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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03539naa a2200313 4500 | |
| 001 | oai:gup.ub.gu.se/239912 | |
| 003 | SwePub | |
| 008 | 240528s2016 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://gup.ub.gu.se/publication/2399122 URI |
| 024 | 7 | a https://doi.org/10.1016/j.accre.2016.06.0062 DOI |
| 040 | a (SwePub)gu | |
| 041 | a eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Rayner, D.P.d 1973u Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences4 aut0 (Swepub:gu)xrayda |
| 245 | 1 0 | a A multi-state weather generator for daily precipitation for the Torne River basin, northern Sweden/western Finland |
| 264 | 1 | b Elsevier BV,c 2016 |
| 520 | a This paper describes a new weather generator e the 10-state empirical model e that combines a 10-state, first-order Markov chain with a non-parametric precipitation amounts model. Using a doubly-stochastic transition-matrix results in a weather generator for which the overall precipitation distribution (including both wet and dry days) and the temporal-correlation can be modified independently for climate change studies. This paper assesses the ability of the 10-state empirical model to simulate daily area-average precipitation in the Torne River catchment in northern Sweden/western Finland in the context of 3 other models: a 10-state model with a parametric (Gamma) amounts model; a wet/dry chain with the empirical amounts model; and a wet/dry chain with the parametric amounts model. The ability to accurately simulate the dis- tribution of multi-day precipitation in the catchment is the primary consideration. Results showed that the 10-state empirical model represented accumulated 2- to 14-day precipitation most realistically. Further, the dis- tribution of precipitation on wet days in the catchment is related to the placement of a wet day within a wet-spell, and the 10-state models represented this realistically, while the wet/dry models did not. Although all four models accurately reproduced the annual and monthly averages in the training data, all models underestimated inter-annual and inter-seasonal variance. Even so, the 10-state empirical model performed best. We conclude that the multi-state model is a promising candidate for hydrological applications, as it simulates multi-day precipitation well, but that further development is required to improve the simulation of interannual variation. | |
| 650 | 7 | a NATURVETENSKAPx Geovetenskap och miljövetenskapx Meteorologi och atmosfärforskning0 (SwePub)105082 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Meteorology and Atmospheric Sciences0 (SwePub)105082 hsv//eng |
| 653 | a Weather generator; Multi-state; Torne River; Precipitation | |
| 700 | 1 | a Achberger, Christine,d 1968u Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences4 aut0 (Swepub:gu)xachch |
| 700 | 1 | a Chen, Deliang,d 1961u Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences4 aut0 (Swepub:gu)xchede |
| 710 | 2 | a Göteborgs universitetb Institutionen för geovetenskaper4 org |
| 773 | 0 | t Advances in Climate Change Researchd : Elsevier BVg 7:1-2, s. 70-81q 7:1-2<70-81x 1674-9278 |
| 856 | 4 | u https://doi.org/10.1016/j.accre.2016.06.006 |
| 856 | 4 8 | u https://gup.ub.gu.se/publication/239912 |
| 856 | 4 8 | u https://doi.org/10.1016/j.accre.2016.06.006 |
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