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Prof. Eun-Sung Chung
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, South Korea

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Research Keywords & Expertise

0 Climate Change
0 Hydrologic Modeling
0 water resources management
0 Urban Hydrology
0 Robust decision making

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Climate Change
Multicriteria decision making method
water resources management
Hydrologic Modeling
Robust decision making

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Preprint content
Published: 25 June 2021
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This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

ACS Style

Mohamed Sanusi Shiru; Eun-Sung Chung. Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria. 2021, 1 .

AMA Style

Mohamed Sanusi Shiru, Eun-Sung Chung. Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria. . 2021; ():1.

Chicago/Turabian Style

Mohamed Sanusi Shiru; Eun-Sung Chung. 2021. "Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria." , no. : 1.

Preprint content
Published: 17 June 2021
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This study compared precipitation projections of Coupled Model Intercomparison Project 5 (CMIP5) and 6 (CMIP6) GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global Precipitation Climatology Centre (GPCC) precipitation climatology of the city was evaluated using different statistical metrics. The best performing GCMs common to both CMIP5 and CMIP6 were selected and subsequently downscaled to GPCC resolution using linear scaling method to spatiotemporal changes in precipitation. The study revealed BCC.CSM1.1(m), IPSL.CM5A.LR, MRI.CGCM3 and MIROC5 of CMIP5 and their equivalents BCC-CSM2-MR, IPSL-CM6A-LR, MRI.ESM2.0 and MIRCO6 of CMIP6 as the most suitable GCMs for the projection of rainfall in Yulin. Changes in precipitation were in the range of -14.0 − 0.0% and − 22.0 − 0.2% during 2021−2060 for RCP4.5 and SSP2-4.5 respectively. The highest decrease of -29.7 ̶ -22.0% was projected by MRI-ESM-2-0 for SSP2-4.5, while − 28.0 − -20.0% by MIROC5 for RCP4.5. For RCP8.5 and SSP5-8.5, precipitation was projected to decrease in the range of -17.0 ̶ -2.0% and − 32.0 ̶ 0.0%, respectively during 2021 ̶ 2060 by most of the GCMs. An increase in precipitation up to 12.3% was projected only by IPSL-CM5A-LR for RCP8.5 for this period. The highest decrease was projected by MIROC5 (-40.2 − -29.0%) for RCP8.5 and IPSL-CM6A-LR (-40.2 − -26.0%) for SSP5-8.5. Overall, the results revealed a higher decrease in precipitation in Yulin city by CMIP6 GCMs compared to those projected by their corresponding GCMs of CMIP5 for both scenarios.

ACS Style

Mohammed Sanusi Shiru; Eun-Sung Chung; Shamsuddin Shahid; Xiao-Jun Wang. Comparison of Precipitation Projections of CMIP5 and CMIP6 Global Climate Models over Yulin, China. 2021, 1 .

AMA Style

Mohammed Sanusi Shiru, Eun-Sung Chung, Shamsuddin Shahid, Xiao-Jun Wang. Comparison of Precipitation Projections of CMIP5 and CMIP6 Global Climate Models over Yulin, China. . 2021; ():1.

Chicago/Turabian Style

Mohammed Sanusi Shiru; Eun-Sung Chung; Shamsuddin Shahid; Xiao-Jun Wang. 2021. "Comparison of Precipitation Projections of CMIP5 and CMIP6 Global Climate Models over Yulin, China." , no. : 1.

Research article
Published: 29 April 2021 in International Journal of Climatology
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This study compared the historical simulations and future projections of precipitation and temperature of Coupled Model Intercomparison Project (CMIP)5 and CMIP6 GCMs to quantify the differences in the projections due to differences in scenarios. Five performance indicators were used to quantify the model reproducibility of the observed precipitation levels at 22 stations for the historical period of 1970‐2005. The percentages of change in precipitation and temperature were estimated for the near (2025‐2060) and far future (2065‐2100) for two Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios of CMIP5 and two Shared Socioeconomic Pathway (SSP)2‐4.5 and SSP5‐8.5 scenarios of CMIP6. The uncertainty in the projection in each case was calculated using the reliability ensemble average (REA) method. As a result, the CMIP6 GCMs showed an improvement compared to the CMIP5 GCMs with regard to the ability to simulate the historical climate. The uncertainty in the precipitation projections was higher for SSPs than that in RCPs. With regard to the temperature, the uncertainty was higher for RCPs than for SSPs. The ensemble means of the precipitation and temperature showed higher changes in the far future compared to the near future for both RCPs and SSPs. This study contributes to improvement in the confidence of future projections using CMIP6 GCMs and bolsters our understanding of the relative uncertainty in SSPs and RCPs.

ACS Style

Young Hoon Song; Eun‐Sung Chung; Shamsuddin Shahid. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s. International Journal of Climatology 2021, 1 .

AMA Style

Young Hoon Song, Eun‐Sung Chung, Shamsuddin Shahid. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s. International Journal of Climatology. 2021; ():1.

Chicago/Turabian Style

Young Hoon Song; Eun‐Sung Chung; Shamsuddin Shahid. 2021. "Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s." International Journal of Climatology , no. : 1.

Journal article
Published: 15 February 2021 in Sustainability
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Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.

ACS Style

Jin Hyuck Kim; Jang Hyun Sung; Eun-Sung Chung; Sang Ug Kim; Minwoo Son; Mohammed Sanusi Shiru. Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5. Sustainability 2021, 13, 2066 .

AMA Style

Jin Hyuck Kim, Jang Hyun Sung, Eun-Sung Chung, Sang Ug Kim, Minwoo Son, Mohammed Sanusi Shiru. Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5. Sustainability. 2021; 13 (4):2066.

Chicago/Turabian Style

Jin Hyuck Kim; Jang Hyun Sung; Eun-Sung Chung; Sang Ug Kim; Minwoo Son; Mohammed Sanusi Shiru. 2021. "Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5." Sustainability 13, no. 4: 2066.

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

Journal article
Published: 24 November 2020 in Sustainability
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This study was conducted to evaluate the variability, trends, volatility, and transition patterns of rainfall in drought-prone northwest Bangladesh. Daily rainfall recorded at five stations for the period 1959–2018 were used for this purpose. Non-parametric tests of variability changes, a modified Mann–Kendall trend test, innovative trend analysis (ITA), a generalized autoregressive conditional heteroscedasticity (GARCH)–jump model, and a Markov chain (MC) were used to assess the variability changes, trends, volatility, and transitions in rainfall to understand the possibility of the persistence of droughts and their predictability. The results showed an overall decrease of variability in annual and seasonal rainfall, but an increase in mean pre-monsoon rainfall and a decrease in mean monsoon rainfall. This caused a decrease in pre-monsoon droughts, but few changes in monsoon droughts. The ITA and rainfall anomaly analysis revealed high temporal variability and, thus, rapid shifts in rainfall regimes, which were also supported by the volatility dynamics and time-varying jumps from the GARCH–jump model and the rapid changes in drought index from the MC analysis. Therefore, the lack of drought in recent years cannot be considered as an indicator of declining droughts in the region.

ACS Style

Mohammad Uddin; Asm Kamal; Shamsuddin Shahid; Eun-Sung Chung. Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh. Sustainability 2020, 12, 9810 .

AMA Style

Mohammad Uddin, Asm Kamal, Shamsuddin Shahid, Eun-Sung Chung. Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh. Sustainability. 2020; 12 (23):9810.

Chicago/Turabian Style

Mohammad Uddin; Asm Kamal; Shamsuddin Shahid; Eun-Sung Chung. 2020. "Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh." Sustainability 12, no. 23: 9810.

Journal article
Published: 15 November 2020 in Sustainability
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Assuring healthy streams in the urban environment is a major goal for restoration scientists, urban planners, and city practitioners around the globe. In South Korea, many urban stream restoration efforts are designed to provide safe water to society and enhance ecological functions. We examined the extent to which the individual interests and different values of multiple stakeholders were considered in previous decision-making in two urban stream restoration projects. The relevant data on stream restoration were collected through the nominal group technique (NGT) and the analytic hierarchy process (AHP) for the two stream cases of a populated inland area and a coastal region in South Korea. The AHP results provide information about the comparative weights of the values of ecological restoration (priority score: 0.487), social restoration (priority score: 0.231), and landscape revitalization (priority score: 0.279) of the Ahn-Yang stream and ecological restoration (priority score: 0.527), social restoration (priority score: 0.182), and landscape revitalization (priority score: 0.290) of the Sahn-Jee stream. The stakeholders of the populated metropolitan area had a relatively high awareness of their role in environmental restoration, thus it was natural for them to place a high value on social restoration.

ACS Style

Chang-Yu Hong; Eun-Sung Chung; Heejun Chang. The Right to Urban Streams: Quantitative Comparisons of Stakeholder Perceptions in Defining Adaptive Stream Restoration. Sustainability 2020, 12, 9500 .

AMA Style

Chang-Yu Hong, Eun-Sung Chung, Heejun Chang. The Right to Urban Streams: Quantitative Comparisons of Stakeholder Perceptions in Defining Adaptive Stream Restoration. Sustainability. 2020; 12 (22):9500.

Chicago/Turabian Style

Chang-Yu Hong; Eun-Sung Chung; Heejun Chang. 2020. "The Right to Urban Streams: Quantitative Comparisons of Stakeholder Perceptions in Defining Adaptive Stream Restoration." Sustainability 12, no. 22: 9500.

Journal article
Published: 27 September 2020 in Water
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This study proposed a deep learning-based model to estimate stream water-use rate (WUR) using precipitation (P) and potential evapotranspiration (PET). Correlations were explored to identify relationships among accumulated meteorological variables for various time durations (three-, four-, five-, and six-month cumulative) and WUR, which revealed that three-month cumulative meteorological variables and WUR were highly correlated. A deep belief network (DBN) based on iterating parameter tuning was developed to estimate WUR using P, PET, and antecedent stream water-use rate (DWUR). The training and validation periods were 2011–2016, and 2017–2019, respectively. The results showed that the PET-DWUR based model provided better performances in Nash–Sutcliff efficiency (NSE), root mean square error (RMSE), and determination coefficient (R2) than the P-PET-DWUR and P-DWUR models. The framework in this study can provide a forecast model for deficiencies of stream water use coupled with a weather forecast model.

ACS Style

Jang Hyun Sung; Young Ryu; Eun-Sung Chung. Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network. Water 2020, 12, 2700 .

AMA Style

Jang Hyun Sung, Young Ryu, Eun-Sung Chung. Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network. Water. 2020; 12 (10):2700.

Chicago/Turabian Style

Jang Hyun Sung; Young Ryu; Eun-Sung Chung. 2020. "Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network." Water 12, no. 10: 2700.

Journal article
Published: 15 September 2020 in Atmospheric Research
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This study compared the historical and future simulations of precipitation in South Korea from INM-CM4 of Coupled Model Intercomparison Project (CMIP) 5 and INM-CM5 of CMIP6 to identify their differences for the projections of corresponding scenarios by three timeframes (annual, summer and winter) and four regions (NW, NE, SW and SE). Six performance indicators were used to quantify the models' reproducibility to precipitation at 22 stations in South Korea for the historical period (1970–2005). Then, the change rates of precipitations in near and far futures (2020–2059 and 2060–2099) were calculated for two representative concentration pathway (RCP) 4.5 and 8.5 and socioeconomic shared pathway (SSP) 2–4.5 and 5–8.5. Their uncertainties were also quantified using standard deviations and interquartile ranges. As a result, CM5 clearly showed a 7.4% improvement in six performance indicators. The change rates in far future were larger but the uncertainties were smaller. But both the rates and uncertainties in NW were the largest. Also, the uncertainties in INM-CM5 were also smaller than in INM-CM4 for all timeframes and the differences between RCP4.5 and SSP2-4.5 were absolutely larger than those between RCP8.5 and SSP5-8.5.

ACS Style

Young Hoon Song; Mohamed Salem Nashwan; Eun-Sung Chung; Shamsuddin Shahid. Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmospheric Research 2020, 247, 105261 .

AMA Style

Young Hoon Song, Mohamed Salem Nashwan, Eun-Sung Chung, Shamsuddin Shahid. Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmospheric Research. 2020; 247 ():105261.

Chicago/Turabian Style

Young Hoon Song; Mohamed Salem Nashwan; Eun-Sung Chung; Shamsuddin Shahid. 2020. "Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea." Atmospheric Research 247, no. : 105261.

Journal article
Published: 11 September 2020 in Sustainability
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This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources.

ACS Style

Young Song; Eun-Sung Chung; Mohammed Shiru. Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs. Sustainability 2020, 12, 7508 .

AMA Style

Young Song, Eun-Sung Chung, Mohammed Shiru. Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs. Sustainability. 2020; 12 (18):7508.

Chicago/Turabian Style

Young Song; Eun-Sung Chung; Mohammed Shiru. 2020. "Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs." Sustainability 12, no. 18: 7508.

Journal article
Published: 26 August 2020 in Science of The Total Environment
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Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of ‘robustness’ measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH3-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

ACS Style

Sijie Tang; Jiping Jiang; Yi Zheng; Yi Hong; Eun-Sung Chung; Asaad Y. Shamseldin; Yan Wei; Xiuheng Wang. Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring. Science of The Total Environment 2020, 753, 142007 .

AMA Style

Sijie Tang, Jiping Jiang, Yi Zheng, Yi Hong, Eun-Sung Chung, Asaad Y. Shamseldin, Yan Wei, Xiuheng Wang. Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring. Science of The Total Environment. 2020; 753 ():142007.

Chicago/Turabian Style

Sijie Tang; Jiping Jiang; Yi Zheng; Yi Hong; Eun-Sung Chung; Asaad Y. Shamseldin; Yan Wei; Xiuheng Wang. 2020. "Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring." Science of The Total Environment 753, no. : 142007.

Journal article
Published: 20 August 2020 in Remote Sensing
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The occurrence and severity of agricultural droughts may not be dependent upon climatic variables alone. Rather increasingly, drought is affected by human interventions such as irrigation. Anthropogenic activity has introduced uncertainty in the assessment of current drought and future drought risk in many parts of the world; neither climatic nor remote sensing data alone are able to assess drought conditions effectively. In response, we present a simple approach to assess drought by combining a remote sensing-based drought index, the Temperature Vegetation Dryness Index (TVDI), climate data (i.e., rainfall and temperature), and field observations to evaluate recent drought conditions in northwestern Bangladesh (NWB). Applying this approach, we gained five insights: (i) the TVDI successfully indicated the drought conditions of NWB and agrees with field observations, (ii) the integrated use of TVDI and climate data (such as rainfall and temperature) provides the best understanding of the difference between meteorological drought and droughts resulting from surface moisture conditions, (iii) the TVDI results agree with rainfall data (r2 = 0.40 in March and r2 = 46 in April) in a part of the study area (NWB) where irrigation is not available, (iv) the TVDI can be used along with climate data to predict the potential risk of drought, and (v) while meteorological drought exists due to low rainfall and high temperature in this NWB in pre-monsoon season, because of widespread irrigation practices, meteorological drought is unable to trigger agricultural drought over most parts of the study area. The findings imply that there is a potential risk of drought in NWB, since any disruption of irrigation water supply could trigger a severe agricultural drought over the whole region. This is similar to what is currently observed over a small part of NWB.

ACS Style

Latifur R. Sarker; Nichol Janet; Siti Mansor; Baharin Ahmad; Shamsuddin Shahid; Eun-Sung Chung; Jeffrey Reid; Eko Siswanto. An Integrated Method for Identifying Present Status and Risk of Drought in Bangladesh. Remote Sensing 2020, 12, 2686 .

AMA Style

Latifur R. Sarker, Nichol Janet, Siti Mansor, Baharin Ahmad, Shamsuddin Shahid, Eun-Sung Chung, Jeffrey Reid, Eko Siswanto. An Integrated Method for Identifying Present Status and Risk of Drought in Bangladesh. Remote Sensing. 2020; 12 (17):2686.

Chicago/Turabian Style

Latifur R. Sarker; Nichol Janet; Siti Mansor; Baharin Ahmad; Shamsuddin Shahid; Eun-Sung Chung; Jeffrey Reid; Eko Siswanto. 2020. "An Integrated Method for Identifying Present Status and Risk of Drought in Bangladesh." Remote Sensing 12, no. 17: 2686.

Water resources and hydrologic engineering
Published: 29 July 2020 in KSCE Journal of Civil Engineering
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This study projected future changes in potential evapotranspiration (PET) over North Korea, which has been exposed to climate change risks. For this purpose, climate change scenarios downscaled at station scale were produced under RCP8.5, which downscale method preserves the long-term trend driven by climate models. Based on the ability to replicate observation, representative climate change scenarios (RCCS) were selected using performance indicators and TOPSIS method. The GCMs having higher spatial resolution were selected as RCCS, and projected that PET would increase in the future. It is found that the inter-model variability of PET in the summer was gradually increased over North Korea and annual mean evapotranspiration would be expected to increase by 1.4 times (F1, 2011–2040), 2.0 times (F2, 2041–2070) and 2.6 times (F3, 2071–2100). In preparation for the deficit of available water due to the increase in evapotranspiration, securing alternative water resources and construction of multi-purpose dams are required.

ACS Style

Young Ryu; Eun-Sung Chung; Seung Beom Seo; Jang Hyun Sung. Projection of Potential Evapotranspiration for North Korea Based on Selected GCMs by TOPSIS. KSCE Journal of Civil Engineering 2020, 24, 2849 -2859.

AMA Style

Young Ryu, Eun-Sung Chung, Seung Beom Seo, Jang Hyun Sung. Projection of Potential Evapotranspiration for North Korea Based on Selected GCMs by TOPSIS. KSCE Journal of Civil Engineering. 2020; 24 (9):2849-2859.

Chicago/Turabian Style

Young Ryu; Eun-Sung Chung; Seung Beom Seo; Jang Hyun Sung. 2020. "Projection of Potential Evapotranspiration for North Korea Based on Selected GCMs by TOPSIS." KSCE Journal of Civil Engineering 24, no. 9: 2849-2859.

Journal article
Published: 27 May 2020 in Journal of Hydro-environment Research
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A multiple variable bias correction approach has been proposed for the projection of the changes in spatial and temporal pattern of rainfall in Borneo Island due to climate change. The ensemble of General Circulation Models (GCMs) was selected through the combination of past performance and envelope approaches. The selected GCMs were downscaled using a Support Vector Machine (SVM) based multiple variable bias correction approach considering that inclusion of multiple circulation variables can provide more information on local climate and able to explicitly account for GCM-inherent error and bias. Finally, an ensemble projection was produced by using Random Forest (RF) regression to simulate the future projection. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and CCSM4 were found most suitable for the projection of rainfall in Borneo with associated uncertainty. The projected changes in rainfall indicate that the rainfall pattern and variation in Borneo are dominated based on the changes of monsoon season, geographical locations and terrains of the region. Overall, there is an increasing rate of rainfall projections in most parts of Borneo. However, the coastal-western region will become drier in contrast to the eastern region, although rainfall variability remains high. The highest increase in rainfall was projected in East Kalimantan in the range of 11.9% to 50.1% for 2070–2099, while the highest decrease was projected in West Kalimantan in the range of −3.7% to −13.0% for 2010–2039. The rainfall was expected to be more distributed during the Southwest monsoon while shorter Northeast monsoon with higher intensity was projected at most regions.

ACS Style

Zulfaqar Sa'Adi; Shamsuddin Shahid; Sahar Hadi Pour; Kamal Ahmed; Eun-Sung Chung; Zaher Mundher Yaseen. Multi-variable model output statistics downscaling for the projection of spatio-temporal changes in rainfall of Borneo Island. Journal of Hydro-environment Research 2020, 31, 62 -75.

AMA Style

Zulfaqar Sa'Adi, Shamsuddin Shahid, Sahar Hadi Pour, Kamal Ahmed, Eun-Sung Chung, Zaher Mundher Yaseen. Multi-variable model output statistics downscaling for the projection of spatio-temporal changes in rainfall of Borneo Island. Journal of Hydro-environment Research. 2020; 31 ():62-75.

Chicago/Turabian Style

Zulfaqar Sa'Adi; Shamsuddin Shahid; Sahar Hadi Pour; Kamal Ahmed; Eun-Sung Chung; Zaher Mundher Yaseen. 2020. "Multi-variable model output statistics downscaling for the projection of spatio-temporal changes in rainfall of Borneo Island." Journal of Hydro-environment Research 31, no. : 62-75.

Journal article
Published: 02 May 2020 in Sustainability
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The present study projected future climate change for the densely populated Central North region of Egypt (CNE) for two representative concentration pathways (RCPs) and two futures (near future: 2020–2059, and far future: 2060–2099), estimated by a credible subset of five global climate models (GCMs). Different bias correction models have been applied to correct the bias in the five interpolated GCMs’ outputs onto a high-resolution horizontal grid. The 0.05° CNE datasets of maximum and minimum temperatures (Tmx, and Tmn, respectively) and the 0.1° African Rainfall Climatology (ARC2) datasets represented the historical climate. The evaluation of bias correction methodologies revealed the better performance of linear and variance scaling for correcting the rainfall and temperature GCMs’ outputs, respectively. They were used to transfer the correction factor to the projections. The five statistically bias-corrected climate projections presented the uncertainty range in the future change in the climate of CNE. The rainfall is expected to increase in the near future but drastically decrease in the far future. The Tmx and Tmn are projected to increase in both future periods reaching nearly a maximum of 5.50 and 8.50 °C for Tmx and Tmn, respectively. These findings highlighted the severe consequence of climate change on the socio-economic activities in the CNE aiming for better sustainable development.

ACS Style

Mohamed Salem Nashwan; Shamsuddin Shahid; Eun-Sung Chung. High-Resolution Climate Projections for a Densely Populated Mediterranean Region. Sustainability 2020, 12, 3684 .

AMA Style

Mohamed Salem Nashwan, Shamsuddin Shahid, Eun-Sung Chung. High-Resolution Climate Projections for a Densely Populated Mediterranean Region. Sustainability. 2020; 12 (9):3684.

Chicago/Turabian Style

Mohamed Salem Nashwan; Shamsuddin Shahid; Eun-Sung Chung. 2020. "High-Resolution Climate Projections for a Densely Populated Mediterranean Region." Sustainability 12, no. 9: 3684.

Journal article
Published: 10 December 2019 in Sustainability
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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.

ACS Style

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 Style

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 (24):7047.

Chicago/Turabian Style

Mohammad 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.

Journal article
Published: 28 November 2019 in Sustainability
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Recent climate change has resulted in the reduction of several surface water bodies (SWBs) all around the globe. These SWBs, such as streams, rivers, lakes, wetlands, reservoirs, and creeks have a positive impact on the cooling of the surrounding climate and, therefore, reduction in SWBs can contribute to the rise of land surface temperature (LST). This study presents the impact of SWBs on the LST across Bangladesh to quantify their roles in the rapid temperature rise of Bangladesh. The moderate resolution imaging spectroradiometer (MODIS) LST and water mask data of Bangladesh for the period 2000–2015 are used for this purpose. Influences of topography and geography on LST were first removed, and then regression analysis was conducted to quantify the impact of SWBs on the LST. The non-parametric Mann–Kendall (MK) test was used to assess the changes in LST and SWBs. The results revealed that SWBs were reduced from 11,379 km2 in 2000 to 9657 km2 in 2015. The trend analysis showed that changes in SWBs have reduced significantly at a 90% level of confidence, which contributed to the acceleration of LST rise in the country due to global warming. The spatial analysis during the specific years showed that an increase in LST can be seen with the reduction of SWBs. Furthermore, the reduction of 100 m2 of SWBs can reduce the LST of the surrounding regions from −1.2 to −2.2 °C.

ACS Style

Najeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim; Rawshan Ali. Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh. Sustainability 2019, 11, 6754 .

AMA Style

Najeebullah Khan, Shamsuddin Shahid, Eun-Sung Chung, Sungkon Kim, Rawshan Ali. Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh. Sustainability. 2019; 11 (23):6754.

Chicago/Turabian Style

Najeebullah Khan; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim; Rawshan Ali. 2019. "Influence of Surface Water Bodies on the Land Surface Temperature of Bangladesh." Sustainability 11, no. 23: 6754.

Research article
Published: 25 November 2019 in Hydrology and Earth System Sciences
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The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

ACS Style

Kamal Ahmed; Dhanapala A. Sachindra; Shamsuddin Shahid; Mehmet C. Demirel; Eun-Sung Chung. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences 2019, 23, 4803 -4824.

AMA Style

Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, Eun-Sung Chung. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences. 2019; 23 (11):4803-4824.

Chicago/Turabian Style

Kamal Ahmed; Dhanapala A. Sachindra; Shamsuddin Shahid; Mehmet C. Demirel; Eun-Sung Chung. 2019. "Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics." Hydrology and Earth System Sciences 23, no. 11: 4803-4824.

Preprint content
Published: 26 August 2019
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Eun-Sung Chung. Response to Sung's comments. 2019, 1 .

AMA Style

Eun-Sung Chung. Response to Sung's comments. . 2019; ():1.

Chicago/Turabian Style

Eun-Sung Chung. 2019. "Response to Sung's comments." , no. : 1.

Preprint content
Published: 26 August 2019
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Eun-Sung Chung. Response to Reviewer 3's comments. 2019, 1 .

AMA Style

Eun-Sung Chung. Response to Reviewer 3's comments. . 2019; ():1.

Chicago/Turabian Style

Eun-Sung Chung. 2019. "Response to Reviewer 3's comments." , no. : 1.