Challenges in modelling river flow and ice regime on the Ningxia–Inner Mongolia reach of the Yellow River, China

• Main Authors: C. Fu, I. Popescu, C. Wang, A. E. Mynett, F. Zhang
• Format: Article
• Language: English
• Published: Copernicus Publications 2014-03-01
• Series: Hydrology and Earth System Sciences
• Online Access: http://www.hydrol-earth-syst-sci.net/18/1225/2014/hess-18-1225-2014.pdf

During winter the Yellow River in China is frequently subjected to ice flood disasters. Possible dike breaking due to ice floods poses a serious threat to the part of the region located along the river, in particular the Ning–Meng reach (including Ningxia Hui and the Inner Mongolia autonomous regions). Due to its special geographical location and river flow direction, the ice dams and jams lead to dike breaking and overtopping on the embankment, which has resulted in huge casualties and property losses throughout history. Therefore, there is a growing need to develop capability in forecasting and analysing river ice floods. Research into ice floods along the river is taking place at the Yellow River Conservancy Commission (YRCC). A numerical model is one of the essential parts of the current research going on at the YRCC, which can be used to supplement the inadequacies in the field and lab studies which are being carried out to help understand the physical processes of river ice on the Yellow River. Based on the available data about the Ning–Meng reach of the Yellow River, the YRCC river ice dynamic model (YRIDM) has been tested for capabilities to conduct ice flood forecasting. The YRIDM can be applied to simulate water level, discharge, water temperature, and ice cover thickness under unsteady-state conditions. Different scenarios were designed to explore the model uncertainty for two bounds (5 and 95%) and probability distribution. The YRIDM is an unsteady-state flow model that can show the basic regular pattern of ice floods; hence it can be used as an important tool to support decision making. The recommendation is that data and research should be continued in order to support the model and to measure improvements.