A novel approach to infer streamflow signals for ungauged basins

In this paper, we present a novel paradigm for inference of streamflow for ungauged basins. Our innovative procedure fuses concepts from both kernel methods and data assimilation. Based on the modularity and flexibility of kernel techniques and the strengths of the variational Bayesian Kalman filter and smoother, we can infer streamflow for ungauged basins whose hydrological and system properties and/or behavior are non-linear and non-Gaussian. We apply the proposed approach to two watersheds, one in California and one in West Virginia. The inferred streamflow signals for the two watersheds appear promising. These preliminary and encouraging validations demonstrate that our new paradigm is capable of providing accurate conditional estimates of streamflow for ungauged basins with unknown and non-linear dynamics.