Abstract
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.
Highlights
Decision-making about the sustainability of water resources requires supporting tools to manage the resources effectively
This study adopted the Coordination of Information on the Environment (CORINE) and MODIS Land Use Land Cover (LULC) dataset and the changes in the land cover characteristics shown in Figure 2 using the mesoscale hydrologic model (mHM) recognized land cover classes
Using the conventional CORINE land cover nomenclature to characterize the land cover in the basin as shown in Table A1, the most dominant land cover obtained in the study period is the natural grasslands, which increased from 718.63 km2 in 1990 to 929.63 km2 in 2012, a slight decline was noticed in 2000 before the increase in subsequent years
Summary
Decision-making about the sustainability of water resources requires supporting tools to manage the resources effectively. Hydrological models are useful tools to capture spatiotemporal variations in hydrological fluxes in many basins. A model consists of various parameters that define the characteristics of the basin. Hydrologic models have become progressive tools for studying the effects of anthropogenic activities and environments on hydrology and ecology [1]. Good modeling should be characterized by a high degree of confidence in the simulated outputs and minimization of uncertainty in model results. These uncertainties may be due to data input integrity, measured output data, non-optimal parameters, and model bias [2]
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