Climate variability alters flood timing across Africa

Modes of climate variability are known to influence rainy season onset, but there is less understanding of how they impact flood timing. We use streamflow reanalysis and gauged observation datasets to examine the influence of the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) across sub-Saharan Africa. We find significant changes in flood timing between positive and negative phases of both IOD and ENSO; in some cases the difference in the timing of annual flood events is more than 3 months. Sensitivity to one or other mode of variability differs regionally. Changes in flood timing are larger than variability in rainy season onset reported in the literature, highlighting the need to understand how the hydrological system alters climate variability signals seen in rainy season onset, length and rainfall totals. Our insights into flood timing could support communities who rely on flood-based farming systems to adapt to climate variability

The utility of impact data in flood forecast verification for anticipatory actions: case studies from Uganda and Kenya

Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data-scarce regions are limited to only a few sparse locations at pre-existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river-gauge observations in Uganda and Kenya. The flood impact data contains semi-quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river-gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data-scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning

Integrated real-time control of water reservoirs with deterministic and probabilistic multi-timescale forecasts: Application to the Lake Como

Hydro-meteorological forecasts are more and more easily available with improving skill over longer timescales and with higher spatiotemporal resolutions. Their uncertainties are commonly represented by ensemble prediction systems which are now dominant at the global and continental scale, while short-term deterministic forecasts are still used, especially in some local contexts. For effective water resources management, it is critical to understand how to use the wealth of information provided by different forecast systems over multiple timescales to help meet the water demand of key competing socio-economic sectors, while reducing short-term impacts and bringing the controlled systems to desirable states in the long term. Real-time control schemes of water reservoirs like Model Predictive Control (MPC) can help meet these goals, by providing a flexible framework to use forecasts proactively and satisfy multiple competing objectives while respecting operational constraints. In this study, we propose a new nested multi-stage stochastic MPC framework integrating the use of both deterministic and ensemble hydrological forecasts over multiple timescales from short-term (60 hours) to seasonal (7 months ahead). We demonstrate the performance of this real-time controller for the Lake Como system, located in the Italian Alps, where a large lake is regulated mainly for irrigation supply and flood control. First, seasonal ensemble forecasts are used to solve a Tree-Based MPC (TB-MPC) problem optimising the reservoir management over several months, by adopting a tree structure to summarise the ensemble information including the resolution of uncertainty in time. Second, the decisions identified so far are used to condition daily operations over a month using sub-seasonal probabilistic forecasts (up to 46 days) under the same TB-MPC approach. Third, the decisions for the first few days are then further adapted to optimize operations three days ahead using deterministic short-term forecasts with MPC. The sub-seasonal and seasonal ensemble (re-)forecasts used are those produced by the European Flood Awareness System (EFAS) from the Copernicus Emergency Management Service which uses ensemble meteorological forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). While EFAS is uncalibrated for the study area, we apply bias-correction techniques to improve the agreement of forecasts with local observations and allow their use for resolving the water-balance within MPC. The short-term 60-h forecasts come from a locally calibrated hydrological model (TOPKAPI) using deterministic weather forecasts from the COSMO (Consortium for Small Scale Modelling) model. The skill of all these forecasts is assessed, as well as the ensemble spread–error relationship for EFAS at different lead times. To evaluate the value of the forecasts we compare the performance of the real-time MPC controller with different benchmarks including perfect forecasts, climatology, and persistence. Finally, we investigate the link between forecast skill and value for reservoir operation, and we compare the performance of the nested MPC framework integrating multi-timescale forecasts with the MPC using single forecasts

Impact-based Flood early warning for rural livelihoods in Uganda

Anticipatory actions are increasingly being taken before an extreme flood event to reduce the impacts on lives and livelihoods. Local contextualised information is required to support real-time local decisions on where and when to act and what anticipatory actions to take. This study defines an impact-based early warning trigger system that integrates flood forecasts with livelihood information, such as crop calendars, to target anticipatory actions better. We demonstrate the application of this trigger system using a flood case study from the Katakwi District in Uganda. First, we integrate information on the local crop cycles with the flood forecasts to define the impact-based trigger system. Second, we verify the impact-based system using historical flood impact information and then compare it with the existing hazard-based system in the context of humanitarian decisions. Study findings show that the impact-based trigger system has an improved probability of flood detection compared to the hazard-based system. The number of missed events are fewer in the impact-based system while the trigger dates are similar in both systems. In a humanitarian context, the two systems trigger anticipatory actions at the same time. However, the impact-based trigger system can be further investigated in a different context (e.g., for livelihood protection) to assess the value of the local information. The impact-based system could also provide a valuable tool to validate the existing hazard-based system, which builds more confidence in its use in informing anticipatory actions. The study findings should therefore open avenues for further dialogue on what the impact-based trigger system could mean within the broader Forecast-based Action landscape towards building the resilience of at-risk communities

Interprétation physique de la mémoire des rivières et application à la prévision saisonnière des crues

International audienceThe various geophysical and hydrological processes involved in the river flow generating process exhibit persistence at several distinct timescales which propagates into the river flow behavior and manifests itself as river memory. We investigate the latter at two seasonal periods of interest, i.e. 3-month High Flow Seasons (HFS) and 1-month Dry Months (DM), by exploiting a dataset of 224 European rivers spanning more than 50 years of daily flow data. We compute the lagged seasonal correlation for the peak flows in the HFS and the average flows in the DM both against the average flows in the antecedent months. We link correlation magnitude to various geophysical catchment characteristics e.g. basin size, presence of lakes, glaciers etc., as well as rainfall-related properties such as seasonality. To exploit the river memory in flood forecasting, we fit a bivariate Meta-Gaussian probability distribution model between peak HFS flow and average pre-HFS flow in order to condition the peak flow distribution in the HFS upon observance of a higher-than-usual (e.g. 95th quantile) flow in the pre-HFS month. The benefit of the suggested methodology is demonstrated by updating a season in advance the flood frequency distribution in real-world applications. Our findings suggest that there is a traceable physical basis for river memory which in turn can be statistically assimilated into flood frequency estimation to improve predictions for technical purposes

Interprétation physique de la mémoire des rivières et application à la prévision saisonnière des crues

International audienceThe various geophysical and hydrological processes involved in the river flow generating process exhibit persistence at several distinct timescales which propagates into the river flow behavior and manifests itself as river memory. We investigate the latter at two seasonal periods of interest, i.e. 3-month High Flow Seasons (HFS) and 1-month Dry Months (DM), by exploiting a dataset of 224 European rivers spanning more than 50 years of daily flow data. We compute the lagged seasonal correlation for the peak flows in the HFS and the average flows in the DM both against the average flows in the antecedent months. We link correlation magnitude to various geophysical catchment characteristics e.g. basin size, presence of lakes, glaciers etc., as well as rainfall-related properties such as seasonality. To exploit the river memory in flood forecasting, we fit a bivariate Meta-Gaussian probability distribution model between peak HFS flow and average pre-HFS flow in order to condition the peak flow distribution in the HFS upon observance of a higher-than-usual (e.g. 95th quantile) flow in the pre-HFS month. The benefit of the suggested methodology is demonstrated by updating a season in advance the flood frequency distribution in real-world applications. Our findings suggest that there is a traceable physical basis for river memory which in turn can be statistically assimilated into flood frequency estimation to improve predictions for technical purposes

Model Predictive Control of water resources systems: A review and research agenda

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments.Transport Engineering and LogisticsDelft Center for Systems and Contro