Spatial Depth Distribution Research Articles

Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning

AbstractRestrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor‐intensive and time‐consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within‐field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1‐m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha–1. Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross‐validated using a k‐fold approach using entire fields as folds. The overall model (using all data) resulted in out‐of‐the bag R2 and RMSE of .66 and 22.8 cm respectively. Shallow (0–30 cm) and deep (0–90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field‐scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site‐specific management based on soil depth maps.

Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure: A case study in Sweden

Due to associated uncertainties, modelling the spatial distribution of depth to bedrock (DTB) is an important and challenging concern in many geo-engineering applications. The association between DTB, the safety and economy of design structures implies that generating more precise predictive models can be of vital interest. In the present study, the challenge of applying an optimally predictive three-dimensional (3D) spatial DTB model for an area in Stockholm, Sweden was addressed using an automated intelligent computing design procedure. The process was developed and programmed in both C++ and Python to track their performance in specified tasks and also to cover a wide variety of different internal characteristics and libraries. In comparison to the ordinary Kriging (OK) geostatistical tool, the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors. The results showed that in the absence of measured data, the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties, thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area. • Two automated deep learning intelligence models through C++ and Python were developed. • 3D pattern of subsurface geo-spatial DTB distributions for Stockholm, Sweden was presented. • The visualised predicted model using C++ showed more accurate results than Python and OK. • Flaws of OK in modelling the limited and heterogeneous distributed data were demonstrated. • The capacity of intelligence models for subsurface DTB characterizing was improved.

Accuracy Assessment of Satellite Derived Bathymetry Model for Depth Extraction in Sorong Shallow Water Area

Marine research has continuously improved the methods in obtaining the related bathymetric data; not only relying on the conventional methods for i.e. echosounder-based methods, but also by incorporating satellite technology for i.e. passive remote sensing technology, in this case, satellite derived bathymetry (SDB). Regarding the SDB method, as we know, variation of sea bed cover can influence the relation between the spectral reflection of shallow water area and the depth of the sea. In this situation, normalization of the sea bed variation is needed. Previous studies have mentioned that the band ratio can help to normalize the variation of sea bed cover. This research is intended to compare the accuracy of satellite derived bathymetry by using single band and band ratio. Four bands of Sentinel 2A (blue, green, red, and NIR bands) are used along with a single beam echosounder (SBES) measurement data published in 2015 used as training and testing data for the SDB model. Furthermore, the influence of sun glint correction to the results was evaluated and the accuracy of the model was estimated. In total there are four single bands and six combinations of band ratio that are used for this research. The results show that green band outperformed band ratio in term of RMSE value. However, visually, only band ratio of blue/green band that provided a much more representative depth spatial distribution especially for shallow water area below 3 m. In this case, band ratio is effective in normalizing the variation of sea bed cover. Furthermore, the use of sun glint correction in the process is also increase accuracies of the SDB model. The highest accuracy was obtained when using green band after sun glint correction with RMSE value 2.999 m while when using band ratio of the blue band to the green band (blue/green), the accuracy was 3.624 m. In conclusion, SDB model to extend methods in obtaining bathymetry data is promising as more images become available free of charge and in various resolutions.

The influence of seasonal temperature variation on embryonic development in two species of spider crabs (Majoidea)

The aim of this study was to analyze the ecological influence of seasonal temperature variations on embryonic development in two species of Majoidea. Leurocyclus tuberculosus and Libinia spinosa. These species inhabit stable populations in the San José Gulf (42°25′41″S, 64°08′27″O, Patagonia-Argentina) in a wide depth range and seasonal temperature variations from 8 °C to 18 °C. The influence of seasonal temperatures on embryonic development was examined in ovigerous females of both species, through a bioassay in laboratory conditions at 8 °C, 14 °C, and 18 °C from gastrula to hatching. Additionally, monthly female samplings were conducted for one year, characterizing their spatial depth distribution associated with the seawater temperature in the study area and their ovigerous/non ovigerous condition. Laboratory experimental results showed that duration of incubation increased 67 % in L. tuberculosus and 55% in L. spinosa by a decline in temperature from 18 °C to 8 °C with one diapause period. The embryonic development for the 3 assayed temperatures was divided in five periods. The assayed temperatures did not affect the morphology of the embryo, and its growth during each period did not present significant differences among the different temperatures. Field analysis showed significant differences in the spatial distribution of ovigerous females which were associated to the assessed temperatures along the year. This distribution might modulate the development of embryos leading to two spawnings synchronized with the phyto- and zooplankton peaks in the San José gulf. Finally, ecological and potential impact of ocean warming in these species was also addressed.

Relationship between coal composition, fracture abundance and initial reservoir permeability: A case study in the Walloon Coal Measures, Surat Basin, Australia

The Surat Basin, which is a major eastern Australian Coal Seam Gas (CSG) reservoir, produces gas from its Middle to Late Jurassic sub-bituminous Walloon Coal Measures, and shows variability in initial reservoir permeability across the basin. For a given lithostatic stress, the presence of vitrinite group or telovitrinite macerals within the coal can influence the fracture abundance within the coal reservoirs of similar rank, which in turn potentially impacts the initial reservoir permeability of the CSG reservoirs. The predictive relationship between megascopic banding character or lithotype, microscopic maceral composition and fracture intensity within CSG reservoirs is well explored in bituminous coals, but not so much in sub-bituminous coals. Here, we analyse wellbore image log data along with the conventional geophysical logs, maceral analysis data, core descriptions validated by core photos from the well completion reports and interpreted permeability data from well tests to systematically investigate the relationship between lithotype, maceral composition with a focus on vitrinite/telovitrinite, and fracture intensity in the sub-bituminous rank Walloon coals, to test their influence on permeability trends across the basin.Permeability-depth trends in the Walloon coals vary regionally across the basin, and show different inclinations depending on coal measures stratigraphy, coal lithotype composition, fracture intensity and stress orientation between local anticlines, monoclines and synclines. Depth normalised permeability and thickness-weighted telovitrinite content data from 55 wells were analysed. Furthermore, analysis of 3.8 km of borehole image logs, along with 156 maceral composition data points and thickness-weighted average lithotypes derived from the core description across 10 vertical wells, revealed a moderately positive relationship between the coal type and fracture abundance. This indicates some coal compositional control on the fracture abundance at this lower rank. The derived average total fracture intensity (cleat and sinusoidal coal fracture) from borehole image logs and thickness-weighted average telovitrinite content from the core analysis also exhibited an exponential increase, with the increasing depth normalised permeability derived from the well test data within the 10 analysed wells. This relationship suggests the influence of the variation in coal composition and fracture intensity on the rheological behaviour of the CSG reservoir within the Walloon Coal Measures. Spatial distribution of depth normalised permeability from the 55 wells across different domains and their relationship with the maceral composition within the study area also suggests the influence of geological structures on the depth normalised permeability. Therefore, though coal composition controls the fracture intensity, under favourable stress and fracture conditions the relationship can be substantially modified in parts of the Surat Basin, and altogether these factors control the rheological behaviour of the Walloon Coal Measures.

Remote Sensing Monitoring of the Bottom Topography in a Shallow Reservoir and the Spatiotemporal Changes of Submerged Aquatic Vegetation Under Water Depth Fluctuations

Monitoring the growth and distribution of submerged aquatic vegetation (SAV) is crucial to the protection and restoration of the ecosystem of inland reservoirs. Considering the high sensitivity of SAV to water depth fluctuations in Guanting Reservoir, China, in this study, we realized the reconstruction of bottom topography by combining changing water level with a long time series remote sensing technology and explored the spatiotemporal succession law of SAV by analyzing the effect of water depth on the spatiotemporal distribution of SAV. Results of water depth spatial distribution in Guanting Reservoir were obtained by using water and land boundary lines to construct underwater terrain contours. The accuracy of estimated water depth data from remote sensing images was verified with measured water depth data, and the average relative error of water depth estimation results was about 0.25 m. The experimental results show that (1) the SWIR bands of Landsat images could avoid the interference of aquatic vegetation and realize the separation of land and water; and (2) after separating water area from land, an SWIR1_NIR index was used to effectively map SAV distribution in the reservoir. The results also indicate that the distribution of SAV in the reservoir is suitable for the water depth range of 0−2 m. Water depth fluctuations cause changes in the spatial distribution of suitable water depth. It is the main reason for the change of SAV distribution area in the reservoir during the past 20 years.

Performance assessment of 2D Zero-Inertia and Shallow Water models for simulating rainfall-runoff processes

Rainfall-runoff simulations are increasingly being performed with physically-based and spatially distributed solvers. The current computational and numerical technology enables the use of full shallow water equations solvers to be applied for these type of flow problems. Nonetheless, Zero-Inertia (diffusive wave) solvers have been historically favoured due to their conceptual and mathematical simplicity in comparison to shallow water solvers, with the working assumption that the simplifications introduced by Zero-Inertia will have some assumable impact on accuracy but will also allow for computational efficiency. Since both types of solvers have been primarily developed, benchmarked and compared to each other for fluvial and floodplain simulations, it is relevant to assess t-he relative performance for rainfall-runoff problems. In this work, both solvers are applied to a set of six well known test cases with reference solutions. The performance of the solvers is assessed in terms of global signatures such as hydrographs and flooded areas, but also in terms of spatial distributions of depth and velocity, as well as computational cost. Furthermore, the comparisons are performed across different spatial resolutions. The results show that for rainfall-runoff problems explicit, finite volumes solvers for both equations provide a similar accuracy, but the shallow water solver requires less computational time. The Zero-Inertia solver was found to be less sensitive to mesh refining than the full shallow water solver.

Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements

AbstractIt is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.

An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.

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

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=0.05842m2/°C·day; R2=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.

Improving Hydrodynamic Modelling: an Analytical Framework for Assessment of Two‐Dimensional Hydrodynamic Models

AbstractTwo‐dimensional hydrodynamic models are increasingly common in riverine research and management. However, input data are not standardized among studies, and assessments of model performance are uncommon, which hinder interpretation of model results and comparisons among studies. Herein, we describe a framework for two‐dimensional hydrodynamic model input data collection, model calibration and validation to evaluate model predictions. We present a logical process for the validation of depth and velocity that recognizes the inherent spatial uncertainty in the field measurements and modelling results. The hydrodynamic model we present as an example shows agreement between predicted and observed water surface elevation, area of inundation and spatial distributions of depth and velocity at calibration and independent validation discharges. If this model development and assessment framework was adopted by others, it would allow comparison between studies and provide a foundation for establishing model performance standards. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Properties of cores of the water masses in the Okhotsk Sea

Spatial distribution of depth and water properties (temperature, salinity, dissolved oxygen content) are considered in detail for cores of the Okhotsk Sea water masses: subsurface, intermediate, and deep, on the base of the most comprehensive oceanographic data set.

Spatial distribution of soil organic matter in two fields on tidal flat sediments (Zhejiang Province, China) differing in duration of paddy management

AbstractDespite of its ecological and economic importance, only limited information is available on spatial variability of soil organic C (OC) in paddy fields during soil development. To obtain deeper insights into the accumulation and spatial distribution of OC with ongoing paddy management, the present study investigates spatial heterogeneities in puddled topsoil layers, in the plough pan, as well as in the underlying subsoil of a 50‐ and 300‐y old paddy site at the area of Cixi (Hangzhou Bay, E China). Sampling of the plots was done on the basis of a regular, orthogonal grid. The grid combined a large‐scale design with distances of 5 m with a small‐scale nested sampling ranging within 0.5 to 1 m. Samples have been taken with an auger in three sampling depths: (1) puddled topsoil layer, (2) dense plough layer, (3) mixed subsoil horizon. All samples were analyzed for their OC and total N concentrations as well as for their soil color (lightness value) to assess relationships between soil lightness and OC amount and distribution. The spatial behavior of all analyzed parameters in the different soil layers was investigated by calculating variograms; the degree of spatial dependence was determined by nugget‐to‐sill ratios. The results show that for each analyzed soil layer, OC and N concentrations were significantly higher in the field with 300 y of paddy management than in the field with 50 y of paddy management. Topsoil OC concentrations changed from 13.8 to 23.6 mg g−1, OC in the plough layer changed from 3.3 to 9.6 mg g−1, and also the subsoil showed a slight increase from 2.9 to 3.3 mg g−1.Although only two points in time were compared, we assume that paddy soil formation is the most likely reason for differences in the spatial distribution of OC between the two sites. Comparing the spatial depth distribution of the 50‐ and 300‐y old paddy sites suggests that the development of a spatial pattern of OC proceeded with time from topsoil to the underlying soil horizons. Measuring soil lightness was insufficient to predict amount and distribution of soil OC in paddy soils, especially in soils with interfering dark‐colored burnt straw residues. The degree of spatial homogeneity was equal in the two sites, suggesting that cultivation practices (puddling, levelling, burning and incorporation of straw residues) most likely maintained a rather high random spatial variability of approx. 45%. The spatial pattern of OC in the topsoil was dissimilar to the plough layer, revealing that transport of OC in the plough pan occurred mainly in preferential flow paths along localized cracks and biopores.

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size, random sampling, predictor quality, and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non-sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non-sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high-resolution LiDAR snow depth observations at six separate 1.17-km2 sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross-validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R2. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR-measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross-validation bias at the spatial scale of inference. We model at the 30-m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R2) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R2 (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R2 scores obtained by LiDAR-measured predictors. Under cross validation, the RMSE and R2 skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30-m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross-validation bias but also maximize true R2. The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.

Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution

Abstract. Topographic index-based hydrological models have gained wide use to describe the hydrological control on the triggering of rainfall-induced shallow landslides at the catchment scale. A common assumption in these models is that a spatially continuous water table occurs simultaneously across the catchment. However, during a rainfall event isolated patches of subsurface saturation form above an impeding layer and their hydrological connectivity is a necessary condition for lateral flow initiation at a point on the hillslope. Here, a new hydrological model is presented, which allows us to account for the concept of hydrological connectivity while keeping the simplicity of the topographic index approach. A dynamic topographic index is used to describe the transient lateral flow that is established at a hillslope element when the rainfall amount exceeds a threshold value allowing for (a) development of a perched water table above an impeding layer, and (b) hydrological connectivity between the hillslope element and its own upslope contributing area. A spatially variable soil depth is the main control of hydrological connectivity in the model. The hydrological model is coupled with the infinite slope stability model and with a scaling model for the rainfall frequency–duration relationship to determine the return period of the critical rainfall needed to cause instability on three catchments located in the Italian Alps, where a survey of soil depth spatial distribution is available. The model is compared with a quasi-dynamic model in which the dynamic nature of the hydrological connectivity is neglected. The results show a better performance of the new model in predicting observed shallow landslides, implying that soil depth spatial variability and connectivity bear a significant control on shallow landsliding.

Spatial mapping of multi-year superimposed ice on the glacier Kongsvegen, Svalbard

AbstractGround-penetrating radar (GPR) and satellite ERS-2 synthetic aperture radar (SAR) are used to map the thickness and extent of the superimposed ice (SI) zone on the surge-type glacier Kongsvegen, Svalbard. GPR imagery shows sub-horizontal SI layers lying unconformably above a discrete boundary. Below this boundary, the ice has a GPR signature similar to that of ice further down-glacier in the ablation zone. This boundary is posited to represent the closing of crevasses that were created during the last surge of Kongsvegen in ∼1948. Open crevasses would have interrupted the formation of sheet layers of SI due to efficient vertical drainage of the snowpack. Aerial photographs suggest that the crevasses closed sometime in the period 1956–66. A classified SAR image from 2003 is used to delineate the extent of the SI zone. The SI extent in the SAR image agrees well with the SI zone mapped by GPR. Using the SI spatial depth distribution, we estimate the mean annual accumulation of superimposed ice to be 0.16 ± 0.06 m w.e. a−1 (locally up to 0.43 m a−1 w.e.). This corresponds to ∼15–33% of the local winter balance and ∼5–10% of the total winter balance measured since 1987.

GIS-based modeling of spawning habitat suitability for walleye in the Sandusky River, Ohio, and implications for dam removal and river restoration

The Sandusky River (Ohio, USA) is the subject of a controversial river restoration proposal involving the removal of a high-head dam to give walleye ( Sander vitreus) access to upstream spawning areas. Habitat suitability index (HSI) models can add useful information for making such environmental decisions. In this work, a one-dimensional hydraulic river model is coupled with a collection of GIS routines programmed into the ArcGIS ® interface for calculating spatial distributions of depth, velocity, and spawning habitat suitability for walleye in a section of the river. The model is evaluated against in situ measurements of depth, velocity, and egg density. Results indicate that the model is accurate at predicting depths, but velocity predictions are generally lower than measured in the field. Despite this limitation, egg densities display a significant positive correlation with HSI ( R 2 = 0.19, P = 0.036), indicating that the model can give a general idea of spawning habitat suitability in the river. The model results indicate that the habitat suitability in the river, and hence reproductive success, is dependent on discharge. Habitat suitability is maximized at discharges of 20–25 m 3/s. Flood events with discharges over 100 m 3/s reduce the amount of highly suitable habitat in the river to less than 1% of the total area.

Spatial depth distribution of the subglacial bed and internal layers in the ice around NGRIP, Greenland, derived with airborne RES

In 1996 and 1997, an airborne RES survey was flown in the context of the North Greenland Ice Core Project (NGRIP). At 75.10°N and 42.30°W, a deep ice core is being drilled for palaeoclimate studies with the objective to recover an undisturbed Eemian sequence. The present survey was specially designed to determine with high lateral resolution the bed topography and the layering of internal reflectors in the vicinity around the drill site. A total of 19,000 km of profiles were flown yielding a rectangular grid with side lengths of 222 by 210 km on 16 flights. The spacing between two flight tracks is about 10 km for most of the area and about 2.5 km in the center of the grid. Detailed maps of ice thickness and subglacial topography have been produced. Moreover, a data set was acquired that enables an independent stratigraphic control on the ice core record of NGRIP. From the map of subglacial topography and from selected profiles, the bed of NGRIP can be characterized as flat, particularly when compared to the region of GRIP/GISP2. There appears to be no flow disturbances that might have altered the stratigraphic order in the lower part of the ice sheet, at least up to 30 km upstream from NGRIP. On the basis of the present study, NGRIP seems to be an ideally chosen drill site in order to recover an undisturbed Eemian sequence.

Investigation of Light-Induced Defect Depth Profile in Hydrogenated Amorphous Silicon Films

ABSTRACTWe report direct measurement of depth profile of light induced defects (LIDs) in hydrogenated amorphous silicon (a-Si:H) films. These depth profiles were measured by ESR measurements combined with layer-by-layer precise wet etching technique. We discuss those LIDs depth profiles in relation to the spatial depth distribution of photocarriers generation and recombination profiles, as well as the defect creation efficiency. In case of uniformly absorbed light, the light induced defects creation takes place uniformly in depth of the a-Si:H film. In case of nonuniformly absorbed light, the LIDs depth profile is spatially wider than photocarrier generation rate profile G(x), and depends on G(x) sublinearly. Moreover, the LID depth profile agrees well with the photocarrier direct recombination rate profile rather than the profile of total recombination rate or that through defects. The obtained results suggest that the LIDs creation takes place at local site where the photocarriers recombine directly.

Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration

This paper describes an individual-based model of sympatric populations of brown and rainbow trout in a stream habitat. The model provides a tool for projecting flow and temperature effects on trout populations by linking the hydraulic component of the instream flow incremental methodology/physical habitat simulation system (IFIM/PHABSIM) to an individual-based population model. PHABSIM simulates the spatial distribution of depth and velocity at different flows, and indirectly, the availability of spawning habitat, cover and feeding station. The individual-based model simulates reproduction, growth and mortality of individual trout as a function of flow and temperature. Population dynamics arise from the survival and reproduction of individual trout. The spatially explicit nature of the model permits evaluation of behavioral responses used by fish to changes in physical habitat. The model has been calibrated to a stream segment in the North Fork Middle Fork Tule River, California. Selected parameters were adjusted to calibrate the model for length and abundance (including production of a new year class) at the end of 1-year simulations for each of 9 years. Predicted and observed lengths were in good agreement, although neither varied appreciably among years. Predicted and observed abundances were not in as good agreement, and differed considerably for some years. These differences reflect a combination of uncertainties in the field data and uncertainties in the model structure and parameter values. Fifty-year simulations indicated that model projections of length and abundance were stationary, although abundance values fluctuated considerably. Seven advantages for using simulation models of this type are emphasized. How to most effectively interpret results from such simulation models as part of instream flow environmental assessments remains a challenge. Variability and uncertainty in both field data and replicate model simulations are realities that have implications for scientists, resource managers, and regulators in projecting growth and abundance responses of fish populations to alternative flow or temperature regimes.