In the basins of Hokkaido, the temperature is not highest but the precipitation is lower in In the basins of Hokkaido, average temperatures were relatively low compared to those in the basins of the SM, so the SWE in the basins of Hokkaido may be more sensitive to changes in precipitation than to changes in air temperature.
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In order to consider the impact of low SWE on water resources, it is necessary to consider the effects of dams and projections of water demand. As mentioned previously, water usage was restricted in Japan in Although the SWE in was less than that in , the lower SWE in would not necessarily lead to decreased water resources because the precipitation in was greater than that in It should be noted that the impacts of low SWE and low precipitation on water resources would not be equivalent, due to the difference between precipitation occurring as snowfall or rainfall.
The impact of the conversion of snowfall to rainfall on water resources would depend on dams and water demand. Hydrological simulations that consider the difference between snowfall and rainfall, as well as the effects of dams and water demand, will be important for further investigations. To drive the model, seven meteorological forcing variables are needed: precipitation; short-wave radiation; long-wave radiation; atmospheric pressure; specific humidity; atmospheric temperature; and wind speed. Kotsuki et al.
Using this system, we conducted a continuous simulation from January to March with 0. The horizontal resolution of JRA55 was 1. In the snow module, the canopy-intercepted snow and ground snow are treated separately. The sum of canopy-intercepted snow and ground snow is defined as the SWE in this study.
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We performed preliminary SiBUC-based simulations at seven observational sites to validate the snow module. At the observational sites, snow depth was measured in addition to the meteorological forcing data. With observational i. To conduct a long-term comparison over a wide area, an index capable of standardising characteristics that differ by region is necessary.
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Therefore, this study introduced a standardised index based on the idea of the standardised precipitation index SPI 19 , a representative index measuring meteorological drought. SPI represents the probability distribution of precipitation normalised to a normal distribution. The value of the SPI illustrates the frequency based on the sigma value of the distribution. While gamma or other distributions have been used in the SPI, we applied the Box—Cox transformation 20 , which is parametric power transformation into a Gaussian distribution, instead of fitting a probabilistic distribution.
We performed a Box—Cox transformation to the mean temperature, total precipitation, and total snowfall from January to February and mean SWE from 1st to 10th March for —, which the definition is identical to that used in Fig. The transformation was conducted for each river basin. Thus, the target variables, mean temperature, total precipitation, total snowfall, and SWE were averaged over each river basin before the transformation. The parameter of the Box—Cox transformation, which determines the accuracy of transformation, was decided by maximum likelihood estimation for each variable and river basin separately.
The derived standard deviation from transformed variables is the index representing the severity in this study. For example, when evaluating an area where SWE is low, larger negative values imply less-common weather phenomena. Although three month or longer is appropriate for SPI measuring meteorological drought, the index in this study uses ten days for SWE and two months for the other variables because these time period can capture the variability.
Kuribayashi, M. Comparison of snow water equivalent estimated in central Japan by high-resolution simulations using different land-surface models.
SOLA 9 , — Barnett, T. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature , — Hara, M. Estimation of the impact of global warming on snow depth in Japan by the pseudo-global-warming method. Kawase, H. Altitude dependency of future snow cover changes over Central Japan evaluated by a regional climate model. Changes in extremely heavy and light snow-cover winters due to global warming over high mountainous areas in central Japan.
Earth Planet. Kotsuki, S. Tanaka, K. Development of the new land surface scheme SiBUC commonly applicable to basin water management and numerical weather prediction model. Doctoral dissertation, graduate school of engineering, Kyoto university Kobayashi, S. The JRA reanalysis: general specifications and basic characteristics. SOLA 14 , 39— Bormann, K. Estimating snow-cover trends from space. Change 8 , — Ishizaka, M. Climatic response of snow depth to recent warmer winter seasons in heavy-snowfall areas in Japan.
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Suzuki, H. Long-term changes in snowfall depth and snow cover depth in and around Niigata prefecture from to analysis using data observed at railway stations. Tenki 53 , — in Japanese with an English summary. Google Scholar.
Yoshikane, T. Simulated snow water equivalent change between the s and s in the Sea of Japan side area using a regional climate model. Ishii, Y. Regional characteristics of variation of snowfall in Japan. Sellers, P. Part I: model formulation. Rutter, N. Evaluation of forest snow processes models SnowMIP2. Box, G. The next unnamed film in the franchise was slated for release in Q3 of [1] , but has been put into abeyance following the arson attack on KyoAni's Building 1, where much of the artwork for the series was created.
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