Publications

Manuscripts under Review 
* indicates students or postdocs.
10 Spor Leal*, L. B., T. Roy, D. R. Uden, K. Schoengold, Hydrological Impacts of the Conservation Reserve Program – A Mini Review, under review in Frontiers in Water.
9 Suriano, Z. J., S. Davidson, R. D. Dixon, T. Roy, Climatological Context of the Severe Rain-on-Snow Flooding Event of March 2019 in Eastern Nebraska, under review in International Journal of Climatology. 
8 Gupta, C., R. Mahmood, P. Flanagan, T. Roy, M. Hayes, and L. Chen, An Assessment of Extreme Precipitation Trends in the Missouri River Basin: Insights from Three Gridded Precipitation Data Sets and Climate Indices Analysis, under review in International Journal of Climatology.
7 Liu, C., T. Roy, D. Tartakovsky, and D. Dwivedi, Baseflow identification via explainable AI with Kolmogorov-Arnold networks, under review in Journal of Geophysical Research - Machine Learning and Computation.
6 Rasiya Koya*, S. and T. Roy, Efficacy of Temporal Fusion Transformers for Runoff Simulation, under review in Water Resources Research. 
5 Kar*, K. K., R. Haggerty*, H. Sharma, D. Dwivedi, and T. Roy, Evapotranspiration partitioning in a semi-arid ecosystem, under review in Hydrological Processes.
4 Mattos et al., Enhancing Radar Rainfall Accuracy: A Novel Bias Correction Scheme for Multi-Duration Events, under review in Journal of Hydrologic Engineering.
3 Budamala, V., T. Roy, and R. Das Bhowmik, A robust skill verification of hindcast decadal experiments on streamflow regimes using CMIP6 data, under review in Journal of Hydrology.
2 Pokharel*, S., T. Roy, and D. Admiraal, Machine Learning-based Peak flow Estimation for Nebraska Streams, under review in International Journal of River Basin Management.
1 Kim*, I., S. Rasiya Koya*, T. Roy, and J. Eun, Seasonal Influences of Precipitation and River Stage on Groundwater Levels in Platte River Watersheds Vulnerable to Spring Floods, under review in Journal of Hydrologic Engineering.
​​Peer-Reviewed Journal Articles
* indicates students or postdocs.
44 Pokharel*, S. and T. Roy (2024), A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM, Journal of Hydroinformatics, doi:10.2166/hydro.2024.114.
43 Van Loon, A. F., S. Kchouk, A. Matanó, F. Tootoonchi, C. Alvarez-Garreton, K. E. A. Hassaballah, M. Wu, M. L. K. Wens, A. Shyrokaya, E. Ridolfi, R. Biella, V. Nagavciuc, M. H. Barendrecht, A. Bastos, L. Cavalcante, F. T. de Vries, M. Garcia, J. Mård, I. N. Streefkerk, C. Teutschbein, R. Tootoonchi, R. Weesie, V. Aich, J. P. Boisier, G. DiBaldassarre, Y. Du, M. Galleguillos, R. Garreaud, M. Ionita, S. Khatami, J. K. L. Koehler, C. H. Luce, S. Maskey, H. D. Mendoza, M. N. Mwangi, I. G. Pechlivanidis, G G. R. Neto, T. Roy, R. Stefanski, P. Trambauer, E. A. Koebele, G. Vico, M. Werner (2024), Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems, Natural Hazards and Earth System Sciences, 24, 3173–3205, doi:10.5194/nhess-24-3173-2024.
42 Srivastava*, S., T. Gerdes*, and T. Roy (2024), County-scale Flood Risk Assessment of Properties and Associated Population in the United States, Natural Hazards, doi:10.1007/s11069-024-06892-8.
41 Almagro, A., A. A. Meira Neto, N. Vergopolan, T. Roy, P. A. Troch, and P. T. S. Oliveira (2024), The drivers of hydrologic behavior in Brazil: insights from a catchment classification, Water Resources Research, doi:10.1029/2024WR037212.
40 Suriano Z. J., S. Davidson, R. D. Dixon, and T. Roy (2024), Ohio River Basin Snow Ablation and the Role of Rain-on-Snow, Hydrological Processes, 38(6), doi:10.1002/hyp.15205.
39 Rasiya Koya*, S., K. K. Kar*, and T. Roy (2024), Northern Pacific Sea-level Pressure Controls Rain-on-Snow in North America, Communications Earth & Environment, doi:10.1038/s43247-024-01431-6.
38 Rasiya Koya*, S., and T. Roy (2024), Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence, Journal of Hydrology, doi:10.1016/j.jhydrol.2024.131301.
37 Aiyelokun, O., Q. B. Pham, O. Aiyelokun, N. T. T. Linh, T. Roy, D. T. Ahn, and E. Lupikasza (2024), Effectiveness of Integrating Ensemble-Based Feature Selection and Novel Gradient Boosted Trees in Runoff Prediction: A Case Study in Vu GiaThu Bon River Basin, Vietnam, Pure and Applied Geophysics, doi:g10.1007/s00024-024-03486-0.
36 Kar*, K. K., T. Roy, S. Zipper, and S. E. Godsey (2024), Nonlinear trends in signatures characterizing non-perennial US streams, Journal of Hydrology, 635, 131131, doi:10.1016/j.jhydrol.2024.131131.
35 Srivastava*, S., N. Kumar*, A. Malakar, S. D. Choudhury, C. Ray, and T. Roy (2024), A Machine Learning-based Probabilistic Approach for Irrigation Scheduling, Water Resources Management, doi:10.1007/s11269-024-03746-7.
34 Rasiya Koya*, S., K. K. Kar*, S. Srivastava*, T. Tadesse, M. Svoboda, and T. Roy (2023), An Autoencoder-based Snow Drought Index, Scientific Reports, doi:10.1038/s41598-023-47999-5.
33 Johnny, C. J., J. C. Titus, and T. Roy (2023), Remote sensing-based drought hazard monitoring and assessment in a semiarid region: a principal component approach, Environmental Research, doi:10.1016/j.envres.2023.117757.
32 Srivastava*, S., and T. Roy (2023), Integrated Flood Risk Assessment of Properties and Associated Population at County Scale for Nebraska, USA, Scientific Reports, doi:10.1038/s41598-023-45827-4.
31 Shen, C., A. Appling, P. Gentine, T. Bandai, H. Gupta, A. Tartakovsky, M. Baity-Jesi, F. Fenicia, D. Kifer, L. Li, X. Liu, W. Ren, Y. Zheng, C. Harman, M. Clark, M. Farthing, D. Feng, P. Kumar, D. Aboelyazeed, F. Rahmani, Y. Song, H. Beck, T. Bindas, D. Dwivedi, K. Fang, M. Höge, C. Rackauckas, B. Mohanty, T. Roy, C. Xu, and K. Lawson (2023), Differentiable modelling to unify machine learning and physical models, Nature Reviews Earth and Environment, doi:10.1038/s43017-023-00450-9.
30 Pokharel*, S., T. Roy, and D. Admiraal (2023), Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction, Environmental Modeling & Software, doi:10.1016/j.envsoft.2023.105730.
29 Srivastava*, S., A. Basche, E., Traylor, and T. Roy (2023), The Efficacy of Conservation Practices in Reducing Floods and Improving Water Quality, Frontiers in Environmental Science, 11, doi:10.3389/fenvs.2023.1136989.
28 Velásquez, N., F. Quintero, S. Rasiya Koya*, T. Roy, and R. Mantilla (2023), Application of HLM-Snow to assess the flood of spring 2019 in Western Iowa, Journal of Hydrology: Regional Studies, 47, 101387, doi:10.1016/j.ejrh.2023.101387.
27 Rasiya Koya*, S., N., Velasquez, M. Rojas, R. Mantilla, K. Harvey, D. Ceynar, F. Quintero, W. F. Krajewski, and T. Roy (2023), Applicability of a Flood Forecasting System for Nebraska Watersheds, Environmental Modeling & Software, 164, 105693, doi:10.1016/j.envsoft.2023.105693.
26 Sánchez, R. A., T. Meixner, T. Roy, T. Ferre, M. Whitaker, and J. Chorover (2023), Physical and Biogeochemical Drivers of Solute Mobilization and Flux through the Critical Zone after Wildfire, Frontiers in Water, 5, doi:10.3389/frwa.2023.1148298.
25 Aliev*, A., S. Rasiya Koya*, I. Kim*, J. Eun, E. Traylor, and T. Roy (2023), Application of neural networks for hydrologic process understanding at a Midwestern watershed, Hydrology, 10(2), 27, doi:10.3390/hydrology10020027.
24 Sikand, M., E. Avery, C. Friedrichsen, and T. Roy (2023), Integrated, Coordinated, Open, and Networked (ICON) Scientific and Societal Relevance, Earth and Space Science, doi:10.1029/2022EA002535.
23 Kishawi*, Y., A. R. Mittelstet, T. E. Gilmore, D. Twidwell, T. Roy, and, N. Shrestha (2022), Impact of Eastern Redcedar encroachment on water resources in the Nebraska Sandhills, Science of The Total Environment, 858, 1, 159696, doi: 10.1016/j.scitotenv.2022.159696. 
22 Mai, J., H. Shen, B. A. Tolson, É. Gaborit, R. Arsenault, J. R. Craig, V. Fortin, L. M. Fry, M. Gauch, D. Klotz, F. Kratzert, N. O'Brien, D. G. Princz, S. Rasiya Koya*, T. Roy, F. Seglenieks, N. K. Shrestha, A. G. T. Temgoua, V. Vionnet, and J. W. Waddell (2022), The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL), Hydrology and Earth System Sciences, 26, 3537-3572, doi:10.5194/hess-26-3537-2022. 
21 Mattos, T. S., P. T. S. Oliveira, L. S. Bruno; G. A. Carvalho, R. B. Pereira, L. L. Crivellaro, M. C. Lucas, and T. Roy (2022), Towards reducing flood risk disasters in a tropical urban basin by the development of flood alert web application, Environmental Modelling & Software, 51, 105367, doi:10.1016/j.envsoft.2022.105367.
20 Sharma, S., K. Dahal, L. Nava, M. R. Gouli, R. Talchabhadel, J. Panthi, T. Roy, and G. R. Ghimire (2021), Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science, Earth and Space Science, doi:10.1029/2021EA002114.
19 Valdes, R., J. B. Valdes, S. Wi, A. Serrat-Capdevila, and T. Roy (2021), Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasting in the Upper Zambezi Basin and its subbasins using Variational Ensemble Forecasting, Hydrology, 8(4), 188, doi:10.3390/hydrology8040188.
18 Gupta, H. V., M. R. Ehsani, T. Roy, M. A. Sans-Fuentes, U. Ehret, and A. Behrangi (2021), Computing accurate probabilistic estimates of One-D Entropy from equiprobable random samples, Entropy, 14, 3480, doi:10.3390/en14123480.
17 Almagro, A., P. T. S. Oliveira, A. A. Meira Neto, T. Roy, and P. Troch (2021), CABra: a novel large-sample dataset for Brazilian catchments, Hydrology and Earth Systems Sciences, 25, 3105–3135, doi:10.5194/hess-25-3105-2021.
16 Mai, J. , B. A. Tolson, H. Shen, É. Gaborit, V. Fortin, N. Gasset, H. Awoye, T. A. Stadnyk, L. M. Fry, E. A. Bradley, F. Seglenieks, A. G. Temgoua, D. G. Princz, S. Gharari, A. Haghnegahdar, M. E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan, M. McLeod, N. Basu, R. Kumar, O. Rakovec, L. Samaniego, S. Attinger, N. K. Shrestha, P. Daggupati, T. Roy, S. Wi, T. Hunter, and J. R. Craig (2021), The Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E), Journal of Hydrologic Engineering, 26(9):05021020, doi:10.1061/(ASCE)HE.1943-5584.0002097.
15 Roy, T. and H. Gupta (2020), How certain are our uncertainty bounds? Accounting for sample variability in Monte Carlo-based uncertainty estimates, Environmental Modeling & Software, 136, 104931, doi:10.1016/j.envsoft.2020.104931.
14 Meira-Neto, A. A., G.-Y. Niu, T. Roy, S. Tyler, and P. A. Troch (2020), Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature, Communications Earth & Environment 1, 56, doi:10.1038/s43247-020-00056-9 (Nature Research Journal).
13 Meira-Neto, A. A., T. Roy, P. T. S. Oliveira, P. A. Troch (2020), An aridity index-based formulation of streamflow components, Water Resources Research, 56(9), doi:10.1029/2020WR027123.
12 Roy, T., X. He, P. Lin, H. Beck, C. Castro, and E. F. Wood (2020), Global evaluation of seasonal precipitation and temperature forecasts from NMME, Journal of Hydrometeorology, 21(11), doi:10.1175/JHM-D-19-0095.1.
11 Roy, T., J. Valdés, A. Serrat-Capdevila, M. Durcik, E. Demaria, R. Valdés-Pineda, and H. Gupta (2020), Detailed Overview of the Multimodel Multiproduct Streamflow Forecasting Platform, Journal of Applied Water Engineering and Research, doi:10.1080/23249676.2020.1799442.
10 Blöschl, G. et al. (2019), Twenty-three Unsolved Problems in Hydrology (UPH) – a community perspective, Hydrological Sciences Journal, 64(10), 1141-1158, doi:10.1080/02626667.2019.1620507.
9 Beck, H. E., M. Pan, T. Roy, G. P. Weedon, F. Pappenberger, A. I. J. M. van Dijk, G. J. Huffman, R. F. Adler, and E. F. Wood (2019), Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrology and Earth Systems Sciences, 23, 207–224, doi:10.5194/hess-23-207-2019.
8 Roy, T., A. J. Martinez, J. E. H. Estrada, Y. Zhang, F. Dominguez, A. Berg, M. Ek, and E. F. Wood (2019), Role of moisture transport and recycling in characterizing droughts: Perspectives from two recent US droughts and the CFSv2 system, Journal of Hydrometeorology, 20, 139-154, doi:10.1175/JHM-D-18-0159.1.
7 Roy, T., J. B. Valdés, B. Lyon, E. M. C. Demaria, A. Serrat-Capdevila, H. V. Gupta, R. Valdés-Pineda, and M. Durcik (2018), Assessing hydrological impacts of short-term climate change in the Mara River basin of East Africa, Journal of Hydrology, 566, 818–829, doi:10.1016/j.jhydrol.2018.08.051.
6 Roy, T., A. Serrat-Capdevila, J. Valdes, M. Durcik, and H. Gupta (2017), Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform, Journal of Hydroinformatics, 19(6), 911-919, doi:10.2166/hydro.2017.111.
5 Jain, A., and T. Roy (2017), Evaporation modeling using neural networks for assessing the self-sustainability of a water body, Lakes and Reservoirs: Research and Management, 22, 123-133, doi:10.1111/lre.12175.
4 Roy, T., H. V. Gupta, A. Serrat-Capdevila, and J. B. Valdes (2017), Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall-runoff model, Hydrology and Earth System Sciences, 21(2), 879–896, doi:10.5194/hess-21-879-2017.
3 Roy, T., A. Serrat-Capdevila, H. Gupta, and J. Valdes (2017), A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting, Water Resources Research, 53, 376-399, doi:10.1002/2016WR019752.
2 Roy, T., N. Schütze, J. Grundmann, M. Brettschneider, and A. Jain (2016), Optimal groundwater management using state-space surrogate models: A case study for an arid coastal region, Journal of Hydroinformatics, 18(4), 666-686, doi:10.2166/hydro.2016.086.
1 Troch, P. A., T. Lahmers, A. Meira, R. Mukherjee, J. W. Pederson, T. Roy, and R. Valdés-Pineda (2015), Catchment Co-evolution: A useful framework for improving predictions of hydrological change?, Water Resources Research, 51, 4903-4922, doi:10.1002/2015WR017032 (50th Anniversary Special Section)

Preprints 
* indicates students or postdocs.
11 Roy, T, S. Srivastava*, and B. Zhang (2024), Reinforcement Learning for Sociohydrology, ArXiv, https://arxiv.org/abs/2405.20772.
10 Pokharel*, S. and T. Roy (2024), A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM, ArXiv, https://doi.org/10.48550/arXiv.2404.07924.
9 Rasiya Koya*, S., K. K. Kar*, S. Srivastava*, T. Tadesse, M. Svoboda, and T. Roy (2023), An Autoencoder-based Snow Drought Index, ArXiv, doi:10.48550/arXiv.2305.13646.
8 Rasiya Koya*, S. and T. Roy (2023), Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence, ArXiv, doi:10.48550/arXiv.2305.12335.
7 Shen, C., A. Appling, P. Gentine, T. Bandai, H. Gupta, A. Tartakovsky, M. Baity-Jesi, F. Fenicia, D. Kifer, L. Li, X. Liu, W. Ren, Y. Zheng, C. Harman, M. Clark, M. Farthing, D. Feng, P. Kumar, D. Aboelyazeed, F. Rahmani, Y. Song, H. Beck, T. Bindas, D. Dwivedi, K. Fang, M. Höge, C. Rackauckas, T. Roy, C. Xu, and K. Lawson (2023), ArXiv, doi:10.48550/arXiv.2301.04027.
6 Mai, J., H. Shen, B. A. Tolson, É. Gaborit, R. Arsenault, J. R. Craig, V. Fortin, L. M. Fry, M. Gauch, D. Klotz, F. Kratzert, N. O'Brien, D. G. Princz, S. Rasiya Koya*, T. Roy, F. Seglenieks, N. K. Shrestha, A. G. T. Temgoua, V. Vionnet, and J. W. Waddell (2022), The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL), Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2022-113.
5 Gupta, H. V., M. R. Ehsani, T. Roy, M. A. Sans-Fuentes, U. Ehret, and A. Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, ArXiv, doi:10.48550/arXiv.2102.12675.
4 Almagro, A., P. T. S. Oliveira, A. A. Meira Neto, T. Roy, and P. Troch (2020), CABra: a novel large-sample dataset for Brazilian catchments, Hydrology and Earth Systems Sciences Discussions, doi:10.5194/hess-2020-521.
3 Beck, H. E., M. Pan, T. Roy, G. P. Weedon, F. Pappenberger, A. I. J. M. van Dijk, G. J. Huffman, R. F. Adler, and E. F. Wood (2018), Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrology and Earth Systems Sciences Discussions, doi:10.5194/hess-2018-481.
2 Troch, P. A., R. Dwivedi, T. Liu, A. Meira-Neto, T. Roy, R. Valdés-Pineda, M. Durcik, S. Arciniega-Esparza, and J. A. Breña-Naranjo (2018), Catchment-scale groundwater recharge and vegetation water use efficiency, Hydrology and Earth Systems Sciences Discussions, doi:10.5194/hess-2018-449.
1 Roy, T., H. V. Gupta, A. Serrat-Capdevila, and J. B. Valdes (2016), Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall-runoff model, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2016-413.

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Book / Book Chapters
3
Pande S., A. Scolobig, J. Adamowski, N. Ajami, G. Carr, A. Castelletti, E. Du, J. Guillaume, T. Krueger, C. D. Pérez-Blanco, and T. Roy (2024), Chapter 4: Methodologies for the study of change in hydrology and society, accepted in IAHS Panta Rhei Synthesis Book.
2
Bhowmik, R. D. and T. Roy (2021), Challenges and Solution Pathways in Water Use through the Lens of COVID-19, chapter in Global Pandemic and Human Security: Technology and Development Perspective, Editors: Rajib Shaw and Anjula Gurtoo, Springer Nature. 
1 Roy, T., N. Schütze, J. Grundmann, and A. Jain (2016), Water management in coastal aquifers by simulation-optimization, LAP Lambert Academic Publishing. ISBN number: 978-3-659-91749-3.
Conferences / Symposiums
* indicates students or postdocs.
88 Rasiya Koya, S. and T. Roy (2024), A Diffusion Inspired Rainfall-Runoff Modeling Framework, HydroML Symposium, May 29-31, Richland, Washington.
87 Roy, T. and S. Srivastava (2024), Assessing Hydrological Impacts of Conservation Practices: Challenges and Opportunities, Exploring Regenerative Agriculture Workshop, May 21-22, Spokane Valley, Washington. 
86 Srivastava*, S., T. Gerdes*, and T. Roy (2024), Analyzing Flood Risk Across U.S. Counties: A Comprehensive Mapping Study, DWFI Research Forum, April 17, Lincoln.
85 Srivastava*, S., N. Kumar*, A. Malakar, S. D. Choudhury, C. Ray, and T. Roy (2024), A Probabilistic Framework for Irrigation Scheduling, AWRA Spring Conference, April 8-10, Tuscaloosa, Alabama.
84 Machhi*, N., S Srivastava*, D. Uden, and T. Roy (2024), Spatiotemporal Patterns of Water Availability in a Nebraska Watershed, Introduce a Girl to Engineering Day, March 20, Omaha.
83 Roy, T., S. Srivastava*, and T. Gerdes* (2024), Flood Risk based on Hazard, Exposure, Vulnerability, and Response, Harnessing the Heartland Workshop, Feb 29, Omaha.
82 Dixon, R. D., T. Roy, Z. J. Suriano, and S. R. Davidson (2024), Climate Model Biases in Rain-on-Snow Days Across the Central United States, American Meteorological Society Annual Meeting, Jan 28 - Feb 1, Baltimore.
81 Patil, A., R. Das Bhowmik, S. Rasiya Koya*, T. Roy, and N. Kumar (2023), Evaluate the Occurrence of Extreme Events in Two Indian Basins Using Rainfall-Runoff Model, AGU Fall Meeting, Dec 11-15, San Francisco.
80 Srivastava*, S., B. Zhang, M. J. Hayes, T. Tadesse, and T. Roy (2023), Application of Reinforcement Learning to Represent Human-Flood Interactions, AGU Fall Meeting, Dec 11-15, San Francisco.
79 Kumar*, N., K. K. Kar*, S. Srivastava*, S. Rasiya Koya*, S. Pokharel*, M. Likins*, and T. Roy (2023), Causal Discovery Methods to Investigate Rain-on-Snow Flooding, AGU Fall Meeting, Dec 11-15, San Francisco.
78 Rasiya Koya*, S. and T. Roy (2023), Streamflow Forecasting with Temporal Fusion Transformers, AGU Fall Meeting, Dec 11-15, San Francisco.
77 Kar*, K. K., and T. Roy (2023), Causal effects of hydroclimatic variables on streamflow signatures in non-perennial streams, AGU Fall Meeting, Dec 11-15, San Francisco.
76 Pokharel*, S., T. Roy, and D. Admiraal (2023), Enhancing Peakflow Estimation in Nebraska with Machine Learning, Nebraska Water Conference, Oct 3-4, Omaha.
75 Kar*, K. K., A. Young, and T. Roy (2023), Influence of Climatic Patterns on Groundwater Levels in Nebraska, Nebraska Water Conference, Oct 3-4, Omaha.
74 Rasiya Koya*, S., K. K. Kar*, and T. Roy (2023), Causal Drivers of Rain-on-Snow Events in North America, Nebraska Water Conference, Oct 3-4, Omaha.
73 Srivastava*, S., T. Gerdes*, and T. Roy (2023), Flood Vulnerability Assessment at the County Scale for the US, Nebraska Water Conference, Oct 3-4, Omaha.
72 Kumar*, N., K. K. Kar*, and T. Roy (2023), Trend for rain-on-snow events across North America, Nebraska Water Conference, Oct 3-4, Omaha. 
71 Roy, T., S. Rasiya Koya*, S. Pokharel*, N. Kumar*, S. Srivastava*, K. K. Kar*, and I. Kim* (2023), Convergent research towards building flood resilience in Nebraska, Nebraska Water Conference, Oct 3-4, Omaha. 
70 Srivastava*, S., A. Basche, E. Traylor, and T. Roy (2023), Using Statistical, Machine Learning, and Causal Discovery Methods to Assess the Use and Impacts of Conservation Practices at the Shell Creek Watershed, Nebraska, Soil Water Conservation Society Annual Conference, Aug 6–9, Des Moines.
69 Blackwell*, B., S. Rasiya Koya*, N. Kumar*, and T. Roy (2023), Causal Drivers of Flood-Induced Water Quality Issues in Nebraska, Nebraska Summer Research Program Symposium, Aug 3, Lincoln.
68 Laiwal, G., R. Wood, D. Admiraal, and T. Roy (2023), Shallow River Ice Flow Impacts on Critical Infrastructure, Hydraulic Measurements & Experimental Methods Conference, Jun 25-29, Fort Collins. 
67 Likins*, M., D. Admiraal, R. Wood, and T. Roy (2023), Using a Hydrodynamic Model and UAS Measurements to Better Predict Short-term and Long-term Channel Adjustments, Hydraulic Measurements & Experimental Methods Conference, Jun 25-29, Fort Collins. 
66 Rasiya Koya*, S. and T. Roy (2023), Application of Temporal Fusion Transformers in Streamflow Prediction, HydroML Symposium, May 22-24, Berkeley.
65 Kar*, K. K., R. Haggerty*, H. Sharma, D. Dwivedi, and T. Roy (2023), Development of a machine learning-based evapotranspiration partitioning framework, HydroML Symposium, May 22-24, Berkeley. 
64 Kumar*, N., D. N. Kumar, and T. Roy (2023), Spatiotemporal analysis and modeling of nonstationarity in hydrological time series, EGU General Assembly, Apr 24–28, Vienna.
63 Rasiya Koya*, S., K. K. Kar*, S. Srivastava*, and T. Roy (2023), SnoDRI: A machine learning based index to measure snow droughts, EGU General Assembly, Apr 23-28, Vienna.
62 Srivastava*, S., N. Kumar*, A. Malakar, S. Das Choudhury, C. Ray, and T. Roy (2023), An ML-based Probabilistic Approach for Irrigation Scheduling, EGU General Assembly, Apr 23-28, Vienna.
61 Pokharel*, S., T. Roy, and D. Admiraal (2023), Enhancing Transportation Safety through Improved Peak Streamflow Prediction using Machine Learning Techniques, EGU General Assembly, Apr 23-28, Vienna.
60 Srivastava*, S. and T. Roy (2023), Flood Risk Assessment in Nebraska using Hazard, Exposure, Vulnerability, and Response as Drivers, DWFI Research Forum, April 13, Lincoln, USA.
59 Newcomer, M. E., N. Dogulu, H. Iravani, M. Dembélé, G. Uysal, T. Roy, S. Fischer, B. Dieppois, S. Dietrich, R. Dwivedi, and A. Tsyplenkov (2023), Open and Free Datasets for Hydrology Research: Insights, Challenges and Opportunities, 9th Global FRIEND-Water Conference, Dakar. 
58 Roy, T. (2023), Enhancing the hydrological drought monitoring capability of the US Drought Monitor, US Drought Monitor Forum, Apr 11-13, Boulder City.
57 Shen, C., A. Appling, P. Gentine, T. Bandai, H. Gupta, A. Tartakovsky, M. Baity-Jesi, F. Fenicia, D. Kifer, L. Li, X. Liu, W. Ren, Y. Zheng, C. Harman, M. Clark, M. Farthing, D. Feng, P. Kumar, D. Aboelyazeed, F. Rahmani, H. Beck, T. Bindas, D. Dwivedi, K. Fang, M. Höge, C. Rackauckas, T. Roy, C. Xu, and K. Lawson (2022), Differentiable modeling in Geosciences to unify machine learning and physical models, AGU Fall Meeting, Dec 12-16, Chicago.
56 Mai, J., H. Shen, B. Tolson, E. Gaborit, R. Arsenault, J. R. Craig, V. Fortin, L. Fry, M. Gauch, D. Klotz, F. Kratzert, N. O'Brien, D. G. Princz, S. Rasiya Koya*, T. Roy, F. Seglenieks, N. Shrestha, A. G. Temgoua, V. Vionnet, and J. M. Waddell (2022), The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL), AGU Fall Meeting, Dec 12-16, Chicago.
55 Pokharel*, S., T. Roy, and D. Admiraal (2022), A Physics-guided Machine Learning Scheme for Predicting Peak Flow in Streams, AGU Fall Meeting, Dec 12-16, Chicago.
54 Rasiya Koya*, S., K. K. Kar*, and T. Roy (2022) Potential Drivers and Spatiotemporal Variability of Rain-on-Snow Events, AGU Fall Meeting, Dec 12-16, Chicago.
53 Srivastava*, S., and T. Roy (2022), Development of a Flood Risk Framework in Context to Public Health Across Nebraska, United States, AGU Fall Meeting, Dec 12-16, Chicago.
52 Dixon, R., and T. Roy (2022), Biases in Rain-on-Snow Days in Climate Models, AGU Fall Meeting, Dec 12-16, Chicago.
51 Sikand, M. V., E. Avery, C. Friedrichsen, and T. Roy (2022), Integrated, Coordinated, Open, and Networked (ICON) Scientific and Societal Relevance, AGU Fall Meeting, Dec 12-16, Chicago.
50 Srivastava*, S. and T. Roy (2022), Assessment of Risk Associated with Flooding in the Context of Public Health in Nebraska, USA​, Platte River Basin Conference, Kearney. 
49 Newcomer, M., N. Dogulu, H Iravani, M. Dembélé, G. Uysal, T. Roy, and S. Fischer (2022), Open and Free Datasets for Hydrology Research: Insights, Challenges and Opportunities, IAHS Scientific Assembly, Montpellier.
48 Walker, D. W., N. Vergopolan, L. Cavalcante, A. Almagro, T. Apurv, D. G. Kingston, T. Roy, K. H. Smith, and N. Wanders (2022), Flash droughts: bridging the understanding between physical definitions and societal impacts, EGU General Assembly, Vienna.
47 Pokharel*, S., D. Admiraal, and T. Roy (2022), A Physics-Based Machine Learning Scheme for Predicting Peak Flows in Nebraska Streams, Student Research Days, UNL, Lincoln.
46 Rasiya Koya*, S. and T. Roy (2022), Incorporating Snow Processes in the Iowa Flood Information System (IFIS) and Evaluating its Applicability to Nebraska, Student Research Days, UNL, Lincoln.
45 Kar*, K. K., S. C. Zipper, and T. Roy (2022), Change detection for hydrological signatures of non-perennial streamflow in United States, UNL Graduate Student Symposium, Feb 25, Lincoln.
44 Kar*, K. K., S. C. Zipper, and T. Roy (2021), Identifying Nonlinear Change in Non-perennial Streamflow, AGU Fall Meeting, Dec 13-17, New Orleans.
43 Rasiya Koya*, S., N. V. Giron, R. Mantilla, M. Rojas, K. Harvey, D. Ceynar, W. F. Krajewski, and T. Roy (2021), Development of a Flood Monitoring System Prototype for a Pilot Basin in Nebraska, AGU Fall Meeting, Dec 13-17, New Orleans.
42 Kim*, I., S. Rasiya Koya*, J. Eun, and T. Roy (2021), Seasonal Effects of Precipitation and River Stage on Groundwater Level in the Midwestern United States, AGU Fall Meeting, Dec 13-17, New Orleans.
41 Pande, S., A. Scolobig, T. Krueger, J. H. A. Guillaume, M. Haeffner, J. F. Adamowski, N. Ajami, D. Perez, A. Castelletti, E. Du, T. Roy, and G. Carr (2021), Methodologies for the study of change in hydrology and society, AGU Fall Meeting, Dec 13-17, New Orleans.
40 Aliev*, A., S. Rasiya Koya*, I. Kim*, and T. Roy (2021), Towards Better Hydrologic Process Understanding at Shell Creek Watershed, AGU Fall Meeting, Dec 13-17, New Orleans.
39 Kar*, K. K., F. K. Khadim, A. K. Gain, and T. Roy (2021), Consequences of morphological and social changes in response to tidal river management, Delft International Conference on Sociohydrology, Sep 6-8, Delft.
38 Aliev*, A., S. Rasiya Koya*, I. Kim*, and T. Roy (2021), Towards Better Hydrologic Process Understanding at Shell Creek Watershed, UNL College of Engineering Summer Undergraduate Research Fair, Aug 3, Lincoln.
37 Harvey, K., T. Roy, and S. Rasiya Koya* (2021), Research Towards an Integrated Food Information System for Nebraska, NEASCE/NITE Transportation Conference, June 4, Virtual.
36 Pande, S., A. Scolobig, T. Kueger, J. Guillaume, M. Haeffner, J. Adamowski, N. Ajami, D. Perez, A. Castelletti, E. Du, T. Roy, and G. Carr (2021), Methodological approaches to studying coupled human-water systems, EGU General Assembly, Apr 19-30, Virtual. 
35 Valdés-Pineda, R., J. B. Valdés, S. Wi, A. Serrat-Capdevila, T. Roy, E. M. C. Demaria, and M. Durcik (2021), Operational Daily Streamflow Forecasts by coupling Variational Ensemble Forecasting and Machine Learning (VEF-ML) approaches, EGU General Assembly, Apr 19-30, Virtual. 
34 Doshi*, S. C., and T. Roy (2020), Assessment of predictability in Downscaling GEFS Precipitation Forecasts, AGU Fall Meeting, Dec 1-17.
33 Barutha, P., L. E. Johnson*, S. Moussavi*, S. C. Doshi*, U. Kreitmair, and T. Roy (2020), Flooding Resilience in Nebraska: An Inter-disciplinary, Mixed-methods analysis of Vulnerability and Resilience in the March 2019 Nebraska Floods, AGU Fall Meeting, Dec 1-17.
32 Sanchez, R. A., T. Roy, and T. Meixner (2020), Post-wildfire Streamflow Reconstruction Using Artificial Neural Network Model, AGU Fall Meeting, Dec 1-17.
31 Kishawi*, Y., C. Liu*, H. Pham*, A. A. M. Neto, P. T. S. Oliveira, T. Roy (2020), A data-based approach for the estimation of streamflow components, AGU Fall Meeting, Dec 1-17.
30 Roy, T., and P. Lin (2020), Seasonal Precipitation and Temperature Predictability Rebounds, AGU Fall Meeting, Dec 1-17. 
29 Doshi*, S., M. G. Valentín, and T. Roy (2020), Surface water and sewer network interface with the inlets, First International Conference on Urban Water Interfaces (UWI), Sep 22-24, Berlin. 
28 Neto, A. A. M., T. Roy, P. T. S. Oliveira, and P. A. A. Troch (2019), A Budyko-type formulation for baseflow and direct runoff, AGU Fall Meeting, Dec 9-13, San Francisco.
27 Beck, H. E., M. Pan, T. Roy, G. P. Weedon, F. Pappenberger, A. I. J. M. van Dijk, G. J. Huffman, R. F. Adler, and E. F. Wood (2019), Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, EGU General Assembly, Apr 7-12, Vienna.
26 Beck, H., M. Pan, T. Roy, and E. F. Wood (2018), Evaluation of 27 precipitation datasets using Stage-IV gauge-radar data for the CONUS, AGU Fall Meeting, Dec 10-14, Washington, D.C. 
25 Roy, T., P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, E. F. Wood ‎(2018), Effects of Hurst-Kolmogorov Dynamics in Intensity-Duration-Frequency Curves, AGU Fall Meeting, Dec 10-14, Washington, D.C.  
24 Troch, P. A., A. A. Meira-Neto, T. Roy, and R. Valdés-Pineda (2018), Climate-based Formulation for Long-term Catchment-scale Baseflow and Direct Runoff, AGU Fall Meeting, Dec 10-14, Washington, D.C.  
23 Roy, T., J. B. Valdes, B. Lyon, E. M. C. Demaria, R. Valdés-Pineda, A. Serrat-Capdevila, M. Durcik, and H. V. Gupta (2017), Short-term climate change impacts on Mara basin hydrology, AGU Fall Meeting, Dec 11-15, New Orleans. 
22 Troch, P., R. Dwivedi, A. A. Meira-Neto, R. Valdés-Pineda, and T. Roy (2017), Catchment-scale groundwater recharge and vegetation water use efficiency, AGU Fall Meeting, Dec 11-15, New Orleans.
21 Rushi, B. R., W. L. Ellenburg, E. C. Adams, A. Flores, A. S. Limaye, R. Valdés-Pineda, T. Roy, J. B. Valdés, F. Mithieu, and S. Omondi (2017), Bias Correction of Satellite Precipitation Products (SPPs) using a User-friendly Tool: A Step in Enhancing Technical Capacity, AGU Fall Meeting, Dec 11-15, New Orleans.
20 Roy, T., J. B. Valdes, A. Serrat-Capdevila, H. V. Gupta, B. Lyon, and M. Durcik (2017), Comparison of two bias correction schemes in the context of climate change impacts assessment in the Mara River basin, EarthWeek Research Symposium, Mar 27-31, University of Arizona, Tucson.
19 Roy, T., H. Gupta, A. Serrat-Capdevila, and J. B. Valdes (2016), Using satellite based actual evapotranspiration estimates to improve streamflow forecasting, AGU Fall Meeting, Dec 12-16, San Francisco.
18 Alemayehu T., T. Roy, A. Serrat-Capdevila, A. van Griensven, and J. Valdes (2016), Simulating streamflow using bias-corrected multiple satellite rainfall products in the Tekeze basin, Ethiopia, AGU Fall Meeting, Dec 12-16, San Francisco.
17 Roy, T., H. Gupta, A. Serrat-Capdevila, and J. Valdes (2016), Improving streamflow forecasting using satellite based ET, Arizona Hydrological Society Annual Symposium, Sep 14-17, Tucson.
16 Roy, T., V. Baker, K. Hirschboeck, and J. Duan (2016), Channel changes and their potential impacts on flood behavior of the Rillito Creek, Tucson, Arizona Hydrological Society Annual Symposium, Sep 14-17, Tucson.
15 Roy, T., A. Serrat-Capdevila, J. Valdes, H. Gupta, E. Demaria, and M. Durcik (2016), A benchmark approach for real-time streamflow monitoring and forecasting, Galileo Circle Scholar Reception, Apr 20, University of Arizona, Tucson.
14 Roy, T., A. Serrat-Capdevila, H. Gupta, J. Valdes, M. Durcik, and  E. Demaria (2016), Probabilistic real-time streamflow forecasting in African basins, EarthWeek Research Symposium, Mar 31-Apr 1, University of Arizona, Tucson.
13 Roy, T., A. Serrat-Capdevila, H. Gupta, J. B. Valdes, E. Demaria, and M. Durcik (2016), An improved streamflow forecasting platform for better decision making, WRRC Annual Conference, Mar 21, Tucson.
12 Roy, T., T. Lahmers, A. Meira, R. Mukherjee, J. W. Pederson, R. Valdés-Pineda, T. Yoshida, and P. A. Troch (2015), Catchment Co-evolution: A useful framework for improving predictions of hydrological change?, AGU Fall Meeting, Dec 14-18, San Francisco.
11 Roy, T., A. Serrat-Capdevila, H. Gupta, and J. B. Valdes (2015), Streamflow Forecasting using Satellite Products: A Benchmark Approach. Can We Reduce Uncertainty by using Multiple Products and Multiple Models?, AGU Fall Meeting, Dec 14-18, San Francisco.
10 Serrat-Capdevila, A., J. Valdes, S. Wi, T. Roy, J. Roberts, and F. Robertson (2015), Seasonal Streamflow Forecasts for African Basins, AGU Fall Meeting, Dec 14-18, San Francisco.
9 Alemayehu, T., T. Roy, A. Serrat-Capdevila, A. van Griensven, J. Valdes, and W. Bauwens (2015), Value of bias-corrected satellite rainfall products in SWAT simulations and comparison with other models in the Mara basin, AGU Fall Meeting, Dec 14-18, San Francisco.
8 Roy, T., A. Serrat-Capdevila, J. Valdes, and H. Gupta (2015), Can we make better predictions by merging multiple models’ forecasts?, Arizona Hydrological Society Annual Symposium, Sep 16-19, Phoenix.
7 Roy, T., T. Lahmers, M. Tso, and H. Gupta (2015), SPSM: A physically-based snowpack accounting model, Arizona Hydrological Society Annual Symposium, Sep 16-19, Phoenix.
6 Roy, T., A. Serrat-Capdevila, H. Gupta, and J. Valdes (2015), Estimating uncertainties in streamflow forecasts using a Bayesian multi-model and multi-product approach, UCOWR/NIWR/CUAHSI Annual Conference, Jun 16-18, Henderson.
5 Roy, T., A. Serrat-Capdevila, J. B. Valdes, H. V. Gupta, E. M. Demaria, and M. Durcik (2015), Near-real-time streamflow monitoring and forecasting along with the estimation of uncertainties in a multi-model multi-product platform, EarthWeek Research Symposium, Apr 7-11, University of Arizona, Tucson.
4 Roy, T., A. Serrat-Capdevila, J. B. Valdes, M. Durcik, H. V. Gupta, and R. Mukherjee (2014), Multi-model and multi-product streamflow forecasting in the African basins, EarthWeek Research Symposium, Apr 8-11, University of Arizona, Tucson.
3 Schütze, N., and T. Roy (2014), Fast neural network surrogates for complex groundwater flow models, Paper 411, International Conference on Hydroinformatics, Aug 17-21, New York. 
2 Serrat-Capdevila, A., J. B. Valdes, T. Roy, R. Mukherjee, M. Durcik, M. Merino, R. Valdes, and H. Gupta (2013), A multi-model real time forecasting prototype system in the Mara Basin (Kenya/Tanzania) in the Lake Victoria Watershed, AGU Fall Meeting, Dec 9-13, San Francisco.
1 Schütze, N., T. Roy, M. Brettschneider, and J. Grundmann (2013), Optimal groundwater management using surrogate models: a case study for an arid coastal region, Geophysical Research Abstracts, vol. 15, p. 12457, EGU General Assembly, Apr 7-14, Vienna.