Univariate Statistics of the RCPs Forced ET–SCI Based Extreme Climate Indices Over Pakistan

The impact of extreme climate on natural ecosystems and socioeconomic systems is more serious than that of the climate’s mean state. Based on the data of 1698 meteorological stations in China from 2001 to 2018, this study calculated the 27 extreme climate indices of the Expert Team on Climate Change Detection and Indices (ETCCDI). Through correlation analysis and collinearity diagnostics, we selected two representative extreme temperature indices and three extreme precipitation indices. The spatial scale of the impact of extreme climate on Normalized Difference Vegetation Index (NDVI) in China during the growing season from 2001 to 2018 was quantitatively analyzed, and the complexity of the dominant factors in different regions was discussed via clustering analysis. The research results show that extreme climate indices have a scale effect on vegetation. There are spatial heterogeneities in the impacts of different extreme climate indices on vegetation, and these impacts varied between the local, regional and national scales. The relationship between the maximum length of a dry spell (CDD) and NDVI was the most spatially nonstationary, and mostly occurred on the local scale, while the effect of annual total precipitation when the daily precipitation amount was more than the 95th percentile (R95pTOT) showed the greatest spatial stability, and mainly manifested at the national scale. Under the current extreme climate conditions, extreme precipitation promotes vegetation growth, while the influence of extreme temperature is more complicated. As regards intensity and range, the impact of extreme climate on NDVI in China over the past 18 years can be categorized into five types: the humidity-promoting type, the cold-promoting and drought-inhibiting compound type, the drought-inhibiting type, the heat-promoting and drought-inhibiting compound type, and the heat-promoting and humidity-promoting compound type. Drought is the greatest threat to vegetation associated with extreme climate in China.

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Future weather types and their influence on mean and extreme climate indices for precipitation and temperature in Central Europe

Meteorologische Zeitschrift ◽

10.1127/0941-2948/2014/0519 ◽

2014 ◽

Vol 23 (3) ◽

pp. 231-252 ◽

Cited By ~ 9

Author(s):

Ulf Riediger ◽

Annegret Gratzki

Keyword(s):

Central Europe ◽

Climate Indices ◽

Weather Types ◽

Extreme Climate ◽

Precipitation And Temperature

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Future projection of the effects of climate change on saffron yield and spatial-temporal distribution of cultivation by incorporating the effect of extreme climate indices

Theoretical and Applied Climatology ◽

10.1007/s00704-020-03241-0 ◽

2020 ◽

Vol 141 (3-4) ◽

pp. 1109-1118

Author(s):

Saeedeh Kouzegaran ◽

Mohamad Mousavi Baygi ◽

Iman Babaeian ◽

Abbas Khashei-Siuki

Keyword(s):

Climate Change ◽

Temporal Distribution ◽

Climate Indices ◽

Future Projection ◽

Extreme Climate

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Historical and future climates over the upper and middle reaches of the Yellow River Basin simulated by a regional climate model in CORDEX

Climate Dynamics ◽

10.1007/s00382-020-05617-4 ◽

2021 ◽

Cited By ~ 1

Author(s):

Xuejia Wang ◽

Deliang Chen ◽

Guojin Pang ◽

Xiaohua Gou ◽

Meixue Yang

Keyword(s):

River Basin ◽

Yellow River ◽

Yellow River Basin ◽

Global Climate Models ◽

The Yellow River ◽

Climate Indices ◽

Extreme Climate ◽

Mean Temperature ◽

Rcp 8.5 ◽

The Yellow River Basin

AbstractDespite the importance of the Yellow River to China, climate change for the middle reaches of the Yellow River Basin (YRB) has been investigated far less than for other regions. This work focuses on future changes in mean and extreme climate of the YRB for the near-term (2021–2040), mid-term (2041–2060), and far-term (2081–2100) future, and assesses these with respect to the reference period (1986–2005) using the latest REgional MOdel (REMO) simulations, driven by three global climate models (GCMs) and assuming historical and future [Representative Concentration Pathway (RCP) 2.6 and 8.5] forcing scenarios, over the CORDEX East Asia domain at 0.22° horizontal resolution. The results show that REMO reproduces the historical mean climate state and selected extreme climate indices reasonably well, although some cold and wet biases exist. Increases in mean temperature are strongest for the far-term in winter, with an average increase of 5.6 °C under RCP 8.5. As expected, the future temperatures of the warmest day (TXx) and coldest night (TNn) increase and the number of frost days (FD) declines considerably. Changes to mean temperature and FD depend on elevation, which could be explained by the snow-albedo feedback. A substantial increase in precipitation (34%) occurs in winter under RCP 8.5 for the far-term. Interannual variability in precipitation is projected to increase, indicating a future climate with more extreme events compared to that of today. Future daily precipitation intensity and maximum 5-day precipitation would increase and the number of consecutive dry days would decline under RCP 8.5. The results highlight that pronounced warming at high altitudes and more intense rainfall could cause increased future flood risk in the YRB, if a high GHG emission pathway is realized.

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Comparative assessment of extreme climate variability and human activities on regional hydrologic droughts in the Weihe River basin, North China

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-369-141-2015 ◽

2015 ◽

Vol 369 ◽

pp. 141-146

Author(s):

H. Shen ◽

L. Ren ◽

F. Yuan ◽

X. Yang

Keyword(s):

River Basin ◽

Human Activity ◽

Human Activities ◽

Climate Extremes ◽

Hydrologic Model ◽

Climate Indices ◽

Extreme Climate ◽

Runoff Series ◽

Weihe River ◽

Weihe River Basin

Abstract. Drought is a comprehensive phenomenon not only resulting from precipitation deficits and climatic factors, but also being related to terrestrial hydrologic conditions and human activities. This paper investigated the relationships among regional hydrologic drought, climate extremes and human activities in the Weihe River basin, northwest China, where is also called Guanzhong Plain. First, the study period was divided into baseline and variation period according to the runoff trend analysis. Subsequently, the variable infiltration capacity (VIC) macroscale distributed hydrologic model was applied to reconstruct the natural runoff series in variation period. Furthermore, the effects of climate change and human activities on runoff were separated by the modelling results. Finally, standardized runoff index (SRI) and extreme climate indices were generated to quantatively assess the relationships among hydrologic droughts, climate extremes and human activity impacts. The results indicated that human activity impacts is a remarkable source of runoff reduction and represented an in-phase pattern of SRI-based drought severity and warm days. It also showed that the SRI-based floods and droughts characteristics are in good correlation with extreme precipitation.

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