the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Feb 2026
A Systemic Shift towards Hydroclimatic Whiplash in India: Event-Based Evidence of Escalating Dry-Wet Transitions since 1951
Abstract. This study presents a comprehensive assessment of the evolution of hydroclimatic extremes, dry spells (DS), wet spells (WS), and their rapid transitions (whiplash) across India from 1951 till 2019. A marked reorganization of extremes emerged around the 1980s regime shift, characterized by widespread intensification of WS and a 40 % rise in DS-affected grids experiencing fewer but longer events. Using Event Coincidence Analysis, trigger relationships between extreme DS and WS are quantified, revealing that trigger coincidence rates exceeded 0.8 after the mid-1980s and increased spatially by nearly 49 % over the west coast, Central India, and the Northeast. Disaggregating transitions demonstrated an emerging dominance of dry-to-wet (DTW) behavior, with an increase of ~14 %, reflecting reorganized monsoon feedbacks. Quantification of whiplash severity revealed a 13 % and 8.7 % rise in extreme and severe whiplash frequencies and a 26.6 % increase in grids exhibiting simultaneous high frequency, duration, and intensity. Spatially, the southwest coast and northern India exhibit ‘persistently high’ risk with no recovery since 1951, while the east coast and central India show ‘emerging’ volatility. Crucially, this intensification translates into a quantifiable ‘climate penalty’ on agriculture: post-2000 wheat yields show persistent negative anomalies with an increase in exposure to extreme whiplash risk, which in turn demands an immediate pivot in adaptation and resource planning.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-6498', Subimal Ghosh, 18 Mar 2026
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AC1: 'Reply on RC1', C.T. Dhanya, 20 Oct 2026
To
The Editor
Hydrology and Earth System Sciences
We sincerely thank the reviewer for their thoughtful and constructive feedback and for the time invested in our manuscript. In addressing the reviewer's comments, we re-implemented the full analysis and subjected every result to a stricter standard of evidence.
The response sheet is attached as the supplement (pdf) below. Comments from the reviewers are written in red, the authors’ responses in black, and changes made to the manuscript in blue.
We believe that the revisions have substantially strengthened the manuscript, and we thank the reviewer again for comments that led us to correct it.
Yours sincerely,
Dhanya C.T.
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AC1: 'Reply on RC1', C.T. Dhanya, 20 Oct 2026
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RC2: 'Comment on egusphere-2025-6498', Anonymous Referee #2, 23 Sep 2026
I am concerned that the novelty of this study is not sufficiently demonstrated. Similar ECA frameworks have already been applied to drought–pluvial transitions, including trigger and precursor coincidence rates, lag windows, spatial patterns, and temporal changes (Deng et al., 2024; He & Sheffield, 2020; Li et al., 2023). The authors should clearly distinguish the present framework from these studies and explain the specific novelty of the proposed risk index and agricultural-impact analysis.
References
Deng, S., Zhao, D., Chen, Z., Liu, L., Zhu, Y., Wang, K., Gao, X., Wu, H., & Zheng, D. (2024). Global distribution and projected variations of compound drought–extreme precipitation events. Earth’s Future, 12(7), e2024EF004809. https://doi.org/10.1029/2024EF004809
He, X., & Sheffield, J. (2020). Lagged compound occurrence of droughts and pluvials globally over the past seven decades. Geophysical Research Letters, 47(14), e2020GL087924. https://doi.org/10.1029/2020GL087924
Li, Y., Cheng, L., Miao, C., & Liu, Z. (2023). Observational uncertainty for global drought–pluvial volatility. Water Resources Research, 59(12), e2022WR034263. https://doi.org/10.1029/2022WR034263
Major Comments:
1- Lines 142–164: A dry spell requires at least 30 consecutive days below the 5th percentile, whereas a wet spell may consist of only one day above the 95th percentile. This strong asymmetry could inherently affect the frequencies and subsequent DTW/WTD transition rates. The authors should justify these definitions, clarify how zero-rainfall days and seasonal percentiles are treated, and provide sensitivity tests to alternative thresholds and durations.
2- Lines 177–185: The Getis–Ord analysis uses a fixed-distance neighborhood, but the selected distance threshold is not reported or justified. More importantly, thousands of grid cells are evaluated at (p<0.05), raising a multiple-testing issue. The authors should report the spatial-weight specification and consider an FDR, permutation-based, or comparable correction for spatial significance.
3- Lines 190–208: The manuscript uses expressions such as “causal trigger” and subsequently links coincidence patterns to specific physical mechanisms. However, temporal coincidence alone cannot distinguish causation from common seasonality, persistence, or shared atmospheric forcing.
4- Lines 339–351: Increasing the temporal window from one to five years will mechanically increase the probability of finding coincident events. Therefore, the reported >50% increase cannot by itself demonstrate multi-annual hydroclimatic “memory.”The authors should compare the observed increase against an null coincidence rate and justify why multi-year windows remain consistent with the definition of rapid whiplash.
5- Lines 68–77: The discussion of long-term hydroclimatic variability and changing precipitation extremes could be strengthened by citing recent regional studies examining historical climate-driven changes in hydroclimatic patterns, such as “Analysis of Historical Global Warming Impacts on Climatological Trends for the Partially Gauged Hirmand River Basin Based on Multiple Data Products and Bias Correction Methods” and "Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districts"
6- Lines 397–437: These labels imply differences in hydrological impact, yet they appear to be defined mainly from combinations of precipitation duration and percentile thresholds. The authors should provide an objective impact-based rationale, uncertainty analysis, or independent evidence.
7- Lines 514–526: The claim of a post-2000 “climate penalty” appears stronger than the supporting evidence. The manuscript attributes negative wheat-yield anomalies to whiplash despite reporting relatively weak correlations and without explicitly controlling for irrigation, temperature, fertilizer use, crop varieties, management changes, or other drivers.
Citation: https://doi.org/10.5194/egusphere-2025-6498-RC2 -
AC2: 'Reply on RC2', C.T. Dhanya, 20 Oct 2026
To
The Editor
Hydrology and Earth System Sciences
We sincerely thank the reviewer for their thoughtful and constructive feedback and for the time invested in our manuscript. In addressing the reviewer's comments, we re-implemented the full analysis and subjected every result to a stricter standard of evidence.
The response sheet is attached as the supplement (pdf) below. Comments from the reviewers are written in red, the authors’ responses in black, and changes made to the manuscript in blue.
We believe that the revisions have substantially strengthened the manuscript, and we thank the reviewer again for comments that led us to correct it.
Yours sincerely,
Dhanya C.T.
-
AC2: 'Reply on RC2', C.T. Dhanya, 20 Oct 2026
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I have read the manuscript and appreciate the authors' excellent work. I found the work technically sound with a few moderate comments, which the authors may think of addressing:
1. What is the role of intraseasonal oscillations and their changes on the conclusions derived by the authors? Are there changes on the 10-20 day and 30-60 day scales that are responsible for the authors' findings?
2. I would love to see authors' comments on the spatial structures of the patterns, for what are the scales of the changes, over small regions, or large regions or both? What about the spatial coherence? Maybe some discussion would be useful.
3. Impacts on agriculture are good extension, but how are the authors making sure that other policy and socio-economic factors are also being considered in their analysis? If not considered, a line specifying this as a limitation would be good.