FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203151693> ?p ?o ?g. }

• W3203151693 abstract "Forecasting temperature extremes especially heat waves are extremely important for developing preparedness and planning mitigation measures, particularly in the context of climate change. The major objective of the present study was to assess the ongoing changes and possible future changes in heat waves and development of robust statistical model for forecasting heat wave which can adapt with changing climate. Pakistan, which is one of the most affected countries of the world to heat waves in recent years was considered as the study area. Novelties of the study are the methods proposed for defining heat waves, reliable projection of heat waves with associated uncertainties, and development of robust forecasting models which can adapt with climate change. Available in-situ temperature records, gauge-based gridded temperature data and temperature simulations of general circulation data (GCM) of Coupled Model Intercomparison Project Phase 5 (CMIP5) were used for defining heat waves and assessment of historical changes and future projections of temperature extremes and heat waves, while the reanalysis atmospheric data of National Centres for Environmental Prediction (NCEP) was used for the development of heat wave forecasting models. A threshold-based approach which able to demarcate the historical heat wave affected area is proposed for defining heat waves, GCMs were selected based on their capability to simulate different characteristics of heat waves and different state-of-the-art machine learning methods (ML) were used for the development of the seasonal and daily heat wave forecasting models. The study revealed that the daily maximum temperature more than 95-th percentile threshold for consecutive five days or more can well reconstruct the spatial pattern of heat wave in Pakistan. The assessment of trends in heat waves based on the derived definition revealed increase in heat wave duration and affected area in Pakistan at a rate of 0.71 days/decade and 1.36% of the total area of Pakistan per decade respectively. Four GCMs namely, CCSM4, CESM1(BGC), CMCC-CM and NorESM1-M were found to have better ability for the projection of all the characteristics of heat waves. The projection of heat waves using the selected GCMs revealed a high increase in the heat wave indices particularly for representative concentration pathways (RCP) 8.5. Heat wave frequency was projected to increase up to 12 events per year in most parts of the country, while some areas would experience heat waves for more than 100 days in a year. The higher increase in heat waves indices was projected in highly populated eastern and southern coastal regions which are already prone to high occurrence of heat waves. Forecasting models were developed for the prediction of triggering date and seasonal number of heat waves days in order to aid in coping and mitigation capacity revealed that the Quantile Regression Forests (QRF) models were able to forecast the triggering and departure dates of heat waves with an accuracy of up to ±5 days. On the other hand, the Support Vector (SVM) model was found to have the higher skills in forecasting number of heat wave days with a month time-lag (R2: 0.89- 0.9, NRMSE%: 32.6-31.8, rSD: 0.98-0.96, and md: 0.8). The analysis of synoptic patterns revealed that the wind vectors, relative humidity and geopotential height are the most potential indicators of heat waves in Pakistan. The forward-rolling based forecasting model proposed for the prediction of heat waves to accommodate the changing pattern of the atmospheric variables responsible for heat waves due to the changes in climate was found to forecast heat waves reliably." @default.
• W3203151693 created "2021-10-11" @default.
• W3203151693 creator A5015456250 @default.
• W3203151693 date "2020-01-01" @default.
• W3203151693 modified "2023-09-23" @default.
• W3203151693 title "Projection and prediction of heat waves for an arid region in the context of climate change" @default.
• W3203151693 cites W1003830771 @default.
• W3203151693 cites W1484318217 @default.
• W3203151693 cites W1486089539 @default.
• W3203151693 cites W1495582736 @default.
• W3203151693 cites W1497571021 @default.
• W3203151693 cites W1502559904 @default.
• W3203151693 cites W1512124184 @default.
• W3203151693 cites W1515170039 @default.
• W3203151693 cites W1533882250 @default.
• W3203151693 cites W1544875105 @default.
• W3203151693 cites W1569334469 @default.
• W3203151693 cites W1572280136 @default.
• W3203151693 cites W1573647811 @default.
• W3203151693 cites W1578261214 @default.
• W3203151693 cites W1582248076 @default.
• W3203151693 cites W1587401723 @default.
• W3203151693 cites W1592164317 @default.
• W3203151693 cites W1603384336 @default.
• W3203151693 cites W1604730974 @default.
• W3203151693 cites W1650177443 @default.
• W3203151693 cites W1675211843 @default.
• W3203151693 cites W1681521146 @default.
• W3203151693 cites W1681926796 @default.
• W3203151693 cites W1742010618 @default.
• W3203151693 cites W1743406137 @default.
• W3203151693 cites W174598607 @default.
• W3203151693 cites W1796961055 @default.
• W3203151693 cites W1833005471 @default.
• W3203151693 cites W1835464942 @default.
• W3203151693 cites W1840372393 @default.
• W3203151693 cites W1840707952 @default.
• W3203151693 cites W1865770566 @default.
• W3203151693 cites W1867005924 @default.
• W3203151693 cites W1868761371 @default.
• W3203151693 cites W1897865373 @default.
• W3203151693 cites W1899516746 @default.
• W3203151693 cites W1906255492 @default.
• W3203151693 cites W1959213299 @default.
• W3203151693 cites W1964298122 @default.
• W3203151693 cites W1964777372 @default.
• W3203151693 cites W1964896449 @default.
• W3203151693 cites W1965731662 @default.
• W3203151693 cites W1965909668 @default.
• W3203151693 cites W1966002639 @default.
• W3203151693 cites W1967460272 @default.
• W3203151693 cites W1967779963 @default.
• W3203151693 cites W1967989039 @default.
• W3203151693 cites W1968325190 @default.
• W3203151693 cites W1970020090 @default.
• W3203151693 cites W1970360626 @default.
• W3203151693 cites W1973609976 @default.
• W3203151693 cites W1974316302 @default.
• W3203151693 cites W1974965531 @default.
• W3203151693 cites W1978333327 @default.
• W3203151693 cites W1980081218 @default.
• W3203151693 cites W1980138005 @default.
• W3203151693 cites W1981551663 @default.
• W3203151693 cites W1981883771 @default.
• W3203151693 cites W1983338648 @default.
• W3203151693 cites W1983717719 @default.
• W3203151693 cites W1985213237 @default.
• W3203151693 cites W1985479415 @default.
• W3203151693 cites W1987720419 @default.
• W3203151693 cites W1988455941 @default.
• W3203151693 cites W1989561469 @default.
• W3203151693 cites W1990151882 @default.
• W3203151693 cites W1991104260 @default.
• W3203151693 cites W1991308688 @default.
• W3203151693 cites W1991487543 @default.
• W3203151693 cites W1995701871 @default.
• W3203151693 cites W1996478169 @default.
• W3203151693 cites W1998990870 @default.
• W3203151693 cites W2001524654 @default.
• W3203151693 cites W2003756933 @default.
• W3203151693 cites W2003938839 @default.
• W3203151693 cites W2004353449 @default.
• W3203151693 cites W2004479753 @default.
• W3203151693 cites W2005715129 @default.
• W3203151693 cites W2005751867 @default.
• W3203151693 cites W2007154532 @default.
• W3203151693 cites W2007670599 @default.
• W3203151693 cites W2008771271 @default.
• W3203151693 cites W2009215426 @default.
• W3203151693 cites W2011094999 @default.
• W3203151693 cites W2012565349 @default.
• W3203151693 cites W2013438202 @default.
• W3203151693 cites W2016531657 @default.
• W3203151693 cites W2018072563 @default.
• W3203151693 cites W2018764772 @default.
• W3203151693 cites W2019340290 @default.
• W3203151693 cites W2020595646 @default.
• W3203151693 cites W2023196340 @default.
• W3203151693 cites W2024369567 @default.
• W3203151693 cites W2024966118 @default.

• next