Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

Kumar, Sachidanand; Chanda, Kironmala; Pasupuleti, Srinivas

doi:10.1007/s00704-020-03088-5

Cited by 29 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, from the entire analysis, spatio-temporal variability and changing pattern of extreme climate indices indicate that the basin will experience a reduction in precipitation and an increase in the number of dry days pushing the basin towards drier weather. This finding is in line with prior studies dealt globally [17,52,53] as well as regionally [54][55][56][57][58]. In particular, Abeysingha et al [56], after analyzing rainfall and temperature trends in the Gomati River basin, concluded that there had been a significant reduction in rainfall, consequently leading to decline in the streamflow coupled with increasing temperature results in dryness in the basin.…”

Section: Bivariate Joint Probability and Return Period Analysissupporting

confidence: 84%

“…In particular, Abeysingha et al [56], after analyzing rainfall and temperature trends in the Gomati River basin, concluded that there had been a significant reduction in rainfall, consequently leading to decline in the streamflow coupled with increasing temperature results in dryness in the basin. Sachidanand et al [58] concluded that the number of 'warm days' per year increased significantly, whereas the number of 'cold days', 'warm nights', and 'cold nights' per year decreased significantly at several locations in India. On the other side, a decreasing trend in precipitation is observed at some Uttar Pradesh locations, including the Gomati basin, highlighting the possibility of dryness in Northern India.…”

Section: Bivariate Joint Probability and Return Period Analysismentioning

confidence: 99%

See 1 more Smart Citation

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

et al. 2021

View full text Add to dashboard Cite

Accelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to -understand these events’ propagation in the near future. We have considered sixteen extreme climate indices defined by the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices from a long-term dataset (1951–2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non-stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain combinations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indicating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16-year bands, and the two-year period was dominant in the global power spectrum around 1970–1990. One interesting finding is that a dominant two-year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes.

show abstract

Section: Bivariate Joint Probability and Return Period Analysissupporting

confidence: 84%

Section: Bivariate Joint Probability and Return Period Analysismentioning

confidence: 99%

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Ghodichore et al, 2018 found that reanalysis dataset (CFSR and MERRA-L) overestimated (underestimated) the high (low) temperatures over the northwestern (southeastern) regions of India. Kumar et al, (2020) found that trend pattern of reanalysis (Climate Prediction Centre, CPC) and observed (IMD) dataset for temperature extremes over India are nearly similar for period 1971-2013. The studies on global reanalysis datasets reported that temperature dataset is strongly affected by atmospheric boundary layer turbulence, the land surface scheme followed in the reanalysis (Wang and Zeng 2013) and there is large undesirable and nonphysical biases in reanalysis datasets which limit their ability to capture long-term trends (Bosilovich et al 2008).…”

Section: Comparison Of Observed and Reanalysis Datasetmentioning

confidence: 87%

Analysis of Temperature Extremes over India Using Sheffield & IMD Dataset

Sharma¹,

Swami²,

Joshi³

2021

Preprint

View full text Add to dashboard Cite

Temperature extremes significantly contribute to climate change; therefore, the analysis of temperature extremes is essential in detecting and attributing climate change. The present study aims to analyze the spatiotemporal variations in the temperature extremes over India, using 0.25° × 0.25° high-resolution Sheffield temperature gridded dataset for a period of 62 years (1951–2012). In addition, the spatial distribution, statistical relation and trend pattern of Sheffield’s temperature dataset is compared with 1°×1°, gridded temperature dataset of Indian Meteorological Department (IMD). The mean of temperature indices show nearly similar spatial distribution in both datasets; however trend pattern of extreme indices show significant differences over different homogeneous temperature regions of India. There is mostly disagreement between the two datasets, for trend patterns of extreme indices at different homogeneous regions, but few grids show reasonable agreement for similar trend pattern. Both datasets reported a significant decreasing trend in TX10p (amount of cool days) and TN10p (amount of cold nights) at maximum grids for the 1951–2012 period. In addition, a significant increasing trend in TX90p (amount of hot days) and WSDI (warm spell duration indicator) at maximum grids during post-1981 and 1951–2012 is observed in both datasets. A comprehensive insight into different characteristics (spatial distribution, statistical relation and trend patterns) of Sheffield’s temperature dataset will help in understanding the applicability of Sheffield temperature dataset for climate modeling and prediction studies over India.

show abstract

“…The threshold values for detecting climate change signals are carefully chosen based on the objectives of the studies. 95 th percentile values are usually adopted for upper thresholds and 5 th percentile values are adopted for lower thresholds in climate change studies globally (Karl et al 1999;Haylock and Nicholls 2000;Zhang et al 2005 a, b;Smith 2011;Knapp et al 2015;Alexander 2016;Kumar et al 2020). The highlight of this study is an attempt to comprehensively fingerprint climate change signals using six thresholds for catering to a wide range of applications that readers deem appropriate.…”

Section: Climate Scenariomentioning

confidence: 99%

Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Vijay

Sivan²,

Mudbhatkal

et al. 2021

J. Hydrol. Eng.

View full text Add to dashboard Cite

This work reports climate change signals and long-term trend analysis of climate variables, meteorological drought, and extreme climate indices over the tropical state of Kerala in India. The trend analysis reveals statistically significant decrease of annual and southwest monsoon rainfall (as much as 63 mm and 55 mm per decade respectively). A decrease in number of annual rainy days (up to 2.8 days/decade) is also reported. Temperature trend analysis indicates an increasing 2 trend with as high as 1.3°C/decade. The spatio-temporal variation of extreme climate indices across Kerala shows a decreasing trend of extreme precipitation indices and an increasing trend of extreme temperature indices. R95 and R95p decreased in northern and southern Kerala whereas R5 index increased in central and southern regions. Warm days have significantly increased whereas cold days exhibit a decreasing trend across the state. The increase in warmer nights is statistically significant whereas colder nights are decreasing in central and southern regions. Meteorological drought using Standardized Precipitation Index (SPI) reveals increasing occurrence of droughts in Kerala with higher frequencies over southern and central Kerala.

show abstract

Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

Cited by 29 publications

References 42 publications

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Analysis of Temperature Extremes over India Using Sheffield & IMD Dataset

Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Contact Info

Product

Resources

About

Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

Cited by 29 publications

References 42 publications

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Analysis of Temperature Extremes over India Using Sheffield &amp; IMD Dataset

Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Contact Info

Product

Resources

About

Analysis of Temperature Extremes over India Using Sheffield & IMD Dataset