Temporal Networks‐Based Approach for Nonstationary Hydroclimatic Modeling and its Demonstration With Streamflow Prediction

Abstract

AbstractLack of stationarity in most of the hydroclimatic variables is no longer a topic of debate rather a reality. It may be hypothesized that alternative methodologies are needed to deal with such nonstationarity and to improve the skill of hydroclimatic modeling/prediction. We propose the concept oftemporal networksin hydroclimatic modeling as a potential solution to this problem. As a typical case, complex association among different hydroclimatic variables and streamflow is considered as an illustrative problem. Evolution of temporal networks over time, obtained through Graphical Modeling (GM), depicts the changes in the model inputs as well as model parameters over time. The proposed concept indicates that the time interval after which the model needs to be updated/recalibrated, referred to as Optimum Recurrence Interval (ORI), is problem specific and is optimized to achieve the best model performance. The proposed concept not only depicts a notable change in the potential predictors for the high and low flow months, but also establishes the different extent of temporal variability for different months, and hence the ORI of model recalibration. As compared to its time‐invariant counterpart, the temporal networks‐based approach shows higher efficacy in capturing the extreme flow events due to its inherent time‐varying characteristics. We recommend the concept of temporal networks to be promising in the context of climate change to capture the time‐varying association. In general, the concept can be applied to other hydroclimatic variables where a time‐varying association is expected due to various reasons including the impacts of climate change.