|
Peer-Reviewed Journals
|
Conference Talks
|
Book Chapters and Reports
|
IF indicates the journal Impact Factor. ** indicates student(s) and post-docs supervised.
See the updated list in Google Scholar
Significant Publications
[40] Human alterations of the global floodplains 1992-2019, Nature Scientific Data 2023. https://doi.org/10.1038/s41597-023-02382-x [IF = 8.7] **Co-authored with PhD student Qianjin Zheng and undergraduate student Itohaosa Isibor. Featured in Nature Outlook and AGU Eos
[39] Avert Bangladesh's looming water crisis through open science and better data, Nature 2022. https://doi.org/10.1038/d41586-022-03373-5 [IF = 55.0]
[38] A call for consistency and integration in global surface water estimates, Environmental Research Letters 2024. DOI: 10.1088/1748-9326/ad1722 [IF = 7.2] ** Co-authored with PhD students Arushi Khare, Bikas Gupta, and Qianjin Zheng
[37] Surface depression and wetland water storage improves major river basin hydrologic predictions, Water Resources Research 2020. https://doi.org/10.1029/2019WR026561 [IF = 5.0] Among journal's top 10 most downloaded articles in 2020
[36] Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2018.10.024 [IF = 6.3]
Published Articles
[35] Barriers to quantifying human alterations of global floodplains and how we can overcome them, Cell Reports Sustainability 2025. https://doi.org/10.1016/j.crsus.2025.100433 [IF =NA] ** Co-authored with PhD student Qianjin Zheng and Post-doc Rezaul Haider. Featured in journal's cover story and Spectrum News
[34] Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities, Environmental Research Letters 2025. https://doi.org/10.1088/1748-9326/ae137b [IF = 7.2] ** Co-authored with PhD students Arushi Khare and Bikas Gupta
[33] Making Earth Observations model-friendly: An interoperable tool enabling comprehensive use of Earth Observations for hydrologic model evaluation, Environmental Modelling & Software 2025. https://doi.org/10.1016/j.envsoft.2025.106618 [IF = 4.6]
[32] Effect of deep learning bias correction and carbon neutrality on projections of future population exposure to extreme precipitation, International Journal of Climatology 2025. https://doi.org/10.1002/joc.70174 [IF = 2.8]
[31] SSGRAM: 3-D Spectral-Spatial Feature Network Enhanced by Graph Attention Map for Hyperspectral Image Classification, IEEE Transactions on Geoscience & Remote Sensing 2025. https://doi.org/10.1109/TGRS.2025.3566070 [IF = 8.6]
[30] Spatiotemporal variability of channel roughness and its substantial impacts on flood modeling errors, Earth's Future 2024. https://doi.org/10.1029/2023EF004257 [IF = 8.2] ** Co-authored with PhD student Krutik Patel
[29] Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network, Environmental Research Letters 2024. https://doi.org/10.1088/1748-9326/ad66e6 [IF = 7.2]
[28] Mapping global non-floodplain wetlands, Earth System Science Data 2023. https://doi.org/10.5194/essd-15-2927-2023 [IF = 13.9]
[27] River basin simulations reveal wide-ranging wetland-mediated nitrate reductions, Environmental Science & Technology 2023. https://doi.org/10.1021/acs.est.3c02161 [IF = 12.4]
[26] Evaluating topography-based approaches for fast floodplain mapping in data-scarce complex-terrain regions: Findings from a Himalayan basin, Journal of Hydrology 2023. https://doi.org/10.1016/j.jhydrol.2023.129309 [IF = 6.3]
[25] Cyber-enabled autocalibration of hydrologic models to support Open Science,” Environmental Modelling & Software 2022. https://doi.org/10.1016/j.envsoft.2022.105561 [IF = 4.6]
[24] The rise of cyberinfrastructures for environmental applications, ASABE Resource: Special Issue on Digital Water 2022. https://elibrary.asabe.org/abstract.asp?aid=53524 [Author’s copy: https://doi.org/10.6084/m9.figshare.20212748 ]
[23] The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset, Nature Scientific Data 2021. https://doi.org/10.1038/s41597-021-01048-w [IF = 8.7] **Co-authored with PhD student Qianjin Zheng
[22] Improving global flood and drought predictions: integrating non-floodplain wetlands into watershed hydrologic models, Environmental Research Letters 2021. https://doi.org/10.1088/1748-9326/ac1fbc [IF = 7.2]
[21] Wetland restoration yields dynamic nitrate responses across the Upper Mississippi River Basin, Environmental Research Communications 2021. https://doi.org/10.1088/2515-7620/ac2125 [IF = 3.0]
[20] Improving agricultural water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling, Science of the Total Environment 2021. https://doi.org/10.1016/j.scitotenv.2021.148177 [IF = 8.0]
[19] Direct integration of numerous dams and reservoirs outflow in continental scale hydrologic modeling, Water Resources Research 2021. https://doi.org/10.1029/2020WR029544 [IF = 5.0]
[18] Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP, Journal of Hydrology 2020. https://doi.org/10.1016/j.jhydrol.2019.124406 [IF = 6.3]
[17] Watershed modeling with remotely sensed Big Data: MODIS Leaf Area Index improves hydrology and water quality predictions, Remote Sensing 2020. https://doi.org/10.3390/rs12132148 [IF = 4.8]
[16] Non-floodplain wetlands affect watershed nutrient dynamics: A critical review, Environmental Science & Technology 2019. https://doi.org/10.1021/acs.est.8b07270 [IF = 12.4]
[15] Rationale and efficacy of assimilating remotely sensed potential evapotranspiration for reduced uncertainty of a hydrologic model, Water Resources Research 2018. https://doi.org/10.1029/2017WR021147 [IF = 5.0]
[14] Spatio-temporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework, Journal of the American Water Resources Association 2018. https://doi.org/10.1111/1752-1688.12585 [IF = 2.2]
[13] Large scale spatially explicit modeling of blue and green water dynamics in a temperate mid-latitude basin, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2018.02.071 [IF = 6.3]
[12] Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2017.11.036 [IF = 6.3]
[11] Hydrologic response to future land use change in the Upper Mississippi River Basin by the end of 21st century, Hydrological Processes 2017. https://doi.org/10.1002/hyp.11282 [IF = 2.9]
[10] Spatial and temporal evaluation of hydrological response to climate and land use change in three South Dakota watersheds, Journal of the American Water Resources Association 2017. https://doi.org/10.1111/1752-1688.12483 [IF = 2.2]
[9] Streamflow response to potential land use and climate changes in the James River watershed, Midwest United States, Journal of Hydrology: Regional Studies 2017. https://doi.org/10.1016/j.ejrh.2017.11.004 [IF = 5.0]
[8] Design of a metadata framework for environmental models with an example hydrologic application in HydroShare, Environmental Modelling & Software 2017. https://doi.org/10.1016/j.envsoft.2017.02.028 [IF = 4.6]
[7] SWATShare – A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models, Environmental Modelling & Software 2016. https://doi.org/10.1016/j.envsoft.2015.10.032 [IF = 4.6]
[6] Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture, Journal of Hydrology 2016. https://doi.org/10.1016/j.jhydrol.2016.02.037 [IF = 6.3]
[5] Improving soil moisture accounting and streamflow prediction in SWAT by incorporating a modified time‐dependent Curve Number method, Hydrological Processes 2016. https://doi.org/10.1002/hyp.10639 [IF = 2.9]
[4] Modeling the effects of future land use change on water quality under multiple scenarios: a case study of low-input agriculture with hay/pasture production, Sustainability of Water Quality and Ecology 2016. https://doi.org/10.1016/j.swaqe.2016.09.001 [IF = 1.6]
[3] Improved sustainability of water supply options in areas with arsenic-impacted groundwater, Water 2013. https://doi.org/10.3390/w5041941 [IF = 3.0]
[2] A comprehensive modeling study on Regional Climate Model application- regional warming projections in monthly resolution under IPCC A1B scenario, Atmosphere 2012. https://doi.org/10.3390/atmos3040557 [IF = 2.5]
[1] Evaluating technological resilience of small drinking water systems under the projected changes of climate, Journal of Water and Climate Change 2012. https://doi.org/10.2166/wcc.2012.019 [IF = 3.1]
Book Chapters & Reports
[2] Merwade, V., Rajib, A., Liu, Z. 2018. “An integrated approach for flood inundation modeling on large scales”. In Jung and Wang (Eds.), Bridging Science and Policy Implication for Managing Climate Extremes, pp. 133-155. DOI: 10.1142/9789813235663_0009. Download Link
[1] Maidment, D., Rajib, A., Lin, P., Clark, E. (Eds.) 2016. National Water Center Innovators Program Summer Institute Report 2016, CUAHSI Technical Report 13, USA, 122p. DOI: 10.4211/technical.20161019.
See the updated list in Google Scholar
Significant Publications
[40] Human alterations of the global floodplains 1992-2019, Nature Scientific Data 2023. https://doi.org/10.1038/s41597-023-02382-x [IF = 8.7] **Co-authored with PhD student Qianjin Zheng and undergraduate student Itohaosa Isibor. Featured in Nature Outlook and AGU Eos
[39] Avert Bangladesh's looming water crisis through open science and better data, Nature 2022. https://doi.org/10.1038/d41586-022-03373-5 [IF = 55.0]
[38] A call for consistency and integration in global surface water estimates, Environmental Research Letters 2024. DOI: 10.1088/1748-9326/ad1722 [IF = 7.2] ** Co-authored with PhD students Arushi Khare, Bikas Gupta, and Qianjin Zheng
[37] Surface depression and wetland water storage improves major river basin hydrologic predictions, Water Resources Research 2020. https://doi.org/10.1029/2019WR026561 [IF = 5.0] Among journal's top 10 most downloaded articles in 2020
[36] Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2018.10.024 [IF = 6.3]
Published Articles
[35] Barriers to quantifying human alterations of global floodplains and how we can overcome them, Cell Reports Sustainability 2025. https://doi.org/10.1016/j.crsus.2025.100433 [IF =NA] ** Co-authored with PhD student Qianjin Zheng and Post-doc Rezaul Haider. Featured in journal's cover story and Spectrum News
[34] Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities, Environmental Research Letters 2025. https://doi.org/10.1088/1748-9326/ae137b [IF = 7.2] ** Co-authored with PhD students Arushi Khare and Bikas Gupta
[33] Making Earth Observations model-friendly: An interoperable tool enabling comprehensive use of Earth Observations for hydrologic model evaluation, Environmental Modelling & Software 2025. https://doi.org/10.1016/j.envsoft.2025.106618 [IF = 4.6]
[32] Effect of deep learning bias correction and carbon neutrality on projections of future population exposure to extreme precipitation, International Journal of Climatology 2025. https://doi.org/10.1002/joc.70174 [IF = 2.8]
[31] SSGRAM: 3-D Spectral-Spatial Feature Network Enhanced by Graph Attention Map for Hyperspectral Image Classification, IEEE Transactions on Geoscience & Remote Sensing 2025. https://doi.org/10.1109/TGRS.2025.3566070 [IF = 8.6]
[30] Spatiotemporal variability of channel roughness and its substantial impacts on flood modeling errors, Earth's Future 2024. https://doi.org/10.1029/2023EF004257 [IF = 8.2] ** Co-authored with PhD student Krutik Patel
[29] Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network, Environmental Research Letters 2024. https://doi.org/10.1088/1748-9326/ad66e6 [IF = 7.2]
[28] Mapping global non-floodplain wetlands, Earth System Science Data 2023. https://doi.org/10.5194/essd-15-2927-2023 [IF = 13.9]
[27] River basin simulations reveal wide-ranging wetland-mediated nitrate reductions, Environmental Science & Technology 2023. https://doi.org/10.1021/acs.est.3c02161 [IF = 12.4]
[26] Evaluating topography-based approaches for fast floodplain mapping in data-scarce complex-terrain regions: Findings from a Himalayan basin, Journal of Hydrology 2023. https://doi.org/10.1016/j.jhydrol.2023.129309 [IF = 6.3]
[25] Cyber-enabled autocalibration of hydrologic models to support Open Science,” Environmental Modelling & Software 2022. https://doi.org/10.1016/j.envsoft.2022.105561 [IF = 4.6]
[24] The rise of cyberinfrastructures for environmental applications, ASABE Resource: Special Issue on Digital Water 2022. https://elibrary.asabe.org/abstract.asp?aid=53524 [Author’s copy: https://doi.org/10.6084/m9.figshare.20212748 ]
[23] The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset, Nature Scientific Data 2021. https://doi.org/10.1038/s41597-021-01048-w [IF = 8.7] **Co-authored with PhD student Qianjin Zheng
[22] Improving global flood and drought predictions: integrating non-floodplain wetlands into watershed hydrologic models, Environmental Research Letters 2021. https://doi.org/10.1088/1748-9326/ac1fbc [IF = 7.2]
[21] Wetland restoration yields dynamic nitrate responses across the Upper Mississippi River Basin, Environmental Research Communications 2021. https://doi.org/10.1088/2515-7620/ac2125 [IF = 3.0]
[20] Improving agricultural water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling, Science of the Total Environment 2021. https://doi.org/10.1016/j.scitotenv.2021.148177 [IF = 8.0]
[19] Direct integration of numerous dams and reservoirs outflow in continental scale hydrologic modeling, Water Resources Research 2021. https://doi.org/10.1029/2020WR029544 [IF = 5.0]
[18] Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP, Journal of Hydrology 2020. https://doi.org/10.1016/j.jhydrol.2019.124406 [IF = 6.3]
[17] Watershed modeling with remotely sensed Big Data: MODIS Leaf Area Index improves hydrology and water quality predictions, Remote Sensing 2020. https://doi.org/10.3390/rs12132148 [IF = 4.8]
[16] Non-floodplain wetlands affect watershed nutrient dynamics: A critical review, Environmental Science & Technology 2019. https://doi.org/10.1021/acs.est.8b07270 [IF = 12.4]
[15] Rationale and efficacy of assimilating remotely sensed potential evapotranspiration for reduced uncertainty of a hydrologic model, Water Resources Research 2018. https://doi.org/10.1029/2017WR021147 [IF = 5.0]
[14] Spatio-temporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework, Journal of the American Water Resources Association 2018. https://doi.org/10.1111/1752-1688.12585 [IF = 2.2]
[13] Large scale spatially explicit modeling of blue and green water dynamics in a temperate mid-latitude basin, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2018.02.071 [IF = 6.3]
[12] Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model, Journal of Hydrology 2018. https://doi.org/10.1016/j.jhydrol.2017.11.036 [IF = 6.3]
[11] Hydrologic response to future land use change in the Upper Mississippi River Basin by the end of 21st century, Hydrological Processes 2017. https://doi.org/10.1002/hyp.11282 [IF = 2.9]
[10] Spatial and temporal evaluation of hydrological response to climate and land use change in three South Dakota watersheds, Journal of the American Water Resources Association 2017. https://doi.org/10.1111/1752-1688.12483 [IF = 2.2]
[9] Streamflow response to potential land use and climate changes in the James River watershed, Midwest United States, Journal of Hydrology: Regional Studies 2017. https://doi.org/10.1016/j.ejrh.2017.11.004 [IF = 5.0]
[8] Design of a metadata framework for environmental models with an example hydrologic application in HydroShare, Environmental Modelling & Software 2017. https://doi.org/10.1016/j.envsoft.2017.02.028 [IF = 4.6]
[7] SWATShare – A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models, Environmental Modelling & Software 2016. https://doi.org/10.1016/j.envsoft.2015.10.032 [IF = 4.6]
[6] Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture, Journal of Hydrology 2016. https://doi.org/10.1016/j.jhydrol.2016.02.037 [IF = 6.3]
[5] Improving soil moisture accounting and streamflow prediction in SWAT by incorporating a modified time‐dependent Curve Number method, Hydrological Processes 2016. https://doi.org/10.1002/hyp.10639 [IF = 2.9]
[4] Modeling the effects of future land use change on water quality under multiple scenarios: a case study of low-input agriculture with hay/pasture production, Sustainability of Water Quality and Ecology 2016. https://doi.org/10.1016/j.swaqe.2016.09.001 [IF = 1.6]
[3] Improved sustainability of water supply options in areas with arsenic-impacted groundwater, Water 2013. https://doi.org/10.3390/w5041941 [IF = 3.0]
[2] A comprehensive modeling study on Regional Climate Model application- regional warming projections in monthly resolution under IPCC A1B scenario, Atmosphere 2012. https://doi.org/10.3390/atmos3040557 [IF = 2.5]
[1] Evaluating technological resilience of small drinking water systems under the projected changes of climate, Journal of Water and Climate Change 2012. https://doi.org/10.2166/wcc.2012.019 [IF = 3.1]
Book Chapters & Reports
[2] Merwade, V., Rajib, A., Liu, Z. 2018. “An integrated approach for flood inundation modeling on large scales”. In Jung and Wang (Eds.), Bridging Science and Policy Implication for Managing Climate Extremes, pp. 133-155. DOI: 10.1142/9789813235663_0009. Download Link
[1] Maidment, D., Rajib, A., Lin, P., Clark, E. (Eds.) 2016. National Water Center Innovators Program Summer Institute Report 2016, CUAHSI Technical Report 13, USA, 122p. DOI: 10.4211/technical.20161019.