Climate input parameters for real-time online risk assessment
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Risk assessment of natural hazards is often based on the actual or forecast weather situation. For estimating such climate-related risks, it is important to obtain weather data as frequently as possible. One commonly used climate interpolation routine is DAYMET, which in its current form is not able to update its database for periods of less than a year. In this paper, we report the construction of a new climate database with a standard interface and implement a framework for providing daily updated weather data for online daily weather interpolations across regions. We re-implement the interpolation routines from DAYMET to be compliant with the data handling in the new framework. We determine the optimal number of stations used in two possible interpolation routines, assess the error bounds using an independent validation dataset and compare the results with a previous validation study based on the original DAYMET implementation. Mean absolute errors are 1°C for maximum and minimum temperature, 28 mm for precipitation, 3.2 MJ/m² for solar radiation and 1 hPa for vapour pressure deficit, which is in the range of the original DAYMET routine. Finally, we provide an example application of the methodology and derive a fire danger index for a 1 km grid over Austria.
KeywordsInterpolation Daily weather data Ecosystem modelling Risk assessment
This research was partly funded by two projects from the Austrian Science Foundation (FWF): The ‘Austrian Forest Fire Research Initiative’ and ‘Application of ergodic theory within ecosystem modelling’. Thanks to the Central Institute for Meteorology and Geodynamics, for providing the daily climate records. We thank Peter E. Thornton for making the original version of DAYMET available, Herbert Formayer for data handling support and Chris Eastaugh for English language editing. We also thank Prof. Thomas Glade for organising the session on Natural Hazards during the EGU conference 2009 in Vienna and the anonymous reviewers.
- Brooker P (1979) Kriging. Eng Min J 180:148–153Google Scholar
- Burgan RE (1988) Revisions to the 1978 national fire-danger rating system. Research paper SE-273. U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, Asheville, NCGoogle Scholar
- Burgan RE, Andrews PL, Bradshaw LS, Chase CH, Hartford RA, Latham DJ (1997) WFAS: wildland fire assessment system. Fire Manag Notes 57:14–17Google Scholar
- Cressie NA (1993) Statistics for spatial data, revised edition. Wiley, New YorkGoogle Scholar
- Eastaugh CS, Petritsch R, Hasenauer H (2010) Climate characteristics across the Austrian forest estate from 1960 to 2008. Austr J Forest Res 127:133–146Google Scholar
- Keetch JJ, Byram GM (1968) A drought index for forest fire control. Research paper SE-38. U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, Asheville, NCGoogle Scholar
- Petritsch R (2002) Anwendung und Validierung des Klimainterpolationsmodells DAYMET in Österreich. Master thesis, University of Natural Resources and Applied Life Sciences, ViennaGoogle Scholar
- Rawat V, Saraf AK, Das J, Sharma K, Shujat Y (2011) Anomalous land surface temperature and outgoing long-wave radiation observations prior to earthquakes in India and Romania. Nat Hazards, doi: 10.1007/s11069-011-9736-5
- Schuster RL (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation, Special Report 247. Transportation Research Board, National Research Council. National Academy Press, Washington, pp 12–35Google Scholar
- Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 ACM national conference, pp 517–524Google Scholar