The Volta Basin Water Allocation System: assessing the impact of small-scale reservoir development on the water resources of the Volta basin, West Africa

Leemhuis, Constanze; Jung, Gerlinde; Kasei, Raymond Abudu; Liebe, Jens

doi:10.5194/adgeo-21-57-2009

Cited by 49 publications

(30 citation statements)

References 4 publications

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“…Reservoirs are essential for the development and management of a river basin's water resources, no matter their size (Liebe et al, 2005;Leemhuis et al, 2009). By increasing the availability of water during low-flow periods (International Commission On Large Dams, 2016), dams often play a key role in water supply, irrigated agriculture, hydropower generation, navigation, cattle breeding, fisheries, etc.…”

Section: Introductionmentioning

confidence: 99%

Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Avisse

Tilmant

Müller³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In river basins with water storage facilities, the availability of regularly updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. However, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political, or legal considerations. This paper proposes a novel approach using Landsat imagery and digital elevation models (DEMs) to retrieve information on storage variations in any inaccessible region. Unlike existing approaches, the method does not require any in situ measurement and is appropriate for monitoring small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2-D dynamic classification of Landsat spectral band information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir, and (iii) a 3-D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighbouring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the normalized root-mean-square error from 10 to 16 % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 to 3.79 km 2 .

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Section: Introductionmentioning

confidence: 99%

Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Avisse

Tilmant

Müller³

et al. 2017

Hydrol. Earth Syst. Sci.

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“…where Wk is the class with k E l , W, u for the land, water and unclassified classes, respectively, and q)k(Xi) the likelihood, q)k(Xi), based on the pixel intensity vector, Xi, for pixel i multiplied with the growing prior, P(wklvj), which is proportional to the posterior probability, according to: (2) where Vj is a conditional variable of the growing prior with state j, which is based on the classification of neighboring pixels; see Table 2. Here, the assumption is made that the likelihood function is independent of the state of the conditional variable, v. Note that a pixel is only classified if its probability of being a member of a class is larger than the probability of remaining unclassified.…”

Section: Basic Growing Bayesian Classifiermentioning

confidence: 99%

“…Currently, the cumulative impact of small reservoirs on water resources at a river basin scale is still debated [2], and the sustainability of small reservoirs under climate change is unknown [3]. In order to plan, manage and improve our understanding of small reservoirs, it is important to monitor the water storage dynamics.…”

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

et al. 2014

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Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

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“…The Guinea savanna agro-ecological zone of West Africa is drawing research attention recently because of the extreme contrast between rainy and dry seasons (ILRI, 1993). Thousands of micro-dams are distributed over the upper and central parts of the Volta Basin (Liebe et al 2005;Liebe et al 2009;Leemhuis et al 2009). The command areas of Tono and Dorongo irrigation schemes in the Upper East Region of Ghana are 2,500 and 10 ha, respectively.…”

Section: Introductionmentioning

confidence: 99%

Stochastic control of a micro-dam irrigation scheme for dry season farming

Unami

Yangyuoru

Alam

et al. 2012

Stoch Environ Res Risk Assess

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Micro-dams are expected to be feasible options for water resources development in semi-arid regions such as the Guinea savanna agro-ecological zone of West Africa. An optimal water management strategy in a microdam irrigation scheme supplying water from an existing reservoir to a potential command area is discussed in this paper based on the framework of stochastic control. Water intake facilities are assumed to consist of photovoltaic pumping system units and hoses. The knowledge of current states of the storage volume of the reservoir and the soil moisture in the command area is fed-back to the intake flow rate. A system of two stochastic differential equations is proposed as a model for the dynamics of the micro-dam irrigation scheme, so that temporally backward solution of the Hamilton-Jacobi-Bellman equation determines an optimal control, which represents the optimal water management strategy. A computational procedure using the finite element method is successfully implemented to provide comprehensive information on the optimal control. The results indicate that the water initially stored in the reservoir can support full irrigation for about 80 days under the optimal water management strategy, which is predominantly based on the demand-side principle. However, the volatility of the soil moisture in the command area must be reasonably small.

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The Volta Basin Water Allocation System: assessing the impact of small-scale reservoir development on the water resources of the Volta basin, West Africa

Cited by 49 publications

References 4 publications

Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Stochastic control of a micro-dam irrigation scheme for dry season farming

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