Multi-Variable SWAT Model Calibration Using Satellite-Based Evapotranspiration Data and Streamflow

被引:19
作者
Koltsida, Evgenia [1 ]
Kallioras, Andreas [1 ]
机构
[1] Natl Tech Univ Athens, Lab Engn Geol & Hydrogeol, Sch Min & Met Engn, Heroon Polytech Str 9, Athens 15780, Greece
关键词
SWAT; streamflow; MODIS; evapotranspiration; hydrological modeling; multi-variable calibration; REMOTELY-SENSED EVAPOTRANSPIRATION; WATER ASSESSMENT-TOOL; UNCERTAINTY; QUALITY; VALIDATION; SOIL; EQUIFINALITY; PERFORMANCE; CATCHMENT; HYDROLOGY;
D O I
10.3390/hydrology9070112
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in Athens, Greece. Three calibration scenarios were performed using streamflow (i.e., single variable), AET (i.e., single variable), and streamflow-AET data together (i.e., multi-variable) to provide insights into how different calibration scenarios affect the hydrological processes of a catchment with complex land use characteristics. The actual evapotranspiration data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration was achieved with the use of the SUFI-2 algorithm in the SWAT-CUP program. The results suggested that the single variable calibrations showed moderately better performance than the multi-variable calibration. However, the multi-variable calibration scenario displayed acceptable outcomes for both streamflow and actual evapotranspiration and indicated reasonably good streamflow estimations (NSE = 0.70; R-2 = 0.86; PBIAS = 6.1%). The model under-predicted AET in all calibration scenarios during the dry season compared to MODIS satellite-based AET. Overall, this study demonstrated that satellite-based AET data, together with streamflow data, can enhance model performance and be a good choice for watersheds lacking sufficient spatial data and observations.
引用
收藏
页数:15
相关论文
共 58 条
[41]  
Nash J.E., 1970, J. Hydrol., V10, P282, DOI [10.1016/0022-1694, DOI 10.1016/0022-1694(70)90255-6]
[42]   Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria [J].
Odusanya, Abolanle E. ;
Mehdi, Bano ;
Schurz, Christoph ;
Oke, Adebayo O. ;
Awokola, Olufiropo S. ;
Awomeso, Julius A. ;
Adejuwon, Joseph O. ;
Schulz, Karsten .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2019, 23 (02) :1113-1144
[43]  
Open Hydrosystem Information Network(OpenHi.net), OBS STREAMFL DAT
[44]   Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT [J].
Parajuli, Prem B. ;
Jayakody, Priyantha ;
Ouyang, Ying .
WATER RESOURCES MANAGEMENT, 2018, 32 (03) :985-996
[45]   Multi-Objective Validation of SWAT for Sparsely-Gauged West African River Basins-A Remote Sensing Approach [J].
Pomeon, Thomas ;
Diekkrueger, Bernd ;
Springer, Anne ;
Kusche, Juergen ;
Eicker, Annette .
WATER, 2018, 10 (04)
[46]   Rationale and Efficacy of Assimilating Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models [J].
Rajib, Adnan ;
Merwade, Venkatesh ;
Yu, Zhiqiang .
WATER RESOURCES RESEARCH, 2018, 54 (07) :4615-4637
[47]   Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration [J].
Rientjes, T. H. M. ;
Muthuwatta, L. P. ;
Bos, M. G. ;
Booij, M. J. ;
Bhatti, H. A. .
JOURNAL OF HYDROLOGY, 2013, 505 :276-290
[48]  
Running S., 2019, User's guide NASA Earth observing system MODIS land algorithm (for collection 6), P1
[49]   Evaluating the added value of multi-variable calibration of SWAT with remotely sensed evapotranspiration data for improving hydrological modeling [J].
Shah, Suraj ;
Duan, Zheng ;
Song, Xianfeng ;
Li, Runkui ;
Mao, Huihui ;
Liu, Junzhi ;
Ma, Tianxiao ;
Wang, Mingyu .
JOURNAL OF HYDROLOGY, 2021, 603
[50]   Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin [J].
Sirisena, T. A. Jeewanthi G. ;
Maskey, Shreedhar ;
Ranasinghe, Roshanka .
REMOTE SENSING, 2020, 12 (22) :1-24