Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

Parajuli, Prem B.; Jayakody, Priyantha; Ouyang, Ying

doi:10.1007/s11269-017-1850-z

Cited by 62 publications

(46 citation statements)

References 39 publications

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“…The actual ET estimates can be used either to constrain the hydrological model or as a model evaluation variable for model calibration and/or validation. For example, Parajuli et al [93] used monthly ET estimates from SEBAL model using MODIS datasets to evaluate the performance of the SWAT model in 2 sub-basins (Merigold and Sunflower) within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. They obtained good model performances with the coefficient of determination (R 2 ) and Nash-Sutcliffe Efficiency (NSE) ranging from 0.79 to 0.82 during model calibration, and 0.71 to 0.78 during model validation.…”

Section: Implications For Hydrological Modelingmentioning

confidence: 99%

Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania’s Kilombero Valley

Senkondo

Munishi

Tumbo³

et al. 2019

Remote Sensing

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Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models’ ET estimates across different land cover classes and different spatial scales revealed that almost all models’ ET estimates were statistically comparable (based on the Wilcoxon’s test and the Levene’s test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

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Section: Implications For Hydrological Modelingmentioning

confidence: 99%

Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania’s Kilombero Valley

Senkondo

Munishi

Tumbo³

et al. 2019

Remote Sensing

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“…In addition, PM model-based evapotranspiration and MODIS evapotranspiration data were used as parameter inputs to compare hydrological simulations for the Mississippi River. These studies found that Nash-Sutcliffe efficiency was significantly higher when using MODIS evapotranspiration data (Parajuli et al, 2018). Further research is now required to investigate the specific reasons for this improvement.…”

Section: Potential Impact Of Errors Caused By Pm Methods In Arid Areasmentioning

confidence: 99%

Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method

et al. 2020

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The Penman-Monteith (PM) method is the most widely used technique to estimate potential worldwide evapotranspiration. However, current research shows that there may be significant errors in the application of this method in arid areas, although questions remain as to the degree of this estimation error and how different surface conditions may affect the estimation error. To address these issues, we evaluated the uncertainty of the PM method under different underlying conditions in an arid area of Northwest China by analyzing data from 84 meteorological stations and various Moderate Resolution Imaging Spectroradiometer (MODIS) products, including land surface temperature and surface albedo. First, we found that when the PM method used air temperature to calculate the slope of the saturation vapor pressure curve, it significantly overestimated the potential evapotranspiration; the mean annual and July-August overestimation was 83.9 and 36.7 mm, respectively. Second, the PM method usually set the surface albedo to a fixed value, which led to the potential evapotranspiration being underestimated; the mean annual underestimation was 27.5 mm, while the overestimation for July to August was 5.3 mm. Third, the PM method significantly overestimated the potential evapotranspiration in the arid area. This difference in estimation was closely related to the underlying surface conditions. For the entire arid zone, the PM method overestimated the potential evapotranspiration by 33.7 mm per year, with an overestimation of 29.0 mm from July to August. The most significant overestimation was evident in the mountainous and plain nonvegetation areas, in which the annual mean overestimation reached 5% and 10%, respectively; during July, there was an estimation of 10% and 20%, respectively. Although the annual evapotranspiration of the plains with better vegetation coverage was slightly underestimated, overestimation still occurred in July and August, with a mean overestimation of approximately 5%. In order to estimate potential evapotranspiration in the arid zone, it is important that we identify a reasonable parameter with which to calibrate the PM formula, such as the slope of the saturation vapor pressure curve, and the surface albedo. We recommend that some parameters must be corrected when using PM in order to estimate potential evapotranspiration in arid regions.

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“…Remote sensing data provide an alternative solution for this problem. Over the past decades, remote sensing data have been widely used for precipitation estimation, land use classification, evapotranspiration inversion, vegetation indices and soil moisture prediction [10][11][12][13][14][15][16][17]. For ungauged basins, remote sensing data are particularly useful in drought forecasting, runoff estimation and rainfall frequency analysis [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Integration of Remote Sensing Evapotranspiration into Multi-Objective Calibration of Distributed Hydrology–Soil–Vegetation Model (DHSVM) in a Humid Region of China

Pan

Liu

Bai

et al. 2018

Water

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This study presents an approach that integrates remote sensing evapotranspiration into multi-objective calibration (i.e., runoff and evapotranspiration) of a fully distributed hydrological model, namely a distributed hydrology–soil–vegetation model (DHSVM). Because of the lack of a calibration module in the DHSVM, a multi-objective calibration module using ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII) and based on parallel computing of a Linux cluster for the DHSVM (εP-DHSVM) is developed. The module with DHSVM is applied to a humid river basin located in the mid-west of Zhejiang Province, east China. The results show that runoff is simulated well in single objective calibration, whereas evapotranspiration is not. By considering more variables in multi-objective calibration, DHSVM provides more reasonable simulation for both runoff (NS: 0.74% and PBIAS: 10.5%) and evapotranspiration (NS: 0.76% and PBIAS: 8.6%) and great reduction of equifinality, which illustrates the effect of remote sensing evapotranspiration integration in the calibration of hydrological models.

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Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

Cited by 62 publications

References 39 publications

Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania’s Kilombero Valley

Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania’s Kilombero Valley

Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method

Integration of Remote Sensing Evapotranspiration into Multi-Objective Calibration of Distributed Hydrology–Soil–Vegetation Model (DHSVM) in a Humid Region of China

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