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A Design Peak Flow Estimation Method for Medium‐Large and Data‐Scarce Watersheds With Frontal Rainfall 1
Author(s) -
Muñoz Enrique,
Arumí José Luis,
Vargas José
Publication year - 2012
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2011.00622.x
Subject(s) - watershed , environmental science , hydrology (agriculture) , estimation , flow (mathematics) , benchmark (surveying) , streamflow , statistics , meteorology , mathematics , computer science , geography , geology , drainage basin , engineering , cartography , geometry , geotechnical engineering , systems engineering , machine learning
Muñoz, Enrique, José Luis Arumí, and José Vargas, 2012. A Design Peak Flow Estimation Method for Medium‐Large and Data‐Scarce Watersheds With Frontal Rainfall. Journal of the American Water Resources Association (JAWRA) 48(3): 439‐448. DOI: 10.1111/j.1752‐1688.2011.00622.x Abstract: We developed a reliable peak flow estimation method for the design of hydraulic structures. The method is valid in medium‐large watersheds (100‐5,000 km 2 ) located in Chile between 32°45′ and 43°50′S, with scarcity of hydro‐meteorological information, and where frontal rainfall prevails. The proposed method requires only rainfall data and geomorphologic descriptors as inputs, and relates the instant peak flow with the time of concentration rainfall flux (the contributing watershed area multiplied by the rainfall). The parameters of the model were defined with peak flows obtained from statistical analyses of historical fluviometric records from 25 watersheds. The quality of the proposed method is evaluated by applying it to three external watersheds different from those used to define model parameters, and comparing it with three other indirect methods and with peak flows obtained from statistical analyses, which were also used as the benchmark. The proposed method estimates peak flows with mean differences of less than 10%, which is two times less than other similar indirect methodologies, making it a recommendable option for estimating design peak flows.