Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Das, Subhadarsini; Das, Jew; Umamahesh, N. V.

doi:10.1002/joc.7145

Cited by 25 publications

(10 citation statements)

References 66 publications

(79 reference statements)

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“…By engaging temperature and precipitation data, Ali et al (2021) identified climate change hotspots in terms of extremes by executing a comparative analysis of local cities with sub-regional rate of change in future at annual and seasonal scales. Das et al (2021) engaged five properties namely frequency, severity, duration, peak, and areal spread of drought indices to analyze climate change hotspots in future projections. Also very recently, researchers identified thermal hotspots through heat index determination and urban heat island mitigation using a numerical microclimate model for their area of study (Perera et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

On the emergence of a predicted climate change signal: When and where it could appear over Pakistan

Khan¹,

Wazir²,

Bokhari³

et al. 2023

EUR J SUSTAIN DEV RES

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Emergence of climate change signal attributed to change in mean temperature can bring serious implications to economic stability of developing countries like Pakistan. Likewise, unawareness of vulnerability in regions of a country can direct mitigation efforts towards unwanted areas instead of towards ones that are genuinely deprived of. To address these two issues for Pakistan, we adopted a compendium of five metrics by using climate model data of near surface mean monthly temperature from output of a general circulation model MRI-ESM2-0 of Meteorological Research Institute (MRI), simulated under historical (1850-2014) and projected (2015-2100) periods for five shared socioeconomic pathways (SSPs) described in the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) originally published in the year 2021. To identify potential hotspot regions, we used four out of the five metrics i.e., change in mean (DM–vulnerability metric), standard Euclidean distance (SED–vulnerability metric), change in standard deviation (DSD–stability metric), and standard score (Z-Score–stability metric) statistics for regional bounds of Pakistan. To investigate emergence of climate change signal, we computed the fifth metric viz. signal to noise ratio (SNR–agility metric) from time series of the near surface mean monthly temperature and checked how rapidly the subject signal emerged out of variability in the studied data under different scenarios. On the estimation of vulnerability and stability, our results revealed that the Himalayan region of Pakistan (the northeast corner) repeatedly appeared to be the most qualified region to be acclaimed as a hotspot due to its reach to optimal echelons in the associated metrics of the DM (more than four degrees), the SED (up to one), the DSD (close to null) and the Z-Score (close to null) under all the studied SSP scenarios. On the estimation of agility, our results revealed that owing to allegedly sustainable scenarios (with low to medium challenges to mitigation), the SSP1, the SSP2, and the SSP4 delayed the evolution of climate change signal (between 2070 to 2100) by at least two decades as compared to allegedly perplexing (high challenges to mitigation) SSP3 and SSP5 scenarios that accelerated the appearance of the signal by crossing the SNR threshold fairly earlier (between 2040 to 2060) in the 21<sup>st</sup> century. With such knowledge at hand, this scientific contribution can advise policymakers and stakeholder agencies to exercise conversant decisions and to equip themselves with evidence to prioritize and target their resources in an informed way over Pakistan region.

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Section: Methodsmentioning

confidence: 99%

On the emergence of a predicted climate change signal: When and where it could appear over Pakistan

Khan¹,

Wazir²,

Bokhari³

et al. 2023

EUR J SUSTAIN DEV RES

View full text Add to dashboard Cite

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“…(2009). Moreover, in past, the same datasets have been used to analyse different extreme events in India (Murari et al ., 2015; Rohini et al ., 2016, 2019; Sharma and Goyal, 2018; Jha et al ., 2019; Das et al ., 2020a, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Quality control has been performed to develop the dataset and the details can be obtained in Srivastava et al (2009). Moreover, in past, the same datasets have been used to analyse different extreme events in India (Murari et al, 2015;Rohini et al, 2016Rohini et al, , 2019Sharma and Goyal, 2018;Jha et al, 2019;Das et al, 2020aDas et al, , 2021.…”

Section: Observed and Future Datasets Of Tmaxmentioning

confidence: 99%

Heat wave magnitude over India under changing climate: Projections from CMIP5 and CMIP6 experiments

Das

Umamahesh

2021

Intl Journal of Climatology

Self Cite

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Atmospheric warming is expected to increase the magnitude and frequency of extreme climatic events namely floods, droughts, heat waves, and so on. Thus, the present study attempts to answer the following research questions; (i) What are the possible implications on future heat wave events due to the projected temperature? (ii) What are the expected changes in heat wave properties under different classifications of heat wave events under different climate scenarios? (iii) Whether the future projection of heat wave events differ significantly under CMIP5 and CMIP6 experiments over India? To answer the above questions, the present study uses the bias‐corrected future projections of maximum temperature (Tmax) from 26 GCMs that is, 13 from CMIP5 and 13 from CMIP6 experiments. In addition, two RCPs (4.5 and 8.5) under CMIP5 and four SSPs (126, 245, 370, and 585) under CMIP6 scenarios are considered. The Heat Wave Magnitude Index (HWMI) is used to characterize the heat wave over India. The essential findings from the study indicate that the Tmax anomaly is likely to increase (SSP126 < SSP245 < SSP370 < SSP585 and RCP4.5 < RCP8.5) in future causing more intense, high frequency, and long duration heat wave events over India. Presently unaffected large regions of southern, northeast, and western parts are expected to be severely affected by heat wave events. More intense heat wave events are likely to occur under RCP8.5 scenario as compared to SSP585 scenario. The percentage of affected area is projected to increase significantly from SSP126 to SSP585 under extreme and very extreme heat wave events. Under RCP8.5 scenario, the average duration of heat wave events for very extreme, and super extreme classifications is significantly higher than SSP585 scenario. Our findings suggest the paramount necessity of adaptation strategies to address the adverse consequences of foreseen heat wave events.

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“…There are many methods/indexes, such as Standardized Precipitation Index (SPI) (Bayat et al, 2015), Rainfall Departure Analysis (Kumar et al, 2020), Standardized Precipitation-Evapotranspiration Index (SPEI) (Das et al, 2021) and so on, have been used to identify the prolonged meteorological drought. Saft et al (2015) introduced a drought definition algorithm that was based on the annual rainfall only, and has been proved to have lower degree of dependence and the stronger robustness than other selected approach in the south-eastern Australia catchments.…”

Section: Identification Of Prolonged Meteorological Droughtmentioning

confidence: 99%

Response of catchment water storage capacity to the prolonged meteorological drought and asymptotic climate variation

Tian

Pan

Wang

2022

Preprint

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Abstract. Studies on the hydrological response to continuous extreme and asymptotic climate change can improve our ability to cope the intensified water-related problems. Most of the existing literature focused on the runoff response to different climate change patterns, while neglected the impacts by the potential variation in the catchment water storage capacity (CWSC) that plays an important role in the transfer of climate input to the catchment runoff. This study aims to identify the response of the CWSC to the long-term meteorological drought and asymptotic climate change systematically. Firstly, the time-varying parameter is derived to reflect the CWSC periodic/abrupt variations under both drought and non-drought periods. Secondly, the change points and varying patterns of the CWSC are analysed based on the Bayesian change point analysis with multiple evaluation criteria. Finally, multiple catchment properties and climate characteristics are used to explore the possible relationship between these variables and the temporal variation characteristic of the CWSC. The catchments suffered from prolonged meteorological drought in southeast Australia are selected as the case study. Results indicate that: (1) the increase of CWSC amplitude change has been observed in 83/92 catchments during the prolonged drought period and the significant shifts in the mean value of the CWSC are detected in 77/92 catchments; (2) the median response time of CWSC for all 92 catchments with significant changes is 641.3 days; (3) the values of CWSC are changed significantly in the catchments with small area\\low elevation\\small slope range\\large forest coverage and high soil water holding capacity. This study might enhance our understanding to the variations in catchment property under different climate-changing patterns.

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Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Cited by 25 publications

References 66 publications

On the emergence of a predicted climate change signal: When and where it could appear over Pakistan

On the emergence of a predicted climate change signal: When and where it could appear over Pakistan

Heat wave magnitude over India under changing climate: Projections from CMIP5 and CMIP6 experiments

Response of catchment water storage capacity to the prolonged meteorological drought and asymptotic climate variation

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