Preprints
https://doi.org/10.5194/egusphere-2023-1755
https://doi.org/10.5194/egusphere-2023-1755
21 Aug 2023
 | 21 Aug 2023

Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil

Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe

Abstract. Regionalization methods dependent on hydrological models comprise techniques for transferring calibrated parameters in instrumented watersheds (donor basins) to non-instrumented watersheds (target basins). This study aims to evaluate regionalization methods for transferring GR4J parameters and predict river flow in catchments from the south of Brazil. We created a dataset for Paraná state with daily hydrological time series (precipitation, evapotranspiration, and river flow) and watershed physiographic and climatological indices for 126 catchments. Rigorous quality control techniques were applied to recover the rainfall history from 1979 to 2020, and manual efforts were made to georeference the fluviometric stations. The regionalization methods compared in this study are based on: simple spatial proximity, physiographic-climatic similarity and regression by Random Forest. Direct regression of Q95 was calculated using Random Forest and compared with indirect methods, i.e. using regionalization of GR4J parameters. A set of 100 basins were used to train the regionalization models and another 26 catchments, pseudo non-instrumented, were used to evaluate and compare the performance of regionalizations. The GR4J model showed acceptable performances for the sample of 126 catchments, 65 % of watersheds presented log-transformed Nash-Sutcliffe coefficient greater than 0.70 during validation period. According to evaluation carried out for the sample of 26 basins, regionalization based on physiographic-climatic similarity showed to be the most robust method for prediction of daily and Q95 reference flow in basins from Paraná state. When increasing the number of donor basins, the method based on spatial proximity has comparable performance to the method based on physiographic-climatic similarity. Based on the physiographic-climatic characteristics of the basins, it was possible to classify 6 distinct groups of watersheds in Paraná. The basins showed similarities in their size, forest cover, urban area, number of days with more than 150 mm of precipitation, and average duration of consecutive dry days.

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Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-1755', John Ding, 23 Aug 2023
    • AC1: 'Reply on CC1', Emilio Graciliano Ferreira Mercuri, 28 Aug 2023
  • RC1: 'Comment on egusphere-2023-1755', Anonymous Referee #1, 06 Oct 2023
    • AC2: 'Reply on RC1', Emilio Graciliano Ferreira Mercuri, 13 Oct 2023
  • RC2: 'Comment on egusphere-2023-1755', Juraj Parajka, 20 Oct 2023
    • AC3: 'Reply on RC2', Emilio Graciliano Ferreira Mercuri, 10 Nov 2023
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe

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Short summary
The authors compared different regionalization methods for river flow prediction in watersheds with few data. We collected data on precipitation, evapotranspiration, river flow, and geographical and climatic factors for 126 catchments in the Paraná state, Brazil. The regionalization method based on physiographic-climatic similarity showed to be the most robust for predicting daily and Q95 reference flow. We also found patterns in data, grouping locations based on similarities.