A Process-Conditioned and Spatially Consistent Method for Reducing Systematic Biases in Modeled Streamflow

Water resources planning often uses streamflow predictions made by hydrologic models. These simulated predictions have systematic errors that limit their usefulness as input to water management models. To account for these errors, streamflow predictions are bias corrected through statistical methods that adjust model predictions based on comparisons to reference datasets (such as observed streamflow). Existing bias correction methods have several shortcomings when used to correct spatially distributed streamflow predictions. First, existing bias correction methods destroy the spatiotemporal consistency of the streamflow predictions when these methods are applied independently at multiple sites across a river network. Second, bias correction techniques are usually built on time-invariant mappings between reference and simulated streamflow without accounting for the processes that underpin the systematic errors. We describe improved bias correction techniques that account for the river network topology and allow for corrections that account for other processes. Further, we present a workflow that allows the user to select whether to apply these techniques separately or in conjunction. We evaluate four different bias correction methods implemented with our workflow in the Yakima River basin in the northwestern United States. We find that all four methods reduce systematic bias in the simulated streamflow. The spatially consistent bias correction methods produce spatially distributed streamflow as well as bias-corrected incremental streamflow, which is suitable for input to water management models. We demonstrate how the spatially consistent method avoids creating flows that are inconsistent between upstream and downstream locations, while performing similar to existing methods. We also find that conditioning on daily minimum temperature, which we use as a proxy for snowmelt processes, improves the timing of the corrected streamflow. Significance StatementTo make streamflow predictions from hydrologic models more informative and useful for water resources management they are often postprocessed by a statistical procedure known as bias correction. In this work we develop and demonstrate bias correction techniques that are specifically tailored to streamflow prediction. These new techniques will make modeled streamflow predictions more useful in complex river systems undergoing climate change.

Automated summary

This article introduces improved bias correction techniques for correcting systematic errors in streamflow predictions from hydrologic models. By considering river network topology and accounting for other processes, these techniques achieve spatial consistency and temporal consistency in the correction. The study found that these methods can reduce systematic bias in simulated streamflow and are suitable for input to water management models. Furthermore, conditioning on daily minimum temperature as a proxy for snowmelt processes can improve the timing of the corrected streamflow.