Many sources suggest the tremendous potential for hydropower generation in Pakistan. However, the current lack of accurate hydrological data and quantitative metrics makes it very difficult to estimate the distribution of power potential across that region. This project will fill this knowledge gap, promoting the optimal utilization of Pakistan’s national hydropower resources.
In the past, high-resolution models have not been practical for areas like Pakistan which have sparse meteorological measurements; however, more physically representative models are now possible due to advances in climatological downscaling techniques. Our group has produced open-source and freely-available modeling tools that can be used to generate climate data (temperature and precipitation) on an approximately 1-km grid over any global land region.
To download the tools and learn more, visit our globalclimatedata.org website.
Through this modeling framework, better gridded time-series of runoff are derivable for the past 100 years, enabling long-term trends and variation in monthly runoff to be analyzed at a higher resolution than previously possible. We are presently working on extending some of these methods to future scenarios to better assess site stability and impacts of climate change. Snow storage and glacier contributions to runoff must be forecast to estimate future viability of microhydro installations because their contributions account for over 50% of the stream flow to more than half of the tributaries to the Indus River. We are also currently working on improved handling of snow storage and glacier contributions in hydrological models suitable for data-scarce regions or for reduced computational cost.
The final portion of this assessment study is to develop a programmable metric for optimization of microhydro unit siting. The metric will likely take into account not only the power potential at each cell but also parameters such as optimal efficiency ranges of the available microhydro units. If there are other human considerations, such as proximity to a village or existing electricity transmission infrastructure, these could also be included given appropriate input information. An example of our envisioned mapping tool is shown in the figure included here.
Throughout this project, we have been working with faculty at the Center for Energy Sciences at the National University of Science and Technology (NUST) in Islamabad, Pakistan. NUST is one of the top technical universities in Pakistan. For more information about the Center for Energy Sciences, please visit their website.