Data-driven modeling and control of droughts

In highly regulated water systems droughts are complex, basin-specific phenomena. The identification of drought drivers is challenged by the coexistence of possibly relevant processes with inconsistent dynamics and origins (natural or anthropic). FRIDA is a fully automated data-driven approach developed to extract relevant drought drivers from a pool of candidate hydro-meteorological predictors at different time aggregations. Selected predictors are then combined into a basin-specific drought index to monitor the state of water resources in highly regulated contexts. The operational value of this index in improving water systems operations is quantified by designing a control policy informed by the index, and contrasting its performance with that of a baseline policy conditioned on basic information only. The approach is demonstrated on Lake Como, Italy, a multipurpose regulated lake operated for flood control and irrigation supply. Results show that the designed index is accurate in representing basin drought conditions, and the overall system performance can improve by nearly 20% when operations are informed with the basin-tailored drought index. The proposed framework is portable across different contexts, where basin-specific drought indexes can support drought characterization and control in a fully data-driven fashion.

Duke Scholars

Author Marta Zaniolo Civil and Environmental Engineering