Abstract
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 is based on a newly compiled hydrological observation dataset with 5 decades of naturalized streamflow at 12 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 6-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 prevails over the former in 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 a 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 main 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.
Highlights
Global warming has fundamentally affected terrestrial hydrological cycle, as well as water-related sectors
The blue line starting in January and ending in June in Fig. 5a shows that the RMSE of streamflow from Ensemble Streamflow Prediction (ESP) simulation is lower than the reverse ESP (revESP) simulation in January and February, indicating that the initial hydrological conditions (ICs) prevail over the meteorological forcings in the streamflow predictability during the first 2 months; the RMSE ratio is larger than 1 from April to June, which suggests that the meteorological forcings are more important for the streamflow prediction after the first 3 months
From the gauge at the headwater region to that at the outlet of the Yellow River basin, ICs significantly contribute to the streamflow predictability for up to 2–5 months for the forecasts initialized in fall and winter, and the meteorological forcings prevail over the ICs in the predictability after the first month for the forecasts initialized in spring and summer (Fig. 5)
Summary
Global warming has fundamentally affected terrestrial hydrological cycle, as well as water-related sectors. Wood and Lettenmaier (2008) applied the assessment framework over two river basins in the western USA and found that ICs yield streamflow forecasting skill for up to 5 months over northern California during the transition period between the wet and dry seasons but have less impact over southern Colorado basin due to a weaker annual cycle of precipitation. Most assessments did not explicitly investigate the role of the IC of the surface water state variables in the streamflow forecasting, where it could be a major source of hydrological forecast uncertainty over rivers with low slope and large floodplains (Paiva et al, 2012). Seasonal hydrological forecasting with multiple climate forecast models will be evaluated in a companion paper, by comparison with the ESP-based hydrological forecasting (Yuan, 2016)
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