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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Quantifying regional low flows under data scarce conditions
Tarekegn Dejen Mengistu1,2,3, Il-Moon Chung2,3, Zenobia Talpur2,3
1Faculty of Civil and Environmental Engineering, Jimma University, Jimma, 378, Ethiopia.
Heliyon
|April 5, 2024
Summary
Estimating regional low flows is crucial for water management and ecosystem health. This study successfully identified the Generalized Pareto model for accurate low-flow quantile prediction in data-limited areas.
Area of Science:
- Hydrology
- Environmental Science
- Statistical Modeling
Background:
- Estimating low flow quantiles is vital for water resource management, ecological health, and system sustainability.
- Data-limited environments pose challenges for accurate low-flow analysis and regional quantification.
Purpose of the Study:
- To quantify regional low flows in data-limited environments.
- To identify the most suitable probability distribution model for low-flow quantile estimation.
Main Methods:
- Analysis of annual minimum 7-day instantaneous streamflow data.
- Homogeneity assessment using discordancy measures.
- Selection of the best-fit probability distribution model using Easy-Fit Statistical Software and goodness-of-fit tests (GOFs).
Main Results:
- All analyzed gaging stations formed a single homogeneous region.
- The Generalized Pareto model was identified as the most suitable distribution for predicting low flow quantiles.
- A high correlation (R² = 0.989) was observed between predicted low flows and catchment area.
Conclusions:
- The Generalized Pareto model provides a statistically sound method for estimating low flow quantiles in data-limited regions.
- Accurate low-flow estimations are valuable for environmental decision-making and mitigating drought impacts on aquatic ecosystems.
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