Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China; Department of Lake Research, Helmholtz Centre for Environmental Research-UFZ, 39114, Magdeburg, Germany.
Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Environ Pollut. 2021 Sep 1;284:117405. doi: 10.1016/j.envpol.2021.117405. Epub 2021 May 19.
River ecosystems are under increasing stress in the background of global change and ever-growing anthropogenic impacts in Central Asia. However, available water quality data in this region are insufficient for a reliable assessment of the current status, which come as no surprise that the limited knowledge of regulating processes for further prediction of solute variations hinders the development of sustainable management strategies. Here, we analyzed a dataset of various water quality variables from two sampling campaigns in 2019 in the catchments of two major rivers in Central Asia-the Amu Darya and Syr Darya Rivers. Our results suggested high spatial heterogeneity of salinity and major ion components along the longitudinal directions in both river catchments, pointing to an increasing influence of human activities toward downstream areas. We linked the modeling outputs from the global nutrient model (IMAGE-GNM) to riverine nutrients to elucidate the effect of different natural and anthropogenic sources in dictating the longitudinal variations of the riverine nutrient concentrations (N and P). Diffuse nutrient loadings dominated the export flux into the rivers, whereas leaching and surface runoff constituted the major fractions for N and P, respectively. Discharge of agricultural irrigation water into the rivers was the major cause of the increases in nutrients and salinity. Given that the conditions in Central Asia are highly susceptible to climate change, our findings call for more efforts to establish holistic management of water quality.
在全球变化和中亚地区不断增长的人为影响的背景下,河流生态系统承受着越来越大的压力。然而,该地区可用的水质数据不足以对现状进行可靠评估,这并不奇怪,因为对调节过程的了解有限,进一步预测溶质变化阻碍了可持续管理战略的发展。在这里,我们分析了 2019 年在中亚两条主要河流——阿姆河和锡尔河的集水区进行的两次采样活动的各种水质变量数据集。我们的结果表明,两条河流流域的纵向方向上盐度和主要离子成分具有高度的空间异质性,表明人类活动对下游地区的影响越来越大。我们将全球养分模型(IMAGE-GNM)的建模输出与河流养分联系起来,以阐明不同自然和人为来源对河流养分浓度(N 和 P)纵向变化的影响。弥散养分负荷主导着进入河流的输出通量,而淋溶和地表径流分别构成了 N 和 P 的主要部分。农业灌溉水排入河流是导致养分和盐分增加的主要原因。鉴于中亚地区的情况极易受到气候变化的影响,我们的研究结果呼吁做出更多努力,以建立水质的整体管理。