Daytime and nighttime heat wave characteristics based on multiple indices over the China–Pakistan economic corridor

Ullah, Safi; You, Qinglong; Ullah, Waheed; Hagan, Daniel Fiifi Tawia; Ali, Amjad; Ali, Gohar; Zhang, Yuqing; Jan, Mushtaq Ahmad; Bhatti, Asher Samuel; Xie, Wei

doi:10.1007/s00382-019-04934-7

Cited by 46 publications

(28 citation statements)

References 112 publications

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“…Further, Fatima et al [69] concluded that with the rapid melting of Hindu Kush-Karakorum-Himalayan glaciers, floods and glacial lake outburst floods (GLOFs) are obvious, and depletion of freshwater availability is the next phase. Southern Pakistan also experiences warming temperatures under the influence of industrialization and transportation boom, land-use change, population pressure, vegetation loss, water resources absence, and pollution (aerosols and other chemical compounds), which pave the way for heatwaves and droughts [8,9,70].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

et al. 2020

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This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June–July–August (JJA) and December–January–February (DJF) seasons for 1970–2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models’ performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 °C) was less than that of DJF (7 to 10 °C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

et al. 2020

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“…A large number of previous studies have established a relationship between rainfall and flooding [17,52,54]. The PRB is characterized by semi-arid climatic conditions, where an enormous amount of rainfall occurs summer season due Asian monsoon system which causes flash flood [63]. The rainfall map was reclassified into five classes with natural breaks.…”

Section: Plos Onementioning

confidence: 99%

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

2020

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Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate floodprone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

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“…In recent decades, the seasonal trend of temperature has increased over northern Pakistan in all seasons (Asmat et al, 2018;Ullah et al, 2019a). The strong, frequent and prolonged heatwaves have persistently increased over southern, central and eastern parts of Pakistan (Ullah et al, 2019b;Sajjad and Ghaffar, 2019).…”

Section: Study Areamentioning

confidence: 99%

Future Changes in Seasonal Temperature Over Pakistan in CMIP6

Karim¹,

Tan²,

Ayugi³

et al. 2021

Preprint

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The present study analyzed seasonal (i.e., Dec-Jan [DJF] and June – August [JJA]) temperature change for the near (2025-2054) and far future (2070-2099) under SSP245, SSP370, and SSP585 scenarios over Pakistan. The anomalies, Mann-Kendall trend tests, Sequential Mann-Kendall trend test (SQMK), and probability density frequency (PDF) analysis were used to investigate future mean temperature variations. The DJF season projected higher increase in temperature in the northern (3.8 oC, 5.1 oC and 6.5 oC), followed by central regions (3.8 oC, 4.9 oC and 6.4 oC) under SSP245, SSP370 and SSP585 scenarios, respectively. The central region is likely to record significant increase in JJA (3.0 oC, 4.4 oC and 5.4 oC) mean temperature in far future under the given SSP scenarios. Compared to historical (PDF), the far future DJF temperature changes revealed significant higher warming over northern, central and then over southern regions under most of SSP scenarios. The southern regions are projected to possible rise in far future JJA temperatures by 2.7 oC, 3.3 oC and 4.3 oC, under SSP245, SSP370 and SSP585, respectively. The PDFs for JJA further verify the highest positive abrupt shift in temperature across the central region and then southern region. The future diverse seasonal temperature changes supports further examination of the associated mechanisms and factors responsible for temperature changes to address climate change.

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Daytime and nighttime heat wave characteristics based on multiple indices over the China–Pakistan economic corridor

Cited by 46 publications

References 112 publications

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

Future Changes in Seasonal Temperature Over Pakistan in CMIP6

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