Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics

Shashikanth, Kulkarni; Ghosh, Subimal; Hari, Vittal; Karmakar, Subhankar

doi:10.1007/s00382-017-3604-2

Cited by 39 publications

(15 citation statements)

References 78 publications

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“…We studied the projected change in extreme rainfall events simulated by the high‐resolution RCM, by computing the index for very wet days, R95p (defined as the number of days when daily mean rainfall exceeds 95th percentile value during the present‐day simulation at each grid point), during JJAS season of 10‐year period from WRF_CCSM4_PD and WRF_CCSM4_FD simulations. It is found that there are widespread increase in the occurrence of wet days over most parts of the Indian region especially most of the coastal areas, central India, and northeast India (not shown) that is consistent with the statistical downscaling study by Shashikanth et al (). Future changes in R95p index for southern WG along with the high topography contours (for 980 and 1,800 m) and for CMZ are shown in Figure c.…”

Section: Projected Changes In Indian Summer Monsoonsupporting

confidence: 88%

Monsoon Climate Change Projection for the Orographic West Coast of India Using High‐Resolution Nested Dynamical Downscaling Model

2018

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An efficient, reliable very high resolution dynamical downscaling model, a regional climate model (Weather Research and Forecasting‐Advanced Research Weather Research and Forecasting) one‐way nested into skillful general circulation model (National Center for Atmospheric Research‐Community Climate System Model version 4), is configured and implemented for ecologically sensitive, densely populated west coast of India encompassing Western Ghats (WG) having complex, meridionally oriented orography and wide biodiversity. This model with 3 km resolution resolves orographic features enabling realistic simulation of physical and dynamical characteristics of present‐day Indian summer monsoon (ISM) and extreme events, particularly recent trends in ISM rainfall over WG as observed. Marked skill of this model provides confidence in its future climate projection at regional scale. Future ISM rainfall projection shows significant increase (reduction) over 50.7% (5.8%) of Indian grid points. Significant reduction (10–20% of mean) over WG is due to upper‐tropospheric warming effect that stabilizes the atmosphere. Projected changes in extreme events show overall increase in warm days and warm nights over India with maximum increase over South India. Projected changes show widespread increase in wet days over most of India and reduction over WG. Projection of consecutive dry days implies wetter future for most parts of India but strengthened drought conditions for WG. Wind extreme projection shows strengthened (weakened) low (high) winds probability over WG and increase (decrease) in very high (low) winds over central India. This study establishes the importance of (i) employing sufficiently high‐resolution model, (ii) using bias‐corrected boundary data, and (iii) configuring model for realistic present‐day climate over complex topographic coastlines such as the west coast, in order to obtain useful climate change information for adaptation measures.

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Section: Projected Changes In Indian Summer Monsoonsupporting

confidence: 88%

Monsoon Climate Change Projection for the Orographic West Coast of India Using High‐Resolution Nested Dynamical Downscaling Model

2018

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“…The annual average rainfall in this area is 200 to 500cm, which increases from the southwestern coastal plains to the mountain areas in the east due to the orographic effect of the Western Ghats (Kuriakose et al, 2009;Sajinkumar et al, 2011). Under the global climate change, extreme rainfall events have hit India frequently (Mishra et al, 2018) and the extreme rainfall events during the monsoon season are expected to increase (Hunt and Menon, 2020;Rai et al, 2019Rai et al, , 2020Shashikanth et al, 2018), making it more vulnerable to slope failures.…”

Section: Study Areamentioning

confidence: 99%

Constructing a complete landslide inventory dataset for the 2018 Monsoon disaster in Kerala, India, for land use change analysis

Lu¹,

Rajaneesh²,

Westen³

et al. 2020

Preprint

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Abstract. Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g. rainfall, earthquake) and the density of the landslides in a particular area, as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of Early Warning Systems, and for evaluating which land use/land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive at reliable results or the above types of analysis. In this study we generated a relatively complete landslide inventory for the 2018 Monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on Object Based Image Analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre-and post-event high-resolution satellite images available in Google Earth, adjusted the two inventories and digitize landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field method and an additional 856 landslides mapped using the visual image (GE) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use/land cover changes as a causal factor for the 2018 Monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failure while 34 % of those impacting on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

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“…Between 1 June and 26 August 2018, the southern Indian state of Kerala witnessed the most severe extreme rainfall event since 1924 (Agarwal, 2018;Megha et al, 2019;Sankar, 2018;Vishnu et al, 2019). The torrential rains triggered several thousand landslides (Singh et al, 2018) and extensive flooding, affecting 5.4 million people in over 1200 villages and causing enormous property losses (buildings, roads, and agriculture damage) and more than 440 casualties (Mishra et al, 2018;Vishnu et al, 2019). Furthermore, the following year, from 8 to 14 August, 2019, Kerala was hit again by another extreme precipitation event, causing more than 100 deaths due to landslides and floods (Koshy, 2019).…”

Section: Introductionmentioning

confidence: 99%

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Hao

Westen²,

Martha

et al. 2020

Earth Syst. Sci. Data

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Abstract. Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

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Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics

Cited by 39 publications

References 78 publications

Monsoon Climate Change Projection for the Orographic West Coast of India Using High‐Resolution Nested Dynamical Downscaling Model

Monsoon Climate Change Projection for the Orographic West Coast of India Using High‐Resolution Nested Dynamical Downscaling Model

Constructing a complete landslide inventory dataset for the 2018 Monsoon disaster in Kerala, India, for land use change analysis

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

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