Toward optimal rainfall for flood prediction in headwater basins – improving soil moisture initialization to close the water budget within observational uncertainty

Abstract

Study region

28 headwater basins along the latitudinal range of the Appalachian Mountains across diverse hydroclimatic and physiographic regions.

Study focus

The objective of this manuscript is to address errors in the initialization of hydrologic models by introducing a physics-based methodology to correct soil moisture (i.e., Initial Condition Correction, ICC) in a manner consistent with the Inverse Rainfall Correction (IRC) methodology proposed by Liao and Barros (2022) to improve Quantitative Precipitation Estimates (QPE). The coupled IRC-ICC framework is demonstrated using a high-resolution hydrologic model for 215 flood-producing events from 2008 to 2024 in 28 headwater basins in the Appalachians.

New hydrological insights for the region

Flood simulations using IRC-ICC QPE, and uncorrected QPE products show a median Kling-Gupta Efficiency (KGE, calculated at 15-minute intervals) of 0.86 versus 0.19, reduction of flood peak timing errors with 90 % versus 20 % of events having peak timing errors within 60 min, and median flood volume errors of 2 % versus −17 %. While the average total precipitation shows a modest increase of 6 % due to ICC, the most significant impact is on spatial variance and on the average maximum rainfall that increases by 68 %. This study establishes the coupled IRC-ICC as a robust general framework for orographic QPE correction and provides a pathway to characterizing and modeling soil moisture uncertainty ahead of extreme precipitation events at high spatial resolution O(100 m).