Mostafa Tarek - Academia.edu (original) (raw)
Papers by Mostafa Tarek
Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets i... more Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies" by Mostafa Tarek et al. Mostafa Tarek et al.
In recent decades, many parts of the African continent have experienced high precipitation variab... more In recent decades, many parts of the African continent have experienced high precipitation variability with periodic drought and flood events. However, the network of streamflow gauges is too sparse in most countries to adequately capture these variations. In addition, no observed reference climatological dataset exists to adequately represent precipitation and temperature changes within all topographic and climatic zones. Consequently, the use of global gridded datasets needs to be considered. This paper aims to use the different available gridded datasets as inputs to a hydrological model to evaluate dataset performance. Nine precipitation and two temperature gridded datasets are used to this effect. The precipitation datasets include two gauged-only products, two satellite products corrected using ground-based observations, four reanalysis products and one merged product of gauge, satellite, and reanalysis. The two temperature datasets include one gauged-only and one reanalysis product. The ten precipitation and two temperature datasets were combined in their 18 possible arrangements for analysis purposes. Each combination was used to force the HMETS lumped hydrological model. The model parameters were calibrated individually for each combination against the streamflow records of 850 African catchments. The Kling-Gupta Efficiency (KGE) was used to evaluate the simulation performance. Results show thatboth temperature datasets performed equally well. Large differences were however observed between precipitation datasets. The MSWEP merged-product was the best-performing precipitation dataset, followed by CHIRPS satellites and ERA5 reanalysis products, respectively. The performance of both gauged-only datasets (CPC and GPCC) was inferior, outlining the limitations of extrapolating information in data-sparse regions.
<p>Recent studies show that the frequency and intensity of extreme precipitation will incre... more <p>Recent studies show that the frequency and intensity of extreme precipitation will increase under a warmer climate. It is expected that extreme convective precipitation will scale at a larger than Clausius–Clapeyron rate and especially so for short-duration rainfall. This has implication on flooding risk, and especially so on small catchments (<500 km<sup>2</sup>) which have a quick response time and are therefore particularly vulnerable to short duration rainfall. The impact of the amplification of extreme precipitation as a function of catchment scale has not been widely studied because most of the climate change impact studies have been conducted at the daily time step or higher. This is because until recently the vast majority of climate model outputs have only been available at the daily time step.</p><p>This study has looked at the amplification of sub-daily, daily, and multiday extreme precipitation and flooding and its dependency on catchment scale. This work uses outputs from the Climex large-ensemble to study the amplification of extreme streamflow with return period from 2 to 300 years and durations from 1 to 24 hours over 133 North-American catchments. Using a large ensemble allows for the accurate empirical computation of extreme events with very large return periods.  Results indicate that future extreme streamflow relative increases are largest for smaller catchments, longer return period, and shorter rainfall durations. Small catchments are therefore more vulnerable to future extreme rainfall than their larger counterparts.</p>
Hydrology and Earth System Sciences
Climate change impact studies require a reference climatological dataset providing a baseline per... more Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using groundbased observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a topdown hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071-2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.
International Journal of Hydrogen Energy
Abstract Graphitic carbon nitride (g-C3N4) has been well-known as an appealing semiconducting mat... more Abstract Graphitic carbon nitride (g-C3N4) has been well-known as an appealing semiconducting material for photocatalytic hydrogen production despite its restricted active sites and poor electronic properties. In this work, exfoliated g-C3N4 nanosheets were synthesised by chemical treatment of the bulk graphitic carbon nitride (gCN) and the nanosheets were further doped with CdO. The photocatalysts produced were extensively characterized by diverse analysis including XRD, BET, XPS, TEM, FESEM, UV-Vis spectroscopy and PL analysis. The BET surface area of CdO/exfoliated g-C3N4, 40.1 m2 g−1 was doubled in comparison to the exfoliated g-C3N4. Numerous electrochemical analyses such as Mott-Schottky, linear weep voltammetry and chronoamperometry were also performed in a standard photoelectrochemical system with three-electrode cell. The hydrothermally synthesised CdO/exfoliated g-C3N4 resulted higher amount of hydrogen evolution (145 μmol/g) for the photoreforming of aqueous formaldehyde than the CdO (20 μmol/g), bulk gCN (58 μmol/g) and exfoliated g-C3N4 (87 μmol/g). The excellent hydrogen production rate using CdO/exfoliated g-C3N4 nanocomposite could be ascribed by higher number of active sites as well as shorter path of the charge carries to the reaction surface. The anticipated Z-Scheme mechanism has demonstrated a synergistic impact between the CdO and exfoliated g-C3N4 where the organic compounds acting as hole scavenger as well as contribute protons, H+ for the effective hydrogen production. Thus, it is clearly confirmed that the newly formulated CdO/exfoliated g-C3N4 has an outstanding potentiality for environmental remediation and conversion sectors.
Journal of Power Sources
Abstract Low power generation and low voltage output is a common problem in microbial fuel cell (... more Abstract Low power generation and low voltage output is a common problem in microbial fuel cell (MFC) run with complex wastewater. Biocatalysts are one of the major components to ensure the high performance of the MFCs. In the present study, palm oil mill effluent (POME) is treated with a combination of Saccharomyces cerevisiae, Klebsiella variicola and Pseudomonas aeruginosa to intensify the power generation and treatment efficiency of the MFC. MFCs are catalyzed by pure cultures exhibited low power generation in the range of 50–103 mW/m2 whereas the yeast-bacteria inoculum demonstrates 5–10 fold higher power generation (500 mW/m2 at 0.67 V) with ~90% COD removal efficiency. The mechanism of enhanced power generation by yeast-bacteria inoculum is unravelled which suggests that Klebsiella variicola and Pseudomonas aeruginosa play a crucial role in transferring the electrons from the bulk phase to the electrode surface through self-produced electron-shuttles and at the same time extract electrons from the yeast leading to high power generation. Moreover, substrate-inoculum synergism also offers higher wastewater treatment efficiency. The findings of the work suggest that the use of substrate-inoculum mutualistic interaction between yeast and bacteria as a profound replacement to the existing bacterial inoculum for achieving higher performance in MFCs.
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IOP Conference Series: Materials Science and Engineering
The present study explored the efficiency of a p-n heterostructured hybrid catalyst CuO-CdS to co... more The present study explored the efficiency of a p-n heterostructured hybrid catalyst CuO-CdS to convert CO2 selectively into methanol by photoelectrochemical (PEC) method under concurrent visible light irradiation and a bias potential -0.4 V vs. NHE. The results showed that the inclusion of CdS with CuO significantly enhanced the activity of PEC CO2 reduction to produce methanol by facilitating the separation of photogenerated electron-hole (e−/h+) pairs through the p-n heterostructured architectures. The yield of methanol, the incident photon current efficiency (IPCE) and quantum efficiency (QE) in PEC CO2 reduction were achieved 35.65 μmoleL−1cm−2, 20.24% and 24.11%, respectively. The present work bears a new understanding into the fabrication of high-performable artificial p-n type heterostructured catalyst which is capable to function as a catalyst for photocathode for the reduction of CO2 and remarkable improvement in methanol yield under visible light illumination.
Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets i... more Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies" by Mostafa Tarek et al. Mostafa Tarek et al.
Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets i... more Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies" by Mostafa Tarek et al. Mostafa Tarek et al.
In recent decades, many parts of the African continent have experienced high precipitation variab... more In recent decades, many parts of the African continent have experienced high precipitation variability with periodic drought and flood events. However, the network of streamflow gauges is too sparse in most countries to adequately capture these variations. In addition, no observed reference climatological dataset exists to adequately represent precipitation and temperature changes within all topographic and climatic zones. Consequently, the use of global gridded datasets needs to be considered. This paper aims to use the different available gridded datasets as inputs to a hydrological model to evaluate dataset performance. Nine precipitation and two temperature gridded datasets are used to this effect. The precipitation datasets include two gauged-only products, two satellite products corrected using ground-based observations, four reanalysis products and one merged product of gauge, satellite, and reanalysis. The two temperature datasets include one gauged-only and one reanalysis product. The ten precipitation and two temperature datasets were combined in their 18 possible arrangements for analysis purposes. Each combination was used to force the HMETS lumped hydrological model. The model parameters were calibrated individually for each combination against the streamflow records of 850 African catchments. The Kling-Gupta Efficiency (KGE) was used to evaluate the simulation performance. Results show thatboth temperature datasets performed equally well. Large differences were however observed between precipitation datasets. The MSWEP merged-product was the best-performing precipitation dataset, followed by CHIRPS satellites and ERA5 reanalysis products, respectively. The performance of both gauged-only datasets (CPC and GPCC) was inferior, outlining the limitations of extrapolating information in data-sparse regions.
<p>Recent studies show that the frequency and intensity of extreme precipitation will incre... more <p>Recent studies show that the frequency and intensity of extreme precipitation will increase under a warmer climate. It is expected that extreme convective precipitation will scale at a larger than Clausius–Clapeyron rate and especially so for short-duration rainfall. This has implication on flooding risk, and especially so on small catchments (<500 km<sup>2</sup>) which have a quick response time and are therefore particularly vulnerable to short duration rainfall. The impact of the amplification of extreme precipitation as a function of catchment scale has not been widely studied because most of the climate change impact studies have been conducted at the daily time step or higher. This is because until recently the vast majority of climate model outputs have only been available at the daily time step.</p><p>This study has looked at the amplification of sub-daily, daily, and multiday extreme precipitation and flooding and its dependency on catchment scale. This work uses outputs from the Climex large-ensemble to study the amplification of extreme streamflow with return period from 2 to 300 years and durations from 1 to 24 hours over 133 North-American catchments. Using a large ensemble allows for the accurate empirical computation of extreme events with very large return periods.  Results indicate that future extreme streamflow relative increases are largest for smaller catchments, longer return period, and shorter rainfall durations. Small catchments are therefore more vulnerable to future extreme rainfall than their larger counterparts.</p>
Hydrology and Earth System Sciences
Climate change impact studies require a reference climatological dataset providing a baseline per... more Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using groundbased observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a topdown hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071-2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.
International Journal of Hydrogen Energy
Abstract Graphitic carbon nitride (g-C3N4) has been well-known as an appealing semiconducting mat... more Abstract Graphitic carbon nitride (g-C3N4) has been well-known as an appealing semiconducting material for photocatalytic hydrogen production despite its restricted active sites and poor electronic properties. In this work, exfoliated g-C3N4 nanosheets were synthesised by chemical treatment of the bulk graphitic carbon nitride (gCN) and the nanosheets were further doped with CdO. The photocatalysts produced were extensively characterized by diverse analysis including XRD, BET, XPS, TEM, FESEM, UV-Vis spectroscopy and PL analysis. The BET surface area of CdO/exfoliated g-C3N4, 40.1 m2 g−1 was doubled in comparison to the exfoliated g-C3N4. Numerous electrochemical analyses such as Mott-Schottky, linear weep voltammetry and chronoamperometry were also performed in a standard photoelectrochemical system with three-electrode cell. The hydrothermally synthesised CdO/exfoliated g-C3N4 resulted higher amount of hydrogen evolution (145 μmol/g) for the photoreforming of aqueous formaldehyde than the CdO (20 μmol/g), bulk gCN (58 μmol/g) and exfoliated g-C3N4 (87 μmol/g). The excellent hydrogen production rate using CdO/exfoliated g-C3N4 nanocomposite could be ascribed by higher number of active sites as well as shorter path of the charge carries to the reaction surface. The anticipated Z-Scheme mechanism has demonstrated a synergistic impact between the CdO and exfoliated g-C3N4 where the organic compounds acting as hole scavenger as well as contribute protons, H+ for the effective hydrogen production. Thus, it is clearly confirmed that the newly formulated CdO/exfoliated g-C3N4 has an outstanding potentiality for environmental remediation and conversion sectors.
Journal of Power Sources
Abstract Low power generation and low voltage output is a common problem in microbial fuel cell (... more Abstract Low power generation and low voltage output is a common problem in microbial fuel cell (MFC) run with complex wastewater. Biocatalysts are one of the major components to ensure the high performance of the MFCs. In the present study, palm oil mill effluent (POME) is treated with a combination of Saccharomyces cerevisiae, Klebsiella variicola and Pseudomonas aeruginosa to intensify the power generation and treatment efficiency of the MFC. MFCs are catalyzed by pure cultures exhibited low power generation in the range of 50–103 mW/m2 whereas the yeast-bacteria inoculum demonstrates 5–10 fold higher power generation (500 mW/m2 at 0.67 V) with ~90% COD removal efficiency. The mechanism of enhanced power generation by yeast-bacteria inoculum is unravelled which suggests that Klebsiella variicola and Pseudomonas aeruginosa play a crucial role in transferring the electrons from the bulk phase to the electrode surface through self-produced electron-shuttles and at the same time extract electrons from the yeast leading to high power generation. Moreover, substrate-inoculum synergism also offers higher wastewater treatment efficiency. The findings of the work suggest that the use of substrate-inoculum mutualistic interaction between yeast and bacteria as a profound replacement to the existing bacterial inoculum for achieving higher performance in MFCs.
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IOP Conference Series: Materials Science and Engineering
The present study explored the efficiency of a p-n heterostructured hybrid catalyst CuO-CdS to co... more The present study explored the efficiency of a p-n heterostructured hybrid catalyst CuO-CdS to convert CO2 selectively into methanol by photoelectrochemical (PEC) method under concurrent visible light irradiation and a bias potential -0.4 V vs. NHE. The results showed that the inclusion of CdS with CuO significantly enhanced the activity of PEC CO2 reduction to produce methanol by facilitating the separation of photogenerated electron-hole (e−/h+) pairs through the p-n heterostructured architectures. The yield of methanol, the incident photon current efficiency (IPCE) and quantum efficiency (QE) in PEC CO2 reduction were achieved 35.65 μmoleL−1cm−2, 20.24% and 24.11%, respectively. The present work bears a new understanding into the fabrication of high-performable artificial p-n type heterostructured catalyst which is capable to function as a catalyst for photocathode for the reduction of CO2 and remarkable improvement in methanol yield under visible light illumination.
Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets i... more Interactive comment on "Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies" by Mostafa Tarek et al. Mostafa Tarek et al.