Nonlinear analysis of rainfall dynamics in California's Sacramento Valley
Sivakumar, B. , Wallender, W.W., Horwath, W.R., Mitchell, J.P., Prentice, S.E., Joyce, B.A. Nonlinear analysis of rainfall dynamics in California's Sacramento Valley Hydrological Processes Volume 20, Issue 8, May 2006, Pages 1723-1736
Sivakumar, B. , Wallender, W.W., Horwath, W.R., Mitchell, J.P.,
Prentice, S.E., Joyce, B.A. Nonlinear analysis of rainfall dynamics
in California's Sacramento Valley Hydrological Processes Volume 20,
Issue 8, May 2006, Pages 1723-1736
Abstract - This study investigates the dynamic nature of rainfall
observed at the Sustainable Agriculture Farming Systems (SAFS) site
in California's Sacramento Valley, which was established to study
the benefits of winter cover cropping in Mediterranean
irrigated-arid systems. Rainfall data of four different temporal
scales (i.e. daily, weekly, biweekly, and monthly) are analysed to
determine the dynamic nature of precipitation in time. In an arid
climate with seasonal precipitation this has large implications for
land and water management, both in the short term and in the long
term. A nonlinear dynamic technique (correlation dimension method)
that uses the phase-space reconstruction and dimension concepts is
employed. Bearing in mind the possible effects of the presence of
zeros (i.e. no rain) on the outcomes of this analysis, an attempt
is also made to compare the dynamic nature of all-year rainfall and
winter rainfall. Analysis of 15 years of data suggests that
rainfall dynamics at this site are dominated by a large number of
variables, regardless of the scales and seasons studied. The
dimension results also suggest that: (1) rainfall dynamics at
coarser resolutions are more irregular than that at finer
resolutions; (2) winter rainfall has a higher variability than
all-year rainfall. These results are indeed useful to gain
information about the complexity of the rainfall process at this
site with respect to (temporal) scales and seasons and, hence, the
appropriate model (high-dimensional) type. However, in view of the
potential effects of certain rainfall data characteristics (e.g.
zeros, measurement errors, scale effects) on the correlation
dimension analysis, the discussion also emphasizes the need for
further verification, and possibly confirmation, of these
results.


