基于GEE研究长江干流梯级大坝建设对河流水温的时空影响

Based on GEE to research the spatiotemporal impact of cascade dam construction on river water temperature in the upper reaches of Yangtze River

摘要

【目的】水温是影响河流水生态系统演变的关键要素,其对生源物质转化、生物栖息与繁衍等物理、化学和生物过程起着重要作用。梯级水电开发阻隔了河流的连续性,改变了河流水温沿程的传递性,严重影响河流水温节律,亟需开展梯级大坝建设前后河流水温沿程的演变规律的研究。【方法】基于Google Earth Engine(GEE)平台,利用Landsat系列遥感数据集,通过大气辐射传输模型反演长江干流2000—2020年水体表面温度,揭示长江干流梨园、阿海、金安桥等梯级水电站建设下表层水温的时空演变规律。【结果】结果表明,建坝前梨园—观音岩段、乌东德—向家坝段、三峡库区段年均表层水温分别为21.58℃、21.31℃和17.79℃,建坝后金沙江中游库区年均表层水温下降明显,下降到15.47℃,其中旱季水温平均下降5.43℃,雨季平均下降2.46℃,坝下年均水温与自然河流水温接近,且与周围实测气温相关性显著;库区内水温呈现片段化分布,各梯级大坝库首温差最大;三峡库区表现出“滞冷”和“滞温”现象。遥感反演的数据与华弹、朱沱水文站点实测数据的纳什效率系数分别为0.925和0.907,均方根误差分别为0.92℃和1.24℃,结果可靠性高。【结论】梯级大坝建设打破了河流水温连续性,使河流水温传递效应显著减弱;各库区库首及回水区域水温呈现显著相关,但库区水温与坝下游泄水水温相关性较弱。遥感方法可有效反演河流表层水温,为河流水温反演及流域水温管理提供方法支持和理论参考。

Abstract

[Objective] Water temperature is a key factor affecting the evolution of river ecosystems, playing an important role in the conversion of biogenic elements, biological habitat, and other physical, chemical, and biological processes. Hydropower development has obstructed the continuity of rivers and altered the transmission of river water temperature along the river,especially under the cascade hydropower development, which has seriously affected the rhythm of river water temperature. It is necessary to reveal the impact patterns of cascade dam construction on water temperature in the Yangtze River mainstream. [Methods] Based on the Google Earth Engine(GEE) platform, Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 TIRS remote sensing datasets were utilized to retrieve surface water temperature of the Yangtze River from 2000 to 2020 through atmospheric radiative transfer models. The retrieved result were validated by comparison with measured data from Zhutuo hydrological station,and the spatiotemporal characteristics of surface water temperature before dam construction( 2000—2005) and after dam construction(2015—2020) were analyzed in the Liyuan-Guanyin section, Wuduode-Xiangjiaba section, and Three Gorges Reservoir area. [Results] The result show that the average annual surface water temperatures before the construction of dams in the Liyuan-Guanyin section, Wuduode-Xiangjiaba section, and Three Gorges Reservoir area were 21. 58 ℃, 21. 31 ℃, and 17. 79 ℃, respectively. After the construction of dams, the water temperature in the middle reaches of the Jinsha River reservoir area decreased significantly to 15. 47 ℃, with an average decrease of 5. 43 ℃ in the dry season and an average decrease of 2. 46 ℃ in the rainy season. The average annual water temperature downstream of the dams was close to the natural river water temperature, and the correlation with the surrounding measured air temperature was significant. Water temperature within the reservoir areas exhibited fragmented distribution, with maximum temperature differences observed at the heads of cascade dams.The Three Gorges Reservoir area exhibited phenomena of “cold retention” and “temperature retention”. The Nash efficiency coefficient between remote sensing inversion and measured data reached 0. 907, with a root mean square error of 1. 24 ℃,indicating high reliability of the result. [Conclusion] The continuity of river water temperature has been disrupted by cascade dam construction, significantly weakening the temperature transmission effect. The water temperature in the head of each reservoir area and the backwater area showed a significant correlation, but the correlation between the water temperature in the reservoir area and the water temperature discharged from dam was weak. Remote sensing method can effectively retrieve river surface water temperature, providing method ological support and theoretical references for river water temperature inversion and watershed water temperature management.

关键词

河流水温 / 梯级水电开发 / Landsat遥感卫星 / Google Earth Engine / 水电站 / 三峡水库 / 水生态系统

Key words

river water temperature / cascade hydropower development / Landsat satellite / Google Earth Engine / hydropower station / Three Gorges Reservoir / water ecosystem

基金资助

国家重点研发计划(2022YFC32020002)

国家自然科学基金项目(52379065)

国家自然科学基金项目(52209087)

中国空间技术研究院CAST创新基金项目