Employing integrated nested laplace approximation with stochastic partial differential equation (INLA-SPDE) modeling for sub-seasonal flood prediction using a globally aggregated disaster database

Global flood reporting has generally improved over the last century, particularly as global disasterdatabases have started archiving and aggregating flood events. While these disaster databases,including EM-DAT, DFO, HANZE, and UNDRR, are helpful tools for flood analysis, they areoften incomplete in their reporting. Therefore, aggregating them to form a more complete databaseis critical. It is equally important when training a disaster-level flood event model using thesedatabases to account for systemic inequality in flood reporting. Here, we present a three-phaseINLA-SPDE flood prediction model which determines relevant climate signals from documentedflood events, determines which countries report floods the most consistently relative to the climatesignals, and projects global latent flood risk on a monthly 0.25-degree scale using the best reportingcountries for calibration. The result is a spatial risk field which ranks relative risk of disaster-levelflood events based on climate conditions one month in advance (sub-seasonal timescale).Predictions at the 0.25-degree level perform well at relative risk ranking (AUC = 81.7 %) butreturns many false positives due to the complexity and rarity of these events (Precision = 6.12 %).However, when aggregating predictions to the country level, this issue is minimized (Precision =49.0 %), meaning country level predictions may be used to determine monthly likelihood of a floodevent occurring, while cell level predictions may be used to determine high risk locations withinthe country. Combined, this modeling methodology allows for prediction of floods beyond thecapabilities of normal disaster database models by separating flood reporting biases from latentclimate signals as much as possible.