Hydrological Research Letters
Online ISSN : 1882-3416
ISSN-L : 1882-3416
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Displaying 1-3 of 3 articles from this issue
  • Yohei Sawada, Shinichi Okugawa, Takayuki Kimizuka
    2022 Volume 16 Issue 4 Pages 73-79
    Published: 2022
    Released on J-STAGE: October 27, 2022
    JOURNAL OPEN ACCESS
    Supplementary material

    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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  • Jun Inaoka, Ken’ichirou Kosugi, Naoya Masaoka, Tetsushi Itokazu, Kimih ...
    2022 Volume 16 Issue 4 Pages 80-86
    Published: 2022
    Released on J-STAGE: November 25, 2022
    JOURNAL OPEN ACCESS
    Supplementary material

    Previous studies have proven that rainfall–runoff characteristics in headwater catchments are affected by many factors, including topography, geology, and vegetation. However, only a few studies have explained the geological effects on rainfall–runoff characteristics based on observations from various catchments. In this study, we conducted runoff observations in 19 headwater catchments in two forests with different geological characteristics; based on these observations, we further conducted direct runoff and hydrograph recessions analyses. The runoff characteristics of the catchments were significantly affected by their local geological settings. Some catchments did not follow average trends, especially catchments with large baseflows, because of the effects of geological structures, such as dips and strikes in sedimentary-rocks and joints in granite. These catchments were likely to have wetter riparian zones, thereby facilitating direct flow, even in the case of reduced rainfall.

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  • Kazuya Takami, Rimpei Kamamoto, Kenji Suzuki, Kosei Yamaguchi, Eiichi ...
    2022 Volume 16 Issue 4 Pages 87-92
    Published: 2022
    Released on J-STAGE: December 01, 2022
    JOURNAL OPEN ACCESS

    Direct and steady observation of newly fallen snow density is difficult because of the effect of snow compaction. We aimed to evaluate a method for estimation of newly fallen snow density using particle size and fall velocity distribution obtained from disdrometer (Parsivel2) for snowfall cases at temperatures below 0°C. As disdrometer observations cannot easily manage cases of mixed hydrometeor such as graupel and aggregate, we considered only the averaged riming degree of snowfall particles as an index without classifying the hydrometeor types. We observed newly fallen snow density using a snow board for 157 cases of snowfall in the winters of 2020–2021 and 2021–2022 in Niigata Prefecture, Japan. Furthermore, we calculated the riming degree for each case using a fraction of squared fall speed with respect to the unrimed aggregate. The results revealed that the averaged riming degree was correlated with density of newly fallen snow. Based on its relationship with the averaged riming degree investigated herein, the newly fallen snow density can be estimated from the particle size and fall speed distribution, which can be automatically observed using a disdrometer without any manual observations via a snowboard.

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