Toward optimal rainfall – Hydrologic QPE correction in headwater basins

Journal Article (Journal Article)

Large errors in Quantitative Precipitation Estimates (QPE) tied to remote-sensing retrieval algorithms remain a challenge especially in complex terrain with fast hydrologic response. We propose a new framework to derive dynamic hydrologic corrections of rainfall in headwater basins that enforces water budget closure, and distributes transient rainfall corrections by Lagrangian backtracking along runoff trajectories constrained by realistic time-of-travel distributions. Downscaled QPE products (250 m resolution) are applied first as input to a distributed hydrologic model to predict runoff trajectories and the event hydrograph at the basin's outlet. Second, time-varying rainfall corrections are derived from the residuals between predicted and observed discharge at the outlet. Finally, the corrections are spatially distributed following the runoff trajectories backward (i.e. trajectories are used as streaklines originating at the basin's outlet). Because nonlinear interactions between rainfall, runoff and storage are transient, the corrections are applied recursively until the shape and volume of the predicted hydrograph are stable. The framework is applied to ground-based (e.g. Stage IV) and satellite-based remote-sensing QPEs (e.g. IMERG) associated with the 49 largest floods 2008–2018 in the Southern Appalachian Mountains, USA. The results show improvements in hydrograph prediction efficiency skill at 15 min timescale from −0.5 to 0.6 on average and up to 0.9 for warm season events, bounding event runoff volume errors with a mean of 3%, and reducing time to peak errors by half an hour on average. Corrected QPEs exhibit nearly perfect correlation and no bias at high elevation gauge locations. Uncertainty in the water budget closure at the event scale is less than the uncertainty in streamflow measurements. Error attribution shows strong organization of QPE corrections according to seasonal weather and rainfall regime, thus providing a path to generalization to ungauged mountain basins.

Full Text

Duke Authors

Cited Authors

  • Liao, M; Barros, AP

Published Date

  • September 15, 2022

Published In

Volume / Issue

  • 279 /

International Standard Serial Number (ISSN)

  • 0034-4257

Digital Object Identifier (DOI)

  • 10.1016/j.rse.2022.113107

Citation Source

  • Scopus