评估混合水文模型在巴基斯坦吉德拉尔盆地复杂条件下的实用性

Zain Syed, Prince Mahmood, Sajjad Haider, Shakil Ahmad
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摘要

水流预报对于水资源管理领域的规划和决策至关重要。巴基斯坦吉德拉尔盆地的特点是高海拔和冰川地形。由于复杂的地形和不确定的气候数据,模拟这类地区的溪流具有挑战性。这种复杂性促使我们探索三种框架(水土评估工具(SWAT)、人工神经网络(ANN)和 SWAT-ANN 混合框架(H2)),在两种不同的气候数据集(观测气候数据集(OC)和调和网格气候数据集(RGC))下模拟吉德拉尔河,从而得出所有六种模型组合。首先通过指数(纳什-萨特克利夫效率、克林-古普塔效率、判定系数、偏差百分比和均方根误差)对模型进行评估,在此基础上,我们进一步对模型进行评分,以反映其在校准和验证阶段的表现。研究结果显示,ANN-RGC 以 53 分排名第一,其次是 H2-RGC(50 分)和 SWAT-RGC(45 分)。排名第四和第五的分别是 SWAT-RGC 和 SWAT-OC(各得 26 分),而 ANN-OC 则排名最后(22 分)。此外,该研究还提出了一种模拟偏差缩放方法,从而减少了衰退和基流偏差,并特别改进了低分模型。尽管 ANN 优于传统模型,但在不确定或数据稀缺的条件下,其作用可能有限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the utility of hybrid hydrological modeling over complex conditions of the Chitral basin, Pakistan

Streamflow forecasting holds pivotal importance for planning and decision-making in the domain of water resources management. The Chitral basin in Pakistan is characterized by high altitude and glaciated terrain. Simulating streamflows in this type of region is challenging due to complex orography and uncertain climate data. This complexity persuaded us to explore three frameworks (soil and water assessment tool (SWAT), artificial neural network (ANN), and hybrid of SWAT–ANN (H2)) for simulating the Chitral river under two different climate datasets (observed climatology (OC) and reconciled gridded climatology (RGC)) to give all six model combinations. Model evaluation was done first by indices (Nash–Sutcliff efficiency, Kling–Gupta efficiency, coefficient of determination, percent bias, and root mean square error) based on which we further assigned scores to models reflecting their performance during calibration and validation epochs. The research revealed that ANN-RGC stood first with 53 points, followed by H2-RGC (50 points) and SWAT-RGC (45 points). Trailing behind in the fourth and fifth positions were SWAT-RGC and SWAT-OC (26 points each), respectively, while ANN-OC finished last (22 points). In addition, this study proposed a bias scaling approach for simulation biases resulting in reduction in recession and baseflow biases and specifically improved low-scoring models. Despite ANN's superiority over conventional models, it could be of limited utility in uncertain or data-scarce conditions.

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