Publications

International Publications

* Corresponding Author

1. AI Improves the Accuracy, Reliability, and Economic Value of Continental-Scale Flood Predictions, (2024), Nature Communications, Under review

2. Ivanov, V. Y., Tran, V. N., Huang, W., Murphy, K., Daneshvar, F., Bednar, J. H., Alexander, G. A., Kim, J., Wright D. B. (2024), Urban flooding is intensified by outdated design guidelines and a lack of a systems approach. Nature Cities, https://doi.org/10.1038/s44284-024-00128-3

3. Tran, T. D., & *Kim, J. (2024), Guidance on the construction and selection process of relatively simple to complex data-driven models for multi-task streamflow forecasting, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-024-02776-2

4. Tran, V. N., Ivanov, V. Y., Huang, W., Murphy, K., Daneshvar, F., Bednar, J. H., Alexander, G. A., Kim, J., Wright D. B. (2024), Connectivity in urbanscapes can cause unintended flood impacts from stormwater systems. Nature Cities, https://doi.org/10.1038/s44284-024-00116-7 

5. Tran, V. N., & *Kim, J. (2024), UIDS: A Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations, Environmental Modelling and Software, 179, 106132, https://doi.org/10.1016/j.envsoft.2024.106132 

6. Paik, K., Jeong, M., Kim, D.-H., & Kim, J. (2024), On the nonlinearity of the catchment instantaneous response function: Insights obtained from dynamic wave modeling, Journal of Hydrology, 637, 131413, https://doi.org/10.1016/j.jhydrol.2024.131413 

7. Vo, T., Doi, M.V., & *Kim, J. (2024), Assessment of future changes in drought characteristics through stochastic downscaling and CMIP6 over South Korea, Stochastic Environmental Research and Risk Assessment. 38, 1955–1979, https://doi.org/10.1007/s00477-024-02664-9

8. Tran, V. N., Ivanov, V. Y., Nguyen, G.T., Tran, A.N., Nguyen, P.H., Kim, D.-H., & *Kim, J. (2024), A Deep Learning Modeling Framework with Uncertainty Quantification for Inflow-Outflow Predictions for Cascade Reservoirs, Journal of Hydrology, 629, 130608, https://doi.org/10.1016/j.jhydrol.2024.130608

9. Tran, T. D., & *Kim, J. (2024), Machine Learning Modeling Structures and Framework for Short-term Forecasting and Long-term Projection of Streamflow, Stochastic Environmental Research and Risk Assessment, 38, 793-813, https://doi.org/10.1007/s00477-023-02621-y

10. Tran, V. N., Ivanov, V. Y., & Kim, J. (2023), Data Reformation - An Innovative Data Processing Technique Enhancing Machine Learning Applicability for Predicting Streamflow Extremes, Advances in Water Resources, 182, https://doi.org/10.1016/j.advwatres.2023.104569

11. Tran, V. N., Ivanov, V. Y., Xu, D., Kim, J. (2023), Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models, Geophysical Research Letters, 50, e2023GL104464, https://doi.org/10.1029/2023GL104464

12. Kim, S., Jeong, M., Kim, J., & Kim, D.-H. (2023), Effects of Soil Particle Size on Relationship between Mound-Puddle Type Microtopography Roughness and Soil Erosion Rate on a Hillslope Basin: Hairsine–Rose Model Analysis, Water Resources Research, 59, e2022WR033879. https://doi.org/10.1029/2022WR033879

13. Ahn, S., Tran, T. D., & *Kim, J. (2022), Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast. Ocean Engineering, 264(15), 112593. https://doi.org/10.1016/j.oceaneng.2022.112593

14. Doi, M.V., & *Kim, J. (2022), Future Projections and Uncertainties of CMIP6 for Hydrological Indicators and Their Discrepancies from CMIP5 over South Korea, Water, 14, 2926. https://doi.org/10.3390/w14182926 

15. Tran, V. N., & *Kim, J. (2022), Robust and Efficient Uncertainty Quantification for Extreme Events that Deviate Significantly from the Training Dataset Using Polynomial Chaos-Kriging, Journal of Hydrology, 609, 127716. https://doi.org/10.1016/j.jhydrol.2022.127716

16. Doi, M. V., & *Kim, J. (2021), Addressing Climate Internal Variability on Future Intensity-Duration-Frequency Curves at Fine Scales across South Korea. Water, 13(20), 2828. https://doi.org/10.3390/w13202828

17. Tran, V. N., & *Kim, J. (2021). A robust surrogate data assimilation approach to real-time forecasting using polynomial chaos expansion. Journal of Hydrology, 598, 126367. https://doi.org/10.1016/j.jhydrol.2021.126367

18. Ivanov, V. Y., Xu, D., Dwelle, M. C., Sargsyan, K., Wright, D. B., Katopodes, N., Kim, J., Tran, V. N., Warnock, A., Fatichi, S., Burlando, P., Caporali, E., Restrepo, P., Sanders, B. F., Chaney, M. M., Nunes, A. M. B., Nardi, F., Vivoni, E. R., Istanbulluoglu, E., Bisht, G., Bras, R. L. (2021). Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting. Geophysical Research Letters, 48(20), e2021GL093585. https://doi.org/10.1029/2021GL093585

19. Tran, T. D., Tran, V. N., & *Kim, J. (2021). Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection. Mathematics, 9(5), 551. https://doi.org/10.3390/math9050551 

20. Tran, V. N., & *Kim, J. (2021). Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion. Water, 13(2), 203. https://doi.org/10.3390/w13020203 

21. Jeong, M., Kim, D.-H., & Kim, J. (2021). Surface runoff hydrograph derivation using a dynamic wave based instantaneous unit hydrograph . Journal of Flood Risk Management, 14(3), e12722. https://doi.org/10.1111/jfr3.12722 

22. Tran, V. N., Dwelle, M. C., Sargsyan, K., Ivanov, V. Y., & *Kim, J. (2020). A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification. Water Resources Research, 56(3), e2019WR025727. https://doi.org/10.1029/2019wr025727 

23. Doi, M.V., & *Kim, J. (2020). Projections on climate internal variability and climatological mean at fine scales over South Korea. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-020-01807-y 

24. Tran, V. N., & *Kim, J. (2019). Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations. Stochastic Environmental Research and Risk Assessment, 33(7), 1453-1476. https://doi.org/10.1007/s00477-019-01703-0 

25. Kim, D., Choi, M., Kim, J., & Kim, U. (2019). Advances in Remote Sensing to Understand Extreme Hydrological Events. Advances in Meteorology, 2019, 8235037. https://doi.org/10.1155/2019/8235037 

26. Kim, J., Lee, J., Kim, D., & Kang, B. (2019). The role of rainfall spatial variability in estimating areal reduction factors. Journal of Hydrology, 568, 416-426. https://doi.org/10.1016/j.jhydrol.2018.11.014 

27. Dwelle, M. C., Kim, J., Sargsyan, K., & Ivanov, V. Y. (2019). Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification. Advances in Water Resources, 125, 13-31. https://doi.org/10.1016/j.advwatres.2019.01.002 

28. Xu, D., Ivanov, V. Y., Kim, J., & Fatichi, S. (2019). On the use of observations in assessment of multi-model climate ensemble. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-018-1621-2 

29. Kim, J., Tanveer, M. E., & Bae, D.-H. (2018). Quantifying climate internal variability using an hourly ensemble generator over South Korea. Stochastic Environmental Research and Risk Assessment, 32(11), 3037-3051. https://doi.org/10.1007/s00477-018-1607-0 

30. Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., & Tarboton, D. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology, 537, 45-60. https://doi.org/10.1016/j.jhydrol.2016.03.026

31. Fatichi, S., Ivanov, V. Y., Paschalis, A., Peleg, N., Molnar, P., Rimkus, S., Kim, J., Burlando, P., & Caporali, E. (2016). Uncertainty partition challenges the predictability of vital details of climate change. Earth's Future, 4(5), 240-251. https://doi.org/10.1002/2015ef000336 

32. Kim, J., Dwelle, M. C., Kampf, S. K., Fatichi, S., & Ivanov, V. Y. (2016). On the non-uniqueness of the hydro-geomorphic responses in a zero-order catchment with respect to soil moisture. Advances in Water Resources, 92, 73-89. https://doi.org/10.1016/j.advwatres.2016.03.019 

33. Kim, J., Ivanov, V. Y., & Fatichi, S. (2016). Soil erosion assessment-Mind the gap. Geophysical Research Letters, 43(24), 12,446-412,456. https://doi.org/10.1002/2016gl071480 

34. Kim, J., Ivanov, V. Y., & Fatichi, S. (2016). Environmental stochasticity controls soil erosion variability. Scientific Reports, 6, 22065. https://doi.org/10.1038/srep22065 

35. Kim, J., Ivanov, V. Y., & Fatichi, S. (2015). Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stochastic Environmental Research and Risk Assessment, 30(3), 923-944. https://doi.org/10.1007/s00477-015-1097-2 

36. Fatichi, S., Katul, G. G., Ivanov, V. Y., Pappas, C., Paschalis, A., Consolo, A., Kim, J., & Burlando, P. (2015). Abiotic and biotic controls of soil moisture spatiotemporal variability and the occurrence of hysteresis. Water Resources Research, 51(5), 3505-3524. https://doi.org/10.1002/2014wr016102 

37. Kim, J., & Ivanov, V. Y. (2015). A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics. Journal of Hydrology, 522, 645-660. https://doi.org/10.1016/j.jhydrol.2015.01.025 

38. Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A., & Sulis, M. (2014). Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 50(2), 1531-1549. https://doi.org/10.1002/2013wr013725 

39. Kim, J., & Ivanov, V. Y. (2014). On the nonuniqueness of sediment yield at the catchment scale: The effects of soil antecedent conditions and surface shield. Water Resources Research, 50(2), 1025-1045. https://doi.org/10.1002/2013wr014580 

40. Kim, D., Kim, J., & Cho, Y.-S. (2014). A Poisson Cluster Stochastic Rainfall Generator That Accounts for the Interannual Variability of Rainfall Statistics: Validation at Various Geographic Locations across the United States. Journal of Applied Mathematics, 2014, 1-14. https://doi.org/10.1155/2014/560390 

41. Warnock, A., Kim, J., Ivanov, V., & Katopodes, N. D. (2014). Self-Adaptive Kinematic-Dynamic Model for Overland Flow. Journal of Hydraulic Engineering, 140(2), 169-181. https://doi.org/10.1061/(asce)hy.1943-7900.0000815 

42. Kim, J., Ivanov, V. Y., & Katopodes, N. D. (2013). Modeling erosion and sedimentation coupled with hydrological and overland flow processes at the watershed scale. Water Resources Research, 49(9), 5134-5154. https://doi.org/10.1002/wrcr.20373 

43. Kim, J., Ivanov, V. Y., & Katopodes, N. D. (2012). Hydraulic resistance to overland flow on surfaces with partially submerged vegetation. Water Resources Research, 48(10). https://doi.org/10.1029/2012wr012047 

44. Kim, J., Warnock, A., Ivanov, V. Y., & Katopodes, N. D. (2012). Coupled modeling of hydrologic and hydrodynamic processes including overland and channel flow. Advances in Water Resources, 37, 104-126. https://doi.org/10.1016/j.advwatres.2011.11.009 

Domestic Publications

1. Kim, J., Cho, H.-R., & Cho, Y.-S. (2014). Projection of Climate Change with Uncertainties: 2. Internal Variability. Journal of the Korean Society of Hazard Mitigation, 14(5), 329-339. https://doi.org/10.9798/kosham.2014.14.5.329 

2. Kim, J., Cho, H.-R., & Cho, Y.-S. (2014). Projection of Climate Change with Uncertainties: 1. GCM and RCP Uncertainties. Journal of the Korean Society of Hazard Mitigation, 14(5), 317-327. https://doi.org/10.9798/kosham.2014.14.5.317 

3. Kim, J., Han, S., & Cho, Y.-S. (2014). A Combined Model of Hydrology, Hydraulics, Erosion and Sediment Transport at Watershed Scale. Journal of the Korean Society of Hazard Mitigation, 14(5), 351-358. https://doi.org/10.9798/kosham.2014.14.5.351 

4. Kim, J., Lee, S. O., Yoon, K. S., & Cho, Y. S. (2008). Application of a two-dimensional flood inundation model based on quad tree grid. Journal of the Korean Society of Hazard Mitigation, 8(3), 129-136. 

5. Kim, J., Kim, H.-J., Lee, S.-O., & Cho, Y.-S. (2007). Numerical Simulation of Flood Inundation with Quadtree Grid. Journal of the Korean Society of Hazard Mitigation, 7(2), 45-52. 

6. Kim, H.-J., Kim, J., Jang, W.-J., & Cho, Y.-S. (2007). Calculation of overtopping discharge with time-dependent aspects of an embankment failure. Journal of the Korean Society of Hazard Mitigation, 7(3), 69-78. 

Patents, Programs, & Service

Patents

1. 인공지능 모형의 계층화된 입력 구성 및 최적화 시스템

2. 전지구 지역파고 단기예보 방법 (출원 10-2023-0169210: 2023.11.29)

3. 캐스케이드 저수지 유입 및 유출 예측 모델 구축 방법 및 그 시스템 (출원 10-2023-0163945: 2023.11.23)

4. 도시 침수 시뮬레이션 시스템 (출원 10-2023-0130432: 2023.09.27) 

5. 장기 예측을 위한 LSTM 기반 기계 학습 시스템 (출원 10-2023-0125400: 2023.09.20)

6. 단기 예측을 위한 LSTM 기반 기계 학습 시스템 (출원 10-2023-0124749: 2023.09.19)

7. 가뭄 예측 방법 및 그 시스템 (출원 10-2023-0106617: 2023.08.16)

8. 댐 유입량을 예측하기 위한 모델의 동작 방법 및 이를 위한 장치 (출원 10-2021-0119091: 2021.09.07)

9. 기후 내적 변동성에 기초하여 극한 강우를 예측하기 위한 장치의 동작 방법 및 그 장치 (출원 10-2021-0128001: 2021.09.28) (등록 10-2618240: 2023.12.21)

10. 대체 필터의 동작 방법 및 이를 이용한 자료 동화 방법 (출원 10-2021-0118542: 2021.09.16) (등록 10-2608732: 2023.11.28)

11. 외삽 현상을 예측하는 대체 모델을 생성하는 방법 및 그 장치 (출원 10-2021-0119222: 2021.09.07) (등록 10-2561402: 2023.07.26)

12. 유량 예측 방법 및 장치 (출원 10-2019-0127138: 2019.10.14) (등록 10-2354616: 2022.01.19)

13. 실시간 앙상블 유량 예보 방법 및 장치 (출원 10-2019-0127228: 2019.10.14) (등록 10-2254817: 2021.05.17)

Programs

1. 멀티태스크 AI 기반 댐유입량 예측 프로그램

2. 유출 상세화를 위한 보조변수 산정 프로그램 (C-2023-056786: 2023.11.23)

3. 유전자 알고리즘을 응용한 시뮬레이션 모형 기반 최적화 프로그램

4. 머신러닝 기반 댐 유입량 예측 프로그램 (C-2021-023917: 2021.05.01)

5. 기후변화에 따른 내적변동성 산정 프로그램 (C-2021-024815: 2021.06.21)

6. 대체모형 기법을 이용한 효율적인 자료동화 프로그램 (C-2021-024814: 2021.06.21)

7. GUI기반 앙상블 확률홍수 예측 프로그램 (C-2021-023918: 2021.06.15)

8. 매개변수 사후분포 앙상블 생성 프로그램 (C-2019-029571: 2019.10.21)

Service

1. (지자체 위원회 위원) 울산시 재해복구사업 사전심의위원; 울산시 광역소하천 관리위원회 위원; 울산시북구사전재해영향성검토위원회; 울산시중구통합자연재해관리위원회; 울산시중구도시계획위원회; 울산시하천기본계획수립자문단; 울산시물재이용관리위원회; 울산시어초관리위원회; 울산시중구기초소하천관리위원회; 울산시지역연안관리심의회; 울산시안전관리민관협력위원회; 울산시지방건설기술심의위원회; 울산시북구 태풍차바 재해복구사업 분석평가 용역 자문위원; 울산시제9기원가분석자문단; 포항시형산강마리나계류장선정위원회;

2. (공기업 위원회 위원) 부산교통공사 기술자문위원회; 울산녹색환경지원센터 연구협의회 위원 및 연구실장; 울산연안특별관리해역 민관산학협의회 위원; 울산지속가능발전협의회; 

3. (낙동강 유역) 낙동강수계관리제도개선을 위한 상하류 포럼 위원; 낙동강유역물관리사업자문단;

4. 경상일보 2024.01.05, https://www.ksilbo.co.kr/news/articleView.html?idxno=989067

5. 경상일보 2024.01.09, https://www.ksilbo.co.kr/news/articleView.html?idxno=989302

6. 경상일보 2024.01.18, https://www.ksilbo.co.kr/news/articleView.html?idxno=990028