The color of environmental noise in river networks

Despite its far-reaching implications for conservation and natural resource management, little is known about the color of environmental noise, or the structure of temporal autocorrelation in random environmental variation, in streams and rivers. Here, we analyze the geography, drivers, and timescale-dependence of noise color in streamflow across the U.S. hydrography, using streamflow series from 7,504 gages. We find that daily and annual flows are dominated by red and white spectra respectively, and spatial variation in noise color is explained by a combination of geographic, hydroclimatic, and anthropogenic variables. Noise color at the daily scale is influenced by stream network position, and land use and water management explain around one third of the spatial variation in noise color irrespective of the timescale considered. Our results highlight the peculiarities of environmental variation regimes in riverine systems, and reveal a strong human fingerprint on the stochastic patterns of streamflow variation in river networks. The files here include raw data & processed datasets of the daily & annual flow noise color. Figure1.zip includes datasets used in Fig.1 & Fig.2; Figure2.zip includes datasets used in Fig.3; Figure3.zip includes datasets used in Fig.4;

尽管环境噪声（environmental noise）的颜色特征——即随机环境变异中的时间自相关结构——对河流生态保护与自然资源管理具有深远意义，但目前学界对溪流与河流中的此类特征仍知之甚少。本研究依托全美7504个水文站的径流序列，分析了美国水系内径流环境噪声颜色的空间分布、驱动因子及其随时间尺度的变化特征。研究发现，日径流与年径流分别以红噪声谱与白噪声谱为主；噪声颜色的空间变异可由地理、水文气候与人为活动变量的组合共同解释。日尺度下的噪声颜色受河网位置影响，而土地利用与水资源管理则可解释约三分之一的噪声颜色空间变异，且该解释力不受所考虑时间尺度的限制。本研究结果凸显了河流系统环境变异模式的独特性，并证实河网径流变异的随机模式中存在显著的人类活动印记。本数据集包含日径流与年径流噪声颜色的原始数据与处理后数据集。其中，Figure1.zip包含图1与图2所用数据集；Figure2.zip包含图3所用数据集；Figure3.zip包含图4所用数据集。