Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins

Yohei Sawada, Shinichi Okugawa, Takayuki Kimizuka
Received 2022/06/14, Accepted 2022/09/21, Published 2022/10/27

Yohei Sawada1), Shinichi Okugawa1), Takayuki Kimizuka2)

1) Institute of Engineering Innovation, the University of Tokyo, Japan
2) Department of Civil Engineering, the University of Tokyo, Japan

Verification processes of rainfall-runoff modeling are important to improve the skill of hydrological models to reproduce water cycles in river basins. It is ideal that newly developed models are compared with many benchmarking conventional models in many river basins as part of the verification process. However, this robust verification is time-consuming if model developers prepare data and models from scratch. Here we present a useful dataset which can accelerate the robust verification of hydrological models. Our newly developed dataset, Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp), provides runoff simulation by 44 calibrated conceptual hydrological models in 135 Japanese river basins as well as meteorological forcing which is necessary to drive conceptual hydrological models. By comparing simulated runoff with river discharge observations which are not used for the calibration of hydrological models, we find that the best models in the 44 models can reproduce observed river runoff with KGE larger than 0.6 in most of the 135 river basins, so that the runoff simulation of MERV-Jp is reasonably accurate. MERV-Jp is publicly available to support all hydrological model developers to robustly verify their model improvement.

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Copyright (c) 2022 The Author(s) CC-BY 4.0

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