Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India DOI Creative Commons
Shoobhangi Tyagi, Sandeep Sahany, Dharmendra Saraswat

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5727 - 5744

Published: Oct. 26, 2024

ABSTRACT Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these rely on the stationarity of biases and sensitivity, which may not hold climate‐sensitive regions (e.g., semi‐arid regions). This study investigates validity assumption across 29 CMIP6 models, encompassing diverse (Dry Warm, Wet Dry Cold, Cold), utilising a region in central India as testbed. implications this projection were comprehensively assessed modelling chain three time periods (the 2030s, 2060s 2090s) based Soil Water Assessment Tool (SWAT) simulations. results highlight non‐stationary nature sensitivity under future scenarios, challenging widespread applicability common uncertainty‐constraining approaches. Moreover, impact non‐stationarity uncertainty was found be strongly influenced by choice GCMs, preprocessing methods change scenarios. In GCMs dominate uncertainty, with dry models exhibiting ~10%–15% higher compared warm is further amplified when interacting biases. However, from mid‐century onwards, bias‐adjustment scenarios significantly shape conditions. These findings emphasise potential bias sensitivity‐based GCM selection reducing near‐future assessment (2030s). For mid‐term long‐term projections, addressing through more viable. offers critical insights prioritise development non‐stationarity‐based approach reliable regions.

Language: Английский

Impacts of Extreme Climate on the Water Resource System in Sichuan Province DOI Open Access

Ma Fang,

Zhi Jun Li

Water, Journal Year: 2024, Volume and Issue: 16(9), P. 1217 - 1217

Published: April 24, 2024

Based on the data of Sichuan Province from 2007 to 2021, extreme climate events in was identified by statistical method, and coupling coordination degree water resources-climate system separate resource analyzed. difference under these two systems, influence mechanism factors is The results show that types gradually transition drought precipitation low temperature high temperature. When are not considered, generally improved distribution more concentrated. Moreover, a simple linear relationship.

Language: Английский

Citations

4
Improving trans-regional hydrological modelling by combining LSTM with big hydrological data DOI Creative Commons
Senlin Tang, Fubao Sun, Qiang Zhang

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102257 - 102257

Published: Feb. 19, 2025

Language: Английский

Citations

0
Role of Aerosols on Prolonged Extreme Heatwave Event over India and its Implication to Atmospheric Boundary Layer DOI

K. B. Betsy,

Sanjay Kumar Mehta

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102513 - 102513

Published: March 1, 2025

Language: Английский

Citations

0
A novel index combining meteorological, hydrological, and ecological anomalies used for ecological drought assessment at a grassland-type basin scale DOI
Yixuan Wang, Tingxi Liu, Limin Duan

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113384 - 113384

Published: March 25, 2025

Language: Английский

Citations

0
Rising occurrence of compound droughts and heatwaves in the Arabian Peninsula linked to large-scale atmospheric circulations DOI
Md Saquib Saharwardi, Waqar Ul Hassan, Hari Prasad Dasari

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 978, P. 179433 - 179433

Published: April 16, 2025

Language: Английский

Citations

0
Characteristics of extreme hourly precipitation variability and influencing factors in the Central Yunnan Urban Agglomeration of China DOI

Hanyu Jin,

Qingping Cheng

Journal of Geographical Sciences, Journal Year: 2025, Volume and Issue: 35(4), P. 886 - 920

Published: April 1, 2025

Language: Английский

Citations

0
Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns DOI Open Access
Lin Liao, Saeed Rad,

Junfeng Dai

et al.

Water, Journal Year: 2024, Volume and Issue: 16(17), P. 2504 - 2504

Published: Sept. 3, 2024

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should deeply explored for its patterns and associations with climatic factors. this study, based on daily precipitation atmospheric temperature datasets from 29 meteorological stations JRB vicinity 1960 2020, 10 indices (6 4 indices) were calculated. The spatial temporal variations of analyzed using Mann–Kendall analysis, explore correlation between circulation linear nonlinear perspectives via Pearson analysis wavelet coherence (WTC), respectively. Results revealed that among six selected indices, Continuous Dry Days (CDD) Wetness (CWD) showed decreasing trend, tended shorter calendar time, while other four an increasing intensity rainfall frequent. except TN10p, which significant three number low-temperature days decreased significantly, duration high increased, basin was warming continuously. Spatially, variation is obvious, mostly located western northern regions, southern northeastern makes regionalized. Linearly, most index, show trend significance obvious. Except Southern Oscillation Index (SOI), correlations Arctic (AO) has strongest CDD. Nonlinearly, NINO3.4, Pacific Decadal (PDO), SOI are not main dominating changes TN90p, average (SDII), maximum amount (RX1day), 5 (Rx5day) clearly associated patterns. This also confirms tend single relationship, but governed by complex response mechanisms. study aims help relevant decision-making authorities cope frequent JRB, provides reference predicting flood, drought waterlogging risks.

Language: Английский

Citations

1
Heatwaves in Hong Kong and their influence on pollution and extreme precipitation DOI
Changyu Li,

W. Wei,

Pak Wai Chan

et al.

Atmospheric Research, Journal Year: 2024, Volume and Issue: unknown, P. 107845 - 107845

Published: Dec. 1, 2024

Language: Английский

Citations

1
Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India DOI Creative Commons
Shoobhangi Tyagi, Sandeep Sahany, Dharmendra Saraswat

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(16), P. 5727 - 5744

Published: Oct. 26, 2024

ABSTRACT Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these rely on the stationarity of biases and sensitivity, which may not hold climate‐sensitive regions (e.g., semi‐arid regions). This study investigates validity assumption across 29 CMIP6 models, encompassing diverse (Dry Warm, Wet Dry Cold, Cold), utilising a region in central India as testbed. implications this projection were comprehensively assessed modelling chain three time periods (the 2030s, 2060s 2090s) based Soil Water Assessment Tool (SWAT) simulations. results highlight non‐stationary nature sensitivity under future scenarios, challenging widespread applicability common uncertainty‐constraining approaches. Moreover, impact non‐stationarity uncertainty was found be strongly influenced by choice GCMs, preprocessing methods change scenarios. In GCMs dominate uncertainty, with dry models exhibiting ~10%–15% higher compared warm is further amplified when interacting biases. However, from mid‐century onwards, bias‐adjustment scenarios significantly shape conditions. These findings emphasise potential bias sensitivity‐based GCM selection reducing near‐future assessment (2030s). For mid‐term long‐term projections, addressing through more viable. offers critical insights prioritise development non‐stationarity‐based approach reliable regions.

Language: Английский

Citations

0