IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Seasonal Precipitation forecasting with large scale climate predictors: A hybrid  wavelet multiresolution -NARX scheme

Rim Ouachani1, Zoubeida Bargaoui2, and Taha Ouarda3
Rim Ouachani et al.
  • 1Higher Institute of Transport and Logistics of Sousse - Sousse University, Tunisia
  • 2National Engineering School of Tunis - El Manar University, Tunisia
  • 3National Institute of Scientific Research - ETE, Canada

Much of northern Tunisia regularly experiences extremes of drought and flooding, with high rainfall variability. Development of reliable and accurate seasonal rainfall forecasts can provide valuable information to help mitigate some of the outcome of floods and enhance water management, particularly for agriculture. Ensemble monthly rainfall forecasts are carried out over horizons ranging from 1 to 6 months using a hybrid wavelet neural network model. The hybrid model called MWD-NARX based on a non-linear autoregressive network with exogenous inputs (NARX) coupled to multiresolution wavelet decomposition (MWD) is developed in this work. First, The MWD is used to decompose the data into different components on various time scale. Then to predict each precipitation decomposition the NARX ensemble model is employed. For an operational forecasting, the forecasts obtained from the decompositions are summed to represent the true precipitation forecast value. The outcomes of MWD-NARX are compared with Artificial Neural Networks (ANN). The seasonal forecasts of average precipitation by sub-basins of the Medjerda river basin are carried out. Large scale climate teleconnection indicators of ENSO, PDO, NAO and Mediterranean Oscillation were used as inputs to the model. The results indicate that exogenous inputs like climatic indices clearly improves the accuracy of forecasts in terms of the coefficient R2 on 82% of SBVs compared to a model that uses only climate indices as inputs with 1 month delay time. It increases then the forecast lead-time up to 6 months. The same conclusion is made when compared to an ANN. The correlation coefficient between observed and forecasted monthly precipitation is ranging from 0.5 to 0.8. It was also found that the MWD-NARX underestimates the extremes. The spatial variability of the quality of the forecasts depends mainly on the local effect of precipitation more than on the quality of the hydrological data observed on the forecasts. It can be concluded that exogenous inputs like climate indices can add some additional information to enhance monthly precipitation forecasts at longer lead-times. The forecasting model coupled to data pre-processing method made it possible to produce very satisfactory forecasts of non-stationary data by extracting significant modes of variability.

How to cite: Ouachani, R., Bargaoui, Z., and Ouarda, T.: Seasonal Precipitation forecasting with large scale climate predictors: A hybrid  wavelet multiresolution -NARX scheme, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-732, https://doi.org/10.5194/iahs2022-732, 2022.