On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

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.

What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?

“…To date, only few investigations identified the dominant sources of predictability in seasonal hydrological forecasting (i.e., the initial hydrological conditions and meteorological forcing) at the continental and global scale; however, these only consider different forcing data, model setups, and benchmarking (Greuell et al, 2019;Li et al, 2009;Shukla & Lettenmaier, 2011;Yossef et al, 2013Yossef et al, , 2017Zhang et al, 2017). The lack of large-sample studies across a variety of modeling settings at multiple spatiotemporal scales and under changing environmental conditions has limited the understanding of how predictability evolves in space and time.…”

Impact of Geology on Seasonal Hydrological Predictability in Alpine Regions by a Sensitivity Analysis Framework

“…Seasonal streamflow generally follows the same pattern as precipitation, with low flows during winter due to snow accumulation in the high-altitude areas and peak flows due to snow melting during summer and cyclonic storms during autumn [27,32]. Three streamflow gauging stations are present in the Passirio catchment (Figure 1b), with the station of Merano denoting the outlet section with a contributing area of approximately 402 km 2 . A set of eight precipitation and temperature stations located in an elevation range of 300-3000 m provide weather data for this catchment.…”

“…Seasonal streamflow predictability is a key element to the efficient design of water-related management strategies as it has a major impact on the resilience of several sectors like hydropower production, water supply and agriculture. Seasonal hydrological forecasts can provide knowledge of the future land surface hydrological conditions several months in advance [1,2], contributing to early preparedness against disasters and to optimal water usage. Therefore, identifying the sources of skill of such forecasts can be crucial for human safety as well as for sustainable water management [3][4][5][6][7].…”

“…Establishment of the dominant factor determining the seasonal forecast skill is crucial to funnel the efforts into improving the appropriate predictive tools, either by developing data assimilation techniques to improve the IC estimates or by enhancing the accuracy of the CF. The relative contribution of the two major sources of seasonal hydrological predictability (i.e., IC, CF) has been investigated in several catchments worldwide [2,14,[21][22][23][24] by applying the ESP/reverse ESP (revESP) framework [25] that compares the predictability stemming from perfect IC and climatological CF (ESP) with the predictability related to known CF and climatological IC (revESP). Recent advances in the ESP/revESP methodology involve the End Point Blending (EPB) [1], a computationally-light alternative to the Variational Ensemble Streamflow Prediction Assessment (VESPA) [15] that accounts for intermediate levels of knowledge between perfect knowledge and climatological (total lack of knowledge), allowing for the calculation of the effect of changes in IC and CF skill on the streamflow forecast skill-a metric termed "skill elasticity" [15].…”

“…Many studies have focused on the impact of catchment location [2,22,25], size [21], elevation [23,24], season and lead time (LT) [2,22,25] on the ESP/revESP skill. A number of studies have also evaluated the effect of geology on the ESP seasonal predictive skill [14,23,24,26].…”