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Daily precipitation-downscaling techniques in three Chinese regions
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Air and Water Science. (Hydrologi)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Air and Water Science. (Hydrologi)
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2006 (English)In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 42, no 11, W11423- p.Article in journal (Refereed) Published
Abstract [en]

Four methods of statistical downscaling of daily precipitation were evaluated on three catchments located in southern, eastern, and central China. The evaluation focused on seasonal variation of statistical properties of precipitation and indices describing the precipitation regime, e. g., maximum length of dry spell and maximum 5-day precipitation, as well as interannual and intra-annual variations of precipitation. The predictors used in this study were mean sea level pressure, geopotential heights at 1000, 850, 700, and 500 hPa, and specific humidity as well as horizontal winds at 850, 700, and 500 hPa levels from the NCEP/NCAR reanalysis with 2.5 degrees x 2.5 degrees resolution for 1961 - 2000. The predictand was daily precipitation from 13 stations. Two analogue methods, one using principal components analysis (PCA) and the other Teweles-Wobus scores (TWS), a multiregression technique with a weather generator producing precipitation (SDSM) and a fuzzy-rule-based weather-pattern-classification method (MOFRBC), were used. Temporal and spatial properties of the predictors were carefully evaluated to derive the optimum setting for each method, and MOFRBC and SDSM were implemented in two modes, with and without humidity as predictor. The results showed that ( 1) precipitation was most successfully downscaled in the southern and eastern catchments located close to the coast, ( 2) winter properties were generally better downscaled, ( 3) MOFRBC and SDSM performed overall better than the analogue methods, ( 4) the modeled interannual variation in precipitation was improved when humidity was added to the predictor set, and ( 5), the annual precipitation cycle was well captured with all methods.

Place, publisher, year, edition, pages
2006. Vol. 42, no 11, W11423- p.
National Category
Earth and Related Environmental Sciences
URN: urn:nbn:se:uu:diva-93487DOI: 10.1029/2005WR004573ISI: 000242747900001OAI: oai:DiVA.org:uu-93487DiVA: diva2:166976
Available from: 2005-09-09 Created: 2005-09-09 Last updated: 2011-05-04Bibliographically approved
In thesis
1. Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation: Comparison of Methods and Climate Regions
Open this publication in new window or tab >>Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation: Comparison of Methods and Climate Regions
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Statistisk nedskalning av nederbörd från storskalig atmosfärscirkulation : Jämförelse mellan metoder och klimatregioner
Abstract [en]

A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables.

The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled.

A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation.

The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.

64 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 93
Hydrology, Statistical, Downscaling, Precipitation, Large-scale circulation, PCA, TWS, Weather pattern, Regression, Weather generator, Hydrologi
National Category
Oceanography, Hydrology, Water Resources
urn:nbn:se:uu:diva-5937 (URN)91-554-6344-4 (ISBN)
Public defence
2005-09-30, Axel Hambergssalen, Geocentrum, Villavägen 16, Uppsala, 10:00
Available from: 2005-09-09 Created: 2005-09-09Bibliographically approved

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