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

Abstract

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...

Assessment of the Role of Snowmelt in a Flood Event in a Gauged Catchment

“…Figure 4 shows two isohyet maps of the event corresponding to the coverage generated from the accumulated precipitation in two of these intervals (the event requires the use of 981 layers). Spatial interpolation is performed by applying a distributed precipitation model based on the use of radial basis functions (RBF) [66][67][68]. From this precipitation coverage, the computer program can generate a different hyetograph for each point of the basin.…”

Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

“…Data‐driven methods, for example SVM, show good performance in simulation and prediction of soil moisture (Bordoni et al, 2017; Ezzahar et al, 2019). Compared with the hydrological models, SVM needs much fewer input variables (Khwairakpam et al, 2018; Koycegiz & Buyukyildiz, 2019). Therefore, the investigation on its application is meaningful for the data limited regions.…”

“…Therefore, the investigation on its application is meaningful for the data limited regions. Some studies prove that it is possible for SVM to achieve similar or even better simulation results than physical models, although they are for streamflow simulation/prediction rather than for soil moisture simulation/prediction (Jajarmizadeh, Kakaei Lafdani, Harun, & Ahmadi, 2014; Koycegiz & Buyukyildiz, 2019; Zhang, Srinivasan, & Van Liew, 2009). For instance, Koycegiz and Buyukyildiz (2019) found that SVM shows better performance in simulating streamflow at the headwater of Çarsamba River, compared with water assessment tool (SWAT) model.…”

Calibration of SWAT and three data-driven models for monthly stream flow simulation in Maharlu Lake Basin

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“…However, few studies have compared both models for daily stream flow estimation. (Koycegiz & Buyukyildiz 2019) investigated the performance of two SWAT and Data-Driven Models (the RBNN and the SVR models) for simulating river runoff in Turkey. The results showed that the data-driven models were more successful than the SWAT model, although the SWAT model is a comprehensive physical model and has a significant problem-solving capability (Eini et al 2020b).…”