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| Spotlight


hydrologic disasters difficult to predict (and hence to prepare for with precision) beyond certain lead times. This is true even with the best of prediction tools, although advances in process models and data-driven methods can enable the forecaster to better approach the theoretical bounds to predictability. While butterfly effects were supposed to be less dominant at climate scales (to the point that climate model simulations did not even run too many initial condition simulations previously), we now know that natural variability (or extreme sensitivity to initial conditions) is a characteristic of global climate and earth system models as well. This realization has exacerbated the ensemble multiplier effect in earth system models, where in addition to emissions pathways and multiple models, ensembles need to be generated for initialisations as well. Furthermore, we are beginning to characterise


the extent to which climate change may increase variability in weather or hydrologic stresses and extremes. While variability and predictability are not identical concepts, increasing variability in climate and weather may lead to lack of predictability in projections.


A combination of grey swans (or the probabilistic viability of relatively rare or even unprecedented extremes) and butterfly effects (or the theoretical bounds to predictability and need for initial-condition ensembles) have serious implications for dam and hydropower constructors, owners, managers, operators, and stakeholders. The construction of dams and hydraulic infrastructures needs to consider both the changes in the intensity (severity) or depth and duration and frequency (IDF or DDF) curves of precipitation and flooding (or drought) extremes, as well as the natural variability and predictability of hydrometeorological systems and extremes through the design life of infrastructures.


Operations and management of dams need to account for the drought-and-deluge-cycles, especially the possibility of unprecedented (yet viable owing to changes in the underlying causal drivers) extremes, and the loss of predictability manifested in different ways from lead times of a few hours to a few weeks, as well as from interannual to decadal scales. Hydropower managers need to be ready to handle changes in extremes, whether heavy precipitation and flash droughts, and longer-term changes in the statistics of precipitation including heavy rainfall and drought events, as well as changes in the patterns of water-related stresses, whether caused by changes in the demand and supply of water or by changes in water temperature. The amplification of threats and risks caused by


grey swans and the limits to predictability imposed by butterfly effects needs to be addressed by a suite of tools and strategies. First, there is a need to develop better tools for


catastrophe modeling which accounts for grey swans and butterfly effects based on physics-grounded extreme value statistics, nonlinear dynamical principles for large model ensembles, and effective blending of process understanding with data-driven methods including artificial intelligence and machine learning. Second, performance-based engineering, which includes design and operations, must be developed to make infrastructures resilient, in a way that optimises


risk-informed engineering with cost or frugality constraints. Besides explicit controls for costs, flexible planning, operations, and design, including grey- green solutions can help, especially for longer-term adaptation to changing design and operations curves. Third, advances in risk modeling and resilient design


needs to be developed in tandem with economic or financial instruments, as well as governance and policy tools and strategies, for holistic management and preparedness. Interested readers may want to look at [1-12] for further reading.


Acknowledgments


Funding was provided by NASA ROSES and Water Resources programmes for the Category 1 project “Remote-sensing data driven Artificial Intelligence for precipitation-Nowcasting (RAIN).” The author thanks the project contributors and partners include Puja Das and August Posch from NU, Nathan Barber, and Michael Hicks from the Tennessee Valley Authority (TVA), Thomas Vandal and Kate Duffy from the NASA SBIR funded startup Zeus AI (both NU PhD alums), Debjani Singh of the US DOE’s Oak Ridge National Laboratory (ORNL), and Katie van Werhoven of the RTI. The climate downscaling and variability work benefited from the work by former SDS Lab PhD students Kate Duffy, Thomas Vandal, Devashish Kumar, Udit Bhatia and Evan Kodra, among others, while the hydropower and dam construction insights benefited from prior work by PhD alumni Udit Bhatia, Nishant Yadav, and Evan Kodra, and by postdoctoral alumna Poulomi Ganguli.


References


[1] Horsburgh, K., Haigh, I.D., Williams, J., De Dominicis, M., Wolf, J., Inayatillah, A. and Byrne, D., 2021. “Grey swan” storm surges pose a greater coastal flood hazard than climate change. Ocean Dynamics, 71(6-7), pp.715-730.


[2] Lin, N. and Emanuel, K., 2016. Grey swan tropical cyclones. Nature Climate Change, 6(1), pp.106-111.


[3] Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M.I. and Wang, J., 2023. Skilful nowcasting of extreme precipitation with NowcastNet. Nature, 619(7970), pp.526-532.


[4] Deser, C., Lehner, F., Rodgers, K.B., Ault, T., Delworth, T.L., DiNezio, P.N., Fiore, A., Frankignoul, C., Fyfe, J.C., Horton, D.E. and Kay, J.E., 2020. Insights from Earth system model initial-condition large ensembles and future prospects. Nature Climate Change, 10(4), pp.277-286.


[5] Kumar, D. and Ganguly, A.R., 2018. Intercomparison of model response and internal variability across climate model ensembles. Climate dynamics, 51(1-2), pp.207-219.


[6] Krishnamurti, T.N., Kishtawal, C.M., LaRow, T.E., Bachiochi, D.R., Zhang, Z., Williford, C.E., Gadgil, S. and Surendran, S., 1999. Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285(5433), pp.1548-1550.


[7] Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R. and Ganguly, A.R., 2017, August. Deepsd: Generating high resolution climate change projections through single image super- resolution. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining (pp. 1663-1672).


[8] Liu, Y., Ganguly, A.R. and Dy, J., 2020, August. Climate downscaling using YNet: A deep convolutional network with skip connections and fusion. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3145-3153).


[9] Aerts, J.C., Botzen, W.W., Emanuel, K., Lin, N., De Moel, H. and Michel-Kerjan, E.O., 2014. Evaluating flood resilience strategies for coastal megacities. Science, 344(6183), pp.473-475.


[10] Ganguly, A.R., Bhatia, U. and Flynn, S.E., 2018. Critical infrastructures resilience: Policy and engineering principles. Routledge. 154 pages.


[11] Northeastern Global News (September 12, 2023, by Tanner Stening): Flooding in Libya a ‘gray swan’ event, but dam infrastructure worldwide ‘not ready’ to meet the demands of a changing climate, expert says. Available from: https://news.northeastern.edu/2023/09/12/libya-flooding/.


[12] Northeastern Global News (December 4, 2023, by Cynthia Hibbert): The short-term rain forecast system is broken. Can AI do a better job of predicting deadly floods? Available from: https://news.northeastern.edu/2023/12/04/flood-prediction-artificial-intelligence/.


www.waterpowermagazine.com | April 2024 | 11


Above: The concept of swans has been introduced into in the assessment of risks and resilience. Grey swans are described as being predictive surprises


Below: Dam failure in Norway 2023. Flexible, science-based, and data-driven planning can help manage disaster risks and ensure progress


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