Hydroclimate Research Group


(Author name in bold denotes current or former group members)

  • Rashid, T., D. Tian. 2024. Improved 30-m evapotranspiration estimates over 145 eddy covariance sites in the contiguous United States: The role of ECOSTRESS, harmonized Landsat Sentinel-2 imagery, climate reanalysis, and deep neural network postprocessing. Water Resources Research, 60(4), p.e2023WR036313.
  • Lesinger, K., D. Tian, H. Wang. 2024. Subseasonal forecast skill of evaporative demand, soil moisture, and flash drought onset from two dynamic models over the contiguous United States. Journal of Hydrometeorology
  • Takhellambam, B.S., Srivastava, P., Lamba, J., Zhao, W., Kumar, H., Tian, D., and Molinari, R.. 2023. Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under Changing climate. Atmospheric Research, p.107122.
  • Schillerberg, T., D. Tian. 2023. Changes in crop failures and their predictions with agroclimatic conditions: Analysis based on earth observations and machine learning over global croplands. Agricultural and Forest Meteorology, 340, p.109620.
  • Wang, F, D. Tian, and M. Carroll. 2023. Customized Deep Learning for Precipitation Bias Correction and Downscaling. Geoscientific Model Development, 16, 535–556.
  • Singh, T. B., P. Srivastava, J. Lamba, R. McGehee, H. Kumar, and D. Tian. 2023. Projected Mid-Century Rainfall Erosivity Under Climate Change Over the Southeastern United States. Science of the Total Environment, p. 161119.
  • Medina, H., D. Tian. 2023. Synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017. Environmental Research Letters, 18, 024020.
  • Zhen, X., W. Huo, and D. Tian, Q. Zhang, A. Sanz-Saez, C. Chen, W. D. Batchelor. 2023. County level calibration strategy to evaluate peanut irrigation water use under different climate change scenarios. European Journal of Agronomy, 143, p.126693.
  • Lesinger, K., D. Tian. 2022. Trends, Variability, and Drivers of Flash Droughts in the Contiguous United States.Water Resources Research, 58, e2022WR032186.
  • Schillerberg, T., D. Tian. Climate Impacts on Crop Productions. In: Zhang, Q., Encyclopedia of Smart Agriculture Technologies. Springer, 2022.
  • Wang, F, D. Tian. 2022. On deep learning-based bias correction and downscaling of multiple climate models simulations. Climate Dynamics, pp.1-18.
  • Singh, T. B., P. Srivastava, J. Lamba, R. McGehee, H. Kumar, and D. Tian. 2022. Temporal disaggregation of hourly projected precipitation over the Southeast United States. Scientific Data, 9(1), pp.1-14.
  • Domeisen, D., and 39 Co-authors including H. Medina and D. Tian. 2022. Advances in the subseasonal prediction of extreme events: Relevant case studies across the globe. Bulletin of the American Meteorological Society, 103(6), E1473-E1501.
  • Ponpetch, K., B. Erko, T. Bekana, T. Kebede, D. Tian, Y. Yang, and S. Liang. 2021. Environmental Drivers and Potential Distribution of Schistosoma mansoni Endemic Areas in Ethiopia. Microorganisms, 9(10), p.2144.
  • Wang, F., D. Tian, L. Lowe, L. Kalin, and J. Lehrter. 2021. Deep learning for daily precipitation and temperature downscaling. Water Resources Research, 57, e2020WR029308
  • Li, Yanzhong, D. Tian, and H. Medina. 2021. Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Postprocessing. Journal of Hydrometeorology, 22(10), pp.2581-2600.
  • Asadi, P., and D. Tian. 2021. Estimating leaf wetness duration with machine learning and climate reanalysis data. Agricultural and Forest Meteorology, 307, p.108548.
  • Li, Yizhuo, D. Tian, G. Feng, W. Yang, L. Feng. 2021. Climate change and cover crop effects on water use efficiency of a corn-soybean rotation system. Agricultural Water Management, 255, p.107042
  • Saminathan, S., H. Medina, S. Mitra, and D. Tian. 2021. Improving short to medium range GEFS precipitation forecast in India. Journal of Hydrology, p.126431
  • Tian, D., X. He, P. Srivastava, and L. Kalin. 2021. A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information. Stochastic Environmental Research and Risk Assessment, pp.1-23.
  • Medina, H., D. Tian, and A. Abebe. 2021. On optimizing a MODIS-based framework for in-season corn yield forecast. International Journal of Applied Earth Observation and Geoinformatics, 95, p.102258.
  • Tasnim, B., Fang, X., Hayworth, J.S., and D. Tian. 2021. Simulating Nutrients and Phytoplankton Dynamics in Lakes: Model Development and Applications. Water, 13(15), p.2088.
  • Lesinger, K., D. Tian, C. Leisner, A. Sanz-Saez. 2020. Impact of Climate Change on Storage Conditions for Major Agricultural Commodities across the Contiguous United States. Climatic Change, pp. 1-19.
  • Schillerberg, T., D. Tian. 2020. Changes of crop failure risks in the United States associated with large-scale climate oscillations in the Atlantic and Pacific Oceans. Environmental Research Letters, 15(6), p.064035.
  • Medina, H., D. Tian. 2020. Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts. Hydrology and Earth System Sciences, 24(2).
  • He, X., L. Estes, M. Konar, D. Tian, D. Anghileri, K. Baylis, T. Evans, J. Sheffield. 2019. Integrated approaches to understanding and reducing drought impact on food security across scales. Current Opinion in Environmental Sustainability, 40, pp. 43-54.
  • Schillerberg, T., D. Tian, and R. Miao. 2019. Spatiotemporal patterns of maize and winter wheat yields in the United States: predictability and impact from climate oscillations. Agricultural and Forest Meteorology, 275 (2019): 208-222.
  • Medina, H., D. Tian, F. Martin, and G. Chirico. 2019. Comparing GEFS, ECMWF, and post-processing methods for ensemble precipitation forecasts over Brazil. Journal of Hydrometeorology, 20, 773-790.
  • Li, Y., C. Liu, W. Yu, D. Tian, and P. Bai. 2019. Response of streamflow to environmental changes: A Budyko-type analysis based on 144 river basins over China. Science of the Total Environment, 664, 824-833
  • Tian, D., M. Pan, and E. F. Wood. 2018. Assessment of a High-resolution Climate Model for Surface Water and Energy Flux Simulations over Global Land: An Inter-comparison with Reanalyses. Journal of Hydrometeorology, 19, 1115-1129.
  • Medina, H., D. Tian, P. Srivastavab, A. Pelosic, G. B. Chiricod. 2018. Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions. Journal of Hydrology. 562, pp.502-517.
  • Cammarano, D., and D. Tian. 2018. The effects of projected climate and climate extremes on a winter and summer crop in the southeast USA. Agricultural and Forest Meteorology. 248. 109-118
  • Tian, D., G. Xie, J. Tian, S. Tseng, C.K. Shum, J. Lee, S. Liang. 2017. Temporal variability and environmental driving factors of harmful algal blooms (HABs) in western Lake Erie, USA. PLoS ONE 12(6): e0179622.
  • Tian, D., E. F. Wood, and X. Yuan. 2017. CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrology and Earth System Sciences, 21, 1477-1490.
  • Tian, D., M. Pan, L. Jia, G. Vincci, and E. F. Wood. 2016. Assessing GFDL High-Resolution Climate Model Water and Energy Budgets from AMIP simulations over Africa. Journal of Geophysical Research-Atmosphere, 121, 8444–8459.
  • Estes, L. D., T. Searchinger, M. Spiegel, D. Tian, S. Sichinga, M. Mwale, L. Kehoe, T. Kuemmerle, A. Berven, N. Chaney, J. Sheffield, E. F. Wood, and K. K. Caylor. 2016. Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier. Philosophical Transactions of Royal Society B, 371(1703).
  • Tian, D., C. J. Martinez, and T. Asefa. 2016. Improving short-term urban water demand forecasts with reforecast analog ensembles. Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0000632, 04016008.
2015 and BEFORE
  • Tian, D., S. Asseng, C. J. Martinez, V. Misra D. Cammarano, and B. Ortiz. 2015. Does decadal climate variation influence wheat and maize production in the southeast USA? Agricultural and Forest Meteorology, 204, 1–9.
  • Tian, D., C. J. Martinez, W. D. Graham, and S. Hwang. 2014. Statistical downscaling multi-model forecasts for seasonal precipitation and surface temperature over southeastern United States. Journal of Climate, 27, 8384–8411.
  • Tian, D. and C. J. Martinez. 2014. The GEFS-based daily reference evapotranspiration (ETo) forecast and its implication for water management in the southeastern United States. Journal of Hydrometeorology, 15, 1152–1165.
  • Tian, D., C. J. Martinez, and W. D. Graham. 2014. Seasonal prediction of regional reference evapotranspiration (ETo) based on Climate Forecast System version 2 (CFSv2). Journal of Hydrometeorology, 15, 1166–1188.
  • Tian, D. and C. J. Martinez. 2012. Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts. Journal of Hydrology, 475(2012), 350-364.
  • Tian, D. and C. J. Martinez. 2012. Forecasting reference evapotranspiration using retrospective forecast analogs in the southeastern United States. Journal of Hydrometeorology, 13, 1874-1892.
  • Tian, D., X. Li, and D. E. Weller. 2012. The responses of hydrological indicators to watershed characteristics. Acta Ecologica Sinica, 32(1):27-37 (in Chinese).
  • Tian, D., X. Li, D. E. Weller, and Z. Bai. 2011. Impacts of land use and impervious surface on stream flow metrics in the Chesapeake Bay watershed. Journal of Natural Resources, 26(6): 1012-1020 (in Chinese).
  • Zhao, H, X. Li, X. Wang, and D. Tian. 2010. Grain size distribution of road-deposited sediment and its contribution to heavy metal pollution in urban runoff in Beijing, China. Journal of Hazardous Materials, 183: 203-210.

Conference Abstracts/Presentations

(Please see the list of conference abstracts/presentations)