Effects of the uncertainties of climate change on the performance of hydropower systems

Abstract

This study's objective is to assess the potential impact of climate change on an example underdesign hydropower system in the Karkheh River basin, Iran. Based on three water resources performance criteria (reliability, resiliency, and vulnerability), a novel framework was proposed to interpret and cope with the uncertainties associated with such assessments. The results demonstrated the acceptable performance of the system in most months, while there were certain signs for rare low-inflows, and consequently low hydropower generated by the system, due to the climate change. It was found that in terms of these three criteria, the best performances in the climate-change condition occurred in May (80% reliability), December (45% resiliency), and April (19% vulnerability). Yet the worst performances occurred in September (2% reliability), July and August (0% resiliency), and in October (39% vulnerability). These results indicated that the reliability and resiliency of the system would be improved under the climate change condition, while due to the increase of low-inflow incidences, the vulnerability of the system would increase. This suggests that, although the system may not face frequent failures, severe blackouts may occur. With timely consideration of future climatic conditions and appropriate adaptive actions, including additional backup systems for reliable and safe electricity generation, future undesired conditions can be avoided in the basin. also have a higher rate of energy demand per capita (Vörösmarty et al. ). Nevertheless, the process of fossil fuel formation takes millenniums, thus making them limited or 591

Investigating the performance of SWAT and IHACRES in simulation streamflow under different climatic regions in Iran

“…The IHACRES model is an applied model used throughout the world that recent successful application of this model caused we chose this model (Sriwongsitanon and Taesombat, 2011;Samuel et al, 2011;Onyutha, 2019). One of the major advantages of this model over other hydrological models is the minimal data input requirement (Zolghadr et al, 2018). This model engages either linear, nonlinear, or multiple regression methods to estimate streamflow (Jakeman et al, 1990;Samuel et al, 2011).…”

“…This indicates that both these models exhibit high performance in humid (vs. arid) regions, which concurs with the results of many other studies (a summary of these studies is shown in Table Ⅶ). In some of these studies (Zolghadr et al, 2018;Sayasane et al, 2016), it was put forward that model weakness in arid regions compared to humid regions may be the result of rainfall intensity and distribution in arid regions.…”

A Hybrid Model-Based Adaptive Framework for the Analysis of Climate Change Impact on Reservoir Performance

“…5(c) shows an increase in the vulnerability of the Dez system across most RCPs, indicating that despite the higher reliability and resiliency of the reservoir, the damages of a failure event will be more severe than historical performance. Zolghadr-Asli et al (2019) also indicated that climate change will increase the reliability and resiliency of hydro-power production of the Karkheh reservoir, located in the same region in Iran, while the vulnerability will also increase and produce severe blackouts. Our results also show that considering the historical release policies of the reservoir and ignoring the effects of climate change in defining the operating policies will result in system failure and will produce serious damages.…”

Preliminary study of soot and polycyclic aromatic hydrocarbons in emitted particles from adobe kilns that use scrap tires as fuel

“…We chose the IHACRES model because it is applied throughout the world and has been recently applied with success (Samuel et al, 2011;Sriwongsitanon and Taesombat, 2011;Onyutha, 2019). One of the major advantages of this model over other hydrological models is the minimal data input requirement (Zolghadr-Asli et al, 2018). It engages either linear, nonlinear, or multiple regression methods to estimate streamflow (Jakeman et al, 1990;Samuel et al, 2011).…”