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

[19] 오현영, Vonihanitriniaina D. Z. Andriamanantena R., Ahmad Priyo Sambodo, 민연홍, 이지성, 곽연수, 서정호, 이재형, 강구협, 홍민기, 신샛별, 박재호, 신제오, 김연주 (2026) 광주 장록 국가습지보호지역 식생 유형에 따른 CO2 및 CH4 플럭스 차이와 연간 CO2 환산 탄소 수지 비교, Journal of Wetlands Research, 28(3), 234-245.

[18] Vonihanitriniaina DZ Andriamanantena R, 오현영, 김연주 (2026) Spatial variability of N2O dynamics in Tropical Headwater Streams of Madagascar, Ecology and Resilient Infrastructure, 13(2), 108-117.

[17] 오현영, Vonihanitriniaina DZ Andriamanantena R, Ahmad Priyo Sambodo, 민연홍, 이지성, 곽연수, 최지원, 김정인, 박채리, 이재현, 강호정, 김연주 (2026) 하천형 습지의 식생 분포에 따른 토양 환경 차이와 토양 호흡 및 메탄 플럭스 특성, Ecology and Resilient Infrastructure, 13(2), 93-107.

[16] 서호철, 김희철, 최수연, 김연주 (2025) 입력 도메인 크기에 따른 레이더 기반 단기 강우 예측 성능 평가, 대한토목학회논문집, 45(3), 339-345.

[15] 최수연, 김연주 (2025) 레이더 및 지상 관즉 강우 자료를 통한 딥러닝 기반 강우 예측 모델 개선, 한국수자원학회논문집, 58(2), 91-103.

[14] 최수연, 김연주 (2025) 물리과정 기반 및 GAN 기반 강수 예측 모델의 예측 성능 평가: 영산강 유역 사례 연구, 한국수자원학회논문집, 58(1), 53-66.

[13] 이종혁, 최수연, 김연주 (2022) LSTM-s2s 모델과 Luong Attention을 첨가한 모델의 시간 단위 댐 유입량 예측, 한국수자원학회논문집, 55(7), 495-504.

[12] 서정호, 지혜원, 김혜진, 김연주 (2022) 기후변화 적응을 위한 우리나라 가뭄 위험도 평가, 한국수자원학회논문집, 55(6), 421-435.

[11] 이재형, 김연주, 채여라 (2020) 기후 및 토지이용 변화 시나리오 기반 한반도 미래 수문학적 및 생태학적 가뭄 전망, 한국수자원학회논문집, 53(6), 427-436.

[10] 김지현, 손소영, 김연주 (2019) 2019년 강원도 산불로 인한 증발산 변화 원격탐사기반 추산​, 한국수자원학회논문집, 52(11), 941-946.

[9] 엄명진, 김정빈, 김문모, 김연주 (2018) CLM 및 CLM-VIC를 이용한 동아시아 지역의 과거 가뭄 분석, Ecology and Resilient Infrastructure, 5(3), 134-144.

[8] 김정빈, 호현주, 엄명진, 김연주 (2018) MODIS 영상을 활용한 한반도의 시공간적 물 이용효율 변동 및 가뭄과의 연관성 분석, 대한원격탐사학회지, 34(4), 553-564.

[7] 박혜선, 김혜진, 채여라, 김연주 (2017) 기후 및 사회·경제 변화를 고려한 한강 유역의 물이용 취약성 평가, 대한토목학회논문집, 37(6), 965-972.

[6] 서호철, 김정빈, 박혜선, 김연주 (2017) 아시아 Fluxnet 자료를 활용한 보완관계 기반 증발산량 추정, 대한토목학회논문집, 37(2), 303-310.

[5] 박혜선, 김정빈, 엄명진, 김연주 (2016) 주관적, 객관적 가중치를 활용한 TOPSIS 기반 단위유역별 물이용 취약성 평가, 한국수자원학회논문집, 49(8), 685-692.

[4] 박혜선, 엄명진, 김정빈, 김연주 (2015) 우리나라의 지역 가뭄 평가를 위한 MODIS DSI 활용, 대한토목학회논문집, 35(6), 1209-1218.

[3] 원광재, 정은성, 김연주, 홍일표 (2014) 국가별 수자원 취약성 지수의 산정, 한국수자원학회논문집, 47(2), 183-194.

[2] 전상묵, 정은성, 이상호, 김연주 (2013) 기후변화 시나리오의 불확실성을 고려하기 위한 로버스트 의사결정 기법의 개발 및 적용, 한국수자원학회논문집, 46(9), 897-907.

[1] 김영규, 정은성, 이길성, 김연주 (2013) 홍수 취약성 평가를 위한 그룹 의사결정 접근법, 한국수자원학회논문집, 46(2), 99-109.

Books/Reports

[6] 김연주 외 (2014) 지속가능한 물이용을 위한 지표 개발 및 적용 방안 연구, 한국환경정책･평가연구원.

[5] 김연주 외 (2014) 가뭄재난 관리를 위한 용수공급 피해 분석 및 대응 연구, 한국환경정책･평가연구원.

[4] 김연주 외 (2013) 가뭄 유형별 기후변화 적응 정책연구, 한국환경정책･평가연구원.

[3] 김연주 외 (2013) 새만금 수질개선 방안 연구 - 용담댐 방류량을 중심으로, 한국환경정책･평가연구원.

[2] 김연주 외 (2010) 기후변화 취약성 지수분석을 통한 국가적응역량 제고방안 모색, 한국환경정책･평가연구원.

[1] 김연주 (2010) 수문･식생 모사를 위한 지표모델의 적용 타당성 분석, 한국환경정책･평가연구원.