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A Comparison of SWAT Model Calibration Techniques for Hydrological Modeling in the Ganga River Watershed

机译:恒河流域水文模型SWAT模型标定技术的比较。

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摘要

The Ganga River,the longest river in India,is stressed by extreme anthropogenic activity and climate change,particularly in the Varanasi region.Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water.In this study,hydrological modeling was applied to Soil and Water Assessment Tool (SWAT) modeling in the Ganga catchment,over a region of 15 621.612 km2 in the southern part of Uttar Pradesh.The primary goals of this study are:① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield;and ② to compare and determine the best calibration algorithm among three popular algorithms-sequential uncertainty fitting version 2 (SUFI-2),the generalized likelihood uncertainty estimation (GLUE),and parallel solution (ParaSol).The input data used in the SWAT were the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM),Landsat-8 satellite imagery,soil data,and daily meteorological data.The watershed of the study area was delineated into 46 sub-watersheds,and a land use/land cover (LULC) map and soil map were used to create hydrological response units (HRUs).Models utilizing SUFI-2,GLUE,and ParaSol methods were constructed,and these algorithms were compared based on five categories:their objective functions,the concepts used,their performances,the values of P-factors,and the values of R-factors.As a result,it was observed that SUFI-2 is a better performer than the other two algorithms for use in calibrating Indian watersheds,as this method requires fewer runs for a computational model and yields the best results among the three algorithms.ParaSol is the worst performer among the three algorithms.After calibrating using SUFI-2,five parameters including the effective channel hydraulic conductivity (CH_K2),the universal soil-loss equation (USLE) support parameter (USLE_P),Manning's n value for the main channel (CH_N2),the surface runoff lag time (SURLAG),and the available water capacity of the soil layer (SOL_AWC) were observed to be the most sensitive parameters for modeling the present watershed.It was also found that the maximum runoff occurred in sub-watershed number 40 (SW#40),while the maximum sediment yield was 50 t·a-1 for SW#36,which comprised barren land.The average evapotranspiration for the basin was 411.55 mm·a-1.The calibrated model can be utilized in future to facilitate investigation of the impacts of LULC,climate change,and soil erosion.
机译:恒河是印度最长的河流,受到极端的人为活动和气候变化的压力,特别是在瓦拉纳西地区。预期的气候变化和人口增长将进一步阻碍水的有效利用。将其应用于北方邦南部恒河集水区15 621.612 km2的土壤和水评估工具(SWAT)建模中。本研究的主要目标是:①测试SWAT的执行和适用性预测径流量和泥沙产量的模型;②比较和确定三种流行的算法中最好的校准算法-顺序不确定度拟合版本2(SUFI-2),广义似然不确定度估计(GLUE)和并行求解(ParaSol)。在SWAT中使用的输入数据是航天飞机雷达地形任务(SRTM)数字高程模型(DEM),Landsat-8卫星图像,土壤数据和每日气象数据。将研究区域的流域划分为46个子流域,并使用土地利用/土地覆被(LULC)图和土壤图创建水文响应单位(HRU)。使用SUFI-2,GLUE和ParaSol方法的模型分别为构造并基于五个类别对这些算法进行了比较:目标函数,使用的概念,其性能,P因子的值和R因子的值。结果发现SUFI-2是与用于校准印度流域的其他两种算法相比,该方法具有更好的性能,因为该方法所需的计算模型运行次数更少,并且在三种算法中产生的结果最好.ParaSol在三种算法中性能最差。使用SUFI- 2,五个参数,包括有效通道水力传导率(CH_K2),通用土壤流失方程(USLE)支持参数(USLE_P),主通道的曼宁n值(CH_N2),地表径流滞后时间(SURLAG)和可用的wa土壤层的三通量(SOL_AWC)被认为是当前流域建模的最敏感参数,并且还发现最大径流发生在40号次流域(SW#40),而最大沉积物产量是SW#36的50 t·a-1,由贫瘠的土地组成。流域的平均蒸散量为411.55 mm·a-1。将来可以使用该校准模型来方便地研究LULC,气候变化,和水土流失。

著录项

  • 来源
    《工程(英文)》 |2018年第005期|643-652|共10页
  • 作者单位

    Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India;

    Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India;

    Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India;

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  • 正文语种 eng
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  • 入库时间 2022-08-19 04:28:19
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