Hydrological modelling of ungauged urban watershed using SWAT model

Sisay, Ermias; Halefom, Afera; Khare, Deepak; Singh, Lakhwinder; Worku, Tesfa

doi:10.1007/s40808-017-0328-6

Cited by 85 publications

(43 citation statements)

References 42 publications

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confidence: 99%

“…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.…”

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confidence: 99%

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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%

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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“…According to Tantawy et al (2007) and Sisay et al (2017), several methodologies have been established to prepare for the allocation and distribution of irrigation (After Falkenmark 1986a, b) water during the planning and management process. The objectives of allocation and distribution plans is to optimize the uses of water and hence land resources.…”

Section: Integrated Water Resources Management and Sustainable Develomentioning

confidence: 99%

Towards integrated water resources management considering hydro-climatological scenarios: an option for sustainable development

Meshesha

Khare

2019

Environ Syst Res

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Background: In the rainfed areas, water, not land, is the foremost restraining resources for better agricultural production to satisfy the growing demand of food and other needs. Water harvesting and enhancing productivity of available water, and not volume of yield per units of land, is therefore a better solution for rainfed agriculture. Under these circumstances, it is necessary toimplement efficient and effective integrated water resources management practices. Elsewhere rainfed dependent highland parts of Ethiopia, in the Beressa watershed most of the rainwater lost in the form of runoff particularly during excess rainfall; therefore, the benefit of rainwater during less rainy season is insignificant. The objective of this study therefore was to assess crop water requirement and net irrigation requirement using CROPWAT8.0 model and Rainfall Contribution Index (RCI) for various crops and to describe the benefits of water harvesting and integrated and sustainable water resources management. Crops growing during the month of February to June (less rainy season) are among the highest irrigation requirements.Results: Based on RCI (as the value far from 1) reflect rainfall contribution is insufficient to satisfy the crop water requirements for the whole growth stages. Therefore, there is a need for supplemental irrigation and rainwater harvesting and a necessity for integrated water resources management. To reduce the intensity of water shortage therefore conservation planning and water management at watershed level in an integrated manner is critical. Similarly, soil and water conservation management practices is essential for the communities to reduce runoff and other resources for future uses and determine the optimal amount of water resources consumption at the irrigation fields is an urgent issues. Conclusions:As a result, there is a need to do more efforts related to water harvesting and integrated water resources management for the sustainability of agriculture and over all human survival. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Urban rainstorm events are often the focus of attention in urban areas; for this purpose, models such as SWMM and STORM are commonly used [18,26,28]. Additionally, some models, such as SWAT and HSPF, are well suited to the evaluation of NPS pollution in a watershed or farmland [17,27,29], but are not applicable to urbanized areas. These NPS evaluation models generally encounter problems with insufficient water quantity or quality modules.…”

Section: Introductionmentioning

confidence: 99%

A Combination Model for Quantifying Non-Point Source Pollution Based on Land Use Type in a Typical Urbanized Area

Wang

Rao

Yang

et al. 2020

Water

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Water pollution poses threats to urban environments and subsequently impacts the ecological health and sustainable development of urban areas. Identifying the spatiotemporal variation in non-point sources (NPS) pollution is a prerequisite for improving water quality. This paper aimed to assess the NPS pollution load and then recognized the spatiotemporal characteristics of the pollution sources in a typical urbanized area. A combination model based on land use type was used to simulate the NPS pollution load. The results showed the following: (1) ponds and farmlands had higher pollution production intensities than other land use types, but the intensity and magnitude of pollution emissions were generally greater in urban areas; (2) monthly and annual total nitrogen (TN) and total phosphorus (TP) emissions had the same pattern as rainfall, and TN and TP emissions accounted for 56.2% and 58.0%, respectively, of the total in summer; (3) TN pollution was more serious than TP pollution in the study area, especially in farmlands; (4) urban runoff (UR) and livestock and poultry breeding (LPB) were the main sources of NPS, TN and TP emissions in the study area. If these NPS pollutants cannot be removed from this area, a large amount of freshwater is needed to dilute the current rivers to meet the requirement of the fourth category of China national environmental quality standards for surface water. This problem is serious in the control of polluted rivers in many cities throughout China.

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Hydrological modelling of ungauged urban watershed using SWAT model

Cited by 85 publications

References 42 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

Towards integrated water resources management considering hydro-climatological scenarios: an option for sustainable development

A Combination Model for Quantifying Non-Point Source Pollution Based on Land Use Type in a Typical Urbanized Area

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