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Many surface urban heat island (SUHI) studies have been conducted around the globe, however there is still a lack of information available regarding the intensity of SUHI (SUHII) in Bangladesh cities. This study focused on diurnal and seasonal SUHI variability, temporal trends and possible drivers in five major cities. Mean annual daytime SUHII ranged from 2.88 °C for Dhaka to 0.84 °C for Rajshahi, while nighttime intensity varied from 1.91 °C (Chittagong) to 0.30 °C (Sylhet). The pre-monsoon period exhibited the greatest magnitude and the seasonal amplitude during the winter season was positive for Dhaka and Khulna but negative for the other cities. Correlation analysis indicated that a dense city population, a high degree of imperviousness and the absence of greenery were likely to act singly, or in combination, to increase urban warming within these cities. An increasing warming trend during daytime was observed. The urban population of Bangladesh is projected to increase substantially in future (i.e., to 81.4 million by 2029), so the findings of this study provide valuable insights into this warming issue and will assist in the development of effective local-scale climate change adaptation plans.
Ashraf Dewan; Grigory Kiselev; Dirk Botje. Diurnal and seasonal trends and associated determinants of surface urban heat islands in large Bangladesh cities. Applied Geography 2021, 135, 102533 .
AMA StyleAshraf Dewan, Grigory Kiselev, Dirk Botje. Diurnal and seasonal trends and associated determinants of surface urban heat islands in large Bangladesh cities. Applied Geography. 2021; 135 ():102533.
Chicago/Turabian StyleAshraf Dewan; Grigory Kiselev; Dirk Botje. 2021. "Diurnal and seasonal trends and associated determinants of surface urban heat islands in large Bangladesh cities." Applied Geography 135, no. : 102533.
Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988–2020), remote sensing images (e.g., MODIS, Landsat 5–8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967–0.999, MAE = 0.022–0.117, RMSE = 0.029–0.148, RAE = 4.48–23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929–0.967), corrected classified instances (CCI: 96.45–98.35), area under the curve (AUC: 0.983–0.997), and Gini coefficients (0.966–0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.
Mahfuzur Rahman; Ningsheng Chen; Ahmed Elbeltagi; Monirul Islam; Mehtab Alam; Hamid Reza Pourghasemi; Wang Tao; Jun Zhang; Tian Shufeng; Hamid Faiz; Muhammad Aslam Baig; Ashraf Dewan. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. Journal of Environmental Management 2021, 295, 113086 .
AMA StyleMahfuzur Rahman, Ningsheng Chen, Ahmed Elbeltagi, Monirul Islam, Mehtab Alam, Hamid Reza Pourghasemi, Wang Tao, Jun Zhang, Tian Shufeng, Hamid Faiz, Muhammad Aslam Baig, Ashraf Dewan. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. Journal of Environmental Management. 2021; 295 ():113086.
Chicago/Turabian StyleMahfuzur Rahman; Ningsheng Chen; Ahmed Elbeltagi; Monirul Islam; Mehtab Alam; Hamid Reza Pourghasemi; Wang Tao; Jun Zhang; Tian Shufeng; Hamid Faiz; Muhammad Aslam Baig; Ashraf Dewan. 2021. "Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh." Journal of Environmental Management 295, no. : 113086.
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.
Hilal Ahmad; Chen Ningsheng; Mahfuzur Rahman; Monirul Islam; Hamid Pourghasemi; Syed Hussain; Jules Habumugisha; Enlong Liu; Han Zheng; Huayong Ni; Ashraf Dewan. Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models. ISPRS International Journal of Geo-Information 2021, 10, 315 .
AMA StyleHilal Ahmad, Chen Ningsheng, Mahfuzur Rahman, Monirul Islam, Hamid Pourghasemi, Syed Hussain, Jules Habumugisha, Enlong Liu, Han Zheng, Huayong Ni, Ashraf Dewan. Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models. ISPRS International Journal of Geo-Information. 2021; 10 (5):315.
Chicago/Turabian StyleHilal Ahmad; Chen Ningsheng; Mahfuzur Rahman; Monirul Islam; Hamid Pourghasemi; Syed Hussain; Jules Habumugisha; Enlong Liu; Han Zheng; Huayong Ni; Ashraf Dewan. 2021. "Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models." ISPRS International Journal of Geo-Information 10, no. 5: 315.
There is currently a lack of knowledge regarding the spatiotemporal variation of day and night surface urban heat island intensity (SUHII) in the major cities of Bangladesh. These cities have a large population base and generally lack the resources to deal with rapid urbanisation impacts, so any increase in urban temperature has the potential to affect people both directly (due to heatwave conditions) or indirectly (due to loss of livelihood). Time series diurnal (day/night) MODIS land surface temperature (LST) data for the period 2000–2019 was used to produce baseline information about SUHI intensity, drivers and temporal trends. Five large cities were selected based on population size and historical urban expansion rates. Results indicated that annual SUHII was greater in the larger cities of Dhaka and Chittagong than in the smaller cities. SUHII observed during the day was also greater than at night. Population (in terms of city size and surface cover), lack of greenness and anthropogenic forcing were major factors affecting SUHII. Trend assessments revealed positive trends during daytime in four out of five cities, while one city recorded negative trends at night. The findings may provide new insights into impacts arising from rapid urbanisation and demographic shifts.
Ashraf Dewan; Grigory Kiselev; Dirk Botje; Golam Iftekhar Mahmud; Hanif Bhuian; Quazi K. Hassan. Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society 2021, 71, 102926 .
AMA StyleAshraf Dewan, Grigory Kiselev, Dirk Botje, Golam Iftekhar Mahmud, Hanif Bhuian, Quazi K. Hassan. Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society. 2021; 71 ():102926.
Chicago/Turabian StyleAshraf Dewan; Grigory Kiselev; Dirk Botje; Golam Iftekhar Mahmud; Hanif Bhuian; Quazi K. Hassan. 2021. "Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends." Sustainable Cities and Society 71, no. : 102926.
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.
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.
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 StyleGhaith 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 StyleGhaith 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.
Violence in Rakhine state of Myanmar forcibly displaced nearly one million Rohingya. They took refuge, from 25 August 2017 to date, in Cox's Bazar–Teknaf peninsula of Bangladesh. Initially, nearly 2,000 ha of forested lands had to be cleared to accommodate them in one of the most ecologically critical areas (ECA) in the peninsula. To support Rohingyas livelihoods, fuelwood collection and illegal logging were widespread since their arrival, causing severe environmental degradation, including loss of a vast amount of forest cover. To devise conservation and protection strategies of a highly sensitive ecosystem, it is imperative to understand the degree of forest cover deterioration and associated impacts related to Rohingya emigration. This study employed satellite images and collateral data to monitor, model spatiotemporal pattern of forest cover degradation, and loss of ecosystem function in Cox's Bazar–Teknaf peninsula. Supervised classification method was used to derive multidate land use/cover data which was then utilized to monitor spatiotemporal pattern of forest cover change from 2017 to 2019. A projection of forest cover loss was also carried out using Markov chain with cellular automata technique. Dynamic modelling was performed to predict changes in forest covers, assuming that displaced Rohingya continues to reside in this environmentally sensitive location. The result revealed that 3,130 ha of forested lands of different categories were transformed into either refugee camps or Rohingya influenced degraded forests between 2017 and 2019. Prediction showed that around 5,115 ha of forest cover may experience loss from 2019–2027. Furthermore, above ground biomass (AGB) and carbon stock estimation indicated a consistent and substantial loss during the study period, which is likely to swell if present deforestation rate continues. The findings have considerable implications in developing conservation decisions, priority interventions and public policies to save ecologically critical area of Bangladesh.
Mohammad Emran Hasan; Li Zhang; Ashraf Dewan; Huadong Guo; Riffat Mahmood. Spatiotemporal pattern of forest degradation and loss of ecosystem function associated with Rohingya influx: A geospatial approach. Land Degradation & Development 2020, 32, 3666 -3683.
AMA StyleMohammad Emran Hasan, Li Zhang, Ashraf Dewan, Huadong Guo, Riffat Mahmood. Spatiotemporal pattern of forest degradation and loss of ecosystem function associated with Rohingya influx: A geospatial approach. Land Degradation & Development. 2020; 32 (13):3666-3683.
Chicago/Turabian StyleMohammad Emran Hasan; Li Zhang; Ashraf Dewan; Huadong Guo; Riffat Mahmood. 2020. "Spatiotemporal pattern of forest degradation and loss of ecosystem function associated with Rohingya influx: A geospatial approach." Land Degradation & Development 32, no. 13: 3666-3683.
Although coastal and inland areas of Bangladesh exhibit distinct physiographic and climatic characteristics, spatiotemporal variation of extreme climatic events are poorly understood in these two areas. This study was an attempt to understand the trends in extreme climatic events in coastal and inland areas over the period 1968‐2018. The missing data in daily maximum and minimum temperature, and daily rainfall datasets were imputed using the multiple imputation by chained equations (MICE) technique and implementing a predictive mean matching algorithm. The imputed datasets were then tested for inhomogeneity using the penalized maximal t (PMT) and modified penalized maximal F (PMF) tests. A quantile matching (QM) algorithm was then applied to homogenize the datasets, which were then used for generating thirteen extreme temperature and nine extreme rainfall indices. The trends were assessed using the Trend Free Pre‐whitened (TFPW) Mann‐Kendall (MK) test and the magnitudes of the changes were determined using the Thiel‐Sen slope estimator. Additionally, standardized anomalies were calculated to understand the seasonal variability of temperature and rainfall over the past five decades. Results suggested that both coastal and inland areas were warming significantly but coastal areas exhibited a higher rate of warming. Although most of the extreme rainfall indices showed statistically non‐significant changes for coastal and inland stations, there is evidence of localized dryness and increased rainfall at individual stations. In particular, the drought‐prone northwestern region of the country experienced decreased rainfall, which is discordant to the results of previous studies. Findings from this study highlighted important local and regional‐scale changes in extreme climate events that were previously overlooked. The findings of this study can help undertake targeted climate change adaptation strategies to save population and resources.
Abu Yousuf Md. Abdullah; Hanif Bhuian; Grigory Kiselev; Ashraf Dewan; Quazi K. Hasan; M. Rafiuddin. Extreme temperature and rainfall events in Bangladesh: a comparison between coastal and inland areas. International Journal of Climatology 2020, 1 .
AMA StyleAbu Yousuf Md. Abdullah, Hanif Bhuian, Grigory Kiselev, Ashraf Dewan, Quazi K. Hasan, M. Rafiuddin. Extreme temperature and rainfall events in Bangladesh: a comparison between coastal and inland areas. International Journal of Climatology. 2020; ():1.
Chicago/Turabian StyleAbu Yousuf Md. Abdullah; Hanif Bhuian; Grigory Kiselev; Ashraf Dewan; Quazi K. Hasan; M. Rafiuddin. 2020. "Extreme temperature and rainfall events in Bangladesh: a comparison between coastal and inland areas." International Journal of Climatology , no. : 1.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide, particularly in densely populated countries such as Bangladesh with inadequate health care facilities. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not have been pragmatically implementable in developing countries due to social and economic reasons (i.e., poor education, less public awareness, massive unemployment). Hence, to elucidate COVID-19 transmission dynamics with respect to the NPI status—e.g., social distancing—this study conducted spatio-temporal analysis using the prospective scanning statistic at district and sub-district levels in Bangladesh and its capital, Dhaka city, respectively. Dhaka megacity has remained the highest-risk “active” cluster since early April. Lately, the central and south eastern regions in Bangladesh have been exhibiting a high risk of COVID-19 transmission. The detected space-time progression of COVID-19 infection suggests that Bangladesh has experienced a community-level transmission at the early phase (i.e., March, 2020), primarily introduced by Bangladeshi citizens returning from coronavirus epicenters in Europe and the Middle East. Potential linkages exist between the violation of NPIs and the emergence of new higher-risk clusters over the post-incubation periods around Bangladesh. Novel insights into the COVID-19 transmission dynamics derived in this study on Bangladesh provide important policy guidelines for early preparations and pragmatic NPI measures to effectively deal with infectious diseases in resource-scarce countries worldwide.
Arif Masrur; Manzhu Yu; Wei Luo; Ashraf Dewan. Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh. International Journal of Environmental Research and Public Health 2020, 17, 5911 .
AMA StyleArif Masrur, Manzhu Yu, Wei Luo, Ashraf Dewan. Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh. International Journal of Environmental Research and Public Health. 2020; 17 (16):5911.
Chicago/Turabian StyleArif Masrur; Manzhu Yu; Wei Luo; Ashraf Dewan. 2020. "Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh." International Journal of Environmental Research and Public Health 17, no. 16: 5911.
The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.
Ifeanyi Ejiagha; M. Ahmed; Quazi Hassan; Ashraf Dewan; Anil Gupta; Elena Rangelova. Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale. Remote Sensing 2020, 12, 2508 .
AMA StyleIfeanyi Ejiagha, M. Ahmed, Quazi Hassan, Ashraf Dewan, Anil Gupta, Elena Rangelova. Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale. Remote Sensing. 2020; 12 (15):2508.
Chicago/Turabian StyleIfeanyi Ejiagha; M. Ahmed; Quazi Hassan; Ashraf Dewan; Anil Gupta; Elena Rangelova. 2020. "Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale." Remote Sensing 12, no. 15: 2508.
The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide. As of mid-June 2020, COVID-19 has spread worldwide with more than 7.7 million confirmed cases and more than 400,000 deaths. The impacts are substantial particularly in developing and densely populated countries like Bangladesh with inadequate health care facilities, where COVID-19 cases are currently surging. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not be pragmatically implementable in developing countries primarily due to social and economic reasons (i.e. poor education, less public awareness, massive unemployment). To shed light on COVID-19 transmission dynamics and impacts of NPI scenarios – e.g. social distancing, this study conducted emerging pattern analysis using the space-time scan statistic at district and thana (i.e. a sub-district or ‘upazila’ with at least one police station) levels in Bangladesh and its capital – Dhaka city, respectively. We found that the central and south eastern regions in Bangladesh are currently exhibiting a high risk of COVID-19 transmission. Dhaka megacity remains as the highest risk “active” cluster since early April. The space-time progression of COVID-19 infection, when validated against the chronicle of government press releases and newspaper reports, suggests that Bangladesh have experienced a community level transmission at the early phase (i.e., March, 2020) primarily introduced by Bangladeshi citizens returning from coronavirus-affected countries in the Europe and the Middle East. A linkage is evident between the violation of NPIs and post-incubation period emergence of new clusters with elevated exposure risk around Bangladesh. This study provides novel insights into the space-time patterns of COVID-19 transmission dynamics and recommends pragmatic NPI implementation for reducing disease transmission and minimizing impacts in a resource-scarce country with Bangladesh as a case-study example.
Arif Masrur; Manzhu Yu; Wei Luo; Ashraf Dewan. Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh. 2020, 1 .
AMA StyleArif Masrur, Manzhu Yu, Wei Luo, Ashraf Dewan. Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh. . 2020; ():1.
Chicago/Turabian StyleArif Masrur; Manzhu Yu; Wei Luo; Ashraf Dewan. 2020. "Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh." , no. : 1.
The severity and frequency of short-duration, but damaging, urban area floods have increased in recent years across the world. Alteration to the urban micro-climate due to global climate change impacts may also exacerbate the situation in future. Sustainable urban stormwater management using low impact development (LID) techniques, along with conventional urban stormwater management systems, can be implemented to mitigate climate-change-induced flood impacts. In this study, the effectiveness of LIDs in the mitigation of urban flood are analyzed to identify their limitations. Further research on the success of these techniques in urban flood mitigation planning is also recommended. The results revealed that LIDs can be an efficient method for mitigating urban flood impacts. Most of the LID methods developed so far, however, are found to be effective only for small flood peaks. They also often fail due to non-optimization of the site-specific and time-varying climatic conditions. Major challenges include identification of the best LID practices for the region of interest, efficiency improvements in technical areas, and site-specific optimization of LID parameters. Improvements in these areas will allow better mitigation of climate-change-induced urban floods in a cost-effective manner and will also assist in the achievement of sustainable development goals for cities.
Sahar Hadi Pour; Ahmad Khairi Abd Wahab; Shamsuddin Shahid; Asaduzzaman; Ashraf Dewan. Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges. Sustainable Cities and Society 2020, 62, 102373 .
AMA StyleSahar Hadi Pour, Ahmad Khairi Abd Wahab, Shamsuddin Shahid, Asaduzzaman, Ashraf Dewan. Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges. Sustainable Cities and Society. 2020; 62 ():102373.
Chicago/Turabian StyleSahar Hadi Pour; Ahmad Khairi Abd Wahab; Shamsuddin Shahid; Asaduzzaman; Ashraf Dewan. 2020. "Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges." Sustainable Cities and Society 62, no. : 102373.
The construction of polders in the coastal region of Bangladesh has significantly modified the patterns of flooding, as well as leading to significant land use/land cover (hereinafter, LULC) changes. The impact of LULC change and flooding on poverty is complex and poorly understood. This study presents a spatiotemporal appraisal of poverty in relation to LULC change and pluvial flood risk in the south western embanked area of Bangladesh. A combination of logistic regression (LR), cellular automata (CA), and Markov Chain models were utilised to predict future LULC based on historical data. Flood risk assessment was performed at present and for future LULC scenarios. A spatial regression model was developed, incorporating multiple parameters to estimate the wealth index (WI) for present-day and future scenarios. In the study area, agricultural lands reduced from 34 % in 2005 to 8% in 2010, while aquaculture land cover increased from 17 % to 39 % during the same time. The rate of LULC change was relatively low between 2010 and 2019. Based on the recent trend, LULC was predicted for the year 2030. Flood risk was positively correlated with LULC and the expected annual damage (EAD) was estimated at $903 million in 2005, which is likely to increase to $2096 million by 2030, considering changes in LULC scenarios. The analysis further showed that the EAD and LULC change were negatively associated with the WI. Despite consistent national GDP growth in Bangladesh in recent years, the rate of increase of WI is likely to be low in the future because flood risk and patterns of LULC change have a negative effect on WI.
Mohammed Sarfaraz Gani Adnan; Abu Yousuf Md Abdullah; Ashraf Dewan; Jim W. Hall. The effects of changing land use and flood hazard on poverty in coastal Bangladesh. Land Use Policy 2020, 99 .
AMA StyleMohammed Sarfaraz Gani Adnan, Abu Yousuf Md Abdullah, Ashraf Dewan, Jim W. Hall. The effects of changing land use and flood hazard on poverty in coastal Bangladesh. Land Use Policy. 2020; 99 ():.
Chicago/Turabian StyleMohammed Sarfaraz Gani Adnan; Abu Yousuf Md Abdullah; Ashraf Dewan; Jim W. Hall. 2020. "The effects of changing land use and flood hazard on poverty in coastal Bangladesh." Land Use Policy 99, no. : .
Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.
Mohammed Sanusi Shiru; Shamsuddin Shahid; Ashraf Dewan; Eun-Sung Chung; Noraliani Alias; Kamal Ahmed; Quazi K. Hassan. Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Scientific Reports 2020, 10, 1 -18.
AMA StyleMohammed Sanusi Shiru, Shamsuddin Shahid, Ashraf Dewan, Eun-Sung Chung, Noraliani Alias, Kamal Ahmed, Quazi K. Hassan. Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Scientific Reports. 2020; 10 (1):1-18.
Chicago/Turabian StyleMohammed Sanusi Shiru; Shamsuddin Shahid; Ashraf Dewan; Eun-Sung Chung; Noraliani Alias; Kamal Ahmed; Quazi K. Hassan. 2020. "Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios." Scientific Reports 10, no. 1: 1-18.
Many young adults are susceptible to obesity issues and the increased health risks associated with a lack of physical activity. Those who are prone to gaining weight include university students. An active transport system (walking and cycling), in combination with well-funded public transport, are essential components of a sustainable urban transport network, offering many benefits to the health of the individual, as well as the environment, economy, and society as a whole. The spatial association between active mobility (i.e. the physical activity of a human being for locomotion) of young adults and the environment, however, is poorly understood. This study presents a GIS-based model to determine association of various environmental (natural and built environment) factors with locational accessibility of active and public transport trips taken by university students. A GIS-based ensemble of Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP) model was established. We analysed the characteristics of locations accessed by university students in relation to eight environmental factors including slope, elevation, land use, population density, travel time, building density, intersection density, and public transport service area. The model was applied to the Grenoble metropolitan region of France, an area well-known for policies which promote active transport. The results indicated that intersection density and land use are strongly associated with active and public transport accessibility, with weights of 0.17 and 0.16, respectively. The presence of infrastructure to support active travel, and regulation to limit vehicular speed, also improved accessibility. Approximately 50% of the area of the Grenoble metropolitan region was defined as accessible and suitable (‘moderate’ to ‘very high’ degree) for active mobility. The results of this study could allow city planners to monitor the existing status of active and public transport facilities, and identify areas that require additional work to improve accessibility.
Khatun E Zannat; Mohammed Sarfaraz Gani Adnan; Ashraf Dewan. A GIS-based approach to evaluating environmental influences on active and public transport accessibility of university students. Journal of Urban Management 2020, 9, 331 -346.
AMA StyleKhatun E Zannat, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan. A GIS-based approach to evaluating environmental influences on active and public transport accessibility of university students. Journal of Urban Management. 2020; 9 (3):331-346.
Chicago/Turabian StyleKhatun E Zannat; Mohammed Sarfaraz Gani Adnan; Ashraf Dewan. 2020. "A GIS-based approach to evaluating environmental influences on active and public transport accessibility of university students." Journal of Urban Management 9, no. 3: 331-346.
The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.
Asif Ishtiaque; Arif Masrur; Yasin Wahid Rabby; Tasnuba Jerin; Ashraf Dewan. Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review. Remote Sensing 2020, 12, 691 .
AMA StyleAsif Ishtiaque, Arif Masrur, Yasin Wahid Rabby, Tasnuba Jerin, Ashraf Dewan. Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review. Remote Sensing. 2020; 12 (4):691.
Chicago/Turabian StyleAsif Ishtiaque; Arif Masrur; Yasin Wahid Rabby; Tasnuba Jerin; Ashraf Dewan. 2020. "Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review." Remote Sensing 12, no. 4: 691.
Drought is considered to be one of the most devastating natural hazards, causing widespread environmental and social damage in many parts of the world. Using standardized precipitation index, this work has assessed changes in the severity–area–frequency (SAF) relationship curve of seasonal droughts in Bangladesh. Changes were estimated for mild, moderate, severe and extreme droughts for the four climatic seasons; winter, pre-monsoon, monsoon, post-monsoon, and for the two major growing seasons; rabi (November to April) and kharif (May to October). Nineteen general circulation models (GCMs) of Couple Model Intercomparison Project 5 were used. The model output statistics approach was used to downscale GCM simulated rainfall for eighteen climate stations in Bangladesh. Changes in the SAF curve were computed for three periods (2010–2039, 2040–2069 and 2070–2099). The uncertainty band of the SAF relationship curve was then computed using the Bayesian bootstrap method at the 95% confidence level. The results reveal that moderate and severe drought categories have the highest return period and are likely to affect the region more than other types of droughts. The kharif season drought was found to be most pronounced and affected significant portions of the country during all return periods and severity categories. Projections also show that monsoon and kharif droughts would increase across Bangladesh in regards of severity and return period. Higher return period droughts were also projected to increase in aerial extent in the middle of this century (2040–2069).
Mahiuddin Alamgir; Najeebullah Khan; Shamsuddin Shahid; Zaher Mundher Yaseen; Ashraf Dewan; Quazi Hassan; Balach Rasheed. Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment 2020, 34, 447 -464.
AMA StyleMahiuddin Alamgir, Najeebullah Khan, Shamsuddin Shahid, Zaher Mundher Yaseen, Ashraf Dewan, Quazi Hassan, Balach Rasheed. Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment. 2020; 34 (2):447-464.
Chicago/Turabian StyleMahiuddin Alamgir; Najeebullah Khan; Shamsuddin Shahid; Zaher Mundher Yaseen; Ashraf Dewan; Quazi Hassan; Balach Rasheed. 2020. "Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios." Stochastic Environmental Research and Risk Assessment 34, no. 2: 447-464.
Rapidly changing river systems can impact people, property and infrastructures. This study investigates bank erosion and accretion of the Padma River in Bangladesh, through space and time, using historical topographic maps, Corona and Landsat images and navigational charts. A geographic information system (GIS) was utilised to quantify the erosion and accretion pattern. In addition, volumetric changes in the riverbed were also investigated. Results indicated that the area of erosion and deposition vary both spatially and temporally. However, erosion was more prominent on the left bank, whilst accretion was high along the right bank, over the study period. Overall, average annual erosion rates were higher than accretion rates (17 km2 year−1 versus 13 km2 year−1). The volumes of morphological change for two epochs correspond to a net volume gain of 338.75 million m3 sediment between 1984 and 1992 but a net loss of 295.20 million m3 during the period 1992–2008. Regression analysis between bank erosion and mean annual flow, peak discharge and mean flood flow showed that two of the three independent variables were significantly associated with bank erosion. The area of large mid-channel bars increased over time, which may have had a role in shaping erosion and accretion processes of the river. As increased runoff is expected in the future, as a result of enhanced rainfall under warmer climate, knowledge of this work will help to determine the morphological response of fluvial systems in Bangladesh and elsewhere.
Ashty Saleem; Ashraf Dewan; Masudur Rahman; Shahrin M. Nawfee; Rajimul Karim; Xi Xi Lu. Spatial and Temporal Variations of Erosion and Accretion: A Case of a Large Tropical River. Earth Systems and Environment 2019, 4, 167 -181.
AMA StyleAshty Saleem, Ashraf Dewan, Masudur Rahman, Shahrin M. Nawfee, Rajimul Karim, Xi Xi Lu. Spatial and Temporal Variations of Erosion and Accretion: A Case of a Large Tropical River. Earth Systems and Environment. 2019; 4 (1):167-181.
Chicago/Turabian StyleAshty Saleem; Ashraf Dewan; Masudur Rahman; Shahrin M. Nawfee; Rajimul Karim; Xi Xi Lu. 2019. "Spatial and Temporal Variations of Erosion and Accretion: A Case of a Large Tropical River." Earth Systems and Environment 4, no. 1: 167-181.
Changes in rainfall and land use/land cover (LULC) can influence river discharge from a catchment in many ways. Homogenized river discharge data from three stations and average rainfall records, interpolated from 13 stations, were examined for long-term trends and decadal variations (1970–2017) in the headwater, upper and middle catchments of the Bagmati River. LULC changes over five decades were quantified using multitemporal Landsat images. Mann–Kendall tests on annual time series showed a significant decrease in river discharge (0.61% per year) from the entire Bagmati catchment, although the decrease in rainfall was statistically insignificant. However, declines in river discharge and rainfall were both significant in upper catchment. Decadal departures from long-term means support these trend results. Over tenfold growth in urban area and a decrease in agricultural land were observed in the upper catchment, while forest cover slightly increased in the entire catchment between 1975 and 2015. Correlation analysis showed a strong association between surface runoff, estimated using the curve number method, observed river discharge and rainfall in the upper catchment, while the relationship was weaker in the headwater catchment. These results were also supported by multiple regression analysis, suggesting that human activities together with climate change have contributed to river discharge changes in the Bagmati catchment.
Dinesh Tuladhar; Ashraf Dewan; Michael Kuhn; Robert J. Corner; Kuhn. The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River. Water 2019, 11, 2444 .
AMA StyleDinesh Tuladhar, Ashraf Dewan, Michael Kuhn, Robert J. Corner, Kuhn. The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River. Water. 2019; 11 (12):2444.
Chicago/Turabian StyleDinesh Tuladhar; Ashraf Dewan; Michael Kuhn; Robert J. Corner; Kuhn. 2019. "The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River." Water 11, no. 12: 2444.