Spatial patterns of sample entropy based on daily precipitation time series in China and their implications for land surface hydrological interactions

Abstract

Entropy is a good index to measure the uncertainty of the precipitation that is a manifestation of complex interactions between water vapour transport and the local land surface processes. However, whether the uncertainty of precipitation time series is highly related to the intensity of these interactions has not been deeply considered before. Thus, sample entropy (SE), the measure of uncertainty based on self‐similarity of the time series instead of a probability distribution, is employed to uncover the relationship between them. The spatial distribution of SE is based on the results calculated from 675 daily precipitation time series (1961–2011) in China. Then, the transfer of SE is extracted following the route of water vapour, water vapour source (WVS), obtained from the NCEP/NCAR reanalysis data set. Finally, the relationship between substantial changes of SE along the WVS route and the underlying conditions are discussed, including the digital elevation model (DEM), plant cover and climatic factors. The results reveal that the SE spatial distribution is closely related to the WV source and its interaction with local climatic regions and underlying conditions, which are manifested by the following aspects: (a) SE exhibits lower values in WVS source regions and increases following the WV transfer route because of diverse continental water vapour composition sources, which makes the system more complicated and increases the system entropy. (b) The pronounced SE increases and decreases indicate WV convergence and substantial topographic uplift, respectively. (c) The spatial distribution of SE is comprehensively dominated by climatic conditions since the boundaries of climatic regions correspond well with the specific SE contours. Therefore, SE spatial distribution indicates where WV originates and how WV interacts with local land surfaces along the WV transfer routes, which offers a new information mechanism perspective to understand the hydrological cycle from precipitation time series.

Multiscale Complexity Analysis of Rainfall in Northeast Brazil

“…Although the climate and the amount of annual rainfall are different in these two distant regions, the rainfall regularity is similar due to strong intra-annual seasonality (3 months of wet season). The applications of multiscale entropy in hydrology have been limited to the analysis of streamflow data, and have been shown to be useful for detecting hydrological changes caused by human activities such as dam and reservoir construction [42,43] Rainfall data were studied with the sample entropy method that evaluates regularity on a temporal scale τ = 1 [30,34,35], while the comprehensive studies on rainfall multiscale dynamics are still ongoing. By using the MMSE method, we obtained entropy values over a wide range of temporal scales, from 1 month to 1 year, which provided a more complete description of rainfall complexity than when analyzed on a single temporal scale.…”

“…SampEn can be used to describe the regularity of temporal series, as it has lower values for time series with more frequent occurrence of sequences of similar consecutive values. Sample entropy method was used in physiology [51,52], geophysics [53], climatology [54], hydrology [30,34,35], and engineering [55].…”

“…These studies include fractal and multifractal methods [22][23][24][25], chaos theory [26,27], information measures [28][29][30], and complex networks [31][32][33] that have been extensively used to assess the degree of nonlinearity and the complexity of rainfall dynamics. Recently, entropy measures have attracted considerable attention as a tool to analyze the irregularity and the rate of information flow in order to access different regimes in hydrological temporal series [34][35][36][37][38].…”

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

“…For the correct selection of similar tolerances remaining in the application of sample entropy, Pincus (1995) determined that the reasonable range is 0.1–0.25 times the SD . In later studies, scholars (Peng et al ., 2009; Wang and Wang, 2015; Xavier et al ., 2019; Zhou and Lei, 2020) have only randomly used values within the empirical value range. However, it is not possible to determine whether the sample entropy achieves the optimal complexity measure performance.…”

Seasonal precipitation variability in mainland China based on entropy theory

“…The precipitation variability can be quantitatively measured based on entropy theory using different precipitation time series (Atieh et al ., 2017; Roushangar and Alizadeh, 2018; Zhou and Lei, 2020). In this study, we investigated the seasonal variability in the distribution of the number of precipitation days and amounts among the different months within a year.…”