A decentralized method is proposed to estimate the two-dimensional horizontal ocean current field using the underwater acoustic sensor networks (UASNs), termed the "UASN-decentralized" method, which integrates the state-of-the-art ocean current field estimation techniques for UASNs: triangle-division-based travel time difference tomography and a spatiotemporal autoregressive model of ocean current dynamics. Moreover, the UASN-decentralized method employs a single-time scale consensus+innovations distributed estimator, called the "distributed information Kalman filter," to perform decentralized estimation and tracking. Given the redundancy of travel time differences when using UASN-based tomography, sensor nodes are classified into two types (i.e., type I and type II) to perform different tasks to reduce computations. A shortest-path-based consensus weight matrix is designed to accommodate fast-varying ocean dynamics. More communication rounds after each sensing are studied as an extension of the adopted single-time scale distributed estimator. Synthetic data are used to verify the decentralized method. Monte Carlo simulations show the feasibility of the proposed method and its robustness to measurement error related problems. With an increased number of communication rounds, the proposed method can also work well for fast-varying dynamics or a lowered sensor measurement rate.