Publications

International Journal Papers

[75] Seo H, Lee JH, Choi S, Kim J, and Kim Y, 2026, Role of river network information in streamflow prediction using Graph WaveNet and entity-aware LSTM models, Journal of Hydrology, doi: 10.1016/j.jhydrol.2026.135584.

[74] Boschee A, Alborzi A, Alexander AC, Arheimer B, Bel Hadj Ali S, Blöschl G, Castelletti A, Chen X, Dogra A, Du E, Duku A, Fiori A, Garcia M, Grimaldi S, Hanasaki N, Kim Y, Kreibich H, Li C, Matin M, Medeiros P, Mehran A, Meira Neto AA, Nakai F, Nakamura S, Nguyen P, Nobert J, Owusu A, Padiyedath Gopalan S, Pereira B, Pouladi P, Pouladi M, Roobavannan M, Sadegh M, Sangiorgio M, Schoppa L, Shrestha A, Sivapalan M, Sousa D, Sunkara SV, Tian F, Trabelsi F, Velpuri N, Volpi E, Wang J, Wang S, Wu J, Yuan X and AghaKouchak A, 2026, The Coupled Hydrology-Human Activity Information (CHHAI) dataset: a global benchmark dataset for model comparison and evaluation, Hydrological Sciences Journal, doi: 10.1080/02626667.2026.2663050.

[73] Seo J, Panahi M, Kim JH,  Bateni S and Kim Y, 2026, Global 0.25-degree gridded snow water equivalent data derived from machine learning using in-situ measurements, Scientific Data, doi: 10.1038/s41597-026-06895-z.

[72] Panda MR, Lee J and Kim Y, 2025, Future supply, demand, and vulnerability of domestic water supply systems in Asian mega cities, Science of The Total Environment, doi: 10.1016/j.scitotenv.2025.180811.

[71] Seo H, Lee E, Kim JH, Choi S, Kolassa J and Kim Y, 2025, Effects of land surface model resolution on soil moisture and wildfire simulations using Community Land Model version 5 – Biogeochemistry, Journal of Hydrology, doi: 10.1016/j.jhydrol.2025.134085.

[70] Kim JH, Choi S, Panahi M, Seo H and Kim Y, 2025, Deep learning-guided urban climate risk mitigation through optimal spatial allocation of green and cool roofs, Earth's Future, doi: 10.1029/2024EF005749.

[69] Kim JH, Choi S, Panahi M, Li D and Kim Y, 2025, Deep learning-based spatial optimization of green and cool roof Implementation for urban heat mitigation, Journal of Environmental Management, doi: 10.1016/j.jenvman.2025.125398.

[68] Abramowitz G, Ukkola A, Hobeichi S, Page JC, Lipson M, Kauwe M, Green S, Brenner C, Frame J, Nearing G, Clark M, Best M, Anthoni P, Arduini G, Boussetta S, Caldararu S, Cho K, Cuntz M, Fairbairn D, Ferguson C, Kim H, Kim Y, Knauer J, Lawrence D, Luo X, Malyshev S, Nitta T, Ogee J, Oleson K, Ottlé K, Peylin P, Rosnay P, Rumbold H, Su B, Vuichard N, Walker A, Wang-Faivre X, Wang Y and  Zeng Y, 2024, On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results, Biogeosciences, doi: 10.5194/bg-21-5517-2024.

[67] Panda MR and Kim Y, 2024, Gridded global dataset of industrial water use predicted using the Random Forest, Scientific Data, doi: 10.1038/s41597-024-04148-5.

[66] Seo H and Kim Y, 2024, Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based Model, Environmental Research Letters, doi: 10.1088/1748-9326/ad8bdc.

[65] Eini N, Janizadeh S, Bateni SM, Jun C and Kim Y, 2024, Estimating equilibrium scour depth around bridge piers with various shapes using interpretable hybrid machine learning models, Ocean Engineering, doi: 10.1016/j.oceaneng.2024.119246.

[64] Kim JH, Sohn S, Wang Z and Kim Y, 2024, Nonuniform response of vegetation phenology to daytime and nighttime warming in urban areas, Communications Earth & Environment, doi: 10.1038/s43247-024-01471-y.

[63] Chi H, Seo H and Kim Y, 2024, Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model, Journal of Hydrology, doi: 10.1016/j.jhydrol.2023.130527.

[62] Mehboob MS and Kim Y, 2024, Impact of climate change on the hydrological projections over a western Himalayan river basin and the associated uncertainties, Journal of Hydrology, doi: 10.1016/j.jhydrol.2023.130460.

[61] Lin Y, Wang D, Meng Y, Sun W, Qui J, Shangguan W, Cai J, Kim Y and Dai Y, 2023, Bias learning improves data driven models for streamflow prediction, Journal of Hydrology: Regional Studies, doi: 10.1016/j.ejrh.2023.101557. 

[60] Song YH, Chung E-S, Shahid S, Kim Y and Kim D, 2023, Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration, Scientific Data, doi: 10.1038/s41597-023-02475-7.

[59] Seo H and Kim Y, 2023, Forcing Global Fire Emissions Database burned-area dataset into Community Land Model version 5.0 - Biogeochemistry: Impacts on carbon and water fluxes at high latitudes, Geoscientific Model Development, doi: 10.5194/gmd-16-4699-2023.

[58] Seo J and Kim Y, 2023, Assessing the likelihood of drought impact occurrence with extreme gradient boosting: A case study on the public water supply in South Korea, Journal of Hydroinformatics, doi: 10.2166/hydro.2023.064.

[57] Oh H, Kim HJ, Mehboob MS, Kim JH and Kim Y, 2023, Sources and uncertainties of future global drought risk with ISIMIP2b climate scenarios and socioeconomic indicators, Science of The Total Environment, doi: 10.1016/j.scitotenv.2022.160371.

[56] Mehboob MS, Kim Y, Lee J and Eidhammer T, 2022, Quantifying the sources of uncertainty for hydrological predictions with WRF-Hydro over the snow-covered region in the Upper Indus Basin, Pakistan, Journal of Hydrology, doi: 10.1016/j.jhydrol.2022.128500.

[55] Kim JH, Kim Y, Kim J, Cho K, Hong J, Hong J-W, Jo S, Park C and Chun JH, 2022, A transiting temperate-subtropical mixed forest: carbon cycle projection and uncertainty, Environmental Research Letters, doi: 10.1088/1748-9326/ac87c0.

[54] Sung K, Kim Y and Yu DJ, 2022, Spatially explicit agent-based approach for human–flood interaction modeling under external support, Journal of Hydrology, doi: 10.1016/j.jhydrol.2022.128175.

[53] Um M-J, Kim Y, Jung K, Lee M, An H, Min I, Kwak J and Park D, 2022, Evaluation of drought propagations with multiple indices in the Yangtze River Basin, Journal of Environmental Management, doi: 10.1016/j.jenvman.2022.115494.

[52] Choi S and Kim Y, 2022, Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional Generative Adversarial Networks for multiple domains, Geoscientific Model Development, doi: 10.5194/gmd-2021-405.

[51] Lee J, Kim Y and Wang D, 2022, Assessing the characteristics of recent drought events in South Korea using WRF-Hydro, Journal of Hydrology, doi: 10.1016/j.jhydrol.2022.127459.

[50] Cho K and Kim Y, 2022, Improving streamflow prediction in the WRF-Hydro model with LSTM networks, Journal of Hydrology, doi: 10.1016/j.jhydrol.2021.127297.

[46] Mehboob MS, Kim Y, Lee J, Um M-J, Erfanian A and Wang GL, 2020, Projection of vegetation impacts on future droughts over West Africa with a coupled RegCM-CLM-CN-DV, Climatic Change, 163(2), 653-668, doi:10.1007/s10584-020-02879-z.

[45] Kim HJ, Cho K, Kim Y, Park H, Lee JW, Kim SJ and Chae Y, 2020, Spatial assessment of water use vulnerability under future climate  and socioeconomic scenarios within a river basin, Journal of Water Resources Planning and Management, 146(7): 05020011, doi: 10.1061/(ASCE)WR.1943-5452.0001235.

​[44] Um M-J, Kim Y, Park D, Jung K, Wang Z, Kim MM and Shin H, 2020, Impacts of potential evapotranspiration on drought phenomena in different regions and climate zones, Science of The Total Environment, 703, doi: 10.1016/j.scitotenv.2019.135590.

[43] Longo M, Knox RG, Levine NM, Swann ALS, Medvigy DM, Dietze MC, Kim Y, Zhang K, Bonal D, Burban B, Camargo PB, Hayek MN, Saleska SR, da Silva R, Bras RL, Wofsy SC and Moorcroft PR, 2019, The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South America, Geoscientific Model Development, doi: 10.5194/gmd-12-4347-2019.

[42] Longo M, Knox RG, Medvigy DM, Levine NM, Dietze MC, Kim Y, Swann ALS, Zhang K, Rollinson CR, Bras RL, Wofsy SC and Moorcroft PR, 2019, The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description, Geoscientific Model Development, doi: 10.5194/gmd-12-4309-2019.

[41] Wang Z, Kim Y, Seo H, Um M-J and Mao J, 2019, Permafrost response to vegetation greenness variation in the Arctic Tundra through positive feedback in surface air temperature and snow cover, Environmental Research Letters, 14(4): 044024  doi: 10.1088/1748-9326/ab0839.

[40] Seo H and​ Kim Y, 2019, Interactive impacts of fire and vegetation dynamics on global carbon and water budgets using Community Land Model version 4.5​, Geoscientific Model Development, 12(1), 457-472, doi: 10.5194/gmd-12-457-2019. 

[39] Kim Y, Kong I, Park H,  Kim HJ​, Kim IJ, Um M-J​, Green PA and​ Vörösmarty CJ, 2018, Assessment of regional threats to human water security adopting the global framework: A case study in South Korea, Science of the Total Environment, 637-638, 1413-1422, doi: 10.1016/j.scitotenv.2018.04.420.

[38] Um M-J, Kim MM, Kim Y and Park D, 2018, Drought assessment with the community land model for 1951–2010 in East Asia​, Sustainability, 10(6), doi: 10.3390/su10062100.

[37] Um M-J, Kim Y and Park  D, 2018, Evaluation and modification of the Drought Severity Index (DSI) in East Asia, Remote Sensing of Environment, 209, 66-76, doi: 10.1016/j.rse.2018.02.044.

[36] Um M-J, Kim Y and Kim J, 2017, Evaluating historical drought characteristics simulated in CORDEX East Asia against observations, International Journal of Climatology, 37(13), 4643-4655, doi: 10.1002/joc.5112.

​[35] Um M-J, Kim Y, Park D and Kim J, 2017, Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014, Hydrology and Earth System Sciences, 21(10), 4989-5007, doi: 10.5194/hess-21-4989-2017.

[34] Um M-J,  Kim Y, Markus M and Wuebbles D, 2017, Modeling nonstationary extreme value distributions with nonlinear functions: an application using multiple precipitation projections for U.S. cities, Journal of Hydrology, 552, 396-406, doi: 10.1016/j.jhydrol.2017.07.007. 

[33] Choi S-U, Kim S, Choi B and Kim Y, 2017, Impact of hydropeaking on downstream fish habitat at the Goesan dam in Korea, Ecohydrology, 10(6), doi: 10.1002/eco.1861.

[32] Um M-J and Kim Y, 2017, Spatial analysis of relative humidity during ungauged periods in a mountainous region, Theoretical and Applied Climatology, 129(3), 1157-1166, doi: 10.1007/s00704-016-1831-9.

[31] Kim JH, Kim Y and Wang GL, 2017, Impacts of boundary condition changes on regional climate projections over West Africa, Journal of Geophysical Research - Atmospheres,  122(11), 5600-5615, doi: 10.1002/2016JD026167.

[30] Kim K-Y and Kim Y, 2017, A comparison of sea level projections based on the observed and reconstructed sea level data around the Korean Peninsula, Climatic Change, 142(1), 23-36, doi: 10.1007/s10584-017-1901-8.

[29] Um M-J and Kim Y, 2017, Spatial variations in temperatures in a mountainous region of Jeju Island, South Korea, International Journal of Climatology, 37, 2431-2423, doi: 10.1002/joc.4854.

[28] Um M-J and Kim Y, 2017, Estimating potential wind energy from sparsely located stations in a mountainous coastal region, Meteorological Applications, 24, 279-289, doi: 10.1002/met.1629.

[27] Um M-J, Kim Y and Park D, 2017, Spatial and temporal variations in reference crop evapotranspiration in a mountainous island, Jeju, in South Korea, Water, 9(4), doi: 10.3390/w9040261.

[26] Sung JH, Chung E-S, Kim Y and Lee B-R, 2017, Meteorological hazard assessment based on trends and abrupt changes in rainfall characteristics on the Korean Peninsula, Theoretical and Applied Climatology, 127, 305-326, doi: 10.1007/s00704-015-1581-0.

[25] Chung E-S, Abdulai PJ, Park H, Kim Y, Ahn S R and Kim SJ, 2017, Multi-criteria assessment of spatial robust water resource vulnerability using the TOPSIS method coupled with objective and subjective weights in the Han river basin, Sustainability, 9(1), doi: 10.3390/su9010029.

[24] Kim Y and Cho K, 2016, Sea level rise around Korea: Analysis of tide gauge station data with the ensemble empirical mode decomposition method, Journal of Hydro-environment, 11, 138-145, doi: 10.1016/j.jher.2014.12.002.

[23] Um M-J, Markus M, Wuebbles D and Kim Y, 2016, Projected variations in the regional clustering of precipitation stations around Chicago, Climate Research, 67(2), 151-163, doi: 10.3354/cr01365.

[22] Kim Y, Moorcroft PR, Aleinov I,  Puma MJ  and Kiang NY,  2015, Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model (Ent TBM version 1.0.1.0.0), Geoscientific Model Development, 8(12), doi: 10.5194/gmd-8-3837-2015. 

[21] Kim Y, Park D, Um M-J and Lee H, 2015, Prioritizing alternatives in strategic environmental assessment (SEA) using VIKOR method with random sampling for data gaps, Expert Systems With Applications, 42(22), 8550-8556, doi: 10.1016/j.eswa.2015.07.010.

[20] Kim Y and Chung E-S, 2015, Robust prioritization of climate change adaptation strategies using the VIKOR method with objective weights, Journal of the American Water Resources Association, 51(5), 1167-1182, doi: 10.1111/jawr.12291.

[19] Park D, Kim Y, Um M-J and Choi S-U, 2015, Robust priority for strategic environmental assessment including incomplete information with multi-criteria decision-making analysis, Sustainability, 7(8), 10233-10249, doi: 10.3390/su70810233.

[18] Kim Y,  Chung E-S, Won K and Gil K, 2015, Robust parameter estimation framework of a rainfall-runoff model using Pareto optimum and minimax regret approach, Water, 7(3), 1246-1263, doi: 10.3390/w7031246.

[17] Kim Y, Chung E-S and Jun SM, 2015, Iterative framework for robust reclaimed wastewater allocation in a changing environment using multi-criteria decision making, Water Resources Management, 29 (2), 295-311, doi: 10.1007/s11269-014-0891-9.

[16] Chung E-S and Kim Y, 2014 , Development of fuzzy multi-criteria approach to prioritize locations of treated wastewater use considering climate change scenarios, Journal of Environmental Management, 146, 505-516, doi: 10.1016/j.jenvman.2014.08.013. 

[15] Chung E-S, Won K, Kim Y and Lee H, 2014, Water resource vulnerability characteristics by district’s population size in a changing climate using subjective and objective weights, Sustainability, 6(9), 61416-6157, doi: 10.3390/su6096141. 

[14] Kim Y and Chung E-S, 2014, An index-based robust decision making framework for watershed management in a changing climate, Science of The Total Environment, 473–474, 88–102, doi: 10.1016/j.scitotenv.2013.12.002.

[13] Kim Y and Chung E-S, 2013, Assessing climate change vulnerability with group multi-criteria decision making approaches: a case study with water resource system in South Korea, Climatic Change, doi: 10.1007/s10584-013-0879-0.

[12] Kim Y and Chung E-S, 2013, Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea, Applied Mathematical Modeling, doi: 10.1016/j.apm.2013.04.040. 

[11] Kim Y, Chung E-S, Jun S-M and Kim SU, 2013, Prioritizing best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS, Resources, Conservation and Recycling, 73, 23-32, doi: 10.1016/j.resconrec.2012.12.009. 

[10] Jun K-S, Chung E-S, Kim Y-G and Kim Y, 2013, A fuzzy multi-criteria approach to flood risk vulnerability of South Korea considering climate change impacts, Expert Systems with Applications, 40, 1003-1013, doi: 10.1016/j.eswa.2012.08.013. 

[9] Kim Y, Kang B and Adams J, 2012, Opposite trends in summer precipitation in South and North Korea, International Journal of Climatology, 32, 2311-2319, doi: 10.1002/joc.3392. 

[8] Kim Y and Chung E-S, 2012, Integrated assessment of climate change and urbanization impact on adaptation strategies: a case study in two small Korean watersheds, Climatic Change, 115, 853-872, doi: 10.1007/s10584-012-0612-4. 

[7] Kim Y and Wang GL, 2012, Soil moisture-vegetation-precipitation feedback over North America: Its sensitivity to soil moisture climatology, Journal of Geophysical Research – Atmospheres, 117, D18115, doi: 10.1029/2012JD017584. 

[6] Kim Y, Knox RG, Longo M, Medvigy D, Hutyra, LR, Pyle EH, Wofsy SC, Bras RL and Moorcroft PR, 2012, Seasonal carbon dynamics and water fluxes in an Amazon rainforest, Global Change Biology, 18, 1322-1334, doi: 10.1111/j.1365-2486.2011.02629.x. 

[5] Wang GL, Kim Y and Wang DG, 2007, Quantifying the strength of soil moisture-precipitation coupling and its sensitivity to changes in surface water budget,  Journal of Hydrometeorology, 8(3), 551-570, doi: 10.1175/JHM573.1. 

[4] Kim Y and Wang GL, 2007, Impact of vegetation feedback on the response of precipitation to antecedent soil moisture anomalies over North America, Journal of Hydrometeorology, 8(3), 534-550, doi: 10.1175/JHM612.1. 

[3] Kim Y and Wang GL, 2007, Impact of initial soil moisture anomalies on subsequent precipitation over North America, Journal of Hydrometeorology, 8(3), 513-533, doi: 10.1175/JHM611.1.

[2] Kim Y and Wang GL, 2005, Modeling seasonal vegetation variation and its validation against Moderate Resolution Imaging Spectroradiometer (MODIS) observations over North America, Journal of Geophysical Research - Atmospheres, 110, D04106, doi: 10.1029/2004JD005436.

[1] Kim Y and Eltahir EAB, 2004, Role of topography in facilitating coexistence of trees and grasses within savannas, Water Resources Research, 40, W07505, doi: 10.1029/2003WR002578.  

International Books/Reports

[3] Bader DA, Hamid R, Kim Y, Blake RA, Cheval S, Goler R, Gornitz V, Horton R, Kadam S, Norouzi H, Pelaccio N and Rosenzweig C, 2025, Third Assessment Report on Climate Change and Cities (ARC3.3), Urban Climate Science: Building the Knowledge Base for City Risk Assessments and Resilience, UCCRN. (in press)

[2] Bader DA, Blake RA, Grimm A, Hamdi R, Kim Y, Horton RM, Rosenzweig C, Alverson K, Gaffin SR and Crane S, 2018,  Second Assessment Report on Climate Change and Cities (ARC3.2), Chapter 2: Urban Climate Science, UCCRN. 

[1] Levy MA, Morel AC, Adamo SB, Barr J, McMullen CP, Dietz T, Lopez-Carr D, Rosa EA, Crawford A, Desombre ER, Gluschankoff M, Goulias K, Jabbou, Kim Y, Debucquet DL, Moreno AR, Msangi S, Paterson M, Pusalmaa B, Tomalty R and Townsend C, 2012, Global Environment Outlook (GEO-5): Environment for the future we want, Chapter 1: Drivers, UNEP.

Domestic Journal Papers/Books/Report

Journals (in Korean)

[20] Oh H, Vonihanitriniaina D.Z. Andriamanantena R., Sambodo AP, Min Y, Lee J, Kwak Y, Seo J, Lee J, Kang G, Hong M, Shin S, Park J, Shin J and Kim Y, 2026, Differences in CO₂ and CH₄ fluxes and annual CO₂-equivalent carbon balance among vegetation yypes in the Gwangju Jangrok national wetland protected area, Journal of Wetlands Research, 28(3), 234-245.

[19] Seo J, Kim Y and Shin J-Y, 2026, A review of legal frameworks and mitigation projects for urban inundation in South Korea and abroad, Journal of Korea Water Resources Association, 59(7), 751-760, doi: 10.3741/jkwra.2026.59.7.751.

[18] Andriamanantena R, Oh H and Kim Y, 2026, Spatial variability of N₂O dynamics in tropical headwater streams of Madagascar, Ecology and Resilient Infrastructure, 13(2), 108-117, doi: 10.17820/eri.2026.13.2.108.

[17] Oh H, Andriamanantena R, Sambodo A, Min Y, Lee JS, Kwak Y, Choi J, Kim J, Park C, Lee JH, Kang H and Kim Y, 2026, Soil environmental variation and carbon fluxes across vegetation types in a riverine wetland, Ecology and Resilient Infrastructure, 13(2), 93-107, doi: 10.17820/eri.2026.13.2.093.

[16] Seo H, Kim H, Choi S and Kim Y, 2025, Evaluation of performances of radar-based rainfall nowcasting models based on input domain size, KSCE Journal of Civil and Environmental Engineering Research, 45(3), 339-345, doi: 10.12652/Ksce.2025.45.3.0339.

[15] Choi S and Kim Y, 2025, Advancing deep learning-based precipitation nowcasting model with radar and ground observation, Journal of Korea Water Resources Association, 58(2), 91-103, doi: 10.3741/JKWRA.2025.58.2.91.

[14] Choi S and Kim Y, 2025, Analyzing the predictive power of GAN-based precipitation prediction model against a process-based model: a case study of the Yeongsangang River basin in South Korea, Journal of Korea Water Resources Association, 58(1), 53-66, doi: 10.3741/JKWRA.2025.58.1.53.

[13] Lee JH, Choi S and Kim Y, 2022, Prediction of dam inflow based on LSTM-s2s model using luong attention, Journal of Korea Water Resources Association, 55(7), 495-504, doi: 10.3741/JKWRA.2022.55.7.495.

[12] Seo J, Chi H, Kim HJ and Kim Y, 2022, Hydrological drought risk assessment for climate change adaptation in South Korea, Journal of Korea Water Resources Association, 55(6), 421-435, doi: 10.3741/JKWRA.2022.55.6.421.

[11] Lee JH, Kim Y and Chae Y, 2020, Projecting future hydrological and ecological droughts with the climate and land use scenarios over the Korean peninsula, Journal of Korea Water Resources Association, 53(6), 427-436, doi: 10.3741/JKWRA.2020.53.6.427.

[10] Kim JH, Sohn S and Kim Y, 2019, The remote-sensing based estimation of the evapotranspiration change due to the 2019 April Gangwon-do wildfire, Journal of Korea Water Resources Association, 52(11), 941-946, doi: 10.3741/JKWRA.2019.52.11.941.

[9] Um MJ, Kim J, Kim MM and Kim Y, 2018, Analysis of historical drought in East Asia with CLM and CLM-VIC, Ecology and Resilient Infrastructure, 5(3), 134-144, doi: 10.17820/eri.2018.5.3.134.

[8] Kim J, Ho H, Um MJ and Kim Y, 2018, Spatial and temporal variations in the water use efficiency and its drought signal on the Korean Peninsula using MODIS-derived products, Korean Journal of Remote Sensing, 34(3), 553-564, doi: 10.7780/kjrs.2018.34.3.10.

[7] Park H, Kim HJ, Chae Y and Kim Y, 2017, Assessment of water use vulnerability considering climate and socioeconomic changes in Han River watershed, KSCE Journal of Civil and Environmental Engineering Research, 37(6), 965-972, doi: 10.12652/Ksce.2017.37.6.0965.

[6] Seo H, Kim J, Park H and Kim Y, 2017, Estimating evapotranspiration with the complementary relationship at Fluxnet sites over Asia, KSCE Journal of Civil and Environmental Engineering Research, 37(2), 303-310, doi: 10.12652/Ksce.2017.37.2.0303.

[5] Park H, Kim J, Um MJ and Kim Y, 2016, Assessment of water use vulnerability in the unit watersheds using TOPSIS approach with subjective and objective weights, Journal of Korea Water Resources Association, 49(8), 685-692, doi: 10.3741/JKWRA.2016.49.8.685.

[4] Park H, Um MJ, Kim J and Kim Y, 2015, MODIS DSI for evaluation of the local drought events in Korea, KSCE Journal of Civil and Environmental Engineering Research, 35(6), 1209-1218, doi: 10.12652/Ksce.2015.35.6.1209.

[3] Won KJ, Chung ES, Kim Y and Hong IP, 2014, Assessment of water resources vulnerability index by nation, Journal of Korea Water Resources Association, 47(2), 183-194, doi: 10.3741/JKWRA.2014.47.2.183.

[2] Jun SM, Chung ES, Lee SH and Kim Y, 2013, Development and application of robust decision making technique considering uncertainty of climatic change scenarios, Journal of Korea Water Resources Association, 46(9), 897-907, doi: 10.3741/JKWRA.2013.46.9.897.

[1] Kim YK, Chung ES, Lee KS and Kim Y, 2013, Group decision making approach to flood vulnerability assessment, Journal of Korea Water Resources Association, 46(2), 99-109, doi: 10.3741/JKWRA.2013.46.2.99.

Books/Reports (in Korean)

[6] Kim Y et al., 2014, Development and application of indicators for sustainable water use (Ⅰ), Korea Environment Institute, Climate Change Policy Research 2014-08. KEI report

[5] Kim Y et al., 2014, Analysis of water supply impacts and response strategies for drought disaster management, Korea Environment Institute, Policy Study 2014-13. KEI report

[4] Kim Y et al., 2013, Climate change adaptation policies for different types of droughts, Korea Environment Institute, Research Report 2013-19. KEI report

[3] Kim Y et al., 2013, Improving water quality in Saemangeum: focusing on releases from Yongdam Dam, Korea Environment Institute, Policy Study 2013-18. KEI report

[2] Kim Y, 2010, Enhancing national climate change adaptation capacity through vulnerability index analysis, Korea Environment Institute, Policy Study 2010-14. KEI report

[1] Kim Y, 2010, Assessing the applicability of a land surface model for simulating hydrology and vegetation in East Asia, Korea Environment Institute, Working Paper 2010-04. KEI report

​© 2026 Department of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea