English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In order to precisely evaluate the snowmelt energy at our flux observation site located in an apple orchard and thereby contribute to reliable flux observation, we derived an equation on the basis of the energy budget theory for predicting the daily snowmelt energy at the snow surface. For practical use at farmers’ fruit orchards, we also derived some simpler prediction equations in which the number of meteorological elements used as explanatory variables was restricted. The optimum values of the parameters for these equations were then determined by using the data for the snow weight, monitored near the flux observation site. The accuracy of these equations was also examined. Using a theoretical equation in which five meteorological elements (daily integrated solar radiation, daily wind run, daily mean snow albedo, air temperature, and specific humidity deficit) and daily integrated clear-sky solar radiation were used as explanatory variables, we could predict the daily snowmelt energy with a root mean square error (RMSE) of 0.84 MJ·m-. The values of the bulk transfer coefficients for sensible and latent heat, estimated on the basis of the optimum values of the parameters for the prediction equation, were slightly smaller than but fairly agreed with those in previous studies. The prediction accuracy hardly decreased in the case of simpler equations in which wind run, specific humidity deficit, and clearsky radiation were not used as explanatory variables. However, the accuracy decreased considerably when solar radiation or snow albedo was excluded. When the daily snowmelt energy was predicted on the basis of only the mean air temperature, RMSE increased up to 1.34 MJ·m-. For farmers’ fruit orchards, it seemed desirable to predict the daily snowmelt energy on the basis of the integrated solar radiation in addition to the mean air temperature.

Equations to predict daily snowmelt energy in a deciduous fruit orchard