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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.
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 StyleObaidullah 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 StyleObaidullah 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.
Reliable precipitation data are often required for conducting hydro-climatological assessments. Therefore, the study aims to detect the inhomogeneity in each calendar month, mean annual and monthly time series precipitation data of Balochistan, an arid province of Pakistan. The inhomogeneity was assessed using standard normal homogeneity test, Buishand range test, Pettitt test and von Neumann ratio test at 95% confidence level. The tests were applied over the precipitation data obtained from 14 meteorological stations for the period 1961–2009. The results of the different tests were classified as ‘useful’, ‘doubtful’ and ‘suspect’ based on the null hypothesis. The result of calendar months and annual series indicates that most of the data were ‘useful’. The output of monthly time series precipitation indicates that the data at Jiwani, Lasbela, Nokkunddi, Ormara, Pasni, Turbat and Zhob stations were ‘useful’ and others are classified into ‘doubtful’ and ‘suspect’ class. It was also observed that von Neumann ratio test is sensitive to minor changes in the precipitation data, and standard normal homogeneity test was found less sensitive to changes.
Kamal Ahmed; Nadeem Nawaz; Najeebullah Khan; Balach Rasheed; Amdadullah Baloch. Inhomogeneity detection in the precipitation series: case of arid province of Pakistan. Environment, Development and Sustainability 2020, 23, 7176 -7192.
AMA StyleKamal Ahmed, Nadeem Nawaz, Najeebullah Khan, Balach Rasheed, Amdadullah Baloch. Inhomogeneity detection in the precipitation series: case of arid province of Pakistan. Environment, Development and Sustainability. 2020; 23 (5):7176-7192.
Chicago/Turabian StyleKamal Ahmed; Nadeem Nawaz; Najeebullah Khan; Balach Rasheed; Amdadullah Baloch. 2020. "Inhomogeneity detection in the precipitation series: case of arid province of Pakistan." Environment, Development and Sustainability 23, no. 5: 7176-7192.
Knowledge of the variability and the changes in potential evapotranspiration (PET) is imperative for agriculture and water resource planning and management. There is a growing concern on alteration of PET because of global climate change. The spatial patterns of the changes in PET for annual and two key cropping seasons of Pakistan namely, Kharif and Rabi, are investigated in the present study for the period 1967–2016. The gauge-based gridded PET data of Climatic Research Unit (CRU) is used for this purpose. The rate of changes in PET over various CRU grid cells of Pakistan is assessed with Sen’s Slope estimator, and the significance of the changes is evaluated using a modified version of Mann-Kendall trend test that has the ability to separate the natural variability of PET from unidirectional trend due to global climate change. Besides, trends in annual and seasonal PET for different 30 years with an interval of 10 years are estimated to assess the variations of the trends with time. The results show higher PET in the southern coastal region and lower PET in the northern mountainous regions. The spatial characteristics of annual PET trend showed a significant decrease (0.74 to 1.65 mm/year) in the south and an increase (1.04 to 1.59 mm/year) in the east during 1967–2016. Among the two crop growing seasons, the PET is found to increase (0.51 to 0.66 mm/year) in a small area in the southeast and decrease over a large area in the center (0.87 to 1.29 mm/year) during Kharif, and only increase (0.38 to 0.85 mm/year) in the southeast during Rabi. The time-varying trends in annual and two crop growing seasons reveal that PET has increased significantly at more grid cells in recent years which indicates higher impact of climate change on PET in recent years in Pakistan.
Kamal Ahmed; Shamsuddin Shahid; Eun-Sung Chung; Nadeem Nawaz; Najeebullah Khan; Balach Rasheed. Divergence of potential evapotranspiration trends over Pakistan during 1967–2016. Theoretical and Applied Climatology 2020, 141, 215 -227.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Eun-Sung Chung, Nadeem Nawaz, Najeebullah Khan, Balach Rasheed. Divergence of potential evapotranspiration trends over Pakistan during 1967–2016. Theoretical and Applied Climatology. 2020; 141 (1-2):215-227.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Eun-Sung Chung; Nadeem Nawaz; Najeebullah Khan; Balach Rasheed. 2020. "Divergence of potential evapotranspiration trends over Pakistan during 1967–2016." Theoretical and Applied Climatology 141, no. 1-2: 215-227.
Multi-Model Ensembles (MMEs) are often employed to reduce the uncertainties related to GCM simulations/projections. The objective of this study was to evaluate the performance of MMEs developed using machine learning (ML) algorithms with different combinations of GCMs ranked based on their performance and determine the optimum number of GCMs to be included in an MME. In this study ML algorithms; Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Relevance Vector Machine (RVM) were used to develop MMEs for annual, monsoon and winter; precipitation (P), maximum (Tmax) and minimum (Tmin) temperature over Pakistan using 36 Coupled Model Intercomparison Project Phase 5 GCMs. GCMs were ranked using Taylor Skill Score for individual seasons and variables, and then using a comprehensive Rating Metric (RM) overall rank of each GCM was determined. It was found that, HadGEM2-AO is the most skilled GCM and IPSL-CM5B-LR is the least skilled GCMs in simulating the 3 climate variables. The performance of MMEs did not improve after the inclusion of about 18 top-ranked GCMs. Thus, it was understood that the optimum performance of MMEs is achieved when about 50% of the top-ranked GCMs are used. The inter-comparison of MMEs developed with ANN, KNN, SVM and RVM revealed that KNN and RVM-based MMEs show better skills. It was found that RVM yields MMEs which show smaller variations in performance over space unlike ANN which displayed large fluctuations in performance over space. KNN and RVM are recommended over SVM and ANN for the development of MMEs over Pakistan.
Kamal Ahmed; D.A. Sachindra; Shamsuddin Shahid; Zafar Iqbal; Nadeem Nawaz; Najeebullah Khan. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research 2019, 236, 104806 .
AMA StyleKamal Ahmed, D.A. Sachindra, Shamsuddin Shahid, Zafar Iqbal, Nadeem Nawaz, Najeebullah Khan. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research. 2019; 236 ():104806.
Chicago/Turabian StyleKamal Ahmed; D.A. Sachindra; Shamsuddin Shahid; Zafar Iqbal; Nadeem Nawaz; Najeebullah Khan. 2019. "Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms." Atmospheric Research 236, no. : 104806.
Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.
Mohammad Rajab Houmsi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Kamal Ahmed; Ghaith Falah Ziarh; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim. Spatial Shift of Aridity and Its Impact on Land Use of Syria. Sustainability 2019, 11, 7047 .
AMA StyleMohammad Rajab Houmsi, Mohammed Sanusi Shiru, Mohamed Salem Nashwan, Kamal Ahmed, Ghaith Falah Ziarh, Shamsuddin Shahid, Eun-Sung Chung, Sungkon Kim. Spatial Shift of Aridity and Its Impact on Land Use of Syria. Sustainability. 2019; 11 (24):7047.
Chicago/Turabian StyleMohammad Rajab Houmsi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Kamal Ahmed; Ghaith Falah Ziarh; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim. 2019. "Spatial Shift of Aridity and Its Impact on Land Use of Syria." Sustainability 11, no. 24: 7047.
Modelling the probable effect of global warming on precipitation over the northern sub-Himalayan region is very important to ensure sustainable water supply for Pakistan. The aim of the study is to develop statistical downscaling models for the projection of precipitation using the outputs of Coupled Model Intercomparison Project Phase 5 global circulation models and using future scenarios. The models were developed considering the Global Precipitation Climatology Centre precipitation data as model predictands. The downscaling models were developed using non-local model output statistics approach based on support vector machine (SVM). Random Forest was applied to formulate multimodal ensemble (MME) for the projection of precipitation. The accuracy of models was judged using the percentage of bias, normalized root mean square error, and the modified index of agreement (md). Results showed that the SVM downscaling model simulated the temporal and spatial distributions of historical precipitation with high skills. The MME showed variations in the range of − 12.68% to 6.31%, − 9.61% to 3.45%, − 8.70% to 9.15%, and − 9.40% to 5.47% for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. The spatial pattern of annual mean rainfall of MME revealed an expansion of high rainfall area, especially in 2070–2099 under all scenarios.
Kamal Ahmed; Zafar Iqbal; Najeebullah Khan; Balach Rasheed; Nadeem Nawaz; Irfan Malik; Mohammad Noor. Quantitative assessment of precipitation changes under CMIP5 RCP scenarios over the northern sub-Himalayan region of Pakistan. Environment, Development and Sustainability 2019, 22, 7831 -7845.
AMA StyleKamal Ahmed, Zafar Iqbal, Najeebullah Khan, Balach Rasheed, Nadeem Nawaz, Irfan Malik, Mohammad Noor. Quantitative assessment of precipitation changes under CMIP5 RCP scenarios over the northern sub-Himalayan region of Pakistan. Environment, Development and Sustainability. 2019; 22 (8):7831-7845.
Chicago/Turabian StyleKamal Ahmed; Zafar Iqbal; Najeebullah Khan; Balach Rasheed; Nadeem Nawaz; Irfan Malik; Mohammad Noor. 2019. "Quantitative assessment of precipitation changes under CMIP5 RCP scenarios over the northern sub-Himalayan region of Pakistan." Environment, Development and Sustainability 22, no. 8: 7831-7845.
Water is gradually becoming scarce in Afghanistan like in many other regions of the globe. The objective of this study was to evaluate the spatial changes in the availability and sustainability of water resources in Afghanistan. The Terrestrial Water Storage (TWS) data of the Gravity Recovery and Climate Experiment (GRACE) satellite obtained from three different institutes, having 1° × 1° spatial resolution for the period 2002–2016 was used for this purpose. Sen’s slope method was used to assess the rate of change, and the Modified Mann–Kendall test was used for the evaluation of the significance of trends in TWS. After, the concept of reliability–resiliency–vulnerability (RRV) was used for assessing the spatial distribution of sustainability in water resources. The results revealed a significant decrease in water availability in the country over the last 15 years. The decrease was found to be highest in the central region where most of the population of the country resides. The reliability in water resources was found high in the northeast Himalayan region and low in the southwest desert; resilience was found low in the central region, while vulnerability was found high in the south and the southeast. Overall, the water resources of the country were found most sustainable in the northeast and southwest and least in the south and the central parts. The maps of water resource sustainability and the changes in water availability produced in the present study can be used for long-term planning of water resources for adaptation to global changes. Besides, those can be used for the management of water resources in a sustainable and judicious manner.
Mohammad Naser Sediqi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Rawshan Ali; Shadan Abubaker; Xiaojun Wang; Kamal Ahmed; Shamsuddin Shahid; Asaduzzaman; Sayed Mir Agha Manawi. Spatio-Temporal Pattern in the Changes in Availability and Sustainability of Water Resources in Afghanistan. Sustainability 2019, 11, 5836 .
AMA StyleMohammad Naser Sediqi, Mohammed Sanusi Shiru, Mohamed Salem Nashwan, Rawshan Ali, Shadan Abubaker, Xiaojun Wang, Kamal Ahmed, Shamsuddin Shahid, Asaduzzaman, Sayed Mir Agha Manawi. Spatio-Temporal Pattern in the Changes in Availability and Sustainability of Water Resources in Afghanistan. Sustainability. 2019; 11 (20):5836.
Chicago/Turabian StyleMohammad Naser Sediqi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Rawshan Ali; Shadan Abubaker; Xiaojun Wang; Kamal Ahmed; Shamsuddin Shahid; Asaduzzaman; Sayed Mir Agha Manawi. 2019. "Spatio-Temporal Pattern in the Changes in Availability and Sustainability of Water Resources in Afghanistan." Sustainability 11, no. 20: 5836.
Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.
Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water 2019, 11, 2083 .
AMA StyleMehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan Irem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water. 2019; 11 (10):2083.
Chicago/Turabian StyleMehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. 2019. "Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration." Water 11, no. 10: 2083.
Groundwater is regarded as one of the most reliable and vulnerable sources of drinking water in many countries. Declining groundwater levels, due to over-exploitation and climate-change impacts, emphasize the need for sustainable management of this valuable resource. The concept of reliability-resiliency-vulnerability (RRV) has been adopted in this study to assess the spatial changes in the sustainability of aquifers for different periods to identify the main factors affecting groundwater sustainability in Pakistan. This is important for the country, as the substantial decline of groundwater levels in recent years has affected the water security of the growing economy. The satellite-based gridded Gravity Recovery and Climate Experiment (GRACE) groundwater anomaly data for the period 2002–2016 were used for this spatial assessment. The results revealed precipitation as the dominant factor associated with changing groundwater storage in Pakistan. A large decrease in aquifer storage was found over the study period. The groundwater-level decline was found to be greater in the region where agriculture is more intense, resulting in over-exploitation of groundwater for irrigation. The reduction of groundwater storage has led to a decrease in sustainability, especially in recent years (2008–2016) compared with previous periods (2002–2010 and 2005–2013). This study emphasized the need for groundwater resource management strategies such as reduction of groundwater abstraction in drought years, rescheduling the crop calendar to take advantage of rainfall, switching to less water-intensive crops, etc., particularly in groundwater depleting regions.
Kamal Ahmed; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Nadeem Nawaz; Najeebullah Khan. The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016. Hydrogeology Journal 2019, 27, 2485 -2496.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Mehmet Cüneyd Demirel, Nadeem Nawaz, Najeebullah Khan. The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016. Hydrogeology Journal. 2019; 27 (7):2485-2496.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Nadeem Nawaz; Najeebullah Khan. 2019. "The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016." Hydrogeology Journal 27, no. 7: 2485-2496.
We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5° resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 °C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.
Ishanch Qutbudin; Mohammed Sanusi Shiru; Ahmad Sharafati; Kamal Ahmed; Nadhir Al-Ansari; Zaher Mundher Yaseen; Shamsuddin Shahid; Xiaojun Wang. Seasonal Drought Pattern Changes Due to Climate Variability: Case Study in Afghanistan. Water 2019, 11, 1096 .
AMA StyleIshanch Qutbudin, Mohammed Sanusi Shiru, Ahmad Sharafati, Kamal Ahmed, Nadhir Al-Ansari, Zaher Mundher Yaseen, Shamsuddin Shahid, Xiaojun Wang. Seasonal Drought Pattern Changes Due to Climate Variability: Case Study in Afghanistan. Water. 2019; 11 (5):1096.
Chicago/Turabian StyleIshanch Qutbudin; Mohammed Sanusi Shiru; Ahmad Sharafati; Kamal Ahmed; Nadhir Al-Ansari; Zaher Mundher Yaseen; Shamsuddin Shahid; Xiaojun Wang. 2019. "Seasonal Drought Pattern Changes Due to Climate Variability: Case Study in Afghanistan." Water 11, no. 5: 1096.
General Circulation Models (GCMs) provide vital information on the likely future climate, much needed for the effective planning and management of water resources. The performance assessment of GCMs has received significant attention in recent years for reliable estimation of future climate. Even though many approaches have been trialled in the ranking of GCMs for the selection of an appropriate ensemble, there is still a need to explore the potential of the state-of-the-art ranking approaches for a more dependable selection of GCMs suitable for a given task, over a region of interest. The present study assessed the potential of a state-of-the-art feature selection method known as Symmetrical Uncertainty (SU) in ranking 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs based on their ability to simulate monthly precipitation and the monthly average of daily maximum and minimum temperature for annual, monsoon and winter seasons. The performance of GCMs was assessed using gridded climate data obtained from Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) and Princeton Global Meteorological Forcing dataset (Prin) over the period 1961-2005 considering Pakistan as the study area. The ranks obtained with SU were compared with those obtained using two well-established ranking approaches; (1) Compromise Programming (CP) and (2) Wavelet-based Skill Score (WSS). According to the results of this study, for the simulation of all the three climate variables in all seasons; CESM1-CAM5, HadGEM2-AO, NorESM1-M and HadGEM2-ES were identified as the best GCMs by SU, whereas CESM1-CAM5, HadGEM2-AO, NorESM1-M and GFDL-CM3 were identified as the best GCMs by CP, and CCSM4, CESM1-CAM5, GFDL-ESM2G and HadGEM2-ES by WSS. The comparison of ranks of GCMs obtained using the same ranking approach but based on different gridded data products showed more or less similar ranks for a given GCM. However, the differences were noticeable when the ranking was conducted with the same gridded data but employing different ranking approaches. The approach presented in this study can be extended to any number of GCMs and can be applied over any region, for the identification of the best performing ensemble of GCMs for a set of climate variables.
Kamal Ahmed; Shamsuddin Shahid; D.A. Sachindra; Nadeem Nawaz; Eun-Sung Chung. Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method. Journal of Hydrology 2019, 573, 281 -298.
AMA StyleKamal Ahmed, Shamsuddin Shahid, D.A. Sachindra, Nadeem Nawaz, Eun-Sung Chung. Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method. Journal of Hydrology. 2019; 573 ():281-298.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; D.A. Sachindra; Nadeem Nawaz; Eun-Sung Chung. 2019. "Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method." Journal of Hydrology 573, no. : 281-298.
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 rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson–Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU); Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research—University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. The result revealed that the performance of different products varies with climate. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications.
Kamal Ahmed; Shamsuddin Shahid; Xiaojun Wang; Nadeem Nawaz; Khan NajeebUllah. Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan. Water 2019, 11, 210 .
AMA StyleKamal Ahmed, Shamsuddin Shahid, Xiaojun Wang, Nadeem Nawaz, Khan NajeebUllah. Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan. Water. 2019; 11 (2):210.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Xiaojun Wang; Nadeem Nawaz; Khan NajeebUllah. 2019. "Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan." Water 11, no. 2: 210.
Changes in the temperature and precipitation have significantly affected water resources and agricultural productions in many countries across the world. The objective of the present study is to analyze the changing patterns of annual and seasonal precipitation and temperature in Iraq for the period 1961–2010. Monthly gridded precipitation and temperature data of Global precipitation climate center (GPCC) and climate research unit (CRU) respectively having a spatial resolution of 0.5° were used in this study to show the spatial pattern in trends. The rate of change in rainfall and temperature was estimated using Sen’s slope method while the significance of change was confirmed using Mann-Kendal test (MK) and the modified Mann-Kendall test (mMK). The results revealed large differences in the number of grid points showing significant changes in rainfall and temperature using MK and mMK methods. The mMK method revealed that the annual rainfall is decreasing at a rate of −1.0 to −5.0 mm/year in the northwest part of Iraq. The seasonal precipitations were found to decrease in spring (−0.4 to −2.56 mm/year) and winter (−0.4 to −2.0 mm/year), increase in summer (0.06 to 0.21 mm/year) at a few grid points and no change in autumn. On the other hand, a sharp rise in annual average of daily mean (0.42 to 0.64 °C/decade), maximum (0.39 to 0.65 °C/decade) and minimum (0.36 to 0.69 °C/decade) temperature was observed.
Saleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Eun-Sung Chung; Xiao-Jun Wang. Characteristics of Annual and Seasonal Trends of Rainfall and Temperature in Iraq. Asia-Pacific Journal of Atmospheric Sciences 2019, 55, 429 -438.
AMA StyleSaleem A. Salman, Shamsuddin Shahid, Tarmizi Ismail, Kamal Ahmed, Eun-Sung Chung, Xiao-Jun Wang. Characteristics of Annual and Seasonal Trends of Rainfall and Temperature in Iraq. Asia-Pacific Journal of Atmospheric Sciences. 2019; 55 (3):429-438.
Chicago/Turabian StyleSaleem A. Salman; Shamsuddin Shahid; Tarmizi Ismail; Kamal Ahmed; Eun-Sung Chung; Xiao-Jun Wang. 2019. "Characteristics of Annual and Seasonal Trends of Rainfall and Temperature in Iraq." Asia-Pacific Journal of Atmospheric Sciences 55, no. 3: 429-438.
The uncertainty assessment of the changes in drought characteristics due to climate change has caught the attention of the scientific community. This study used gauge-based gridded precipitation data obtained from Global Precipitation Climatology Centre (GPCC) to reconstruct historical droughts and downscale future precipitation projected by seven general circulation models (GCMs) of Coupled Model Inter-comparison Project phase 5 (CMIP5) under four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Support vector machine (SVM) and quantile mapping were used for downscaling and GCM bias correction, respectively. The model performances were assessed based on statistical measures. The historical and future projected precipitation data were finally used to characterize the seasonal droughts using Standardized Precipitation Index (SPI) for different crop growing periods. The drought severity-area-frequency (SAF) curves for the historical (1961-2010) and three future periods (2010-2039, 2040-2069, and 2070-2099) were developed. The uncertainty band of future drought SAF curves was estimated using Bayesian bootstrap (BB) at a 95% confidence level. As a result, SVM was successful in downscaling the precipitation of all selected CMIP5 GCMs. The seasonal ensemble of GCMs projected an increase in precipitation ranging from 8% to 41% under all scenarios. The historical SAF curves revealed that for equal drought severity, larger areas are affected by droughts having higher return periods. Future projections of droughts revealed the increase in affected area for lower severity and return period droughts and the decrease for higher severity and return period droughts. The uncertainty bands of drought SAF curves with higher return periods were found much wider compared to those with lower return periods which indicates more uncertainty in the projection of higher severity and return period droughts.
Kamal Ahmed; Shamsuddin Shahid; Eun-Sung Chung; Xiao-Jun Wang; Sobri Bin Harun. Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan. Journal of Hydrology 2019, 570, 473 -485.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Eun-Sung Chung, Xiao-Jun Wang, Sobri Bin Harun. Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan. Journal of Hydrology. 2019; 570 ():473-485.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Eun-Sung Chung; Xiao-Jun Wang; Sobri Bin Harun. 2019. "Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan." Journal of Hydrology 570, no. : 473-485.
The northern sub-Himalayan region is the primary source of water for a large part of Pakistan. Changes in precipitation and precipitation extremes in the area may have severe impacts on water security and hydrology of Pakistan. The objective of the study is to evaluate the spatial characteristics of the trends in annual and seasonal precipitation and precipitation extremes in Gilgit Baltistan, the northern administrative territory of Pakistan surrounded by Hindu Kush, Karakoram, and the Himalayan regions. The daily gridded rainfall data (1951–2007) of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) at 0.25° spatial resolution was used for evaluating the trends. The novelty of the present study is the application of the modified Mann-Kendall (MMK) trend for the evaluation of the significance of precipitation trends in order to differentiate the secular trends from climate natural variability. Besides, cumulative distribution function (CDF) plots of daily rainfall for the early (1951–1980) and later (1981–2007) periods were used to show the changes in extremes. The results revealed no significant change in annual precipitation but increase in summer rainfall in the range of 0.25 to 1.25 mm/year in the upper part and decrease in winter precipitation from 0 to − 0.25 mm/year in the west part of the region. Annual number of rainy days was also found to decrease in winter up to − 1.33 days/decade where the region receives a major portion of total precipitation. The decrease in winter rainfall and rainy days caused an increase in continuous dry days (around 0.27 days/year) and decrease in continuous wet days (up to − 0.26 days/year). Trend analysis and CDF plot revealed that though the numbers of rainy days are decreasing, the numbers of extreme rainfall days are increasing, which indicates rainfall become more erratic and intense in the region. The increases in both continuous dry days and extreme rainfall days indicate more droughts and floods may have adverse impacts on the hydrology of Pakistan.
Zafar Iqbal; Shamsuddin Shahid; Kamal Ahmed; Termizi Ismail; Nadeem Nawaz. Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan. Theoretical and Applied Climatology 2019, 137, 2755 -2769.
AMA StyleZafar Iqbal, Shamsuddin Shahid, Kamal Ahmed, Termizi Ismail, Nadeem Nawaz. Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan. Theoretical and Applied Climatology. 2019; 137 (3-4):2755-2769.
Chicago/Turabian StyleZafar Iqbal; Shamsuddin Shahid; Kamal Ahmed; Termizi Ismail; Nadeem Nawaz. 2019. "Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan." Theoretical and Applied Climatology 137, no. 3-4: 2755-2769.
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.
The uncertainties in climate projections in arid regions are quite high due to the large variability of climate and the lack of high-quality climate observations. In this study, an ensemble of four Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Model (GCM) namely GISS-E2-H, HadGEM2-ES, MIROC5, and NorESM1-M simulations was downscaled for the assessment of the spatiotemporal changes in precipitation in the data-scarce arid province (Balochistan) of Pakistan for four Representative Concentration Pathway (RCP) scenarios. The gauge-based gridded precipitation data of the Global Precipitation Climatology Centre (GPCC) having a spatial resolution of 0.5° was used for this purpose. Support Vector Machine (SVM) was used for the development of non-local model output statistics (MOS) downscaling models for each grid by linking the GPCC precipitation with the GCM simulated precipitation across a spatial domain (latitudes 03°–45° N and longitudes 42°–92° E). Then, Random Forest (RF) algorithm was used to develop the multi-model ensemble (MME) of downscaled precipitation projections. The performances of the models were assessed in terms of normalized root mean square error (NRMSE), percentage of bias (PBIAS), and modified index of agreement (md). The results indicated that the non-local SVM-based MOS models coupled with RF MME can simulate historical precipitation over the region quite well. The MME of GCMs projected changes in the annual, monsoon, and winter precipitation in the range of − 30% to 30% for different RCPs. Overall, the MME of GCMs indicated an increase in precipitation in the monsoon-dominated wetter regions in the east, while a decrease in winter precipitation dominated arid region in the west. A decrease in annual precipitation over the majority of the southeast, east, and northeastern arid regions was projected which may increase the aridity in the region.
Kamal Ahmed; Shamsuddin Shahid; Nadeem Nawaz; Najeebullah Khan. Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach. Theoretical and Applied Climatology 2018, 137, 1347 -1364.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Nadeem Nawaz, Najeebullah Khan. Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach. Theoretical and Applied Climatology. 2018; 137 (1-2):1347-1364.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Nadeem Nawaz; Najeebullah Khan. 2018. "Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach." Theoretical and Applied Climatology 137, no. 1-2: 1347-1364.
Assessment of the influence of climate variables on drought characteristics is important for adaption to changing pattern of droughts due to climate change. The objective of this study is to assess the changing characteristics of droughts due to climate variability and change during two major cropping seasons (Rabi and Kharif) for the period 1901–2010 over the diverse climate of Pakistan. The gauge-based gridded precipitation and temperature data with a spatial resolution of 0.5° is used for the reconstruction of droughts using standardized precipitation evapotranspiration index (SPEI). The temporal variations in droughts and their relationships with precipitation and temperature are assessed using a 50-year moving window with a 10-year time step. The annual maximum series (AMS) approach is used to estimate the return periods of seasonal droughts and the modified Mann-Kendal trend test is applied to assess the significance of trends in climate variables and drought index. The results showed that drought severity is increasing in the predominantly arid and semi-arid regions for both cropping seasons, while it is decreasing in western disturbance (WD) influenced high winter precipitation region during Rabi season. Temperature is found as the dominating factor for defining droughts in arid and semi-arid regions while the precipitation in WD influenced region. An increase in temperature in the range of 0.001 to 0.025 °C per year and almost no change in precipitation have caused decreases in Rabi SPEI in the range of −0.011 to −0.025 per year in the arid region. On the other hand, increases in precipitation in the range of 1.01–2.0 mm/year have caused increases in Kharif SPEI in WD influenced region in the range of 0.016–0.02 per year. However, rises in temperature in most part of the country has caused an increase in drought frequency in both seasons in the areas where droughts are less frequent. The results indicate that rising temperature due to global warming would increase drought severity and frequency in most part of the predominantly arid country.
Kamal Ahmed; Shamsuddin Shahid; Nadeem Nawaz. Impacts of climate variability and change on seasonal drought characteristics of Pakistan. Atmospheric Research 2018, 214, 364 -374.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Nadeem Nawaz. Impacts of climate variability and change on seasonal drought characteristics of Pakistan. Atmospheric Research. 2018; 214 ():364-374.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Nadeem Nawaz. 2018. "Impacts of climate variability and change on seasonal drought characteristics of Pakistan." Atmospheric Research 214, no. : 364-374.
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.
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 StyleTarmizi 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 StyleTarmizi 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.