Details
| Original language | English |
|---|---|
| Article number | 862 |
| Journal | Water (Switzerland) |
| Volume | 10 |
| Issue number | 7 |
| Publication status | Published - 28 Jun 2018 |
Abstract
Long and continuous series of precipitation in a high temporal resolution are required for several purposes, namely, urban hydrological applications, design of flash flood control structures, etc. As data of the temporally required resolution is often available for short period, it is advantageous to develop a precipitation model to allow for the generation of long synthetic series. A stochastic model is applied for this purpose, involving an alternating renewal process (ARP) describing a system consisting of spells that can take two possible states: wet or dry. Stochastic generation of rainfall time series using ARP models is straight forward for single site simulation. The aim of this work is to present an extension of the model to spatio-temporal simulations. The proposed methodology combines an occurrence model to define in which locations rainfall events occur simultaneously with a multivariate copula to generate synthetic events. Rainfall series registered in different regions of Germany are used to develop and test the methodology. Results are compared with an existing method in which long independent time series of rainfall events are transformed to spatially dependent ones by permutation of their order. The proposed model shows to perform as a satisfactory extension of the ARP model for multiple sites simulations.
Keywords
- Multivariate copula, Spatial consistency, Stochastic rainfall model
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Social Sciences(all)
- Geography, Planning and Development
- Agricultural and Biological Sciences(all)
- Aquatic Science
- Environmental Science(all)
- Water Science and Technology
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In: Water (Switzerland), Vol. 10, No. 7, 862, 28.06.2018.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas
AU - Poduje, Ana Claudia Callau
AU - Haberlandt, Uwe
N1 - Funding information: This research was funded by the German Federal Ministry of Education and Research (BMBF) grant number Förderkennzeichen 033W002A. Acknowledgments: The authors would like to thank the colleagues Bora Shehu and Anne Fangmann for their help and useful comments. Special thanks go to Benedikt Gräler for his constructive suggestions regarding some of the explanations involving Vine copulas. The results presented in this study are part of a research project about synthetic generation of time series of precipitation for the optimal planning and operation of urban drainage systems (SYNOPSE), funded by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung BMBF) who are grateful acknowledged. We are also thankful for the provision and right to use data from the German National Weather Service (Deutscher Wetterdienst DWD) and to the Open Access fund of Leibniz Universität Hannover for funding the publication of this article. The authors appreciate the constructive suggestions from the academic editor and three anonymous reviewers which helped improve the quality of the manuscript.
PY - 2018/6/28
Y1 - 2018/6/28
N2 - Long and continuous series of precipitation in a high temporal resolution are required for several purposes, namely, urban hydrological applications, design of flash flood control structures, etc. As data of the temporally required resolution is often available for short period, it is advantageous to develop a precipitation model to allow for the generation of long synthetic series. A stochastic model is applied for this purpose, involving an alternating renewal process (ARP) describing a system consisting of spells that can take two possible states: wet or dry. Stochastic generation of rainfall time series using ARP models is straight forward for single site simulation. The aim of this work is to present an extension of the model to spatio-temporal simulations. The proposed methodology combines an occurrence model to define in which locations rainfall events occur simultaneously with a multivariate copula to generate synthetic events. Rainfall series registered in different regions of Germany are used to develop and test the methodology. Results are compared with an existing method in which long independent time series of rainfall events are transformed to spatially dependent ones by permutation of their order. The proposed model shows to perform as a satisfactory extension of the ARP model for multiple sites simulations.
AB - Long and continuous series of precipitation in a high temporal resolution are required for several purposes, namely, urban hydrological applications, design of flash flood control structures, etc. As data of the temporally required resolution is often available for short period, it is advantageous to develop a precipitation model to allow for the generation of long synthetic series. A stochastic model is applied for this purpose, involving an alternating renewal process (ARP) describing a system consisting of spells that can take two possible states: wet or dry. Stochastic generation of rainfall time series using ARP models is straight forward for single site simulation. The aim of this work is to present an extension of the model to spatio-temporal simulations. The proposed methodology combines an occurrence model to define in which locations rainfall events occur simultaneously with a multivariate copula to generate synthetic events. Rainfall series registered in different regions of Germany are used to develop and test the methodology. Results are compared with an existing method in which long independent time series of rainfall events are transformed to spatially dependent ones by permutation of their order. The proposed model shows to perform as a satisfactory extension of the ARP model for multiple sites simulations.
KW - Multivariate copula
KW - Spatial consistency
KW - Stochastic rainfall model
UR - http://www.scopus.com/inward/record.url?scp=85049607269&partnerID=8YFLogxK
U2 - 10.3390/w10070862
DO - 10.3390/w10070862
M3 - Article
AN - SCOPUS:85049607269
VL - 10
JO - Water (Switzerland)
JF - Water (Switzerland)
SN - 2073-4441
IS - 7
M1 - 862
ER -