Abstract

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

Highlights

  • In the coming decades, climate change will likely become a primary problem affecting hydrological regimes and flood hazard conditions

  • The mean absolute error (MAE) values vary in the ranges of 0.1–20 for annual maximum precipitation, percent of bias (PBIAS) values range − 40 to 30, precipitation weighting root means square error (PWRMSE) values range 0–0.1, and RR − 0.25 to 0.55 in all the selected catchments

  • The results of climate variables from Coupled model intercomparison project phase 6 (CMIP6) data set are consistent with previous studies and applied for the first time in estimation of future flood hazard under varying climate conditions and associated uncertainty estimation

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Summary

Introduction

Climate change will likely become a primary problem affecting hydrological regimes and flood hazard conditions. According to the IPCC report, significant changes in atmospheric temperature, precipitation, humidity, and circulation are expected, resulting in increased extreme events, including floods, droughts, heatwaves, heavy rainfall, and more intense cyclones (Knutti and Sedláček 2013; IPPC 2013). According to Serdeczny et al (2016) and Rojas et al (2013), flood frequency will be increased due to climate change and will have a massive socio-economic impacts. Serdeczny et al (2016) stated that there have been climate-linked changes in the magnitude, frequency and timing of flood characteristics in different regions of Africa. The understanding of catchment-scale flood hazard projections requires a chain of linked concepts and comprehensive framework. Developing a comprehensive cascade modeling that considers these principal components of climate change and hydrologic modeling framework is crucial

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