Spatio-temporal changes of precipitation in Chengdu from 1980 to 2016 based on Mann-Kendall test and information entropy
WANG Xiaoxuan1, ZUO Xiaoqing1, YANG Zenan1, ZHANG Yanmei2, XIE Wenbin1
1. School of Land and Resources Engineering, Kunming University of Science and Technology, 650093, Kunming, China;
2. The Third Institute of Photogrammetry and Remote Sensing of National Bureau of Surveying, Mapping and Geographic Information, 610100, Chengdu, China
Abstract：[Background] According to studies from the International Intergovernmental Panel on Climate Change (IPCC), the global climate has undergone profound changes during the past ten decades, so has the process mechanism and spatio-temporal distribution pattern of precipitation in recent years. Precipitation plays a crucial role in the water cycle, and energy flow among the atmosphere, vegetation, soil, and other systems, resulting in remarkable environmental disturbances. Chengdu city is located in Sichuan Basin of southwestern China, characterized by the subtropical humid monsoon. Studying the characteristics of precipitation in Chengdu city may provide a significant scientific basis and theoretical support for management of water resources and warning of meteorological disaster here.[Methods] Based on the daily precipitation data of 14 meteorological stations in Chengdu city from 1980 to 2016, the one-dimensional trend of precipitation in this area was studied using simple regression analysis. The abrupt variability of precipitation in time series was detected by the Mann-Kendall method, and the uncertainty and stability of the precipitation were detected by information entropy. On this basis, the visual expression of information entropy distribution regarding regional precipitation was analyzed through the technology of GIS (geographic information system).[Results] 1) The chronological changes of precipitation from 1980 to 2016 present variability, with the highest value of precipitation in 2013 at 1 274.77 mm and the lowest in 1997 at 768.6 mm. The average annual precipitation over the Chengdu city is 950.10 mm, showing a trend of gradual decrease with a tendency of -2.049 mm/a. The changing tendency of seasonal precipitation is less obvious. No significant decrease of precipitation (P>0.05) is found in spring, summer, autumn and winter, and neither significant increase in autumn. 2) Chengdu city has much uneven rainfall distribution. The average precipitation decreases from the east to the west, with the center located in hilly regions of the eastern hilly region and the lowest in the northern side of the western foothills region. The uncertainty of precipitation varies with time units. 3) The information entropy reaches the highest for annual precipitation, followed by summer precipitation, spring and autumn precipitation, and monthly precipitation in order. It indicates that the summer rainfall tends to be as rich and steady as ever, and the precipitation in autumn and spring will become increasingly changeable and difficult to be forecasted accurately and timely.[Conclusions] In recent decades, the precipitation in Chengdu city has exhibited certain temporal and spatial variation. The changing tendency and uncertainty of precipitation in Chengdu city has a close correlation with regional and global climate change. Nevertheless, the specific and detailed process mechanism remains unclear. It is found in this study that the more the precipitation is, the smaller the information entropy and the more uncertainty the precipitation is.
王枭轩, 左小清, 杨泽楠, 张艳梅, 谢文斌. 基于Mann-Kendall检验和信息熵的成都市1980-2016年降水时空变化[J]. 中国水土保持科学, 2019, 17(4): 26-33.
WANG Xiaoxuan, ZUO Xiaoqing, YANG Zenan, ZHANG Yanmei, XIE Wenbin. Spatio-temporal changes of precipitation in Chengdu from 1980 to 2016 based on Mann-Kendall test and information entropy. SSWC, 2019, 17(4): 26-33.
SOLOMON S. IPCC (2007):Climate change the physical science basis[C]//AGU Fall Meeting, 2007.
[2]
LAL R. Soil Carbon sequestration impacts on global climate change and food security[J]. Science, 2004, 304(5677):1623.
[3]
KOVATS S, AKHTAR R. Climate, climate change and human health in Asian cities[J]. Environment & Urbanization, 2008, 20(1):165
[4]
CHERNOGOR L F, ROZUMENKO V T. Earth-atmosphere-geospace as an open nonlinear dynamical system[J]. Radio Physics and Radio Astronomy, 2012, 13(2):120.
[5]
SIEPIELSKI A M, MORRISSEY M B, BUORO M, et al. Response to Comment on "Precipitation drives global variation in natural selection"[J]. Science, 2018, 359(6374):eaan5760.
[6]
HEVESI J A, ISTOK J D, FLINT A L. Precipitation Estimation in Mountainous Terrain Using Multivariate Geostatistics. Part I:Structural Analysis[J]. Journal of Applied Meteorology, 1992, 31(7):661.
[7]
SAIEDI N, BESALATPOUR A A, SHIRANI H, et al. Aggregation and fractal dimension of aggregates formed in sand dunes stabilized by PistachioPAM and PistachioPVAC mulches[J]. European Journal of Soil Science, 2017, 68(5):783.
[8]
HAWKINS E, SUTTON R. The potential to narrow uncertainty in projections of regional precipitation change[J]. Climate Dynamics, 2011, 37(1/2):407.
[9]
PASCALE S, LUCARINI V, XUE F, et al. Analysis of observed and modeled rainfall seasonality with new indicators based on information entropy[C]//EGU General Assembly Conference. EGU General Assembly Conference Abstracts, 2014.
[10]
ADERA S, LARSEN L, LEVY M C, et al. Functional connectivity of precipitation networks in the Brazilian Rainforest-Savanna Transition Zone[C]//AGU Fall Meeting. AGU Fall Meeting Abstracts, 2016.
[11]
BRUNSELL N A. A multiscale information theory approach to assess spatial-temporal variability of daily precipitation[J]. Journal of Hydrology, 2010, 385(1):165.
[12]
黄家俊,张强,张生,陈晓宏.基于信息熵的新疆降水时空变异特征研究[J].生态学报,2017,37(13):4444. HUANG J J, ZHANG Q, ZHANG S, CHEN X H.Information entropy-based analysis of spatial and temporal variation in precipitation in Xinjiang[J]. Acta Ecologica Sinica, 2017, 37(13):4444.
[13]
郑泳杰,张强,张生,陈永勤.基于信息熵的淮河流域降水时空变异特征研究[J].水电能源科学,2016,34(1):1. ZHENG Y J, ZHANG Q, CHEN Y Q. Information entropy investigation on variability of precipitation in Huai river[J].Water Resource and Power, 2016,34(1):1.
[14]
余冲,刘冀,董晓华.基于信息熵的湖北省降雨时空变化规律研究[J].水电能源科学,2011,29(1):6. YU C, LIU J, DONG X H. Information entropy-based spatial and temporal variation rule of rainfall in Hubei Province[J]. Water Resourse and Power, 2011, 29(1):6.
[15]
HAMED K H. Trend detection in hydrologic data:the Mann-Kendall trend test under the scaling hypothesis[J]. Journal of Hydrology, 2008, 349(3/4):350.
[16]
SHANNON C E. Communication theory of secrecy systems[J]. Bell Labs Technical Journal, 1949, 28(4):656.
[17]
SINGH R N, MANGLIK A. Entropy-information perspective to radiogenic heat distribution in continental crust[J]. Nonlinear Processes in Geophysics, 2013, 20(3):423.
2019, Vol. 17 
