| description abstract | Streamflow records from hydrologic models are attractive for use in operational hydrology, such as a streamflow frequency analysis. The amount of bias inherent to simulated streamflow from hydrologic models is often unknown, but it is likely present in derivative products. Therefore, a workflow may help determine where streamflow frequency analysis is credibly feasible from simulated streamflow and allow for a systematic way to assess and correct for bias. The proposed workflow consists of hydrologically matching model output locations with streamflow-gauging station (stream gauge) locations, computing the desired statistic from the simulated and observed streamflow record, computing the differences between the simulated and observed statistic (i.e., the bias), and constructing generalized additive models (GAMs) from the differences to determine bias corrections. The US Geological Survey, in cooperation with the Gulf Coast Ecosystem Restoration Council and the US Environmental Protection Agency, is testing the proposed workflow on a low-streamflow frequency (LFF) analysis. Simulated streamflows for the LFF analysis were sourced from a machine-learning model that estimated daily streamflow at Level-12 hydrologic unit code (HUC12) pour points (outlets) in the southern and southeastern US for 1950–2010. The comparison data set consists of 497 stream gauges that are coincident with a HUC12 outlet. The simulated LFF statistics were being overestimated on average; thus, there are limits to using simulated streamflow for frequency analysis. The magnitude of the overprediction generally increases where no-flow conditions are common. Bias corrections determined from the GAMs decreased the magnitude of bias observed in the simulated LFF statistics on average, suggesting it is feasible to expand the operational use of simulated streamflows to frequency analyses with the proposed workflow. The proposed workflow could be advantageous to practitioners interested in leveraging existing and future simulated streamflow data sets with regional and or global coverage. Globally, the direct observations of streamflow needed to characterize the likelihood of a hydrologic event (such as a flood or drought) are not readily available, and hydrologic models that predict streamflow at ungauged locations can ostensibly solve this problem. However, assumptions used to create these models could affect the accuracy of the results from streamflow analyses conducted with the modeled data. To address this issue, a workflow is proposed to assess and correct for any biases. The workflow involves matching the model output locations with streamflow-gauging station locations where there are direct observations, computing the desired streamflow statistic from the modeled and observed streamflow records, calculating the differences between them, and constructing models to determine correction values. The proposed workflow was tested on modeled streamflow data from the southern and southeastern US, and the results showed that there is some potential in using the predicted streamflow to solve operational problems like the statistical characterization of drought events. | |
