In this paper, we develop a simple analysis method to infer some properties of the watershed processes from daily streamflow data. The method is built on a simple streamflow model with a link to rainfall stochasticity, which characterizes the streamflow as a series of overlapping gamma distribution-shaped pulses. The key premise of the method is that the complex streamflow processes can be effectively captured by simply dividing streamflow into two regimes. Specifically in this method, the gamma pulse model is applied separately to low- and high-flow regimes. We demonstrate the application of the method to five watersheds and show that it is capable of capturing at least two important statistical properties of streamflow, namely the probability density function and the autocorrelation function for wide ranges of values (i.e., from low to large flows and time lags, respectively).