Characteristics of observed rainfall over Odisha: An extreme vulnerable zone in the east coast of India

Swain, Madhusmita; Sinha, P. C.; Pattanayak, Sujata; Guhathakurta, Pulak; Mohanty, U. C.

doi:10.1007/s00704-019-02983-w

Cited by 14 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the eastern part exhibits a changing deficit volume pattern from north to south, closely resembling the spatial rainfall trend (Swain et al, 2020). More irrigation and the type of irrigation in northern India modify the intra-seasonal properties of monsoonal precipitation, causing delayed arrival of droughts (Devanand et al, 2019).…”

Section: Spatiotemporal Evolution Of Streamflow Droughtsmentioning

confidence: 84%

Streamflow drought onset and severity explained by non‐linear responses between climate‐catchment and land surface processes

Raut,

Ganguli,

Kumar

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

Knowledge of drought onset and its relationship with drought severity (deficit volume) is crucial for providing timely information for reservoir operations, irrigation scheduling, devising cropping choices and patterns and managing surface and groundwater water resources. An analysis of the relationship between drought onset timing and deficit volume can help in drought hazard assessments and associated risks. Despite its importance, little attention has been paid to understand the drought onset timing and its potential linkage with deficit volume for effective drought monitoring and its impact assessment. Further, only a few studies have explored the role of environmental controls, encompassing the interaction between climate, catchment and land‐surface processes in influencing streamflow droughts and associated characteristics such as onset time and severity. This study leverages quality‐controlled streamflow observations from 1965 to 2018 to unveil regional patterns of streamflow drought onset, at‐site trends in deficit volume and detect non‐linear relationships between onset timing and deficit volume across 82 rain‐fed catchments in peninsular India (8°–24° N, 72°–87° E). We show that around 12% of catchments show an earlier onset of streamflow droughts in conjunction with a decreasing trend in deficit volume. Further, approximately one‐third of the catchments show a significant non‐linear dependency between drought deficit volume and onset time. Among catchment controls, such as soil and topographic properties, we found soil organic carbon stock and stock as dominant drivers controlling the streamflow drought onset time. Likewise, sand content and vertical distance to channel network control the streamflow deficit volume. Finally, the linkages between inferred dominant low‐flow generation mechanisms and the specific combinations of environmental controls are synthesized in a conceptual diagram that might assist in developing appropriate models for low‐flow simulations and predictions, especially across ungauged sites. The new insights add value to understanding the chain of physical processes linking climatic and physiographic controls on streamflow droughts, which can support drought forecasting and climate impact assessment efforts.

show abstract

Section: Spatiotemporal Evolution Of Streamflow Droughtsmentioning

confidence: 84%

Streamflow drought onset and severity explained by non‐linear responses between climate‐catchment and land surface processes

Raut,

Ganguli,

Kumar

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Besides these, Bhubaneswar also experiences heatwave conditions during summer regularly. In addition, Swain et al, 2020 showed that urbanization-linked extreme rainfall events and their intensity are increasing over Bhubaneswar.…”

Section: Study Regionmentioning

confidence: 99%

“…Another study using the Empirical Orthogonal function analysis found that 0.77 °C in the observed warming of 1.37 °C over Korea is due to the urban effect, and the greenhouse effect causes the remaining 0.60 °C (Kim & Kim, 2011). In India, several studies related to UHI and its effect on temperature were conducted over multiple cities (Butsch et al, 2017;Mohan et al, 2013Mohan et al, , 2012Sethi et al, 2022;Sussman et al, 2019;Swain et al, 2017Swain et al, , 2020. However, systematically segregating and quantifying the impact of urbanization and the regional effect (related to climate change) is still unexplored over the Indian region.…”

Section: Introductionmentioning

confidence: 99%

A modelling study on quantifying the impact of urbanization and regional effects on the wintertime surface temperature over a rapidly-growing tropical city

et al. 2022

Self Cite

View full text Add to dashboard Cite

Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.

show abstract

“…Pai et al (2014) had used the inverse distance weighted interpolation approach to prepare the dataset for the period 1901-2010. This dataset has been widely used in monsoon and meteorological drought studies over the Indian region (Mukherjee et al, 2018;Bisht et al, 2019;Swain et al, 2020).…”

Section: Study Area and Data Usedmentioning

confidence: 99%

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

Samantaray

Ramadas

Panda

2020

Intl Journal of Climatology

View full text Add to dashboard Cite

Drought patterns are better understood by analysing the joint dependence among long-term drought characteristics: severity, duration, and frequency. Changes in these drought characteristics are also likely due to potential climate change, for instance, increased drought severity and duration, increased frequency of drought events, and increased spatial extent of drought impacts. In this study, we performed a regional-scale analysis of potential changes in meteorological drought characteristics in a tropical region in India under a warming future climate scenario. The meteorological drought characteristics were computed using the Standardized Precipitation Index. The Coordinated Regional Downscaling Experiment South Asia Regional Climate Model future precipitation simulation under the RCP 4.5 scenario was bias-corrected and utilized for future drought frequency analysis. Regionalization of meteorological drought characteristics was performed using the simple k-means clustering technique to identify the homogeneous regions for baseline and future periods. Bivariate copula was utilized to model the dependence among the drought characteristics in order to develop the S-D-F curves. The changes in regionalization and regional S-D-F curves under future potential climate change were then investigated. The results of the study suggest an increase in drought severity by 2-3% for long-term droughts (duration more than 5 months) in the future as indicated by upward shift and changes in the slope of the regional S-D-F curves. There is a projected increase in the occurrence of long-term droughts in the future when compared to short-term (duration of 1 to 3 months) droughts. We also observed a considerable spatial shift of drought hotspots, the severe drought-prone regions lie in the eastern part of the study region in the future. Future projections of regional S-D-F curves and Spatiotemporal drought characteristics obtained from the proposed framework may be utilized for devising drought mitigation strategies.

show abstract

Characteristics of observed rainfall over Odisha: An extreme vulnerable zone in the east coast of India

Cited by 14 publications

References 26 publications

Streamflow drought onset and severity explained by non‐linear responses between climate‐catchment and land surface processes

Streamflow drought onset and severity explained by non‐linear responses between climate‐catchment and land surface processes

A modelling study on quantifying the impact of urbanization and regional effects on the wintertime surface temperature over a rapidly-growing tropical city

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

Contact Info

Product

Resources

About