Analysis of Rainfall-Runoff Characteristic at Mountainous Watershed Using GeoWEPP and SWAT Model

2021 ◽  
Vol 28 (2) ◽  
pp. 31-44
Author(s):  
Jisu Kim ◽  
Minseok Kim ◽  
Jin Kwan Kim ◽  
Hyun-Joo Oh ◽  
Choongshik Woo
Keyword(s):  
Swat Model ◽  
Rainfall Runoff ◽  
Mountainous Watershed

scholarly journals Comparative evaluation of conceptual and physical rainfall–runoff models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
R. K. Jaiswal ◽  
Sohrat Ali ◽  
Birendra Bharti
Keyword(s):  
Water Balance ◽  
Swat Model ◽  
Water Balance Model ◽  
Optimization Techniques ◽  
Balance Model ◽  
Series Data ◽  
Climatic Data ◽  
Rainfall Runoff ◽  
Tank Model ◽  
Australian Water

AbstractThe design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. In the present study, an attempt has been made to compare two conceptual models including TANK and Australian water balance model (AWBM) and a physically distributed but lumped on HRUs scale SWAT model for Tandula basin of Chhattisgarh (India). The daily data of reservoirs levels, evaporation, seepage and releases were used in a water balance model to compute runoff from the catchment for the period of 24 years from 1991 to 2014. The rainfall runoff library (RRL) tool was used to set up TANK model and AWBM using auto and genetic algorithm, respectively, and SWAT model with SWATCUP application using sequential uncertainty fitting as optimization techniques. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

scholarly journals Calibration and validation of SWAT model and estimation of water balance components of Shaya mountainous watershed, Southeastern Ethiopia

2013 ◽  
Vol 10 (11) ◽  
pp. 13955-13978 ◽  
Author(s):  
A. A. Shawul ◽  
T. Alamirew ◽  
M. O. Dinka
Keyword(s):  
Best Management Practices ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Watershed Modeling ◽  
Coefficient Of Determination ◽  
Water Yield ◽  
Time Step ◽  
Monthly Streamflow ◽  
Mountainous Watershed

Abstract. To utilize water resources in a sustainable manner, it is necessary to understand the quantity and quality in space and time. This study was initiated to evaluate the performance and applicability of the physically based Soil and Water Assessment Tool (SWAT) model in analyzing the influence of hydrologic parameters on the streamflow variability and estimation of monthly and seasonal water yield at the outlet of Shaya mountainous watershed. The calibrated SWAT model performed well for simulation of monthly streamflow. Statistical model performance measures, coefficient of determination (r2) of 0.71, the Nash–Sutcliffe simulation efficiency (ENS) of 0.71 and percent difference (D) of 3.69, for calibration and 0.76, 0.75 and 3.30, respectively for validation, indicated good performance of the model simulation on monthly time step. Mean monthly and annual water yield simulated with the calibrated model were found to be 25.8 mm and 309.0 mm, respectively. Overall, the model demonstrated good performance in capturing the patterns and trend of the observed flow series, which confirmed the appropriateness of the model for future scenario simulation. Therefore, SWAT model can be taken as a potential tool for simulation of the hydrology of unguaged watershed in mountainous areas, which behave hydro-meteorologically similar with Shaya watershed. Future studies on Shaya watershed modeling should address the issues related to water quality and evaluate best management practices.

Uncertainty of SWAT model at different DEM resolutions in a large mountainous watershed

2014 ◽  
Vol 53 ◽  
pp. 132-144 ◽  
Author(s):  
Peipei Zhang ◽  
Ruimin Liu ◽  
Yimeng Bao ◽  
Jiawei Wang ◽  
Wenwen Yu ◽  
...  
Keyword(s):  
Swat Model ◽  
Mountainous Watershed

scholarly journals Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India

2013 ◽  
Vol 45 (1) ◽  
pp. 71-83
Author(s):  
Jan Adamowski
Keyword(s):  
Support Vector Regression ◽  
Water Flow ◽  
Base Flow ◽  
Support Vector ◽  
Morphological Parameters ◽  
Rainfall Runoff ◽  
Limited Data ◽  
Total Flow ◽  
Direct Runoff ◽  
Mountainous Watershed

Abstract Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India. In the ecologically sensitive Himalayan region, land transformations and utilization of natural resources have modified water flow patterns. To ascertain future sustainable water supply it is necessary to predict water flow from the watersheds as affected by rainfall and morphological parameters. Although such predictions may be made using available process- -based models, in mountainous and hilly areas it is extremely difficult to determine the numerous parameters needed to run such models, thus limiting their applicability. Artificial intelligence (AI) based models are a possible alternative in such circumstances. In this study an AI technique, support vector machines (SVM), was used for modeling the rainfall-runoff relationship from three hilly watersheds in the state of Uttaranchal, India. Different SVM models were developed to predict direct runoff, base flow, and total flow based on the daily rainfall, runoff, and morphological parameters collected from each watershed. The results confirm the potential of SVM models in the prediction of runoff, base flow, and total flow in hilly areas.

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