The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime
The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime
复制标题
DOI：
10.1029/2023wr034484
复制
发表时间：
2023-07
影响因子：
5.4
通讯作者：
Brian C. Brown;Aimee H. Fulerton;D. Kopp;Flavia Tromboni;Arial J. Shogren;J. Webb;C. Ruffing;M. Heaton;Lenka Kuglerová;Daniel C. Allen;L. McGill;J. Zarnetske;M. Whiles;Jeremy B. Jones;Benjamin W. Abbott
中科院分区：
文献类型：
--
作者：
Brian C. Brown;Aimee H. Fulerton;D. Kopp;Flavia Tromboni;Arial J. Shogren;J. Webb;C. Ruffing;M. Heaton;Lenka Kuglerová;Daniel C. Allen;L. McGill;J. Zarnetske;M. Whiles;Jeremy B. Jones;Benjamin W. Abbott
河流的流量在时间尺度上变化，从几分钟到几千年不等。这些水流的波动受到全球不同因素的调节，例如，在某些生态系统中，大坝会抑制多日变化，植被会减弱洪峰。影响流动的物理、生物和人为因素的相对重要性是一个活跃的研究领域，寻找描述整体流动状态的通用语言也是一个相关问题。在这里，我们使用1988年至2016年全球3000多个站点的每日河流流量数据集来解决这两个主题。我们首先研究了常见流态量化方法之间的相似性，包括传统的流量度量、小波和傅立叶分析。在所有这些方法中，流动数据显示出低维结构（即简单和一致的模式），表明基本机制限制了流动状态。其中一种模式是日变异性与年变异性呈负相关。此外，河流流量数据中的低维结构仅与一小部分集水区特征密切相关，包括集水区面积、降水和温度，但与生物群落、水坝表面积或水坝数量无关。我们在一个框架中讨论这些发现，旨在为从事河流研究和管理的许多社区提供便利，同时强调让数据结构指导机制推理和跨学科讨论的重要性。
River flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, by dams suppressing multi‐day variability or vegetation attenuating flood peaks in some ecosystems. The relative importance of the physical, biological, and human factors influencing flow is an active area of research, as is the related question of finding a common language for describing overall flow regime. Here, we addressed both topics using a daily river discharge data set for over 3,000 stations across the globe from 1988 to 2016. We first studied similarities between common flow regime quantification methods, including traditional flow metrics, wavelets, and Fourier analysis. Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. One such pattern was that day‐to‐day variability was negatively correlated with year‐to‐year variability. Additionally, the low‐dimensional structure in river flow data correlated closely with only a small number of catchment characteristics, including catchment area, precipitation, and temperature—but notably not biome, dam surface area, or number of dams. We discuss these findings in a framework intended to be accessible to the many communities engaged in river research and management, while stressing the importance of letting structure in data guide both mechanistic inference and interdisciplinary discussion.