Using Climate Factors to Estimate Flood Economic Loss Risk

Estimation of economic loss is essential for stakeholders to manage flood risk.Most flooding events are closely related to extreme precipitation,which is influenced by large-scale climate factors.Considering the lagged influence of climate factors,we developed a flood-risk assessment framework and used Hunan Province in China as an example to illustrate the risk assessment process.The main patterns of precipitation—as a connection between climate factors and flood economic losses—were extracted by the empirical orthogonal function(EOF)analysis.We identified the correlative climate factors through crosscorrelation analysis and established a multiple stepwise linear regression model to forecast future precipitation patterns.Risk assessment was done based on the main precipitation patterns.Because the economic dataset is limited,a Monte Carlo simulation was applied to simulate 1000-year flood loss events under each precipitation regime(rainy,dry,normal years)to obtain aggregate exceedance probability(AEP)and occurrence exceedance probability(OEP)curves.We found that precipitation has a strong influence on economic loss risk,with the highest risk in rainy years.Regional economic development imbalances are the potential reason for the varying economic loss risks in different regions of Hunan Province.As the climate indices with at least several months prediction lead time are strong indicators in predicting precipitation,the framework we developed can estimate economic loss risk several months in advance.

Evaluation of Early Action Mechanisms in Peru Regarding Preparedness for El Nino

In this article,we provide an impact evaluation of an intervention in Peru regarding preparedness for El Nino impacts in Picsi District of Chiclayo Province in Peru’s northwestern coastal Lambayeque region.This effort involved the provision of special kits that reduce the potential damage to homes as a consequence of rainfall and floods associated with an El Nino-Southern Oscillation event.Information was collected in 2016 when this Forecast-based Financing early action was activated by an El Nino forecast,and after a coastal El Nino actually struck in 2017.This dual database permits us to estimate the impact of the intervention on the damage level of homes by comparing those homes supported by the program with those homes not receiving pilot-program support.This comparison is achieved by using propensity score matching techniques,which identify the most comparable homes to the ones that were supported by the intervention.The main findings of the study suggest a positive impact of the program in terms of its effectiveness in mitigating the damage caused by the 2017 El Ni?o.These results suggest a drop in the scale of house damage(less damage)by around 63%for a home that received the modular kit treatment.When considering other specifications of the model,the decrease in the scale of house damage improves up to approximately 66%.