An assessment of the potential for cloud computing and satellite thermal infrared sensing to produce meaningful river temperature insights for hydropower operations - Valman, Sam (2021)
Hydropower interacts heavily with river temperature to; meet regulations, maximise profits, and maintain dam safety. Often the operational decisions that dictate this interaction are made without monitoring of river temperature, and so it is proposed that satellite remote sensing may provide a quasi-regular cost-effective method to improve this. This dissertation assesses the viability of using Google Earth Engine cloud computing and Landsat 8 Thermal Infrared satellite measurements to provide actionable insights for hydropower managers. The method was tested in three large rivers (the Saint John River in Canada, the Colorado River in the USA, and the Ganges in India) to assess transferability. No previous study has attempted to extract river temperature from multiple sites in a single study. Three different methods were tested to find the most accurate atmospheric correction algorithm for the task of river temperature measurement. The Statistical Mono-Window algorithm was found to produce the most accurate comparison to kinetic temperature loggers on the Saint John River (±2oc) with a R2 value of 0.96 (n=40, p<0.001). However, this method was not transferable to the Colorado River indicating application in rivers without validation data should be carried out with caution. A Python Package named SatTemp (Valman, 2021b) was developed to assist hydropower operators in implementing the method along with a dashboard app to disseminate results (Valman, 2021a). Concerns were raised with the “black box” nature of Google Earth Engine and this App, meaning that errors and nuances in the method may be missed. These would need to be addressed before this method can be provided to hydropower operators.