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Many efficient forecasting models have been found to fail or show low skill due to the changes in the predictor–predictand relationship with the changes in global climate. An attempt has been taken to develop a climate change resilient heatwave prediction model using machine learning (ML) algorithms known as Support Vector Machines (SVM), random forest and artificial neural network. The National Centres for Environmental Prediction/National Centre for Atmospheric Research reanalysis data of ocean-atmospheric variables were used as the predictors of ML models for forecasting the number of heatwave days (HWDs) in the summer of Pakistan. An SVM based recursive feature elimination method was used to select the skilful predictors. The ML models were developed by considering a moving window of 29 years with a time step of 5 years to incorporate the changes in the relation of HWDs with its predictors due to climate change. The result showed changes in the relationship of HWDs with all the ocean-atmospheric variables considered in this study as probable predictors, which indicates the necessity of forward-rolling approach proposed in this study for the development of climate change resilient forecasting model. The relative performance of ML showed the higher capability of SVM to predict HWDs with an %NRMSE of 36, R2 of 0.87, md score of 0.76 and an rSD of 0.88 during the validation period. The result revealed the potential of SVM model to be used for reliable forecasting of heatwaves in the context of climate change.
Najeebullah Khan; Shamsuddin Shahid; Tarmizi Bin Ismail; Farida Behlil. Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change. Stochastic Environmental Research and Risk Assessment 2021, 1 -19.
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Tarmizi Bin Ismail, Farida Behlil. Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change. Stochastic Environmental Research and Risk Assessment. 2021; ():1-19.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Tarmizi Bin Ismail; Farida Behlil. 2021. "Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change." Stochastic Environmental Research and Risk Assessment , no. : 1-19.
Natural variability of climate considerably affects hydro-climatic trend significance, and therefore, removal of such influence is essential to understand the unidirectional trends due to global warming. The objective of this study was to evaluate the trends in precipitation extremes in the arid province of Pakistan by removing the natural variability of climate to understand the effect of global warming on precipitation extremes during two major cropping seasons, Rabi and Kharif. Daily precipitation data of APHRODITE (Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation) for the period 1951–2015 was used for this purpose. An improved form of classical Mann-Kendall (MK) test known as modified Mann-Kendall (MMK) was used which can estimate trends by discarding the influence of natural cycles present in time series. The results were compared with the classical MK test to show the novelty in the findings of this investigation. The results revealed a large influence of climate fluctuations on the trends in all the extreme precipitation indices for both seasons. The reduction in trend significance was noticed between 25 and 100% for different precipitation indices when MMK instead of MK test was used. The reduction was observed more for the positive trends in the indices compared with negative trends. The results revealed that global warming caused an increase in total annual precipitation at a rate of 2.8–34.8 mm/decade during 1951–2015. Besides, the annual number of extreme precipitation days was found to increase in the north by 0.1–0.84 days/decade and the number of annual precipitation days to decrease in the west for all seasons up to − 8.6 days/decade. An increase in continuous precipitation days was detected by 0.6–1.0 day/decade in the northeast while a decrease by − 0.5 to − 1.0 days/decade in the southwest and northwest. The continuous dry days decreased in the north and the central regions by up to − 6.3 days/decade while a rise in 1-day maximum precipitation by 6.6–35 mm/decade in the central north. Analysis of results revealed that the overestimation of trends by classical MK test is more in the arid region of Pakistan compared with other regions.
Najeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Farida Behlil; Mohamad S.J. Darwish. Spatiotemporal changes in precipitation extremes in the arid province of Pakistan with removal of the influence of natural climate variability. Theoretical and Applied Climatology 2020, 142, 1447 -1462.
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Eun-Sung Chung, Farida Behlil, Mohamad S.J. Darwish. Spatiotemporal changes in precipitation extremes in the arid province of Pakistan with removal of the influence of natural climate variability. Theoretical and Applied Climatology. 2020; 142 (3):1447-1462.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Farida Behlil; Mohamad S.J. Darwish. 2020. "Spatiotemporal changes in precipitation extremes in the arid province of Pakistan with removal of the influence of natural climate variability." Theoretical and Applied Climatology 142, no. 3: 1447-1462.
Climate change has increased frequency, severity and areal extent of droughts across the world in the last few decades magnifying their adverse impacts. Prediction of droughts is immensely helpful in early warning and preparing the most vulnerable communities to their adverse impacts. For the first time, this study investigated the potential of developing drought prediction models over Pakistan using three state-of-the-art Machine Learning (ML) techniques; Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbour (KNN). Three categories of droughts; moderate, severe, and extreme considering two major cropping seasons called Rabi and Kharif were estimated using Standardized Precipitation Evaporation Index (SPEI) and then predicted using the predictor data obtained from the National Centres for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis database. Also, for the first time in drought modelling, a novel feature selection approach called Recursive Feature Elimination (RFE) was used for identifying optimum sets of predictors. In validation, SVM-based models were able to better capture the temporal and spatial characteristics of droughts over Pakistan compared to those by ANN and KNN-based models. KNN which was used in developing drought models for the first time displayed limited performance in comparison to that by SVM and ANN-based drought models, in validation. It was found that in the Rabi season SPEI is positively correlated with relative humidity over the Mediterranean Sea and the region north of the Caspian Sea. In the Kharif season, SPEI is positively correlated with the humid region over the south-eastern part of the Bay of Bengal and the regions north of the Mediterranean and Caspian Seas. In developing a drought prediction model for Pakistan, relative humidity, temperature and wind speed should be considered with a domain which encompasses the Mediterranean Sea, the region north of the Caspian Sea, the Indian Ocean and the Arabian Sea.
Najeebullah Khan; D.A. Sachindra; Shamsuddin Shahid; Kamal Ahmed; Mohammed Sanusi Shiru; Nadeem Nawaz. Prediction of droughts over Pakistan using machine learning algorithms. Advances in Water Resources 2020, 139, 103562 .
AMA StyleNajeebullah Khan, D.A. Sachindra, Shamsuddin Shahid, Kamal Ahmed, Mohammed Sanusi Shiru, Nadeem Nawaz. Prediction of droughts over Pakistan using machine learning algorithms. Advances in Water Resources. 2020; 139 ():103562.
Chicago/Turabian StyleNajeebullah Khan; D.A. Sachindra; Shamsuddin Shahid; Kamal Ahmed; Mohammed Sanusi Shiru; Nadeem Nawaz. 2020. "Prediction of droughts over Pakistan using machine learning algorithms." Advances in Water Resources 139, no. : 103562.
Drought is considered to be one of the most devastating natural hazards, causing widespread environmental and social damage in many parts of the world. Using standardized precipitation index, this work has assessed changes in the severity–area–frequency (SAF) relationship curve of seasonal droughts in Bangladesh. Changes were estimated for mild, moderate, severe and extreme droughts for the four climatic seasons; winter, pre-monsoon, monsoon, post-monsoon, and for the two major growing seasons; rabi (November to April) and kharif (May to October). Nineteen general circulation models (GCMs) of Couple Model Intercomparison Project 5 were used. The model output statistics approach was used to downscale GCM simulated rainfall for eighteen climate stations in Bangladesh. Changes in the SAF curve were computed for three periods (2010–2039, 2040–2069 and 2070–2099). The uncertainty band of the SAF relationship curve was then computed using the Bayesian bootstrap method at the 95% confidence level. The results reveal that moderate and severe drought categories have the highest return period and are likely to affect the region more than other types of droughts. The kharif season drought was found to be most pronounced and affected significant portions of the country during all return periods and severity categories. Projections also show that monsoon and kharif droughts would increase across Bangladesh in regards of severity and return period. Higher return period droughts were also projected to increase in aerial extent in the middle of this century (2040–2069).
Mahiuddin Alamgir; Najeebullah Khan; Shamsuddin Shahid; Zaher Mundher Yaseen; Ashraf Dewan; Quazi Hassan; Balach Rasheed. Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment 2020, 34, 447 -464.
AMA StyleMahiuddin Alamgir, Najeebullah Khan, Shamsuddin Shahid, Zaher Mundher Yaseen, Ashraf Dewan, Quazi Hassan, Balach Rasheed. Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment. 2020; 34 (2):447-464.
Chicago/Turabian StyleMahiuddin Alamgir; Najeebullah Khan; Shamsuddin Shahid; Zaher Mundher Yaseen; Ashraf Dewan; Quazi Hassan; Balach Rasheed. 2020. "Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios." Stochastic Environmental Research and Risk Assessment 34, no. 2: 447-464.
Recent climate change has resulted in the reduction of several surface water bodies (SWBs) all around the globe. These SWBs, such as streams, rivers, lakes, wetlands, reservoirs, and creeks have a positive impact on the cooling of the surrounding climate and, therefore, reduction in SWBs can contribute to the rise of land surface temperature (LST). This study presents the impact of SWBs on the LST across Bangladesh to quantify their roles in the rapid temperature rise of Bangladesh. The moderate resolution imaging spectroradiometer (MODIS) LST and water mask data of Bangladesh for the period 2000–2015 are used for this purpose. Influences of topography and geography on LST were first removed, and then regression analysis was conducted to quantify the impact of SWBs on the LST. The non-parametric Mann–Kendall (MK) test was used to assess the changes in LST and SWBs. The results revealed that SWBs were reduced from 11,379 km2 in 2000 to 9657 km2 in 2015. The trend analysis showed that changes in SWBs have reduced significantly at a 90% level of confidence, which contributed to the acceleration of LST rise in the country due to global warming. The spatial analysis during the specific years showed that an increase in LST can be seen with the reduction of SWBs. Furthermore, the reduction of 100 m2 of SWBs can reduce the LST of the surrounding regions from −1.2 to −2.2 °C.
Najeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim; Rawshan Ali. Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh. Sustainability 2019, 11, 6754 .
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Eun-Sung Chung, Sungkon Kim, Rawshan Ali. Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh. Sustainability. 2019; 11 (23):6754.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim; Rawshan Ali. 2019. "Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh." Sustainability 11, no. 23: 6754.
Performance of 31 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) was assessed according to their ability to reconstruct the different properties of heat waves (HWs); HW frequency, HW duration and HW index estimated using Princeton Global Meteorological Forcing (PGF) daily temperature data for the period 1961 to 2005 in order to generate an ensemble for the projection of HWs in Pakistan. The GCMs were selected based on three criteria: (1) ability to replicate the decadal variability in HW properties, (2) ability to reconstruct the spatial distribution of HW properties based on Taylor skill score, (3) replicate the annual time series of HW properties based on standard statistical indices and compromise programming. Results revealed four GCMs: CCSM4, CESM1(BGC), CMCC-CM and NorESM1-M are the most suitable for the projection of HWs over Pakistan. Projection of HWs using the selected GCMs revealed increase in the frequency and severity of HWs in most parts of Pakistan for both the radiative concentration pathway (RCP4.5 and RCP8.5) scenarios used in the study. The frequency of HWs was projected to increase up to 12 events per year while the duration was projected to increase up to 100 days in a year during 2060 to 2099 for the highest emission scenario. Overall, the HWs were projected to be more frequent and longer duration in the east and the southern coastal regions.
Najeebullah Khan; Shamsuddin Shahid; Kamal Ahmed; Xiaojun Wang; Rawshan Ali; Tarmizi Ismail; Nadeem Nawaz. Selection of GCMs for the projection of spatial distribution of heat waves in Pakistan. Atmospheric Research 2019, 233, 104688 .
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Kamal Ahmed, Xiaojun Wang, Rawshan Ali, Tarmizi Ismail, Nadeem Nawaz. Selection of GCMs for the projection of spatial distribution of heat waves in Pakistan. Atmospheric Research. 2019; 233 ():104688.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Kamal Ahmed; Xiaojun Wang; Rawshan Ali; Tarmizi Ismail; Nadeem Nawaz. 2019. "Selection of GCMs for the projection of spatial distribution of heat waves in Pakistan." Atmospheric Research 233, no. : 104688.
The presence of long‐term persistence (LTP) in hydro‐climatic time series can lead to considerable change in the significance of trend. Therefore, past findings of climatic trend analysis without considering LTP in time series has become a disputable issue. The objective of this study is to assess the spatial patterns in the trends of annual and seasonal rainfall amounts and extremes in Peninsular Malaysia considering LTP. Daily rainfall data of APHRODITE (Asian Precipitation ‐ Highly‐Resolved Observational Data Integration Towards Evaluation) for the period 1951‐2007 was used to assess the trends using classical Mann‐Kendall (MK) test and the modified version of Mann‐Kendall (MMK) test, which can remove the influence of LTP in significance of trends. The results indicate that significant trends in different rainfall indices of Peninsular Malaysia obtained using MK test reduced drastically when LTP was taken into consideration. There was almost no change in annual and seasonal rainfall amounts, which contradicts with the findings of previous studies. Field significance of regional trends revealed increase in wet spells at an average rate of 4.8 and 4.9 days/decade in the Southeast and the Southwest respectively during Northeast Monsoon, decrease in rainy days by ‐1.4 days/decade in the North and increase in dry spells by 1.0 day/decade in the Southeast and maximum one‐day rainfall by 1.7 mm/decade in the West Coast during Southwest Monsoon. The results indicate that the trends in rainfall indices reported in the maritime continent in previous studies should be re‐evaluated as most of those are due to LTP. This article is protected by copyright. All rights reserved.
Najeebullah Khan; Sahar Hadi Pour; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Eun‐Sung Chung; Nadeem Nawaz; Xiao‐Jun Wang. Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia in the presence of long‐term persistence. Meteorological Applications 2019, 26, 655 -670.
AMA StyleNajeebullah Khan, Sahar Hadi Pour, Shamsuddin Shahid, Tarmizi Ismail, Kamal Ahmed, Eun‐Sung Chung, Nadeem Nawaz, Xiao‐Jun Wang. Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia in the presence of long‐term persistence. Meteorological Applications. 2019; 26 (4):655-670.
Chicago/Turabian StyleNajeebullah Khan; Sahar Hadi Pour; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Eun‐Sung Chung; Nadeem Nawaz; Xiao‐Jun Wang. 2019. "Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia in the presence of long‐term persistence." Meteorological Applications 26, no. 4: 655-670.
The rising temperature due to global warming has caused an increase in frequency and severity of heat waves across the world. A statistical model known as Quantile Regression Forests (QRF) has been proposed in this study for the prediction of heat waves in Pakistan for different time-lags using synoptic climate variables. The gridded daily temperature data of Princeton's Global Meteorological Forcing (PGF) was used for the reconstruction of historical heat waves and the National Centers for Environmental Prediction (NCEP) reanalysis data was used to select the appropriate set of predictors to forecast the heat waves using QRF. The performance of QRF in prediction of heat waves was compared with classical random forest (RF). The results showed superior performance of QRF in detecting heat waves compared to RF. The QRF model was able to predict the triggering and departure dates of heat waves with 1 to 10 days lead times at various levels of accuracy. The model was able to predict the triggering dates of 2 to 3 out of 3 heat waves in the month of May, 8 to 12 out of 13 heat waves in June and 2 out of 2 in July and the departure dates of 3 out of 3 in May, 10 out of 13 in June and 2 out of 2 in July with an accuracy of up to ±5 days. The evaluation of different atmospheric variables revealed that wind and relative humidity are the major factors that define the heat waves in Pakistan. The research proved the advantage of QRF model to predict the conditional quantiles that help to explain some extreme behaviors of temperature.
Najeebullah Khan; Shamsuddin Shahid; Liew Juneng; Kamal Ahmed; Tarmizi Ismail; Nadeem Nawaz. Prediction of heat waves in Pakistan using quantile regression forests. Atmospheric Research 2019, 221, 1 -11.
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Liew Juneng, Kamal Ahmed, Tarmizi Ismail, Nadeem Nawaz. Prediction of heat waves in Pakistan using quantile regression forests. Atmospheric Research. 2019; 221 ():1-11.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Liew Juneng; Kamal Ahmed; Tarmizi Ismail; Nadeem Nawaz. 2019. "Prediction of heat waves in Pakistan using quantile regression forests." Atmospheric Research 221, no. : 1-11.
The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.
Najeebullah Khan; Shamsuddin Shahid; Kamal Ahmed; Tarmizi Ismail; Nadeem Nawaz; Minwoo Son. Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets. Water 2018, 10, 1793 .
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Kamal Ahmed, Tarmizi Ismail, Nadeem Nawaz, Minwoo Son. Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets. Water. 2018; 10 (12):1793.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Kamal Ahmed; Tarmizi Ismail; Nadeem Nawaz; Minwoo Son. 2018. "Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets." Water 10, no. 12: 1793.
Increased frequency and severity of heat wave is one of the immediate and certain impacts of rising temperature due to global warming. A number of heat wave related indices considering both daily maximum and minimum temperature are proposed in this paper to assess the changes in different characteristics of heat waves in Pakistan, which is one of the most vulnerable countries of the world to extreme temperature. Gridded daily temperature dataset of Princeton’s Global Meteorological Forcing for the period 1948–2010 was used for this purpose. The results revealed daily maximum temperature more than 95-th percentile threshold for consecutive 5 days or more can well reconstruct the spatial pattern of heat wave in Pakistan. The results revealed that intense heat waves in Pakistan are mostly occurred in the southwest. However, heat waves are most devastating when those occur in highly populated southeast region. It was found that major heat waves in Pakistan occurred in 1952, 1978, 1984, 1988, 2002, 2006, 2009 and 2010 which affected 55.7, 71.1, 74.0, 72.3, 48.9, 60.6, 41.8 and 82.9% population respectively. The trends in heat wave indices revealed significant increases in the indices calculated based on both the maximum and minimum temperatures. Duration of heat wave was found to increase at a rate of 0.71 days/decade, while the duration and affected area having both maximum and minimum temperature above 95-th percentiles are found to increase at a rate of 0.95 days/decade and 1.36% of total area of Pakistan per decade respectively.
Najeebullah Khan; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Nadeem Nawaz. Trends in heat wave related indices in Pakistan. Stochastic Environmental Research and Risk Assessment 2018, 33, 287 -302.
AMA StyleNajeebullah Khan, Shamsuddin Shahid, Tarmizi Ismail, Kamal Ahmed, Nadeem Nawaz. Trends in heat wave related indices in Pakistan. Stochastic Environmental Research and Risk Assessment. 2018; 33 (1):287-302.
Chicago/Turabian StyleNajeebullah Khan; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Nadeem Nawaz. 2018. "Trends in heat wave related indices in Pakistan." Stochastic Environmental Research and Risk Assessment 33, no. 1: 287-302.