Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

Sirisena, T. A. J. G.; Maskey, Shreedhar; Ranasinghe, Roshanka

doi:10.3390/rs12223768

Cited by 49 publications

(28 citation statements)

References 71 publications

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“…The differences in the sensitivity of the parameters are due to different data used in the calibration process. Similar outcomes were obtained by Sirisena et al [16] and Moriasi et al [49]. Sirisena et al [16] concluded that the most sensitive parameters for evapotranspiration were connected to soil properties.…”

Section: Discussionsupporting

confidence: 76%

“…Similar outcomes were obtained by Sirisena et al [16] and Moriasi et al [49]. Sirisena et al [16] concluded that the most sensitive parameters for evapotranspiration were connected to soil properties. Moriasi et al [49] pointed out that the high sensitivity of the soil parameters to AET indicated a connection between actual evaporation and soil water.…”

Section: Discussionsupporting

confidence: 76%

“…In the single variable calibrations, the two variables (i.e., streamflow and actual evapotranspiration) were calibrated separately, and the performance of the second variable was evaluated. In the multi-variable calibration the two variables were calibrated together using a multi-variable objective function and assigning equal weights to each variable [16,44]. The Nash-Sutcliffe model efficiency (NS) was used as an objective function, and 900 simulations per iteration were performed and up to three iterations.…”

Section: Model Calibration and Sensitivity Analysismentioning

confidence: 99%

“…In addition, unknown procedures to the modeler, such as unidentified discharges, agricultural activities, and dumping of construction materials, interfere with the natural behavior of the system and increase the uncertainty in streamflow calibration [13,14]. Therefore, incorporating remote sensing data in model calibration can increase model accuracy, capture the spatial and temporal heterogeneity of hydrological processes, and be a promising alternative for catchments lacking sufficient observations [13,15,16]. Satellite-based actual evapotranspiration (AET) data can be used to constrain hydrological parameters associated with the water balance [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, a study in southern India [13] used MODIS satellite-based AET data to calibrate the SWAT model and suggested that satellite-based AET data with a monthly temporal resolution can be used to reduce equifinality obtained from traditional streamflow calibration. In addition, a study in Myanmar [16] calibrated the SWAT model using (Global Land Evaporation Amsterdam Model (GLEAM) satellite-based AET data and measured streamflow. This study suggested that constraining the model with actual evapotranspiration and streamflow data (i.e., multi-variable calibration) can produce good results for both variables.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Koltsida

Kallioras

2022

Hydrology

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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; R2 = 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.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionsupporting

confidence: 76%

Section: Model Calibration and Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Koltsida

Kallioras

2022

Hydrology

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Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation-Insights from a Randomized, Large-Sample Experiment

Nguyen

Tran

et al. 2022

Preprint

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Remotely sensed evapotranspiration (ET RS ) is increasingly used for streamflow estimation. Earlier reports are conflicting as to whether ET RS is useful in improving streamflow estimation skills. We believe that it is because earlier works used calibrated models and explored only small subspaces of the complex relationship between model skills for streamflow (Q) and ET. To shed some light on this complex relationship, we design a novel randomized, large sample experiment to explore the full ET-Q skill space, using seven catchments in Vietnam and four global ET RS products. For each catchment and each ET RS product, we employ 10,000 SWAT (Soil and Water Assessment Tool) model runs whose parameters are randomly generated via Latin Hypercube sampling. We then assess the full joint distribution of streamflow and ET skills using all model simulations. Resultsshow that the relationship between ET and streamflow skills varies with regions, ET RS products, and the selected performance indices. This relationship even changes with different ranges of ET skills. Parameter sensitivity analysis indicates that the most sensitive parameters could have opposite contributions to ET and streamflow skills. Conditional probability assessment reveals that with certain ET RS products, the probabilities of having good streamflow skills are high and increase with better ET skills, but for other ET RS products, good model skills for streamflow are only achievable with certain intermediate ranges of ET skills, not the best ones. Overall, our study provides a useful approach for evaluating the value of ET RS for streamflow estimation.

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Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions

Anand,

Oinam,

Wieprecht

et al. 2024

Hydrological Processes

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Calibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R2) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46–0.81 and 0.41–0.83, whereas during validation R2 and KGE were found to be in the range of 0.40–0.79 and 0.53–0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region.

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Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

Cited by 49 publications

References 71 publications

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

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

Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation-Insights from a Randomized, Large-Sample Experiment

Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions

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