Space-time disaggregation of precipitation and temperature across different climates and spatial scales

• Published in: Journal of Hydrology: Regional Studies
• Main Authors: Korbinian Breinl, Giuliano Di Baldassarre
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2019
• Subjects: Physical geography; GB3-5030; Geology; QE1-996.5; Subarctic; Tundra
• Online Access: https://doi.org/10.1016/j.ejrh.2018.12.002; https://doaj.org/article/9c7b4ffa7c4d4511a76e063ceb4f06c8

Study region: This study focuses on two study areas: the Province of Trento (Italy; 6200 km²), and entire Sweden (447000km²). The Province of Trento is a complex mountainous area including subarctic, humid continental and Tundra climates. Sweden, instead, is mainly dominated by a subarctic climate in the North and an oceanic climate in the South.Study focus: Hydrological predictions often require long weather time series of high temporal resolution. Daily observations typically exceed the length of sub-daily observations, and daily gauges are more widely available than sub-daily gauges. The issue can be overcome by disaggregating daily into sub-daily values. We present an open-source tool for the non-parametric space-time disaggregation of daily precipitation and temperature into hourly values called spatial method of fragments (S-MOF). A large number of comparative experiments was conducted for both S-MOF and MOF in the two study regions.New hydrological insights for the region: Our experiments demonstrate the applicability of the univariate and spatial method of fragments in the two temperate/subarctic study regions where snow processes are important. S-MOF is able to produce consistent precipitation and temperature fields at sub-daily resolution with acceptable method related bias. For precipitation, although climatologically more complex, S-MOF generally leads to better results in the Province of Trento than in Sweden, mainly due to the smaller spatial extent of the former region. Keywords: Precipitation, Temperature, Disaggregation, Space-time scaling, Non-parametric, Method of fragments