Suriano Micaela, Caram Leonidas Facundo, Rosso Osvaldo Anibal
Departamento de Hidráulica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Las Heras 2214, Buenos Aires C1127AAR, Argentina.
Laboratorio de Redes y Sistemas Móviles, Departamento de Electrónica, Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires C1063ACV, Argentina.
Entropy (Basel). 2024 Jan 9;26(1):0. doi: 10.3390/e26010056.
This paper analyzes the temporal evolution of streamflow for different rivers in Argentina based on information quantifiers such as statistical complexity and permutation entropy. The main objective is to identify key details of the dynamics of the analyzed time series to differentiate the degrees of randomness and chaos. The permutation entropy is used with the probability distribution of ordinal patterns and the Jensen-Shannon divergence to calculate the disequilibrium and the statistical complexity. Daily streamflow series at different river stations were analyzed to classify the different hydrological systems. The complexity-entropy causality plane (CECP) and the representation of the Shannon entropy and Fisher information measure (FIM) show that the daily discharge series could be approximately represented with Gaussian noise, but the variances highlight the difficulty of modeling a series of natural phenomena. An analysis of stations downstream from the Yacyretá dam shows that the operation affects the randomness of the daily discharge series at hydrometric stations near the dam. When the station is further downstream, however, this effect is attenuated. Furthermore, the size of the basin plays a relevant role in modulating the process. Large catchments have smaller values for entropy, and the signal is less noisy due to integration over larger time scales. In contrast, small and mountainous basins present a rapid response that influences the behavior of daily discharge while presenting a higher entropy and lower complexity. The results obtained in the present study characterize the behavior of the daily discharge series in Argentine rivers and provide key information for hydrological modeling.
本文基于统计复杂度和排列熵等信息量化指标，分析了阿根廷不同河流的流量时间演变。主要目的是识别分析时间序列动态的关键细节，以区分随机性和混沌程度。排列熵与序数模式的概率分布以及 Jensen-Shannon 散度一起用于计算不平衡和统计复杂度。分析了不同河站的日流量序列，以对不同的水文系统进行分类。复杂度-熵因果关系平面（CECP）以及香农熵和费舍尔信息测度（FIM）的表示表明，日流量序列可以用高斯噪声近似表示，但方差突出了对一系列自然现象进行建模的难度。对亚西雷塔大坝下游站点的分析表明，该运行影响了大坝附近水文站日流量序列的随机性。然而，当站点更下游时，这种影响会减弱。此外，流域面积在调节该过程中起着相关作用。大型集水区的熵值较小，并且由于在较大时间尺度上的积分，信号噪声较小。相比之下，小型山区流域呈现出快速响应，这会影响日流量行为，同时具有较高的熵和较低的复杂度。本研究获得的结果表征了阿根廷河流日流量序列的行为，并为水文建模提供了关键信息。
