Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density
- Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume predictionWater fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.…
Author details: | Bruno Pereira, Pedro Henrique Augusto MedeirosORCiD, Till FranckeORCiDGND, Geraldo RamalhoORCiD, Saskia FörsterORCiD, Jose Carlos De AraujoORCiD |
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DOI: | https://doi.org/10.1080/02626667.2019.1566727 |
ISSN: | 0262-6667 |
ISSN: | 2150-3435 |
Title of parent work (English): | Hydrological sciences journal = Journal des sciences hydrologiques |
Publisher: | Routledge, Taylor & Francis Group |
Place of publishing: | Abingdon |
Publication type: | Article |
Language: | English |
Year of first publication: | 2019 |
Publication year: | 2019 |
Release date: | 2021/04/21 |
Tag: | high-density reservoir network; remote sensing; reservoir volume; semi-arid; water height-area-volume curve |
Volume: | 64 |
Issue: | 1 |
Number of pages: | 14 |
First page: | 66 |
Last Page: | 79 |
Funding institution: | Brazilian Coordination of Higher Education Personnel (CAPES) [424/14]; German Academic Exchange Service (DAAD)Deutscher Akademischer Austausch Dienst (DAAD); Brazilian National Council of Scientific and Technological Development (CNPq)National Council for Scientific and Technological Development (CNPq) [455883/2014-9] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |