Abstract
Predicting soil moisture of arable lands under climate change is important and useful for yield prediction and adaptation under the climate change. For predicting soil moisture condition of agricultural lands, spatial and temporal scale of input climate data is favorable to have similar scales of water and heat transport phenomena at agricultural lands. We investigated methods of downscaling the General Circulation Model (GCM) projections, which generally have monthly resolution, to hourly scale for predicting soil water condition of agricultural lands. In this study, stochastic weather generators were used to reproduce climatic variables. Predicted soil moisture well responded to rainfall events when downscaled hourly rainfall records produced by using weather generators and the monthly GCM projections were used as input data. Direct use of rainfall records of monthly GCM projections could not produce acceptable response of soil moisture content. These results suggested proper methods for temporal downscaling of rainfall records are important to give precise prediction of soil moisture of agricultural land under future climate change. Also, weather generator is one of the useful tools for acceptable temporal downscaling.
