Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

International audienceThis article investigates the improvement of the operation of a four-reservoir system in the Seine River basin, France, by use of deterministic and ensemble weather forecasts and real-time control. In the current management, each reservoir is operated independently from the others and following prescribed rule-curves, designed to reduce floods and sustain low flows under the historical hydrological conditions. However, this management system is inefficient when inflows are significantly different from their seasonal average and may become even more inadequate to cope with the predicted increase in extreme events induced by climate change. In this work, a centralized real-time control system is developed to improve reservoirs operation by exploiting numerical weather forecasts that are becoming increasingly available. The proposed management system implements a well-established optimization technique, model predictive control (MPC), and its recently modified version that can incorporate uncertainties, tree-based model predictive control (TB-MPC), to account for deterministic and ensemble forecasts respectively. The management system is assessed by simulation over historical events and compared to the no-forecasts strategy based on rule-curves. Simulation results show that the proposed real-time control system largely outperforms the no-forecasts management strategy, and that explicitly considering forecast uncertainty through ensembles can compensate for the loss in performance due to forecast inaccuracy

Multi-timescale hydro-meteorological forecasts for the optimal control of the multipurpose Lake Como

Hydrometeorological forecast products have seen an increase in their availability, accuracy and reliability, and they are expected to be an essential tool to support adaptive and robust control of water systems under changing hydroclimatic conditions. In this study, we investigate how the most valuable information (lead time and forecasted variable) from multi-scale forecasts can be selected and used to inform the optimal control of multipurpose water reservoirs. The framework is composed of Input Variable Selection algorithms, supporting the extraction of the most informative policy inputs, from a set of different hydrological forecasts ranging from short to long term, coupled with the Evolutionary Multi-Objective Direct Policy Search method, for designing Pareto optimal control policies conditioned on forecast information. This approach is tested on the Lake Como system, a regulated lake in Northern Italy which is controlled for preventing floods along the lake shores, as well as for providing irrigation supply to downstream users and avoiding low lake levels. We expect to extract the best subset or combinations of lead times from a suite of forecast products, including short-term local deterministic forecasts as well as sub-seasonal and seasonal large-scale ensemble forecasts provided by the European Flood Awareness System (EFAS), part of the Copernicus Emergency Management Service. The candidate variables proposed as inputs for the IVS include different statistics extracted from these forecasts, over a variety of temporal scales and spatial domains. The performance of the designed forecast-informed control policies is contrasted against various benchmarks, including controllers relying on perfect forecasts as well as baseline control policies not informed by any forecast. Beside improving the controller performance, results are expected to provide insights on how to address the intrinsic bias of forecast products and to highlight the role of forecast uncertainty in optimal control design

Advancing the operation of multipurpose water reservoirs with multi-timescale forecasts: Application to Lake Como

Water management can benefit from recent advances in hydroclimatic forecasting, like the growing accuracy of forecasts over longer lead times. However, it is critical to understand how to use the wealth of information available over multiple timescales in the most effective way. For water system control, hydrological forecasts can be used with two approaches, either an on-line one, which acquires the forecasts in real-time in an optimization routine at each control time step, or a data-driven off-line one, learning a policy that includes forecasts as inputs based on historical reforecasts. In this study, we compare the performance of an on-line and off-line control scheme to understand how multi-timescale forecasts can be best used to inform the optimal operation of multipurpose water reservoirs. The on-line control scheme is a new nested multi-stage stochastic Model Predictive Control (MPC) framework integrating the use of forecasts over multiple timescales. The MPC framework can use multiple forecasts proactively and satisfy competing objectives but is affected by the forecast bias and optimization challenges arising with longer control horizons. Off-line control schemes like Direct Policy Search (DPS) can help overcome these challenges. The off-line framework combines an Input Variable Selection algorithm, for extracting the most informative policy inputs from a set of forecast variables over different lead times, with the Evolutionary Multi-Objective DPS method, for designing Pareto-optimal control policies conditioned on forecast information. We test the performance of these two control schemes for the Lake Como system in Northern Italy, where a large lake is regulated mainly for irrigation supply and flood control. The forecasts used include deterministic and ensemble hydrological forecasts over multiple timescales from short-term (60 h) to sub-seasonal and seasonal lead times (7 months). The performance of the two forecast-informed operating policies is compared with different benchmarks, including the historical management with no forecasts and a perfect operating policy. Beside improving the policy performance with respect to single timescale forecasts, results provide insights on the role of the forecast bias and uncertainty at different timescales in reservoir policy design

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

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 or 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 El Niño-Southern Oscillation (ENSO) phases when the sea surface temperature (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

Assessment of global reanalysis precipitation for hydrological modelling in data–scarce regions: a case study of Kenya

Study region 19 flood prone catchments in Kenya, Eastern Africa Study focus Flooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the region While ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya