Addressing effective real-time flood forecasting for upstream artificial reservoirs through predictive uncertainty &#160;
Authors: Barbetta Silvia , Sahoo Bhabagrahi , Bonaccorsi Bianca , Moramarco Tommaso , Nanda Trushnamayee , Chatterjee Chandranath , Todini Ezio 
Published: March 2022

&lt;p&gt;The impact of flood events is usually approached through structural measures, such as riverbanks and dams able to mitigating, although not fully eliminating flooding risk. Therefore, complementary non-structural measures, mainly real-time Flood Forecasting and Warning Systems (FFWSs), usually combined with operational decision support systems, must be developed to improve the population safety and resilience. Flood forecasting models, essential components of FFWSs, provide deterministic forecasts of discharge or water levels at critical sections on forecast horizons to support the decision-makers activities. Unfortunately, under the uncertainty of future events, predictions must be probabilistic, to be effective and to guarantee the required robustness to the decision makers &lt;em&gt;(Todini, 2017). &lt;/em&gt;&lt;/p&gt;&lt;p&gt;Many studies are available in the literature on generating probabilistic forecasting starting from a deterministic forecast and considering the error distribution. Alternatively, the introduction of the Hydrological Uncertainty Processor (&lt;em&gt;Krzysztofowicz, 1999&lt;/em&gt;) has posed the basis for the estimation of the predictive uncertainty, PU, that is the probability of occurrence of a future value conditional on all the available information, usually provided by forecasting models.&lt;/p&gt;&lt;p&gt;In this context, for estimating the PU, Todini (&lt;em&gt;2008&lt;/em&gt;) proposed the Model Conditional Processor (MCP) which allows for the analytical treatment of the multivariate probability densities after converting both observations and model predictions into the Normal space. Afterwards, MCP was extended to the multi-model approach (&lt;em&gt;Barbetta et al., 2017&lt;/em&gt;) enabling a decision based on &amp;#8220;multiple forecasts&amp;#8221; of different deterministic models at the same time.&lt;/p&gt;&lt;p&gt;With the aim to shed light on the benefits of using PU, the multi-model MCP is applied to discharge forecasts at sites along Indian rivers. Specifically, a data-driven model, i.e. a novel Wavelet-based Non-linear AutoRegressive with eXogenous inputs (WNARX) model and the grid-based semi-distributed VIC hydrological model are used to this end. The future estimates of the river discharge coming into artificial reservoirs, provided by VIC and WNARX models (&lt;em&gt;Nanda et al., 2019&lt;/em&gt;) at the same time, are used to feed simultaneously the MCP; thus, showing the benefits in terms of improved effectiveness of the future prediction. The analysis is performed for the Hirahud dam along the Manhanadi River: the results indicate that the methodology could be able to provide effective probabilistic real-time inflow forecasting to be used during significant floods as an appropriate support for the artificial reservoir management.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Barbetta S.&lt;/em&gt;&lt;em&gt;, Coccia G., Moramarco T., Brocca L., and Todini E. (2017). &lt;/em&gt;&lt;em&gt;Improving the effectiveness of real-time flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach,&amp;#160;J. of Hydrol., 51, 555-576.&amp;#160;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Krzysztofowicz, R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model,&lt;/em&gt;&lt;em&gt; Water Resour. Res., 35, 2739&amp;#8211;2750.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Nanda, T., Sahoo, B., Chatterjee, C. (2019). &lt;/em&gt;&lt;em&gt;Enhancing real-time streamflow forecasts with wavelet-neural network-based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model. J. Hydrol., 575, pp. 890&amp;#8211;910. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Todini, E. A model conditional processor to assess predictive uncertainty in flood forecasting. Int. J. River&amp;#160;&lt;/em&gt;&lt;em&gt;Basin Manag. 2008, 6, 123&amp;#8211;137.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Todini E. Flood Forecasting and Decision Making in the new Millennium. Where are We?, Water Resour Manage. 2017, doi:10.1007/s11269-017-1693-7, pp.1-19.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&amp;#160;&lt;/em&gt;&lt;/p&gt;