Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control

The Seine River, in France, flows through territories of large economic value, among which the metropolitan area of Paris. A system of four reservoirs operates upstream to regulate the river flows in order to protect the area against extreme events, such as floods and droughts. Current reservoirs management is based on reactive filling curves, designed from an analysis of historical hydrological regimes. The efficiency of this management strategy is jeopardized when inflows are significantly different from their seasonal average. To improve the current management strategy, we investigated the use of Tree-Based Model Predictive Control (TB-MPC). TB-MPC is a proactive and centralized method that uses information available in real-time, as ensemble weather forecasts. Reservoir management is tested under past hydro-climatic conditions using time series of ensemble weather forecasts produced by ECMWF (European Centre for Medium-Range Weather Forecasts) and weather observations. The performance of TB-MPC is compared to that of deterministic Model Predictive Control (MPC), showing the benefits of considering forecasts uncertainty by using ensemble forecasts

Beyond El Niño: unsung climate modes drive African floods

The El Niño Southern Oscillation (ENSO) dominates the conversation about predictability of climate extremes and early warning and preparedness for floods and droughts, but in Africa other modes of climate variability are also known to influence rainfall anomalies. In this study, we compare the role of ENSO in driving flood hazard over sub-Saharan Africa with modes of climate variability in the Indian and Atlantic Oceans. This is achieved by applying flood frequency approaches to a hydrological reanalysis dataset and streamflow observations for different phases of the ENSO, Indian Ocean Dipole and Tropical South Atlantic climate modes. Our results highlight that Indian and Atlantic Ocean modes of climate variability are equally as important as ENSO for driving changes in the frequency of impactful floods across Africa. We propose that in many parts of Africa a larger consideration of these unsung climate modes could provide improved seasonal predictions of associated flood hazard and better inform adaptation to the changing climate

Hydrological model pre-selection with a filter sequence for the national flood forecasting system in Kenya

The choice of model for operational flood forecasting is not simple because of different process representations, data scarcity issues and propagation of errors and uncertainty down the modelling chain. An objective decision needs to be made for the choice of the modelling tools. However, this decision is complex because all parts of the process have inherent uncertainty. This paper provides a model selection with a filter sequence for flood forecasting applications in data scarce regions, using Kenya as an example building on the existing literature, concentrating on six aspects: (i) process representation, (ii) model applicability to different climatic and physiographic settings, (iii) data requirements and model resolution, (iv) ability to be downscaled to smaller scales, (v) availability of model code, and (vi) possibility of adoption of the model into an operation flood forecasting system. In addition, we review potential models based on the proposed criteria and apply a decision tree as a filter sequence to provide insights on the possibility of model applicability. We summarise and tabulate an evaluation of the reviewed models based on the proposed criteria and propose the potential model candidates for flood applications in Kenya. This evaluation serves as an objective model pre-selection criterion to propose a modelling tool that can be adopted in development and operational flood forecasting to the end-users of an early warning system that can help mitigate the effects of floods in data scarce regions such as Keny

A decision-led evaluation approach for flood forecasting system developments: An application to the Global Flood Awareness System in Bangladesh

Scientific and technical changes to flood forecasting models are implemented to improve forecasts. However, responses to such changes are complex, particularly in global models, and evaluation of improvements remains focussed on generalised skill assessments and not on the most relevant outcomes for those taking decisions. Recently, the Global Flood Awareness System (GloFAS) flood forecasting model has been upgraded from version 2.1 to 3.1 with a significant change to its hydrological model structure. In the updated version 3.1, a single fully configured hydrological model (LISFLOOD) has been adopted, including ground water and river routing processes, instead of two coupled models, a land surface and a simplified hydrological model, of the previous version 2.1. This study aims to evaluate changes in the simulated behaviour of floods and the forecast skill of the two GloFAS versions based on different decision criteria for early action. We evaluate GloFAS reforecasts for the Brahmaputra and the Ganges Rivers in Bangladesh for the period 1999– 2018. For the Brahmaputra River, the old GloFAS 2.1 version performs better than the 3.1 version, especially in predicting low-(90th percentile) and medium-level (95th percentile) floods. For the Ganges, GloFAS 3.1 shows improved probability of detection of low to medium-level floods compared to version 2.1, especially for lead times longer than 10 days. Both versions show limited skill for more extreme floods (99th percentile) but results are less robust for these less frequent floods given the lower number of events. Using lead-time dependent thresholds improves the false alarm ratio while reducing the probability of detection. The changes in model structures influence the model performance in a complex and varied way and forecast skill needs further investigation across regions and decision-making criteria. Understanding the skill changes between different model versions is important for decision-makers, however, focused case studies such as this should also be used by model developers to guide future changes to the system to ensure that they lead to improvements in decision-making ability

Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin

Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive/negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979-2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative ENSO phases when the SST anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing however does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods