Flood insurance is an important financial measure for flood risk management. However, a significant protection gap in flood insurance exists in many countries due to high flood insurance costs. Reducing flood insurance costs for both policyholders and insurance companies is crucial for implementing flood insurance. In this study, we derive fundamental mathematical principles for reducing overall insurance costs, including premiums and risk reserves, by introducing the geographic complementarity of flood risk based on portfolio theory. We also propose a reasonable premium allocation framework among multiple individual policyholders using cooperative game theory (CGT), which can benefit all policyholders and achieve stable allocation. The proposed approach is illustrated using China as a case study. The results show that there are low correlation coefficients of flood losses across different provinces and river basins in China, showing high geographic risk complementarity. Compared to independent insurance in each province, national flood insurance can reduce total premiums by 14.8% and total risk reserves by 60.8%. The Yangtze River Basin shows the greatest premium reduction by pooling its internal flood risk, compared with independent insurance by provinces. The Yellow and Huai River Basin shows the greatest premium reduction when pooling national flood risk, compared with pooling risk within individual river basins. In summary, geographic complementarity in flood risk has significant effects on reducing flood insurance costs, implying that China can employ this characteristic to implement a national flood insurance program. Furthermore, the proposed approach can also offer new insights for enhancing catastrophe insurance affordability in more countries.

Electricity production by hydropower is negatively affected by drought. To understand, monitor and manage risks of less than normal streamflow for hydroelectricity production (HP) at the global scale, we developed an HP model that simulates time series of monthly HP worldwide and thus enables analyzing and monitoring the impact of drought on HP. The HP model is based on a new global hydropower database (GHD), containing 8748 geo-localized plant records, and on monthly streamflow values computed by the global hydrological model WaterGAP. The GHD includes 43 attributes and covers 91.8% of the globally installed capacity. The HP model can capture the interannual variability of country-scale HP that was caused by both (de)commissioning of hydropower plants and streamflow variability. It can also simulate the streamflow drought and its impact on HP reasonably well. A drought risk analysis for period 1975−2016 revealed the reduction of HP that is exceeded in 1 out of 10 years. 71 out of 134 countries with hydropower suffer from a reduction of more than 20% of average HP, and 20 countries from a reduction of more than 40%. We suggest four indices for monitoring the drought impact on HP in grid cells and on total electricity production in countries. These indices quantify the impact in terms of either relative reduction or anomaly. Applying the developed HP model, these indices can be included in global drought monitoring systems and inform stakeholders such as hydropower producer and national energy agencies about the reduced energy production due to streamflow drought.