Hydrobiogeochemical (HBGC) processes are often spatially discrete (hotspots) and temporally restricted (hot moments) due to variability in controlling factors like energy inputs, lithologic characteristics, vegetation, etc. Although this scale-dependence of HBGC processes accounts for a high percentage of nutrient cycling, the ability to identify and incorporate them into numerical simulation models remains a significant challenge. This session invites contributions that identify spatial and temporal patterns in biogeochemical variables, implement such variability in process-based models, and/or document the extent that hot spots/moments influence larger-scale system functioning. This session will focus on exchanging ideas on (1) new techniques (e.g., machine learning) for identifying HBGC hotspots and hot moments across scales, (2) experimental, sensing and modeling techniques that can map variable porosity, permeability, mineral coatings or geologic structures that affect solute transport, (3) incorporating this spatio-temporal variability in models, and (4) quantifying the significance of incorporating this emergent behavior in reactive transport models.