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Prediction of flow duration curves for ungauged basins

Published:2017-02 Issue: Volume:545 Page:383-394
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Atieh Maya,Taylor Graham,M.A. Sattar Ahmed,Gharabaghi Bahram

Funder

GreenBug Energy

Ontario Ministry of Natural Resources

Ontario Power Generation

Grand River Conservation Authority

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference93 articles.

1. Effects of water flow regulation on ecosystem functioning in a Mediterranean river network assessed by wood decomposition;Abril;Sci. Total Environ.,2015

2. Information theory as an extension of the maximum likelihood principle;Akaike,1973

3. A neural net model for environmental flow estimation at the Ebro River Basin, Spain;Alcazar;J. Hydrol.,2008

4. Map correlation method: selection of a reference stream gauge to estimate daily stream flow at ungauged catchments;Archfield;Water Resour. Res.,2010

5. Relative impact of anthropogenic modifications versus climate change on the natural flow regimes of rivers in the Northern Rocky Mountains, United States;Arrigoni;Water Resour. Res.,2010

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1. Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins;Water Resources Research;2024-07

2. Connecting flow duration curve and precipitation duration curve based on the relationship deduced from machine learning in the watersheds of northern China;Journal of Hydrology;2024-05

3. Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution;Hydrological Processes;2024-02

4. Case study of continental‐scale hydrologic modeling's ability to predict daily streamflow percentiles for regulatory application;JAWRA Journal of the American Water Resources Association;2024-01-12

5. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches;HydroResearch;2024

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