Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

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.

Infectious Diseases Associated with Hydrometeorological Hazards in Europe: Disaster Risk Reduction in the Context of the Climate Crisis and the Ongoing COVID-19 Pandemic

“…With the climate crisis now evident and evolving, hydrometeorological hazards and related disasters are already on an upward trend, which is expected to increase even more in the upcoming years (e.g., [ 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 ]), greatly affecting or sometimes even suspending activities in many sectors of daily life. This increase in hydrometeorological hazards and related disasters affects the interaction between lithosphere, hydrosphere, and biosphere, which in turn can form multi-hazard conditions with the simultaneous occurrence or parallel evolution of hydrometeorological hazards including floods and biological health hazards including infectious disease outbreaks.…”

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

“…The river discharge time series is extracted for the proposed site from the global data series. The predictions offered by the climate model datasets comprise of a lot biases, courtesy of the use of different grid structures and parameterization of the fluxes [44]. Without resolving the biases in the model, the accuracy of the result from that dataset will be compromised.…”

Calibração e validação de modelos hidrológicos para uma bacia hidrográfica do estado de Goiás

“…Nos estudos de Kim et al (2022) os valores de NSE encontrados foram superiores a 0,80 para os períodos de validação e calibração. Enquanto que Meresa et al (2022) analisaram 5 bacias hidrográficas e seus valores de NSE variaram entre 0,5 e 0,75.…”

“…Em outros casos vários modelos são selecionados para possibilitar uma análise mais abrangente. O HyMOD foi selecionado, em conjunto com outros modelos para avaliar a disponibilidade hídrica de bacias hidrográfica localizadas na Etiópia (Meresa et al, 2022) e no oeste da África (Chabi et al, 2021). Já no estudo desenvolvido por Satgé et al (2021) o HyMOD foi utilizado em conjunto com o GR4j para investigar a confiabilidade dos dados disponíveis para dez bacias hidrográficas na região Amazônica.…”