| 引用本文: | 王嘉诚,李渊,施坤,朱广伟,张运林,李慧赟,朱梦圆,郭宇龙,张芝镪.1987—2022年新安江水库(千岛湖)水面面积时空变化及其与水位、蓄水量的响应关系.湖泊科学,2024,36(3):913-926. DOI:10.18307/2024.0343 |
| Wang Jiacheng,Li Yuan,Shi Kun,Zhu Guangwei,Zhang Yunlin,Li Huiyun,Zhu Mengyuan,Guo Yulong,Zhang Zhiqiang.Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage. J. Lake Sci.2024,36(3):913-926. DOI:10.18307/2024.0343 |
|
| |
|
|
| 本文已被：浏览 1764次 下载 1547次 |
码上扫一扫！ |
|
|
| 1987—2022年新安江水库(千岛湖)水面面积时空变化及其与水位、蓄水量的响应关系 |
|
王嘉诚1, 李渊1, 施坤2,3, 朱广伟2,3, 张运林2,3, 李慧赟2, 朱梦圆2, 郭宇龙4, 张芝镪1
|
|
1.浙江工商大学旅游与城乡规划学院,杭州 310018;2.中国科学院南京地理与湖泊研究所,湖泊与环境国家重点实验室,南京 210008;3.中国科学院大学,北京 100049;4.河南农业大学资源与环境学院,郑州 450002
|
|
| 摘要: |
| 水面面积、水位、蓄水量是水库水资源管理的重要基础数据,遥感是湖库水体提取、水位和蓄水量估算的重要技术手段。由于不同水体提取方法的适用性差异、测高卫星数据的有限时间覆盖度和开源数据集的时空分辨率不足等原因,湖库水面面积、水位、蓄水量的长时序、高频率时空变化监测仍存在一定挑战。本研究以新安江水库为研究区,结合多源遥感、气象、水文和土地利用等数据,基于Google Earth Engine云平台,运用水体指数法,分析1987—2022年新安江水库水面面积时空变化特征,构建水位-水面面积、水位-蓄水量和水面面积-蓄水量响应关系,探究水面面积时空变化成因。结果表明:(1) Landsat 5、Landsat 8和哨兵2号数据的最佳水体提取指数分别为AWEIsh和GNDWI,F1-score分别为91.93%、91.03%和93.14%。相比于开放数据集GSWED(32.61%)、JRC GSW(76.17%)和ReaLSAT(69.76%),基于最优水体指数的水体提取结果具有最高的F1-score(91.26%);(2)时间上,1987—2022年新安江水库水面面积呈显著上升趋势(R2=0.20,P=0.01),总体增长速率为0.96 km2/a;空间上,永久性水体(淹没频率大于75%)面积占比为73.44%,主要分布在湖心等水体开阔区域;季节性水体(淹没频率＞25%且≤75%)面积占比为10.17%,主要分布在湖汊区域;(3)三次多项式函数可以较好地模拟新安江水库水位-水面面积、水位-蓄水量和水面面积-蓄水量的响应关系;(4)千岛湖流域上游降水和人类活动导致的土地利用变化是影响新安江水库水面面积动态变化的主要因素。 |
| 关键词: 新安江水库 水体指数 水面面积 水位 蓄水量 |
| DOI：10.18307/2024.0343 |
| 分类号: |
| 基金项目:国家自然科学基金项目(U22A20561,41922005,42071333)、中国科学院科研仪器研制项目(YJKYYQ20200071)、中国科学院南京地理与湖泊研究所青年科学家小组项目(E1SL002)、浙江工商大学“数字+”学科建设管理项目(SZJ2022B014)和浙江工商大学研究生科研创新基金项目(EDYB202205,YBXM2023014)联合资助。 |
|
| Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage |
|
Wang Jiacheng1, Li Yuan1, Shi Kun2,3, Zhu Guangwei2,3, Zhang Yunlin2,3, Li Huiyun2, Zhu Mengyuan2, Guo Yulong4, Zhang Zhiqiang1
|
|
1.School of Tourism and Urban & Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, P. R. China;2.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;3.University of Chinese Academy of Sciences, Beijing 100049, P. R. China;4.College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, P. R. China
|
| Abstract: |
| Water area, water level and water storage of reservoirs are fundamental data for water resource management. The method of satellite-based remote sensing has been demonstrated as an effective method for the estimation of water area, water level and water storage. However, the different applicability of the remote water extraction method and the limited time coverage of altimetry data bring difficulties to the study of the spatiotemporal variation characteristics of reservoir water area, water level, and water storage and their response relationships. Although many open-source data of reservoir water area and reservoir water storage at regional- and global-scale were published, these data were still hard to satisfy the long-term and high-frequency remote estimating reservoir dynamics due to its limitation in spatiotemporal resolution. Xin'anjiang Reservoir (Lake Qiandao), the largest man-made reservoir in eastern China, is a popular tourist destination and an important drinking water source in the Yangtze River Delta region. Here, we used the water index to extract the water area of the Xin'anjiang Reservoir from 1987 to 2022 with Landsat series data (1987-2022) and Sentinel-2 data (2019-2022), established response relationships of water level-water area, water level-water storage, and water area-water storage based on in-situ long-term water level and water storage data, and explored the causes of spatiotemporal changes in water area with land use data and hydrometeorological data. The results showed that: (1) The optimal water indexes for the Landsat series data and Sentinel-2 data to extract water extent in the Xin'anjiang Reservoir were AWEIsh and GNDWI, respectively. Specifically, the F1-score are 91.93%, 91.03% and 93.14% for Landsat 5, Landsat 8 and Sentinel-2, respectively. Our method showed the highest F1-score value (91.26%) than the open-source dataset, GSWED (32.61%), JRC GSW (76.17%) and ReaLSAT (69.76%). (2) Temporally, the water area of the Xin'anjiang Reservoir had exhibited an upward trend from 1987 to 2022 (R2=0.20; P=0.01), with an overall growth rate of 0.96 km2/a. Spatially, permanent water bodies (with a water inundation frequency greater than 75%) accounted for 73.44% of the water area, primarily distributed in open areas. Seasonal water bodies (with a water inundation frequency greater than 25% and less than or equal to 75%) accounted for 10.17% of the water area, primarily distributed in the lake branch area. (3) The third-degree polynomial function showed the best performance in fitting the response relationships of water level-water area, water level-water storage, and water area-water storage in Xin'anjiang Reservoir. (4) The main factors causing dynamic changes in the water area of the Xin'anjiang Reservoir were precipitation in the upper reaches of the Lake Qiandao basin and land use changes caused by human activities. |
| Key words: Xin'anjiang Reservoir water index water area water level water storage |
|
|
|
|
