Techniques to evaluate the modifier process of National Weather Service flood forecasts

Abstract

The operational hydrologists of the United States’ National Weather Service (NWS) develop river forecasts as guidance for those at risk of flood damage and update those flood forecasts in real-time as more information becomes available. To do so they rely on experience and intuition to adjust the inputs, state variables, and parameters of hydrologic models. NWS hydrologists use the term “modifiers” to refer collectively to these adjustments. This paper demonstrates the development and application of tools (statistical and graphical) to aid operational hydrologists in the achievement of accurate flood forecasts. Analysis of variance (ANOVA) identifies the relative contribution to forecast uncertainty of each modifier. Heat map visualizations illustrate for operational hydrologists the basin, lead-time, and season-specific effects of their modifiers choices. The tools provide operational hydrologists with insight into which of three commonly applied modifiers (precipitation, soil moisture, and unit hydrograph shape) are most likely to provide improvement in flood forecast accuracy. The tools are demonstrated for a case study of four watersheds within in the Ohio River Valley, using data for flood events sampled from 1990 to 2018. The findings of this research show that operational hydrologists in the Ohio River Basin would do well apply no modifiers in the winter (leaving hydrologic input variables and parameters at baseline values). And though the forecast might be improved by real-time adjustments to the unit hydrograph in summer months, recommendations for particular unit hydrograph modification levels cannot be made with confidence. These findings call into question the modifier adjustment program as a standard process. In the evaluated cases, modifiers do not systematically improve flood forecasts. Improvement may be more efficiently achieved through better calibration of hydrologic models or techniques for reduction of precipitation uncertainty.