Identification and Regionalization of Streamflow Routing Parameters Using Machine Learning for the HLM Hydrological Model in Iowa-Reference-Cited by-同舟云学术

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Identification and Regionalization of Streamflow Routing Parameters Using Machine Learning for the HLM Hydrological Model in Iowa

Published:2022-07 Issue:7 Volume:14 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Velásquez Nicolás¹^ORCID,Mantilla Ricardo²^ORCID,Krajewski Witold¹³^ORCID,Quintero Felipe¹^ORCID,Zanchetta André D. L.⁴

Affiliation:

1. Iowa Flood Center University of Iowa Iowa City IA USA

2. Department of Civil Engineering University of Manitoba Winnipeg MB Canada

3. Department of Civil and Environmental Engineering University of Iowa Iowa City IA USA

4. Department of Civil Engineering McMaster University Hamilton ON Canada

Funder

Mid-America Transportation Center, University of Nebraska-Lincoln

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002855

Reference41 articles.

1. Random forests and decision trees;Ali J.;International Journal of Computer Science Issues,2012

2. Global-scale regionalization of hydrologic model parameters

3. Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

4. Rainfall-Runoff Modeling of Ungauged Catchments

5. A spatially distributed flash flood forecasting model

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1. Contiguous United States hydrologic modeling using the Hillslope Link Model TETIS;JAWRA Journal of the American Water Resources Association;2024-08-11

2. Ensemble Kalman Inversion for upstream parameter estimation and indirect streamflow correction: A simulation study;Advances in Water Resources;2023-11

3. Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale;ACS ES&T Water;2023-08-23

4. Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning;Remote Sensing;2022-09-15

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