Abstract

Initial conditions (ICs) and climate forecasts (CFs) are the two primary sources of seasonal hydrological forecast skill. However, their relative contribution to predictive skill remains unclear in China. In this study, we investigate the relative roles of ICs and CFs in cumulative runoff (CR) and soil moisture (SM) forecasts using 31-year (1980–2010) ensemble streamflow prediction (ESP) and reverse-ESP (revESP) simulations with the Variable Capacity Infiltration (VIC) hydrologic model. The results show that the relative importance of ICs and CFs largely depends on climate regimes. The influence of ICs is stronger in a dry or wet-to-dry climate regime that covers the northern and western interior regions during the late fall to early summer. In particular, ICs may dominate the forecast skill for up to three months or even six months during late fall and winter months, probably due to the low precipitation value and variability in the dry period. In contrast, CFs become more important for most of southern China or during summer months. The impact of ICs on SM forecasts tends to cover larger domains than on CR forecasts. These findings will greatly benefit future work that will target efforts towards improving current forecast levels for the particular regions and forecast periods.

Highlights

  • Seasonal hydrological forecasts can provide reliable and timely information of land surface hydrologic conditions several months in advance

  • When it comes to October, the initial moisture storage is implied as the dominant source of cumulative runoff (CR) predictability for most of northern China, but the predictive skill over the southeastern part of China primarily comes from the climate forecasts

  • initial conditions (ICs) and climate forecasts (CFs) are the primary two factors governing the performance of seasonal hydrological forecasts

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Summary

Introduction

Seasonal hydrological forecasts can provide reliable and timely information of land surface hydrologic conditions several months in advance. To facilitate seasonal hydrological forecasts, physical hydrological models are typically initialized with refined initial conditions (ICs) (i.e., antecedent hydrologic states) and fed with seasonal climate forecasts (CFs) [3,4]. Depending on how CFs are generated, the model-based seasonal hydrological forecast is generally grouped into two categories: (1) the Ensemble Streamflow Prediction (ESP) method [5]. Seasonal hydrological forecast skill is attributed to the prior knowledge of ICs (primarily snowpack, soil moisture, surface water, and groundwater) on the forecast start date and the posterior information. Water 2017, 9, 902 of CFs during the forecast period. Identifying which of these two factors dominates the forecast skill has great implications for the target efforts towards substantially improving the forecast level

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