Entropy-based assessment and zoning of rainfall distribution

Liu, Bingjun; Chen, Xiaohong; Lian, Yanqing; Lili, Walim

doi:10.1016/j.jhydrol.2013.03.020

Cited by 40 publications

(16 citation statements)

References 25 publications

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“…The climatic region boundaries are the comprehensive indicators of precipitation, temperature, humidity and local terrain (Wu 2008;Zhang, 2010). However, the relationship between the climatic boundaries and entropy has not been discussed in studies evaluating the PWRA based on information entropy of the precipitation time series (Kawachi et al, 2001;Maruyama et al, 2005;Brunsell, 2010;Hasan and Dunn, 2011;Zhao et al, 2011;Liu et al, 2013). Further, SE along the climatic region boundaries decreases from 0.28 in humid regions to 0.17 in arid regions.…”

Section: Se Variation Along Climatic Region Boundariesmentioning

confidence: 99%

“…These studies have reported that entropy is weakly correlated to the mean annual precipitation. The regional-scale and watershed-scale PWRA have been widely evaluated in several locations, for example, Texas (Mishra et al, 2009;Hao and Singh, 2013), Xinjiang Province (Zhao et al, 2011) and the Pearl River Basin in China (Liu et al, 2013). These studies have demonstrated that the entropy of the precipitation time series is closely related to PWRA.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Lei

2019

Intl Journal of Climatology

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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.

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Section: Se Variation Along Climatic Region Boundariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhou

Lei

2019

Intl Journal of Climatology

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“…Likewise, alternative forms of entropy, derived from information theory (Shannon, 1948) have been used to study morphological patterns in streams. This makes entropy an important concept in geomorphic studies, reflecting its relevance to precipitation and stream flow characteristics (Singh, 1997;Mayurama et al, 2005;Chen et al, 2008;Mishra et al, 2009, Brunsell, 2010Liu et al, 2013;Salas and Poveda, 2015) as important drivers of drainage networks. Tejedor et al (2017) using information entropy studied the optimality principle in self-organized deltas in relation to the transport of water and sediment fluxes.…”

Section: Introductionmentioning

confidence: 99%

An entropy based morphological variability assessment of a large braided river

Chembolu

Dutta

2018

Earth Surf Processes Landf

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The present paper utilizes entropy to investigate variability in the surface areas of sandbars distribution of the Brahmaputra River, India. The intensity entropy disorder index (IDI) was computed for the dry and the post‐monsoon season for a selected reach of the river. The planform disorder index (PDI) was computed to represent the annual scale planform change due to seasonal flood. Finally, annual monsoon and peak flood stream power were evaluated and related to PDI. The results showed that the intensity disorder index followed a decreasing trend in dry and post‐monsoon seasons; tending towards lower deviation from a uniform distribution. It was noted that except for extreme flood years the PDI trend correlated well with monsoonal stream power. Distinctive trends in stream power were also observed in the present study. © 2018 John Wiley & Sons, Ltd.

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“…The entropy concept was introduced by Shannon (1948) to measure the uncertainties of random variables. Later, the researches on applying entropy theory to quantitatively assess uncertainties of hydrologic variables have become a research focus (Liu et al, 2013;Singh, 1997). The studies on features of meteorological system demonstrated that meteorological factors of the terrestrial atmosphere have the characteristic of self-similarity (Monin and Obukhov, 1954), which exists from free troposphere to boundary layer of the atmosphere (Nieuwstadt, 1984;Shao and Hacher, 1990).…”

Section: Introductionmentioning

confidence: 99%