Demirci, U.; Demirel, M.C. Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model. Agronomy2023, 13, 534.
Demirci, U.; Demirel, M.C. Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model. Agronomy 2023, 13, 534.
Demirci, U.; Demirel, M.C. Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model. Agronomy2023, 13, 534.
Demirci, U.; Demirel, M.C. Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model. Agronomy 2023, 13, 534.
Abstract
The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on soil properties and crop-vegetation dynamics. Mostly coarse scale meteorological forcing is used to estimate water balance at the catchment outlet only. Mesoscale hydrologic model (mHM) is one of the rare models that incorporates remote sensing data i.e. leaf area index (LAI) and aspect to improve actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduced a new weighting parameter “alphax” that allows user to incorporate both LAI and aspect together for PET scaling. With this mHM code enhancement, the modeler has an also option of using raw PET with no scaling. In this study, streamflow, and AET are simulated using the mesoscale Hydrological Model (mHM) in Main (Germany) basin for the period of 2002-2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using Optimization Software Toolkit (OSTRICH). For calibration and evaluation, KGE metric is used for water balance and SPAEF metric is used for evaluating spatial patterns of AET. Our results show that AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.
Copyright:
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