An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions

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 realtime 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...

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

“…However, over the northern and northwestern dry regions, the initial hydrologic conditions are expected to still strongly impact SM forecasts for longer lead times (three-month), particularly when the forecasts are initialized in dry (November-March) or wet-to-dry transition (October) months. In addition, in line with the previous findings [24], the summer RMSE ratio (June-August; orange lines) tends to converge at a specific value with increased lead times over the regions with strong seasonality in hydro-climate. This suggests that wet ICs will largely benefit the prediction of drought development and duration occurring at the end of the wet season.…”

“…In addition, the ESP/revESP diagnosis framework only focuses on two end points that reflect zero (i.e., directly taken from observational data) and perfect information (i.e., the ensemble taken from the full climatological period) of CFs and ICs, which prevents a comprehensive understanding of their relative roles at varying levels of uncertainty. What's more, the initial moisture storage only includes two primary state variables (SM and SWE), without consideration of other water storage components (e.g., surface water, groundwater), which could be important for one-month seasonal streamflow predictions in downstream regions [19,24]. This implies that more detailed components of initial storage, rather than the general SM and SWE, should be considered in future assessments.…”

“…In reality, China is characterized by many types of hydro-climatic regimes, illustrating that the dominant factor controlling seasonal hydrological forecast skill is related to obvious space-time heterogeneity. To date, limited studies have been pursued to investigate this, e.g., over the Three Gorges Dam [30], the Hanjiang River basin [23], the Yellow River basin [24], and Southwest China [13]. However, these studies were either conducted by different methods or issued at some specific basins, leaving it hard to draw a consistent conclusion under the same diagnosis framework across the whole country.…”

“…Thereafter, this artificial diagnosis approach was extended to eastern U.S. river basins [17] and across the total U.S. domain [18], respectively. These studies consistently reported that the strong influence of ICs on runoff and soil moisture forecasts is expected to last for one month across most of the U.S., and even for up to five months during the wet-to-dry transition period; while the role of CFs starts to become important with the increases in forecast lead, especially during the reverse transition period (i.e., dry-to-wet) and over southeastern and northeastern U.S. To date, given the effectiveness of such an ESP/revESP scheme, numerous studies have employed it to determine the primary contributor to seasonal hydrological skill or uncertainties over many major river basins globally [19][20][21][22][23][24].…”

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

“…Several studies have shown that state variables such as soil moisture or snow water equivalent can provide relevant information to extend predictability for lead times from one to several months ahead (e.g. Wood and Lettenmaier, 2008;Shukla et al, 2013;Yang et al, 2014;Yuan et al, 2016). The influence of initial conditions depends on the period of the year and on the location of watersheds (e.g.…”