Peng Bai

Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions

AbstractPotential evapotranspiration (PET), which determines the upper limit of actual evapotranspiration (AET), is a necessary input in monthly hydrological models. In this study, the sensitivities of monthly hydrological models to different PET inputs are investigated in 37 catchments under different climatic conditions. Four types of PET estimation methods (i.e., Penman–Monteith, Hargreaves–Samani, Jensen–Haise, and Hamon) give significantly different PET values in the 37 catchments. However, similar runoff simulations are produced based on different PET inputs in both nonhumid and humid regions. It is found that parameter calibration of the hydrological model can eliminate the influences of different PET inputs on runoff simulations in both nonhumid and humid regions. However, the influences of parameter calibration on the simulated water balance components, including AET and water storage change (WSC), are different in nonhumid and humid regions. In nonhumid regions, simulated runoff, AET, and WSC ar...

Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

The Global Land Data Assimilation System (GLDAS) project estimates long-term runoff based on land surface models (LSMs) and provides a potential way to solve the issue of non-existent streamflow data in gauge-sparse regions such as the Tibetan Plateau (TP). However, the reliability of GLDAS runoff data must be validated before being practically applied. In this study, the streamflows simulated by four LSMs (CLM, Noah, VIC, and Mosaic) in GLDAS coupled with a river routing model are evaluated against observed streamflows in five river basins on the TP. The evaluation criteria include four aspects: monthly streamflow value, seasonal cycle of streamflow, annual streamflow trend, and streamflow component partitioning. The four LSMs display varying degrees of biases in monthly streamflow simulations: systematic overestimations are found in the Noah (1.74 ≤ bias ≤ 2.75) and CLM (1.22 ≤ bias ≤ 2.53) models, whereas systematic underestimations are observed in the VIC (0.36 ≤ bias ≤ 0.85) and Mosaic (0.34 ≤ bias ≤ 0.66) models. The Noah model shows the best performance in capturing the temporal variation in monthly streamflow and the seasonal cycle of streamflow, while the VIC model performs the best in terms of bias statistics. The Mosaic model provides the best performance in modeling annual runoff trends and runoff component partitioning. The possible reasons for the different performances of the LSMs are discussed in detail. In order to achieve more accurate streamflow simulations from the LSMs in GLDAS, suggestions are made to further improve the accuracy of the forcing data and parameterization schemes in all models.

Incorporating vegetation dynamics noticeably improved performance of hydrological model under vegetation greening

Numerous hydrological models calculate actual evaporation as a function of potential evaporation (PET) and soil moisture stress. There are some limitations for such empirical equations since they do not consider vegetation changes, and therefore cannot account for the different responses of soil evaporation and plant transpiration to changes in environmental factors and cannot be used for evaluating the impacts of vegetation changes. Here, we investigated whether incorporating a physically based evaporation scheme into a grid-based hydrological model can improve the accuracy of hydrological simulations. The original and modified hydrological models were evaluated in a basin which has experienced rapid vegetation greening. The model evaluations were performed using streamflow observations, soil moisture observations and water balance-based evaporation estimates. Results indicated that the modified model can provide better evaporation simulations than the original model during the period of vegetation greening. The streamflow and soil moisture simulations by the modified model over the same period benefitted significantly from the improvement in evaporation simulations and exhibited better consistency with in situ observations than the original model. This study underscores the importance of including vegetation change information in evaporation estimates and demonstrated that the physically based evaporation equation can be used in hydrological models to improve the hydrological simulations under vegetation greening conditions.