An experimental seasonal hydrological forecasting system over the Yellow River basin – Part I: Understanding the role of initial hydrological conditions
Authors: Yuan Xing , Ma Feng , Wang Linying , Zheng Ziyan , Ma Zhuguo , Ye Aizhong , Peng Shaoming 
Published: March 2016

Abstract. The hydrological cycle over the Yellow River has been altered by the climate change and human interventions greatly during past decades, with a decadal drying trend mixed with a large variation of seasonal hydrological extremes. To provide support for the adaptation to a changing environment, an experimental seasonal hydrological forecasting system is established over the Yellow River basin. The system draws from a legacy of a global hydrological forecasting system that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach, but with a higher resolution and a spatially disaggregated calibration procedure that are based on a newly compiled hydrological observation dataset with five decades of naturalized streamflow at twelve mainstream gauges and a newly released meteorological observation dataset including 324 meteorological stations over the Yellow River basin. While the evaluation of the NMME-based seasonal hydrological forecasting will be presented in a companion paper to explore the added values from climate forecast models, this paper investigates the role of initial hydrological conditions (ICs) by carrying out six-month Ensemble Streamflow Prediction (ESP) and reverse ESP-type simulations for each calendar month during 1982–2010 with the hydrological models in the forecasting system, i.e., a large scale land surface hydrological model and a global routing model that is regionalized over the Yellow River. In terms of streamflow predictability, the ICs outweigh the meteorological forcings up to 2–5 months during the cold and dry seasons, but the latter dominate the predictability after the first month during the warm and wet seasons. For the streamflow forecasts initialized at the end of the rainy season, the influence of ICs for lower reaches of the Yellow River can be 5 months longer than that for the upper reaches, while such difference drops to 1 month during the rainy season. Based on an additional ESP type simulation without the initialization of the river routing model, it is found that the initial surface water state is the dominant source of streamflow predictability during the first month, beyond which other sources of terrestrial memory become more important. During the dry/wet periods, the dominance of ICs on the streamflow predictability can be extended by a month even in the rainy season, suggesting the usefulness of the ESP forecasting approach after the onset of the hydrological extreme events. Similar results are found for the soil moisture predictability, but with longer influences from ICs. And the simulations indicate that the soil moisture memory is longer over the middle reaches than those over the upper and lower reaches of the Yellow River. The naturalized hydrological predictability analysis in this study will provide a guideline for establishing an operational hydrological forecasting system as well as for managing the risks of hydrological extremes over the Yellow River basin.