Verification of Long-Term Ensemble Evapotranspiration Hindcast Using a Conditional Nonlinear Optimal Parameter Perturbation Ensemble Prediction Method on the Tibetan Plateau

In this research, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) method is employed to propose an ensemble prediction method titled as the conditional nonlinear optimal perturbation related to parameters ensemble prediction (CNOP-PEP) method. Within the CNOP-PEP method, all ensemble members are generated by the CNOP-P method. To explore the operability and validity of the CNOP-PEP method, long-term evapotranspiration (ET) hindcast skill is evaluated at 13 sites on the Tibetan Plateau (TP) during the period of 2001-2018 with a land surface model. Two traditional ensemble prediction methods (the stochastically perturbed parameters (SPP) method and the one-at-a-time (OAT) method) are also applied to assess the ET hindcast skills. The numerical results indicate that the CNOP-PEP method shows the best hindcast skill for estimated ET at 13 stations on the TP among the three methods (CNOP-PEP, OAT, and SPP methods) during the period of 2001-2018. This suggests that the CNOP-PEP method is helpful and effective for long-term and interannual hydrological predictions, such as long-term and interannual ET predictions. The numerical results also indicate that ensemble members with certain special properties of fast-growing types should be selected to implement ensemble prediction compared to these ordinary and traditional samples. The CNOP-PEP method could supply special samples to improve the ensemble prediction and hindcast skill of hydrological cycle. To enhance the ensemble prediction skill of evapotranspiration (ET) on the Tibetan Plateau, physical parameters in a numerical model are perturbed using ensemble techniques. Ensemble members about the physical parameters are generated by three ensemble techniques. The numerical results show the ensemble hindcast skills of ET are improved by using ensemble techniques. And, ensemble members with high hindcast skill are a rapidly evolving type of parametric perturbations, which could cause large hindcast uncertainties compared to the reference state, using the CNOP-PEP method. Hence, the CNOP-PEP method could be employed to improve the hindcast or prediction skill of hydrological cycle to reduce the uncertainties in model physical parameters. And, the uncertainties in model physical parameters also play a key role in ensemble prediction of hydrological cycle. The model physical parameters errors should be paid attention to enhance the forecast and hindcast skill of hydrological cycle. The ensemble hindcast skills of evapotranspiration (ET) using three ensemble prediction methods are evaluated Higher ensemble forecasting skills with a conditional nonlinear optimal perturbation related to parameters ensemble prediction method are found Physical parameter errors with maximum impact play a key role in ensemble prediction of hydrological cycle