The impact of climate change on Al-wala basin based on geomatics, hydrology and climate models

IF 2.3 Q2 REMOTE SENSING Applied Geomatics Pub Date : 2024-08-25 DOI:10.1007/s12518-024-00578-3
Farah Kloub, Samih B. Al Rawashdeh, Ghayda Al Rawashdeh
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Abstract

Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11o C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R2), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results.

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基于地学、水文学和气候模型的气候变化对 Al-wala 盆地的影响
约旦受到气候变化的严重影响,在过去的十年中,约旦的年降水量基本处于大幅波动和下降的状态,这对农民和淡水供应造成了严重后果。本研究利用地理信息技术、谷歌地球引擎(GEE)和机器学习代码分析了 2013 年至 2024 年期间气候变化对 Al-Wala 盆地的影响。水土评估工具(SWAT)模型用于模拟 2064 年之前的水文过程。此外,还使用气象研究所地球系统模型(MRI-ESM2-0)来预测未来 Al-Wala 大坝湖泊水面面积的变化。年度卫星图像:下载并增强了覆盖研究区域期间的 Lanadsat 和哨兵卫星图像。它们为本研究提供了必要的信息。结果观测到年降雨量的重要波动,以及预计未来几年年降雨量的波动增减。总体而言,与基准情景相比,年平均径流量将增加 10%。预计全年最低气温将比其速率高出 0.09 摄氏度至 0.11 摄氏度,这将使蒸发率增加约 0.03%。利用 SWAT 模型进行的敏感性分析确定了 17 个参数中的 6 个参数。按月计算的回归系数(R2)、纳什和苏特克利夫效率(NSE)均高于 0.60,表明模型结果令人满意。
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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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