Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management

Regime-based approach recently becomes an important strategy while considering aquatic ecosystems in environmental flow management. The key element for supporting this strategy is long streamflow data which is usually not available for determining natural flow regimes. This study uses a back-propaga...

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Bibliographic Details
Published in:Water resources management 2016-10, Vol.30 (13), p.4571-4584
Main Authors: Yang, Han-Chung, Suen, Jian-Ping, Chou, Shih-Kai
Format: Article
Language:English
Subjects:
Aquatic ecosystems
Aquatic life
Atmospheric Sciences
Back propagation
Civil Engineering
Correlation coefficient
Creeks & streams
Drainage
Earth and Environmental Science
Earth Sciences
Engineering
Environment
Environment management
Environmental management
Estimates
Geomorphology
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrologic data
Hydrologic sciences
Hydrology/Water Resources
Indicators
Mathematical models
Natural flow
Neural networks
Rainfall
River ecology
Simulation
Stations
Stream discharge
Stream flow
Studies
Water resources
Water resources management
Water runoff
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Summary:Regime-based approach recently becomes an important strategy while considering aquatic ecosystems in environmental flow management. The key element for supporting this strategy is long streamflow data which is usually not available for determining natural flow regimes. This study uses a back-propagation network to estimate ungauged natural flow regimes. A set of the upper reaches of Taiwan’s 42 flow stations with non-human control streamflow and at least 20 years daily flow data is used to quantify the natural flow regimes using 31 Indicators of Hydrologic Alteration (IHA). Watershed geomorphologic characteristic factors and rainfall parameters are used to classify homogeneous flow regime areas. The results show that there are three types of flow regimes from the flow stations, and each group of indicators in the IHA has different correlations with different geomorphologic characteristic factors and rainfall parameters. The results of using an artificial neural network model to estimate IHA show that the group average percent error fell from 21 % to 8 % and the average correlation coefficient was over 0.7, indicating that the model presented in this study is able to accurately estimate the natural flow regime in ungauged stations. Instead of predicting daily streamflow, this study estimates indicator values for ease of ecological water resources management.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-016-1437-0