Estimation of the 3D correlation structure of an alluvial aquifer from surface-based multi-frequency ground-penetrating radar reflection data     

Zhiwei Xu  James Irving  Kyle Lindsay  John Bradford  Peimin Zhu  Klaus Holliger 《Geophysical Prospecting》2020,68(2):678-689

Knowledge about the stochastic nature of heterogeneity in subsurface hydraulic properties is critical for aquifer characterization and the corresponding prediction of groundwater flow and contaminant transport. Whereas the vertical correlation structure of the heterogeneity is often well constrained by borehole information, the lateral correlation structure is generally unknown because the spacing between boreholes is too large to allow for its meaningful inference. There is, however, evidence to suggest that information on the lateral correlation structure may be extracted from the correlation statistics of the subsurface reflectivity structure imaged by surface-based ground-penetrating radar measurements. To date, case studies involving this approach have been limited to 2D profiles acquired at a single antenna centre frequency in areas with limited complementary information. As a result, the practical reliability of this methodology has been difficult to assess. Here, we extend previous work to 3D and consider reflection ground-penetrating radar data acquired using two antenna centre frequencies at the extensively explored and well-constrained Boise Hydrogeophysical Research Site. We find that the results obtained using the two ground-penetrating radar frequencies are consistent with each other, as well as with information from a number of other studies at the Boise Hydrogeophysical Research Site. In addition, contrary to previous 2D work, our results indicate that the surface-based reflection ground-penetrating radar data are not only sensitive to the aspect ratio of the underlying heterogeneity, but also, albeit to a lesser extent, to the so-called Hurst number, which is a key parameter characterizing the local variability of the fine-scale structure.  相似文献   

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers     

J. Ignacio López-Moreno  Leena Leppänen  Bartłomiej Luks  Ladislav Holko  Ghislain Picard  Alba Sanmiguel-Vallelado  Esteban Alonso-González  David C. Finger  Ali N. Arslan  Katalin Gillemot  Aynur Sensoy  Arda Sorman  M. Cansaran Ertaş  Steven R. Fassnacht  Charles Fierz  Christoph Marty 《水文研究》2020,34(14):3120-3133

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.  相似文献   

Sources of variability in springwater chemistry in Fool Creek,a high-elevation catchment of the Rocky Mountains,Colorado, USA     

Charles C. Rhoades  Timothy S. Fegel  Timothy P. Covino  Kathleen A. Dwire  Kelly Elder 《水文研究》2021,35(3):e14089

Springs are the point of origin for most headwater streams and are important regulators of their chemical composition. We analysed solute concentrations of water emerging from 57 springs within the 3 km² Fool Creek catchment at the Fraser Experimental Forest and considered sources of spatial variation among them and their influence on the chemical composition of downstream water. On average, calcium and acid neutralizing capacity (bicarbonate-ANC) comprised 50 and 90% of the cation and anion charge respectively, in both spring and stream water. Variation in inorganic chemical composition among springs reflected distinct groundwater sources and catchment geology. Springs emerging through glacial deposits in the upper portion of the catchment were the most dilute and similar to snowmelt, whereas lower elevation springs were more concentrated in cations and ANC. Water emerging from a handful of springs in a geologically faulted portion of the catchment were more concentrated than all others and had a predominant effect on downstream ion concentrations. Chemical similarity indicated that these springs were linked along surface and subsurface flowpaths. This survey shows that springwater chemistry is influenced at nested spatial scales including broad geologic conditions, elevational and spatial attributes and isolated local features. Our results highlight the role of overlapping factors on solute export from headwater catchments.  相似文献   

Cognitive and Affective Predictors of Illinois Residents’ Perceived Risks from Gray Wolves     

Adam C. Landon  Maarten H. Jacobs  Craig A. Miller  Jerry J. Vaske  Brent D. Williams 《社会与自然资源》2020,33(5):574-593

Abstract

Increasing wolf populations are a concern for wildlife managers in the Midwestern U.S. Understanding the psychological mechanisms that contribute to public perceptions of risk will enable development of strategies that seek to mitigate these risks, and suggest where outreach efforts may facilitate acceptance of wolves. We examined the psychological factors that influence Illinois residents’ perceived risks from wolves. We hypothesized that individuals’ perceived risks from wolves were a function of their attitudes toward wolves, negative affect toward wolves, and basic beliefs about wildlife. Data were obtained from a survey of the Illinois public (n?=?784). Negative affect and attitudes toward wolves were direct predictors of perceived risks. Basic beliefs predicted attitudes and negative affect toward wolves. Negative affect predicted attitudes. Basic beliefs had direct and indirect effects on perceived risks.  相似文献   

Discrete element modeling of transversely isotropic rocks with non-continuous planar fabrics under Brazilian test     

Xu  Guowen  Gutierrez  Marte  He  Chuan  Meng  Wei 《Acta Geotechnica》2020,15(8):2277-2304

Acta Geotechnica - A new numerical approach based on the particle discrete element method (PDEM) is developed to investigate the mechanical behavior of transversely isotropic rocks with...  相似文献   

The Radial Basis Functions Method for Improved Numerical Approximations of Geological Processes in Heterogeneous Systems     

Piret  Cécile  Dissanayake  Nadun  Gierke  John S.  Fornberg  Bengt 《Mathematical Geosciences》2020,52(4):477-497

Mathematical Geosciences - A robust, high order modeling approach is introduced, based on the finite difference-based radial basis functions method, for solving the groundwater flow equation in the...  相似文献   

Study on the performance of the micropile-mechanically stabilized earth wall     

Zhi-chao Zhang  Ronald Y. S. Pak  Yu-min Chen  Han-long Liu 《山地科学学报》2018,15(4):825-844

The Micropile-Mechanically Stabilized Earth (MSE) wall, specially designed for mountain roads, is proposed to improve the MSE wall local stability, global stability and impact resistance of roadside barriers. Model tests and the corresponding numerical modeling were conducted to validate the serviceability of the Micropile-MSE wall and the reliability of the numerical method. Then, a parametric study of the stress and deformation of Micropile-MSE wall based on the backfill strength and interfacial friction angle between backfill and backslope is conducted to evaluate its performance. The test results indicate that the surcharge-induced horizontal earth pressure, base pressure and lateral displacement of the wall panel of Micropile-MSE wall decrease. The corresponding numerical results are nearly equal to the measured values. The basic failure mode of MSE wall in steep terrain is the sliding of backfill along the backslope, while A-frame style micropiles are capable of preventing the sliding trend. The maximum resultant displacement can be decreased by 6.25% to 46.9% based on different interfacial friction angles, and the displacement can be reduced by 6% ~ 56.1% based on different backfill strengths. Furthermore, the reduction increases when the interfacial friction angle and internal friction angle of backfill decrease. In addition, the lateral displacement of wall panel, the deformation of backfill decrease and the tension strain of geogrid obviously, which guarantees the MSE wall functions and provides good conditions for mountain roads.  相似文献   

Governing access and allocation in the Anthropocene     

《Global Environmental Change》2014

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Provenance of sediment in the Labrador Current: a record of hinterland glaciation over the past 125 ka          下载免费PDF全文

LONGJIANG MAO  DAVID J. W. PIPER  FRANCKY SAINT‐ANGE  JOHN T. ANDREWS  MARKUS KIENAST 《第四纪科学杂志》2014,29(7):650-660

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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          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

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.  相似文献