Comparison of Calibration Approaches of the Soil and Water Assessment Tool (SWAT) Model in a Tropical Watershed

Makumbura, Randika K.; Gunathilake, Miyuru B.; Samarasinghe, Jayanga T.; Confesor, Remegio; Muttil, Nitin; Rathnayake, Upaka

doi:10.3390/hydrology9100183

Cited by 8 publications

(2 citation statements)

References 61 publications

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“…The SWAT and CA-Markov model used in this study all have certain internal systematic errors, and although they act as reference values for future studies of runoff change and its driving factors, their accuracy still needs to be further improved [51,52]. Therefore, the next step needs to comprehensively and accurately identify the depressions characteristics of DEM, develop and improve the underlying code of the SWAT model, and combine with more powerful computational capabilities and high-precision LUCC data, so as to obtain the runoff-change characteristics and the reliable future-prediction results that better reflect the actual watershed [53,54].…”

Section: Uncertainty Analysismentioning

confidence: 99%

Combined Effects of Land Use/Cover Change and Climate Change on Runoff in the Jinghe River Basin, China

Liu,

Guan,

Huang

et al. 2023

Atmosphere

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In the context of global warming and intensified human activities, the quantitative assessment of the combined effects of land use/cover change (LUCC) and climate change on the hydrological cycle is crucial. This study was based on the simulation results of future climate and LUCC in the Jinghe River Basin (JRB) using the GFDL–ESM2M and CA–Markov combined with the SWAT models to simulate the runoff changes under different scenarios. The results revealed that the future annual precipitation and average temperature in the JRB are on the increase, and the future LUCC changes are mainly reflected in the increase in forest and urban lands and decrease in farmlands. Changes in runoff in the JRB are dominated by precipitation, and the frequency of extreme events increases with the increase in the concentration of CO2 emissions. Under four climate scenarios, the contribution of future climate change to runoff changes in the JRB is −8.06%, −27.30%, −8.12%, and +1.10%, respectively, whereas the influence of future LUCC changes is smaller, ranging from 1.14–1.64%. In response to the future risk of increasing water-resources stress in the JRB, the results of this study can provide a scientific basis for ecological protection and water-resources management and development.

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Section: Uncertainty Analysismentioning

confidence: 99%

Combined Effects of Land Use/Cover Change and Climate Change on Runoff in the Jinghe River Basin, China

Liu,

Guan,

Huang

et al. 2023

Atmosphere

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“…Furthermore, these models are widely used to enhance the discussion of water control [4]. These models are widely used as accessible tools in the subjects of water resources, engineering, and management [5]. Furthermore, most of these models can also be used to forecast and flood perdition [6].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Runoff and Flooding in Urban Areas based on Hydrology Models: A Literature Review

2024

IJG

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Hydrological models significantly estimate water movements and their distribution at local and catchment sizes. They have been used to simulate the relative between rainfall and runoff. They enhance city planners and hydrology communities to investigate the complex relationship between precipitation and runoff in catchment and city sizes. Water movement is a vital matter, especially on the ground. This study reviews several hydrological modeling studies in different environments, including spatial analysis in the Watershed Modelling System (WMS). It seeks to consider only the most critical Hydrology and Hydraulic Modelling to investigate water in a watershed. Furthermore, this review gives a fundamental understanding of the empirical hydrology methods used to calculate runoff and a brief for 2D denominational. This review found that Hydrology Modelling is based on a) Loss Methods, b) Direct Run-off, and c) 2 Dimensional. This review concluded that the popular hydrology models applied in different environments are the Hydrologic Engineering Center-Hedrologic Modeling System (HEC-HMS) and Soil Water Assessment Tool (SWAT). Also, Hydrologic Engineering Center-River Analysis System (HEC-RAS) sites are at the top of the hydraulic water depth (1D) and water Distributed (2D) models to assess flood plans. Hydrological models became a postnatal application to understand flood risk in both gauged and ungauged catchments.

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A hybrid approach to improvement of watershed water quality modeling by coupling process–based and deep learning models

Jeong,

Kim

et al. 2024

Water Environment Research

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Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process‐based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time‐consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data‐driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short‐Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5‐year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub‐basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT‐LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling.Practitioner points We aimed to enhance process‐based models for watershed water‐quality modeling. The Soil and Water Assessment Tool‐Long Short‐Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub‐basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

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Comparison of Calibration Approaches of the Soil and Water Assessment Tool (SWAT) Model in a Tropical Watershed

Cited by 8 publications

References 61 publications

Combined Effects of Land Use/Cover Change and Climate Change on Runoff in the Jinghe River Basin, China

Combined Effects of Land Use/Cover Change and Climate Change on Runoff in the Jinghe River Basin, China

Investigation of Runoff and Flooding in Urban Areas based on Hydrology Models: A Literature Review

A hybrid approach to improvement of watershed water quality modeling by coupling process–based and deep learning models

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