Assessing the impact of PET estimation methods on hydrologic model performance
Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potenti...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2021-04-01
|
| Series: | Hydrology Research |
| Subjects: | |
| Online Access: | http://hr.iwaponline.com/content/52/2/373 |
| _version_ | 1818399887282143232 |
|---|---|
| author | Dilhani Ishanka Jayathilake Tyler Smith |
| author_facet | Dilhani Ishanka Jayathilake Tyler Smith |
| author_sort | Dilhani Ishanka Jayathilake |
| collection | DOAJ |
| description | Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments. HIGHLIGHTS
Model sensitivity to potential evapotranspiration (PET) errors was explored based on eco-regional and Budyko classifications.;
Although the model was not found to be sensitive to eco-region classification, the sensitivity varied along the water- to energy-limited continuum.;
This information, critically, can be used to better allocate limited resources for performing data collection and modeling and has benefits in data-scarce regions.; |
| first_indexed | 2024-12-14T07:27:49Z |
| format | Article |
| id | doaj.art-d90488509a81419586ca1dc236d803ee |
| institution | Directory Open Access Journal |
| issn | 1998-9563 2224-7955 |
| language | English |
| last_indexed | 2024-12-14T07:27:49Z |
| publishDate | 2021-04-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Hydrology Research |
| spelling | doaj.art-d90488509a81419586ca1dc236d803ee2022-12-21T23:11:28ZengIWA PublishingHydrology Research1998-95632224-79552021-04-0152237338810.2166/nh.2020.066066Assessing the impact of PET estimation methods on hydrologic model performanceDilhani Ishanka Jayathilake0Tyler Smith1 Department of Physical & Environmental Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699, USA Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments. HIGHLIGHTS Model sensitivity to potential evapotranspiration (PET) errors was explored based on eco-regional and Budyko classifications.; Although the model was not found to be sensitive to eco-region classification, the sensitivity varied along the water- to energy-limited continuum.; This information, critically, can be used to better allocate limited resources for performing data collection and modeling and has benefits in data-scarce regions.;http://hr.iwaponline.com/content/52/2/373budyko classificationhydrologic modelpotential evapotranspirationuncertaintyvariability |
| spellingShingle | Dilhani Ishanka Jayathilake Tyler Smith Assessing the impact of PET estimation methods on hydrologic model performance Hydrology Research budyko classification hydrologic model potential evapotranspiration uncertainty variability |
| title | Assessing the impact of PET estimation methods on hydrologic model performance |
| title_full | Assessing the impact of PET estimation methods on hydrologic model performance |
| title_fullStr | Assessing the impact of PET estimation methods on hydrologic model performance |
| title_full_unstemmed | Assessing the impact of PET estimation methods on hydrologic model performance |
| title_short | Assessing the impact of PET estimation methods on hydrologic model performance |
| title_sort | assessing the impact of pet estimation methods on hydrologic model performance |
| topic | budyko classification hydrologic model potential evapotranspiration uncertainty variability |
| url | http://hr.iwaponline.com/content/52/2/373 |
| work_keys_str_mv | AT dilhaniishankajayathilake assessingtheimpactofpetestimationmethodsonhydrologicmodelperformance AT tylersmith assessingtheimpactofpetestimationmethodsonhydrologicmodelperformance |