Preprints
https://doi.org/10.5194/hess-2021-278
https://doi.org/10.5194/hess-2021-278

  08 Jul 2021

08 Jul 2021

Review status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID: a method for precipitation temporal downscaling for sediment delivery assessment

Pedro Henrique Lima Alencar1,2, Eva Nora Paton1, and José Carlos de Araújo2 Pedro Henrique Lima Alencar et al.
  • 1Technische Universität Berlin, Institut für Ökologie, Ernst-Reuter-Platz 1 10587 Berlin, Germany
  • 2Universidade Federal do Ceará, Departamento de Engenharia Agrícola, Campus do Pici Fortaleza, Brazil

Abstract. Scarcity of precipitation data is yet a problem in erosion modelling, especially when working in remote and data scarce areas. While much effort was made to use remote sensing and reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with area varying from 10−3 to 10+2 km2 and broad timespan of monitoring (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for the rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21 % and a Nash-Sutcliffe Efficiency of 0.96 (rather than 105 % and −4.49, respectively

Pedro Henrique Lima Alencar et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-278', Anonymous Referee #1, 17 Aug 2021
    • AC1: 'Reply on RC1', Pedro Henrique Lima Alencar, 23 Aug 2021
  • RC2: 'Comment on hess-2021-278', Anonymous Referee #2, 26 Aug 2021
    • AC2: 'Reply on RC2', Pedro Henrique Lima Alencar, 05 Sep 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-278', Anonymous Referee #1, 17 Aug 2021
    • AC1: 'Reply on RC1', Pedro Henrique Lima Alencar, 23 Aug 2021
  • RC2: 'Comment on hess-2021-278', Anonymous Referee #2, 26 Aug 2021
    • AC2: 'Reply on RC2', Pedro Henrique Lima Alencar, 05 Sep 2021

Pedro Henrique Lima Alencar et al.

Data sets

MEDRID-SyPOME Pedro Alencar, Eva Paton José Carlos de Araújo https://github.com/pedroalencar1/MEDRID-SyPOME

Model code and software

MEDRID-SyPOME Pedro Alencar, José Carlos de Araújo https://github.com/pedroalencar1/MEDRID-SyPOME

Pedro Henrique Lima Alencar et al.

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Short summary
Knowing how long and how fast it rained on a particular day is not often an easy (or cheap) task. It requires equipment and constant monitoring. It can be even harder if you live in an isolated area or if the day you are interested in is so much in the past that such pieces of equipment were not even in the market. In this paper, we propose a new way to assess such information and also show how it can help to model sediment transport and siltation in watersheds.