Changes in precipitation and temperature in Xiangjiang River Basin, China

Global warming brings a huge challenge to society and human being. Understanding historic and future potential climate change will be beneficial to regional crop, forest, and water management. This study aims to analyze the precipitation and temperature changes in the historic period and future period 2021–2050 in the Xiangjiang River Basin, China. The Mann–Kendall rank test for trend and change point analysis was used to analyze the changes in trend and magnitude based on historic precipitation and temperature time series. Four global climate models (GCMs) and a statistical downscaling approach, LARS-WG, were used to estimate future precipitation and temperature under RCP4.5. The results show that annual precipitation in the basin is increasing, although not significant, and will probably continue to increase in the future on the basis of ensemble projections of four GCMs. Temperature is increasing in a significant way and all GCMs projected continuous temperature increase in the future. There will be more extreme events in the future, including both extreme precipitation and temperature.

[1]  Mikhail A. Semenov,et al.  A serial approach to local stochastic weather models , 1991 .

[2]  T. Cinco,et al.  Long-term trends and extremes in observed daily precipitation and near surface air temperature in the Philippines for the period 1951–2010 , 2014 .

[3]  E. Im,et al.  An assessment of future dryness over Korea based on the ECHAM5-RegCM3 model chain under A1B emission scenario , 2012, Asia-Pacific Journal of Atmospheric Sciences.

[4]  Impact of climate change on hydro-climatic variables in Xiangjiang River basin, China , 2011, 2011 International Symposium on Water Resource and Environmental Protection.

[5]  Richard M. Vogel,et al.  Trends in floods and low flows in the United States: impact of spatial correlation , 2000 .

[6]  M. J. Booij,et al.  Uncertainty in future high flows in Qiantang River Basin, China , 2015 .

[7]  Song‐You Hong,et al.  Future climate change scenarios over Korea using a multi-nested downscaling system: A pilot study , 2010 .

[8]  Chenghai Wang,et al.  Trends in precipitation recycling over the Qinghai-Xizang Plateau in last decades , 2014 .

[9]  Variability of minimum temperature across Libya (1945–2009) , 2013 .

[10]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[11]  Yue‐Ping Xu,et al.  Impact of climate change on 24‐h design rainfall depth estimation in Qiantang River Basin, East China , 2012 .

[12]  Seok-Geun Oh,et al.  Impact of lateral boundary conditions on precipitation and temperature extremes over South Korea in the CORDEX regional climate simulation using RegCM4 , 2013, Asia-Pacific Journal of Atmospheric Sciences.

[13]  M. Suh,et al.  Prospects of using Bayesian model averaging for the calibration of one-month forecasts of surface air temperature over South Korea , 2013, Asia-Pacific Journal of Atmospheric Sciences.

[14]  Ashish Sharma,et al.  An error estimation method for precipitation and temperature projections for future climates , 2012 .

[15]  D. Jacob,et al.  Modeling extreme precipitation events—a climate change simulation for Europe , 2004 .

[16]  Chong-yu Xu,et al.  Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China , 2013 .

[17]  G. Meehl,et al.  Going to the Extremes , 2006 .

[18]  M. Semenov,et al.  Use of multi-model ensembles from global climate models for assessment of climate change impacts. , 2010 .

[19]  M. Semenov.  Simulation of extreme weather events by a stochastic weather generator , 2008 .

[20]  R. Macdonald,et al.  Effects of local and global change on an inland sea: the Strait of Georgia, British Columbia, Canada , 2009 .

[21]  Kay,et al.  Comparison of uncertainty sources for climate change impacts : flood frequency in England , 2009 .

[22]  L. Vincent,et al.  Changes in Daily and Extreme Temperature and Precipitation Indices for Canada over the Twentieth Century , 2006, Data, Models and Analysis.

[23]  Song‐You Hong,et al.  Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model , 2014, Theoretical and Applied Climatology.

[24]  C. W. Richardson,et al.  Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates , 1998 .

[25]  Xuebin Zhang,et al.  Temperature and precipitation trends in Canada during the 20th century , 2000, Data, Models and Analysis.

[26]  R. Wilby,et al.  A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK , 2006 .

[27]  Sheng Yue,et al.  The influence of autocorrelation on the ability to detect trend in hydrological series , 2002 .

[28]  C. F. Kossack,et al.  Rank Correlation Methods , 1949 .

[29]  Song‐You Hong,et al.  Future Changes in Surface Runoff over Korea Projected by a Regional Climate Model under A1B Scenario , 2014 .

[30]  Taha B. M. J. Ouarda,et al.  Automated regression-based statistical downscaling tool , 2008, Environ. Model. Softw..

[31]  J. Oh,et al.  Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea , 2011 .

[32]  Yue‐Ping Xu,et al.  Detection of trends in precipitation extremes in Zhejiang, east China , 2011, Theoretical and Applied Climatology.

[33]  G. Fu,et al.  Uncertainties in SWAT extreme flow simulation under climate change , 2014 .

[34]  Mathieu Vrac,et al.  Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change , 2011 .

[35]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[36]  U. C. Chaube,et al.  Long-term historic changes in climatic variables of Betwa Basin, India , 2014, Theoretical and Applied Climatology.

[37]  H. B. Mann.  Nonparametric Tests Against Trend , 1945 .

[38]  H. Moradkhani,et al.  Assessing the uncertainties of hydrologic model selection in climate change impact studies , 2011 .

[39]  S. Yue,et al.  Canadian streamflow trend detection: impacts of serial and cross-correlation , 2003 .

[40]  H. Hayhoe,et al.  Comparison of LARS-WG and AAFC-WG stochastic weather generators for diverse Canadian climates , 2004 .

[41]  L. J. Lane,et al.  Chapter 2. WEATHER GENERATOR , 1995 .

[42]  Song‐You Hong,et al.  Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP , 2014, Climate Dynamics.