A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

Streamflow reconstruction involves estimating streamflow volumes in the distant past using statistical techniques and long range climate proxies. Conventionally, this is done using linear regression techniques which model streamflow as a linear function of climatic inputs. These models do not consider catchment dynamics, although in reality, streamflow volume depends on both climatic inputs and the state of the catchment (whether it is wet or dry). Here, we contribute a novel reconstruction technique, based on linear dynamical systems, that models explicitly the catchment state and its interaction with climatic inputs. We apply it to a case study of the Ping River in northern Thailand. Our model improves reconstruction skills significantly over linear regression. Furthermore, the model’s state variable reveals a history in which the catchment shifted between wet and dry regimes. The model can also be used readily to generate streamflow scenarios, which are useful for reservoir operation studies. With only a marginal increase in computation but many desirable features, our model can replace linear regression. Our work has strengthened the values and widened the applications of streamflow reconstruction.