05 Apr 2023
 | 05 Apr 2023

Accounting for Precipitation Asymmetry in a Multiplicative Random Cascades Disaggregation Model

Kaltrina Maloku, Benoit Hingray, and Guillaume Evin

Abstract. Analytical Multiplicative Random Cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies simple scaling laws to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on temporal scale, precipitation intensity and a so-called precipitation asymmetry index.

Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence in the model leads to a drastic improvement of model performance for all statistics related to precipitation temporal persistence (wet/dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry/wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs.

The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.

Kaltrina Maloku et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-544', Anonymous Referee #1, 12 May 2023
  • RC2: 'Comment on egusphere-2023-544', Anonymous Referee #2, 12 May 2023

Kaltrina Maloku et al.

Kaltrina Maloku et al.


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Short summary
High-resolution precipitation, needed for many applications in hydrology, are typically rare worldwide. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, Multiplicative Random Cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.