Investigating the Limitations of Multi‐Model Ensembling of Climate Model Outputs in Capturing Climate Extremes DOI
Velpuri Manikanta,

V. Manohar Reddy,

Jew Das

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5711 - 5726

Published: Oct. 24, 2024

ABSTRACT In the context of climate change, widespread practice directly employing Multi‐Model Ensembles (MMEs) for projecting future extremes, without prior evaluation MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis ensemble means derived from CMIP6‐based models, including both simple and weighted averages precipitation (SEMP WEMP) temperature (SEMT WEMT) time series, as well (SEME) (WEME) extremes based on model‐by‐model analysis. The study evaluates efficacy MMEs capturing mean annual values ETCCDI indices over India period 1951–2014, utilising IMD gridded data set reference. results reveal that SEME WEME consistently align closely with across various indices. At same time, SEMP WEMP display underestimation biases ranging 20% to 80% all indices, except CWD, where there is an overestimation bias. Moreover, underestimate CDD overestimate indicating systematic bias these means, while demonstrate satisfactory performance. SEMT WEMT exhibit notable summary, adopting leads more robust assessment respectively. These findings highlight limitations traditional methodologies reproducing observed extreme events climatic zones India, offering essential insights refining models improving reliability projections specific Indian subcontinent.

Language: Английский

Past and future joint return period of precipitation extremes over South Asia and Southeast Asia DOI

V. M. Reddy,

Litan Kumar Ray

Global and Planetary Change, Journal Year: 2024, Volume and Issue: 239, P. 104495 - 104495

Published: June 12, 2024

Language: Английский

Citations

5
Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin DOI Open Access
Li Tang, Xiaohui Sun

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1424 - 1424

Published: May 16, 2024

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation multiple regression identify key factors influencing these variations their interrelationships. During study period, lake averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging 0.003 184.372 km2. coefficient variation spans 3.043 217.436, indicating considerable variability stability. Six primary determined have a significant impact fluctuations: 24 h precipitation, maximum daily hours sunshine, wind speed, minimum relative humidity, source region generally showed positive correlation. For speed (m/s), 28 correlations, five twenty-three negative coefficients −0.34 −0.63, average −0.47, an overall between speed. precipitation (mm), 36 had showing correlation, larger lakes. Furthermore, 117 sufficient model, predictive capabilities various models for showcased distinct advantages, random forest model outperforming others dataset 65 lakes, Ridge is best Lasso performs 20 Linear only 4 cases. provides fit due its ability handle large number variables consider interactions, thereby offering fitting effect. These insights are crucial understanding influence within Basin instrumental developing data.

Language: Английский

Citations

1
Non‐Stationary Flash Drought Analysis and Its Teleconnection With Low‐Frequency Climatic Oscillations DOI

Kanak Priya,

V. M. Reddy,

Litan Kumar Ray

et al.

Hydrological Processes, Journal Year: 2024, Volume and Issue: 38(10)

Published: Oct. 1, 2024

ABSTRACT Flash droughts, characterised by their rapid onset and significant impacts on local communities agriculture, pose challenges for monitoring mitigation efforts due to unpredictable nature. Therefore, this study aims investigate the occurrence, characteristics influencing factors of flash droughts in Ganga River Basin (GRB) period 1981–2020. are identified using pentad averaged root zone soil moisture (PRZSM). The Mann‐Kendall trend test is used determine spatial temporal pattern drought characteristics. Furthermore, a multivariate index (MFDI) developed account combined effects Finally, wavelet coherence analysis evaluates relationship between climatic oscillations MFDI at sub‐basin scale. Utilising revised identification approach incorporating non‐stationary cumulative distribution functions (CDFs), identifies GRB, particularly emphasising higher occurrences Chambal Upper Yamuna Sub‐basins. Analysis under stationary conditions reveals increased frequency, severity decline rates, highlighting impact evaporation latent heat flux. Sub‐basin demonstrates with DMI shorter time scales (1 4‐year scales), while Lower displays pronounced association NINO3.4 (5.65‐year scale), indicating climate dynamics these regions. These findings provide valuable insights monitoring, prediction management strategies changing climate, need integrated approaches address complex interplay variability GRB.

Language: Английский

Citations

1
Change and coincidence risk analysis of floods in the Mahanadi River Basin, India DOI Creative Commons

S. Ravichandran,

V. M. Reddy,

Litan Kumar Ray

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(9), P. 4254 - 4277

Published: Sept. 1, 2024

ABSTRACT Coincidence flood risk due to the simultaneous occurrences on both mainstream and its tributary results in downstream inundation of a confluence. Therefore, this study was taken up for coincidence analysis Mahanadi River basin considering annual maximum (AM) peak over threshold (POT) series. In study, Mann–Kendall trend test performed analyze magnitudes, while circular statistics used persistence timing. The joint distributions between streams were established using bivariate copula functions magnitudes occurrence dates as variables. MK revealed mixture significant insignificant trends AM series selected stations, POT Additionally, showed high level persistence. It is evident from that Seorinarayan–Bamnidhi confluence point with value 7.63 × 10−3. increases mostly late July mid-September, more frequently occurring events. obtained will help prioritizing hazard zones effective mitigation strategies basin.

Language: Английский

Citations

0
Investigating the Limitations of Multi‐Model Ensembling of Climate Model Outputs in Capturing Climate Extremes DOI
Velpuri Manikanta,

V. Manohar Reddy,

Jew Das

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5711 - 5726

Published: Oct. 24, 2024

ABSTRACT In the context of climate change, widespread practice directly employing Multi‐Model Ensembles (MMEs) for projecting future extremes, without prior evaluation MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis ensemble means derived from CMIP6‐based models, including both simple and weighted averages precipitation (SEMP WEMP) temperature (SEMT WEMT) time series, as well (SEME) (WEME) extremes based on model‐by‐model analysis. The study evaluates efficacy MMEs capturing mean annual values ETCCDI indices over India period 1951–2014, utilising IMD gridded data set reference. results reveal that SEME WEME consistently align closely with across various indices. At same time, SEMP WEMP display underestimation biases ranging 20% to 80% all indices, except CWD, where there is an overestimation bias. Moreover, underestimate CDD overestimate indicating systematic bias these means, while demonstrate satisfactory performance. SEMT WEMT exhibit notable summary, adopting leads more robust assessment respectively. These findings highlight limitations traditional methodologies reproducing observed extreme events climatic zones India, offering essential insights refining models improving reliability projections specific Indian subcontinent.

Language: Английский

Citations

0