Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Understanding groundwater movement within karst aquifers remains challenging because flow-defining conduit and fracture networks are both complex and inaccessible. In Grand Canyon National Park, dye tracers have been used to establish flow paths for springs that support ecosystems and park operations. Unfortunately, these point-to-point studies are limited when attempting to extrapolate flow paths over thousands of square kilometers. We introduce a mobile lidar-based methodology that resolves groundwater flow-defining structures from actively-discharging stream caves within the aquifer. This methodology enabled efficient collection of centimeter-scale 3D data from over 10 km of remote caves from the Redwall (Mississippian) and Muav (Cambrian) limestones in the North Rim of the Grand Canyon. Our methodology achieved total compounding errors of less than 0.5% and shows strong agreement with traditional cave maps. We find geologic structures exposed within these caves are consistent across kilometers of cave passages, indicating groundwater flow exploits joint sets and bedding dip direction. These patterns suggest that present-day flow paths within the North Rim of Grand Canyon National Park are, in part, a product of regional faulting and uplift. This lidar-derived structural characterization enables karst network flow pattern identification that would be otherwise unavailable from traditional methods.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398496 | PMC |
http://dx.doi.org/10.1038/s41598-025-17472-6 | DOI Listing |