Geochemical Variability in Karst-Siliciclastic Aquifer Spring Discharge, Kaibab Plateau, Grand Canyon

2020 ◽  
Vol 26 (3) ◽  
pp. 367-381
Author(s):  
Alexander J. Wood ◽  
Abraham E. Springer ◽  
Benjamin W. Tobin

ABSTRACT The source area of groundwater for springs discharging from lithologically variably perched aquifers is essential to understand when establishing baseline aquifer characteristics. Stratigraphic data from hydrostratigraphic outcrops and geochemical data from springs were used to characterize the hydrogeology of a remote, data-poor aquifer. This study focuses on the hydrogeological variability within the shallow karst-siliciclastic Coconino (C) aquifer on the Kaibab Plateau, north of Grand Canyon National Park. Stratigraphic data were collected from 8 locations, and 22 C aquifer springs were sampled for 18 months. Stable isotope analyses indicate that groundwater is biased to winter recharge in the form of snow and shows similar isotopic signature for groundwater storage areas for all C aquifer springs. Stratigraphic analyses show that the primary water-bearing unit in the C aquifer thins dramatically from south to north and has evaporite lithofacies directly above the unit. Principal component analysis (PCA) indicates that the hydrogeochemistry is influenced by SO42−, Cl−, Mg2+, Ca+, specific conductivity, alkalinity, and δD variability. The stratigraphic variability influences geochemistry at multiple locations and has geochemical variabilities that correlate with changing lithology. Based on the PCA results, groundwater sub-basins were delineated based on geochemical variability. This study provides new analytical tools for land managers and karst hydrogeologists to evaluate lithologically complex aquifers by evaluating the stratigraphy and with high-resolution data. Cost-effective stratigraphic analyses and high-resolution spring sampling can and should be used to evaluate lithologically complex aquifers in remote, data-poor regions.

2016 ◽  
Vol 233 ◽  
pp. 238-252 ◽  
Author(s):  
I.A. Thomas ◽  
P.-E. Mellander ◽  
P.N.C. Murphy ◽  
O. Fenton ◽  
O. Shine ◽  
...  

Author(s):  
A. V. Crewe ◽  
M. Ohtsuki

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).


2020 ◽  
Author(s):  
Miranda Terwilliger ◽  
Cynthia Hartway ◽  
Kate Schoenecker ◽  
Gregory Holm ◽  
Linda Zeigenfuss ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.


2009 ◽  
Vol 474 (1-2) ◽  
pp. 271-284 ◽  
Author(s):  
L. Tosi ◽  
P. Teatini ◽  
L. Carbognin ◽  
G. Brancolini

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi

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