Busari, I.O.; Demirel, M.C.; Newton, A. Effect of Using Multi-year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water2021, 13, 1538.
Busari, I.O.; Demirel, M.C.; Newton, A. Effect of Using Multi-year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water 2021, 13, 1538.
Busari, I.O.; Demirel, M.C.; Newton, A. Effect of Using Multi-year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water2021, 13, 1538.
Busari, I.O.; Demirel, M.C.; Newton, A. Effect of Using Multi-year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water 2021, 13, 1538.
Abstract
This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.
Keywords
LULC; mHM; CORINE; MODIS; Hydrologic Modeling; Model Calibration
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
