Dr. Tirusew Asefa


Dr. Tirusew Asefa
Courtesy Professor
Twitter @TirusewAsefa


Dr. Tirusew Asefa is the planning & decision support lead at Tampa Bay Water, one of the largest water supply utilities in Southeast, USA. He is responsible for all activities of the agency’s design and implementation of water resources models and decision support tools for operations and planning of water resources projects. He is responsible for leading a group of professionals working on water supply projects planning modeling that range between $200 to $400 million.

Currently, he is also a chair of Florida Water and Climate Alliance, where he leads the group’s effort in building a stakeholder-scientist partnership that is committed to increasing the relevance of climate science data and tools to support decision-making in water resources management, planning and operations in Florida.

As a forward looking and innovative practitioner Dr. Asefa is frequently invited as an independent evaluator for several projects by NSF, NOAA, the Water Research Foundation among others. He has written more several peer-reviewed articles in various area of water resources management. Dr Asefa is the recipient of 2022 Outstanding Practitioner in Water Resources Engineering Award by American Academy of Water Resources Engineers.


As part of courses given by Dr. Ghebremichael

  • IDS 6245 - Sustainable Water Resource Management: Doing more with less
  • IDS 6248 - Water Resources Planning

Dr Asefa gives hand on experience to students on several topics: decision making under deep uncertainty, urban water demand forecast, demand management, water supply planning, and practical approach of conducting climate change impact assessment and adaptation approaches in water resources management.

Research Interest

In addition to his management role at the Tampa Bay Water, Dr. Asefa is highly involved in research activities. He has published several peer-reviewed articles in various area of water resources management.

Some of his main research interest topics include:

  • Water supply management, systems analysis, and modeling
  • Climate variability & Climate changes
  • Integrated Hydrologic Modeling, Integrated Water Resources Management (IWRM)
  • Planning under deep uncertainty, and development of adaptation pathways for regional water resource management
  • Integrated river basin management (both flood and droughts)
  • Statistical learning tools/intelligent algorithms
  • Risk, reliability, and water resources management



  • Wang, H., Wanakule, N., Asefa, T., Erkyihun S., Basdekas L., and Hayslett, R. 2021., Application of Multi-Objective Optimization for multiple Supply resources allocation, Journal of Water Resource Planning and Management, in press, 2022

  • Gorelick, D., Gold, D., Asefa, T., Svrdlin, S., Wang, H., Wanakule, N., Reed Patrick and Characklis G., 2022, Water supply infrastructure investment requires adaptive financial assessment: an enhanced exploratory modeling framework to evaluate coupled financial and water supply dynamics, Journal of Water Resource Planning and Management, in press, 2022

  • Eladawy, A., Asefa, T., and El Nour, S. 2022, Comment on “Egypt's water budget deficit and suggested mitigation policies for the Grand Ethiopian Renaissance Dam filling scenarios”, Environmental Research Letter Vol 17, Number 7, 2022.

  • Wang, Asefa, T., Misra, V., and Bhardwaj, A. 2022, Assessing the Value of a Regional Climate Model’s Rainfall Forecasts in Improving Dry-Season Streamflow Predictions, Journal of Water Resource Planning and Management, DOI:10.1061/(ASCE)WR.1943-5452.0001571, 2022

  • Wang, H., Wanakule, N., Asefa, T. and Erkyihun, S, 2022, A risk-based framework to evaluate infrastructure investment option for a water supply system, Journal of Environmental Engineering, November 2022 | Volume 148, Issue 11, 2022

  • Wang, H., Asefa, T. and Thornburgh, J. 2022. Integrating water quality and streamflow into prediction of chemical dosage in a drinking water treatment plant using machine learning algorithm. Water Supply Vol 22 No 3, 2803 doi: 10.2166/ws.2021.435.

  • Ayelew, D.W., T. Asefa, M. A. Moges and S. M. Leyew, 2022, Evaluating the potential impact of climate change on the hydrology of Ribb catchment, Lake Tana Basin, Ethiopia, Journal of Water and Climate Change. Vol 13 No 1, 190 doi: 10.2166/wcc.2021.


  • Wang, H., Asefa, T., Erkyihun S. 2021. Inter annual variability of the summer and extreme wet daily precipitation in the Southeastern United States, Journal of Hydrology, Vol 603, https://doi.org/10.1016/j.jhydrol.2021.127013, 2021

  • Wang, H., Wanakule, N., Asefa, T., 2021. Examining optimal groundwater management at multi-time scales in the Tampa Bay region, Journal of Water Resource Planning and Management, Vol. 147, Issue 12, 2021.

  • Bhardwaj, A., Misra V., Kirtman, B., Asefa, T., Maran, C., Morris, K., Carter, E., Martinez, C., Roberts D. 2021. Experimental high-resolution winter seasonal climate reforecast for Florida. Weather and Forecasting 36(4):1169–1182 2021.

  • Wang, H., Asefa, T., Sarkar A. 2021. A novel non-homogeneous hidden Markov model for simulating and predicting monthly rainfall, Theoretical and Applied Climatology 143(7):1-12, DOI:10.1007/s00704-020-03447-2


  • Wang, H., Asefa, T., Wanakule, N., Geurink, J. 2020. Evaluating potential impacts of short-term augmentation of groundwater production on groundwater levels in Tampa Bay region. Journal of Water Resource Planning and Management, 147(2) DOI:10.1061/(ASCE)WR.1943-5452.0001314, 2020

  • Misra, V., Irani, T., Staal, L., Morris, K., Asefa, T., Martinez, C., and Graham, W. 2020. The Florida Water and Climate Alliance (FloridaWCA): Developing a Stakeholder–Scientist Partnership to Create Actionable Science in Climate Adaptation and Water Resource Management. Bulletin of the American Meteorological Society 102(2):1-38 DOI:10.1175/BAMS-D-19-0302.1, 2020.

  • Wang, H., Asefa, T., Wanakule, N. and Adams, A. 2020. Application of decision support tools for seasonal water supply management incorporating systems uncertainties and operational constraints, Journal of Water Resource Planning and Management, 2020

  • AWWA (2020). M71 manual. Climate Adaptation Plans: Adaptive Management Strategies for Utilities. Chapter 4: Principle of Engineering Practice. https://www.awwa.org/Portals/0/Awwa/Publishing/Manuals/M71LookInside.pdf?ver=2021-01-26-093207-03


  • Wang, H., Asefa, T., Bracciano, B., Adams, A., and Wanakule, N. Proactive water shortage mitigation integrating system optimization and input uncertainty, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2019.01.071, 2019
  • Wang, H. and Asefa, T. (2019). Drought monitoring, mitigation, and adaptation; In: Melesse, A. W. Abtew and S. Gabriel (Eds.), Extreme Hydrology & Climate Variability Book Chapter, Elsevier Institute, Chapter 36.
  • Panaou, T., Asefa, T. and M. Nachabe (2019). Performance evaluation of a water supply system under a changing climate; In: Melesse, A. W. Abtew and S. Gabriel (Eds.), Extreme Hydrology & Climate Variability Book Chapter, Elsevier Institute Chapter 37.


  • Chang, S., Graham, W., Geurink, J., Wanakule, N., and Asefa, T.: Evaluation of impact of climate change and anthropogenic change on regional hydrology, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-91, 2018.
  • Panaou, T., Asefa, T. and M. Nachabe, 2018. Keeping Us Honest: Examining climate states and transition probabilities of precipitation projections in General Circulation Models, Journal of Water Resources Planning and Management, 144(4): April 2018.

Pre 2018-

  • Wang, H. and T. Asefa, 2017, Impact of Different Types of ENSO Conditions on Seasonal Precipitation and Streamflow in the Southeastern United States, International Journal of Climatology, doi.org/10.1002/joc.5257.
  • Obeysekera, J., Graham, W., Sukop, M. C., Asefa, T., Wang, D., Ghebremichael, K., et al. (2017). Implications of climate change on Florida's water resources. In E. P. Chassignet, J. W. Jones, V.
  • Di, T., Martinez, C. and Asefa T., 2016, Improving short-term urban water demand with reforecast analog, Journal of Water Resources Planning and Management.
  • Fullerton, T., Walke, A. and Asefa T., 2016, Short-run water demand forecast accuracy for the Tampa Bay area, Journal of American Water Works Association.
  • Asefa T., 2015. Innovative systems-based decision support: A tale for the real world, Journal of Water Resources Planning and Management. Invited editorial commentary, 141(9), 01815001
  • Asefa, T., A. Adams, and N. Wanakule, 2015, A level of service concept for planning future water supply projects under probabilistic demand and supply framework, Journal of American Water Resources Association, 51(5) pp: 1272-285, DOI: 10.1111/1752-1688.12309.
  • Asefa, T., A. Adams, and I. Kajtezovic-Blankenship, 2014, A tale of integrated regional water supply planning: Meshing socio-economic, policy, governance, and sustainability desires together, Journal of Hydrology, doi:10.1016/j.jhydrol.2014.05.047, vol. 519, Part C, pp. 2632-2641
  • Asefa, T., J. Clayton, A. Adams, D. Anderson 2014, Performance Evaluation of Water Supply Utilities under Varying Climatic Condition: Reliability, Resilience, Vulnerability, and Beyond, Journal of Hydrology, Cited the most downloaded article in the past 90 days-Journal of Hydrology January through-April 2014.

  • Asefa, T., N. Wanakule, A. Adams, J. Shelby and J. Clayton 2014, On the Use of system Performance Metrics for Assessing Incremental Water-use Permit, Journal of Water Resources Planning & Management.

  • Asefa, T. and A. Adams, 2013, Reducing bias corrected precipitation projections uncertainties: A Bayesian based indicator weighting approach, Journal of Regional Environmental Change 13:111-120 DOI 10.1007/s10113-013-0431-9

  • Syewoon, H., Asefa, T., and Seungwoo, C. 2013, Assessing the utility of rainfall forecast for weekly groundwater level forecast in Tampa Bay Region, Florida, Journal of the Korean society of Agricultural Engineers 55(6), pp. 1 - 9.

  • Syewoon, H., C. Martinez and Asefa, T. 2012, Assessing the benefits of incorporating rainfall forecasts into monthly flow forecast system of Tampa Bay Water, Florida, Journal of the Korean society of Agricultural Engineers 54(4), pp. 127 - 135.

  • Asefa T. 2009, Ensemble stream flow forecast: A GLUE-Based Neural Network Approach, Journal of American Water Resources Association (JAWRA) 45(5) j.1155-1163.
    (Selected as Journal cover page and highlight of the October 2009 issue)

  • Asefa T., Wanakule N. and A. Adams, 2007, Field-scale applicability of three types of Artificial Neural Networks to predict groundwater levels, Journal of American Water Resources Association (JAWRA) 43(5):1-12. DOI: 10.1111 ⁄ j.1752-1688.2007.00107.
    (Selected as Journal cover page highlight of the October 2007 issue)

  • Gill K., Asefa, T. and M. McKee, 2007, Effect of missing data on performances of Learning Algorithms: Implications to Imputation Techniques, 43, W07416, doi:10.1029/2006WR005298, Water Resources Research.

  • Asefa, T., Kemblowski, W.M., McKee, M. and A., Khalil, 2006, Multi-time Scale Stream Flow Prediction: The Support Vector Machines Approach, Journal of Hydrology. (318), 7-16.

  • Gill K., Asefa, T., Kemblowski, W.M. and M. McKee, 2006, Soil moisture prediction using Support Vector Machines, Journal of American Water resources Association, 42(4), 1033-1046.

  • Khalil A., M. McKee, M. Kemblowski, Asefa T., and L. Bastidas, 2006, Multi-objective analysis of chaotic dynamic systems with sparse learning machines, 29, 72-88, Advances in Water Resources.

  • Asefa, T., M. Kemblowski, U. Lall, and G. Urroz, 2005, Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series, Water Resources Research, 41, W12422, doi:10.1029/2004WR003785

  • Asefa T., Kemblowski M., G. Urroz and M. McKee, 2005, Support Vector Machines for ground water quality monitoring network design, 43(3), pp.423-422, Ground Water.

  • Khalil A., M McKee, MW Kemblowski and Asefa T., 2005, Basin-Scale Water Management and Forecasting using Artificial Neural Networks, Journal of American Water Resources Association 41(1):195-208.

  • Khalil, A., M. McKee, M. Kemblowski, and Asefa. T., 2005, Sparse Bayesian learning machines for real-time management of reservoir releases, Water Resources Research, 41, W11401, doi:10.1029/2004WR003891.

  • Asefa, T., M. W. Kemblowski, G. Urroz, M. McKee, and A. Khalil, 2004, Support vectors–based groundwater head observation networks design, Water Resources Research, 40, W11509, doi:10.1029/2004WR003304.

  • Batelaan, O., Van Campenhout, A., Asefa, T., De Smedt, F. and Triest, L., 2002, Groundwater Discharge and Recharges in the Land-use Planning Project of Grote-Nete, 1: Characterization by means of Hydrological Modeling, Vegetation Mapping and GIS (in Dutch), Water, May 2002, pp 1- 10. (Abstract in English).

  • Batelaan, O., Van Campenhout, A., Asefa, T. and De Smedt, F., 2002, Groundwater Discharge and Recharges in the Land-use Planning Project Grote-Nete, 2: Effect of Land-use Changes (in Dutch), Water, June 2002, pp 1-9. (Abstract in English).

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