Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world's population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

Abstract. Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydrometeorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a) weather timescale (0-6 days) and (b) seasonal timescale conditions (up to 6 months) before the event. The 7-day precipitation preceding a flood event (PRE7) and the standardized precipitation evapotranspiration index (SPEI) are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet) values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.

Flood early warning systems play a more substantial role in risk mitigation than ever before. Hydrological forecasts, which are an essential part of these systems, are used to trigger action against floods around the world. This research presents an evaluation framework, where the skills of the Global Flood Awareness System (GloFAS) are assessed in Peru for the years 2009-2015. Simulated GloFAS discharges are compared against observed ones for 10 river gauges. Forecasts skills are assessed from two perspectives: (i) by calculating verification scores at every river section against simulated discharges and (ii) by comparing the flood signals against reported events. On average, river sections with higher discharges and larger upstream areas perform better. Raw forecasts provide correct flood signals for 82% of the reported floods, but exhibit low verification scores. Post-processing of raw forecasts improves most verification scores, but reduces the percentage of the correctly forecasted reported events to 65%.

Given the large investments in dike construction and reinforcements, optimisation of these interventions could contribute to significant cost savings. In this study, a probabilistic method is developed for obtaining the geometry of a river dike cross‐section that fulfils a specified target reliability at a minimal cost. The method takes into account multiple relevant failure mechanisms and various geometric parameters of the dike. Large numbers of cross‐sections are generated and the reliability for each section is computed using FORM, Monte Carlo sampling, and numerical integration. The method is applied to several cases representing typical river dike profiles that are found in the Netherlands. Results show that for the optimal geometry the largest fraction of the target failure probability is assigned to those mechanisms with a relatively high cost of reinforcement. Also, comparison with the current semi‐probabilistic design guidelines shows that a decrease in costs of up to 15% could be reached with the new optimisation method.