Modeling of discharge and sediment transport through the SWAT model in the basin of Harraza (Northwest of Algeria)

Hallouz, Faiza; Meddi, Mohamed; Mahé, Gil; Rahmani, Salah Eddine Ali; Keddar, Abdelkader

doi:10.1016/j.wsj.2017.12.004

Cited by 59 publications

(25 citation statements)

References 18 publications

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“…SWAT, a physically-based model frequently used by different disciplines, evaluates the watershed from a wider perspective [9][10][11][12][13][14]. The SWAT model is widely used in the simulation of the quality and quantity of surface and groundwater, in estimating the environmental impacts of different land use/land management practices and climate change, in calculating loads from pollutants, in evaluating best management practices, and in the simulation of various hydrological processes (runoff, infiltration, evapotranspiration, lateral flow, tile drainage, return flow, sediment etc) [15]. SWAT employs two different methods, the soil conservation services-curve number (SCS-CN) and the Green Ampt-MeinLarsen, for streamflow estimation [16][17][18][19].…”

mentioning

confidence: 99%

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Köyceğiz

Büyükyıldız

2019

Water

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Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R 2 ) = 0.787 and Nash-Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R 2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models. explicit knowledge of the physical behavior of the system [8]. In addition, adequate data should be provided for the training process in data-driven models. SWAT, a physically-based model frequently used by different disciplines, evaluates the watershed from a wider perspective [9][10][11][12][13][14]. The SWAT model is widely used in the simulation of the quality and quantity of surface and groundwater, in estimating the environmental impacts of different land use/land management practices and climate change, in calculating loads from pollutants, in evaluating best management practices, and in the simulation of various hydrological processes (runoff, infiltration, evapotranspiration, lateral flow, tile drainage, return flow, sediment etc) [15]. SWAT employs two different methods, the soil conservation services-curve number (SCS-CN) and the Green Ampt-MeinLarsen, for streamflow estimation [16][17][18][19]. Concurrent use of a digital elevation model (DEM), land use/land cover (LULC), and soil map alongside meteorological inputs also enables spatial analysis of the outputs produced by the model. As it includes physical inputs, the SWAT model yields successful results also in ungauged catchments [20,21].AI models such as support vector machines (SVM), artificial neural networks (ANN) and adaptive network-based fuzzy inference system (ANFIS) are widely used in estimating hydrological and meteorological phenomena. Tongal [22] used a chaotic approach (k-nearest neighbor-kNN) and neural networks (feed-forward neural networks, FFNN) the non-linear estimation of the streamflow of Yamula station in Kızılırmak Basin and found that the kNN model was more successful than the FFNN model for streamflow estimation. Buyukyildiz et al. [23] used five different methods, including support vector regression (SVR), artificial neur...

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confidence: 99%

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Köyceğiz

Büyükyıldız

2019

Water

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“…At this point, it should be mentioned that a model calibrated, for example, for discharge, may not be adequate for prediction of sediment, or for application to another region, or for application to another time period [36]. However, a lot of research works has been done successfully in the past by calibrating the model with the discharge and simulating sediments [37] or implementing land use change scenarios [38]. In future research, the previous assumption will be considered by using the SWAT-CUP for calibration and validation purposes.…”

Section: Swat Calibration and Validationmentioning

confidence: 99%

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

Samarinas

Tziolas

Zalidis

2020

IJGI

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The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.

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“…The SWAT model can reliably predict the streamflow in a watershed for a longer period giving a good idea of year-round flow rates of streams or rivers for extended periods which is essential for the calculation of hydropower. This reliability of the SWAT model has resulted in its application in watershed management, agricultural and hydropower applications throughout the world [17][18][19]. The SWAT model makes the simulation of flow quicker and reliable in a watershed [20].…”

Section: Description Of the Swat Modelmentioning

confidence: 99%

Theoretical Hydrokinetic Power Potential Assessment of the U-Tapao River Basin Using GIS

et al. 2020

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Conventional hydropower technologies such as dams have been criticized due to their negative environmental effects which have necessitated the development of new technologies for sustainable development of hydropower energy. Hydrokinetic (HK) energy is one such emerging renewable energy technology and, in this study, a theoretical potential assessment was done using a Geographic Information System (GIS) and Soil and Water Assessment Tool (SWAT) hydrological model, for the U-Tapao river basin (URB), a major tributary of the Songkhla lake basin (SLB) in southern Thailand. The SWAT was calibrated and validated with SWAT calibration and uncertainty (SWAT-CUP)-SUFI 2 programs using the observed discharge data from the gauging stations within the watershed. The model performance was evaluated based on the Nash–Sutcliffe efficiency (NSE) and the coefficient of determination (R2) values, achieving 0.62 and 0.60, respectively, for calibration, and 0.65 and 0.68 for validation which is considered acceptable and can be used to represent flow estimation. The theoretical HK potential was estimated to be 71.9 MW along the 77.18 km U-Tapao river, which could be developed as a renewable and reliable energy source for the communities living around the river. The method developed could also be applied to river systems around the world for resource and time efficient HK potential assessments.

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Modeling of discharge and sediment transport through the SWAT model in the basin of Harraza (Northwest of Algeria)

Cited by 59 publications

References 18 publications

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

Theoretical Hydrokinetic Power Potential Assessment of the U-Tapao River Basin Using GIS

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