A spatial time series framework for modeling daily precipitationat regional scales
In this paper, a framework for stochastic spatiotemporal modeling of daily precipitation in a hindcast mode is presented. Observed precipitation levels in space and time are modeled as a joint realization of a collection of space-indexed time series, one for each spatial location. Time series model parameters are spatially varying, thus capturing space-time interactions. Stochastic simulation, i.e., the procedure of generating alternative precipitation realizations (synthetic fields) over the space-time domain of interest (Deutsch and Journel, 1998), is employed for ensemble prediction. The simulated daily precipitation fields reproduce a data-based histogram and spatiotemporal covariance model, and identify the measured precipitation values at the rain gauges (conditional simulation). Such synthetic precipitation fields can be used in a Monte Carlo framework for risk analysis studies in hydrologic impact assessment investigations.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Director, Office of Science; National Aeronautics andSpace Administration
- DOE Contract Number:
- DE-AC02-05CH11231; NASA:NASA/RESAC GRANTNS-2791
- OSTI ID:
- 861006
- Report Number(s):
- LBNL-49156; JHYDA7; R&D Project: 43AY01; BnR: 400409900; TRN: US200603%%67
- Journal Information:
- Journal of Hydrology, Vol. 297, Issue 1-4; Related Information: Journal Publication Date: 09/01/2004; ISSN 0022-1694
- Country of Publication:
- United States
- Language:
- English
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