4.4 Article

Spatiotemporal Variability in Future Extreme Temperatures and Rainfall in the Yangtze River Basin: Update Using Bias-Corrected Climate Projections Fitted by Stationary and Nonstationary Model

Journal

JOURNAL OF HYDROLOGIC ENGINEERING
Volume 24, Issue 11, Pages -

Publisher

ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)HE.1943-5584.0001847

Keywords

Climate extremes; Generalized extreme value distribution; Uncertainty; Nonstationary; Bias correction

Funding

  1. National Key R&D Program of China [2018YFC0407902]
  2. National Natural Science Foundation of China [U1765201, 51609061, 51709237]
  3. Fundamental Research Funds for the Central Universities [2018B11314]
  4. Science and Technology Planning Project of the Department of Water Resources of Zhejiang Province [RA1603]
  5. Zhejiang provincial scientific research institutes [2017F30009]
  6. Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

Ask authors/readers for more resources

The Yangtze River is the third largest river basin in the world. With the advancement of research methods and data quality, the understanding of extreme climate changes in the Yangtze River Basin is constantly updated. This study used bias corrected climate projections fitted by stationary and nonstationary extreme generalized extreme value models to quantify historical warm, cold, and rainfall extremes in the Yangtze River Basin and their possible future changes. The uncertainty resulting from the intermodel and emission scenario differences was also discussed. The future annual maximum (minimum) temperature will possibly increase by 1.45 degrees C-4.02 degrees C (1.07 degrees C-2.03 degrees C), 1.54 degrees C-4.45 degrees C (0.99 degrees C-1.76 degrees C), and 1.60 degrees C-4.91 degrees C (0.84 degrees C-1.54 degrees C) in 2100 at the 10-, 20-, and 50-year return periods, respectively. The precipitation extremes are expected to increase by 6.4%-11.6%, 6.6%-12.5%, and 7.0%-14.6% at the 10-, 20-, and 50-year return periods, respectively. The warming trend and spatiotemporal distribution are mainly affected by monsoon climate, altitude, and ocean characteristics, and it is more pronounced in the middle and lower reaches of the river for warm extremes, but in plateau and coastal regions for cold extremes. The stationary generalized extreme value (GEV) model may lead to an overestimation of simulated temperature and precipitation extremes, with a deviation of <1.0 degrees C for temperature extremes and <10% for precipitation extremes.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available