Future precipitation extremes over India from the CORDEX-South Asia experiments

Rai, P.; Choudhary, Anubhav; Dimri, A. P.

doi:10.1007/s00704-019-02784-1

Cited by 27 publications

(20 citation statements)

References 55 publications

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“…This increase follows a pattern with the maxima over the southern peninsular region (36–48 days) followed by the central and eastern Indian region (20–36) and western India and part of the western part of Gangetic plains (4–20 days). The previous suite of CORDEX‐SA experiments also suggests a marked increase in the frequency of CWD over central India (Rai et al ., 2019). Interestingly, the eastern J&K which consists of the western Himalayas and northern Arunachal Pradesh also reflects an increase of CWD per year (36–48).…”

Section: Resultsmentioning

confidence: 99%

Present and future changes in precipitation characteristics during Indian summer monsoon in CORDEX‐CORE simulations

Maharana

Kumar

Das

et al. 2020

Intl Journal of Climatology

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This work examines the changes in precipitation characteristics over the Indian region using the latest high‐resolution CORDEX‐CORE model simulations. Individual RCM (COSMO, RegCM4.7, and REMO) experiments, their ensembles, and all RCM ensembles are examined to evaluate their performance using various statistical metrics. It aims to provide a holistic idea for choosing better models for specific analysis (variability, climatology, temporal evolution, indices, etc.) of precipitation distribution over India. The COSMO model experiments show lesser mean bias and have a high correlation coefficient (>0.8) compared to REMO and RegCM4. Each RCM ensemble performs better than its members in representing the spatial patterns of precipitation. Interestingly, the RCM experiments downscaling the forcings from MPI_ESM GCMs and ERA‐Interim outperforms other RCM experiments in the present day period. The probability distribution function reflects that the mean frequency and the intensity of precipitation are best represented in the RegCM4 ensemble. Further, most RCMs agree to a robust increase in low‐intensity rainfall (>0.1–4 mm⋅day‑1) in the future under the RCP8.5 scenario. The intraseasonal variability in terms of active and break spells are best represented in the RegCM4 ensemble. There exists uncertainty in terms of projection of active phases, while all model experiments agree to the decrease in the break phases in the near future compared to the present day. The projected consecutive dry (wet) days and their spells will decrease (increase) over India. The heavy precipitation events are expected to increase (by 18–32 days) along with its contribution (by 14–48%) towards the total precipitation over entire India indicating a possible increase in flooding events in the future. The present‐day ISM characteristics is well captured in the model ensemble compared to individual experiments. The model ensemble indicates towards a robust increase in low‐intensity rainfall in the future. The very heavy precipitation events and its contribution towards total precipitation are increasing in the future.

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Section: Resultsmentioning

confidence: 99%

Present and future changes in precipitation characteristics during Indian summer monsoon in CORDEX‐CORE simulations

Maharana

Kumar

Das

et al. 2020

Intl Journal of Climatology

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“…Ensemble and multiple ensembles give reliable mean precipitation and temperature with observed values. Simulations with different greenhouse gas scenarios are likely to produce increasing precipitation over India in near future [93]. The transfer of simulated values across different scales, models and regions is still a challenge.…”

Section: Discussionmentioning

confidence: 99%

“…Model selection to evaluate future climate change considers the fact that increasing greenhouse gases plays a major role. Future climate changes due to rising greenhouse gases are expected to increase both the intensity and frequency of precipitation in central India from 2070 to 2099 [93]. Several studies reveal the impact of future climate change extremes over India in terms of precipitation such as increased (floods) or scarcity (drought) or number of wet and dry days.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation

Chokkavarapu¹,

Mandla²

2019

SN Appl. Sci.

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Water resources are naturally influenced by weather, topography, geology and environment. These factors cause difficulties in evaluating future water resources under changing climate. The Intergovernmental Panel on Climate Change (IPCC) reported that the increasing greenhouse gases which can cause sea level rise increase frequency of storms, heavy rainfall events and droughts. In order to quantify the future change of the hydrological cycle rainfall, different modeling approaches like the use of global climate models, regional climate models and hydrological models (for local scale about 200 km 2) are being used. This paper aims to investigate the challenges in modeling because of different parameter or variable or model conceptualization uncertainties using different scenarios, downscaling methods and climatic variables. This work helps to quantify future water resources to maintain quality of water to support aquatic life, agriculture and industrial needs. Challenges in climate change impact analysis in the present research and knowledge gaps are also discussed.

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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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Future precipitation extremes over India from the CORDEX-South Asia experiments

Cited by 27 publications

References 55 publications

Present and future changes in precipitation characteristics during Indian summer monsoon in CORDEX‐CORE simulations

Present and future changes in precipitation characteristics during Indian summer monsoon in CORDEX‐CORE simulations

Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation

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

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