CON-SST-RAIN: Continuous Stochastic Space-Time Rainfall generation based on Markov chains and transposition of weather radar data

In this study, we present CON -SST -RAIN, a novel stochastic space-time rainfall generator specialized for model -based urban drainage design and planning. CON -SST -RAIN is based on Markov Chains for sequences of dry/rainy days and uses stochastic storm transposition (SST) to generate realistic rainfall fields from weather radar data. CON -SST -RAIN generates continuous areal rainfall time series at arbitrary lengths. We propose a method for updating the Markov Chains by each passing year to better incorporate low -frequency variation in inter -annual rainfall values. The performance of CON -SST -RAIN is tested against multi -year records from rain gauges at both point and catchment scales. We find that updating the Markov Chains has a significant impact on the inter -annual variation of rainfall, but has little effect on mean annual/seasonal precipitation and dry/wet spell lengths. CON -SST -RAIN shows good preservation of extreme rain rates (including sub -hourly values) compared to observed rain gauge data and the original SST framework.