Corresponding author (*) & EDAM members

2024

60. Jeong, H., Jung, E., Lee, Y., Lee, J.Y., and Lee, S.*, 2024. Predicting the distribution of soil heavy metals across mining sites using machine learning. [in preparation]

59. Lee, B., Kim, M.G., McCarty, G.W., Zhang, X., Qi, J., Lee, S.*, 2024. Assessing the parameter uncertainty of SWAT-C using vegetation constraints. [in preparation]

58. Lee, Y., Zhang, X., and Lee, S.*, 2024. The impacts of winter cover crops on soil organic carbon within an agricultural watershed using SWAT-C [in preparation]

57. Lee, Y., Ling, D., Li, X., Zhang, X., Jeong, H., Lee, B., McCarty, G.W.*, and Lee, S.*, 2024. Predicting the distribution of soil organic carbons using machine learning models. Catena [under review]

56. Kim, D., Lee, Y., Zhang, X., McCarty, G.W., Qi, J., Cho K.*, and Lee, S.*, 2024. Climate change impacts on in-stream carbon cycling dynamics using a water quality model. Carbon Balance and Management [under review]

55. Lee, J., Kim, D., Hong, S., Yun, D., Kwon, D., Hill, R.L., Gao, F., Zhang, X., Cho, K.H., Lee, S., and Pachepsky, Y., 2024. Comparative efficiency of SWAT and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland. Water Research [under review].

54. Lee, B., Im, J.K., Han, J.W., Kang, T., Kim, W., and Lee, S.*, 2024. Benefits of multiple remotely sensed datasets and machine learning models to predict the Chlorophyll-a concentration. Environmental Science and Pollution Research [under review]

53. Kim, S., McCormick, B., Carlson, K., Lee, S., Kim, G., Seo, Y.-H., Lee, K.*, and Kang, W.*, 2024. Characterization of flowback and produced water from shale oil and gas wells fractured with different levels of recycled water. Water Environment Research [under review]

52.  Lee, Y., Kim, C., Jeong, H., Kim, D., Lee, B., Kim, S., Jeon, D., and Lee, S.*, 2024. Assessing the spatial distribution of groundwater NO3-N concentration across Jeju Island, South Korea, using machine learning models with geospatial data. Journal of Contaminant Hydrology [under review]

51. Han, J.W., Kim, T.H., Lee, S., Kang, T., and Im, J., 2024. Machine learning and explainable AI for Chlorophyll-a prediction in Namhan River Watershed, South Korea. Ecological Indicator [in revision]

50. Lee, S.*, Kim, D., McCarty, G.W., Anderson, M., Gao, F., Lei, F., Moglen, G.E., Zhang, X., Yen, H., Qi, J., Crow, W., Yeo, I-Y, and Sun, L., 2024. Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data. Heliyon [in revision]

Before the member of Korea university (~ Mar 2024)

2024

49.  Jeong, H., Lee, B., Kim, D., Lim, K.J., and Lee, S.*, 2024. Improving prediction capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty. Journal of Hydrology. 633: 130942

48. Kim, Y., Yu, J., Lee, S.*, and Jeon, S.*, 2024. Efficiency analysis of best management practices under climate change conditions in the So-okcheon watershed, South Korea. Frontiers in Environmental Science. 12: 1297289.

47. Luo, X., Risal, A., Qi, J.*, Lee., S., Zhang, X., Alferi, J., and McCarty G.W. 2024. Modeling lateral carbon fluxes for agroecosystems in the Mid-Atlantic region: control factors and importance for carbon budget. Science of the Total Environment. 912: 169128.

2023

46. Lee, S.*, Lee, J., Song, J., Lee, B., and McCarty, G.W.*, 2023. Detecting causal relationship between upland non-floodplain wetlands and downstream water through groundwater using convergent cross mapping. Scientific reports. 13:17220.

45. Lee, D., Shin, J., Kim, T., Lee, S., Kim, D.,  Park, Y., and Cha, Y.*, 2023. Hybrid model for daily streamflow and phosphorus load prediction. Water science and technology. 88(4):975-990.

44. Kwon, Y., Cha, Y., Park, Y., and Lee, S.*, 2023. Assessing the impacts of dam and weir operation on streamflow predictions using LSTM across South Korea. Scientific reports. 13: 9296.

43. Kim, S., Omur-Ozbe, P., Carlson, K., Lee, S., Kim, E.-S., Hwang, M.-J., Son, J.-H.*, and Kang, W.*, 2023. Organics and inorganics in flowback/produced water from shale gas operation: treatment applications and glycol identification. Water Reuse.  13(2): 282-293.

42. Dangol, S., Zhang, X.*, Liang, X.-Z., Anderson, M., Crow, W., Lee, S., Moglen, G.E., and McCarty, G.W.,  2023.  Multivariate calibration of the SWAT model using remotely sensed datasets. Remote sensing. 15(9): 2417.

41. Lee, S.*, Jeong, H., Lee, J., Lee, Y., Kim, C., Hwang, W., Park, M., Hyun, S., Seo, S., and Lee, J.*, 2023. Classifying cropland vulnerability to pollutant loads across South Korea under climate change conditions using Soil Vulnerability Index. Agricultural Water Management. 282: 108273.

40. Phung, Q.*, Thompson, A., Baffaut, C., Witthaus, L.M., Veith, T.L., Bosch, D., McCarty, G., and Lee, S.,  2023. Assessing soil vulnerability index classification with respect to rainfall characteristics. Journal of Soil and Water Conservation. 78(2): 1-13.

39. Kim, Y., Park, S.-B.*, Lee, S.*, and Park, Y.-K.*, 2023. Comparison of PM2.5 prediction performance of the three deep learning models: A case study of Seoul, Daejeon, and Busan. Journal of Industrial and Engineering Chemistry. 120: 159-169.

38. Lee, J., Abbas, A., McCarty G.W., Zhang, X., Lee, S.*, and Cho, K.H.*, 2023. Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay. Journal of Hydrology. 617-B: 128916.

2022

37. Lee, S.*, Qi, J., McCarty G.W., Anderson, M., Yang, Y., Zhang, X., Moglen, G.E., Kwak, D., Kim, H., Lakshmi, V., and Kim, S., 2022. Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed. Agricultural Water Management. 264: 107503.

2021

36. Qiu, H., Qi, J.*, Lee., S., Moglen, G.E., McCarty G.W., Chen, M., and Zhang, X.*, 2021. Effects of temporal resolution of river routing on hydrologic modeling and aquatic ecosystem health assessment with the SWAT model. Environmental Modelling & Software. 146:105232.

35. Qi, J., Lee., S., Du, X., Ficklin, D.L, Wang, Q., Myers, D., Singh, D., Moglen, G.E., McCarty G.W., Zhou, Y., and Zhang, X.*, 2021. Coupling terrestrial and aquatic thermal processes for improving stream temperature modeling at the watershed scale. Journal of Hydrology. 603B:126983.

34. Ling, D., McCarty, G.W.*, Xia, L., Martin, C.R., Qianfeng W., Lee, S., Audra, L.H., and Zhenhua, Z., 2021. Spatial extrapolation of topographic models for mapping soil organic carbon using local samples. Geoderma. 404:115290.

33. Lee, S.*, Qi, J., McCarty, G.W., Yeo, I.Y., Zhang, X., Moglen, G.E., and Du, L., 2021. Uncertainty assessment of multi-parameter, multi-GCM, and multi-RCP simulations for streamflow and non-floodplain wetland (NFW) water storage. Journal of Hydrology. 600:126564.

32. Lee, S.*, Qi, J., Kim, H., McCarty, G.W., Moglen, G.E., Zhang, X., and Du, L., 2021. Utility of remotely sensed evapotranspiration products on assessing an improved model structure. Sustainability. 13(4):2375.

2020

31. Lee, S., McCarty, G.W.*, Lang. M.W., and Li. X., 2020. Overview of the USDA Mid-Atlantic Regional Wetland Conservation Effects Assessment Project. Journal of Soil and Water Conservation. 75(5): 727-738.  

30. Backhaus, P.J.*, Lee, S., Nassry, M., McCarty, G.W, Lang, M.W., and Brooks, R.P., 2020. Evaluating a remote wetland functional assessment along an alternation gradient in coastal plain depressional wetlands. Journal of Soil and Water Conservation. 75(5): 684-694. 

Before the member of the University of Seoul (~ Aug. 2020)

2020

29. Kim, H., Lee, S.*, Cosh, M.H., Lakshimi, V., Kwon, Y., and McCarty, G.W., 2021. Assessment and Combination of SMAP and Sentinel-1A/B Derived Soil Moisture Estimates with Land Surface Model Data in the Mid-Atlantic Coastal Plain, U.S.A. IEEE Transactions on Geoscience and Remote Sensing. 59(2), 991-1011. (online publication date: June 2020) 

28. Qi, J., Zhang, X., Lee, S., Wu, Y, Moglen, E.G., and McCarty, G.W., 2020. Modeling Sediment Diagenesis Processes on Riverbed to Better Quantify Aquatic Carbon Fluxes and Stocks in a small watershed of mid-Atlantic region. Carbon Balance and Management. 15(1): 1-14.

27. Li, X.*, McCarty, G.W.*, Du, L., and Lee, S., 2020. Use of topographic models for mapping soil properties and processes. Soil Systems. 4(2): 32. 

26. Hively W.D.*, Lee, S., Sadeghi, A.M., McCarty, G.W., Lamb, B.T., Soroka, A., Keppler, J., Yeo, I.-Y., and Moglen G.E., 2020. Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing and Soil and Water Assessment Tool (SWAT) modeling. Journal of Soil and Water Conservation. 75(3): 362-375. 

25. Lee, S.*, McCarty, G., Moglen, G.E., Li. X., and Wallace, W.C., 2020. Assessing the effectiveness of riparian buffer strips on reducing organic nitrogen loads in the Coastal Plain of the Chesapeake Bay watershed using a watershed model. Journal of Hydrology. 585: 124779. 

24. Du, L., McCarty, G.W.*, Zhang, X., Lang, M.W., Vanderhoof, M.K., Li, X., Huang, C., Lee, S., and Zou, Z., 2020. Mapping forested wetland inundation in the Delmarva Peninsula, USA using deep convolutional neural networks. Remote Sensing. 12(4): 644. 

23. Lee, S.*, McCarty, G.W., Moglen, G.E., Lang, M.W, Jones, C.N., Palmer, M., Yeo, I.–Y, Anderson, M., Sadeghi, A.M., and Rabenhorst, M.C., 2020. Seasonal drivers of geographically isolated wetland hydrology in a low-gradient, coastal plain landscape. Journal of Hydrology. 583: 124608. 

22. Lee, S.*, Yen, H., Yeo, I.-Y., Moglen, G.E., Rabenhorst, M.C., and McCarty, G.W.*, 2020. Use of multiple modules and Bayesian Model Averaging to assess structural uncertainty of catchment-scale wetland modeling in a Coastal Plain landscape. Journal of Hydrology. 582: 124544. 

21. Qi, J., Du, X., Zhang, X.*, Lee, S., Wu, Y., Deng, J., Moglen, G.E., Sadeghi, A.M., and McCarty, G.W., 2020. Modeling Riverine Dissolved and Particulate Organic Carbon Fluxes from Two Small watersheds in the Northeastern United States. Environmental Modelling & Software. 124: 104601. 

20.  Baffaut, C.*, Lohani, S., Thompson, A.R., Davis, A.R., Aryal, N., Bjorneberg, D.L., Binger, R.L., Dabney, L.F., Duriancik, L.F0, James, D.E., King, K.W., Lee, S., McCarty, G.W., Pease, L.A., Reba, M.L., Sedeghi, A.M., Tomer, M.D., Williams, M.R., and Yasarer, L.M.W., 2020. Evaluation of the Soil Vulnerability Index for artificially drained cropland across eight Conservation Effect Assessment Project watersheds. Journal of Soil and Water Conservation. 75(1): 28 - 41. 

19. Qi, J., Lee, S., Zhang, X.*, Yang Q., McCarty, G.W., and Glenn, M.E., 2020. Effects of surface runoff and infiltration partition methods on hydrological modeling: a comparison of four schemes in two watersheds in the Northeastern US. Journal of Hydrology. 581: 124415. 

2019

18. Lee, S., Yeo, I. –Y*, Lang, M.W., McCarty, G.W., Sadeghi, A.M., Sharifi, A., Jin, H., and Liu, Y., 2019. Improving the catchment scale wetland modeling using remotely sensed data. Environmental Modelling & Software. 122: 104069. 

17. Yen, H.*, Park, S., Arnold, J.G., Srinivasan, R., Chawanda, C.J., Wang, R., Feng, Q., Wu, J., Miao, C., Bieger, K., Daggupati, P., Griensven, A., Kalin, L., Lee, S., Sheshukov, A.Y., White, M.J., Yuan, Y., Yeo, I.-Y., Zhang, M., and Zhang, X., 2019. IPEAT+: A Built-in Optimization and Automatic Calibration Tool of SWAT+. Water. 11(8): 1681. 

16. Qi, J., Zhang, X.*, Lee, S., Moglen, G.E., Sadeghi, A.M., and McCarty, G.W., 2019. A coupled surface water storage and subsurface water dynamics model in SWAT for characterizing hydroperiod of geographically isolated wetlands. Advances in Water Resources. 131: 103380. 

15. Sharifi, A.*, Lee, S.*, McCarty, G.W., Lang, M.W., Jeong, J., Sadeghi, A.M., and Rabenhorst, M.C., 2019. Enhancement of Agricultural Policy / Environment eXtender (APEX) model to assess effectiveness of wetland water quality functions. Water. 11(3): 606. 

14. Yeo, I.–Y.*, Lee, S., Lang, M.W., Yetemen, O., McCarty, G.W., Sadeghi, A.M., and Evenson, G., 2019. Mapping the landscape-level hydrological connectivity of headwater wetlands to downstream water: a catchment modeling approach – Part 2. Science of the Total Environment. 653: 1557 – 1570. 

13. Yeo, I.–Y.*, Lang, M.W., Lee, S., McCarty, G.W., Sadeghi, A.M., Yetemen, O., and Huang, C., 2019. Mapping the landscape-level hydrological connectivity of headwater wetlands to downstream water: a geospatial modeling approach – Part I. Science of the Total Environment. 653: 1546 – 1556. 

12. Lee, S.*, McCarty, G.W., Moglen, G.E., Lang, M.W., Sadeghi, A.M., Green, T.R., Yeo, I.–Y., and Rabenhorst, M.C., 2019. Effects of subsurface soil characteristics on wetland-groundwater interaction in the Coastal Plain of the Chesapeake Bay watershed. Hydrological Processes. 33(2): 305–315. 

2018

11. Lee, S.*, Wallace, C.W., Sadeghi, A.M., McCarty, G.W., Zhong, H., and Yeo, I.–Y., 2018. Impacts of Global Circulation Model (GCM) bias and WXGEN on modeling hydrologic variables. Water. 10:764. 

10. Lee, S.*, Yeo, I. –Y, Lang, M.W., Sadeghi, A.M., McCarty, G.W., Moglen, G.E., and Evenson, G., 2018. Assessing the cumulative impacts of wetlands on the watershed hydrology using the SWAT model coupled with improved wetland modules. Journal of Environmental Management. 223: 37-48. 

9. Lee, S.*, Sadeghi, A.M., McCarty, G.W., Baffaut, C., Lohani, S., Thompson, A., Yeo, I. –Y., and Wallace, C.W., 2018. Assessing the suitability of the Soil Vulnerability Index (SVI) on identifying croplands vulnerable to nitrogen loss using the SWAT model. CATENA. 167:1-12. 

8. Wallace, C.W.*, McCarty, G.W.*, Lee, S., Brooks, R.P., Veith, T.L., Kleinman, P.J.A., and Sadeghi, A.M., 2018. Evaluating Concentrated Flowpaths in Riparian Forest Buffer Contributing Areas using LiDAR Imagery and Topographic Metrics. Remote Sensing. 10(4):614.

7. Lee, S.*, Yeo, I.–Y, Sadeghi, A.M., McCarty, G.W., Hively, W.D., Lang, M.W., and Sharifi, A., 2018. Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change scenarios using the SWAT model. Hydrology and Earth System Sciences. 22(1): 689-708. 

2017

6. Sharifi, A.*, Yen, H., Wallace, C.W., McCarty, G.W., Crow, W., Momen, B., Lang, M.W., Sadeghi, A.M., Lee, S., Denver, J., and Rabenhorst, M.C., 2017. Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-based Model. Water. 9(11):895. 

5. Lee, S.*, Sadeghi, A. M., Yeo, I. –Y, McCarty, G.W, and Hively, W.D., 2017. Assessing the impacts of future climate conditions on the effectiveness of winter cover crops in reducing nitrate loads into the Chesapeake Bay Watershed using the SWAT model. Transactions of the ASABE. 60(6): 1939-1955.