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Title: Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Abstract

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km2. Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfallmore » volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.« less

Authors:
ORCiD logo [1];  [2];  [3];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Iowa, Iowa City, IA (United States)
  3. Berkshire Hathaway Specialty Insurance, San Ramon, CA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1843668
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Advances in Water Resources
Additional Journal Information:
Journal Volume: 161; Journal ID: ISSN 0309-1708
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Radar-rainfall error; Resolution; Width function; Basin-wide rainfall volume error; MRMS; IFC

Citation Formats

Ghimire, Ganesh R., Krajewski, Witold F., Ayalew, Tibebu B., and Goska, Radoslaw. Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs. United States: N. p., 2022. Web. doi:10.1016/j.advwatres.2022.104145.
Ghimire, Ganesh R., Krajewski, Witold F., Ayalew, Tibebu B., & Goska, Radoslaw. Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs. United States. https://doi.org/10.1016/j.advwatres.2022.104145
Ghimire, Ganesh R., Krajewski, Witold F., Ayalew, Tibebu B., and Goska, Radoslaw. Sat . "Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs". United States. https://doi.org/10.1016/j.advwatres.2022.104145. https://www.osti.gov/servlets/purl/1843668.
@article{osti_1843668,
title = {Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs},
author = {Ghimire, Ganesh R. and Krajewski, Witold F. and Ayalew, Tibebu B. and Goska, Radoslaw},
abstractNote = {Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km2. Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.},
doi = {10.1016/j.advwatres.2022.104145},
journal = {Advances in Water Resources},
number = ,
volume = 161,
place = {United States},
year = {Sat Jan 29 00:00:00 EST 2022},
month = {Sat Jan 29 00:00:00 EST 2022}
}

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