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- Title
Prediction of Streamflow Recession Curves in Gauged and Ungauged Basins.
- Authors
Singh, Shailesh Kumar; Griffiths, George A.
- Abstract
Prediction of the time for perennial outflow from a natural basin to recede from the mean to some low flow value is an important practical and difficult problem in water resource management. This study aims to gain further understanding of this complex problem and to put forward new practical and accurate methods for predicting flow recessions between nominated limits in both gauged and ungauged basins. For a gauged basin, a three parameter recession model is employed to estimate the recession time, from day‐to‐day, as flow recedes from mean flow using previously measured site recession curves and a library of recession curve shapes generated by the RObust Parameter Estimation algorithm. The model is tested using data from 10 New Zealand basins which are diverse in low flow hydrological behavior and yields a Median Absolute Error (MAE) of 1 day. Another new model is also developed to predict recession time in an ungauged basin using catchment characteristics and information from master recession curves in a suite of 10 reference basins geologically and hydrologically similar to the ungauged basin, as assessed by a Random Forest model. Model performance is robust with a MAE of 1 day and the models advance the use of past flow records to enable more accurate predictions to be obtained. They can be applied elsewhere with confidence although further testing is desirable. Key Points: A methodology for successive prediction in real time of the fall recession curve ranging from mean flow values is presentedAccess to a library of modeled recorded as well as generated possible recession curves enables close matching of real time measurementsPrediction of recession time at an ungauged site is achievable by using information from reference basins of similar hydrological character
- Subjects
NEW Zealand; RECESSIONS; STREAMFLOW; GAGING; RANDOM forest algorithms; WATERSHEDS; PARAMETER estimation; CURVES
- Publication
Water Resources Research, 2021, Vol 57, Issue 11, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2021WR030618