The paper was submitted to Water Resources Research May 17, 1999.
Abstract. Streamflow disaggregation is used to preserve statistical
attributes of time series across multiple sites and time scales. Several
algorithms for spatial disaggregation and for disaggregation of annual
to monthly flows are available. However, the disaggregation of monthly
to daily or weekly to daily flows remains a challenge. A new algorithm
for simultaneously disaggregating monthly flows at a number of sites and
daily flows at an index site, to daily flows at a number of sites on a
drainage network is presented. The continuity of flow in time across months
at each site as well as the inter-site flow pattern is preserved. The disaggregated
daily flows at the multiple sites are conditioned on the spatial (across
site) pattern of monthly flows at the respective sites. The probability
distribution of the vector of disaggregated flows conditional on the multi-site
monthly flows is approximated nonparametrically using the k-nearest neighbors
of the monthly spatial flow pattern. A constrained optimization problem
is solved to adaptively estimate the disaggregated flows in space and time
for each such neighborhood. An application to data from a tributary of
the Colorado River is used to illustrate the modeling process.
Keywords: Stochastic Hydrology, Nonparametric Methods, Streamflow
The paper can be accessed by downloading the following pdf files
Disag.pdf
Fig
4.pdf
Fig5.pdf
Fig6.pdf
Fig7.pdf
Fig8.pdf
Fig9.pdf
Fig10.pdf