Streamflow disaggregation is used to preserve statistical attributes of tim
e series across multiple sites and timescales. Several algorithms for spati
al disaggregation and for disaggregation of annual to monthly flows are ava
ilable. However, the disaggregation of monthly to daily or weekly to daily
flows remains a challenge. A new algorithm is presented for simultaneously
disaggregating monthly flows at a number of sites and daily flows at an ind
ex site to daily flows at a number of sites on a drainage network. The cont
inuity of flow in time across months at each site as well as the intersite
flow pattern are preserved. The disaggregated daily flows at the multiple s
ites are conditioned on the spatial (across site) pattern of monthly flows
at the respective sites. The probability distribution of the vector of disa
ggregated flows conditional on the multisite monthly flows is approximated
nonparametrically using the k nearest neighbors of the monthly spatial flow
pattern. A constrained optimization problem is solved to adaptively estima
te 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 illu
strate the modeling process.