Estimation of water surface elevations for the Everglades, Florida

Monica Palaseanu; Leonard Pearlstine

doi:10.1016/j.cageo.2007.08.004

Home/Archive/USGS

USGS · 70184374

Estimation of water surface elevations for the Everglades, Florida

Monica Palaseanu·Leonard Pearlstine

View original source ↗Export metadata ↓

Abstract

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring gages and modeling methods that provides scientists and managers with current (2000–present) online water surface and water depth information for the freshwater domain of the Greater Everglades. This integrated system presents data on a 400-m square grid to assist in (1) large-scale field operations; (2) integration of hydrologic and ecologic responses; (3) supporting biological and ecological assessment of the implementation of the Comprehensive Everglades Restoration Plan (CERP); and (4) assessing trophic-level responses to hydrodynamic changes in the Everglades. This paper investigates the radial basis function multiquadric method of interpolation to obtain a continuous freshwater surface across the entire Everglades using radio-transmitted data from a network of water-level gages managed by the US Geological Survey (USGS), the South Florida Water Management District (SFWMD), and the Everglades National Park (ENP). Since the hydrological connection is interrupted by canals and levees across the study area, boundary conditions were simulated by linearly interpolating along those features and integrating the results together with the data from marsh stations to obtain a continuous water surface through multiquadric interpolation. The absolute cross-validation errors greater than 5 cm correlate well with the local outliers and the minimum distance between the closest stations within 2000-m radius, but seem to be independent of vegetation or season.

Explore related subjects

Computers & Geosciences Article

Source-reported bounding extent: 25.075648445630527° to 26.912273826625587° latitude; -82.232666015625° to -80.0244140625° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Monica Palaseanu, Leonard Pearlstine. 2008. Estimation of water surface elevations for the Everglades, Florida. https://doi.org/10.1016/j.cageo.2007.08.004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗

USGS2022

gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring

gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.

Computers & Geosciences↗

USGS2022

MTH5: An archive and exchangeable data format for magnetotelluric time series data

Magnetotellurics (MT) is a passive electromagnetic geophysical method that measures variations in subsurface electrical resistivity. MT data are collected in the time domain and processed in the frequency domain to produce estimates of a transfer function representing the Earth’s electrical structure. Unfortunately, the MT community lacks metadata and data standards for time series data. As the community grows and findability, accessibility, interoperability, and reuse of digital assets (FAIR) data principles are enforced by government and funding agencies, a standard is needed for time series data. Presented here is a hierarchical data format (MTH5) that is logically formatted to how MT data are collected. Open-source Python packages are also described to read, write, and manipulate MTH5 files. These include a package to deal with metadata ( mt_metadata ) based on standards developed by the Working Group for Magnetotelluric Data Handling and Software assembled by the Incorporated Research Institutions for Seismology (IRIS), and mth5 : a package to interact with MTH5 files that uses mt_metadata . Example code and workflows are presented.

Computers & Geosciences↗