Estimation of small reservoir storage capacities in a semi-arid environment

Liebe, Jens; Giesen, Nick van de; Andreini, M.

doi:10.1016/j.pce.2005.06.011

Cited by 200 publications

(172 citation statements)

References 6 publications

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“…Little information is available for informal/private SRs in Ghana. Liebe (2002) and Liebe et al (2005) identified 504 dugouts and small reservoirs in the Upper East Region of Ghana ( Figure 6) and estimated their storage capacity (Table 3). It is assumed that SRs are only present in North of Ghana, hence our estimation will rely solely on these two papers.…”

Section: Ii21 Small Reservoirsmentioning

confidence: 99%

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A decision-support tool for water allocation in the Volta Basin

Condappa

Chaponnière

Lemoalle

2009

Water International

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The first version of a decision-support tool (DST) for the management of the transboundary water resource of the Volta Basin is presented in this article. The DST coupled a hydrologic model with the Water Evaluation and Planning (WEAP) water-allocation model. It was calibrated and validated by reproducing observed river flows and water stored in the hydropower reservoirs, respectively. It considered the impact of possible future climate changes (potentially critical) and the development of upstream small reservoirs (leading to upstream-downstream trade-offs) on the Akosombo hydropower scheme. This DST may foster transboundary dialogue for the integrated management of the basin's water resources

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Section: Ii21 Small Reservoirsmentioning

confidence: 99%

“…• reservoirs with a surface less than 1 ha are discarded as Liebe et al (2005) judged their characterisation / identification unreliable (limit of remote sensing analysis), making a set of 154 SRs (out of the 504),…”

Section: Ii21 Small Reservoirsmentioning

confidence: 99%

A decision-support tool for water allocation in the Volta Basin

Condappa

Chaponnière

Lemoalle

2009

Water International

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“…. All the reservoirs may be considered as Categories 1 and 2 according to classification proposed by Liebe, Van de Giesen and Andreini (2005).…”

Section: Automatic Detection Of Small Reservoirs In the Quixaba Creekmentioning

confidence: 99%

“…Compared to the detection sensitivity of the Landsat images, the results obtained in QCB are considerably better. For example, Liebe, Van de Giesen and Andreini (2005) in the Upper East Region of Ghana classified 504 reservoirs, but had to disregard 348 with acreage less than 1 ha. In the QCB, 59 small reservoirs could be classified in Category 1 (less than 3 ha) according to classification mentioned before.…”

Section: Automatic Detection Of Small Reservoirs In the Quixaba Creekmentioning

confidence: 99%

“…For this reason, it is important to define the classes and categories of storage adopted by the authors. These classes are: small (less than 1 million m 3 ) (LIEBE; VAN DE GIESEN;ANDREINI, 2005;RODRIGUES et al, 2012), middle (1-3 million m 3 ) and large (more than 3 million m 3 ) (ICOLD, 2011). The small ones may also be divided into three categories according to Rodrigues et al (2012) based on Liebe, Van de Giesen and Andreini (2005): Category 1, with surface areas between 1 and 3 ha; Category 2 between 3 and 10 ha; Category 3 between 10 and 50 ha.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of reservoirs for water supply in Northeast Brazil using high resolution remote sensing

Nascimento¹,

Neto

2017

RBRH

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This paper reports the application of information acquired by aerial survey to characterize water supply reservoirs in the Pajeú River Basin (Pernambuco State/Brazil). The survey was carried out with digital cameras of high spatial resolution and laser relief profiling (LiDAR technology). Two areas were selected to apply the remote sensing products. Small reservoirs in the Quixaba Creek Basin were identified based on their topographic characteristics. Given that the small reservoirs are "depressions" in the terrain, they can be "filled", resulting in a new Digital Terrain Model (DTM). The difference between the filled DTM and the original DTM makes it possible to identify the reservoirs. A summary of the results is: 61 reservoirs were correctly detected; 18 reservoirs were not detected; 13 reservoirs were detected erroneously. In another application, the storage capacity of the reservoirs belonging to the hydrosystems of Pajeú River Basin was estimated. The storage in these reservoirs and maximum surface area were estimated using DTM and geoprocessing tools. From the total of 31 reservoirs evaluated, eight were completely empty at the time of the LiDAR data collection. The official registers reported 83.83 million m 3 for the storage capacity of these eight reservoirs, whereas our applications estimated the value at 70.23 million m 3 . This difference is explained by the loss of volume in the reservoirs due to the process of sediment transport.Keywords: LiDAR; Semiarid; Water resources; Storage capacity. RESUMOEste artigo apresenta a aplicação de produtos adquiridos por levantamento aéreo para caracterização de reservatórios de abastecimento de água na Bacia do Rio Pajeú (Pernambuco/Brasil). O levantamento foi feito com câmeras digitais de alta resolução espacial e perfilamento de relevo a laser (tecnologia LiDAR). Foram selecionadas duas áreas para aplicação dos produtos de sensoriamento remoto. Pequenos reservatórios na Bacia do Riacho Quixaba foram identificados com base em suas características topográficas. Considerando que os pequenos reservatórios são "depressões" no terreno, eles podem ser "preenchidos" resultando em um novo MDT. A diferença entre o MDT preenchido e o MDT original torna possível a identificação dos reservatórios. O resumo dos resultados é: 61 reservatórios foram corretamente detectados; 18 reservatórios não foram detectados; 13 reservatórios foram detectados erroneamente. Em outra aplicação, foi estimada a capacidade de armazenamento dos reservatórios pertencentes aos hidrossistemas da Bacia do Rio Pajeú. O armazenamento nos reservatórios e a área superficial máxima foram estimados utilizando o MDT e ferramentas de geoprocessamento.

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Determining water reservoir characteristics with global elevation data

Bemmelen

Mann

Ridder

et al. 2016

Geophysical Research Letters

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Quantification of human impact on water, sediment, and nutrient fluxes at the global scale demands characterization of reservoirs with an accuracy that is presently unavailable. This letter presents a new method, based on virtual dam placement, to make accurate estimations of area‐volume relationships of large reservoirs, using solely readily available elevation data. The new method is based on regional similarity of area‐volume relationships. The essence of the method is that virtual reservoirs are created in the vicinity of an existing reservoir to derive area‐volume relationships for the existing reservoir. The derived area‐volume relationships reproduced in situ bathymetric data well. An intercomparison for twelve reservoirs resulted in an average R2 = 0.93. This is a significant improvement on estimates using the best existing global regression model, which gives R2 = 0.54 for the same set of reservoirs.

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Estimation of small reservoir storage capacities in a semi-arid environment

Cited by 200 publications

References 6 publications

A decision-support tool for water allocation in the Volta Basin

A decision-support tool for water allocation in the Volta Basin

Characterization of reservoirs for water supply in Northeast Brazil using high resolution remote sensing

Determining water reservoir characteristics with global elevation data

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