Multi-model ensemble approaches for simulation of evapotranspiration of karst agroforestry ecosystems

Water shortages frequently occur in karst areas, and there is an urgent need to quantify water fluxes to provide information for sustainable management of water resources. Thus, a variety of models have been developed to simulate the water balance process, including actual evapotranspiration (ETc), which is a key variable for linking water and energy cycles. However, high heterogeneity of the land surface makes it hard to get precise complete set of parameters for single model, and a single model often has uncertainties in simulating evapotranspiration in karst regions. Therefore, this study integrated three well-known individual models (Penman-Monteith, PM; Priestley and Taylor, PT; and Shuttleworth-Wallace, SW) with two multi-model ensemble approaches (Bayesian model averaging, BMA; and simple model average, SA) to enhance ETc modeling in a subtropical humid karst catchment. Results show that: 1) The individual models show different strengths for different ecosystems, which could be attributed to differences in the underlying landscape surface characteristics; 2) individual models exhibited seasonal uncertainties. For example, simulated ETc (ETs) by the PM and PT model was lower than ETo (observed ETc) during November-March but higher than during April-October for forest-grass mixed and grass ecosystems; 3) Two multi-model ensemble approaches (R2 ≥ 0.85) performed better than any individual model (R2 ≤ 0.85) most likely because multi-model ensemble approaches reduce model uncertainties by weakening the bias of individual models. •Penman-Monteith, Priestley and Taylor, and Shuttleworth-Wallace models exhibited seasonal uncertainties.•Multi-model ensemble approaches, integrating two or more models, performed better than individual models.•The contribution of Shuttleworth-Wallace model to Bayesian Model Averaging approach was the highest.