Benchmark decadal forecast skill for terrestrial water storage estimated by an elasticity framework

Enda Zhu, Xing Yuan () and Andrew W. Wood
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Enda Zhu: Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences
Xing Yuan: Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences
Andrew W. Wood: Research Applications Laboratory, NCAR

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: Abstract A reliable decadal prediction of terrestrial water storage (TWS) is critical for a sustainable management of freshwater resources and infrastructures. However, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the baseline skill remains unknown. Here we use decadal climate hindcasts and perform hydrological ensemble simulations to estimate a benchmark decadal forecast skill for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. The initial condition skill elasticity is higher than climate forecast skill elasticity over many river basins at 1–4 years lead, suggesting the dominance of initial conditions at short leads. However, our benchmark skill for TWS is significantly higher than initial conditions-based forecast skill over 25 and 31% basins for the leads of 1–4 and 3–6 years, and incorporating climate prediction can significantly increase TWS prediction skill over half of the river basins at long leads, especially over mid- and high-latitudes. Our findings imply the possibility of improving decadal TWS forecasts by using dynamical climate prediction information, and the necessity of using the new benchmark skill for verifying the success of decadal hydrological forecasts.

Date: 2019
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DOI: 10.1038/s41467-019-09245-3

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