics Data System (ADS)

Ade, Christiana; Hestir, Erin L.; Khanna, Shruti; Ustin, Susan L.

2016-08-01

Around the world wetlands are critical to human societies and ecosystems, providing services such as habitat, water, food and fiber, flood and nutrient control, and cultural, recreational and religious value. However, the dynamic nature of tidal wetlands makes measuring ecosystem responses to climate change, seasonal inundation regimes, and anthropogenic disturbance from current and previous Earth observing sensors challenging due to limited spatial and temporal resolutions. Sentinel- 2 will directly address this challenge by providing high spatial resolution data with frequent revisit time. This pilot study aims to develop methodology for future Sentinel-2 products and highlight the variability of tidal wetland ecosystems, thereby demonstrating the necessity of improved spatial particularly temporal resolution. Here the simulated Sentinel-2 dataset from the SPOT-5 Take 5 experiment reveals the capacity of the new sensor to simultaneously assess tidal wetland ecosystem phenology and water quality in inland waters.

Spatial variability of heavy metals in the coastal soils under long-term reclamation

NASA Astrophysics Data System (ADS)

Wang, Lin; Coles, Neil A.; Wu, Chunfa; Wu, Jiaping

2014-12-01

The coastal plain of Cixi City, China, has experienced over 1000 years of reclamation. With the rapid development of agriculture and industry after reclamation, successive inputs into agricultural soils have drastically modified the soil environment. To determine the spatial distribution of heavy metals and to evaluate the influence of anthropogenic activities, a total of 329 top soil samples were taken along a transect on the coastal plain. The samples collected across 11 sea dikes, were selected by a nested sampling methodology. Total Cu, Fe, Mn, Ni, Pb, and Zn concentrations, as well as their diethylenetriamine penta-acetic acid (DTPA) extractable (available) concentrations were determined. Results indicated that except for Zn concentrations, there was neither heavy metals pollution nor mineral deficiency in the soils. Heavy metals exhibited considerable spatial variability, obvious spatial dependence, and close relationships on the reclaimed land. For most metals, the reclamation history was the main influencing factor. Metals concentrations generally showed discontinuities around the position of sea dikes, and the longer reclamation histories tended to have higher metals concentrations than the recently reclaimed sectors. As for Cu and Zn total concentrations, stochastic factors, like industrial waste discharge, fertilization and pesticide application, probably led to the high nugget effect and altered this relationship. The 6th and 10th zones generally had the highest total metals concentrations, due to the concentration of household appliance manufacturers in these reclaimed areas. The first two zones were characterized by high available metals concentrations, probably due to the alternant flooding and emergence, low pH values and high organic matter contents in these paddy field soils. From the 3rd to 7th zones with the same land use history and soil type, metals concentrations, especially available concentrations, showed homogeneity. The nested sampling method adopted demonstrated that the 500-m interval was enough to capture the spatial variation of the metals. These results were useful in evaluating the variation in the environment quality of the soils under long-term reclamation and to formulate plans for future reclamation projects.

The spatial variable glacier mass loss over the southeast Tibet Plateau and the climate cause analyses

NASA Astrophysics Data System (ADS)

Ke, L.; Ding, X.; Song, C.; Sheng, Y.

2016-12-01

Temperate glaciers can be highly sensitive to global climate change due to relatively humid and warm local climate. Numerous temperate glaciers are distributed in the southeastern Tibet Plateau (SETP) and their changes are still poorly represented. Based on a latest glacier inventory and ICESat altimetry measurements, we examine the spatial heterogeneity of glacier change in the SETP (including the central and eastern Nyainqêntanglha ranges) and further analyze its relation with climate change by using station-based and gridded meteorological data. Our results show that SETP glaciers experienced drastic surface lowering at about -0.84±0.26 m a-1 on average over 2003-2008. Debris-covered ice thinned at an average rate of -1.13±0.32 m a-1, in comparison with -0.92±0.17 m a-1 over the debris-free ice areas. The thinning rate is the strongest in the southeastern sub-region (up to -1.24 m a-1 ) and moderate ( -0.45 m a-1 ) in the central and northwestern parts, which is in general agreement with the pattern of surface mass changes based on the GRACE gravimetry observation. Long-term climate data at weather stations show that, in comparison with the period of 1992-2002, mean temperature increased by 0.46 °C - 0.59 °C in the recent decade (2003-2013); while the change of summer precipitation exhibited remarkably spatial variability, following a southeast-northwest contrasting pattern (decreasing by over 10% in the southeast, to stable level in the central region, and increment up to 10% in the northwest). This spatially variable precipitation change is consistent with results from CN05 grid data and ERA re-analysis data, and agrees well with the spatial pattern of glacier surface elevation changes. The results suggest that overall negative glacier mass balances in SETP are governed by temperature rising, while the different precipitation change could contribute to inconsistent glacier thinning rates. The spatial pattern of precipitation decrease and mass loss might be tele-connected with the dynamics of the Indian summer monsoon.

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.

Can APEX Represent In-Field Spatial Variability and Simulate Its Effects On Crop Yields?

USDA-ARS?s Scientific Manuscript database

Precision agriculture, from variable rate nitrogen application to precision irrigation, promises improved management of resources by considering the spatial variability of topography and soil properties. Hydrologic models need to simulate the effects of this variability if they are to inform about t...

Association between climate variability and malaria epidemics in the East African highlands.

PubMed

Zhou, Guofa; Minakawa, Noboru; Githeko, Andrew K; Yan, Guiyun

2004-02-24

The causes of the recent reemergence of Plasmodium falciparum epidemic malaria in the East African highlands are controversial. Regional climate changes have been invoked as a major factor; however, assessing the impact of climate in malaria resurgence is difficult due to high spatial and temporal climate variability and the lack of long-term data series on malaria cases from different sites. Climate variability, defined as short-term fluctuations around the mean climate state, may be epidemiologically more relevant than mean temperature change, but its effects on malaria epidemics have not been rigorously examined. Here we used nonlinear mixed-regression model to investigate the association between autoregression (number of malaria outpatients during the previous time period), seasonality and climate variability, and the number of monthly malaria outpatients of the past 10-20 years in seven highland sites in East Africa. The model explained 65-81% of the variance in the number of monthly malaria outpatients. Nonlinear and synergistic effects of temperature and rainfall on the number of malaria outpatients were found in all seven sites. The net variance in the number of monthly malaria outpatients caused by autoregression and seasonality varied among sites and ranged from 18 to 63% (mean=38.6%), whereas 12-63% (mean=36.1%) of variance is attributed to climate variability. Our results suggest that there was a high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in the highlands, and that climate variability played an important role in initiating malaria epidemics in the East African highlands.

Spatially Resolved Measurements of CO2 and CH4 Concentration and Gas-Exchange Velocity Highly Influence Carbon-Emission Estimates of Reservoirs

PubMed Central

2017-01-01

The magnitude of diffusive carbon dioxide (CO2) and methane (CH4) emission from man-made reservoirs is uncertain because the spatial variability generally is not well-represented. Here, we examine the spatial variability and its drivers for partial pressure, gas-exchange velocity (k), and diffusive flux of CO2 and CH4 in three tropical reservoirs using spatially resolved measurements of both gas concentrations and k. We observed high spatial variability in CO2 and CH4 concentrations and flux within all three reservoirs, with river inflow areas generally displaying elevated CH4 concentrations. Conversely, areas close to the dam are generally characterized by low concentrations and are therefore not likely to be representative for the whole system. A large share (44–83%) of the within-reservoir variability of gas concentration was explained by dissolved oxygen, pH, chlorophyll, water depth, and within-reservoir location. High spatial variability in k was observed, and kCH4 was persistently higher (on average, 2.5 times more) than kCO2. Not accounting for the within-reservoir variability in concentrations and k may lead to up to 80% underestimation of whole-system diffusive emission of CO2 and CH4. Our findings provide valuable information on how to develop field-sampling strategies to reliably capture the spatial heterogeneity of diffusive carbon fluxes from reservoirs. PMID:29257874

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Spatial vulnerability assessments by regression kriging

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

2016-04-01

Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

NASA Astrophysics Data System (ADS)

B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

2014-02-01

Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (~1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible and 90m for thermal) satellite platforms. Results showed that spatial variations in crop yield were related to a satellite derived canopy stress index (CSIsat) and a moisture stress index (MSIsat). A weather station level canopy stress index (CSIws) calculated at midday was correlated to the CSIsat at late morning. In addition, a strong linear relationship was observed between EVI and LST at point scale throughout the crop growth period. Differences were smallest at anthesis when the canopy closure was highest. This suggests that LST imagery data around flowering could be used to calculate crop stress over large areas of the crop. The harvested yield was related (R2 = 0.67) to CSIsat using a fix date across all fields. This relationship improved (R2 = 0.92) using both indices from all five dates across all fields during the crop growth period. Here we successfully showed that satellite derived crop attributes (CSIsat and MSIsat) can account for most of the variability in final crop yield and that they can be used to predict crop yield at field scales. Applications of these results could enhance the ability of producers to hedge their financial on -farm crop production losses due to in-season water stress by taking crop insurance. This is likely to further improve their adaptive capacity and thus strengthening the long-term viability of the industry domestically and elsewhere.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

PubMed

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

Uncertainty assessment method for the Cs-137 fallout inventory and penetration depth.

PubMed

Papadakos, G N; Karangelos, D J; Petropoulos, N P; Anagnostakis, M J; Hinis, E P; Simopoulos, S E

2017-05-01

Within the presented study, soil samples were collected in year 2007 at 20 different locations of the Greek terrain, both from the surface and also from depths down to 26 cm. Sampling locations were selected primarily from areas where high levels of 137 Cs deposition after the Chernobyl accident had already been identified by the Nuclear Engineering Laboratory of the National Technical University of Athens during and after the year of 1986. At one location of relatively higher deposition, soil core samples were collected following a 60 m by 60 m Cartesian grid with a 20 m node-to-node distance. Single or pair core samples were also collected from the remaining 19 locations. Sample measurements and analysis were used to estimate 137 Cs inventory and the corresponding depth migration, twenty years after the deposition on Greek terrain. Based on these data, the uncertainty components of the whole sampling-to-results procedure were investigated. A cause-and-effect assessment process was used to apply the law of error propagation and demonstrate that the dominating significant component of the combined uncertainty is that due to the spatial variability of the contemporary (2007) 137 Cs inventory. A secondary, yet also significant component was identified to be the activity measurement process itself. Other less-significant uncertainty parameters were sampling methods, the variation in the soil field density with depth and the preparation of samples for measurement. The sampling grid experiment allowed for the quantitative evaluation of the uncertainty due to spatial variability, also by the assistance of the semivariance analysis. Denser, optimized grid could return more accurate values for this component but with a significantly elevated laboratory cost, in terms of both, human and material resources. Using the hereby collected data and for the case of a single core soil sampling using a well-defined sampling methodology quality assurance, the uncertainty component due to spatial variability was evaluated to about 19% for the 137 Cs inventory and up to 34% for the 137 Cs penetration depth. Based on the presented results and also on related literature, it is argued that such high uncertainties should be anticipated for single core samplings conducted using similar methodology and employed as 137 Cs inventory and penetration depth estimators. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drinking Water Quality Criterion - Based site Selection of Aquifer Storage and Recovery Scheme in Chou-Shui River Alluvial Fan

NASA Astrophysics Data System (ADS)

Huang, H. E.; Liang, C. P.; Jang, C. S.; Chen, J. S.

2015-12-01

Land subsidence due to groundwater exploitation is an urgent environmental problem in Choushui river alluvial fan in Taiwan. Aquifer storage and recovery (ASR), where excess surface water is injected into subsurface aquifers for later recovery, is one promising strategy for managing surplus water and may overcome water shortages. The performance of an ASR scheme is generally evaluated in terms of recovery efficiency, which is defined as percentage of water injected in to a system in an ASR site that fulfills the targeted water quality criterion. Site selection of an ASR scheme typically faces great challenges, due to the spatial variability of groundwater quality and hydrogeological condition. This study proposes a novel method for the ASR site selection based on drinking quality criterion. Simplified groundwater flow and contaminant transport model spatial distributions of the recovery efficiency with the help of the groundwater quality, hydrological condition, ASR operation. The results of this study may provide government administrator for establishing reliable ASR scheme.

Spring onset variations and long-term trends from new hemispheric-scale products and remote sensing

NASA Astrophysics Data System (ADS)

Dye, D. G.; Li, X.; Ault, T.; Zurita-Milla, R.; Schwartz, M. D.

2015-12-01

Spring onset is commonly characterized by plant phenophase changes among a variety of biophysical transitions and has important implications for natural and man-managed ecosystems. Here, we present a new integrated analysis of variability in gridded Northern Hemisphere spring onset metrics. We developed a set of hemispheric temperature-based spring indices spanning 1920-2013. As these were derived solely from meteorological data, they are used as a benchmark for isolating the climate system's role in modulating spring "green up" estimated from the annual cycle of normalized difference vegetation index (NDVI). Spatial patterns of interannual variations, teleconnections, and long-term trends were also analyzed in all metrics. At mid-to-high latitudes, all indices exhibit larger variability at interannual to decadal time scales than at spatial scales of a few kilometers. Trends of spring onset vary across space and time. However, compared to long-term trend, interannual to decadal variability generally accounts for a larger portion of the total variance in spring onset timing. Therefore, spring onset trends identified from short existing records may be aliased by decadal climate variations due to their limited temporal depth, even when these records span the entire satellite era. Based on our findings, we also demonstrated that our indices have skill in representing ecosystem-level spring phenology and may have important implications in understanding relationships between phenology, atmosphere dynamics and climate variability.

The spatial and temporal variability of groundwater recharge in a forested basin in northern Wisconsin

USGS Publications Warehouse

Dripps, W.R.; Bradbury, K.R.

2010-01-01

Recharge varies spatially and temporally as it depends on a wide variety of factors (e.g. vegetation, precipitation, climate, topography, geology, and soil type), making it one of the most difficult, complex, and uncertain hydrologic parameters to quantify. Despite its inherent variability, groundwater modellers, planners, and policy makers often ignore recharge variability and assume a single average recharge value for an entire watershed. Relatively few attempts have been made to quantify or incorporate spatial and temporal recharge variability into water resource planning or groundwater modelling efforts. In this study, a simple, daily soil-water balance model was developed and used to estimate the spatial and temporal distribution of groundwater recharge of the Trout Lake basin of northern Wisconsin for 1996-2000 as a means to quantify recharge variability. For the 5 years of study, annual recharge varied spatially by as much as 18 cm across the basin; vegetation was the predominant control on this variability. Recharge also varied temporally with a threefold annual difference over the 5-year period. Intra-annually, recharge was limited to a few isolated events each year and exhibited a distinct seasonal pattern. The results suggest that ignoring recharge variability may not only be inappropriate, but also, depending on the application, may invalidate model results and predictions for regional and local water budget calculations, water resource management, nutrient cycling, and contaminant transport studies. Recharge is spatially and temporally variable, and should be modelled as such. Copyright ?? 2009 John Wiley & Sons, Ltd.

Three dimensional simulation of spatial and temporal variability of stratospheric hydrogen chloride

NASA Technical Reports Server (NTRS)

Kaye, Jack A.; Rood, Richard B.; Jackman, Charles H.; Allen, Dale J.; Larson, Edmund M.

1989-01-01

Spatial and temporal variability of atmospheric HCl columns are calculated for January 1979 using a three-dimensional chemistry-transport model designed to provide the best possible representation of stratospheric transport. Large spatial and temporal variability of the HCl columns is shown to be correlated with lower stratospheric potential vorticity and thus to be of dynamical origin. Systematic longitudinal structure is correlated with planetary wave structure. These results can help place spatially and temporally isolated column and profile measurements in a regional and/or global perspective.

Spatial versus Day-To-Day Within-Lake Variability in Tropical Floodplain Lake CH4 Emissions – Developing Optimized Approaches to Representative Flux Measurements

PubMed Central

Peixoto, Roberta B.; Machado-Silva, Fausto; Marotta, Humberto; Enrich-Prast, Alex; Bastviken, David

2015-01-01

Inland waters (lakes, rivers and reservoirs) are now understood to contribute large amounts of methane (CH4) to the atmosphere. However, fluxes are poorly constrained and there is a need for improved knowledge on spatiotemporal variability and on ways of optimizing sampling efforts to yield representative emission estimates for different types of aquatic ecosystems. Low-latitude floodplain lakes and wetlands are among the most high-emitting environments, and here we provide a detailed investigation of spatial and day-to-day variability in a shallow floodplain lake in the Pantanal in Brazil over a five-day period. CH4 flux was dominated by frequent and ubiquitous ebullition. A strong but predictable spatial variability (decreasing flux with increasing distance to the shore or to littoral vegetation) was found, and this pattern can be addressed by sampling along transects from the shore to the center. Although no distinct day-to-day variability were found, a significant increase in flux was identified from measurement day 1 to measurement day 5, which was likely attributable to a simultaneous increase in temperature. Our study demonstrates that representative emission assessments requires consideration of spatial variability, but also that spatial variability patterns are predictable for lakes of this type and may therefore be addressed through limited sampling efforts if designed properly (e.g., fewer chambers may be used if organized along transects). Such optimized assessments of spatial variability are beneficial by allowing more of the available sampling resources to focus on assessing temporal variability, thereby improving overall flux assessments. PMID:25860229

Spatial variability in intertidal macroalgal assemblages on the North Portuguese coast: consistence between species and functional group approaches

NASA Astrophysics Data System (ADS)

Veiga, P.; Rubal, M.; Vieira, R.; Arenas, F.; Sousa-Pinto, I.

2013-03-01

Natural assemblages are variable in space and time; therefore, quantification of their variability is imperative to identify relevant scales for investigating natural or anthropogenic processes shaping these assemblages. We studied the variability of intertidal macroalgal assemblages on the North Portuguese coast, considering three spatial scales (from metres to 10 s of kilometres) following a hierarchical design. We tested the hypotheses that (1) spatial pattern will be invariant at all the studied scales and (2) spatial variability of macroalgal assemblages obtained by using species will be consistent with that obtained using functional groups. This was done considering as univariate variables: total biomass and number of taxa as well as biomass of the most important species and functional groups and as multivariate variables the structure of macroalgal assemblages, both considering species and functional groups. Most of the univariate results confirmed the first hypothesis except for the total number of taxa and foliose macroalgae that showed significant variability at the scale of site and area, respectively. In contrast, when multivariate patterns were examined, the first hypothesis was rejected except at the scale of 10 s of kilometres. Both uni- and multivariate results indicated that variation was larger at the smallest scale, and thus, small-scale processes seem to have more effect on spatial variability patterns. Macroalgal assemblages, both considering species and functional groups as surrogate, showed consistent spatial patterns, and therefore, the second hypothesis was confirmed. Consequently, functional groups may be considered a reliable biological surrogate to study changes on macroalgal assemblages at least along the investigated Portuguese coastline.

Tannat grape composition responses to spatial variability of temperature in an Uruguay's coastal wine region

NASA Astrophysics Data System (ADS)

Fourment, Mercedes; Ferrer, Milka; González-Neves, Gustavo; Barbeau, Gérard; Bonnardot, Valérie; Quénol, Hervé

2017-09-01

Spatial variability of temperature was studied in relation to the berry basic composition and secondary compounds of the Tannat cultivar at harvest from vineyards located in Canelones and Montevideo, the most important wine region of Uruguay. Monitoring of berries and recording of temperature were performed in 10 commercial vineyards of Tannat situated in the southern coastal wine region of the country for three vintages (2012, 2013, and 2014). Results from a multivariate correlation analysis between berry composition and temperature over the three vintages showed that (1) Tannat responses to spatial variability of temperature were different over the vintages, (2) correlations between secondary metabolites and temperature were higher than those between primary metabolites, and (3) correlation values between berry composition and climate variables increased when ripening occurred under dry conditions (below average rainfall). For a particular studied vintage (2013), temperatures explained 82.5% of the spatial variability of the berry composition. Daily thermal amplitude was found to be the most important spatial mode of variability with lower values recorded at plots nearest to the sea and more exposed to La Plata River. The highest levels in secondary compounds were found in berries issued from plots situated as far as 18.3 km from La Plata River. The increasing knowledge of temperature spatial variability and its impact on grape berry composition contributes to providing possible issues to adapt grapevine to climate change.

Spatial variability in acoustic backscatter as an indicator of tissue homogenate production in pulsed cavitational ultrasound therapy.

PubMed

Parsons, Jessica E; Cain, Charles A; Fowlkes, J Brian

2007-03-01

Spatial variability in acoustic backscatter is investigated as a potential feedback metric for assessment of lesion morphology during cavitation-mediated mechanical tissue disruption ("histotripsy"). A 750-kHz annular array was aligned confocally with a 4.5 MHz passive backscatter receiver during ex vivo insonation of porcine myocardium. Various exposure conditions were used to elicit a range of damage morphologies and backscatter characteristics [pulse duration = 14 micros, pulse repetition frequency (PRF) = 0.07-3.1 kHz, average I(SPPA) = 22-44 kW/cm2]. Variability in backscatter spatial localization was quantified by tracking the lag required to achieve peak correlation between sequential RF A-lines received. Mean spatial variability was observed to be significantly higher when damage morphology consisted of mechanically disrupted tissue homogenate versus mechanically intact coagulation necrosis (2.35 +/- 1.59 mm versus 0.067 +/- 0.054 mm, p < 0.025). Statistics from these variability distributions were used as the basis for selecting a threshold variability level to identify the onset of homogenate formation via an abrupt, sustained increase in spatially dynamic backscatter activity. Specific indices indicative of the state of the homogenization process were quantified as a function of acoustic input conditions. The prevalence of backscatter spatial variability was observed to scale with the amount of homogenate produced for various PRFs and acoustic intensities.

Spatial diversity of rocky midlittoral macro-invertebrates associated with the endangered species Patella ferruginea (Mollusca: Gastropoda) of Tunisian coastline

NASA Astrophysics Data System (ADS)

Tlig-Zouari, Sabiha; Rabaoui, Lotfi; Fguiri, Hosni; Diawara, Moctar; Ben Hassine, Oum Kalthoum

2010-04-01

The present study focuses on horizontal spatial variability of benthic macrofauna associated with Patella ferruginea. Thirty-six samples collected at 12 transects belonging to 4 midlittoral sites along the rocky Tunisian coastline, were examined. A total of 44 species belonging to 5 taxa were found. Multivariate analysis applied on gathered data did not show a horizontal spatial variability at small scale (between transects), but at large scale, between sites as well as sectors. Thus, three groups of communities were identified (GI: Korbous and El Haouaria; GIIa: Zembra Island and GIIb: Kelibia). The distribution of species abundance within these groups revealed that crustaceans were the most abundant taxon, due to the overwhelming dominance of Chthamalus stellatus. This substratum appeared to create favourable micro-habitats for the installation of molluscs including gastropods. Regarding the low diversity index ( H') and evenness ( J), they seemed to reflect a disturbance and a demographic unbalance within these communities. The heterogeneity of substrate surface, created by C. stellatus specimens appeared to be caused by various complex interactions established between the key components of these communities in particular suspension feeders, predators, herbivorous molluscs and macroalgae. Thus, the dynamic status of each of these communities is the result of these complex interactions.

High-Resolution Atmospheric Emission Inventory of the Argentine Enery Sector

NASA Astrophysics Data System (ADS)

Puliafito, Salvador Enrique; Castesana, Paula; Allende, David; Ruggeri, Florencia; Pinto, Sebastián; Pascual, Romina; Bolaño Ortiz, Tomás; Fernandez, Rafael Pedro

2017-04-01

This study presents a high-resolution spatially disaggregated inventory (2.5 km x 2.5 km), updated to 2014, of the main emissions from energy activities in Argentina. This inventory was created with the purpose of improving air quality regional models. The sub-sectors considered are public electricity and heat production, cement production, domestic aviation, road and rail transportation, inland navigation, residential and commercial, and fugitive emissions from refineries and fuel expenditure. The pollutants considered include greenhouse gases and ozone precursors: CO2, CH4, NOx, N2O VOC; and other gases specifically related to air quality including PM10, PM2.5, SOx, Pb and POPs. The uncertainty analysis of the inventories resulted in a variability of 3% for public electricity generation, 3-6% in the residential, commercial sector, 6-12% terrestrial transportation sector, 10-20% in oil refining and cement production according to the considered pollutant. Aviation and maritime navigation resulted in a higher variability reaching more than 60%. A comparison with the international emission inventory EDGAR shows disagreements in the spatial distribution of emissions, probably due to the finer resolution of the map presented here, particularly as a result of the use of new spatially disaggregated data of higher resolution that is currently available.

Natural and human-induced variability in the composition of fish assemblages in the Northwestern Cuban shelf.

PubMed

González-Sansón, Gaspar; Aguilar, Consuelo; Hernández, Ivet; Cabrera, Yureidy; Suarez-Montes, Noelis; Bretos, Fernando; Guggenheim, David

2009-09-01

The main goal of the study was to obtain field data to build a baseline of fish assemblage composition that can be used comparatively for future analyses of the impact of human actions in the region. A basic network of 68 sampling stations was defined for the entire region (4,050 km2). Fish assemblage species and size composition was estimated using visual census methods at three different spatial scales: a) entire region, b) inside the main reef area and c) along a human impact coastal gradient. Multivariate numerical analyses revealed habitat type as the main factor inducing spatial variability of fish community composition, while the level of human impact appears to play the main role in fish assemblage composition changes along the coast. A trend of decreasing fish size toward the east supports the theory of more severe human impact due to overfishing and higher urban pollution in that direction. This is the first detailed study along the northwest coast of Cuba that focuses on fish community structure and the natural and human-induced variations at different spatial scales for the entire NW shelf. This research also provides input for a more comprehensive understanding of coastal marine fish communities' status in the Gulf of Mexico basin.

The effect of sensory uncertainty due to amblyopia (lazy eye) on the planning and execution of visually-guided 3D reaching movements.

PubMed

Niechwiej-Szwedo, Ewa; Goltz, Herbert C; Chandrakumar, Manokaraananthan; Wong, Agnes M F

2012-01-01

Impairment of spatiotemporal visual processing in amblyopia has been studied extensively, but its effects on visuomotor tasks have rarely been examined. Here, we investigate how visual deficits in amblyopia affect motor planning and online control of visually-guided, unconstrained reaching movements. Thirteen patients with mild amblyopia, 13 with severe amblyopia and 13 visually-normal participants were recruited. Participants reached and touched a visual target during binocular and monocular viewing. Motor planning was assessed by examining spatial variability of the trajectory at 50-100 ms after movement onset. Online control was assessed by examining the endpoint variability and by calculating the coefficient of determination (R(2)) which correlates the spatial position of the limb during the movement to endpoint position. Patients with amblyopia had reduced precision of the motor plan in all viewing conditions as evidenced by increased variability of the reach early in the trajectory. Endpoint precision was comparable between patients with mild amblyopia and control participants. Patients with severe amblyopia had reduced endpoint precision along azimuth and elevation during amblyopic eye viewing only, and along the depth axis in all viewing conditions. In addition, they had significantly higher R(2) values at 70% of movement time along the elevation and depth axes during amblyopic eye viewing. Sensory uncertainty due to amblyopia leads to reduced precision of the motor plan. The ability to implement online corrections depends on the severity of the visual deficit, viewing condition, and the axis of the reaching movement. Patients with mild amblyopia used online control effectively to compensate for the reduced precision of the motor plan. In contrast, patients with severe amblyopia were not able to use online control as effectively to amend the limb trajectory especially along the depth axis, which could be due to their abnormal stereopsis.

The Effect of Sensory Uncertainty Due to Amblyopia (Lazy Eye) on the Planning and Execution of Visually-Guided 3D Reaching Movements

PubMed Central

Niechwiej-Szwedo, Ewa; Goltz, Herbert C.; Chandrakumar, Manokaraananthan; Wong, Agnes M. F.

2012-01-01

Background Impairment of spatiotemporal visual processing in amblyopia has been studied extensively, but its effects on visuomotor tasks have rarely been examined. Here, we investigate how visual deficits in amblyopia affect motor planning and online control of visually-guided, unconstrained reaching movements. Methods Thirteen patients with mild amblyopia, 13 with severe amblyopia and 13 visually-normal participants were recruited. Participants reached and touched a visual target during binocular and monocular viewing. Motor planning was assessed by examining spatial variability of the trajectory at 50–100 ms after movement onset. Online control was assessed by examining the endpoint variability and by calculating the coefficient of determination (R2) which correlates the spatial position of the limb during the movement to endpoint position. Results Patients with amblyopia had reduced precision of the motor plan in all viewing conditions as evidenced by increased variability of the reach early in the trajectory. Endpoint precision was comparable between patients with mild amblyopia and control participants. Patients with severe amblyopia had reduced endpoint precision along azimuth and elevation during amblyopic eye viewing only, and along the depth axis in all viewing conditions. In addition, they had significantly higher R2 values at 70% of movement time along the elevation and depth axes during amblyopic eye viewing. Conclusion Sensory uncertainty due to amblyopia leads to reduced precision of the motor plan. The ability to implement online corrections depends on the severity of the visual deficit, viewing condition, and the axis of the reaching movement. Patients with mild amblyopia used online control effectively to compensate for the reduced precision of the motor plan. In contrast, patients with severe amblyopia were not able to use online control as effectively to amend the limb trajectory especially along the depth axis, which could be due to their abnormal stereopsis. PMID:22363549

Spatially explicit simulation of hydrologically controlled carbon and nitrogen cycles and associated feedback mechanisms in a boreal ecosystem

NASA Astrophysics Data System (ADS)

Govind, Ajit; Chen, Jing Ming; Ju, Weimin

2009-06-01

Ecosystem models that simulate biogeochemical processes usually ignore hydrological controls that govern them. It is quite possible that topographically driven water fluxes significantly influence the spatial distribution of C sources and sinks because of their large contribution to the local water balance. To investigate this, we simulated biogeochemical processes along with the associated feedback mechanisms in a boreal ecosystem using a spatially explicit hydroecological model, boreal ecosystem productivity simulator (BEPS)-TerrainLab V2.0, that has a tight coupling of ecophysiological, hydrological, and biogeochemical processes. First, the simulated dynamics of snowpack, soil temperature, net ecosystem productivity (NEP), and total ecosystem respiration (TER) were validated with high-frequency measurements for 2 years. The model was able to explain 80% of the variability in NEP and 84% of the variability in TER. Further, we investigated the influence of topographically driven subsurface base flow on soil C and N cycling and on the spatiotemporal patterns of C sources and sinks using three hydrological modeling scenarios that differed in hydrological conceptualizations. In general, the scenarios that had nonexplicit hydrological representation overestimated NEP, as opposed to the scenario that had an explicit (realistic) representation. The key processes controlling the NEP differences were attributed to the combined effects of variations in photosynthesis (due to changes in stomatal conductance and nitrogen (N) availability), heterotrophic respiration, and autotrophic respiration, all of which occur simultaneously affecting NEP. Feedback relationships were also found to exacerbate the differences. We identified six types of NEP differences (biases), of which the most commonly found was due to an underestimation of the existing C sources, highlighting the vulnerability of regional-scale ecosystem models that ignore hydrological processes.

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

DOE PAGES

Kim, Young-Min; Zhou, Ying; Gao, Yang; ...

2014-11-16

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

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

Kim, Young-Min; Zhou, Ying; Gao, Yang

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Mesoscale structure of microplankton and mesoplankton assemblages under contrasting oceanographic conditions in the Catalan Sea (NW Mediterranean)

NASA Astrophysics Data System (ADS)

Villate, Fernando; Uriarte, Ibon; Olivar, M. Pilar; Maynou, Francesc; Emelianov, Mikhail; Ameztoy, Iban

2014-11-01

The abundance, composition and mesoscale variability of the microplankton (53-200 μm) and the mesoplankton (0.2-2 mm) fractions in relation to oceanographic factors and phytoplankton biomass were compared off the Catalan coast (NW Mediterranean) during the summer stratification (June) and autumn mixing (November) periods in 2005. This work aims to determine whether the two plankton fractions that more contribute to fish larval diet respond to a common variable environment, and this study constitutes the first attempt to analyse, in parallel, the spatial structure of both fractions in this area. From June to November microplankton abundance increased mainly by the increase of dinoflagellates, tintinnids and radiolarians, and mesoplankton decreased due mainly to the decrease of long-horned dinoflagellates, cladocerans, doliolids and appendicularians. Plankton mesoscale variability in relation to environmental variables showed higher complexity in June, where environmental horizontal and vertical gradients were more marked than in November. In June, the major mode of variability of the microplankton was mainly accounted by the patchy distribution of several tintinnid species dominated by Rhabdonella spiralis associated to the subsurface phytoplankton biomass. The main mode of variability of the mesoplankton was related to the intrusion of the Ebro river plume and the related aggregation of doliolids and cladocerans, dominated by Evadne spinifera. In November, the major variability pattern in both fractions was a combination of inshore-offshore and eastern-western gradients in taxa distributions shaped mainly by the course of the Catalan Current along the shelf-break. Spatial differences in planktonic food pathways in each period are discussed on the basis of literature on plankton feeding habits and types, and on the diet of fish larvae of the main species from the same surveys.

The new MOPREDAS database and the monthly precipitation trends in Spain (December 1945- November 2005)

NASA Astrophysics Data System (ADS)

Gonzalez-Hidalgo, Jose Carlos; Brunetti, Michele; Martin, De Luis

2010-05-01

Precipitation is one of the most important climate elements directly affecting human society, economic activities and natural systems; at the same time it is the most variable climate element, and its changes can be detected only if a spatially dense network of observations is used. Due to this, the last AR4 report renewed interest in the study of precipitation, and suggests focusing on detailed sub-regional studies, with a preference for those areas where water is a scarce resource with heavy demands placed on it. We have developed the new MOPREDAS database (MOnthly PREcipitation DAtabase of Spain) by exploiting the total amount of data available at Spanish Meteorological Agency (AEMET, formerly INM). These provide a total of 2670 complete and homogeneous series for the period 1946-2005 after exhaustive quality control and reconstruction processes, and at present is the most complete and extensive monthly precipitation dataset uptodated in Spain, including dense information up to 1500 m o.l.s.. MOPREDAS has been created with the aim of analyzing the behaviour of precipitation in the conterminous provinces of Spain, and to validate the downscaling of climate models on a detailed spatial level. To this end, the station data were also interpolated on a regular grid, at 1/10 of degree of resolution, over the whole Spain. Trend analysis (Mann-Kendall text, p <0,10) confirms great spatial and temporal variability in the behaviour of precipitation across Spain between 1946-2005. Except March, June and October, no generalized significant pattern have been found, but subregional areas with homogeneous trend were detected. MOPREDAS shows a global decrease of precipitation in March that affects 68.9% of Spain and 31.8% in June, while in October the area affected by positive trends is 33.7% of land (p<0.10). We detected numerous sub-regional coherent patterns well delineated by topographic factors, and passing unnoticed until now due to inadequate data density. These results suggest that both global and local factors affect the spatial distribution of trends in the Iberian Peninsula, being mountain chains the most significant geographical factor in determining the spatial distribution of monthly trends on a detailed, sub-regional spatial scale.

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

Treesearch

Emma Vakili; Chad M. Hoffman; Robert E. Keane; Wade T. Tinkham; Yvette Dickinson

2016-01-01

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the...

Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

Zooplankton variability in the subtropical estuarine system of Paranaguá Bay, Brazil, in 2012 and 2013

NASA Astrophysics Data System (ADS)

Salvador, Bianca; Bersano, José Guilherme F.

2017-12-01

Spatial and temporal dynamics of zooplankton assemblages were studied in the Paranaguá Estuarine System (southern Brazil), including data from the summer (rainy) and winter (dry) periods of 2012 and 2013. Zooplankton and environmental data were collected at 37 stations along the estuary and examined by multivariate methods. The results indicated significantly distinct assemblages; differences in abundance were the major source of variability, mainly over the temporal scale. The highest abundances were observed during rainy periods, especially in 2012, when the mean density reached 16378 ind.m-3. Winter assemblages showed lower densities but higher species diversity, due to the more extensive intrusion of coastal waters. Of the 14 taxonomic groups recorded, Copepoda was the most abundant and diverse (92% of total abundance and 22 species identified). The coastal copepods Acartia lilljeborgi (44%) and Oithona hebes (26%) were the most important species in both abundance and frequency, followed by the estuarine Pseudodiaptomus acutus and the neritic Temora turbinata. The results indicated strong influences of environmental parameters on the community structure, especially in response to seasonal variations. The spatial distribution of species was probably determined mainly by their preferences and tolerances for specific salinity conditions. On the other hand, the abundances were strongly related to higher water temperature and precipitation rates, which can drive nutrient inputs and consequently food supply in the system, due to intense continental drainage.

The AIROPA software package: milestones for testing general relativity in the strong gravity regime with AO

NASA Astrophysics Data System (ADS)

Witzel, Gunther; Lu, Jessica R.; Ghez, Andrea M.; Martinez, Gregory D.; Fitzgerald, Michael P.; Britton, Matthew; Sitarski, Breann N.; Do, Tuan; Campbell, Randall D.; Service, Maxwell; Matthews, Keith; Morris, Mark R.; Becklin, E. E.; Wizinowich, Peter L.; Ragland, Sam; Doppmann, Greg; Neyman, Chris; Lyke, James; Kassis, Marc; Rizzi, Luca; Lilley, Scott; Rampy, Rachel

2016-07-01

General relativity can be tested in the strong gravity regime by monitoring stars orbiting the supermassive black hole at the Galactic Center with adaptive optics. However, the limiting source of uncertainty is the spatial PSF variability due to atmospheric anisoplanatism and instrumental aberrations. The Galactic Center Group at UCLA has completed a project developing algorithms to predict PSF variability for Keck AO images. We have created a new software package (AIROPA), based on modified versions of StarFinder and Arroyo, that takes atmospheric turbulence profiles, instrumental aberration maps, and images as inputs and delivers improved photometry and astrometry on crowded fields. This software package will be made publicly available soon.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Modeling the spatial and temporal variability in climate and primary productivity across the Luquillo Mountains, Puerto Rico.

Treesearch

Hongqing Wanga; Charles A.S. Halla; Frederick N. Scatenab; Ned Fetcherc; Wei Wua

2003-01-01

There are few studies that have examined the spatial variability of forest productivity over an entire tropical forested landscape. In this study, we used a spatially-explicit forest productivity model, TOPOPROD, which is based on the FORESTBGC model, to simulate spatial patterns of gross primary productivity (GPP), net primary productivity (NPP), and respiration over...

Spatial patterns of distribution and abundance of Harrisia portoricensis, an endangered Caribbean cactus

Treesearch

J. Rojas-Sandoval; E. J. Melendez-Ackerman; NO-VALUE

2013-01-01

Aims The spatial distribution of biotic and abiotic factors may play a dominant role in determining the distribution and abundance of plants in arid and semiarid environments. In this study, we evaluated how spatial patterns of microhabitat variables and the degree of spatial dependence of these variables influence the distribution and abundance of the endangered...

A multi-scale comparison of trait linkages to environmental and spatial variables in fish communities across a large freshwater lake.

PubMed

Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J

2011-07-01

Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.

Spatial Variability of the Topsoil Organic Carbon in the Moso Bamboo Forests of Southern China in Association with Soil Properties

PubMed Central

Zhang, Houxi; Zhuang, Shunyao; Qian, Haiyan; Wang, Feng; Ji, Haibao

2015-01-01

Understanding the spatial variability of soil organic carbon (SOC) must be enhanced to improve sampling design and to develop soil management strategies in terrestrial ecosystems. Moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forests have a high SOC storage potential; however, they also vary significantly spatially. This study investigated the spatial variability of SOC (0-20 cm) in association with other soil properties and with spatial variables in the Moso bamboo forests of Jian’ou City, which is a typical bamboo hometown in China. 209 soil samples were collected from Moso bamboo stands and then analyzed for SOC, bulk density (BD), pH, cation exchange capacity (CEC), and gravel content (GC) based on spatial distribution. The spatial variability of SOC was then examined using geostatistics. A Kriging map was produced through ordinary interpolation and required sample numbers were calculated by classical and Kriging methods. An aggregated boosted tree (ABT) analysis was also conducted. A semivariogram analysis indicated that ln(SOC) was best fitted with an exponential model and that it exhibited moderate spatial dependence, with a nugget/sill ratio of 0.462. SOC was significantly and linearly correlated with BD (r = −0.373**), pH (r = −0.429**), GC (r = −0.163*), CEC (r = 0.263**), and elevation (r = 0.192**). Moreover, the Kriging method requires fewer samples than the classical method given an expected standard error level as per a variance analysis. ABT analysis indicated that the physicochemical variables of soil affected SOC variation more significantly than spatial variables did, thus suggesting that the SOC in Moso bamboo forests can be strongly influenced by management practices. Thus, this study provides valuable information in relation to sampling strategy and insight into the potential of adjustments in agronomic measure, such as in fertilization for Moso bamboo production. PMID:25789615

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

NASA Astrophysics Data System (ADS)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Validation of WRF-Chem air quality simulations in the Netherlands at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, A.; Lowe, D.; Kuenen, J. J. P.; Denier Van Der Gon, H.; Vrekoussis, M.

2017-12-01

Air pollution is the single most important environmental hazard for publichealth, and especially nitrogen dioxide (NO2) plays a key role in air qualityresearch. With the aim of improving the quality and reproducibility ofmeasurements of NO2 vertical distribution from MAX-DOAS instruments, theCINDI-2 campaign was held in Cabauw (NL) in September 2016.The measurement site was rural, but surrounded by several major pollutioncenters. Due to this spatial heterogeneity of emissions, as well as themeteorological conditions, high spatial and temporal variability in NO2 mixingratios were observed.Air quality models used in the analysis of the measured data must have highspatial resolution in order to resolve this fine spatial structure. Thisremains a challenge even today, mostly due to the uncertainties and largespatial heterogeneity of emission data, and the need to parameterize small-scaleprocesses.In this study, we use the state-of-the-art version 3.9 of the Weather Researchand Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutantconcentrations over the Netherlands, to facilitate the analysis of the CINDI-2NO2 measurements. The model setup contains three nested domains withhorizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are takenfrom the TNO-MACC III inventory and, where available, from the Dutch PollutantRelease and Transfer Register (Emissieregistratie), at a spatial resolution of 7and 1 km, respectively. We use the Common Reactive Intermediates gas-phasechemical mechanism (CRIv2-R5) with the MOSAIC aerosol module.The high spatial resolution of model and emissions will allow us to resolve thestrong spatial gradients in the NO2 concentrations measured during theCINDI-2 campaign, allowing for an unprecedented level of detail in theanalysis of individual pollution sources.

Relevance of anisotropy and spatial variability of gas diffusivity for soil-gas transport

NASA Astrophysics Data System (ADS)

Schack-Kirchner, Helmer; Kühne, Anke; Lang, Friederike

2017-04-01

Models of soil gas transport generally do not consider neither direction dependence of gas diffusivity, nor its small-scale variability. However, in a recent study, we could provide evidence for anisotropy favouring vertical gas diffusion in natural soils. We hypothesize that gas transport models based on gas diffusion data measured with soil rings are strongly influenced by both, anisotropy and spatial variability and the use of averaged diffusivities could be misleading. To test this we used a 2-dimensional model of soil gas transport to under compacted wheel tracks to model the soil-air oxygen distribution in the soil. The model was parametrized with data obtained from soil-ring measurements with its central tendency and variability. The model includes vertical parameter variability as well as variation perpendicular to the elongated wheel track. Different parametrization types have been tested: [i)]Averaged values for wheel track and undisturbed. em [ii)]Random distribution of soil cells with normally distributed variability within the strata. em [iii)]Random distributed soil cells with uniformly distributed variability within the strata. All three types of small-scale variability has been tested for [j)] isotropic gas diffusivity and em [jj)]reduced horizontal gas diffusivity (constant factor), yielding in total six models. As expected the different parametrizations had an important influence to the aeration state under wheel tracks with the strongest oxygen depletion in case of uniformly distributed variability and anisotropy towards higher vertical diffusivity. The simple simulation approach clearly showed the relevance of anisotropy and spatial variability in case of identical central tendency measures of gas diffusivity. However, until now it did not consider spatial dependency of variability, that could even aggravate effects. To consider anisotropy and spatial variability in gas transport models we recommend a) to measure soil-gas transport parameters spatially explicit including different directions and b) to use random-field stochastic models to assess the possible effects for gas-exchange models.

Photometric variability in earthshine observations.

PubMed

Langford, Sally V; Wyithe, J Stuart B; Turner, Edwin L

2009-04-01

The identification of an extrasolar planet as Earth-like will depend on the detection of atmospheric signatures or surface non-uniformities. In this paper we present spatially unresolved flux light curves of Earth for the purpose of studying a prototype extrasolar terrestrial planet. Our monitoring of the photometric variability of earthshine revealed changes of up to 23% per hour in the brightness of Earth's scattered light at around 600 nm, due to the removal of specular reflection from the view of the Moon. This variability is accompanied by reddening of the spectrum and results from a change in surface properties across the continental boundary between the Indian Ocean and Africa's east coast. Our results based on earthshine monitoring indicate that specular reflection should provide a useful tool in determining the presence of liquid water on extrasolar planets via photometric observations.

X-ray microtomography analysis of soil structure deformation caused by centrifugation

NASA Astrophysics Data System (ADS)

Schlüter, Steffen; Leuther, Frederic; Vogler, Steffen; Vogel, Hans-Jörg

2016-04-01

Centrifugation provides a fast method to measure soil water retention curves over a wide moisture range. However, deformation of soil structure may occur at high angular velocities in the centrifuge. The objective of this study was to capture these changes in soil structure with X-ray microtomography and to measure local deformations via digital volume correlation. Two samples were investigated that differ in texture and rock content. A detailed analysis of the pore space reveals an interplay between shrinkage due to drying and soil compaction due to compression. Macroporosity increases at moderate angular velocity because of crack formation due to moisture release. At higher angular velocities, corresponding to capillary pressure of <-100kPa, macroporosity decreases again because of structure deformation due to compression. While volume changes due to swelling clay minerals are immanent to any drying process, the compaction of soil is a specific drawback of the centrifugation method. A new protocol for digital volume correlation was developed to analyze the spatial heterogeneity of deformation. In both samples the displacement of soil constituents is highest in the top part of the sample and exhibits high lateral variability explained by the spatial distribution of macropores in the sample. Centrifugation should therefore only be applied after the completion of all other hydraulic or thermal experiments, or any other analysis that depends on the integrity of soil structure.

X-ray microtomography analysis of soil structure deformation caused by centrifugation

NASA Astrophysics Data System (ADS)

Schlüter, S.; Leuther, F.; Vogler, S.; Vogel, H.-J.

2016-01-01

Centrifugation provides a fast method to measure soil water retention curves over a wide moisture range. However, deformation of soil structure may occur at high angular velocities in the centrifuge. The objective of this study was to capture these changes in soil structure with X-ray microtomography and to measure local deformations via digital volume correlation. Two samples were investigated that differ in texture and rock content. A detailed analysis of the pore space reveals an interplay between shrinkage due to drying and soil compaction due to compression. Macroporosity increases at moderate angular velocity because of crack formation due to moisture release. At higher angular velocities, corresponding to capillary pressure of ψ < -100 kPa, macroporosity decreases again because of structure deformation due to compression. While volume changes due to swelling clay minerals are immanent in any drying process, the compaction of soil is a specific drawback of the centrifugation method. A new protocol for digital volume correlation was developed to analyze the spatial heterogeneity of deformation. In both samples the displacement of soil constituents is highest in the top part of the sample and exhibits high lateral variability explained by the spatial distribution of macropores in the sample. Centrifugation should therefore only be applied after the completion of all other hydraulic or thermal experiments, or any other analysis that depends on the integrity of soil structure.

Geoelectrical characterisation of basement aquifers: the case of Iberekodo, southwestern Nigeria

NASA Astrophysics Data System (ADS)

Aizebeokhai, Ahzegbobor P.; Oyeyemi, Kehinde D.

2018-03-01

Basement aquifers, which occur within the weathered and fractured zones of crystalline bedrocks, are important groundwater resources in tropical and subtropical regions. The development of basement aquifers is complex owing to their high spatial variability. Geophysical techniques are used to obtain information about the hydrologic characteristics of the weathered and fractured zones of the crystalline basement rocks, which relates to the occurrence of groundwater in the zones. The spatial distributions of these hydrologic characteristics are then used to map the spatial variability of the basement aquifers. Thus, knowledge of the spatial variability of basement aquifers is useful in siting wells and boreholes for optimal and perennial yield. Geoelectrical resistivity is one of the most widely used geophysical methods for assessing the spatial variability of the weathered and fractured zones in groundwater exploration efforts in basement complex terrains. The presented study focuses on combining vertical electrical sounding with two-dimensional (2D) geoelectrical resistivity imaging to characterise the weathered and fractured zones in a crystalline basement complex terrain in southwestern Nigeria. The basement aquifer was delineated, and the nature, extent and spatial variability of the delineated basement aquifer were assessed based on the spatial variability of the weathered and fractured zones. The study shows that a multiple-gradient array for 2D resistivity imaging is sensitive to vertical and near-surface stratigraphic features, which have hydrological implications. The integration of resistivity sounding with 2D geoelectrical resistivity imaging is efficient and enhances near-surface characterisation in basement complex terrain.

Exploring the spatial variability of soil properties in an Alfisol Catena

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

Rosemary, F.; Vitharana, U. W. A.; Indraratne, S. P.

Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzedmore » for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing crossvariograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m –1 ; OC: 0.1–1.37%; CEC: 0.44– 11.51 cmol (+) kg –1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena. Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system.« less

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Air Pollution Measurements by Citizen Scientists and NASA Satellites: Data Integration and Analysis

NASA Astrophysics Data System (ADS)

Gupta, P.; Maibach, J.; Levy, R. C.; Doraiswamy, P.; Pikelnaya, O.; Feenstra, B.; Polidori, A.

2017-12-01

PM2.5, or fine particulate matter, is a category of air pollutant consisting of solid particles with effective aerodynamic diameter of less than 2.5 microns. These particles are hazardous to human health, as their small size allows them to penetrate deep into the lungs. Since the late 1990's, the US Environmental Protection Agency has been monitoring PM2.5 using a network of ground-level sensors. Due to cost and space restrictions, the EPA monitoring network remains spatially sparse. That is, while the network spans the extent of the US, the distance between sensors is large enough that significant spatial variation in PM concentration can go undetected. To increase the spatial resolution of monitoring, previous studies have used satellite data to estimate ground-level PM concentrations. From imagery, one can create a measure of haziness due to aerosols, called aerosol optical depth (AOD), which then can be used to estimate PM concentrations using statistical and physical modeling. Additionally, previous research has identified a number of meteorological variables, such as relative humidity and mixing height, which aide in estimating PM concentrations from AOD. Although the high spatial resolution of satellite data is valuable alone for forecasting air quality, higher resolution ground-level data is needed to effectively study the relationship between PM2.5 concentrations and AOD. To this end, we discuss a citizen-science PM monitoring network deployed in California. Using low-cost PM sensors, this network achieves higher spatial resolution. We additionally discuss a software pipeline for integrating resulting PM measurements with satellite data, as well as initial data analysis.

Impact of spatial variability and sampling design on model performance

NASA Astrophysics Data System (ADS)

Schrape, Charlotte; Schneider, Anne-Kathrin; Schröder, Boris; van Schaik, Loes

2017-04-01

Many environmental physical and chemical parameters as well as species distributions display a spatial variability at different scales. In case measurements are very costly in labour time or money a choice has to be made between a high sampling resolution at small scales and a low spatial cover of the study area or a lower sampling resolution at the small scales resulting in local data uncertainties with a better spatial cover of the whole area. This dilemma is often faced in the design of field sampling campaigns for large scale studies. When the gathered field data are subsequently used for modelling purposes the choice of sampling design and resulting data quality influence the model performance criteria. We studied this influence with a virtual model study based on a large dataset of field information on spatial variation of earthworms at different scales. Therefore we built a virtual map of anecic earthworm distributions over the Weiherbach catchment (Baden-Württemberg in Germany). First of all the field scale abundance of earthworms was estimated using a catchment scale model based on 65 field measurements. Subsequently the high small scale variability was added using semi-variograms, based on five fields with a total of 430 measurements divided in a spatially nested sampling design over these fields, to estimate the nugget, range and standard deviation of measurements within the fields. With the produced maps, we performed virtual samplings of one up to 50 random points per field. We then used these data to rebuild the catchment scale models of anecic earthworm abundance with the same model parameters as in the work by Palm et al. (2013). The results of the models show clearly that a large part of the non-explained deviance of the models is due to the very high small scale variability in earthworm abundance: the models based on single virtual sampling points on average obtain an explained deviance of 0.20 and a correlation coefficient of 0.64. With increasing sampling points per field, we averaged the measured abundance of the sampling within each field to obtain a more representative value of the field average. Doubling the samplings per field strongly improved the model performance criteria (explained deviance 0.38 and correlation coefficient 0.73). With 50 sampling points per field the performance criteria were 0.91 and 0.97 respectively for explained deviance and correlation coefficient. The relationship between number of samplings and performance criteria can be described with a saturation curve. Beyond five samples per field the model improvement becomes rather small. With this contribution we wish to discuss the impact of data variability at sampling scale on model performance and the implications for sampling design and assessment of model results as well as ecological inferences.

A new high resolution permafrost map of Iceland from Earth Observation data

NASA Astrophysics Data System (ADS)

Barnie, Talfan; Conway, Susan; Balme, Matt; Graham, Alastair

2017-04-01

High resolution maps of permafrost are required for ongoing monitoring of environmental change and the resulting hazards to ecosystems, people and infrastructure. However, permafrost maps are difficult to construct - direct observations require maintaining networks of sensors and boreholes in harsh environments and are thus limited in extent in space and time, and indirect observations require models or assumptions relating the measurements (e.g. weather station air temperature, basal snow temperature) to ground temperature. Operationally produced Land Surface Temperature maps from Earth Observation data can be used to make spatially contiguous estimates of mean annual skin temperature, which has been used a proxy for the presence of permafrost. However these maps are subject to biases due to (i) selective sampling during the day due to limited satellite overpass times, (ii) selective sampling over the year due to seasonally varying cloud cover, (iii) selective sampling of LST only during clearsky conditions, (iv) errors in cloud masking (v) errors in temperature emissivity separation (vi) smoothing over spatial variability. In this study we attempt to compensate for some of these problems using a bayesian modelling approach and high resolution topography-based downscaling.

How spatial and temporal rainfall variability affect runoff across basin scales: insights from field observations in the (semi-)urbanised Charlotte watershed

NASA Astrophysics Data System (ADS)

Ten Veldhuis, M. C.; Smith, J. A.; Zhou, Z.

2017-12-01

Impacts of rainfall variability on runoff response are highly scale-dependent. Sensitivity analyses based on hydrological model simulations have shown that impacts are likely to depend on combinations of storm type, basin versus storm scale, temporal versus spatial rainfall variability. So far, few of these conclusions have been confirmed on observational grounds, since high quality datasets of spatially variable rainfall and runoff over prolonged periods are rare. Here we investigate relationships between rainfall variability and runoff response based on 30 years of radar-rainfall datasets and flow measurements for 16 hydrological basins ranging from 7 to 111 km2. Basins vary not only in scale, but also in their degree of urbanisation. We investigated temporal and spatial variability characteristics of rainfall fields across a range of spatial and temporal scales to identify main drivers for variability in runoff response. We identified 3 ranges of basin size with different temporal versus spatial rainfall variability characteristics. Total rainfall volume proved to be the dominant agent determining runoff response at all basin scales, independent of their degree of urbanisation. Peak rainfall intensity and storm core volume are of secondary importance. This applies to all runoff parameters, including runoff volume, runoff peak, volume-to-peak and lag time. Position and movement of the storm with respect to the basin have a negligible influence on runoff response, with the exception of lag times in some of the larger basins. This highlights the importance of accuracy in rainfall estimation: getting the position right but the volume wrong will inevitably lead to large errors in runoff prediction. Our study helps to identify conditions where rainfall variability matters for correct estimation of the rainfall volume as well as the associated runoff response.

Spatial-Orientation Priming Impedes Rather than Facilitates the Spontaneous Control of Hand-Retraction Speeds in Patients with Parkinson’s Disease

PubMed Central

Yanovich, Polina; Isenhower, Robert W.; Sage, Jacob; Torres, Elizabeth B.

2013-01-01

Background Often in Parkinson’s disease (PD) motor-related problems overshadow latent non-motor deficits as it is difficult to dissociate one from the other with commonly used observational inventories. Here we ask if the variability patterns of hand speed and acceleration would be revealing of deficits in spatial-orientation related decisions as patients performed a familiar reach-to-grasp task. To this end we use spatial-orientation priming which normally facilitates motor-program selection and asked whether in PD spatial-orientation priming helps or hinders performance. Methods To dissociate spatial-orientation- and motor-related deficits participants performed two versions of the task. The biomechanical version (DEFAULT) required the same postural- and hand-paths as the orientation-priming version (primed-UP). Any differences in the patients here could not be due to motor issues as the tasks were biomechanically identical. The other priming version (primed-DOWN) however required additional spatial and postural processing. We assessed in all three cases both the forward segment deliberately aimed towards the spatial-target and the retracting segment, spontaneously bringing the hand to rest without an instructed goal. Results and Conclusions We found that forward and retracting segments belonged in two different statistical classes according to the fluctuations of speed and acceleration maxima. Further inspection revealed conservation of the forward (voluntary) control of speed but in PD a discontinuity of this control emerged during the uninstructed retractions which was absent in NC. Two PD groups self-emerged: one group in which priming always affected the retractions and the other in which only the more challenging primed-DOWN condition was affected. These PD-groups self-formed according to the speed variability patterns, which systematically changed along a gradient that depended on the priming, thus dissociating motor from spatial-orientation issues. Priming did not facilitate the motor task in PD but it did reveal a breakdown in the spatial-orientation decision that was independent of the motor-postural path. PMID:23843963

Interferometric Synthetic Aperture Radar to capture spatial variability of local land-based subsidence

NASA Astrophysics Data System (ADS)

Bekaert, D. P.; Hamlington, B.; Buzzanga, B. A.; Jones, C. E.

2017-12-01

The rate of relative sea level rise results from a combination of land subsidence and rising seas associated with global warming on long timescales and exacerbated by shifts in ocean dynamics on shorter timescales. An understanding of the current-day magnitude of each component is needed to create accurate projections of future relative sea level rise upon which to base planning efforts. Current day land-based subsidence rates derived from GPS often lack the spatial resolution to capture the local spatial variability needed when assessing the impact of relative sea-level rise. Interferometric Synthetic Aperture Radar (InSAR) is an attractive technique that has the potential to provide a measurement every 20-30m when good signal coherence is maintained. In practice, coastal regions are challenging for InSAR due to variable vegetation cover and soil moisture, which can be in part mitigated by applying advanced time-series InSAR techniques. After applying time-series InSAR, derived rates need to be combined with GPS to tie relative subsidence rates into a geodetic reference frame. Given the need to make projections of relative sea-level rise it is particularly important to propagate all uncertainties during the different processing stages. Here we provide results from ALOS and Sentinel-1 over Hampton Roads area in the Chesapeake Bay region, which is experiencing one of the highest rates of relative sea level rise on the Atlantic coast of the United States. Although the current derived subsidence rates have large uncertainties, it is expected that this will improve with the decadal observations from Sentinel-1.

A comprehensive overview of the spatial and temporal variability of apple bud dormancy release and blooming phenology in Western Europe

NASA Astrophysics Data System (ADS)

Legave, Jean Michel; Blanke, Michael; Christen, Danilo; Giovannini, Daniela; Mathieu, Vincent; Oger, Robert

2013-03-01

In the current context of global warming, an analysis is required of spatially-extensive and long-term blooming data in fruit trees to make up for insufficient information on regional-scale blooming changes and determinisms that are key to the phenological adaptation of these species. We therefore analysed blooming dates over long periods at climate-contrasted sites in Western Europe, focusing mainly on the Golden Delicious apple that is grown worldwide. On average, blooming advances were more pronounced in northern continental (10 days) than in western oceanic (6-7 days) regions, while the shortest advance was found on the Mediterranean coastline. Temporal trends toward blooming phase shortenings were also observed in continental regions. These regional differences in temporal variability across Western Europe resulted in a decrease in spatial variability, i.e. shorter time intervals between blooming dates in contrasted regions (8-10-day decrease for full bloom between Mediterranean and continental regions). Fitted sequential models were used to reproduce phenological changes. Marked trends toward shorter simulated durations of forcing period (bud growth from dormancy release to blooming) and high positive correlations between these durations and observed blooming dates support the notion that blooming advances and shortenings are mainly due to faster satisfaction of the heating requirement. However, trends toward later dormancy releases were also noted in oceanic and Mediterranean regions. This could tend toward blooming delays and explain the shorter advances in these regions despite similar or greater warming. The regional differences in simulated chilling and forcing periods were consistent with the regional differences in temperature increases.

Variability of the raindrop size distribution at small spatial scales

NASA Astrophysics Data System (ADS)

Berne, A.; Jaffrain, J.

2010-12-01

Because of the interactions between atmospheric turbulence and cloud microphysics, the raindrop size distribution (DSD) is strongly variable in space and time. The spatial variability of the DSD at small spatial scales (below a few km) is not well documented and not well understood, mainly because of a lack of adequate measurements at the appropriate resolutions. A network of 16 disdrometers (Parsivels) has been designed and set up over EPFL campus in Lausanne, Switzerland. This network covers a typical operational weather radar pixel of 1x1 km2. The question of the significance of the variability of the DSD at such small scales is relevant for radar remote sensing of rainfall because the DSD is often assumed to be uniform within a radar sample volume and because the Z-R relationships used to convert the measured radar reflectivity Z into rain rate R are usually derived from point measurements. Thanks to the number of disdrometers, it was possible to quantify the spatial variability of the DSD at the radar pixel scale and to show that it can be significant. In this contribution, we show that the variability of the total drop concentration, of the median volume diameter and of the rain rate are significant, taking into account the sampling uncertainty associated with disdrometer measurements. The influence of this variability on the Z-R relationship can be non-negligible. Finally, the spatial structure of the DSD is quantified using a geostatistical tool, the variogram, and indicates high spatial correlation within a radar pixel.

Timing of floods in southeastern China: Seasonal properties and potential causes

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Gu, Xihui; Singh, Vijay P.; Shi, Peijun; Luo, Ming

2017-09-01

Flood hazards and flood risks in southeastern China have been causing increasing concerns due to dense population and highly-developed economy. This study attempted to address changes of seasonality, timing of peak floods and variability of occurrence date of peak floods using circular statistical methods and the modified Mann-Kendall trend detection method. The causes of peak flood changes were also investigated. Results indicated that: (1) floods were subject to more seasonality and temporal clustering when compared to precipitation extremes. However, seasonality of floods and extreme precipitation was subject to spatial heterogeneity in northern Guangdong. Similar changing patterns of peak floods and extreme precipitation were found in coastal regions; (2) significant increasing/decreasing seasonality, but no confirmed spatial patterns, were observed for peak floods and extreme precipitation. Peak floods in northern Guangdong province had decreasing variability, but had larger variability in coastal regions; (3) tropical cyclones had remarkable impacts on extreme precipitation changes in coastal regions of southeastern China, and peak floods as well. The landfalling of tropical cyclones was decreasing and concentrated during June-September; this is the major reason for earlier but enhanced seasonality of peak floods in coastal regions. This study sheds new light on flood behavior in coastal regions in a changing environment.

Detecting seasonal flood changes in the Upper Danube River basin

NASA Astrophysics Data System (ADS)

Kohnová, Silvia; Jeneiová, Katarína; Parajka, Juraj; Hall, Julia; Marková, Romana

2017-04-01

Due to a number of large-scale floods observed worldwide in recent years, the analysis of changes in long-term hydrological time series is becoming increasingly important. This study focuses on the Upper Danube region, which was struck by many flood events in the past decade. The flood seasonality of the study region, defined as the area of Germany, the Czech Republic, Switzerland, Austria and Slovakia, is examined to interpret the dominant flood processes. A spatial assessment of the seasonality indices of the annual maximum discharges and the seasonal discharges (derived from daily average discharges) was conducted for 117 gauging stations. Hot spots for potential changes in the mean dates of occurrence of the discharges were identified, and the results were linked with derived spatial characteristics for the catchments. The first results of the study of the seasonal discharges revealed that the variability of occurrence of summer floods is higher than winter floods in lowlands of the upper Danube catchment. In high Alpine catchments the winter floods variability of occurrence is the same or higher than for the summer floods. The summer season floods tend to appear for all catchment sizes in the same time period. With increased magnitude of floods in the summer season, the variability of occurrence of the floods is higher.

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

PubMed

Fu, Michael J; Cavuşoğlu, M Cenk

2012-12-01

Haptic interface research benefits from accurate human arm models for control and system design. The literature contains many human arm dynamic models but lacks detailed variability analyses. Without accurate measurements, variability is modeled in a very conservative manner, leading to less than optimal controller and system designs. This paper not only presents models for human arm dynamics but also develops inter- and intrasubject variability models for a stylus-based haptic device. Data from 15 human subjects (nine male, six female, ages 20-32) were collected using a Phantom Premium 1.5a haptic device for system identification. In this paper, grip-force-dependent models were identified for 1-3-N grip forces in the three spatial axes. Also, variability due to human subjects and grip-force variation were modeled as both structured and unstructured uncertainties. For both forms of variability, the maximum variation, 95 %, and 67 % confidence interval limits were examined. All models were in the frequency domain with force as input and position as output. The identified models enable precise controllers targeted to a subset of possible human operator dynamics.

Stochastic calculus of protein filament formation under spatial confinement

NASA Astrophysics Data System (ADS)

Michaels, Thomas C. T.; Dear, Alexander J.; Knowles, Tuomas P. J.

2018-05-01

The growth of filamentous aggregates from precursor proteins is a process of central importance to both normal and aberrant biology, for instance as the driver of devastating human disorders such as Alzheimer's and Parkinson's diseases. The conventional theoretical framework for describing this class of phenomena in bulk is based upon the mean-field limit of the law of mass action, which implicitly assumes deterministic dynamics. However, protein filament formation processes under spatial confinement, such as in microdroplets or in the cellular environment, show intrinsic variability due to the molecular noise associated with small-volume effects. To account for this effect, in this paper we introduce a stochastic differential equation approach for investigating protein filament formation processes under spatial confinement. Using this framework, we study the statistical properties of stochastic aggregation curves, as well as the distribution of reaction lag-times. Moreover, we establish the gradual breakdown of the correlation between lag-time and normalized growth rate under spatial confinement. Our results establish the key role of spatial confinement in determining the onset of stochasticity in protein filament formation and offer a formalism for studying protein aggregation kinetics in small volumes in terms of the kinetic parameters describing the aggregation dynamics in bulk.

Evidence of a robust relationship between solar-induced chlorophyll fluorescence and gross primary productivity across dryland ecosystems of southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J.; Kimball, J. S.; He, M.; Yan, D.; Hudson, A.; Barnes, M.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2017-12-01

Satellite remote sensing provides unmatched spatiotemporal information on multiple facets of vegetation dynamics including seasonal to interannual total photosynthesis, termed gross primary productivity (GPP). Yet, our understanding of the relationship between GPP and remote sensing observations - and how this relationship changes with scale, biophysical constraint, vegetation type, etc. - remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have high spatial and temporal variability and are under-represented by long-term, continuous field measurements. Here, utilizing a new synthesis of eddy covariance flux tower data for southwestern North America, we present a first assessment of the ability of novel satellite remote sensing vegetation proxies to accurately capture seasonal to interannual GPP dynamics across the region. We evaluate the greenness-based Enhanced Vegetation Index (EVI) and emerging proxies linked to plant physiological function, Solar-Induced Fluorescence (SIF) and Photochemical Reflectivity Index (PRI). We find that SIF observations more consistently correlate with seasonal GPP dynamics (R = 0.90) compared to EVI (R = 0.85) and PRI (R = 0.78). More, we find that SIF observations are also more sensitive to interannual GPP variability (linear slope = 0.80) relative to EVI (linear slope = 0.63) and PRI (linear slope = 0.35). This is likely due to increased sensitivity of SIF to GPP during periods of decoupling between greenness and photosynthesis due to water-limitation / stomatal closure. Conversely, EVI and PRI observations better capture spatial GPP variability between flux tower sites. These results suggest that combinations of these independent vegetation growth proxies could yield synergistic improvements in satellite-based GPP estimates.

Fluxes of dissolved organic carbon and nitrogen to the northern Indian Ocean from the Indian monsoonal rivers

NASA Astrophysics Data System (ADS)

Krishna, M. S.; Prasad, V. R.; Sarma, V. V. S. S.; Reddy, N. P. C.; Hemalatha, K. P. J.; Rao, Y. V.

2015-10-01

Dissolved organic carbon (DOC) and nitrogen (DON) were measured in 27 major and medium monsoonal estuaries along the Indian coast during southwest monsoon in order to understand the spatial variability in their concentrations and fluxes to the northern Indian Ocean. A strong spatial variability (~20-fold) in DOC and DON was observed in the Indian monsoonal estuaries due to variable characteristics of the catchment area and volume of discharge. It is estimated that the Indian monsoonal estuaries transport ~2.37 ± 0.47 Tg (1 Tg = 1012 g) of DOC and ~0.41 ± 0.08 Tg of DON during wet period to the northern Indian Ocean. The Bay of Bengal receives 3 times higher DOC and DON (1.82 and 0.30 Tg, respectively) than the Arabian Sea (0.55 and 0.11 Tg). Catchment area normalized fluxes of DOC and DON were found to be higher in the estuaries located in the southwestern than the estuaries from other regions of India. It was attributed to relatively higher soil organic carbon, biomass carbon, and heavy rainfall in catchment areas of the rivers from the former region. It has been noticed that neither catchment area nor discharge volume of the river controls the fluxes of DOC and DON to the northern Indian Ocean. Since the total load of DOC and DON is strongly linked to the volume of discharge, alterations in the freshwater discharge due to natural or anthropogenic activities may have significant influence on organic matter fluxes to the Indian coastal waters and its impact on microbial food web dynamics needs further evaluation.

Quantifying the processes controlling intraseasonal mixed-layer temperature variability in the tropical Indian Ocean

NASA Astrophysics Data System (ADS)

Halkides, D. J.; Waliser, Duane E.; Lee, Tong; Menemenlis, Dimitris; Guan, Bin

2015-02-01

Spatial and temporal variation of processes that determine ocean mixed-layer (ML) temperature (MLT) variability on the timescale of the Madden-Julian Oscillation (MJO) in the Tropical Indian Ocean (TIO) are examined in a heat-conserving ocean state estimate for years 1993-2011. We introduce a new metric for representing spatial variability of the relative importance of processes. In general, horizontal advection is most important at the Equator. Subsurface processes and surface heat flux are more important away from the Equator, with surface heat flux being the more dominant factor. Analyses at key sites are discussed in the context of local dynamics and literature. At 0°, 80.5°E, for MLT events > 2 standard deviations, ocean dynamics account for more than two thirds of the net tendency during cooling and warming phases. Zonal advection alone accounts for ˜40% of the net tendency. Moderate events (1-2 standard deviations) show more differences between events, and some are dominated by surface heat flux. At 8°S, 67°E in the Seychelles-Chagos Thermocline Ridge (SCTR) area, surface heat flux accounts for ˜70% of the tendency during strong cooling and warming phases; subsurface processes linked to ML depth (MLD) deepening (shoaling) during cooling (warming) account for ˜30%. MLT is more sensitive to subsurface processes in the SCTR, due to the thin MLD, thin barrier layer and raised thermocline. Results for 8°S, 67°E support assertions by Vialard et al. (2008) not previously confirmed due to measurement error that prevented budget closure and the small number of events studied. The roles of MLD, barrier layer thickness, and thermocline depth on different timescales are examined.

Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements

NASA Astrophysics Data System (ADS)

Castro, S. L.; Emery, W. J.; Tandy, W., Jr.; Good, W. S.

2017-12-01

Technological advances in spatial resolution of observations have revealed the importance of short-lived ocean processes with scales of O(1km). These submesoscale processes play an important role for the transfer of energy from the meso- to small scales and for generating significant spatial and temporal intermittency in the upper ocean, critical for the mixing of the oceanic boundary layer. Submesoscales have been observed in sea surface temperatures (SST) from satellites. Satellite SST measurements are spatial averages over the footprint of the satellite. When the variance of the SST distribution within the footprint is small, the average value is representative of the SST over the whole pixel. If the variance is large, the spatial heterogeneity is a source of uncertainty in satellite derived SSTs. Here we show evidence that the submesoscale variability in SSTs at spatial scales of 1km is responsible for the spatial variability within satellite footprints. Previous studies of the spatial variability in SST, using ship-based radiometric data suggested that variability at scales smaller than 1 km is significant and affects the uncertainty of satellite-derived skin SSTs. We examine data collected by a calibrated thermal infrared radiometer, the Ball Experimental Sea Surface Temperature (BESST), flown on a UAV over the Arctic Ocean and compare them with coincident measurements from the MODIS spaceborne radiometer to assess the spatial variability of SST within 1 km pixels. By taking the standard deviation of all the BESST measurements within individual MODIS pixels we show that significant spatial variability exists within the footprints. The distribution of the surface variability measured by BESST shows a peak value of O(0.1K) with 95% of the pixels showing σ < 0.45K. More importantly, high-variability pixels are located at density fronts in the marginal ice zone, which are a primary source of submesoscale intermittency near the surface in the Arctic Ocean. Wavenumber spectra of the BESST SSTs indicate a spectral slope of -2, consistent with the presence of submesoscale processes. Furthermore, not only is the BESST wavenumber spectra able to match the MODIS SST spectra well, but also extends the spectral slope of -2 by 2 decades relative to MODIS, from wavelengths of 8km to 0.08km.

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation

NASA Technical Reports Server (NTRS)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.

2010-01-01

Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.

A new approach in space-time analysis of multivariate hydrological data: Application to Brazil's Nordeste region rainfall

NASA Astrophysics Data System (ADS)

Sicard, Emeline; Sabatier, Robert; Niel, HéLèNe; Cadier, Eric

2002-12-01

The objective of this paper is to implement an original method for spatial and multivariate data, combining a method of three-way array analysis (STATIS) with geostatistical tools. The variables of interest are the monthly amounts of rainfall in the Nordeste region of Brazil, recorded from 1937 to 1975. The principle of the technique is the calculation of a linear combination of the initial variables, containing a large part of the initial variability and taking into account the spatial dependencies. It is a promising method that is able to analyze triple variability: spatial, seasonal, and interannual. In our case, the first component obtained discriminates a group of rain gauges, corresponding approximately to the Agreste, from all the others. The monthly variables of July and August strongly influence this separation. Furthermore, an annual study brings out the stability of the spatial structure of components calculated for each year.

Ecosystem variability along the estuarine salinity gradient: Examples from long-term study of San Francisco Bay

USGS Publications Warehouse

Cloern, James E.; Jassby, Alan D.; Schraga, Tara; Kress, Erica S.; Martin, Charles A.

2017-01-01

The salinity gradient of estuaries plays a unique and fundamental role in structuring spatial patterns of physical properties, biota, and biogeochemical processes. We use variability along the salinity gradient of San Francisco Bay to illustrate some lessons about the diversity of spatial structures in estuaries and their variability over time. Spatial patterns of dissolved constituents (e.g., silicate) can be linear or nonlinear, depending on the relative importance of river-ocean mixing and internal sinks (diatom uptake). Particles have different spatial patterns because they accumulate in estuarine turbidity maxima formed by the combination of sinking and estuarine circulation. Some constituents have weak or no mean spatial structure along the salinity gradient, reflecting spatially distributed sources along the estuary (nitrate) or atmospheric exchanges that buffer spatial variability of ecosystem metabolism (dissolved oxygen). The density difference between freshwater and seawater establishes stratification in estuaries stronger than the thermal stratification of lakes and oceans. Stratification is strongest around the center of the salinity gradient and when river discharge is high. Spatial distributions of motile organisms are shaped by species-specific adaptations to different salinity ranges (shrimp) and by behavioral responses to environmental variability (northern anchovy). Estuarine spatial patterns change over time scales of events (intrusions of upwelled ocean water), seasons (river inflow), years (annual weather anomalies), and between eras separated by ecosystem disturbances (a species introduction). Each of these lessons is a piece in the puzzle of how estuarine ecosystems are structured and how they differ from the river and ocean ecosystems they bridge.

Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

NASA Astrophysics Data System (ADS)

Ryu, D.; Liu, S.; Western, A. W.; Webb, J. A.; Lintern, A.; Leahy, P.; Wilson, P.; Watson, M.; Waters, D.; Bende-Michl, U.

2016-12-01

The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites, spatial variations in water quality are likely from the agricultural inputs (fertilises) and for the Cluster 2 sites, the differences in hydrological transport is responsible for large spatial variations in water quality. These findings can be applied to water quality assessment along with establish effective water and land management in the future.

Measuring variability in trophic status in the Lake Waco/Bosque River Watershed

PubMed Central

Rodriguez, Angela D; Matlock, Marty D

2008-01-01

Background Nutrient management in rivers and streams is difficult due to the spatial and temporal variability of algal growth responses. The objectives of this project were to determine the spatial and seasonal in situ variability of trophic status in the Lake Waco/Bosque River watershed, determine the variability in the lotic ecosystem trophic status index (LETSI) at each site as indicators of the system's nutrient sensitivity, and determine if passive diffusion periphytometers could provide threshold algal responses to nutrient enrichment. Methods We used the passive diffusion periphytometer to measure in-situ nutrient limitation and trophic status at eight sites in five streams in the Lake Waco/Bosque River Watershed in north-central Texas from July 1997 through October 1998. The chlorophyll a production in the periphytometers was used as an indicator of baseline chlorophyll a productivity and of maximum primary productivity (MPP) in response to nutrient enrichment (nitrogen and phosphorus). We evaluated the lotic ecosystem trophic status index (LETSI) using the ratio of baseline primary productivity to MPP, and evaluated the trophic class of each site. Results The rivers and streams in the Lake Waco/Bosque River Watershed exhibited varying degrees of nutrient enrichment over the 18-month sampling period. The North Bosque River at the headwaters (NB-02) located below the Stephenville, Texas wastewater treatment outfall consistently exhibited the highest degree of water quality impact due to nutrient enrichment. Sites at the outlet of the watershed (NB-04 and NB-05) were the next most enriched sites. Trophic class varied for enriched sites over seasons. Conclusion Seasonality played a significant role in the trophic class and sensitivity of each site to nutrients. Managing rivers and streams for nutrients will require methods for measuring in situ responses and sensitivities to nutrient enrichment. Nutrient enrichment periphytometers show significant potential for use in nutrient gradient studies. PMID:18271947

Measuring variability in trophic status in the Lake Waco/Bosque River Watershed.

PubMed

Rodriguez, Angela D; Matlock, Marty D

2008-01-11

Nutrient management in rivers and streams is difficult due to the spatial and temporal variability of algal growth responses. The objectives of this project were to determine the spatial and seasonal in situ variability of trophic status in the Lake Waco/Bosque River watershed, determine the variability in the lotic ecosystem trophic status index (LETSI) at each site as indicators of the system's nutrient sensitivity, and determine if passive diffusion periphytometers could provide threshold algal responses to nutrient enrichment. We used the passive diffusion periphytometer to measure in-situ nutrient limitation and trophic status at eight sites in five streams in the Lake Waco/Bosque River Watershed in north-central Texas from July 1997 through October 1998. The chlorophyll a production in the periphytometers was used as an indicator of baseline chlorophyll a productivity and of maximum primary productivity (MPP) in response to nutrient enrichment (nitrogen and phosphorus). We evaluated the lotic ecosystem trophic status index (LETSI) using the ratio of baseline primary productivity to MPP, and evaluated the trophic class of each site. The rivers and streams in the Lake Waco/Bosque River Watershed exhibited varying degrees of nutrient enrichment over the 18-month sampling period. The North Bosque River at the headwaters (NB-02) located below the Stephenville, Texas wastewater treatment outfall consistently exhibited the highest degree of water quality impact due to nutrient enrichment. Sites at the outlet of the watershed (NB-04 and NB-05) were the next most enriched sites. Trophic class varied for enriched sites over seasons. Seasonality played a significant role in the trophic class and sensitivity of each site to nutrients. Managing rivers and streams for nutrients will require methods for measuring in situ responses and sensitivities to nutrient enrichment. Nutrient enrichment periphytometers show significant potential for use in nutrient gradient studies.

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model

PubMed Central

Cao, Chunxiang; Xu, Min; Pandit, Shreejana

2018-01-01

Both the number of cases of dengue fever and the areas of outbreaks within Nepal have increased significantly in recent years. Further expansion and range shift is expected in the future due to global climate change and other associated factors. However, due to limited spatially-explicit research in Nepal, there is poor understanding about the present spatial distribution patterns of dengue risk areas and the potential range shift due to future climate change. In this context, it is crucial to assess and map dengue fever risk areas in Nepal. Here, we used reported dengue cases and a set of bioclimatic variables on the MaxEnt ecological niche modeling approach to model the climatic niche and map present and future (2050s and 2070s) climatically suitable areas under different representative concentration pathways (RCP2.6, RCP6.0 and RCP8.5). Simulation-based estimates suggest that climatically suitable areas for dengue fever are presently distributed throughout the lowland Tarai from east to west and in river valleys at lower elevations. Under the different climate change scenarios, these areas will be slightly shifted towards higher elevation with varied magnitude and spatial patterns. Population exposed to climatically suitable areas of dengue fever in Nepal is anticipated to further increase in both 2050s and 2070s on all the assumed emission scenarios. These findings could be instrumental to plan and execute the strategic interventions for controlling dengue fever in Nepal. PMID:29360797

Experimental whole-lake increase of dissolved organic carbon concentration produces unexpected increase in crustacean zooplankton density

USGS Publications Warehouse

Kelly, Patrick T.; Craig, Nicola; Solomon, Christopher T.; Weidel, Brian C.; Zwart, Jacob A.; Jones, Stuart E.

2016-01-01

The observed pattern of lake browning, or increased terrestrial dissolved organic carbon (DOC) concentration, across the northern hemisphere has amplified the importance of understanding how consumer productivity varies with DOC concentration. Results from comparative studies suggest these increased DOC concentrations may reduce crustacean zooplankton productivity due to reductions in resource quality and volume of suitable habitat. Although these spatial comparisons provide an expectation for the response of zooplankton productivity as DOC concentration increases, we still have an incomplete understanding of how zooplankton respond to temporal increases in DOC concentration within a single system. As such, we used a whole-lake manipulation, in which DOC concentration was increased from 8 to 11 mg L−1 in one basin of a manipulated lake, to test the hypothesis that crustacean zooplankton production should subsequently decrease. In contrast to the spatially derived expectation of sharp DOC-mediated decline, we observed a small increase in zooplankton densities in response to our experimental increase in DOC concentration of the treatment basin. This was due to significant increases in gross primary production and resource quality (lower seston carbon-to-phosphorus ratio; C:P). These results demonstrate that temporal changes in lake characteristics due to increased DOC may impact zooplankton in ways that differ from those observed in spatial surveys. We also identified significant interannual variability across our study region, which highlights potential difficulty in detecting temporal responses of organism abundances to gradual environmental change (e.g., browning).

Extense historical droughts in Spain derived from documentary sources

NASA Astrophysics Data System (ADS)

Dominguez-Castro, F.; García-Herrera, R.; Barriendos, M.

2009-09-01

Documentary records, specially those from rogation ceremonies have been extensively used to build proxy series of droughts and floods in Spain. Most of the work done previously has focused in the abstraction of the documents and building of the individual series, but less attention has been paid to the joint analysis of this type of records. This is problematic because, due to the diversity of Spanish climates, the climatological meaning of the rogation ceremonies changes depending on the considered region. This paper aims to analyse the spatial extension of drought events from the rogation records from Barcelona, Bilbao, Gerona, Murcia, Seville, Tarragona, Toledo, Tortosa and Zamora, which cover the 16th to 19th centuries. The representativeness of each of them is analysed taking into account the local climate and the series variability. Then the spatial scale of the recorded droughts is examined at seasonal scale. The results show high multidecadal variability, with the driest periods at national scale recorded during the 1680s, 1730s and 1780s. Finally, the dry years of 1680, 1683 and 1817 are analysed in detail.

Asymmetric multiscale multifractal analysis of wind speed signals

NASA Astrophysics Data System (ADS)

Zhang, Xiaonei; Zeng, Ming; Meng, Qinghao

We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.

Spatial heterogeneity of within-stream methane concentrations

NASA Astrophysics Data System (ADS)

Crawford, John T.; Loken, Luke C.; West, William E.; Crary, Benjamin; Spawn, Seth A.; Gubbins, Nicholas; Jones, Stuart E.; Striegl, Robert G.; Stanley, Emily H.

2017-05-01

Streams, rivers, and other freshwater features may be significant sources of CH4 to the atmosphere. However, high spatial and temporal variabilities hinder our ability to understand the underlying processes of CH4 production and delivery to streams and also challenge the use of scaling approaches across large areas. We studied a stream having high geomorphic variability to assess the underlying scale of CH4 spatial variability and to examine whether the physical structure of a stream can explain the variation in surface CH4. A combination of high-resolution CH4 mapping, a survey of groundwater CH4 concentrations, quantitative analysis of methanogen DNA, and sediment CH4 production potentials illustrates the spatial and geomorphic controls on CH4 emissions to the atmosphere. We observed significant spatial clustering with high CH4 concentrations in organic-rich stream reaches and lake transitions. These sites were also enriched in the methane-producing mcrA gene and had highest CH4 production rates in the laboratory. In contrast, mineral-rich reaches had significantly lower concentrations and had lesser abundances of mcrA. Strong relationships between CH4 and the physical structure of this aquatic system, along with high spatial variability, suggest that future investigations will benefit from viewing streams as landscapes, as opposed to ecosystems simply embedded in larger terrestrial mosaics. In light of such high spatial variability, we recommend that future workers evaluate stream networks first by using similar spatial tools in order to build effective sampling programs.

Spatial heterogeneity in the carrying capacity of sika deer in Japan

PubMed Central

Iijima, Hayato; Ueno, Mayumi

2016-01-01

Abstract Carrying capacity is 1 driver of wildlife population dynamics. Although in previous studies carrying capacity was considered to be a fixed entity, it may differ among locations due to environmental variation. The factors underlying variability in carrying capacity, however, have rarely been examined. Here, we investigated spatial heterogeneity in the carrying capacity of Japanese sika deer ( Cervus nippon ) from 2005 to 2014 in Yamanashi Prefecture, central Japan (mesh with grid cells of 5.5×4.6 km) by state-space modeling. Both carrying capacity and density dependence differed greatly among cells. Estimated carrying capacities ranged from 1.34 to 98.4 deer/km 2 . According to estimated population dynamics, grid cells with larger proportions of artificial grassland and deciduous forest were subject to lower density dependence and higher carrying capacity. We conclude that population dynamics of ungulates may vary spatially through spatial variation in carrying capacity and that the density level for controlling ungulate abundance should be based on the current density level relative to the carrying capacity for each area. PMID:29692470

Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. The paper will conclude by discussing the user needs of satellite rainfall retrievals from a climate change modelling prospective.

A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica.

PubMed

Svob, Sienna; Arroyo-Mora, J Pablo; Kalacska, Margaret

2014-12-01

The high spatio-temporal variability of aboveground biomass (AGB) in tropical forests is a large source of uncertainty in forest carbon stock estimation. Due to their spatial distribution and sampling intensity, pre-felling inventories are a potential source of ground level data that could help reduce this uncertainty at larger spatial scales. Further, exploring the factors known to influence tropical forest biomass, such as wood density and large tree density, will improve our knowledge of biomass distribution across tropical regions. Here, we evaluate (1) the variability of wood density and (2) the variability of AGB across five ecosystems of Costa Rica. Using forest management (pre-felling) inventories we found that, of the regions studied, Huetar Norte had the highest mean wood density of trees with a diameter at breast height (DBH) greater than or equal to 30 cm, 0.623 ± 0.182 g cm -3 (mean ± standard deviation). Although the greatest wood density was observed in Huetar Norte, the highest mean estimated AGB (EAGB) of trees with a DBH greater than or equal to 30 cm was observed in Osa peninsula (173.47 ± 60.23 Mg ha -1 ). The density of large trees explained approximately 50% of EAGB variability across the five ecosystems studied. Comparing our study's EAGB to published estimates reveals that, in the regions of Costa Rica where AGB has been previously sampled, our forest management data produced similar values. This study presents the most spatially rich analysis of ground level AGB data in Costa Rica to date. Using forest management data, we found that EAGB within and among five Costa Rican ecosystems is highly variable. Combining commercial logging inventories with ecological plots will provide a more representative ground level dataset for the calibration of the models and remotely sensed data used to EAGB at regional and national scales. Additionally, because the non-protected areas of the tropics offer the greatest opportunity to reduce rates of deforestation and forest degradation, logging inventories offer a promising source of data to support mechanisms such as the United Nations REDD + (Reducing Emissions from Tropical Deforestation and Degradation) program.

Thermohaline variability in the Adriatic and Northern Ionian Seas observed from the Argo floats during 2010-2014

NASA Astrophysics Data System (ADS)

Kovačević, Vedrana; Ursella, Laura; Gačić, Miroslav; Notarstefano, Giulio; Menna, Milena; Bensi, Manuel; Civitarese, Giuseppe; Poulain, Pierre-Marie

2015-04-01

The Adriatic Sea is the northernmost basin of the Eastern Mediterranean Sea (EMed). At its southern end, the basin communicates with the adjacent Ionian Sea through the 80 km wide and 850 m deep Strait of Otranto. Due to the river discharge in the north and due to the strong winter cooling, the Adriatic is both a dilution basin and the dense water formation region. The basin-wide circulation is cyclonic. The circulation is however, energetic also at smaller spatial and temporal scales, and several circulation cells and mesoscale features are regularly observed equally along the littoral and in the open sea. The North Adriatic Dense Water (NAdDW) formed during winter is the densest water of the whole Mediterranean Sea (up to 1060 kg/m3). It flows as a density driven bottom current from the northern shelf toward south, filling the deep layers of the middle and southern Adriatic pits. The deep open-sea area of the South Adriatic Pit (SAP, 1200 m) feels the influence of a water mass exchange through the Strait of Otranto. Specifically, it receives salty and warm surface and Levantine Intermediate Waters from the Ionian Sea. Through the open-sea winter convection that homogenizes and ventilates 400-800 m thick upper water column, this salty water contributes to the formation of the Adriatic Deep Water (AdDW, 1029.17-1029.20 kg/m3), which is not as dense as the NAdDW. Both dense waters eventually mix and spill across the sill ventilating the deep and bottom layers of the Ionian Sea, and driving the deep thermohaline cell of the EMed. Thermohaline properties of the Adriatic Sea vary at wide spatial and temporal scales, and this in turn affects the properties of its dense waters. The long-term scales are of a particular interest, as they are often associated with the biogeochemical and biotic variability such as intrusion of alien species into the Adriatic Sea and interconnection with the adjacent Ionian basin. Due to the extremely variable meteo- and climatic conditions, the signal of the Adriatic dense waters can be fairly irregular and impulsive. Sporadic in-situ surveys by research vessels are not always sufficient to capture this irregularity and its consequences on the circulation. The Lagrangian platforms are disseminated within the whole Mediterranean through the international Argo program. They are a useful tool to assess some of the spatial and temporal variability in the two basins. Combining the information from the floats and in-situ CTD profiles from oceanographic campaigns, we picture the inter-annual variability of the thermohaline properties in general during 2010-2014. In addition, the peculiarities of the very dense water overflow that during 2012 spilled out form the Strait of Otranto into the Northern Ionian is evidenced. Also, by the remotely sensed sea surface topography, we depict the most prominent circulation features of the upper layer.

Crop traceability and remote sensing in tree fruit

NASA Astrophysics Data System (ADS)

Perry, Eileen M.; Rupp, Richard; Davenport, Joan; Leal, Juliano; Pierce, Francis J.; Schulthess, Urs

2004-01-01

Fresh market fruit crops such as apples have not employed precision agriculture tools, partially due to the labor intensive nature of the cropping systems. In this paper we describe new research in the development of precision agriculture tools for tree fruit, including the ability to track spatially variable orchard data before harvest through to the packing plant. Remote sensing is a key component of this system, and remote sensing products are being evaluated for their usefulness in guiding orchard management.

Trait-based plant ecology: moving towards a unifying species coexistence theory : Features of the Special Section.

PubMed

Escudero, Adrián; Valladares, Fernando

2016-04-01

Functional traits are the center of recent attempts to unify key ecological theories on species coexistence and assembling in populations and communities. While the plethora of studies on the role of functional traits to explain patterns and dynamics of communities has rendered a complex picture due to the idiosyncrasies of each study system and approach, there is increasing evidence on their actual relevance when aspects such as different spatial scales, intraspecific variability and demography are considered.

Examining Impulse-Variability in Kicking.

PubMed

Chappell, Andrew; Molina, Sergio L; McKibben, Jonathon; Stodden, David F

2016-07-01

This study examined variability in kicking speed and spatial accuracy to test the impulse-variability theory prediction of an inverted-U function and the speed-accuracy trade-off. Twenty-eight 18- to 25-year-old adults kicked a playground ball at various percentages (50-100%) of their maximum speed at a wall target. Speed variability and spatial error were analyzed using repeated-measures ANOVA with built-in polynomial contrasts. Results indicated a significant inverse linear trajectory for speed variability (p < .001, η2= .345) where 50% and 60% maximum speed had significantly higher variability than the 100% condition. A significant quadratic fit was found for spatial error scores of mean radial error (p < .0001, η2 = .474) and subject-centroid radial error (p < .0001, η2 = .453). Findings suggest variability and accuracy of multijoint, ballistic skill performance may not follow the general principles of impulse-variability theory or the speed-accuracy trade-off.

Spatial Distribution of a Large Herbivore Community at Waterholes: An Assessment of Its Stability over Years in Hwange National Park, Zimbabwe.

PubMed

Chamaillé-Jammes, Simon; Charbonnel, Anaïs; Dray, Stéphane; Madzikanda, Hillary; Fritz, Hervé

2016-01-01

The spatial structuring of populations or communities is an important driver of their functioning and their influence on ecosystems. Identifying the (in)stability of the spatial structure of populations is a first step towards understanding the underlying causes of these structures. Here we studied the relative importance of spatial vs. interannual variability in explaining the patterns of abundance of a large herbivore community (8 species) at waterholes in Hwange National Park (Zimbabwe). We analyzed census data collected over 13 years using multivariate methods. Our results showed that variability in the census data was mostly explained by the spatial structure of the community, as some waterholes had consistently greater herbivore abundance than others. Some temporal variability probably linked to Park-scale migration dependent on annual rainfall was noticeable, however. Once this was accounted for, little temporal variability remained to be explained, suggesting that other factors affecting herbivore abundance over time had a negligible effect at the scale of the study. The extent of spatial and temporal variability in census data was also measured for each species. This study could help in projecting the consequences of surface water management, and more generally presents a methodological framework to simultaneously address the relative importance of spatial vs. temporal effects in driving the distribution of organisms across landscapes.

Spatial Distribution of a Large Herbivore Community at Waterholes: An Assessment of Its Stability over Years in Hwange National Park, Zimbabwe

PubMed Central

Chamaillé-Jammes, Simon; Charbonnel, Anaïs; Dray, Stéphane; Madzikanda, Hillary; Fritz, Hervé

2016-01-01

The spatial structuring of populations or communities is an important driver of their functioning and their influence on ecosystems. Identifying the (in)stability of the spatial structure of populations is a first step towards understanding the underlying causes of these structures. Here we studied the relative importance of spatial vs. interannual variability in explaining the patterns of abundance of a large herbivore community (8 species) at waterholes in Hwange National Park (Zimbabwe). We analyzed census data collected over 13 years using multivariate methods. Our results showed that variability in the census data was mostly explained by the spatial structure of the community, as some waterholes had consistently greater herbivore abundance than others. Some temporal variability probably linked to Park-scale migration dependent on annual rainfall was noticeable, however. Once this was accounted for, little temporal variability remained to be explained, suggesting that other factors affecting herbivore abundance over time had a negligible effect at the scale of the study. The extent of spatial and temporal variability in census data was also measured for each species. This study could help in projecting the consequences of surface water management, and more generally presents a methodological framework to simultaneously address the relative importance of spatial vs. temporal effects in driving the distribution of organisms across landscapes. PMID:27074044

Health risks from large-scale water pollution: trends in Central Asia.

PubMed

Törnqvist, Rebecka; Jarsjö, Jerker; Karimov, Bakhtiyor

2011-02-01

Limited data on the pollution status of spatially extensive water systems constrain health-risk assessments at basin-scales. Using a recipient measurement approach in a terminal water body, we show that agricultural and industrial pollutants in groundwater-surface water systems of the Aral Sea Drainage Basin (covering the main part of Central Asia) yield cumulative health hazards above guideline values in downstream surface waters, due to high concentrations of copper, arsenic, nitrite, and to certain extent dichlorodiphenyltrichloroethane (DDT). Considering these high-impact contaminants, we furthermore perform trend analyses of their upstream spatial-temporal distribution, investigating dominant large-scale spreading mechanisms. The ratio between parent DDT and its degradation products showed that discharges into or depositions onto surface waters are likely to be recent or ongoing. In river water, copper concentrations peak during the spring season, after thawing and snow melt. High spatial variability of arsenic concentrations in river water could reflect its local presence in the top soil of nearby agricultural fields. Overall, groundwaters were associated with much higher health risks than surface waters. Health risks can therefore increase considerably, if the downstream population must switch to groundwater-based drinking water supplies during surface water shortage. Arid regions are generally vulnerable to this problem due to ongoing irrigation expansion and climate changes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Using long-term data to predict fish abundance: the case of Prochilodus lineatus (Characiformes, Prochilodontidae) in the intensely regulated upper Paraná River

USGS Publications Warehouse

Piana, Pitágoras A.; Cardoso, Bárbara F.; Dias, Joilson; Gomes, Luiz C.; Agostinho, Angelo A.; Miranda, Leandro E.

2017-01-01

Populations show spatial-temporal fluctuations in abundance, partly due to random processes and partly due to self-regulatory processes. We evaluated the effects of various external factors on the population numerical abundance of curimba Prochilodus lineatus in the upper Paraná River floodplain, Brazil, over a 19-year period. Panel data analysis was applied to examine the structure of temporal and spatial abundance while controlling auto-regressive processes and spatial non-homogeneity variances that often obscure relationships. As sources of population variation, we considered predation, competition, selected abiotic variables, construction of a dam upstream of the study area, water level and flood intensity during the spawning period. We found that biological interactions (predation and competition) were not significantly related to variations in curimba abundance; specific conductance was a space indicator of abundance, apparently linked to the biology of the species; intensity of floods determined inter-annual variation in abundances; Porto Primavera Dam negatively impacted the abundances at sites in the floodplain directly affected by discharges from the dam. Panel data analysis was a powerful tool that identified the need for intense flooding to maintain high abundances of curimba in the upper Paraná River. We believe our results apply to other species with similar life strategy.

Spatial variation of PM elemental composition between and within 20 European study areas--Results of the ESCAPE project.

PubMed

Tsai, Ming-Yi; Hoek, Gerard; Eeftens, Marloes; de Hoogh, Kees; Beelen, Rob; Beregszászi, Timea; Cesaroni, Giulia; Cirach, Marta; Cyrys, Josef; De Nazelle, Audrey; de Vocht, Frank; Ducret-Stich, Regina; Eriksen, Kirsten; Galassi, Claudia; Gražuleviciene, Regina; Gražulevicius, Tomas; Grivas, Georgios; Gryparis, Alexandros; Heinrich, Joachim; Hoffmann, Barbara; Iakovides, Minas; Keuken, Menno; Krämer, Ursula; Künzli, Nino; Lanki, Timo; Madsen, Christian; Meliefste, Kees; Merritt, Anne-Sophie; Mölter, Anna; Mosler, Gioia; Nieuwenhuijsen, Mark J; Pershagen, Göran; Phuleria, Harish; Quass, Ulrich; Ranzi, Andrea; Schaffner, Emmanuel; Sokhi, Ranjeet; Stempfelet, Morgane; Stephanou, Euripides; Sugiri, Dorothea; Taimisto, Pekka; Tewis, Marjan; Udvardy, Orsolya; Wang, Meng; Brunekreef, Bert

2015-11-01

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial patterns of development drive water use

USGS Publications Warehouse

Sanchez, G.M.; Smith, J.W.; Terando, Adam J.; Sun, G.; Meentemeyer, R.K.

2018-01-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non‐spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio‐economic and environmental variables and two water use variables: a) domestic water use, and b) total development‐related water use (a combination of public supply, domestic self‐supply and industrial self‐supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio‐economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water‐efficient land use planning.

Spatial Patterns of Development Drive Water Use

NASA Astrophysics Data System (ADS)

Sanchez, G. M.; Smith, J. W.; Terando, A.; Sun, G.; Meentemeyer, R. K.

2018-03-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non-spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio-economic and environmental variables and two water use variables: a) domestic water use, and b) total development-related water use (a combination of public supply, domestic self-supply and industrial self-supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio-economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water-efficient land use planning.

Measuring spatial variability in soil characteristics

DOEpatents

Hoskinson, Reed L.; Svoboda, John M.; Sawyer, J. Wayne; Hess, John R.; Hess, J. Richard

2002-01-01

The present invention provides systems and methods for measuring a load force associated with pulling a farm implement through soil that is used to generate a spatially variable map that represents the spatial variability of the physical characteristics of the soil. An instrumented hitch pin configured to measure a load force is provided that measures the load force generated by a farm implement when the farm implement is connected with a tractor and pulled through or across soil. Each time a load force is measured, a global positioning system identifies the location of the measurement. This data is stored and analyzed to generate a spatially variable map of the soil. This map is representative of the physical characteristics of the soil, which are inferred from the magnitude of the load force.

Seasonal variability of convectively coupled equatorial waves (CCEWs) in recent high-top CMIP5 models

NASA Astrophysics Data System (ADS)

Zakaria, Dzaki; Lubis, Sandro W.; Setiawan, Sonni

2018-05-01

Tropical weather system is controlled by periodic atmospheric disturbances ranging from daily to subseasonal time scales. One of the most prominent atmospheric disturbances in the tropics is convectively coupled equatorial waves (CCEWs). CCEWs are excited by latent heating due to a large-scale convective system and have a significant influence on weather system. They include atmospheric equatorial Kelvin wave, Mixed Rossby Gravity (MRG) wave, Equatorial Rossby (ER) wave and Tropical Depression (TD-type) wave. In this study, we will evaluate the seasonal variability of CCEWs activity in nine high-top CMIP5 models, including their spatial distribution in the troposphere. Our results indicate that seasonal variability of Kelvin waves is well represented in MPI-ESM-LR and MPI-ESM-MR, with maximum activity occurring during boreal spring. The seasonal variability of MRG waves is well represented in CanESM2, HadGEM2-CC, IPSL-CM5A-LR and IPSL-CM5A-MR, with maximum activity observed during boreal summer. On the other hand, ER waves are well captured by IPSL-CM5A-LR and IPSL-CM5A-MR and maximize during boreal fall; while TD-type waves, with maximum activity observed during boreal summer, are well observed in CanESM2, HadGEM2-CC, IPSL-CM5A-LR and IPSL-CM5A-MR. Our results indicate that the skill of CMIP5 models in representing seasonal variability of CCEWs highly depends on the convective parameterization and the spatial or vertical resolution used by each model.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Rainfall variability over southern Africa: an overview of current research using satellite and climate model data

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.

Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia

NASA Astrophysics Data System (ADS)

Mahmud, Kashif; Mariethoz, Gregoire; Baker, Andy; Treble, Pauline C.

2018-02-01

Cave drip water response to surface meteorological conditions is complex due to the heterogeneity of water movement in the karst unsaturated zone. Previous studies have focused on the monitoring of fractured rock limestones that have little or no primary porosity. In this study, we aim to further understand infiltration water hydrology in the Tamala Limestone of SW Australia, which is Quaternary aeolianite with primary porosity. We build on our previous studies of the Golgotha Cave system and utilize the existing spatial survey of 29 automated cave drip loggers and a lidar-based flow classification scheme, conducted in the two main chambers of this cave. We find that a daily sampling frequency at our cave site optimizes the capture of drip variability with the least possible sampling artifacts. With the optimum sampling frequency, most of the drip sites show persistent autocorrelation for at least a month, typically much longer, indicating ample storage of water feeding all stalactites investigated. Drip discharge histograms are highly variable, showing sometimes multimodal distributions. Histogram skewness is shown to relate to the wetter-than-average 2013 hydrological year and modality is affected by seasonality. The hydrological classification scheme with respect to mean discharge and the flow variation can distinguish between groundwater flow types in limestones with primary porosity, and the technique could be used to characterize different karst flow paths when high-frequency automated drip logger data are available. We observe little difference in the coefficient of variation (COV) between flow classification types, probably reflecting the ample storage due to the dominance of primary porosity at this cave site. Moreover, we do not find any relationship between drip variability and discharge within similar flow type. Finally, a combination of multidimensional scaling (MDS) and clustering by k means is used to classify similar drip types based on time series analysis. This clustering reveals four unique drip regimes which agree with previous flow type classification for this site. It highlights a spatial homogeneity in drip types in one cave chamber, and spatial heterogeneity in the other, which is in agreement with our understanding of cave chamber morphology and lithology.

Can we improve streamflow simulation by using higher resolution rainfall information?

NASA Astrophysics Data System (ADS)

Lobligeois, Florent; Andréassian, Vazken; Perrin, Charles

2013-04-01

The catchment response to rainfall is the interplay between space-time variability of precipitation, catchment characteristics and antecedent hydrological conditions. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. One of the characteristics which distinguishes distributed from lumped models is their ability to represent explicitly the spatial variability of precipitation and catchment characteristics. The sensitivity of runoff hydrographs to the spatial variability of forcing data has been a major concern of researchers over the last three decades. However, although the literature on the relationship between spatial rainfall and runoff response is abundant, results are contrasted and sometimes contradictory. Several studies concluded that including information on rainfall spatial distribution improves discharge simulation (e.g. Ajami et al., 2004, among others) whereas other studies showed the lack of significant improvement in simulations with better information on rainfall spatial pattern (e.g. Andréassian et al., 2004, among others). The difficulties to reach a clear consensus is mainly due to the fact that each modeling study is implemented only on a few catchments whereas the impact of the spatial distribution of rainfall on runoff is known to be catchment and event characteristics-dependent. Many studies are virtual experiments and only compare flow simulations, which makes it difficult to reach conclusions transposable to real-life case studies. Moreover, the hydrological rainfall-runoff models differ between the studies and the parameterization strategies sometimes tend to advantage the distributed approach (or the lumped one). Recently, Météo-France developed a rainfall reanalysis over the whole French territory at the 1-kilometer resolution and the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Based on this new high resolution product, we propose a framework to evaluate the improvements in streamflow simulation by using higher resolution rainfall information. Semi-distributed modelling is performed for different spatial resolution of precipitation forcing: from lumped to semi-distributed simulations. Here we do not work on synthetic (simulated) streamflow, but with actual measurements, on a large set of 181 French catchments representing a variety of size and climate. The rainfall-runoff model is re-calibrated for each resolution of rainfall spatial distribution over a 5-year sub-period and evaluated on the complementary sub-period in validation mode. The results are analysed by catchment classes based on catchment area and for various types of rainfall events based on the spatial variability of precipitation. References Ajami, N. K., Gupta, H. V, Wagener, T. & Sorooshian, S. (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298(1-4), 112-135. Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C. & Loumagne, C. (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), 1-9.

Direct generation of spatial quadripartite continuous variable entanglement in an optical parametric oscillator.

PubMed

Liu, Kui; Guo, Jun; Cai, Chunxiao; Zhang, Junxiang; Gao, Jiangrui

2016-11-15

Multipartite entanglement is used for quantum information applications, such as building multipartite quantum communications. Generally, generation of multipartite entanglement is based on a complex beam-splitter network. Here, based on the spatial freedom of light, we experimentally demonstrated spatial quadripartite continuous variable entanglement among first-order Hermite-Gaussian modes using a single type II optical parametric oscillator operating below threshold with an HG0245° pump beam. The entanglement can be scalable for larger numbers of spatial modes by changing the spatial profile of the pump beam. In addition, spatial multipartite entanglement will be useful for future spatial multichannel quantum information applications.

Wet-season spatial variability of N2O emissions from a tea field in subtropical central China

NASA Astrophysics Data System (ADS)

Fu, X.; Liu, X.; Li, Y.; Shen, J.; Wang, Y.; Zou, G.; Li, H.; Song, L.; Wu, J.

2015-01-01

Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability of N2O emissions from a red-soil tea field in Hunan province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10-10.30 a.m.) ranged from -1.73 to 1659.11 g N ha-1 d-1 and were positively skewed with an average flux of 102.24 g N ha-1 d-1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt), total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r=0.57-0.71, p<0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r= 0.74 and RMSE =1.18) outperformed ordinary kriging (r= 0.18 and RMSE =1.74), regression kriging with the sample position as a predictor (r= 0.49 and RMSE =1.55) and cokriging with SOCt as a covariable (r= 0.58 and RMSE =1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of the 4.0 ha tea field ranged from 148.2 to 208.1 g N d-1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed, and more importantly providing accurate regional estimation of N2O emissions from tea-planted soils.

Wet-season spatial variability in N2O emissions from a tea field in subtropical central China

NASA Astrophysics Data System (ADS)

Fu, X.; Liu, X.; Li, Y.; Shen, J.; Wang, Y.; Zou, G.; Li, H.; Song, L.; Wu, J.

2015-06-01

Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability in N2O emissions from a red-soil tea field in Hunan Province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10:00-10:30 a.m.) ranged from -1.73 to 1659.11 g N ha-1 d-1 and were positively skewed with an average flux of 102.24 g N ha-1 d-1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt) and total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r = 0.57-0.71, p < 0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r = 0.74 and RMSE = 1.18) outperformed ordinary kriging (r = 0.18 and RMSE = 1.74), regression kriging with the sample position as a predictor (r = 0.49 and RMSE = 1.55) and cokriging with SOCt as a covariable (r = 0.58 and RMSE = 1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of 4.0 ha tea field ranged from 148.2 to 208.1 g N d-1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed and, more importantly, providing accurate regional estimation of N2O emissions from tea-planted soils.

Temporal and spatial variability in North Carolina piedmont stream temperature

Treesearch

J.L. Boggs; G. Sun; S.G. McNulty; W. Swartley; Treasure E.; W. Summer

2009-01-01

Understanding temporal and spatial patterns of in-stream temperature can provide useful information to managing future impacts of climate change on these systems. This study will compare temporal patterns and spatial variability of headwater in-stream temperature in six catchments in the piedmont of North Carolina in two different geological regions, Carolina slate...

Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

USGS Publications Warehouse

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

PubMed

Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

2014-02-01

In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

Evaluation of modeled bacteria loads along an impaired stream reach receiving discharge from a municipal separate storm sewer system in Independence, Mo.

USGS Publications Warehouse

Flickinger, Allison; Christensen, Eric D.

2017-01-01

The Little Blue River in Jackson County, Missouri, was listed as impaired in 2012 due to Escherichia coli (E. coli) from urban runoff and storm sewers. A study was initiated to characterize E. coli concentrations and loads to aid in the development of a total maximum daily load implementation plan. Longitudinal sampling along the stream revealed spatial and temporal variability in E. coli loads. Regression models were developed to better represent E. coli variability in the impaired reach using continuous hydrologic and water-quality parameters as predictive parameters. Daily loads calculated from main-stem samples were significantly higher downstream compared to upstream even though there was no significant difference between the upstream and downstream measured concentrations and no significant conclusions could be drawn from model-estimated loads due to model-associated uncertainty. Increasing sample frequency could decrease the bias and increase the accuracy of the modeled results.

Remote sensing using MIMO systems

DOEpatents

Bikhazi, Nicolas; Young, William F; Nguyen, Hung D

2015-04-28

A technique for sensing a moving object within a physical environment using a MIMO communication link includes generating a channel matrix based upon channel state information of the MIMO communication link. The physical environment operates as a communication medium through which communication signals of the MIMO communication link propagate between a transmitter and a receiver. A spatial information variable is generated for the MIMO communication link based on the channel matrix. The spatial information variable includes spatial information about the moving object within the physical environment. A signature for the moving object is generated based on values of the spatial information variable accumulated over time. The moving object is identified based upon the signature.

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Probabilistic atlas based labeling of the cerebral vessel tree

NASA Astrophysics Data System (ADS)

Van de Giessen, Martijn; Janssen, Jasper P.; Brouwer, Patrick A.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2015-03-01

Preoperative imaging of the cerebral vessel tree is essential for planning therapy on intracranial stenoses and aneurysms. Usually, a magnetic resonance angiography (MRA) or computed tomography angiography (CTA) is acquired from which the cerebral vessel tree is segmented. Accurate analysis is helped by the labeling of the cerebral vessels, but labeling is non-trivial due to anatomical topological variability and missing branches due to acquisition issues. In recent literature, labeling the cerebral vasculature around the Circle of Willis has mainly been approached as a graph-based problem. The most successful method, however, requires the definition of all possible permutations of missing vessels, which limits application to subsets of the tree and ignores spatial information about the vessel locations. This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases. The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set. With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.

The effect of short-range spatial variability on soil sampling uncertainty.

PubMed

Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

2008-11-01

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

Geographical Gradients in Argentinean Terrestrial Mammal Species Richness and Their Environmental Correlates

PubMed Central

Márquez, Ana L.; Real, Raimundo; Kin, Marta S.; Guerrero, José Carlos; Galván, Betina; Barbosa, A. Márcia; Olivero, Jesús; Palomo, L. Javier; Vargas, J. Mario; Justo, Enrique

2012-01-01

We analysed the main geographical trends of terrestrial mammal species richness (SR) in Argentina, assessing how broad-scale environmental variation (defined by climatic and topographic variables) and the spatial form of the country (defined by spatial filters based on spatial eigenvector mapping (SEVM)) influence the kinds and the numbers of mammal species along these geographical trends. We also evaluated if there are pure geographical trends not accounted for by the environmental or spatial factors. The environmental variables and spatial filters that simultaneously correlated with the geographical variables and SR were considered potential causes of the geographic trends. We performed partial correlations between SR and the geographical variables, maintaining the selected explanatory variables statistically constant, to determine if SR was fully explained by them or if a significant residual geographic pattern remained. All groups and subgroups presented a latitudinal gradient not attributable to the spatial form of the country. Most of these trends were not explained by climate. We used a variation partitioning procedure to quantify the pure geographic trend (PGT) that remained unaccounted for. The PGT was larger for latitudinal than for longitudinal gradients. This suggests that historical or purely geographical causes may also be relevant drivers of these geographical gradients in mammal diversity. PMID:23028254

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

PubMed Central

Baron, Christian; Sultan, Benjamin; Balme, Maud; Sarr, Benoit; Traore, Seydou; Lebel, Thierry; Janicot, Serge; Dingkuhn, Michael

2005-01-01

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level. PMID:16433096

Effect of land use on the spatial variability of organic matter and nutrient status in an Oxisol

NASA Astrophysics Data System (ADS)

Paz-Ferreiro, Jorge; Alves, Marlene Cristina; Vidal Vázquez, Eva

2013-04-01

Heterogeneity is now considered as an inherent soil property. Spatial variability of soil attributes in natural landscapes results mainly from soil formation factors. In cultivated soils much heterogeneity can additionally occur as a result of land use, agricultural systems and management practices. Organic matter content (OMC) and nutrients associated to soil exchange complex are key attribute in the maintenance of a high quality soil. Neglecting spatial heterogeneity in soil OMC and nutrient status at the field scale might result in reduced yield and in environmental damage. We analyzed the impact of land use on the pattern of spatial variability of OMC and soil macronutrients at the stand scale. The study was conducted in São Paulo state, Brazil. Land uses were pasture, mango orchard and corn field. Soil samples were taken at 0-10 cm and 10-20 cm depth in 84 points, within 100 m x 100 m plots. Texture, pH, OMC, cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, H, Al) and resin extractable phosphorus were analyzed.. Statistical variability was found to be higher in parameters defining the soil nutrient status (resin extractable P, K, Ca and Mg) than in general soil properties (OMC, CEC, base saturation and pH). Geostatistical analysis showed contrasting patterns of spatial dependence for the different soil uses, sampling depths and studied properties. Most of the studied data sets collected at two different depths exhibited spatial dependence at the sampled scale and their semivariograms were modeled by a nugget effect plus a structure. The pattern of soil spatial variability was found to be different between the three study soil uses and at the two sampling depths, as far as model type, nugget effect or ranges of spatial dependence were concerned. Both statistical and geostatistical results pointed out the importance of OMC as a driver responsible for the spatial variability of soil nutrient status.

Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo.

PubMed

Van der Laan, Carina; Verweij, Pita A; Quiñones, Marcela J; Faaij, André Pc

2014-12-01

Land use and land cover change occurring in tropical forest landscapes contributes substantially to carbon emissions. Better insights into the spatial variation of aboveground biomass is therefore needed. By means of multiple statistical tests, including geographically weighted regression, we analysed the effects of eight variables on the regional spatial variation of aboveground biomass. North and East Kalimantan were selected as the case study region; the third largest carbon emitting Indonesian provinces. Strong positive relationships were found between aboveground biomass and the tested variables; altitude, slope, land allocation zoning, soil type, and distance to the nearest fire, road, river and city. Furthermore, the results suggest that the regional spatial variation of aboveground biomass can be largely attributed to altitude, distance to nearest fire and land allocation zoning. Our study showed that in this landscape, aboveground biomass could not be explained by one single variable; the variables were interrelated, with altitude as the dominant variable. Spatial analyses should therefore integrate a variety of biophysical and anthropogenic variables to provide a better understanding of spatial variation in aboveground biomass. Efforts to minimise carbon emissions should incorporate the identified factors, by 1) the maintenance of lands with high AGB or carbon stocks, namely in the identified zones at the higher altitudes; and 2) regeneration or sustainable utilisation of lands with low AGB or carbon stocks, dependent on the regeneration capacity of the vegetation. Low aboveground biomass densities can be found in the lowlands in burned areas, and in non-forest zones and production forests.

[Research progress on hydrological scaling].

PubMed

Liu, Jianmei; Pei, Tiefan

2003-12-01

With the development of hydrology and the extending effect of mankind on environment, scale issue has become a great challenge to many hydrologists due to the stochasticism and complexity of hydrological phenomena and natural catchments. More and more concern has been given to the scaling issues to gain a large-scale (or small-scale) hydrological characteristic from a certain known catchments, but hasn't been solved successfully. The first part of this paper introduced some concepts about hydrological scale, scale issue and scaling. The key problem is the spatial heterogeneity of catchments and the temporal and spatial variability of hydrological fluxes. Three approaches to scale were put forward in the third part, which were distributed modeling, fractal theory and statistical self similarity analyses. Existing problems and future research directions were proposed in the last part.

Simulation of ground-water flow in the Prairie du Chien-Jordan and overlying aquifers near the Mississippi River, Fridley, Minnesota

USGS Publications Warehouse

Lindgren, R.J.

1990-01-01

Spatially variable leakage to the confined-drift and St. Peter aquifers in the steady-state simulation for 1885-1930 ranged from 1.0 to 2.3 inches per year. Leakage to the confined-drift and St. Peter aquifers in the steady-state simulation for 1970-79 increased 0 to 3.0 inches per year above the initial steady-state results. This increase represents additional leakage caused by the lowering of hydraulic heads due to ground-water withdrawals. Simulated leakage to the confined-drift and St. Peter aquifers for the transient simulation for 1987 varied both seasonally (0.4 to 2.1 inches per stress period) and spatially (2.6 to 5.7 inches per year). 

A Transient Transit Signature Associated with the Young Star RIK-210

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

David, Trevor J.; Hillenbrand, Lynne A.; Howard, Andrew W.

We find transient transit-like dimming events within the K2 time series photometry of the young star RIK-210 in the Upper Scorpius OB association. These dimming events are variable in depth, duration, and morphology. High spatial resolution imaging revealed that the star is single and radial velocity monitoring indicated that the dimming events cannot be due to an eclipsing stellar or brown dwarf companion. Archival and follow-up photometry suggest the dimming events are transient in nature. The variable morphology of the dimming events suggests they are not due to a single spherical body. The ingress of each dimming event is alwaysmore » shallower than egress, as one would expect for an orbiting body with a leading tail. The dimming events are periodic and synchronous with the stellar rotation. However, we argue it is unlikely the dimming events could be attributed to anything on the stellar surface based on the observed depths and durations. Variable obscuration by a protoplanetary disk is unlikely on the basis that the star is not actively accreting and lacks the infrared excess associated with an inner disk. Rather, we explore the possibilities that the dimming events are due to magnetospheric clouds, a transiting protoplanet surrounded by circumplanetary dust and debris, eccentric orbiting bodies undergoing periodic tidal disruption, or an extended field of dust or debris near the corotation radius.« less

Variability of growing degree days in Poland in response to ongoing climate changes in Europe.

PubMed

Wypych, Agnieszka; Sulikowska, Agnieszka; Ustrnul, Zbigniew; Czekierda, Danuta

2017-01-01

An observed increase in air temperature can lead to significant changes in the phenology of plants and, consequently, changes in agricultural production. The aim of the study was to evaluate the spatial differentiation of thermal resources in Poland and their variability during a period of changing thermal conditions in Europe. Since the variability of thermal conditions is of paramount importance for perennial crops, the study focused on apple, plum, and cherry orchard regions in Poland. The analysis was conducted for the period of 1951-2010 using air temperature daily data. Thermal resources have been defined using the growing degree days (GDD) index calculated independently for the whole year and during in frost-free season for three air temperature thresholds: 0, 5, and 10 °C, which determine the non-winter period, growing season, and the period of full plant growth, respectively. In addition, due to the high significance for perennials in particular, the incidence and intensity of frost during flowering were calculated. In this study, a detailed analysis of the spatial differentiation of thermal resources was first performed, followed by an evaluation of long-term variability and associated change patterns. The obtained results confirmed an increase in thermal resources in Poland as a consequence of the lengthening of the growing season. However, the frequency and intensity of spring frost, especially during flowering or even during ripening of plants, remain a threat to harvests in both the eastern and western parts of the country.

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

NASA Technical Reports Server (NTRS)

Lerner, Jeffrey A.; Jedlovec, Gary J.; Kidder, Stanley Q.

1996-01-01

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

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

Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modesmore » and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this article, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.« less

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

DOE PAGES

Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef; ...

2016-10-04

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modesmore » and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this article, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.« less

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

NASA Astrophysics Data System (ADS)

Han, Weiqing; Meehl, Gerald A.; Stammer, Detlef; Hu, Aixue; Hamlington, Benjamin; Kenigson, Jessica; Palanisamy, Hindumathi; Thompson, Philip

2017-01-01

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth's climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.

Shifting patterns of ENSO variability from a 492-year South Pacific coral core

NASA Astrophysics Data System (ADS)

Tangri, N.; Linsley, B. K.; Mucciarone, D.; Dunbar, R. B.

2017-12-01

Anticipating the impacts of ENSO in a changing climate requires detailed reconstructions of changes in its timing, amplitude, and spatial pattern, as well as attempts to attribute those changes to external forcing or internal variability. A continuous coral δ18O record from American Samoa, in the tropical South Pacific, sheds light on almost five centuries of these changes. We find evidence of internally-driven 50-100 year cycles with broad peaks of high variability punctuated by short transitions of low variability. We see a long, slow trend towards more frequent ENSO events, punctuated by sharp decreases in frequency; the 20th century in particular shows a strong trend towards higher-frequency ENSO. Due to the unique location of American Samoa with respect to ENSO sea surface temperature (SST) anomalies, we infer changes in the spatial pattern of ENSO. American Samoa currently lies on the ENSO 3.4 nodal line - the boomerang shape that separates waters warmed by El Niño from those that cool. Closer examination reveals that SST around American Samoa displays opposing responses to Eastern and Central Pacific ENSO events. However, this has not always been the case; in the late 19th and early 20th century, SST responded similarly to both flavors of ENSO. We interpret this to mean a geographic narrowing towards the equator of the eastern Pacific El Niño SST anomaly pattern in the first half of the 20th century.

Quantitative variability of renewable energy resources in Norway

NASA Astrophysics Data System (ADS)

Christakos, Konstantinos; Varlas, George; Cheliotis, Ioannis; Aalstad, Kristoffer; Papadopoulos, Anastasios; Katsafados, Petros; Steeneveld, Gert-Jan

2017-04-01

Based on European Union (EU) targets for 2030, the share of renewable energy (RE) consumption should be increased at 27%. RE resources such as hydropower, wind, wave power and solar power are strongly depending on the chaotic behavior of the weather conditions and climate. Due to this dependency, the prediction of the spatiotemporal variability of the RE resources is more crucial factor than in other energy resources (i.e. carbon based energy). The fluctuation of the RE resources can affect the development of the RE technologies, the energy grid, supply and prices. This study investigates the variability of the potential RE resources in Norway. More specifically, hydropower, wind, wave, and solar power are quantitatively analyzed and correlated with respect to various spatial and temporal scales. In order to analyze the diversities and their interrelationships, reanalysis and observational data of wind, precipitation, wave, and solar radiation are used for a quantitative assessment. The results indicate a high variability of marine RE resources in the North Sea and the Norwegian Sea.

Impacts of maintenance dredged material disposal on macrobenthic structure and secondary productivity.

PubMed

Bolam, S G; Barry, J; Bolam, T; Mason, C; Rumney, H S; Thain, J E; Law, R J

2011-10-01

The results of a monitoring programme to assess the spatial impacts associated with ongoing dredged material disposal activity at a dispersive, coastal disposal site (southwest UK) are described. Benthic impacts were assessed using benthic community structure and secondary productivity estimates. Analyses of univariate indices (including secondary production) and multivariate community structure revealed differences between stations inside and those outside the disposal site were minimal. Generally, stations within and outside the disposal site were characterised by the same species. Regression models indicated that the variability in biological structure and secondary production was predominantly accounted for by natural variables (e.g., depth, sediment granulometry) with only a small amount of residual variability being due to contaminant variables. Thus, the elevated levels of certain contaminants in the vicinity of the disposal area were not sufficient to result in significant ecological or ecotoxicological changes. We ascribe such findings partly to the dispersive nature of the disposal site. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Temporal variation of meandering intensity and domain-wide lateral oscillations of the Gulf Stream

NASA Technical Reports Server (NTRS)

Lee, Tong; Cornillon, Peter

1995-01-01

The path of the Gulf Stream exhibits two modes of variability: wavelike spatial meanders associated with instability processes and large-sale lateral shifts of the path presumably due to atmospheric forcing. The objectives of this study are to examine the temporal variation of the intensity of spatial meandering in the stream, to characterize large-scale lateral oscillations in the stream's path, and to study the correlation betwen these two dynamically distinct modes of variability. The data used for this analysis are path displacemets ofthe Gulf Stream between 75 deg and 60 deg W obtained from AVHRR-derived (Advanced Very High Resolution Radiometer) infrared images for the period April 1982 through December 1989. Meandering intensity, measured by the spatial root-mean-sqaure displacement of the stream path, displays a 9-month dominant periodicity which is persistent through the study period. The 9-month fluctuation in meandering intensity may be related to the interaction of Rosseby waves with the stream. Interannual variation of meandering intensity is also found to be significant, with meandering being mich more intense during 1985 than it was in 1987. Annual variation, however,is weak and not well-defined.The spatially averaged position of the stream, which reflects nonmeandering large-scale lateral oscillations of the stream path, is dominated by an annual cycle. On average, the mean position is farthest north in November and farthest south in April. The first empirical orthogonal function mode of the space-time path displacements represents lateral oscillatins that are in-phase over the space-time domain. Interannual oscillations are also observed and are found to be weaker than the annual oscillation. The eigenvalue of the first mode indicates that about 21.5% of the total space-time variability of the stream path can be attibuted to domain-wide lateral oscillation. The correlation between meandering intensity and domain-wide lateral oscillations is very weak.

Regional Hydroclimatic Impacts of Contemporary Amazonian Deforestation and Their Spatiotemporal Variability - An Integrated Study Using Remotely Sensed Data and Numerical Tools

NASA Astrophysics Data System (ADS)

Medvigy, D.; Khanna, J.

2016-12-01

The Amazon rainforest has been under deforestation for more than four decades. Recent investigation of the regional hydroclimatic impacts of the past three decades of deforestation has revealed a strong scale-dependence of the atmospheric response to land use change. Contemporary deforestation, affecting spatial scales of a few hundreds of kilometers, has resulted in a spatial redistribution of the local dry season rainfall, with downwind and upwind deforested regions receiving respectively 30% more and 30% less rainfall from the area mean. This phenomenon is attributable to a `dynamical' response of the boundary layer air to a reduction in surface roughness due to deforestation, apparent in both satellite and numerically simulated data. This response is starkly different from a spatially uniform increase in non-precipitating cloudiness triggered by small scale clearings, prevalent in the early phases of deforestation. This study investigates the `generalizability' of the dynamical mechanism to understand its impacts on a continually deforested Amazonia. In particular, we investigate the spatiotemporal variability of the dynamical mechanism. The nature of this investigation demands long time series and large spatial converge datasets of the hydroclimate. As such, satellite imagery of clouds (GridSat) and precipitation (PERSIANN and TRMM) has proven particularly useful in facilitating this analysis. The analysis is further complemented by a reanalysis product (ERA-interim) and numerical simulations (using a variable resolution GCM). Results indicate the presence of the dynamical mechanism during local dry and transition seasons effecting the mean precipitation during this period. Its effect on the transition season precipitation can be important for the local dry season length. The dynamical mechanism also occurs in atmospheric conditions which are otherwise less conducive to thermally triggered convection. Hence, this mechanism, which effects the seasons most important for regional ecology, emerges as a possibly impactful convective triggering mechanism. This study provides context for thinking about the climate of a future, more patchily deforested Amazonia that is more favorable to the dynamical mechanism.

Modeling Soil Organic Carbon Variation Along Climatic and Topographic Trajectories in the Central Andes

NASA Astrophysics Data System (ADS)

Gavilan, C.; Grunwald, S.; Quiroz, R.; Zhu, L.

2015-12-01

The Andes represent the largest and highest mountain range in the tropics. Geological and climatic differentiation favored landscape and soil diversity, resulting in ecosystems adapted to very different climatic patterns. Although several studies support the fact that the Andes are a vast sink of soil organic carbon (SOC) only few have quantified this variable in situ. Estimating the spatial distribution of SOC stocks in data-poor and/or poorly accessible areas, like the Andean region, is challenging due to the lack of recent soil data at high spatial resolution and the wide range of coexistent ecosystems. Thus, the sampling strategy is vital in order to ensure the whole range of environmental covariates (EC) controlling SOC dynamics is represented. This approach allows grasping the variability of the area, which leads to more efficient statistical estimates and improves the modeling process. The objectives of this study were to i) characterize and model the spatial distribution of SOC stocks in the Central Andean region using soil-landscape modeling techniques, and to ii) validate and evaluate the model for predicting SOC content in the area. For that purpose, three representative study areas were identified and a suite of variables including elevation, mean annual temperature, annual precipitation and Normalized Difference Vegetation Index (NDVI), among others, was selected as EC. A stratified random sampling (namely conditioned Latin Hypercube) was implemented and a total of 400 sampling locations were identified. At all sites, four composite topsoil samples (0-30 cm) were collected within a 2 m radius. SOC content was measured using dry combustion and SOC stocks were estimated using bulk density measurements. Regression Kriging was used to map the spatial variation of SOC stocks. The accuracy, fit and bias of SOC models was assessed using a rigorous validation assessment. This study produced the first comprehensive, geospatial SOC stock assessment in this undersampled region that serves as a baseline reference to assess potential impacts of climate and land use change.

Assessing the Importance of Cross-Stream Transport in Bedload Flux Estimates from Migrating Dunes: Colorado River, Grand Canyon National Park

NASA Astrophysics Data System (ADS)

Leary, K. P.; Buscombe, D.; Schmeeckle, M.; Kaplinski, M. A.

2017-12-01

Bedforms are ubiquitous in sand-bedded rivers, and understanding their morphodynamics is key to quantifying bedload transport. As such, mechanistic understanding of the spatiotemporal details of sand transport through and over bedforms is paramount to quantifying total sediment flux in sand-bedded river systems. However, due to the complexity of bedform field geometries and migration in natural settings, our ability to relate migration to bedload flux, and to quantify the relative role of tractive and suspended processes in their dynamics, is incomplete. Recent flume and numerical investigations indicate the potential importance of cross-stream transport, a process previously regarded as secondary and diffusive, to the three-dimensionality of bedforms and spatially variable translation and deformation rates. This research seeks to understand and quantify the importance of cross-stream transport in bedform three-dimensionality in a field setting. This work utilizes a high-resolution (0.25 m grid) data set of bedforms migrating in the channel of the Colorado River in Grand Canyon National Park. This data set comprises multi-beam sonar surveys collected at 3 different flow discharges ( 283, 566, and 1076 m3/s) along a reach of the Colorado River just upstream of the Diamond Creek USGS gage. Data were collected every 6 minutes almost continuously for 12 hours. Using bed elevation profiles (BEPs), we extract detailed bedform geometrical data (i.e. bedform height, wavelength) and spatial sediment flux data over a suite of bedforms at each flow. Coupling this spatially extensive data with a generalized Exner equation, we conduct mass balance calculations that evaluate the possibility, and potential importance, of cross-stream transport in the spatial variability of translation and deformation rates. Preliminary results suggest that intra-dune cross-stream transport can partially account for changes in the planform shape of dunes and may play an important role in spatially variable translation and deformation rates. Parameterization of cross-stream sediment transport could lead to accounting for ambiguities in bedload flux calculations caused by dune deformation, which in turn could significantly improve overall calculation of bedload and total load sediment transport in sand bedded rivers.

Predicting ecosystem stability from community composition and biodiversity.

PubMed

de Mazancourt, Claire; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B; Haegeman, Bart; Wayne Polley, H; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J; Loreau, Michel

2013-05-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species' responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22-75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability. © 2013 Blackwell Publishing Ltd/CNRS.

Predicting ecosystem stability from community composition and biodiversity

USGS Publications Warehouse

Mazancourt, Claire de; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B.; Haegeman, Bart; Polley, H. Wayne; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J.; Loreau, Michel

2013-01-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species’ responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22–75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability.

The relative roles of environment, history and local dispersal in controlling the distributions of common tree and shrub species in a tropical forest landscape, Panama

USGS Publications Warehouse

Svenning, J.-C.; Engelbrecht, B.M.J.; Kinner, D.A.; Kursar, T.A.; Stallard, R.F.; Wright, S.J.

2006-01-01

We used regression models and information-theoretic model selection to assess the relative importance of environment, local dispersal and historical contingency as controls of the distributions of 26 common plant species in tropical forest on Barro Colorado Island (BCI), Panama. We censused eighty-eight 0.09-ha plots scattered across the landscape. Environmental control, local dispersal and historical contingency were represented by environmental variables (soil moisture, slope, soil type, distance to shore, old-forest presence), a spatial autoregressive parameter (??), and four spatial trend variables, respectively. We built regression models, representing all combinations of the three hypotheses, for each species. The probability that the best model included the environmental variables, spatial trend variables and ?? averaged 33%, 64% and 50% across the study species, respectively. The environmental variables, spatial trend variables, ??, and a simple intercept model received the strongest support for 4, 15, 5 and 2 species, respectively. Comparing the model results to information on species traits showed that species with strong spatial trends produced few and heavy diaspores, while species with strong soil moisture relationships were particularly drought-sensitive. In conclusion, history and local dispersal appeared to be the dominant controls of the distributions of common plant species on BCI. Copyright ?? 2006 Cambridge University Press.

Soil variability in engineering applications

NASA Astrophysics Data System (ADS)

Vessia, Giovanna

2014-05-01

Natural geomaterials, as soils and rocks, show spatial variability and heterogeneity of physical and mechanical properties. They can be measured by in field and laboratory testing. The heterogeneity concerns different values of litho-technical parameters pertaining similar lithological units placed close to each other. On the contrary, the variability is inherent to the formation and evolution processes experienced by each geological units (homogeneous geomaterials on average) and captured as a spatial structure of fluctuation of physical property values about their mean trend, e.g. the unit weight, the hydraulic permeability, the friction angle, the cohesion, among others. The preceding spatial variations shall be managed by engineering models to accomplish reliable designing of structures and infrastructures. Materon (1962) introduced the Geostatistics as the most comprehensive tool to manage spatial correlation of parameter measures used in a wide range of earth science applications. In the field of the engineering geology, Vanmarcke (1977) developed the first pioneering attempts to describe and manage the inherent variability in geomaterials although Terzaghi (1943) already highlighted that spatial fluctuations of physical and mechanical parameters used in geotechnical designing cannot be neglected. A few years later, Mandelbrot (1983) and Turcotte (1986) interpreted the internal arrangement of geomaterial according to Fractal Theory. In the same years, Vanmarcke (1983) proposed the Random Field Theory providing mathematical tools to deal with inherent variability of each geological units or stratigraphic succession that can be resembled as one material. In this approach, measurement fluctuations of physical parameters are interpreted through the spatial variability structure consisting in the correlation function and the scale of fluctuation. Fenton and Griffiths (1992) combined random field simulation with the finite element method to produce the Random Finite Element Method (RFEM). This method has been used to investigate the random behavior of soils in the context of a variety of classical geotechnical problems. Afterward, some following studies collected the worldwide variability values of many technical parameters of soils (Phoon and Kulhawy 1999a) and their spatial correlation functions (Phoon and Kulhawy 1999b). In Italy, Cherubini et al. (2007) calculated the spatial variability structure of sandy and clayey soils from the standard cone penetration test readings. The large extent of the worldwide measured spatial variability of soils and rocks heavily affects the reliability of geotechnical designing as well as other uncertainties introduced by testing devices and engineering models. So far, several methods have been provided to deal with the preceding sources of uncertainties in engineering designing models (e.g. First Order Reliability Method, Second Order Reliability Method, Response Surface Method, High Dimensional Model Representation, etc.). Nowadays, the efforts in this field have been focusing on (1) measuring spatial variability of different rocks and soils and (2) developing numerical models that take into account the spatial variability as additional physical variable. References Cherubini C., Vessia G. and Pula W. 2007. Statistical soil characterization of Italian sites for reliability analyses. Proc. 2nd Int. Workshop. on Characterization and Engineering Properties of Natural Soils, 3-4: 2681-2706. Griffiths D.V. and Fenton G.A. 1993. Seepage beneath water retaining structures founded on spatially random soil, Géotechnique, 43(6): 577-587. Mandelbrot B.B. 1983. The Fractal Geometry of Nature. San Francisco: W H Freeman. Matheron G. 1962. Traité de Géostatistique appliquée. Tome 1, Editions Technip, Paris, 334 p. Phoon K.K. and Kulhawy F.H. 1999a. Characterization of geotechnical variability. Can Geotech J, 36(4): 612-624. Phoon K.K. and Kulhawy F.H. 1999b. Evaluation of geotechnical property variability. Can Geotech J, 36(4): 625-639. Terzaghi K. 1943. Theoretical Soil Mechanics. New York: John Wiley and Sons. Turcotte D.L. 1986. Fractals and fragmentation. J Geophys Res, 91: 1921-1926. Vanmarcke E.H. 1977. Probabilistic modeling of soil profiles. J Geotech Eng Div, ASCE, 103: 1227-1246. Vanmarcke E.H. 1983. Random fields: analysis and synthesis. MIT Press, Cambridge.

Spatial heterogeneity of within-stream methane concentrations

USGS Publications Warehouse

Crawford, John T.; Loken, Luke C.; West, William E.; Crary, Benjamin; Spawn, Seth A.; Gubbins, Nicholas; Jones, Stuart E.; Striegl, Robert G.; Stanley, Emily H.

2017-01-01

Streams, rivers, and other freshwater features may be significant sources of CH4 to the atmosphere. However, high spatial and temporal variabilities hinder our ability to understand the underlying processes of CH4 production and delivery to streams and also challenge the use of scaling approaches across large areas. We studied a stream having high geomorphic variability to assess the underlying scale of CH4 spatial variability and to examine whether the physical structure of a stream can explain the variation in surface CH4. A combination of high-resolution CH4 mapping, a survey of groundwater CH4 concentrations, quantitative analysis of methanogen DNA, and sediment CH4 production potentials illustrates the spatial and geomorphic controls on CH4 emissions to the atmosphere. We observed significant spatial clustering with high CH4 concentrations in organic-rich stream reaches and lake transitions. These sites were also enriched in the methane-producing mcrA gene and had highest CH4 production rates in the laboratory. In contrast, mineral-rich reaches had significantly lower concentrations and had lesser abundances of mcrA. Strong relationships between CH4and the physical structure of this aquatic system, along with high spatial variability, suggest that future investigations will benefit from viewing streams as landscapes, as opposed to ecosystems simply embedded in larger terrestrial mosaics. In light of such high spatial variability, we recommend that future workers evaluate stream networks first by using similar spatial tools in order to build effective sampling programs.

Analysis of spatial patterns informs community assembly and sampling requirements for Collembola in forest soils

NASA Astrophysics Data System (ADS)

Dirilgen, Tara; Juceviča, Edite; Melecis, Viesturs; Querner, Pascal; Bolger, Thomas

2018-01-01

The relative importance of niche separation, non-equilibrial and neutral models of community assembly has been a theme in community ecology for many decades with none appearing to be applicable under all circumstances. In this study, Collembola species abundances were recorded over eleven consecutive years in a spatially explicit grid and used to examine (i) whether observed beta diversity differed from that expected under conditions of neutrality, (ii) whether sampling points differed in their relative contributions to overall beta diversity, and (iii) the number of samples required to provide comparable estimates of species richness across three forest sites. Neutrality could not be rejected for 26 of the forest by year combinations. However, there is a trend toward greater structure in the oldest forest, where beta diversity was greater than predicted by neutrality on five of the eleven sampling dates. The lack of difference in individual- and sample-based rarefaction curves also suggests randomness in the system at this particular scale of investigation. It seems that Collembola communities are not spatially aggregated and assembly is driven primarily by neutral processes particularly in the younger two sites. Whether this finding is due to small sample size or unaccounted for environmental variables cannot be determined. Variability between dates and sites illustrates the potential of drawing incorrect conclusions if data are collected at a single site and a single point in time.

When will trends in European mean and heavy daily precipitation emerge?

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-03-01

A multi-model ensemble of regional climate projections for Europe is employed to investigate how the time of emergence (TOE) for seasonal sums and maxima of daily precipitation depends on spatial scale. The TOE is redefined for emergence from internal variability only; the spread of the TOE due to imperfect climate model formulation is used as a measure of uncertainty in the TOE itself. Thereby, the TOE becomes a fundamentally limiting timescale and translates into a minimum spatial scale on which robust conclusions can be drawn about precipitation trends. Thus, minimum temporal and spatial scales for adaptation planning are also given. In northern Europe, positive winter trends in mean and heavy precipitation, and in southwestern and southeastern Europe, summer trends in mean precipitation already emerge within the next few decades. However, across wide areas, especially for heavy summer precipitation, the local trend emerges only late in the 21st century or later. For precipitation averaged to larger scales, the trend, in general, emerges earlier.

Implicit learning of non-spatial sequences in schizophrenia

PubMed Central

MARVEL, CHERIE L.; SCHWARTZ, BARBARA L.; HOWARD, DARLENE V.; HOWARD, JAMES H.

2006-01-01

Recent studies have reported abnormal implicit learning of sequential patterns in patients with schizophrenia. Because these studies were based on visuospatial cues, the question remained whether patients were impaired simply due to the demands of spatial processing. This study examined implicit sequence learning in 24 patients with schizophrenia and 24 healthy controls using a non-spatial variation of the serial reaction time test (SRT) in which pattern stimuli alternated with random stimuli on every other trial. Both groups showed learning by responding faster and more accurately to pattern trials than to random trials. Patients, however, showed a smaller magnitude of sequence learning. Both groups were unable to demonstrate explicit knowledge of the nature of the pattern, confirming that learning occurred without awareness. Clinical variables were not correlated with the patients' learning deficits. Patients with schizophrenia have a decreased ability to develop sensitivity to regularly occurring sequences of events within their environment. This type of deficit may affect an array of cognitive and motor functions that rely on the perception of event regularity. PMID:16248901

High Performance Geostatistical Modeling of Biospheric Resources

NASA Astrophysics Data System (ADS)

Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

2004-12-01

We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

Variation in the Composition and In Vitro Proinflammatory Effect of Urban Particulate Matter from Different Sites

PubMed Central

Manzano-León, Natalia; Quintana, Raúl; Sánchez, Brisa; Serrano, Jesús; Vega, Elizabeth; Vázquez-López, Inés; Rojas-Bracho, Leonora; López-Villegas, Tania; O’Neill, Marie S.; Vadillo-Ortega, Felipe; De Vizcaya-Ruiz, Andrea; Rosas, Irma

2015-01-01

Spatial variation in particulate matter–related health and toxicological outcomes is partly due to its composition. We studied spatial variability in particle composition and induced cellular responses in Mexico City to complement an ongoing epidemiologic study. We measured elements, endotoxins, and polycyclic aromatic hydrocarbons in two particle size fractions collected in five sites. We compared the in vitro proinflammatory response of J774A.1 and THP-1 cells after exposure to particles, measuring subsequent TNFα and IL-6 secretion. Particle composition varied by site and size. Particle constituents were subjected to principal component analysis, identifying three components: C1 (Si, Sr, Mg, Ca, Al, Fe, Mn, endotoxin), C2 (polycyclic aromatic hydrocarbons), and C3 (Zn, S, Sb, Ni, Cu, Pb). Induced TNFα levels were higher and more heterogeneous than IL-6 levels. Cytokines produced by both cell lines only correlated with C1, suggesting that constituents associated with soil induced the inflammatory response and explain observed spatial differences. PMID:23335408

Spatial Variation of Selenium in Appalachian Coal Seams

NASA Astrophysics Data System (ADS)

Le, L.; Tyner, J. S.; Perfect, E.; Yoder, D. C.

2013-12-01

The potential environmental impacts from coal extraction have led to many investigations of the geochemistry of coal. Previous studies have shown that selenium (Se) is an environmental contaminant due to its mutagenic effects on sensitive macro-organisms as a result of bioaccumulation in affected waters. Some regulatory authorities have responded by requiring the sampling of coal seams and adjacent rock for Se prior to authorizing a given coal mining permit. In at least one case, a single continuous rock core was sampled for Se to determine the threshold of Se across a 2.2 square kilometer proposed surface coal mine. To examine the adequacy of such an approach, we investigated the spatial variability and correlation of a West Virginia Geological and Economic Survey (WVGES) dataset of Se concentrations from coal seams collected within Appalachia (1088 samples). We conducted semi-variogram and Kriging cross-validation analyses on six coal seams from the dataset. Our findings suggest no significant spatial correlation of Se within a given coal seam.

Effects of Fiber Type and Size on the Heterogeneity of Oxygen Distribution in Exercising Skeletal Muscle

PubMed Central

Liu, Gang; Mac Gabhann, Feilim; Popel, Aleksander S.

2012-01-01

The process of oxygen delivery from capillary to muscle fiber is essential for a tissue with variable oxygen demand, such as skeletal muscle. Oxygen distribution in exercising skeletal muscle is regulated by convective oxygen transport in the blood vessels, oxygen diffusion and consumption in the tissue. Spatial heterogeneities in oxygen supply, such as microvascular architecture and hemodynamic variables, had been observed experimentally and their marked effects on oxygen exchange had been confirmed using mathematical models. In this study, we investigate the effects of heterogeneities in oxygen demand on tissue oxygenation distribution using a multiscale oxygen transport model. Muscles are composed of different ratios of the various fiber types. Each fiber type has characteristic values of several parameters, including fiber size, oxygen consumption, myoglobin concentration, and oxygen diffusivity. Using experimentally measured parameters for different fiber types and applying them to the rat extensor digitorum longus muscle, we evaluated the effects of heterogeneous fiber size and fiber type properties on the oxygen distribution profile. Our simulation results suggest a marked increase in spatial heterogeneity of oxygen due to fiber size distribution in a mixed muscle. Our simulations also suggest that the combined effects of fiber type properties, except size, do not contribute significantly to the tissue oxygen spatial heterogeneity. However, the incorporation of the difference in oxygen consumption rates of different fiber types alone causes higher oxygen heterogeneity compared to control cases with uniform fiber properties. In contrast, incorporating variation in other fiber type-specific properties, such as myoglobin concentration, causes little change in spatial tissue oxygenation profiles. PMID:23028531

High Resolution Flash Flood Forecasting Using a Wireless Sensor Network in the Dallas-Fort Worth Metroplex

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.

2017-12-01

In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.

Quantifying Landscape Spatial Pattern: What Is the State of the Art?

Treesearch

Eric J. Gustafson

1998-01-01

Landscape ecology is based on the premise that there are strong links between ecological pattern and ecological function and process. Ecological systems are spatially heterogeneous, exhibiting consid-erable complexity and variability in time and space. This variability is typically represented by categorical maps or by a collection of samples taken at specific spatial...

Predicting probability of occurrence and factors affecting distribution and abundance of three Ozark endemic crayfish species at multiple spatial scales

USGS Publications Warehouse

Nolen, Matthew S.; Magoulick, Daniel D.; DiStefano, Robert J.; Imhoff, Emily M.; Wagner, Brian K.

2014-01-01

We found that a range of environmental variables were important in predicting crayfish distribution and abundance at multiple spatial scales and their importance was species-, response variable- and scale dependent. We would encourage others to examine the influence of spatial scale on species distribution and abundance patterns.

The Effects of Spatial Stimulus-Response Compatibility on Choice Time Production Accuracy and Variability

ERIC Educational Resources Information Center

Rakitin, Brian C.

2005-01-01

Five experiments examined the relations between timing and attention using a choice time production task in which the latency of a spatial choice response is matched to a target interval (3 or 5 s). Experiments 1 and 2 indicated that spatial stimulus-response incompatibility increased nonscalar timing variability without affecting timing accuracy…

Spatial patterns of throughfall isotopic composition at the event and seasonal timescales

NASA Astrophysics Data System (ADS)

Allen, Scott T.; Keim, Richard F.; McDonnell, Jeffrey J.

2015-03-01

Spatial variability of throughfall isotopic composition in forests is indicative of complex processes occurring in the canopy and remains insufficiently understood to properly characterize precipitation inputs to the catchment water balance. Here we investigate variability of throughfall isotopic composition with the objectives: (1) to quantify the spatial variability in event-scale samples, (2) to determine if there are persistent controls over the variability and how these affect variability of seasonally accumulated throughfall, and (3) to analyze the distribution of measured throughfall isotopic composition associated with varying sampling regimes. We measured throughfall over two, three-month periods in western Oregon, USA under a Douglas-fir canopy. The mean spatial range of δ18O for each event was 1.6‰ and 1.2‰ through Fall 2009 (11 events) and Spring 2010 (7 events), respectively. However, the spatial pattern of isotopic composition was not temporally stable causing season-total throughfall to be less variable than event throughfall (1.0‰; range of cumulative δ18O for Fall 2009). Isotopic composition was not spatially autocorrelated and not explained by location relative to tree stems. Sampling error analysis for both field measurements and Monte-Carlo simulated datasets representing different sampling schemes revealed the standard deviation of differences from the true mean as high as 0.45‰ (δ18O) and 1.29‰ (d-excess). The magnitude of this isotopic variation suggests that small sample sizes are a source of substantial experimental error.

Spatial-temporal variability of soil water content in a cropland-shelterbelt-desert site in an arid inland river basin of Northwest China

NASA Astrophysics Data System (ADS)

Shen, Qin; Gao, Guangyao; Hu, Wei; Fu, Bojie

2016-09-01

Knowledge of the spatial-temporal variability of soil water content (SWC) is critical for understanding a range of hydrological processes. In this study, the spatial variance and temporal stability of SWC were investigated in a cropland-shelterbelt-desert site at the oasis-desert ecotone in the middle of the Heihe River Basin, China. The SWC was measured on 65 occasions to a depth of 2.8 m at 45 locations during two growing seasons from 2012 to 2013. The standard deviation of the SWC versus the mean SWC exhibited a convex upward relationship in the shelterbelt with the greatest spatial variation at the SWC of around 22.0%, whereas a linearly increasing relationship was observed for the cropland, desert, and land use pattern. The standard deviation of the relative difference was positively linearly correlated with the SWC (p < 0.05) for the land use pattern, whereas such a relationship was not found in the three land use types. The spatial pattern of the SWC was more time stable for the land use pattern, followed by desert, shelterbelt, and cropland. The spatial pattern of SWC changed dramatically among different soil layers. The locations representing the mean SWC varied with the depth, and no location could represent the whole soil profile due to different soil texture, root distribution and irrigation management. The representative locations of each soil layer could be used to estimate the mean SWC well. The statistics of temporal stability of the SWC could be presented equally well with a low frequency of observation (30-day interval) as with a high frequency (5-day interval). Sampling frequency had little effect on the selection of the representative locations of the field mean SWC. This study provides useful information for designing the optimal strategy for sampling SWC at the oasis-desert ecotone in the arid inland river basin.

Effects of spatial structure of population size on the population dynamics of barnacles across their elevational range.

PubMed

Fukaya, Keiichi; Okuda, Takehiro; Nakaoka, Masahiro; Noda, Takashi

2014-11-01

Explanations for why population dynamics vary across the range of a species reflect two contrasting hypotheses: (i) temporal variability of populations is larger in the centre of the range compared to the margins because overcompensatory density dependence destabilizes population dynamics and (ii) population variability is larger near the margins, where populations are more susceptible to environmental fluctuations. In both of these hypotheses, positions within the range are assumed to affect population variability. In contrast, the fact that population variability is often related to mean population size implies that the spatial structure of the population size within the range of a species may also be a useful predictor of the spatial variation in temporal variability of population size over the range of the species. To explore how population temporal variability varies spatially and the underlying processes responsible for the spatial variation, we focused on the intertidal barnacle Chthamalus dalli and examined differences in its population dynamics along the tidal levels it inhabits. Changes in coverage of barnacle populations were monitored for 10.5 years at 25 plots spanning the elevational range of this species. Data were analysed by fitting a population dynamics model to estimate the effects of density-dependent and density-independent processes on population growth. We also examined the temporal mean-variance relationship of population size with parameters estimated from the population dynamics model. We found that the relative variability of populations tended to increase from the centre of the elevational range towards the margins because of an increase in the magnitude of stochastic fluctuations of growth rates. Thus, our results supported hypothesis (2). We also found that spatial variations in temporal population variability were well characterized by Taylor's power law, the relative population variability being inversely related to the mean population size. Results suggest that understanding the population dynamics of a species over its range may be facilitated by taking the spatial structure of population size into account as well as by considering changes in population processes as a function of position within the range of the species. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

Implementation of Solute Transport in the Vadose Zone into the `HYDRUS Package for MODFLOW'

NASA Astrophysics Data System (ADS)

Simunek, J.; Beegum, S.; Szymkiewicz, A.; Sudheer, K. P.

2017-12-01

The 'HYDRUS package for MODFLOW' was developed by Seo et al. (2007) and Twarakavi et al. (2008) to simultaneously evaluate transient water flow in both unsaturated and saturated zones. The package, which is based on the HYDRUS-1D model (Šimůnek et al., 2016) simulating unsaturated water flow in the vadose zone, was incorporated into MODFLOW (Harbaugh et al., 2000) simulating saturated groundwater flow. The HYDRUS package in the coupled model can be used to represent the effects of various unsaturated zone processes, including infiltration, evaporation, root water uptake, capillary rise, and recharge in homogeneous or layered soil profiles. The coupled model is effective in addressing spatially-variable saturated-unsaturated hydrological processes at the regional scale, allowing for complex layering in the unsaturated zone, spatially and temporarily variable water fluxes at the soil surface and in the root zone, and with alternating recharge and discharge fluxes (Twarakavi et al., 2008). One of the major limitations of the coupled model was that it could not be used to simulate at the same time solute transport. However, solute transport is highly dependent on water table fluctuations due to temporal and spatial variations in groundwater recharge. This is an important concern when the coupled model is used for analyzing groundwater contamination due to transport through the unsaturated zone. The objective of this study is to integrate the solute transport model (the solute transport part of HYDRUS-1D for the unsaturated zone and MT3DMS (Zheng and Wang, 1999; Zheng, 2009) for the saturated zone) into an existing coupled water flow model. The unsaturated zone component of the coupled model can consider solute transport involving many biogeochemical processes and reactions, including first-order degradation, volatilization, linear or nonlinear sorption, one-site kinetic sorption, two-site sorption, and two-kinetic sites sorption (Šimůnek and van Genuchten, 2008). Due to complex interactions at the groundwater table, certain modifications of the pressure head (compared to the original coupling) and solute concentration profiles were incorporated into the HYDRUS package. The developed integrated model is verified using HYDRUS-2D and analyzed for its computational time requirements.

Estimates of reservoir methane emissions based on a spatially ...

EPA Pesticide Factsheets

Global estimates of methane (CH4) emissions from reservoirs are poorly constrained, partly due to the challenges of accounting for intra-reservoir spatial variability. Reservoir-scale emission rates are often estimated by extrapolating from measurement made at a few locations; however, error and bias associated with this approach can be large and difficult to quantify. Here we use a generalized random tessellation survey (GRTS) design to generate estimates of central tendency and variance at multiple spatial scales in a reservoir. GRTS survey designs are probabilistic and spatially balanced which eliminates bias associated with expert judgment in site selection. GRTS surveys also allow for variance estimates that account for spatial pattern in emission rates. Total CH4 emission rates (i.e. sum of ebullition and diffusive emissions) were 4.8 (±2.1), 33.0 (±10.7), and 8.3 (±2.2) mg CH4 m-2 h-1 in open-waters, tributary associated areas, and the entire reservoir for the period in August 2014 during which 115 sites were sampled across an 7.98 km2 reservoir in Southwestern, Ohio, USA. Tributary areas occupy 12% of the reservoir surface, but were the source of 41% of total CH4 emissions, highlighting the importance of riverine-lacustrine transition zones. Ebullition accounted for >90% of CH4 emission at all spatial scales. Confidence interval estimates that incorporated spatial pattern in CH4 emissions were up to 29% narrower than when spatial independence

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Spatial correlation of probabilistic earthquake ground motion and loss

USGS Publications Warehouse

Wesson, R.L.; Perkins, D.M.

2001-01-01

Spatial correlation of annual earthquake ground motions and losses can be used to estimate the variance of annual losses to a portfolio of properties exposed to earthquakes A direct method is described for the calculations of the spatial correlation of earthquake ground motions and losses. Calculations for the direct method can be carried out using either numerical quadrature or a discrete, matrix-based approach. Numerical results for this method are compared with those calculated from a simple Monte Carlo simulation. Spatial correlation of ground motion and loss is induced by the systematic attenuation of ground motion with distance from the source, by common site conditions, and by the finite length of fault ruptures. Spatial correlation is also strongly dependent on the partitioning of the variability, given an event, into interevent and intraevent components. Intraevent variability reduces the spatial correlation of losses. Interevent variability increases spatial correlation of losses. The higher the spatial correlation, the larger the variance in losses to a port-folio, and the more likely extreme values become. This result underscores the importance of accurately determining the relative magnitudes of intraevent and interevent variability in ground-motion studies, because of the strong impact in estimating earthquake losses to a portfolio. The direct method offers an alternative to simulation for calculating the variance of losses to a portfolio, which may reduce the amount of calculation required.

Bottom-up and climatic forcing on the worldwide population of leatherback turtles.

PubMed

Saba, Vincent S; Spotila, James R; Chavez, Francisco P; Musick, John A

2008-05-01

Nesting populations of leatherback turtles (Dermochelys coriacea) in the Atlantic and western Indian Oceans are increasing or stable while those in the Pacific are declining. It has been suggested that leatherbacks in the eastern Pacific may be resource limited due to environmental variability derived from the El Niño Southern Oscillation (ENSO), but this has yet to be tested. Here we explored bottom-up forcing and the responding reproductive output of nesting leatherbacks worldwide. We achieved this through an extensive review of leatherback nesting and migration data and by analyzing the spatial, temporal, and quantitative nature of resources as indicated by net primary production at post-nesting female migration and foraging areas. Leatherbacks in the eastern Pacific were the smallest in body size and had the lowest reproductive output due to less productive and inconsistent resources within their migration and foraging areas. This derived from natural interannual and multidecadal climate variability together with an influence of anthropogenic climate warming that is possibly affecting these natural cycles. The reproductive output of leatherbacks in the Atlantic and western Indian Oceans was nearly twice that of turtles in the eastern Pacific. The inconsistent nature of the Pacific Ocean may also render western Pacific leatherbacks susceptible to a more variable reproductive output; however, it appears that egg harvesting on nesting beaches is their major threat. We suggest that the eastern Pacific leatherback population is more sensitive to anthropogenic mortality due to recruitment rates that are lower and more variable, thus accounting for much of the population differences compared to Atlantic and western Indian turtles.

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River

PubMed Central

Radinger, Johannes; Wolter, Christian; Kail, Jochem

2015-01-01

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. PMID:26569119

Adaptations of a physical-based hydrological model for alpine catchments. Application to the upper Durance catchment.

NASA Astrophysics Data System (ADS)

Lafaysse, Matthieu; Hingray, Benoit

2010-05-01

The impact of global change on water resources is expected to be especially pronounced in mountainous areas. Future hydrological scenarios required for impact studies are classically simulated with hydrological models from future meteorological scenarios based on GCMs outputs. Future hydrological regimes of French rivers were estimated following this methodology by Boé et al. (2009) with the physical-based hydrological model SAFRAN-ISBA-MODCOU (SIM), developed by Météo-France. Scenarios obtained for the Alps seem however not very reliable due to the poor performance achieved by the model for the present climate over this region. This work presents possible improvements of SIM for a more relevant simulation of alpine catchments hydrological behavior. Results obtained for the upper Durance catchment (3580 km2) are given for illustration. This catchment is located in Southern French Alps. Its outlet is the Serre-Ponçon lake, a large dam operated for hydropower production, with a key role for water supply in southeastern France. With altitudes ranging from 700 to 4100 meters, the catchment presents highly seasonal flows: minimum and maximum discharges are observed in winter and spring respectively due to snow accumulation and melt, low flows are sustained by glacier melt in late summer (39 km2 are covered by glaciers), major floods can be observed in fall due to large liquid precipitation amounts. Two main limitations of SIM were identified for this catchment. First the 8km-side grid discretization gives a bad representation of the spatial variability of hydrological processes induced by elevation and orientation. Then, low flows are not well represented because the model doesn't include deep storage in aquifers nor ice melt from glaciers. We modified SIM accordingly. For the first point, we applied a discretization based on topography : we divided the catchment in 9 sub-catchments and further 300 meters elevation bands. The vertical variability of meteorological inputs and vegetation cover could be thus better accounted for. Then, each elevation band is divided in 7 exposure classes, in order to represent the influence on snow cover of the solar radiation spatial variability . This discretisation results in 539 Hydrological Units where hydrological processes are assumed to be homogeneous. For the second point, we first included the possibility for glacier melt in previous discretization. We next added a conceptual non-linear underground reservoir in order to simulate water retention by aquifers. These adaptations lead to a clear improvement of simulations for all the hydrometric stations. Daily simulated discharges fit well with measurements (Nash score = 0.8). The model has a good ability to simulate interannual variability and it is robust under a long simulation period (1959-2006). This encourages us to use it in a modified climate context. We studied the effect of each model improvement with a set of sensitivity tests. Accounting for elevation bands allows simulating more persistent snow cover at high altitudes, contributing later to river flows. Adding underground storage leads to delay the snowmelt runoff transfer in river. The exposure influence is not so sensitive for discharges simulation, but it gives a more accurate description of the spatial variability of snow cover. Although glaciered areas are very small compared to total basin area, a better simulation of summer low flows is obtained including a glacier melt module. Despite previous improvements, winter low flows are still slightly underestimated. As suggested by a simple sensitivity analysis, this could be partly due to the fact that the model doesn't correctly simulate basal snowmelt by ground heat flow.

Spatial variability assessment of soil nutrients in an intense agricultural area, a case study of Rugao County in Yangtze River Delta Region, China

NASA Astrophysics Data System (ADS)

Zhao, Yongcun; Xu, Xianghua; Darilek, Jeremy Landon; Huang, Biao; Sun, Weixia; Shi, Xuezheng

2009-05-01

Topsoil samples (0-20 cm) ( n = 237) were collected from Rugao County, China. Geostatistical variogram analysis, sequential Gaussian simulation (SGS), and principal component (PC) analysis were applied to assess spatial variability of soil nutrients, identify the possible areas of nutrient deficiency, and explore spatial scale of variability of soil nutrients in the county. High variability of soil nutrient such as soil organic matter (SOM), total nitrogen (TN), available P, K, Fe, Mn, Cu, Zn, and B concentrations were observed. Soil nutrient properties displayed significant differences in their spatial structures, with available Cu having strong spatial dependence, SOM and available P having weak spatial dependence, and other nutrient properties having moderate spatial dependence. The soil nutrient deficiency, defined here as measured nutrient concentrations which do not meet the advisory threshold values specific to the county for dominant crops, namely rice, wheat, and rape seeds, was observed in available K and Zn, and the deficient areas covered 38 and 11%, respectively. The first three PCs of the nine soil nutrient properties explained 62.40% of the total variance. TN and SOM with higher loadings on PC1 are closely related to soil texture derived from different parent materials. The PC2 combined intermediate response variables such as available Zn and P that are likely to be controlled by land use and soil pH. Available B has the highest loading on PC3 and its variability of concentrations may be primarily ascribed to localized anthropogenic influence. The amelioration of soil physical properties (i.e. soil texture) and soil pH may improve the availability of soil nutrients and the sustainability of the agricultural system of Rugao County.

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

Exploring the effect of drought extent and interval on the Florida snail kite: Interplay between spatial and temporal scales

USGS Publications Warehouse

Mooij, Wolf M.; Bennetts, Robert E.; Kitchens, Wiley M.; DeAngelis, Donald L.

2002-01-01

The paper aims at exploring the viability of the Florida snail kite population under various drought regimes in its wetland habitat. The population dynamics of snail kites are strongly linked with the hydrology of the system due to the dependence of this bird species on one exclusive prey species, the apple snail, which is negatively affected by a drying out of habitat. Based on empirical evidence, it has been hypothesised that the viability of the snail kite population critically depends not only on the time interval between droughts, but also on the spatial extent of these droughts. A system wide drought is likely to result in reduced reproduction and increased mortality, whereas the birds can respond to local droughts by moving to sites where conditions are still favourable. This paper explores the implications of this hypothesis by means of a spatially-explicit individual-based model. The specific aim of the model is to study in a factorial design the dynamics of the kite population in relation to two scale parameters, the temporal interval between droughts and the spatial correlation between droughts. In the model high drought frequencies led to reduced numbers of kites. Also, habitat degradation due to prolonged periods of inundation led to lower predicted numbers of kites. Another main result was that when the spatial correlation between droughts was low, the model showed little variability in the predicted numbers of kites. But when droughts occurred mostly on a system wide level, environmental stochasticity strongly increased the stochasticity in kite numbers and in the worst case the viability of the kite population was seriously threatened.

Distribution of Escherichia coli in a coastal lagoon (Venice, Italy): Temporal patterns, genetic diversity and the role of tidal forcing.

PubMed

Perini, L; Quero, G M; García, E Serrano; Luna, G M

2015-12-15

Despite its worldwide importance as fecal indicator in aquatic systems, little is known about the diversity of Escherichia coli in the environment and the factors driving its spatial distribution. The city of Venice (Italy), lying at the forefront of a large European lagoon, is an ideal site to study the mechanisms driving the fate of fecal bacteria, due to the huge fluxes of tourists, the city's unique architecture (causing poor efficiency of sewages treatment), and the long branching network of canals crossing the city. We summarize the results of a multi-year investigation to study the temporal dynamics of E. coli around the city, describe the population structure (by assigning isolates to their phylogenetic group) and the genotypic diversity, and explore the role of environmental factors in determining its variability. E. coli abundance in water was highly variable, ranging from being undetectable up to 10(4) Colony Forming Units (CFU) per 100 ml. Abundance did not display significant relationships with the water physico-chemical variables. The analysis of the population structure showed the presence of all known phylogroups, including extra-intestinal and potentially pathogenic ones. The genotypic diversity was very high, as likely consequence of the heterogeneous input of fecal bacteria from the city, and showed site-specific patterns. Intensive sampling during the tidal fluctuations highlighted the prominent role of tides, rather than environmental variables, as source of spatial variation, with a more evident influence in water than sediments. These results, the first providing information on the genetic properties, spatial heterogeneity and influence of tides on E. coli populations around Venice, have implications to manage the fecal pollution, and the associated waterborne disease risks, in coastal cities lying in front of lagoons and semi-enclosed basins. Copyright © 2015 Elsevier Ltd. All rights reserved.

A global map of mangrove forest soil carbon at 30 m spatial resolution

NASA Astrophysics Data System (ADS)

Sanderman, Jonathan; Hengl, Tomislav; Fiske, Greg; Solvik, Kylen; Adame, Maria Fernanda; Benson, Lisa; Bukoski, Jacob J.; Carnell, Paul; Cifuentes-Jara, Miguel; Donato, Daniel; Duncan, Clare; Eid, Ebrahem M.; Ermgassen, Philine zu; Ewers Lewis, Carolyn J.; Macreadie, Peter I.; Glass, Leah; Gress, Selena; Jardine, Sunny L.; Jones, Trevor G.; Ndemem Nsombo, Eugéne; Mizanur Rahman, Md; Sanders, Christian J.; Spalding, Mark; Landis, Emily

2018-05-01

With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m‑3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86–729 Mg C ha‑1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work are intended to serve nations seeking to include mangrove habitats in payment-for- ecosystem services projects and in designing effective mangrove conservation strategies.

Climate and Edaphic Controls on Humid Tropical Forest Tree Height

NASA Astrophysics Data System (ADS)

Yang, Y.; Saatchi, S. S.; Xu, L.

2014-12-01

Uncertainty in the magnitude and spatial variations of forest carbon density in tropical regions is due to under sampling of forest structure from inventory plots and the lack of regional allometry to estimate the carbon density from structure. Here we quantify the variation of tropical forest structure by using more than 2.5 million measurements of canopy height from systematic sampling of Geoscience Laser Altimeter System (GLAS) satellite observations between 2004 to 2008 and examine the climate and edaphic variables influencing the variations. We used top canopy height of GLAS footprints (~ 0.25 ha) to grid the statistical mean and 90 percentile of samples at 0.5 degrees to capture the regional variability of large trees in tropics. GLAS heights were also aggregated based on a stratification of tropical regions using soil, elevation, and forest types. Both approaches provided consistent patterns of statistically dominant large trees and the least heterogeneity, both as strong drivers of distribution of high biomass forests. Statistical models accounting for spatial autocorrelation suggest that climate, soil and spatial features together can explain more than 60% of the variations in observed tree height information, while climate-only variables explains about one third of the first-order changes in tree height. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as organic matters, all present independent but statistically significant relationships to tree height variations. The results confirm other landscape and regional studies that soil fertility, geology and climate may jointly control a majority of the regional variations of forest structure in pan-tropics and influencing both biomass stocks and dynamics. Consequently, other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.

High-resolution (SIMS) versus bulk sulfur isotope patterns of pyrite in Proterozoic microbialites with diverse mat textures

NASA Astrophysics Data System (ADS)

Gomes, M. L.; Fike, D. A.; Bergmann, K.; Knoll, A. H.

2015-12-01

Sulfur (S) isotope signatures of sedimentary pyrite preserved in marine rocks provide a rich suite of information about changes in biogeochemical cycling associated with the evolution of microbial metabolisms and oxygenation of Earth surface environments. Conventionally, these S isotope records are based on bulk rock measurements. Yet, in modern microbial mat environments, S isotope compositions of sulfide can vary by up to 40‰ over a spatial range of ~ 1 mm. Similar ranges of S isotope variability have been found in Archean pyrite grains using both Secondary Ion Mass Spectrometry and other micro-analytical techniques. These micron-scale patterns have been linked to changes in rates of microbial sulfate reduction and/or sulfide oxidation, isotopic distillation of the sulfate reservoir due to microbial sulfate reduction, and post-depositional alteration. Fine-scale mapping of S isotope compositions of pyrite can thus be used to differentiate primary environmental signals from post-depositional overprinting - improving our understanding of both. Here, we examine micron-scale S isotope patterns of pyrite in microbialites from the Mesoproterozoic-Neoproterozoic Sukhaya Tunguska Formation and Neoproterozoic Draken Formation in order to explore S isotope variability associated with different mat textures and pyrite grain morphologies. A primary goal is to link modern observations of how sulfide spatial isotope distributions reflect active microbial communities present at given depths in the mats to ancient processes driving fine-sale pyrite variability in microbialites. We find large (up to 60‰) S isotope variability within a spatial range of less than 2.5cm. The micron-scale S isotope measurements converge around the S isotope composition of pyrite extracted from bulk samples of the same microbialites. These micron-scale pyrite S isotope patterns have the potential to reveal important information about ancient biogeochemical cycling in Proterozoic mat environments with implications for interpreting S isotope signatures from the geological record.

Impacts of multiple global environmental changes on African crop yield and water use efficiency: Implications to food and water security

NASA Astrophysics Data System (ADS)

Pan, S.; Yang, J.; Zhang, J.; Xu, R.; Dangal, S. R. S.; Zhang, B.; Tian, H.

2016-12-01

Africa is one of the most vulnerable regions in the world to climate change and climate variability. Much concern has been raised about the impacts of climate and other environmental factors on water resource and food security through the climate-water-food nexus. Understanding the responses of crop yield and water use efficiency to environmental changes is particularly important because Africa is well known for widespread poverty, slow economic growth and agricultural systems particularly sensitive to frequent and persistent droughts. However, the lack of integrated understanding has limited our ability to quantify and predict the potential of Africa's agricultural sustainability and freshwater supply, and to better manage the system for meeting an increasing food demand in a way that is socially and environmentally or ecologically sustainable. By using the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed the spatial and temporal patterns of crop yield, evapotranspiration (ET) and water use efficiency across entire Africa in the past 35 years (1980-2015) and the rest of the 21st century (2016-2099). Our preliminary results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion (about 50%), elevated atmospheric CO2 concentration, and nitrogen deposition. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Climate extremes especially droughts and heat wave have largely reduced crop yield in the most vulnerable regions. Our results indicate that N fertilizer could be a major driver to improve food security in Africa. Future climate warming could reduce crop yield and shift cropland distribution. Our study further suggests that improving water use efficiency through land management practices including the increased uses of fertilizers and irrigation will be the key for reducing the loss of crop yield in a warming climate and extreme weather.

Potential for negative emissions of greenhouse gases (CO2, CH4 and N2O) through coastal peatland re-establishment: Novel insights from high frequency flux data at meter and kilometer scales

NASA Astrophysics Data System (ADS)

Windham-Myers, Lisamarie; Bergamaschi, Brian; Anderson, Frank; Knox, Sara; Miller, Robin; Fujii, Roger

2018-04-01

High productivity temperate wetlands that accrete peat via belowground biomass (peatlands) may be managed for climate mitigation benefits due to their global distribution and notably negative emissions of atmospheric carbon dioxide (CO2) through rapid storage of carbon (C) in anoxic soils. Net emissions of additional greenhouse gases (GHG)—methane (CH4) and nitrous oxide (N2O)—are more difficult to predict and monitor due to fine-scale temporal and spatial variability, but can potentially reverse the climate mitigation benefits resulting from CO2 uptake. To support management decisions and modeling, we collected continuous 96 hour high frequency GHG flux data for CO2, CH4 and N2O at multiple scales—static chambers (1 Hz) and eddy covariance (10 Hz)—during peak productivity in a well-studied, impounded coastal peatland in California’s Sacramento Delta with high annual rates of C fluxes, sequestering 2065 ± 150 g CO2 m‑2 y‑1 and emitting 64.5 ± 2.4 g CH4 m‑2 y‑1. Chambers (n = 6) showed strong spatial variability along a hydrologic gradient from inlet to interior plots. Daily (24 hour) net CO2 uptake (NEE) was highest near inlet locations and fell dramatically along the flowpath (‑25 to ‑3.8 to +2.64 g CO2 m‑2 d‑1). In contrast, daily net CH4 flux increased along the flowpath (0.39 to 0.62 to 0.88 g CH4 m‑2 d‑1), such that sites of high daily CO2 uptake were sites of low CH4 emission. Distributed, continuous chamber data exposed five novel insights, and at least two important datagaps for wetland GHG management, including: (1) increasing dominance of CH4 ebullition fluxes (15%–32% of total) along the flowpath and (2) net negative N2O flux across all sites as measured during a 4 day period of peak biomass (‑1.7 mg N2O m‑2 d‑1 0.51 g CO2 eq m‑2 d‑1). The net negative emissions of re-established peat-accreting wetlands are notably high, but may be poorly estimated by models that do not consider within-wetland spatial variability due to water flowpaths.

Temporal and spatial characteristics of annual and seasonal rainfall in Malawi

NASA Astrophysics Data System (ADS)

Ngongondo, Cosmo; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena M.; Alemaw, Berhanu

2010-05-01

An understanding of the temporal and spatial characteristics of rainfall is central to water resources planning and management. However, such information is often limited in many developing countries like Malawi. In an effort to bridge the information gap, this study examined the temporal and spatial charecteristics of rainfall in Malawi. Rainfall readings from 42 stations across Malawi from 1960 to 2006 were analysed at monthly, annual and seasonal scales. The Malawian rainfall season lasts from November to April. The data were firstly subjected to quality checks through the cumulative deviations test and the Standard Normal Homogeinity Test (SNHT). Monthly distribution in a typical year, called heterogeneity, was investigated using the Precipitation Concentration Index (PCI). Further, normalized precipitation anomaly series of annual rainfall series (AR) and the PCI (APCI) were used to test for interannual rainfall variability. Spatial variability was characterised by fitting the Spatial Correlation function (SCF). The nonparametric Mann-Kendall statistic was used to investigate the temporal trends of the various rainfall variables. The results showed that 40 of the stations passed both data quality tests. For the two stations that failed, the data were adjusted using nearby stations. Annual and seasonal rainfall were found to be characterised by high spatial variation. The country mean annual rainfall was 1095 mm with mean interannual variability of 26%. The highland areas to the north and southeast of the country exhibited the highest rainfall and lowest interannual variability. Lowest rainfall coupled with high interannual variability was found in the Lower Shire basin, in the southern part of Malawi. This simillarity is the pattern of annual and seasonal rainfall should be expected because all stations had over 90% of their observed annual rainfall in the six month period between November and April. Monthly rainfall was found to be highly variable both temporally and spatially. None of the stations have stable monthly rainfall regimes (mean PCI of less than 10). Stations with the highest mean rainfall were found to have a lower interannual variability. The rainfall stations showed low spatial correlations for annual, monthly as well as seasonal timescales indicating that the data may not be suitable for spatial interpolation. However, some structure (i.e. lower correlation with distance) could be observed when aggregating the data at 50 mile intervals. The annual and seasonal rainfall series were dominated by negative trends. The spatial distribution of the trends can be described as heterogeneous, although most of the stations in the southern region have negative trends. At the monthly timescale, 37 of the stations show a negative trend with four of the stations, all in the south, showing significant negative trends. On the other hand, only 5 stations show positive trends with only one significant trend in the south. Keywords: Malawi, rainfall trends, spatial variation

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

Kumar, R.; Barth, M. C.; Nair, V. S.

This study examines differences in the surface black carbon (BC) aerosol loading between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identifies dominant sources of BC in South Asia and surrounding regions during March-May 2006 (Integrated Campaign for Aerosols, Gases and Radiation Budget, ICARB) period. A total of 13 BC tracers are introduced in the Weather Research and Forecasting Model coupled with Chemistry to address these objectives. The model reproduced the temporal and spatial variability of BC distribution observed over the AS and the BoB during the ICARB ship cruise and captured spatial variability at the inlandmore » sites. In general, the model underestimates the observed BC mass concentrations. However, the model-observation discrepancy in this study is smaller compared to previous studies. Model results show that ICARB measurements were fairly well representative of the AS and the BoB during the pre-monsoon season. Elevated BC mass concentrations in the BoB are due to 5 times stronger influence of anthropogenic emissions on the BoB compared to the AS. Biomass burning in Burma also affects the BoB much more strongly than the AS. Results show that anthropogenic and biomass burning emissions, respectively, accounted for 60 and 37% of the average +/- standard deviation (representing spatial and temporal variability) BC mass concentration (1341 +/- 2353 ng m(-3)) in South Asia. BC emissions from residential (61 %) and industrial (23 %) sectors are the major anthropogenic sources, except in the Himalayas where vehicular emissions dominate. We find that regional-scale transport of anthropogenic emissions contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions in different regions of South Asia.« less

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

Kumar, R.; Barth, M. C.; Nair, V. S.

This study examines differences in the surface black carbon (BC) aerosol loading between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identifies dominant sources of BC in South Asia and surrounding regions during March–May 2006 (Integrated Campaign for Aerosols, Gases and Radiation Budget, ICARB) period. A total of 13 BC tracers are introduced in the Weather Research and Forecasting Model coupled with Chemistry to address these objectives. The model reproduced the temporal and spatial variability of BC distribution observed over the AS and the BoB during the ICARB ship cruise and captured spatial variability at the inlandmore » sites. In general, the model underestimates the observed BC mass concentrations. However, the model–observation discrepancy in this study is smaller compared to previous studies. Model results show that ICARB measurements were fairly well representative of the AS and the BoB during the pre-monsoon season. Elevated BC mass concentrations in the BoB are due to 5 times stronger influence of anthropogenic emissions on the BoB compared to the AS. Biomass burning in Burma also affects the BoB much more strongly than the AS. Results show that anthropogenic and biomass burning emissions, respectively, accounted for 60 and 37% of the average ± standard deviation (representing spatial and temporal variability) BC mass concentration (1341 ± 2353 ng m -3) in South Asia. BC emissions from residential (61%) and industrial (23%) sectors are the major anthropogenic sources, except in the Himalayas where vehicular emissions dominate. We find that regional-scale transport of anthropogenic emissions contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions in different regions of South Asia.« less

Spatio-temporal variability of the North Sea cod recruitment in relation to temperature and zooplankton.

PubMed

Nicolas, Delphine; Rochette, Sébastien; Llope, Marcos; Licandro, Priscilla

2014-01-01

The North Sea cod (Gadus morhua, L.) stock has continuously declined over the past four decades linked with overfishing and climate change. Changes in stock structure due to overfishing have made the stock largely dependent on its recruitment success, which greatly relies on environmental conditions. Here we focus on the spatio-temporal variability of cod recruitment in an effort to detect changes during the critical early life stages. Using International Bottom Trawl Survey (IBTS) data from 1974 to 2011, a major spatio-temporal change in the distribution of cod recruits was identified in the late 1990s, characterized by a pronounced decrease in the central and southeastern North Sea stock. Other minor spatial changes were also recorded in the mid-1980s and early 1990s. We tested whether the observed changes in recruits distribution could be related with direct (i.e. temperature) and/or indirect (i.e. changes in the quantity and quality of zooplankton prey) effects of climate variability. The analyses were based on spatially-resolved time series, i.e. sea surface temperature (SST) from the Hadley Center and zooplankton records from the Continuous Plankton Recorder Survey. We showed that spring SST increase was the main driver for the most recent decrease in cod recruitment. The late 1990s were also characterized by relatively low total zooplankton biomass, particularly of energy-rich zooplankton such as the copepod Calanus finmarchicus, which have further contributed to the decline of North Sea cod recruitment. Long-term spatially-resolved observations were used to produce regional distribution models that could further be used to predict the abundance of North Sea cod recruits based on temperature and zooplankton food availability.

Soil Greenhouse Gas Emissions from a Subtropical Mangrove in Hong Kong

NASA Astrophysics Data System (ADS)

Lai, D. Y. F.; Xu, J.

2014-12-01

The concept of "blue carbon" has received increasing attention recently, which points to the potential role of vegetated coastal wetlands in carbon sequestration. Yet, the magnitude and controls of greenhouse gas emissions from coastal wetland ecosystems, especially mangroves in the subtropical regions, are still largely unknown. In this study, we conducted chamber measurements in the Mai Po Marshes Nature Reserve of Hong Kong at monthly intervals to characterize the spatial and temporal variability of the emission of greenhouse gases, including CO2, CH4 and N2O from mangrove soils, and examine the influence of environmental and biotic variables on greenhouse gas fluxes. We found the highest mean CH4 and N2O emissions in autumn and the highest CO2 flux in summer. Along the tidal gradient, we observed significantly higher CH4 and N2O emissions from the middle zones and landward zones, respectively, while no clear spatial variation of CO2 emissions was observed. There were significantly higher soil greenhouse gas emissions from sites dominated by Avicennia marina than those dominated by Kandelia obovata, which might be due to the presence of pneumatophores which facilitated gas transport. We found a significant, negative correlation between CH4 flux and soil NO3-N concentration, while CO2 flux was positively correlation with total Kjeldahl nitrogen content. Soil temperature was positively correlated with the emissions of all three greenhouse gases, while water table depth was positively and negatively correlated with CH4 and N2O emissions, respectively. Our findings demonstrate the high spatial and temporal variability of greenhouse gas emissions from mangrove soils which could be attributed in part to the differences in environmental conditions and dominant plant species.

Multiangular Contributions for Discriminate Seasonal Structural Changes in the Amazon Rainforest Using MODIS MAIAC Data

NASA Astrophysics Data System (ADS)

Moura, Y. M.; Hilker, T.; Galvão, L. S.; Santos, J. R.; Lyapustin, A.; Sousa, C. H. R. D.; McAdam, E.

2014-12-01

The sensitivity of the Amazon rainforests to climate change has received great attention by the scientific community due to the important role that this vegetation plays in the global carbon, water and energy cycle. The spatial and temporal variability of tropical forests across Amazonia, and their phenological, ecological and edaphic cycles are still poorly understood. The objective of this work was to infer seasonal and spatial variability of forest structure in the Brazilian Amazon based on anisotropy of multi-angle satellite observations. We used observations from the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra and Aqua) processed by a new Multi-Angle Implementation Atmospheric Correction Algorithm (MAIAC) to investigate how multi-angular spectral response from satellite imagery can be used to analyze structural variability of Amazon rainforests. We calculated differences acquired from forward and backscatter reflectance by modeling the bi-directional reflectance distribution function to infer seasonal and spatial changes in vegetation structure. Changes in anisotropy were larger during the dry season than during the wet season, suggesting intra-annual changes in vegetation structure and density. However, there were marked differences in timing and amplitude depending on forest type. For instance differences between reflectance hotspot and darkspot showed more anisotropy in the open Ombrophilous forest than in the dense Ombrophilous forest. Our results show that multi-angle data can be useful for analyzing structural differences in various forest types and for discriminating different seasonal effects within the Amazon basin. Also, multi-angle data could help solve uncertainties about sensitivity of different tropical forest types to light versus rainfall. In conclusion, multi-angular information, as expressed by the anisotropy of spectral reflectance, may complement conventional studies and provide significant improvements over approaches that are based on vegetation indices alone.

Intra-urban and street scale variability of BTEX, NO 2 and O 3 in Birmingham, UK: Implications for exposure assessment

NASA Astrophysics Data System (ADS)

Vardoulakis, Sotiris; Solazzo, Efisio; Lumbreras, Julio

2011-09-01

Automatic monitoring networks have the ability of capturing air pollution episodes, as well as short- and long-term air quality trends in urban areas that can be used in epidemiological studies. However, due to practical constraints (e.g. cost and bulk of equipment), the use of automatic analysers is restricted to a limited number of roadside and background locations within a city. As a result, certain localised air pollution hotspots may be overlooked or overemphasised, especially near heavily trafficked street canyons and intersections. This has implications for compliance with regulatory standards and may cause exposure misclassification in epidemiological studies. Apart from automatic analysers, low cost passive diffusion tubes can be used to characterise the spatial variability of air pollution in urban areas. In this study, BTEX, NO 2 and O 3 data from a one-year passive sampling survey were used to characterise the intra-urban and street scale spatial variability of traffic-related pollutants in Birmingham (UK). In addition, continuous monitoring of NO 2, NO x, O 3, CO, SO 2, PM 10 and PM 2.5 from three permanent monitoring sites was used to identify seasonal and annual pollution patterns. The passive sampling measurements allowed us to evaluate the representativeness of a permanent roadside monitoring site that has recorded some of the highest NO 2 and PM 10 concentrations in Birmingham in recent years. Dispersion modelling was also used to gain further insight into pollutant sources and dispersion characteristics at this location. The strong spatial concentration gradients observed in busy streets, as well as the differences between roadside and urban background levels highlight the importance of appropriate positioning of air quality monitoring equipment in cities.

Remote Sensing Characterization of Two-dimensional Wave Forcing in the Surf Zone

NASA Astrophysics Data System (ADS)

Carini, R. J.; Chickadel, C. C.; Jessup, A. T.

2016-02-01

In the surf zone, breaking waves drive longshore currents, transport sediment, shape bathymetry, and enhance air-sea gas and particle exchange. Furthermore, wave group forcing influences the generation and duration of rip currents. Wave breaking exhibits large gradients in space and time, making it challenging to measure in situ. Remote sensing technologies, specifically thermal infrared (IR) imagery, can provide detailed spatial and temporal measurements of wave breaking at the water surface. We construct two-dimensional maps of active wave breaking from IR imagery collected during the Surf Zone Optics Experiment in September 2010 at the US Army Corps of Engineers' Field Research Facility in Duck, NC. For each breaker identified in the camera's field of view, the crest-perpendicular length of the aerated breaking region (roller length) and wave direction are estimated and used to compute the wave energy dissipation rate. The resultant dissipation rate maps are analyzed over different time scales: peak wave period, infragravity wave period, and tidal wave period. For each time scale, spatial maps of wave breaking are used to characterize wave forcing in the surf zone for a variety of wave conditions. The following phenomena are examined: (1) wave dissipation rates over the bar (location of most intense breaking) have increased variance in infragravity wave frequencies, which are different from the peak frequency of the incoming wave field and different from the wave forcing variability at the shoreline, and (2) wave forcing has a wider spatial distribution during low tide than during high tide due to depth-limited breaking over the barred bathymetry. Future work will investigate the response of the variability in wave setup, longshore currents and rip currents, to the variability in wave forcing in the surf zone.

Spatio-Temporal Variability of the North Sea Cod Recruitment in Relation to Temperature and Zooplankton

PubMed Central

Nicolas, Delphine; Rochette, Sébastien; Llope, Marcos; Licandro, Priscilla

2014-01-01

The North Sea cod (Gadus morhua, L.) stock has continuously declined over the past four decades linked with overfishing and climate change. Changes in stock structure due to overfishing have made the stock largely dependent on its recruitment success, which greatly relies on environmental conditions. Here we focus on the spatio-temporal variability of cod recruitment in an effort to detect changes during the critical early life stages. Using International Bottom Trawl Survey (IBTS) data from 1974 to 2011, a major spatio-temporal change in the distribution of cod recruits was identified in the late 1990s, characterized by a pronounced decrease in the central and southeastern North Sea stock. Other minor spatial changes were also recorded in the mid-1980s and early 1990s. We tested whether the observed changes in recruits distribution could be related with direct (i.e. temperature) and/or indirect (i.e. changes in the quantity and quality of zooplankton prey) effects of climate variability. The analyses were based on spatially-resolved time series, i.e. sea surface temperature (SST) from the Hadley Center and zooplankton records from the Continuous Plankton Recorder Survey. We showed that spring SST increase was the main driver for the most recent decrease in cod recruitment. The late 1990s were also characterized by relatively low total zooplankton biomass, particularly of energy-rich zooplankton such as the copepod Calanus finmarchicus, which have further contributed to the decline of North Sea cod recruitment. Long-term spatially-resolved observations were used to produce regional distribution models that could further be used to predict the abundance of North Sea cod recruits based on temperature and zooplankton food availability. PMID:24551103

Controls on the spatial variability of key soil properties: comparing field data with a mechanistic soilscape evolution model

NASA Astrophysics Data System (ADS)

Vanwalleghem, T.; Román, A.; Giraldez, J. V.

2016-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of a geostatistical versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

Spatial variation in attributable risks.

PubMed

Congdon, Peter

2015-01-01

The attributable risk (AR) measures the contribution of a particular risk factor to a disease, and allows estimation of disease rates specific to that risk. While previous studies consider variability in ARs over demographic categories, this paper considers the extent of spatial variability in ARs estimated from multilevel data with confounders both at individual and geographic levels. A case study considers the AR for diabetes in relation to elevated BMI, and area rates for diabetes attributable to excess weight. Contextual adjustment includes known area variables, and unobserved spatially clustered influences, while spatial heterogeneity (effect modification) is considered in terms of varying effects of elevated BMI by neighbourhood deprivation category. The application is to patient register data in London, with clear evidence of spatial variation in ARs, and in small area diabetes rates attributable to excess weight. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of spatial variability in visible and near-infrared spectral images

USGS Publications Warehouse

Chavez, P.S.

1992-01-01

The visible and near-infrared bands of the Landsat Thematic Mapper (TM) and the Satellite Pour l'Observation de la Terre (SPOT) were analyzed to determine which band contained more spatial variability. It is important for applications that require spatial information, such as those dealing with mapping linear features and automatic image-to-image correlation, to know which spectral band image should be used. Statistical and visual analyses were used in the project. The amount of variance in an 11 by 11 pixel spatial filter and in the first difference at the six spacings of 1, 5, 11, 23, 47, and 95 pixels was computed for the visible and near-infrared bands. The results indicate that the near-infrared band has more spatial variability than the visible band, especially in images covering densely vegetated areas. -Author

Spatial and temporal variability in forest growth in the Olympic Mountains, Washington: sensitivity to climatic variability.

Treesearch

Melisa L. Holman; David L. Peterson

2006-01-01

We compared annual basal area increment (BAI) at different spatial scales among all size classes and species at diverse locations in the wet western and dry northeastern Olympic Mountains. Weak growth correlations at small spatial scales (average R = 0.084-0.406) suggest that trees are responding to local growth conditions. However, significant...

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

Sinistore, Julie C.; Reinemann, D. J.; Izaurralde, Roberto C.

Spatial variability in yields and greenhouse gas emissions from soils has been identified as a key source of variability in life cycle assessments (LCAs) of agricultural products such as cellulosic ethanol. This study aims to conduct an LCA of cellulosic ethanol production from switchgrass in a way that captures this spatial variability and tests results for sensitivity to using spatially averaged results. The Environment Policy Integrated Climate (EPIC) model was used to calculate switchgrass yields, greenhouse gas (GHG) emissions, and nitrogen and phosphorus emissions from crop production in southern Wisconsin and Michigan at the watershed scale. These data were combinedmore » with cellulosic ethanol production data via ammonia fiber expansion and dilute acid pretreatment methods and region-specific electricity production data into an LCA model of eight ethanol production scenarios. Standard deviations from the spatial mean yields and soil emissions were used to test the sensitivity of net energy ratio, global warming potential intensity, and eutrophication and acidification potential metrics to spatial variability. Substantial variation in the eutrophication potential was also observed when nitrogen and phosphorus emissions from soils were varied. This work illustrates the need for spatially explicit agricultural production data in the LCA of biofuels and other agricultural products.« less

Optimization techniques for integrating spatial data

USGS Publications Warehouse

Herzfeld, U.C.; Merriam, D.F.

1995-01-01

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration. ?? 1995 International Association for Mathematical Geology.

Preliminary results of spatial modeling of selected forest health variables in Georgia

Treesearch

Brock Stewart; Chris J. Cieszewski

2009-01-01

Variables relating to forest health monitoring, such as mortality, are difficult to predict and model. We present here the results of fitting various spatial regression models to these variables. We interpolate plot-level values compiled from the Forest Inventory and Analysis National Information Management System (FIA-NIMS) data that are related to forest health....

Biogeographical drivers of ragweed pollen concentrations in Europe

NASA Astrophysics Data System (ADS)

Matyasovszky, István; Makra, László; Tusnády, Gábor; Csépe, Zoltán; Nyúl, László G.; Chapman, Daniel S.; Sümeghy, Zoltán; Szűcs, Gábor; Páldy, Anna; Magyar, Donát; Mányoki, Gergely; Erostyák, János; Bodnár, Károly; Bergmann, Karl-Christian; Deák, Áron József; Thibaudon, Michel; Albertini, Roberto; Bonini, Maira; Šikoparija, Branko; Radišić, Predrag; Gehrig, Regula; Rybníček, Ondřej; Severova, Elena; Rodinkova, Victoria; Prikhodko, Alexander; Maleeva, Anna; Stjepanović, Barbara; Ianovici, Nicoleta; Berger, Uwe; Seliger, Andreja Kofol; Weryszko-Chmielewska, Elżbieta; Šaulienė, Ingrida; Shalaboda, Valentina; Yankova, Raina; Peternel, Renata; Ščevková, Jana; Bullock, James M.

2017-06-01

The drivers of spatial variation in ragweed pollen concentrations, contributing to severe allergic rhinitis and asthma, are poorly quantified. We analysed the spatiotemporal variability in 16-year (1995-2010) annual total (66 stations) and annual total (2010) (162 stations) ragweed pollen counts and 8 independent variables (start, end and duration of the ragweed pollen season, maximum daily and calendar day of the maximum daily ragweed pollen counts, last frost day in spring, first frost day in fall and duration of the frost-free period) for Europe (16 years, 1995-2010) as a function of geographical coordinates. Then annual total pollen counts, annual daily peak pollen counts and date of this peak were regressed against frost-related variables, daily mean temperatures and daily precipitation amounts. To achieve this, we assembled the largest ragweed pollen data set to date for Europe. The dependence of the annual total ragweed pollen counts and the eight independent variables against geographical coordinates clearly distinguishes the three highly infected areas: the Pannonian Plain, Western Lombardy and the Rhône-Alpes region. All the eight variables are sensitive to longitude through its temperature dependence. They are also sensitive to altitude, due to the progressively colder climate with increasing altitude. Both annual total pollen counts and the maximum daily pollen counts depend on the start and the duration of the ragweed pollen season. However, no significant changes were detected in either the eight independent variables as a function of increasing latitude. This is probably due to a mixed climate induced by strong geomorphological inhomogeneities in Europe.

An analytical formulation of two‐dimensional groundwater dispersion induced by surficial recharge variability

USGS Publications Warehouse

Swain, Eric D.; Chin, David A.

2003-01-01

A predominant cause of dispersion in groundwater is advective mixing due to variability in seepage rates. Hydraulic conductivity variations have been extensively researched as a cause of this seepage variability. In this paper the effect of variations in surface recharge to a shallow surficial aquifer is investigated as an important additional effect. An analytical formulation has been developed that relates aquifer parameters and the statistics of recharge variability to increases in the dispersivity. This is accomplished by solving Fourier transforms of the small perturbation forms of the groundwater flow equations. Two field studies are presented in this paper to determine the statistics of recharge variability for input to the analytical formulation. A time series of water levels at a continuous groundwater recorder is used to investigate the temporal statistics of hydraulic head caused by recharge, and a series of infiltrometer measurements are used to define the spatial variability in the recharge parameters. With these field statistics representing head fluctuations due to recharge, the analytical formulation can be used to compute the dispersivity without an explicit representation of the recharge boundary. Results from a series of numerical experiments are used to define the limits of this analytical formulation and to provide some comparison. A sophisticated model has been developed using a particle‐tracking algorithm (modified to account for temporal variations) to estimate groundwater dispersion. Dispersivity increases of 9 percent are indicated by the analytical formulation for the aquifer at the field site. A comparison with numerical model results indicates that the analytical results are reasonable for shallow surficial aquifers in which two‐dimensional flow can be assumed.