Effectiveness of introducing crop coefficient and leaf area index to enhance evapotranspiration simulations in hydrologic models

摘要

Evapotranspiration (ET) is an essential component of the hydrological cycle and plays a critical role in water resource management. However, ET is often overlooked in order to transform rainfall to runoff for better streamflow simulation. Hydrological models are commonly used to estimate areal actual evapotranspiration (AET) after calibration against observed discharge. However, classical approaches are often inadequate to appropriately simulate other hydrologic components. Hence, it is important to introduce natural heterogeneity to enhance hydrological processes and reduce water balance errors. In this study, the effectiveness of introducing a constant crop coefficient (K-c), flux tower-based K-c, and leaf area index (LAI) to three hydrological models (Three-Parametric Hydrologic Model [TPHM], Genie Rural a 4 parametres Journalier [GR4J], and Catchment hydrologic cycle Assessment Tool [CAT]) is assessed for the simulation of daily streamflow and AET in a mountainous mixed forest watershed (8.54 km(2)) in South Korea. The results show that the streamflow simulations after introduction of K-c and LAI to the original models are quite similar. However, the effectiveness of the AET estimation was significantly enhanced after introduction of the flux tower-based K-c and LAI. The information criterion computed to compare the models reveals that the flux tower-based K-c-simulated AET demonstrated better agreement with the observed AET. The Pearson's correlation coefficients (R-2) of the TPHM (8%), GR4J (55%), and CAT (55%) models also showed improvements that were greater than the constant based K-c simulation. Similarly, the limitations of the three models with respect to capturing seasonal variation as well as high and low flows were enhanced after the introduction of the flux tower-based K-c, which adequately reproduced hydrological processes with minimum water balance errors and bias. A regression analysis revealed the potential of estimating K-c as a linear function of LAI (R-2 = 0.84). The results of this study indicate that introduction of K-c and LAI to the conceptual rainfall-runoff models can be considered an effective approach to reduce water balance errors and uncertainties in hydrological models and improve the reliability of climate change studies and water resource management.