Precipitation Complexity and its Spatial Difference in the Taihu Lake Basin, China

Due to the rapid urbanization development, the precipitation variability in the Taihu Lake basin (TLB) in East China has become highly complex over the last decades. However, there is limited understanding of the spatiotemporal variability of precipitation complexity and its relationship with the urbanization development in the region. In this article, by considering the whole urbanization process, we use the SampEn index to investigate the precipitation complexity and its spatial differences in different urbanization areas (old urban area, new urban area and suburbs) in TLB. Results indicate that the precipitation complexity and its changes accord well with the urbanization development process in TLB. Higher urbanization degrees correspond to greater complexity degrees of precipitation. Precipitation in old urban areas shows the greatest complexity compared with that in new urban areas and suburbs, not only for the entire precipitation process but also the precipitation extremes. There is a significant negative correlation between the annual precipitation and its SampEn value, and the same change of precipitation can cause a greater complexity change in old urbanization areas compared with the new urban areas and suburbs. It is noted that the enhanced precipitation complexity in a new urban area during recent decades cannot be ignored facing the expanding urbanization.

[1]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[2]  Youpeng Xu,et al.  Impacts of Urbanization on Precipitation in Taihu Lake Basin, China , 2014 .

[3]  Demetris Koutsoyiannis,et al.  Entropy based derivation of probability distributions: A case study to daily rainfall , 2012 .

[4]  Song Songbai.  Study and application of complexity model for hydrological system , 2013 .

[5]  Zhuang Chengbin.  Analysis on complexity of monthly runoff series based on sample entropy in the Dongjiang river , 2009 .

[6]  Geoff Kite,et al.  Use of time series analysis to detect climatic change , 1989 .

[7]  Vijay P. Singh,et al.  Gradation of complexity and predictability of hydrological processes , 2015 .

[8]  F. Ludwig,et al.  Global water resources affected by human interventions and climate change , 2013, Proceedings of the National Academy of Sciences.

[9]  Steven M. Pincus.  Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[10]  Mahdouh Shahin,et al.  Statistical Analysis in Water Resources Engineering , 1993 .

[11]  Vijay P. Singh,et al.  A Tsallis entropy-based redundancy measure for water distribution networks , 2015 .

[12]  Chien-Ming Chou,et al.  Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales , 2014, Stochastic Environmental Research and Risk Assessment.

[13]  Marc Sciamanna,et al.  Introduction to the issue on Physics and Applications of Laser Dynamics (IS-PALD 2013). , 2014, Optics express.

[14]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Vijay P. Singh,et al.  Entropy‐Aided Evaluation of Meteorological Droughts Over China , 2017 .

[16]  Yang Shaopu.  Sample entropy-based roller bearing fault diagnosis method , 2012 .

[17]  Gwo-Fong Lin,et al.  The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan , 2017 .

[18]  S. Pincus.  Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[19]  Li Na,et al.  INFLUENCE OF URBANIZATION ON PRECIPITATION IN SUZHOU CITY , 2006 .

[20]  Vijay P. Singh,et al.  Evaluation of rainfall networks using entropy: II. Application , 1992 .

[21]  Vijay P. Singh,et al.  Evaluation of rainfall networks using entropy: I. Theoretical development , 1992 .

[22]  Vijay P. Singh,et al.  Entropy-based derivation of generalized distributions for hydrometeorological frequency analysis , 2018 .

[23]  Lin Nai-feng.  The Response Relationship between Underlying Surface Changing and Climate Change in the Taihu Basin , 2013 .

[24]  Jian Sun,et al.  A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data , 2018 .

[25]  George Manis,et al.  Fast computation of approximate entropy , 2008, Comput. Methods Programs Biomed..

[26]  Vijay P. Singh,et al.  Entropy Theory for Hydrologic Modeling , 2005 .

[27]  Romy R. Ravines,et al.  A joint model for rainfall–runoff: The case of Rio Grande Basin , 2008 .

[28]  Pan Guang-bo.  EFFECT OF URBANIZATION ON REGIONAL PRECIPITATION IN SUZHOU-WUXI-CHANGZHOU AREA , 2010 .

[29]  B. Lehner,et al.  An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales , 2017 .

[30]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[31]  James P. McNamara,et al.  The essential value of long‐term experimental data for hydrology and water management , 2017 .

[32]  M. Jha,et al.  Hydrologic Time Series Analysis: Theory and Practice , 2012 .

[33]  Dong Wang,et al.  The relation between periods’ identification and noises in hydrologic series data , 2009 .

[34]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[35]  Dongpu Cao,et al.  Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling , 2016, IEEE Transactions on Industrial Electronics.

[36]  Huijuan Cui,et al.  Tsallis Entropy Theory for Modeling in Water Engineering: A Review , 2017, Entropy.

[37]  Vijay P. Singh,et al.  Evaluation of the significance of abrupt changes in precipitation and runoff process in China , 2018 .