Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data

Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an importa...

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Autores principales:	Henrique Haas, Furkan Dosdogru, Latif Kalin, Haw Yen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	land use/cover change SWAT NLCD uncertainty flow regimes soft data Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/be0c3ce980eb4b3281c9db23708fcd9a
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spelling	oai:doaj.org-article:be0c3ce980eb4b3281c9db23708fcd9a2021-11-11T19:52:21ZSoft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data10.3390/w132129472073-4441https://doaj.org/article/be0c3ce980eb4b3281c9db23708fcd9a2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/2947https://doaj.org/toc/2073-4441Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (<i>NSE)</i>, coefficient of determination (<i>R</i><sup>2</sup>), and model percent bias (<i>PBIAS</i>). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats.Henrique HaasFurkan DosdogruLatif KalinHaw YenMDPI AGarticleland use/cover changeSWATNLCDuncertaintyflow regimessoft dataHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 2947, p 2947 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	land use/cover change SWAT NLCD uncertainty flow regimes soft data Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	land use/cover change SWAT NLCD uncertainty flow regimes soft data Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Henrique Haas Furkan Dosdogru Latif Kalin Haw Yen Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
description	Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (<i>NSE)</i>, coefficient of determination (<i>R</i><sup>2</sup>), and model percent bias (<i>PBIAS</i>). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats.
format	article
author	Henrique Haas Furkan Dosdogru Latif Kalin Haw Yen
author_facet	Henrique Haas Furkan Dosdogru Latif Kalin Haw Yen
author_sort	Henrique Haas
title	Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
title_short	Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
title_full	Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
title_fullStr	Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
title_full_unstemmed	Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
title_sort	soft data in hydrologic modeling: prediction of ecologically relevant flows with alternate land use/land cover data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/be0c3ce980eb4b3281c9db23708fcd9a
work_keys_str_mv	AT henriquehaas softdatainhydrologicmodelingpredictionofecologicallyrelevantflowswithalternatelanduselandcoverdata AT furkandosdogru softdatainhydrologicmodelingpredictionofecologicallyrelevantflowswithalternatelanduselandcoverdata AT latifkalin softdatainhydrologicmodelingpredictionofecologicallyrelevantflowswithalternatelanduselandcoverdata AT hawyen softdatainhydrologicmodelingpredictionofecologicallyrelevantflowswithalternatelanduselandcoverdata
_version_	1718431432622211072

Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data

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