Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng 252000, China.
Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China.
Sci Total Environ. 2022 Mar 1;810:151188. doi: 10.1016/j.scitotenv.2021.151188. Epub 2021 Oct 25.
Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors.
湖泊透明度通常通过塞奇圆盘深度(SDD)来衡量,由于其与总悬浮物、叶绿素-a 和营养物质密切相关,因此是湖泊营养状态的可靠替代指标。我们使用现场测量的 SDD 和匹配的 Landsat 图像对其进行训练,建立了各种回归模型来估算全球湖泊的 SDD。我们选择了一个统一的模型,该模型具有良好的时空可转移性,并且有可能在不同年份使用嵌入 Google Earth Engine(GEE)中的高质量 Landsat 顶空(TOA)图像来绘制 SDD。该统一模型成功地进行了校准(n=3586 个数据点,R=0.84,MAPE=29.8%),与全球 2235 个湖泊的实测 SDD 进行了对比,验证(n=1779,R=0.76,MAPE=38.8%)也表现出稳定的性能。该统一模型针对过去四十年(1984-2020 年)发射的不同 Landsat 传感器(L5-TM、L7-ETM+、L8-OLI)的历史 SDD 测量值进行了调整,从而确认了其时间稳定性。使用 GEE 主要使用 2019 年获取的 OLI TOA 产品绘制了全球 SDD,以检查全球范围内所有湖泊水质清澈度(湖泊表面积≥1 公顷)的空间变化。2019 年,全球湖泊平均透明度为 3.13±1.71 米,但由于集水区水文和景观设置、湖泊形态、海拔和人为影响等因素,表现出显著的空间变异性。由于高纬度地区水深较大且人类干扰较小,欧洲(4.18±1.82 米)和北美的湖泊(3.84±1.77 米)水质清澈度最高。南美洲(2.50±2.33 米)、亚洲(2.44±1.63 米)和非洲(2.36±0.72 米)的湖泊透明度处于中等水平。除南极洲外,大洋洲(1.97±1.48 米)的湖泊透明度最低。此外,我们还使用绘制的 SDD 来评估水体富营养状态,使用 Carlson 营养状态指数。我们的结果表明,2019 年,全球约 63.6%的湖泊面积和 47.8%的湖泊总数(2219627/4646056)为寡营养型,而约 23.6%的面积百分比和 37.1%的湖泊数量为富营养型,主要是因为它们位于以农业和城市为主的流域。这项研究首次为面积≥1 公顷的全球湖泊提供了 30 米分辨率的水质清澈度信息,并有助于了解与 TSM(r=0.95)、Chl-a(r=0.73)、总磷(r=0.75)、总氮(r=0.60)相关的水质清澈度,这也可以为营养水平评估提供水质清澈度数据和技术支持。该统一模型可以作为一种强大的研究工具,用于长期监测水生生态系统,并评估其对人为干扰和气候变化相关压力的恢复能力。