Streamflow prediction uncertainty analysis and verification of SWAT model in a tropical watershed

<p>Semi-distributed model of SWAT based on physical-chemical spatial information has been an effective tool for simulating hydrological cycle in the basin whereas it can&#8217;t completely restore all natural processes. Therefore, uncertainty analysis is needed to be conducted in order to achieve the reliability of the model. Yalong River Basin (YLRB), which is listed as the top ten hydropower bases in China, contains abundant water resources with plentiful runoff. Here a case study in YLRB was conducted to explore the parameter uncertainties of the SWAT model to runoff simulations based on multiple optimization algorithms. The following results were obtained: 1) setting the same objective function of Nash&#8211;Sutcliffe Efficiency, three optimization methods including Sequential Uncertainty Fitting version 2 (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE) and Particle Swarm Optimization (PSO) all performed satisfactory fitting results and produced similar parameter ranges in YLRB, while SUFI-2 achieved better uncertainty analysis, followed by PSO and the last GLUE; 2) five general sensitive parameters to model output were ALPHA_BF, CH_K2, SOL_K(1), GW_REVAP and ESCO based on above three algorithms; 3) from the contribution network analysis in economics, the positive correlation between ALPHA_BF and CH_K2 exhibited the highest weight among all parameter relationships; and 4) the much lower sensitivity of parameter CN2 to streamflow in YLRB revealed that most commonly modified parameter CN2 was less applicable to land with adequate surface water than dry land. This work will be conducive to further hydrological analysis based on a reliable fitting model for this hydropower watershed. Additionally, this work will provide references and insights for sensitive parameter modification and prediction uncertainty reduction of streamflow simulation furthermore contributing to an optimal water resource management.</p>

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Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2393-2014 ◽

2014 ◽

Vol 18 (6) ◽

pp. 2393-2413 ◽

Cited By ~ 34

Author(s):

H. Sellami ◽

I. La Jeunesse ◽

S. Benabdallah ◽

N. Baghdadi ◽

M. Vanclooster

Keyword(s):

Model Prediction ◽

Hydrological Model ◽

Swat Model ◽

Behavioral Model ◽

Model Parameters ◽

Ungauged Catchments ◽

Southern France ◽

Model Parameter ◽

Ungauged Catchment ◽

Prediction Uncertainty

Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

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Effects of dynamic land use inputs on improvement of SWAT model performance and uncertainty analysis of outputs

Journal of Hydrology ◽

10.1016/j.jhydrol.2018.06.063 ◽

2018 ◽

Vol 563 ◽

pp. 874-886 ◽

Cited By ~ 12

Author(s):

Qingrui Wang ◽

Ruimin Liu ◽

Cong Men ◽

Lijia Guo ◽

Yuexi Miao

Keyword(s):

Land Use ◽

Uncertainty Analysis ◽

Swat Model ◽

Model Performance

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Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽

10.3390/w11030594 ◽

2019 ◽

Vol 11 (3) ◽

pp. 594 ◽

Cited By ~ 9

Author(s):

Majid Fereidoon ◽

Manfred Koch ◽

Luca Brocca

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Assessment Tool ◽

Swat Model ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Hydrological Models ◽

Streamflow Prediction ◽

Soil Moisture Data ◽

Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

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Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽

10.3390/w11061177 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1177 ◽

Cited By ~ 2

Author(s):

Lufang Zhang ◽

Baolin Xue ◽

Yuhui Yan ◽

Guoqiang Wang ◽

Wenchao Sun ◽

...

Keyword(s):

Uncertainty Analysis ◽

River Basin ◽

Model Uncertainty ◽

Assessment Tool ◽

Rapid Development ◽

Swat Model ◽

Observation Data ◽

Hydrological Models ◽

Distributed Hydrological Models ◽

Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

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