Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China

• Main Authors: L. Liu, Y. P. Xu, S. L. Pan, Z. X. Bai
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-08-01
• Series: Hydrology and Earth System Sciences
• Online Access: https://www.hydrol-earth-syst-sci.net/23/3335/2019/hess-23-3335-2019.pdf

<p>In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and the maximum and minimum temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). <span class="inline-formula"><i>N</i></span> simulations are proposed to account for parameter uncertainty in VIC. Results show that when trade-offs between multiple objectives are significant, <span class="inline-formula"><i>N</i></span> simulations are recommended for better simulation and forecasting. This is why better results are obtained for the Nugesha and Yangcun stations. Our ensemble flood forecasting system can skillfully predict the maximum floods with a lead time of more than 10&thinsp;d and can predict about 7&thinsp;d ahead for meltwater-related components. The accuracy of forecasts for the first floods is inferior, with a lead time of only 5&thinsp;d. The base-flow components for the first floods are insensitive to lead time, except at the Nuxia station, whilst for the maximum floods an obvious deterioration in performance with lead time can be recognized. The meltwater-induced surface runoff is the most poorly captured component by the forecast system, and the well-predicted rainfall-related components are the major contributor to good performance. The performance in 7&thinsp;d accumulated flood volumes is better than the peak flows.</p>