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Article

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

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Centre of Marine and Environmental Research (CIMA), Gambelas Campus, University of Algarve, 8005-139 Faro, Portugal
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Faculty of Marine and Environmental Sciences, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
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Department of Biological, Geological and Environmental Sciences, University of Bologna, 48123 Ravenna, Italy
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Department of Civil Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
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Department of Earth, Environmental and Marine Sciences (DCTMA), Gambelas Campus, University of Algarve, 8005-139 Faro, Portugal
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Author to whom correspondence should be addressed.
Academic Editor: David Post
Water 2021, 13(11), 1538; https://doi.org/10.3390/w13111538
Received: 7 May 2021 / Revised: 27 May 2021 / Accepted: 28 May 2021 / Published: 30 May 2021
(This article belongs to the Special Issue Hydrologic Modelling for Water Resources and River Basin Management)
Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) 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. View Full-Text
Keywords: CORINE; Hydrologic Modeling; LULC; mHM; MODIS CORINE; Hydrologic Modeling; LULC; mHM; MODIS
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MDPI and ACS Style

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. https://doi.org/10.3390/w13111538

AMA Style

Busari IO, Demirel MC, 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(11):1538. https://doi.org/10.3390/w13111538

Chicago/Turabian Style

Busari, Ibrahim O., Mehmet C. Demirel, and Alice Newton. 2021. "Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model" Water 13, no. 11: 1538. https://doi.org/10.3390/w13111538

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