Geostatistical approach for the assessment of the water reservoir capacity in arid regions: a case study of the Akdarya reservoir,Uzbekistan

The paper presents the results of a new geostatistical approach to generate bathymetric surface models from point measurement converted into continuous contour surfaces of reservoir bottoms in Uzbekistan. Sedimentation of reservoirs raises engineering, environmental and economical issues for the communities around the world in areas affected by a strong water deficit. Because of Uzbekistan’s arid climatic conditions, and uneven spatial and temporal water resources distribution, responsive and innovative water availability assessment surveys of all major water reservoirs are required. Bathymetric surveying is a traditional method that is carried out for the estimation of reservoir volumes and surface areas of the corresponding reservoir stages in order to assess the water availability. Volume and surface area differences derived from multiple surveys of a reservoir provide storage loss estimates over time due to sedimentation. However, two main factors, such as intensive field data measurement and post data-processing, often limit the frequency of these surveys. Alternatively, innovative depth measurement technologies coupled with contouring and surface mapping programs provide automated reservoir volume and surface area calculations. This significantly reduces time, workload and financial burdens for reservoir sedimentation projects. This research proposes the use of geostatistical approach to assess the reservoir sedimentation in the Akdarya reservoir of Uzbekistan. The geostatistical approach includes (semi-) variogram analysis and interpolation (kriging and simulations—turning bands) techniques predicting values at unsampled locations for generating digital bathymetric surface models of reservoir bottom conditions in order to calculate the volume and surface area at a given water elevation. The simulation enables to have range of reservoir volumes and surface areas with the same probability, in comparison to the kriging and traditional methods. This gives a real estimation of the resource availability for water operators to manage natural resources and hydraulic infrastructure in a sustainable manner.