Identification of the best multi-model combination for simulating river discharge

- Calibrated and validated data of eight hydrological models are used.
- Ten multi model ensemble methods are evaluated for streamflow estimation.
- A new SCORE is proposed for evaluating multi model ensemble.
- An ensemble of size five is found to perform well for the study catchment.
- The best ensemble outperforms even the best individual models.

SummaryThis paper focuses on the selection of the best multi-model ensemble method that is subsequently used to create an ensemble for the discharge estimation in a catchment of the Mahanadi river basin in India. Ten different multi-model ensemble methods, viz., mean, median, trimmed mean, unconstrained and constrained multiple linear regression, weighted mean based on calibration performance (two variants), linear programming, simple model average and multi model super ensemble, are compared using calibrated and validated data of eight popular hydrological models, MIKE SHE, SWAT, HEC-HMS, AWBM, SIMHYD, SACRAMENTO, SMAR and TANK. Constrained multiple linear regression (MLR_C) method is found to be the most suitable multi-model ensemble method for the study area. MLR_C method is subsequently used to develop 189 possible multi-model ensembles. These ensembles are evaluated for categorical and temporal accuracy, using a proposed SCORE that includes normalized relative operating characteristic (ROC) area and normalized number of skillful days. The results show that an ensemble having five models, one physically based (SWAT) and four conceptual (AWBM, SIMHYD, SACRAMENTO and SMAR), performs the best for the chosen catchment. The best performing ensemble also outperforms all eight individual models in simulating the observed discharge and flow volume. Furthermore, uncertainty in simulating river discharge due to all sources collectively is analyzed through uncertainty analysis.