Abstract
Daily peak stream discharge data, collected over time, are typically characterized by a few large peaks separated by runs of small values, where peaks correspond to the occurrence of storms. Furthermore, the peak discharge on the first day of a storm has little or no relationship to the previous day's discharge. These characteristics are not present in standard Gaussian time series models in which a zig-zag behavior not conducive to runs of small values is observed and the present value always depends on the previous value. However, they can be successfully captured with non-Gaussian time series models.
Daily peak stream discharge between 1926 and 1953 of Kaukonahua Stream, Hawaii is analyzed using a new exponential autoregressive (NEAR) time series model. The distribution of the length of contiguous periods in which the stream discharge stays below a fixed percentage of the average is estimated. This estimate is shown to be closer to the actual distribution than that obtained using standard Gaussian time series models, with data from the same stream obtained during two disjoint time periods 1926-1952 and 1960-1996 .
Keywords
stochastic modeling of streamflow; NEAR; exponential autoregressive; runs; non-Gaussian time series
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Perera, S.
(2001).
"AN ANALYSIS OF DAILY PEAK STREAM DISCHARGE USING A NON-GAUSSIAN TIME SERIES MODEL,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1226
AN ANALYSIS OF DAILY PEAK STREAM DISCHARGE USING A NON-GAUSSIAN TIME SERIES MODEL
Daily peak stream discharge data, collected over time, are typically characterized by a few large peaks separated by runs of small values, where peaks correspond to the occurrence of storms. Furthermore, the peak discharge on the first day of a storm has little or no relationship to the previous day's discharge. These characteristics are not present in standard Gaussian time series models in which a zig-zag behavior not conducive to runs of small values is observed and the present value always depends on the previous value. However, they can be successfully captured with non-Gaussian time series models.
Daily peak stream discharge between 1926 and 1953 of Kaukonahua Stream, Hawaii is analyzed using a new exponential autoregressive (NEAR) time series model. The distribution of the length of contiguous periods in which the stream discharge stays below a fixed percentage of the average is estimated. This estimate is shown to be closer to the actual distribution than that obtained using standard Gaussian time series models, with data from the same stream obtained during two disjoint time periods 1926-1952 and 1960-1996 .
