Evaluating changes and estimating seasonal precipitation for the Colorado River Basin using a stochastic nonparametric disaggregation technique

Precipitation estimation is an important and challenging task in hydrology because of high variability and changing climate. This research involves (1) analyzing changes (trend and step) in seasonal precipitation and (2) estimating seasonal precipitation by disaggregating water year precipitation using a k-nearest neighbor (KNN) nonparametric technique for 29 climate divisions encompassing the Colorado River Basin. Water year precipitation data from 1900 to 2008 are subdivided into four seasons (i.e., autumn, winter, spring, and summer). Two statistical tests (Mann-Kendall and Spearman's rho) are used to evaluate trend changes, and a rank sum test is used to identify the step change in seasonal precipitation. The results indicate a decrease in the upper basin and an increase in the lower basin winter precipitation resulting from an abrupt step change. The effect of El Nino-Southern Oscillations in relation to seasonal precipitation is also evaluated by removing the historic El Nino events. Decreasing winter and spring season precipitation trends for the upper basin are not linked to El Nino. Corroborating evidence of changes in seasonal precipitation is established by analyzing the trends in snow telemetry (SNOTEL) data and streamflow at the Lees Ferry gauge. KNN disaggregation results indicate satisfactory seasonal precipitation estimates during winter and spring seasons compared to autumn and summer seasons, and the superiority of KNN results is established when compared with the first-order periodic autoregressive parametric approach. The analysis of seasonal changes and estimates of precipitation can help water managers to better manage the water resources in the Colorado River Basin.