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This study evaluates the performance of an enhanced Kohonen self-organizing map (eKSOM)-based bias-correction technique and compares the results with a copula-based bias-correction approach and the traditional quantile mapping (QM) to correct the daily multi-model ensemble short- to medium-range rainfall forecast products of the India Meteorological Department (IMD-MME) in the Hirakud Reservoir catchment in India. The copula and eKSOM outperformed the raw IMD-MME and QM rainfall across all lead times. While both the copula and eKSOM-based bias-corrected forecasts could satisfactorily provide the temporal pattern of the observed rainfall, the corresponding eKSOM-based forecasts presented better skills in capturing seasonality in the observed rainfall. The streamflow at one- to five-day lead times are simulated by forcing a suitable hydrological model, MIKE11 NAM (Nedbør Afstrømnings Model)-HD (Hydrodynamic) with the raw and bias-corrected rainfall inputs. The overall performance evaluation reveals significant improvement in both the rainfall and streamflow forecasts by copula and eKSOM, with the latter performing most accurately at higher lead times.

hydrological sciences journal

Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts