Next Article in Journal
Removal Efficiency and Mechanism of Cr(VI) from Aqueous Solution by Maize Straw Biochars Derived at Different Pyrolysis Temperatures
Previous Article in Journal
Zootechnical Farm Wastewaters in Ecuador: A Treatment Proposal and Cost-benefit Analysis
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Comparison of Surface Water Volume Estimation Methodologies That Couple Surface Reflectance Data and Digital Terrain Models

School of Life and Environmental Sciences, The University of Sydney, New South Wales 2006, Australia
Author to whom correspondence should be addressed.
Water 2019, 11(4), 780;
Received: 6 February 2019 / Revised: 26 March 2019 / Accepted: 11 April 2019 / Published: 15 April 2019
(This article belongs to the Section Hydrology)
PDF [13015 KB, uploaded 23 April 2019]
  |     |  


Uncertainty about global change requires alternatives to quantify the availability of water resources and their dynamics. A methodology based on different satellite imagery and surface elevation models to estimate surface water volumes would be useful to monitor flood events and reservoir storages. In this study, reservoirs with associated digital terrain models (DTM) and continuously monitored volumes were selected. The inundated extent was based on a supervised classification using surface reflectance in Landsat 5 images. To estimate associated water volumes, the DTMs were sampled at the perimeter of inundated areas and an inverse distance weighting interpolation was used to populate the water elevation inside the flooded polygons. The developed methodology (IDW) was compared against different published methodologies to estimate water volumes from digital elevation models, which assume either a flat water surface using the maximum elevation of inundated areas (Max), and a flat water surface using the median elevation of the perimeter of inundated areas (Median), or a tilted surface, where water elevations are based on an iterative focal maximum statistic with increasing window sizes (FwDET), and finally a tilted water surface obtained by replacing the focal maximum statistic from the FwDET methodology with a focal mean statistic (FwDET_mean). Volume estimates depend strongly on both water detection and the terrain model. The Max and the FwDET methodologies are highly affected by the water detection step, and the FwDET_mean methodology leads to lower volume estimates due to the iterative smoothing of elevations, which also tends to be computationally expensive for big areas. The Median and IDW methodologies outperform the rest of the methods, and IDW can be used for both reservoir and flood volume monitoring. Different sources of error can be observed, being systematic errors associated with the DTM acquisition time and the reported volumes, which for example fail to consider dynamic sedimentation processes taking place in reservoirs. Resolution effects account for a fraction of errors, being mainly caused by terrain curvature. View Full-Text
Keywords: flood mapping; water volumes; remote sensing; Google Earth Engine flood mapping; water volumes; remote sensing; Google Earth Engine

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Fuentes, I.; Padarian, J.; van Ogtrop, F.; Vervoort, R.W. Comparison of Surface Water Volume Estimation Methodologies That Couple Surface Reflectance Data and Digital Terrain Models. Water 2019, 11, 780.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top