首页> 外文期刊>Journal of hydrologic engineering >Effect of Calibration and Validation Decisions on Streamflow Modeling for a Heterogeneous and Low Runoff-Producing River Basin in India
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Effect of Calibration and Validation Decisions on Streamflow Modeling for a Heterogeneous and Low Runoff-Producing River Basin in India

机译:校准和验证决策对印度非均质低产河流域流量模型的影响

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摘要

Spatial heterogeneity of a river basin increases the predicting uncertainty of streamflow using hydrological models, and for such river basins calibration and prediction become a challenge. For a large and low flow-producing river basin like the Pennar basin of India, single-site calibration may ignore spatial heterogeneity, which leads to the use of a multiple-site calibration approach. The present study used Soil and Water Assessment Tool (SWAT) model to develop a real-world numerical model for predicting streamflow in a large, low runoff-producing river basin and evaluated the performance of the model under single-site and multiple-site calibration approaches. Under multiple-site approach, the large basin was divided into smaller subbasins, and the calibrated parameters were sequentially applied to the entire basin, and so local conditions were incorporated very effectively into the calibration process. The results demonstrated the superiority of a multiple-site calibration over a single-site calibration approach in predicting streamflow. The overall improvement of model performance in predicting streamflow using multiple-site approach over single-site approach reached as high as 133% during calibration and 140% during validation, as observed at the basin outlet (Chennur station). It was also observed that, within the basin, precipitation contributed only 15.8% to surface runoff, 2.6% to total aquifer recharge, and 69.3% to evapotranspiration. Uncertainty analysis indicated that more than 64% of the observed streamflow was bracketed by a 95% prediction uncertainty (PPU) band under the multiple-site approach. Thus, the multiple-site calibration approach demonstrated its ability to improve model performance by capturing the heterogeneity of a low runoff-producing basin like Pennar.
机译:流域的空间异质性增加了使用水文模型预测水流的不确定性,对于此类流域,标定和预测成为一项挑战。对于像印度Pennar盆地这样的大流量低流量流域,单站点标定可能会忽略空间异质性,从而导致使用多站点标定方法。本研究使用土壤和水评估工具(SWAT)模型开发了一个现实世界的数值模型,用于预测大型,低径流量生产流域的水流量,并在单站点和多站点校准下评估了该模型的性能。方法。在多站点方法下,将大盆地分成较小的子盆地,并将校正后的参数依次应用于整个盆地,因此将局部条件非常有效地纳入了校正过程。结果表明,在预测流量方面,多站点校准优于单站点校准方法。在盆地出口(Chennur站)观察到,在使用多站点方法比单站点方法预测流量方面,模型性能的总体改进在校准期间高达133%,在验证过程中高达140%。还观察到,在盆地内,降水仅对地表径流贡献了15.8%,对含水层总补给量的贡献为2.6%,对蒸散量的贡献为69.3%。不确定性分析表明,在多站点方法下,超过64%的观测流量被95%的预测不确定性(PPU)带包围。因此,多站点标定方法证明了它能够通过捕获低产流盆地(如Pennar)的非均质性来改善模型性能。

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