Identification of Soil Moisture–Precipitation Feedback Based on Temporal Information Partitioning Networks

The underlying mechanisms by which soil moisture might affect precipitation are essential for weather prediction and climate change projection. Process-based models that capable of providing physical explanation often tripped in concerns about model uncertainty structure, data, parameters, etc. information theory has provided a new paradigm to explain the feedback system. This study focused on identify moisture–precipitation based direct long-term observations using an information-based method, Temporal Information Partitioning Networks (TIPNets). Normalized transfer entropy was proposed describe strength. In comparison with Granger causality correlation, TIPNets method independent parameterizations spatiotemporal resolution effects, also elucidate information-theoretic insights. results case Illinois, indicated existed significant during May mid-July. bidirectional dominant lags 3–10 6–17 days were both shorter summer. Areas strong located south-central Illinois. Besides, wooded grassland coverage most related land use type strength, could be one kind positive mechanism