Impacts of climate change on hydropower generation and developing adaptation measures through hydrologic modeling and multi-objective optimization

The climate change resulting from anthropogenic factors is driving governments and policy-makers to provide additional thrust on renewable energy. Hydropower, which is the dominant renewable component of the energy-mix, is also under threat due to the changing climate conditions. The present study aims to quantify the impact of climate change on hydropower generation, the associated revenues and subsequently suggest the adaptation measures through adaptive reservoir management. A modeling chain consisting of hydrologic and hydropower simulation models is adopted to evaluate the impacts of projected climate change on hydropower generation. Calibrated hydrologic models forced with the climate data from various climate models have been widely employed for future streamflow projection. A reliable modelling framework should ensure the simulation of reality with limited uncertainty, thus enhancing its predictive ability. In the literature, the hydrologic model assessment is reported to be inadequate when carried out based on only statistical objectives or limited number of evaluation metrics. In the present research, the thrust is given on improving the hydrologic model simulation through model diagnostic assessment, incorporating hydrologic signatures and multi-objective model calibration. Multi-objective evolutionary algorithm (MOEA) is coupled with the hydrologic model, Soil and Water Assessment Tool (SWAT), to perform model calibration. The methodology was first tested for Saugeen River watershed in Southern Ontario and then applied to the Magpie River watershed model located in Northern Ontario. The uncertainties contributed by the hydrologic models have generally been given a lesser focus in climate change impact analysis. In the present research, the uncertainty emanating from model parameters was investigated and found to dominate during some periods. The accounting of hydrologic model uncertainty is found to be vital for providing an improved assessment. Steephill Falls hydroelectric project located on Magpie River in Northern Ontario is considered as a case study for assessing climate change impacts on hydropower. The results show that the annual generation is not considerably affected but there is a significant seasonal redistribution on energy production. The changes in the hydropower revenues compared to the present level for the four seasons viz., winter, spring, summer and autumn are estimated to be 21.1%, 18.4%, -13.4% and -15.9%, respectively, for mid-century and 23.1%, 19.5%, -20.1% and -22.9% for end-century scenarios. In order to reduce the vulnerability of hydropower system to climate change and consequently mitigate the impacts, it will be profitable for the project owners to provide suitable adaptation measures. Adaptive reservoir management through multi-objective optimization of reservoir level was found to be an effective approach to develop adaptation measures provided additional live storage is made available. It also reduced the vulnerability of the system to climate change by 24%. The seasonal alteration in the energy production will require the project owners to arrange modification in power purchase/sharing agreement with the buyers

Climate model bias correction for nonstationary conditions

In most of the climate change impacts assessment studies, climate model bias is considered to be stationary between the control and scenario periods. A Few methods are found in the literature that addresses the issue of nonstationarity in correcting the bias. To overcome the shortcomings reported in these approaches, three new methods of bias correction (NBC_μ, NBC_σ, and NBC_bs) are presented. The methods are improvised versions of previous techniques relying on distribution mapping. The methods are tested using split sample approach over 50-year historical period for nine climate stations in Ontario, using six regional climate models. The average bias reduction improvement (BRI) by new methods, in mean daily and monthly precipitation, was found to be 73.9%, 74.3%, and 77.4%, respectively, higher than that obtained by the previous methods (eQM 67.7% and CNCDFm_NP 64.1%). Thus, the methods are found to be more effective in accounting for nonstationarity in the model bias.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author