Spatial–temporal variability and extreme climate indices of precipitation in a coastal watershed of southeastern Brazil

2021 ◽  
Vol 193 (11) ◽  
Author(s):  
Letícia Guarnier ◽  
Gilberto Fonseca Barroso
Keyword(s):  
Temporal Variability ◽  
Southeastern Brazil ◽  
Climate Indices ◽  
Coastal Watershed ◽  
Extreme Climate

scholarly journals Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Tarun Pant ◽  
Maheswaran Rathinasamy ◽  
Ankit Agarwal
Keyword(s):  
Wavelet Transform ◽  
Temporal Variability ◽  
Global Climate ◽  
Phase Relationship ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Coherence Analysis ◽  
Cross Wavelet Transform ◽  
Wavelet Coherence Analysis ◽  
Cross Wavelet

Abstract With the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis was also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.

Relationship between vegetation change and extreme climate indices on the Inner Mongolia Plateau, China, from 1982 to 2013

2018 ◽  
Vol 89 ◽  
pp. 101-109 ◽  
Author(s):  
Chunlan Li ◽  
Jun Wang ◽  
Richa Hu ◽  
Shan Yin ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Inner Mongolia ◽  
Vegetation Change ◽  
Climate Indices ◽  
Extreme Climate ◽  
Inner Mongolia Plateau

scholarly journals On the use of mean and extreme climate indices to predict sugar yield in western Fiji

2020 ◽  
Vol 29 ◽  
pp. 100271
Author(s):  
Simon McGree ◽  
Sergei Schreider ◽  
Yuriy Kuleshov ◽  
Bipendra Prakash
Keyword(s):  
Sugar Yield ◽  
Climate Indices ◽  
Extreme Climate

Extreme climate indices in Brazil: evaluation of downscaled earth system models at high horizontal resolution

2020 ◽  
Vol 54 (11-12) ◽  
pp. 5065-5088 ◽  
Author(s):  
Alvaro Avila-Diaz ◽  
Gabriel Abrahão ◽  
Flavio Justino ◽  
Roger Torres ◽  
Aaron Wilson
Keyword(s):  
Horizontal Resolution ◽  
Climate Indices ◽  
Earth System ◽  
System Models ◽  
Extreme Climate ◽  
High Horizontal Resolution ◽  
Earth System Models

scholarly journals Spatial Heterogeneity and Complexity of the Impact of Extreme Climate on Vegetation in China

2021 ◽  
Vol 13 (10) ◽  
pp. 5748
Author(s):  
Shuang Li ◽  
Feili Wei ◽  
Zheng Wang ◽  
Jiashu Shen ◽  
Ze Liang ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Extreme Temperature ◽  
Climate Indices ◽  
Natural Ecosystems ◽  
Extreme Climate ◽  
Dry Spell ◽  
Compound Type ◽  
The Impact

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.

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

2021 ◽  
Vol 5 (3) ◽  
pp. em0166
Author(s):  
Ahmad Khan Burhan ◽  
Azmat Hayat Khan ◽  
Syed Ahsan Ali Bukhari ◽  
Khurram Riaz
Keyword(s):  
Climate Indices ◽  
Extreme Climate ◽  
Univariate Statistics

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

2021 ◽  
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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