Developing High-Resolution Remote Sensing Technology into an Advanced Knowledge Management System to Assess Small-Scale Hydropower Potential in Kazakhstan

Information and communication technology-led growth in remote sensing and image processing has unexplored potential of applications in the management of water resources and environmental systems for assessing hydropower potential and monitoring their hydropower production and performance. This project aims to advance the application of remote sensing in the context of managing water resource by integrating high-resolution remote sensing technology into a knowledge management system for the management of water resources and the infrastructure to control and harvest its power generating potential.

Beyond its vast amount of oil, gas, and coal, the Republic of Kazakhstan has a hydraulic potential of about 170 billion kWh per year, with the technological potential being 62 billion kWh per year. Despite this enormous potential, only 27 billion kWh is being generated with hydroelectric power systems. The economic potential of small hydropower generating units alone is estimated at 7.5–11.0 terawatt-hours per year, of which only 5% is produced (United Nations Development Programme (UNDP) Kazakhstan, Water Resources of Kazakhstan in the new millennium, Report # UNDPKAZ 07, Almaty, 2004). However, their sustainable use demands a thorough restructuring of the existing system of natural water resource allocation and use as suggested by Zàuìrbek (National programme for water resources management in the Republic of Kazakhstan. KazNatAGRU, Almaty, 2013) which is largely in line with the literature on Integrated Water Resource Management (IWRM) (Biswas, Water Int 29(2):248–256, 2004; Jewitt, Phys Chem Earth 27(11–22):887–895, 2002; Thomas and Durham, Desalination 156(1–3):21–28, 2003).

Our knowledge management system has the aim to support the user to develop credible and defensible water resource management plans in line with IWRM objectives and in particular:

Such applications (Argote et al., Manag Sci 49(4):571–582, 2003; Boddy et al., Autom Constr 16(5):596–606, 2007; Dalcanale et al., Environ Manag 47(3):443–455, 2011; Liao, Expert Syst Appl 25(2):155–164, 2003; Sandwahalia et al. 2008; Toman, Water Resour Manag IV 103:667–676, 2007) with the integration of a Geo-Information Systems (Chen et al., J Hydro Environ Res 4(3):253–264, 2010) have significant potential as a platform for managing environmental, water resource, and infrastructure data. This mapping and geo-spatial analysis of information will see further advancements by integrating models and near real-time remote monitoring systems (Burman, Image Signal Process Remote Sens V 3871:348–357, 1999; Chen et al. 2008; Legleiter et al. Earth Surf Process Landf 34:1039–1059, 2009; Lyzenga, Shallow-water reflectance modeling with applications to remote sensing of ocean floor. Proceeding of 13th international Symposium on remote sensing of environment, pp 583–602, 1979) thus providing a wide range of data modalities to enable high-speed visualization and analytics. To be tested as a prototype, this project will result in a comprehensive technical and economic feasibility study for investing in hydropower generation by quantifying flow volumes, identifying potential sites for water storage, and assessing geographic elevation gradients that are suitable to produce electricity with small hydropower stations.