Evolution of Wet and Dry Spells Based on Original and Corrected Precipitation Data in Southwest China, 1961–2019

Gauge-measured precipitation data have long been recognized to underestimate actual precipitation due to wind-induced error, trace precipitation, and wetting loss, which affects the spatial and temporal characteristics of precipitation. In this study, we examined spatial and temporal differences in wet and dry spell indices based on original (Po) and corrected (Pc) precipitation data and their correlations with large-scale circulation indices (LSCIs) in Southwest China during 1961–2019. The main conclusions were: (1) Pc-based trends in wet/dry spell indices were generally more pronounced than Po-based. Specifically, when Pc-based, more stations had significant changes in the MWS, MLWS, MPWS, PWS95, FWW, FDW, MDS, MLDS, NLDS, and DDS95 indices, while fewer had significant changes in the NWS, NDS, FDD, and FWD indices. (2) Spearman’s results showed that more LSCIs were significantly related to the Pc-based wet/dry spell indices than Po-based. Po-based and Pc-based MWS, Po-based MDS, and Pc-based NLDS were significantly related to the most LSCIs. Therefore, taking them as examples, wavelet transform coherence (WTC) and partial wavelet coherence (PWC) were used to explore the coherence with LSCIs. WTC results showed South Asian Summer Monsoon Index (SASMI) + Po-based MWS, Arctic Oscillation (AO) + Po-based MDS, SASMI + Pc-based MWS, Asia Polar Vortex Intensity Index (APVI) + Pc-based NLDS exhibited the most obvious periodic resonance with main resonance periods of 2.13~7.8 year, 2.19~10.41 year, 2.13~12.13 year, 2.75~18.56 year, respectively. Since WTC may arbitrarily ignore the interaction between LSCIs, PWC is adopted for further analysis. PWC results showed the coherence of AO +Po-based MDS significantly increased after eliminating the Nino Eastern Pacific index (NEP) influence, with the main resonance period of 6.56~18.56 year. This study clearly demonstrated that corrected precipitation data should be used to improve the accuracy of drought assessments, climate models, eco-hydrological models, etc.

[1]  K. Ostad-Ali-Askari,et al.  Estimating potential reference evapotranspiration using time series models (case study: synoptic station of Tabriz in northwestern Iran) , 2022, Applied Water Science.

[2]  K. Ostad-Ali-Askari,et al.  Investigation of meteorological variables on runoff archetypal using SWAT: basic concepts and fundamentals , 2022, Applied Water Science.

[3]  R. Allan,et al.  Consistent Trends in Dry Spell Length in Recent Observations and Future Projections , 2022, Geophysical Research Letters.

[4]  Yixuan Wang,et al.  Long-term spatiotemporal variability in occurrences of wet and dry days across South Mongolian Plateau , 2021 .

[5]  A. Gobin,et al.  Spatio-temporal variability of dry and wet spells and their influence on crop yields , 2021 .

[6]  Z. Ma,et al.  The unevenness in observed daily precipitation in mainland China , 2021, Theoretical and Applied Climatology.

[7]  Junguo Liu,et al.  Extreme precipitation variability across the Lancang‐Mekong River Basin during 1952–2015 in relation to teleconnections and summer monsoons , 2021 .

[8]  Z. Zuo,et al.  Changing structures of summertime heatwaves over China during 1961–2017 , 2021, Science China Earth Sciences.

[9]  L. Amekudzi,et al.  Characterisation of wet and dry spells and associated atmospheric dynamics at the Pra River catchment of Ghana, West Africa , 2021 .

[10]  Jianqi Sun,et al.  Characteristics of spring consecutive dry days with different durations across China based on the objective zoning approach , 2021, Atmospheric Science Letters.

[11]  C. Reason,et al.  Dry Spells, Wet Days, and Their Trends Across Southern Africa During the Summer Rainy Season , 2021, Geophysical Research Letters.

[12]  Xi Chen,et al.  Climate and land use influences on changing spatiotemporal patterns of mountain vegetation cover in southwest China , 2021, Ecological Indicators.

[13]  A. Mailhot,et al.  Evolution of Dry and Wet Spells Under Climate Change Over North‐Eastern North America , 2021, Journal of Geophysical Research: Atmospheres.

[14]  Wei Hu,et al.  Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences , 2021 .

[15]  Kaicun Wang,et al.  Contrast patterns and trends of lapse rates calculated from near-surface air and land surface temperatures in China from 1961 to 2014. , 2020, Science bulletin.

[16]  E. Dai,et al.  Spatio-temporal analysis of the human footprint in the Hengduan Mountain region: Assessing the effectiveness of nature reserves in reducing human impacts , 2020, Journal of Geographical Sciences.

[17]  A. Mishra,et al.  Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation , 2020, Nature Communications.

[18]  Hongfei Zhao,et al.  Evapotranspiration as a response to climate variability and ecosystem changes in southwest, China , 2020, Environmental Earth Sciences.

[19]  Xiaomeng Song,et al.  Potential linkages of precipitation extremes in Beijing-Tianjin-Hebei region, China, with large-scale climate patterns using wavelet-based approaches , 2020, Theoretical and Applied Climatology.

[20]  G. Ren,et al.  Precipitation Trends Over Mainland China From 1961–2016 After Removal of Measurement Biases , 2020, Journal of Geophysical Research: Atmospheres.

[21]  G. Hegerl,et al.  Human influence strengthens the contrast between tropical wet and dry regions , 2020, Environmental Research Letters.

[22]  P. Drobinski,et al.  Evolution of Mediterranean extreme dry spells during the wet season under climate change , 2019, Regional Environmental Change.

[23]  G. Ren,et al.  Bias Correction of Gauge Data and its Effect on Precipitation Climatology over Mainland China , 2019, Journal of Applied Meteorology and Climatology.

[24]  S. Vicente‐Serrano,et al.  Impact of the dry-day definition on Mediterranean extreme dry-spell analysis , 2019, Natural Hazards and Earth System Sciences.

[25]  Lu Gao,et al.  Statistical analyses of spatial and temporal variabilities in total, daytime, and nighttime precipitation indices and of extreme dry/wet association with large-scale circulations of Southwest China, 1961–2016 , 2019, Atmospheric Research.

[26]  S. Vicente‐Serrano,et al.  Impact of the dry day definition on Mediterranean extreme dry spells analysis , 2019 .

[27]  Yi Li,et al.  Bias correction of precipitation data and its effects on aridity and drought assessment in China over 1961-2015. , 2018, The Science of the total environment.

[28]  Yi Li,et al.  Bias correction of the observed daily precipitation and re‐division of climatic zones in China , 2018 .

[29]  Asim Biswas,et al.  Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses , 2017 .

[30]  Zhuguo Ma,et al.  Characteristics of consecutive dry days variations in China , 2017, Theoretical and Applied Climatology.

[31]  Josyane Ronchail,et al.  Evolution of wet‐day and dry‐day frequency in the western Amazon basin: Relationship with atmospheric circulation and impacts on vegetation , 2016 .

[32]  X. Qin,et al.  Seasonal and Interannual Variability of Wet and Dry Spells over Two Urban Regions in the Western Maritime Continent , 2016 .

[33]  R. Wu,et al.  Spatial and temporal characteristics of summer precipitation events spanning different numbers of days over Asia , 2016 .

[34]  S. Seneviratne,et al.  Assessment of future changes in water availability and aridity , 2015, Geophysical research letters.

[35]  C. Shope,et al.  Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution , 2015 .

[36]  Shanlei Sun,et al.  The spatial and temporal analysis of precipitation concentration and dry spell in Qinghai, northwest China , 2015, Stochastic Environmental Research and Risk Assessment.

[37]  Yifei Zhao,et al.  Spatial distribution and temporal trends in precipitation extremes over the Hengduan Mountains region, China, from 1961 to 2012 , 2014 .

[38]  N. Diffenbaugh,et al.  Observed changes in extreme wet and dry spells during the South Asian summer monsoon season , 2014 .

[39]  B. Schraven,et al.  Time matters: shifting seasonal migration in Northern Ghana in response to rainfall variability and food insecurity , 2014 .

[40]  Richard P. Allan,et al.  Observed and simulated precipitation responses in wet and dry regions 1850–2100 , 2013 .

[41]  C. Simmer,et al.  Changes in the Duration of European Wet and Dry Spells during the Last 60 Years , 2013 .

[42]  Baolong Wang,et al.  Extreme drought changes in Southwest China from 1960 to 2009 , 2013, Journal of Geographical Sciences.

[43]  James R. Gord,et al.  Application of the Cross Wavelet Transform and Wavelet Coherence to OH-PLIF in Bluff-Body Stabilized Flames , 2013 .

[44]  Johnny C. L. Chan,et al.  Geophysical Applications of Partial Wavelet Coherence and Multiple Wavelet Coherence , 2012 .

[45]  V. Singh,et al.  Spatial–temporal changes of precipitation structure across the Pearl River basin, China , 2012 .

[46]  K. Tian,et al.  Wetland changes and droughts in southwestern China , 2012 .

[47]  Vijay P. Singh,et al.  Analysis of the periods of maximum consecutive wet days in China , 2011 .

[48]  N. Singh,et al.  The Wet and Dry Spells across India during 1951-2007 , 2010 .

[49]  Daqing Yang,et al.  Effects of bias correction on precipitation trend over China , 2007 .

[50]  Robert Lund,et al.  A Review and Comparison of Changepoint Detection Techniques for Climate Data , 2007 .

[51]  Francis W. Zwiers,et al.  Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes , 2005 .

[52]  T. Koike,et al.  A Bias-Corrected Precipitation Climatology for China , 2004 .

[53]  Aslak Grinsted,et al.  Nonlinear Processes in Geophysics Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series , 2022 .

[54]  Khaled H. Hamed,et al.  A modified Mann-Kendall trend test for autocorrelated data , 1998 .

[55]  Thomas R. Karl,et al.  Overcoming biases of precipitation measurement : a history of the USSR experience , 1991 .

[56]  W. Kruskal.  Ordinal Measures of Association , 1958 .

[57]  Jianqi Sun,et al.  Decadal change of extreme consecutive dry days in spring over the middle and lower reaches of the Yangtze River around the early 2000s: The synergistic effect of mega-El Niño/Southern Oscillation, Atlantic Multidecadal Oscillation, and Arctic sea ice , 2022, Atmospheric Research.

[58]  S. Vicente‐Serrano,et al.  Impact of the dry day definition on Mediterranean extreme dry 1 spells analysis 2 3 , 2019 .

[59]  Jie Lin,et al.  Changing characteristics of wet/dry spells during 1961–2008 in Sichuan province, southwest China , 2015, Theoretical and Applied Climatology.

[60]  Shang Ke-zheng.  Temporal and spatial characteristic of rainless days in mainland China in recent 50 years , 2011 .

[61]  A. Jemain,et al.  Fitting the distribution of dry and wet spells with alternative probability models , 2009 .

[62]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[63]  H. Alexandersson.  A homogeneity test applied to precipitation data , 1986 .

[64]  H. Theil.  A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .