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Dr. Tarmizi Ismail
Universiti Teknologi Malaysia

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0 Atmospheric Science
0 Climatology
0 Hydrologic Modeling
0 climate change
0 Hydrology and Water Resources

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Preprint content
Published: 26 July 2021
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Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming (CP) and multicriteria group decision–making methods (MCGDM) to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall (MMK) test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit (CRU). Assessment of TBI trends using CPC data revealed an increase in the minimum temperature in the coldest month over the whole basin at a rate of 0.03 to 0.08\(℃\) per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2\(℃\) and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

ACS Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Saad Sh Sammen; Anurag Malik; Xiaojun Wang. Selection of the Gridded Temperature Dataset for Assessment of Thermal Bioclimatic Environment Changes in Amu Darya River Basin. 2021, 1 .

AMA Style

Obaidullah Salehie, Tarmizi Ismail, Shamsuddin Shahid, Saad Sh Sammen, Anurag Malik, Xiaojun Wang. Selection of the Gridded Temperature Dataset for Assessment of Thermal Bioclimatic Environment Changes in Amu Darya River Basin. . 2021; ():1.

Chicago/Turabian Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Saad Sh Sammen; Anurag Malik; Xiaojun Wang. 2021. "Selection of the Gridded Temperature Dataset for Assessment of Thermal Bioclimatic Environment Changes in Amu Darya River Basin." , no. : 1.

Original paper
Published: 13 April 2021 in Theoretical and Applied Climatology
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Reference evapotranspiration (ETo) is one of the foremost elements of the hydrology cycle which is essential for water resources management and irrigation applications. The current study is emphasized on the implementation of evolutionary computing models (i.e., gene expression programming (GEP)) for the simulation daily ETo in different locations of Peninsular Malaysia. The ETo models are developed using various input combinations of meteorological variables including air temperature (mean, maximum, and minimum), relative humidity, solar radiation, and mean wind speed. The in situ measurements of the ET are used to validate the model’s performance. The performance of the proposed GEP model is also compared with five well-established empirical formulations (EFs) developed based on the related climatological variability. The attained results evidenced the potential of GEP-derived ETo models in terms of all the statistical measures used. The best GEP model attained when all the meteorological variables are incorporated. However, the study revealed that the use of only temperature information can provide substantial predictability compared to EFs at all the studied stations across Peninsular Malaysia. This confirms the applicability of GEP in simulating ETo with fewer meteorological variables. The major advantage of GEP compared to other black box artificial intelligence algorithms is that GEP provides a set of equations which can be used by practitioners for reliable estimation of ETo at field with a fewer meteorological variable and, thus, can have wide applicability in water resources management.

ACS Style

Mohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology 2021, 144, 1419 -1434.

AMA Style

Mohd Khairul Idlan Muhammad, Shamsuddin Shahid, Tarmizi Ismail, Sobri Harun, Ozgur Kisi, Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology. 2021; 144 (3):1419-1434.

Chicago/Turabian Style

Mohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. 2021. "The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia." Theoretical and Applied Climatology 144, no. 3: 1419-1434.

Original paper
Published: 12 March 2021 in Theoretical and Applied Climatology
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Accurate representation of precipitation over time and space is vital for hydro-climatic studies. Appropriate selection of gridded precipitation data (GPD) is important for regions where long-term in situ records are unavailable and gauging stations are sparse. This study was an attempt to identify the best GPD for the data-poor Amu Darya River basin, a major source of freshwater in Central Asia. The performance of seven GPDs and 55 precipitation gauge locations was assessed. A novel algorithm, based on the integration of a compromise programming index (CPI) and a global performance index (GPI) as part of a multi-criteria group decision-making (MCGDM) method, was employed to evaluate the performance of the GPDs. The CPI and GPI were estimated using six statistical indices representing the degree of similarity between in situ and GPD properties. The results indicated a great degree of variability and inconsistency in the performance of the different GPDs. The CPI ranked the Climate Prediction Center (CPC) precipitation as the best product for 20 out of 55 stations analysed, followed by the Princeton University Global Meteorological Forcing (PGF) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS). Conversely, GPI ranked the CPC product the best product for 25 of the stations, followed by PGF and CHRIPS. Integration of CPI and GPI ranking through MCGDM revealed that the CPC was the best precipitation product for the Amu River basin. The performance of PGF was also closely aligned with that of CPC.

ACS Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Kamal Ahmed; S Adarsh; Asaduzzaman; Ashraf Dewan. Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin. Theoretical and Applied Climatology 2021, 144, 985 -999.

AMA Style

Obaidullah Salehie, Tarmizi Ismail, Shamsuddin Shahid, Kamal Ahmed, S Adarsh, Asaduzzaman, Ashraf Dewan. Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin. Theoretical and Applied Climatology. 2021; 144 (3-4):985-999.

Chicago/Turabian Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Kamal Ahmed; S Adarsh; Asaduzzaman; Ashraf Dewan. 2021. "Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin." Theoretical and Applied Climatology 144, no. 3-4: 985-999.

Journal article
Published: 16 February 2021 in Atmospheric Research
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Global climate models (GCMs) of Coupled Model Intercomparison Project 6 (CMIP6) has designed with new socioeconomic pathway scenarios to incorporate the socioeconomic changes along with greenhouse gas emissions to project future climate. Performance of 35 GCMs of CMIP6 was evaluated in this study in replicating APHRODITE rainfall in the Mainland South-East Asia (MSEA) for the period 1975–2014. Compromised programming (CP) based on four spatial statistical metrics were used for the ranking of the GCMs and Jenk's natural break classification was employed to find the most suitable subset of GCMs for MSEA. The results showed that majority of CMIP6 GCMs can capture the rainfall climatological of MSEA. The performance of the GCMs was different in terms of different metrics. Integration of all metrics using CP showed MRI-ESM2-0, EC-Earth3 and EC-Earth3-Veg as the most suitable subset of GCMs for rainfall projections in MSEA. The performance assessment of the selected GCMs revealed their ability to simulate the annual mean rainfall climatology in the central and southern region of MSEA with a bias less than 25%. Relatively higher biases (−25 to −75%) were noticed in the western coastal region of Myanmar where observed rainfall is the highest. The identified CMIP6 GCMs can be employed for climate change projections and impact assessments in MSEA after correcting the associated biases.

ACS Style

Zafar Iqbal; Shamsuddin Shahid; Kamal Ahmed; Tarmizi Ismail; Ghaith Falah Ziarh; Eun-Sung Chung; Xiaojun Wang. Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia. Atmospheric Research 2021, 254, 105525 .

AMA Style

Zafar Iqbal, Shamsuddin Shahid, Kamal Ahmed, Tarmizi Ismail, Ghaith Falah Ziarh, Eun-Sung Chung, Xiaojun Wang. Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia. Atmospheric Research. 2021; 254 ():105525.

Chicago/Turabian Style

Zafar Iqbal; Shamsuddin Shahid; Kamal Ahmed; Tarmizi Ismail; Ghaith Falah Ziarh; Eun-Sung Chung; Xiaojun Wang. 2021. "Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia." Atmospheric Research 254, no. : 105525.

Journal article
Published: 02 February 2021 in Sustainability
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An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

ACS Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability 2021, 13, 1549 .

AMA Style

Ziauddin Safari, Sayed Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung, Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability. 2021; 13 (3):1549.

Chicago/Turabian Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. 2021. "Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing." Sustainability 13, no. 3: 1549.

Journal article
Published: 25 December 2020 in Atmospheric Research
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The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000–2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and − 3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2–0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards.

ACS Style

Ghaith Falah Ziarh; Shamsuddin Shahid; Tarmizi Bin Ismail; Asaduzzaman; Ashraf Dewan. Correcting bias of satellite rainfall data using physical empirical model. Atmospheric Research 2020, 251, 105430 .

AMA Style

Ghaith Falah Ziarh, Shamsuddin Shahid, Tarmizi Bin Ismail, Asaduzzaman, Ashraf Dewan. Correcting bias of satellite rainfall data using physical empirical model. Atmospheric Research. 2020; 251 ():105430.

Chicago/Turabian Style

Ghaith Falah Ziarh; Shamsuddin Shahid; Tarmizi Bin Ismail; Asaduzzaman; Ashraf Dewan. 2020. "Correcting bias of satellite rainfall data using physical empirical model." Atmospheric Research 251, no. : 105430.

Journal article
Published: 20 August 2020 in Atmospheric Research
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In recent years the use of remotely sensed precipitation products in hydrological studies has become increasingly common. The capability of the products in producing rainfall intensity-duration-frequency (IDF) relationships, however, has not been examined in any great detail. The performance of four remote-sensing-based gridded rainfall data processing algorithms (GSMaP_NRT, GSMaP_GC, PERSIANN and TRMM_3B42V7) was evaluated to determine the ability to generate reliable IDF curves. The work was undertaken in Peninsular Malaysia. The best-fitted probability distribution functions (PDFs) of rainfall totals for different durations were used to generate the IDF curves. The accuracy of the gridded IDF curves was evaluated by comparing observed versus estimated IDF curves at 80 locations. The results revealed that a generalized extreme value (GEV) distribution had the best fit to the rainfall intensity for different durations at 62% of the stations, and this was then used to develop the IDF curves. A comparison of these remote sensing derived IDF curves with the observed IDF data revealed that the GSMaP_GC product performed best. In general, the satellite-based precipitation products tended to underestimate the IDF curves. The GSMaP_GC IDF curves were found to be the least biased (8%–27%) compared to the TRMM_3B42V7 IDF curves (65%–67%). The biases in rainfall intensity of different return periods for GSMaP_GC for all grid points were estimated. These results can be used in designing hydraulic structures where gauged data are unavailable.

ACS Style

Muhammad Noor; Tarmizi Ismail; Shamsuddin Shahid; Asaduzzaman; Ashraf Dewan. Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia. Atmospheric Research 2020, 248, 105203 .

AMA Style

Muhammad Noor, Tarmizi Ismail, Shamsuddin Shahid, Asaduzzaman, Ashraf Dewan. Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia. Atmospheric Research. 2020; 248 ():105203.

Chicago/Turabian Style

Muhammad Noor; Tarmizi Ismail; Shamsuddin Shahid; Asaduzzaman; Ashraf Dewan. 2020. "Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia." Atmospheric Research 248, no. : 105203.

Journal article
Published: 19 June 2020 in Water Policy
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Reduction of uncertainty in climate change projections is a major challenge in impact assessment and adaptation planning. General circulation models (GCMs) along with projection scenarios are the major sources of uncertainty in climate change projections. Therefore, the selection of appropriate GCMs for a region can significantly reduce uncertainty in climate projections. In this study, 20 GCMs were statistically evaluated in replicating the spatial pattern of monsoon propagation towards Peninsular Malaysia at annual and seasonal time frames against the 20th Century Reanalysis dataset. The performance evaluation metrics of the GCMs for different time frames were compromised using a state-of-art multi-criteria decision-making approach, compromise programming, for the selection of GCMs. Finally, the selected GCMs were interpolated to 0.25° × 0.25° spatial resolution and bias-corrected using the Asian Precipitation – Highly-Resolved Observational Integration Towards Evaluation (APHRODITE) rainfall as reference data. The results revealed the better performance of BCC-CSM1-1 and HadGEM2-ES in replicating the historical rainfall in Peninsular Malaysia. The bias-corrected projections of selected GCMs revealed a large variation of the mean, standard deviation and 95% percentile of daily rainfall in the study area for two futures, 2020–2059 and 2060–2099 compared to base climate.

ACS Style

Saleem A. Salman; Mohamed Salem Nashwan; Tarmizi Ismail; Shamsuddin Shahid. Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Water Policy 2020, 51, 781 -798.

AMA Style

Saleem A. Salman, Mohamed Salem Nashwan, Tarmizi Ismail, Shamsuddin Shahid. Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Water Policy. 2020; 51 (4):781-798.

Chicago/Turabian Style

Saleem A. Salman; Mohamed Salem Nashwan; Tarmizi Ismail; Shamsuddin Shahid. 2020. "Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia." Water Policy 51, no. 4: 781-798.

Articles
Published: 07 November 2019 in Engineering Applications of Computational Fluid Mechanics
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The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.

ACS Style

Rajab Homsi; Mohammed Sanusi Shiru; Shamsuddin Shahid; Tarmizi Ismail; Sobri Bin Harun; Nadhir Al-Ansari; Kwok-Wing Chau; Zaher Mundher Yaseen. Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics 2019, 14, 90 -106.

AMA Style

Rajab Homsi, Mohammed Sanusi Shiru, Shamsuddin Shahid, Tarmizi Ismail, Sobri Bin Harun, Nadhir Al-Ansari, Kwok-Wing Chau, Zaher Mundher Yaseen. Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics. 2019; 14 (1):90-106.

Chicago/Turabian Style

Rajab Homsi; Mohammed Sanusi Shiru; Shamsuddin Shahid; Tarmizi Ismail; Sobri Bin Harun; Nadhir Al-Ansari; Kwok-Wing Chau; Zaher Mundher Yaseen. 2019. "Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria." Engineering Applications of Computational Fluid Mechanics 14, no. 1: 90-106.

Journal article
Published: 07 August 2019 in Sustainability
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Selection of appropriate empirical reference evapotranspiration (ETo) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ETo models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ETo estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ETo models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ETo, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ETo models.

ACS Style

Mohd Khairul Idlan Muhammad; Mohamed Nashwan; Shamsuddin Shahid; Tarmizi Ismail; Young Song; Eun-Sung Chung. Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia. Sustainability 2019, 11, 4267 .

AMA Style

Mohd Khairul Idlan Muhammad, Mohamed Nashwan, Shamsuddin Shahid, Tarmizi Ismail, Young Song, Eun-Sung Chung. Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia. Sustainability. 2019; 11 (16):4267.

Chicago/Turabian Style

Mohd Khairul Idlan Muhammad; Mohamed Nashwan; Shamsuddin Shahid; Tarmizi Ismail; Young Song; Eun-Sung Chung. 2019. "Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia." Sustainability 11, no. 16: 4267.

Journal article
Published: 06 December 2018 in Water
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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.

ACS Style

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 Style

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 (12):1793.

Chicago/Turabian Style

Najeebullah 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.

Journal article
Published: 28 November 2018 in Water
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This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

ACS Style

Muhammad Noor; Tarmizi Ismail; Eun-Sung Chung; Shamsuddin Shahid; Jang Hyun Sung. Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios. Water 2018, 10, 1750 .

AMA Style

Muhammad Noor, Tarmizi Ismail, Eun-Sung Chung, Shamsuddin Shahid, Jang Hyun Sung. Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios. Water. 2018; 10 (12):1750.

Chicago/Turabian Style

Muhammad Noor; Tarmizi Ismail; Eun-Sung Chung; Shamsuddin Shahid; Jang Hyun Sung. 2018. "Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios." Water 10, no. 12: 1750.

Journal article
Published: 19 November 2018 in Malaysian Journal of Civil Engineering
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A copula-based methodology is presented in this study for bivariate flood frequency analysis of Kelantan river basin located in Northeast Malaysia. The joint dependence structures of three flood characteristics, namely, peak flow (Q), flood volume (V) and flood duration (D) were modelled using t-Copula. Various univariate distribution functions of flood variables were fitted with observed flood variables to find the best distributions. Cumulative joint distribution functions (CDF) of peak flow and volume (Q-F), peak flow and duration (Q-D) and volume and duration (V-D) revealed that return period of joint return periods are much higher compared to univariate return period. The joint probabilities of occurrence of 0.8, 0.6, 0.4, 0.2 and 0 can be expected when flood duration greater than 65 h, 54 h, 46 h, and 32 h, and the flood volume higher than 0.62 km3, 0.33 km3, 0.25 km3, and 0.22 km3 respectively.

ACS Style

Mahiuddin Alamgir; Tarmizi Ismail; Muhammad Noor. BIVARIATE FREQUENCY ANALYSIS OF FLOOD VARIABLES USING COPULA IN KELANTAN RIVER BASIN. Malaysian Journal of Civil Engineering 2018, 30, 1 .

AMA Style

Mahiuddin Alamgir, Tarmizi Ismail, Muhammad Noor. BIVARIATE FREQUENCY ANALYSIS OF FLOOD VARIABLES USING COPULA IN KELANTAN RIVER BASIN. Malaysian Journal of Civil Engineering. 2018; 30 (3):1.

Chicago/Turabian Style

Mahiuddin Alamgir; Tarmizi Ismail; Muhammad Noor. 2018. "BIVARIATE FREQUENCY ANALYSIS OF FLOOD VARIABLES USING COPULA IN KELANTAN RIVER BASIN." Malaysian Journal of Civil Engineering 30, no. 3: 1.

Journal article
Published: 19 September 2018 in Measurement
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ACS Style

Saleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Alaa M. Al-Abadi; Xiao-Jun Wang; Eun-Sung Chung. Selection of gridded precipitation data for Iraq using compromise programming. Measurement 2018, 132, 87 -98.

AMA Style

Saleem A. Salman, Shamsuddin Shahid, Tarmizi Ismail, Alaa M. Al-Abadi, Xiao-Jun Wang, Eun-Sung Chung. Selection of gridded precipitation data for Iraq using compromise programming. Measurement. 2018; 132 ():87-98.

Chicago/Turabian Style

Saleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Alaa M. Al-Abadi; Xiao-Jun Wang; Eun-Sung Chung. 2018. "Selection of gridded precipitation data for Iraq using compromise programming." Measurement 132, no. : 87-98.

Original paper
Published: 28 August 2018 in Stochastic Environmental Research and Risk Assessment
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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.

ACS Style

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 Style

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 (1):287-302.

Chicago/Turabian Style

Najeebullah 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.

Journal article
Published: 05 August 2018 in Malaysian Journal of Civil Engineering
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A copula based methodology is presented in this study for bivariate flood frequency analysis over a station over a Kelantan river basin located in Northeast Malaysia. The joint dependence structures of three flood characteristics, namely, peak flow, flood volume and flood duration were modelled using Gumble Copula. Various univariate distribution functions of flood variables were fitted with observed flood variables to find the best distributions (eg. generalized pareto, log-normal, exponential, gamma distribution, weibull, gumbel, cauchy). The results of study revealed that different variable fits with different distributions and the correlation analysis among variables showed a strong association. Cumulative joint distribution functions (CDF) of peakflow and volume, peakflow and duration and volume and duration revealed that return period of joint return periods are much higher.

ACS Style

Tarmizi Ismail; Kamal Ahmed; Mahiuddin Alamgir; Mohammad Noor Kakar; Abu Bakar Fadzil. BIVARIATE FLOOD FREQUENCY ANALYSIS USING GUMBEL COPULA. Malaysian Journal of Civil Engineering 2018, 30, 1 .

AMA Style

Tarmizi Ismail, Kamal Ahmed, Mahiuddin Alamgir, Mohammad Noor Kakar, Abu Bakar Fadzil. BIVARIATE FLOOD FREQUENCY ANALYSIS USING GUMBEL COPULA. Malaysian Journal of Civil Engineering. 2018; 30 (2):1.

Chicago/Turabian Style

Tarmizi Ismail; Kamal Ahmed; Mahiuddin Alamgir; Mohammad Noor Kakar; Abu Bakar Fadzil. 2018. "BIVARIATE FLOOD FREQUENCY ANALYSIS USING GUMBEL COPULA." Malaysian Journal of Civil Engineering 30, no. 2: 1.

Journal article
Published: 07 July 2018 in Atmospheric Research
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A hybrid approach by combining the past performance and the envelope methods has been proposed for the selection of an ensemble of general circulation models (GCMs) of Couple Model Intercomparison phase 5 (CMIP5) for the projection of spatiotemporal changes in annual and seasonal temperatures of Iraq for four representative concentration pathways (RCP) scenarios. A filter known as Symmetrical Uncertainty (SU) was used to rank the GCMs considering their ability to simulate monthly average of daily maximum and minimum temperature for the historical period (1961–2005). The highest rank GCMs that represents the widest range of projection was then selected for the projection of temperature through statistical downscaling. A linear bias correction approach was used for the downscaling of temperature, a random forest regression was used to generate multi-model ensemble (MME) mean of projections and a quantile regression (QR) was used to assess the trends in projections at 95% level of confidence. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and MIROC-ESM were found most suitable for projection of temperature of Iraq. Ensemble mean of the selected GCMs revealed increases in minimum temperatures in the range of 1.5–2.4 °C, 1.6–3.6 °C, 1.2–4.2 °C, and 1.3–6.2 °C and maximum temperatures in the range of 1.7–2.9 °C, 1.8–4.4 °C, 1.5–4.9 °C, and 1.7–6.8 °C under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively during 2070–2099. Higher increases in temperatures were projected in the north and northeast of Iraq where the average temperature is usually low, which indicates that spatial distribution of temperature would be more homogeneous in future compared to base years. The maximum temperature was found to increase more in winter while the minimum in summer. The quantile regression revealed that average summer maximum temperature may reach almost 50 °C, while the sub-zero temperature will gradually become rare during winter.

ACS Style

Saleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Xiao-Jun Wang. Selection of climate models for projection of spatiotemporal changes in temperature of Iraq with uncertainties. Atmospheric Research 2018, 213, 509 -522.

AMA Style

Saleem A. Salman, Shamsuddin Shahid, Tarmizi Ismail, Kamal Ahmed, Xiao-Jun Wang. Selection of climate models for projection of spatiotemporal changes in temperature of Iraq with uncertainties. Atmospheric Research. 2018; 213 ():509-522.

Chicago/Turabian Style

Saleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Xiao-Jun Wang. 2018. "Selection of climate models for projection of spatiotemporal changes in temperature of Iraq with uncertainties." Atmospheric Research 213, no. : 509-522.

Journal article
Published: 01 July 2018 in Atmósfera
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Homogeneity evaluations are usually performed on the total annual precipitation data, which often fails to detect non-homogeneity in seasonal precipitation. Furthermore, it is required to assess homogeneity using multiple methods as the performance of homogeneity testing methods depend on the distribution of the data. This is particularly important for the arid region where distributions of seasonal and annual rainfall are often non-normal. The homogeneity of annual and monthly precipitation datasets of 14 meteorological stations located in the arid region of Pakistan was assessed in this study using the Pettitt’s test, the standard normal homogeneity test (SNHT), the cumulative deviation test, the von Neumann’s ratio test, the Bayesian test, the Worsley’s likelihood ratio test, and Student’s t -test at a 95% confidence level. The rainfall series were categorized into three classes, namely “useful”, “doubtful” and “suspect” based on the results of different homogeneity tests. Results suggest that rainfall time series for most of the months in all the stations are useful. The rainfall time series are found doubtful for the month of June at two stations, for April at one station, and suspect for November at only one station. On the other hand, the annual series were found useful at 12 stations and suspect at two stations. Comparison of different homogeneity tests revealed that SNHT and Worsley’s tests are the most sensitive, and cumulative deviation test is the least sensitive to changes in monthly precipitation data. In the case of annual series, the von Neumann’s test was found most sensitive compared to other tests.

ACS Style

Kamal Ahmed; Shamsuddin Shahid; Tarmizi Ismail; Nadeem Nawaz; Xiao-Jun Wang. Absolute homogeneity assessment of precipitation time series in an arid region of Pakistan. Atmósfera 2018, 31, 301 -316.

AMA Style

Kamal Ahmed, Shamsuddin Shahid, Tarmizi Ismail, Nadeem Nawaz, Xiao-Jun Wang. Absolute homogeneity assessment of precipitation time series in an arid region of Pakistan. Atmósfera. 2018; 31 (3):301-316.

Chicago/Turabian Style

Kamal Ahmed; Shamsuddin Shahid; Tarmizi Ismail; Nadeem Nawaz; Xiao-Jun Wang. 2018. "Absolute homogeneity assessment of precipitation time series in an arid region of Pakistan." Atmósfera 31, no. 3: 301-316.

Journal article
Published: 30 April 2018 in Malaysian Journal of Civil Engineering
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Downscaling Global Circulation Model (GCM) output is important in order tounderstand the present climate as well as future climate changes at local scale. In this study,Random Forest (RF) was used to downscale the mean daily rainfall at Kota Bahru meteorologicalstation located in Kelantan Malaysia. The RF model was used to downscale daily rainfall fromGCM of Coupled Model Intercomparison Project Phase 5 (CMIP5), BCC-CSM1.1. The potentialpredictors were selected using stepwise regression at grid points located around the study area.Quantile mapping was used to remove the bias in the prediction. The results showed that the RFmodel was able to establish a good relation between observed and downscaled rainfall. TheQuantile mapping was found to perform well to correct errors in prediction. The statisticalmeasures of performance of downscaling and bias correction approaches show that they are ableto replicate daily observed rainfall with Nash-Schutclif efficiency greater than 0.75 for all themonths. It can be concluded that RF and Quantile mapping are reliable and effective methods fordownscaling rainfall.

ACS Style

Muhammad Noor; Tarmizi Ismail. DOWNSCALING OF DAILY AVERAGE RAINFALL OF KOTA BHARU KELANTAN, MALAYSIA. Malaysian Journal of Civil Engineering 2018, 30, 1 .

AMA Style

Muhammad Noor, Tarmizi Ismail. DOWNSCALING OF DAILY AVERAGE RAINFALL OF KOTA BHARU KELANTAN, MALAYSIA. Malaysian Journal of Civil Engineering. 2018; 30 (1):1.

Chicago/Turabian Style

Muhammad Noor; Tarmizi Ismail. 2018. "DOWNSCALING OF DAILY AVERAGE RAINFALL OF KOTA BHARU KELANTAN, MALAYSIA." Malaysian Journal of Civil Engineering 30, no. 1: 1.

Journal article
Published: 01 December 2017 in Atmospheric Research
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ACS Style

Saleem Abdulridha Salman; Shamsuddin Shahid; Tarmizi Ismail; Eun-Sung Chung; Alaa M. Al-Abadi. Long-term trends in daily temperature extremes in Iraq. Atmospheric Research 2017, 198, 97 -107.

AMA Style

Saleem Abdulridha Salman, Shamsuddin Shahid, Tarmizi Ismail, Eun-Sung Chung, Alaa M. Al-Abadi. Long-term trends in daily temperature extremes in Iraq. Atmospheric Research. 2017; 198 ():97-107.

Chicago/Turabian Style

Saleem Abdulridha Salman; Shamsuddin Shahid; Tarmizi Ismail; Eun-Sung Chung; Alaa M. Al-Abadi. 2017. "Long-term trends in daily temperature extremes in Iraq." Atmospheric Research 198, no. : 97-107.