Comparative analysis of evapotranspiration models for lake Urmia: Implications for water resource management in semi-arid regions

IF 3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Jafar Chabokpour
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Abstract

This paper presents a detailed study of evapotranspiration estimation over the period of 1974–2017 in the Lake Urmia basin, Iran. Traditional empirical models like Penman-Monteith, Hargreaves-Samani, Priestley-Taylor, and Thornthwaite are compared in this study with some advanced soft computing techniques, including artificial neural networks, adaptive neuro-fuzzy inference systems, and support vector regression. The results show that the soft computing techniques always perform better, particularly ANN, in comparison to traditional models for both accuracy and adaptability over various climatic conditions. Among the traditional approaches, the Penman-Monteith model performs best. It also introduces a new ET model dimensionally consistent, developed through dimensional analysis, which works comparably to the Penman-Monteith model. This long-term trend analysis reveals a highly significant annual increase in ET of about 5.2 mm y−1, with a change point detected in the year 1995. The study further discusses the effect of land use changes on ET patterns, showing remarkable increases in agricultural and urban areas of about 23.7 % and 156.3 %, respectively, over the study period. Sensitivity analyses, in fact, show that accurate ET estimation is very important where temperature and solar radiation measurements are concerned. In this respect, different statistical techniques like wavelet analysis and principal component analysis will be used to create nuanced insight into ET dynamics within the Lake Urmia basin. Moreover, the paper investigates models' performance for differing climatic conditions and their ability to capture extreme ET events. In this respect, the comprehensive approach to and the intercomparison of ET processes in semi-arid regions presented in this study are very useful.
本文对伊朗乌尔米耶湖盆地 1974-2017 年期间的蒸散估计进行了详细研究。本研究将彭曼-蒙蒂斯、哈格里夫斯-萨马尼、普里斯特利-泰勒和索恩斯韦特等传统经验模型与一些先进的软计算技术(包括人工神经网络、自适应神经模糊推理系统和支持向量回归)进行了比较。结果表明,与传统模型相比,软计算技术在各种气候条件下的准确性和适应性方面总是表现更好,尤其是人工神经网络。在传统方法中,Penman-Monteith 模型表现最佳。本报告还介绍了通过维度分析开发的一种新的蒸散发模型,该模型在维度上与彭曼-蒙蒂斯模型相一致,其效果与彭曼-蒙蒂斯模型相当。长期趋势分析表明,每年的蒸散发增加量非常显著,约为 5.2 毫米 y-1,变化点出现在 1995 年。研究进一步讨论了土地利用变化对蒸散发模式的影响,结果显示,在研究期间,农业和城市地区的蒸散发分别显著增加了约 23.7% 和 156.3%。事实上,敏感性分析表明,在涉及温度和太阳辐射测量时,精确的蒸散发估算非常重要。在这方面,将使用不同的统计技术,如小波分析和主成分分析,以深入了解乌尔米耶湖流域的蒸散发动态。此外,本文还研究了不同气候条件下模型的性能及其捕捉极端蒸散发事件的能力。在这方面,本研究提出的半干旱地区蒸散发过程的综合方法和相互比较非常有用。
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来源期刊
Physics and Chemistry of the Earth
Physics and Chemistry of the Earth 地学-地球科学综合
CiteScore
5.40
自引率
2.70%
发文量
176
审稿时长
31.6 weeks
期刊介绍: Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001. Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers. The journal covers the following subject areas: -Solid Earth and Geodesy: (geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy). -Hydrology, Oceans and Atmosphere: (hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology). -Solar-Terrestrial and Planetary Science: (solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).
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