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Biogeochemical processes are often spatially discrete (hot spots) and temporally isolated (hot moments) due to variability in controlling factors like hydrologic fluxes, lithological characteristics, bio-geomorphic features, and external forcing. Although these hot spots and hot moments (HSHMs) account for a high percentage of carbon, nitrogen and nutrient cycling within the Critical Zone, the ability to identify and incorporate them into reactive transport models remains a significant challenge. This chapter provides an overview of the hot spots hot moments (HSHMs) concepts, where past work has largely focused on carbon and nitrogen dynamics within riverine systems. This work is summarized in the context of process-based and data-driven modeling approaches, including a brief description of recent research that casts a wider net to incorporate Hg, Fe and other Critical Zone elements, and focuses on interdisciplinary approaches and concepts. The broader goal of this chapter is to provide an overview of the gaps in our current understanding of HSHMs, and the opportunities therein, while specifically focusing on the underlying parameters and processes leading to their prognostic and diagnostic representation in reactive transport models. 

It is important to understand how point measurements across spatially heterogeneous ecosystems are scaled to represent these systems. Stream biogeochemistry presents an illustrative example because water quality concerns within stream networks and recipient water bodies motivate heterogeneous watershed studies. Measurements of the stream water‐groundwater (SW‐GW) interface (i.e., the shallow stream subsurface) are well‐documented for point‐scale sampling density measurements (i.e., cm²–m²features), but poorly characterized for network‐scale sampling density measurements (i.e., km²; stream reaches and networks). Sampling the SW‐GW interface is more time and labor intensive than surface water sampling, meaning sample point selection must be made with care for network‐scale analyses. In this study, we endeavor to determine which of two common spatial sampling schemes is appropriate for characterizing SW‐GW interface biogeochemistry across a third‐order stream network, focusing on dissolved organic carbon. The first scheme, called Local Sampling, focuses on characterizing small‐scale (< 10 m²) variability produced by the local physical and biogeochemical heterogeneity, with fewer points across the stream network. The second scheme, called Longitudinal Sampling, has approximately the same number of measurements distributed over many more points across the stream network with less local variability characterization. This comparison reveals that selection of a Local Sampling versus a Longitudinal Sampling scheme influences the biogeochemical pattern interpretation at the stream network scale. Additionally, this study found that increasing observation efforts at the local scale added limited information for reach‐ to network‐scale biogeochemical patterns, suggesting that emphasis should be placed on characterizing variability across broader spatial scales with the Longitudinal Sampling approach.

 

Novel observation techniques (e.g., smart tracers) for characterizing coupled hydrological and biogeochemical processes are improving understanding of stream network transport and transformation dynamics. In turn, these observations are thought to enable increasingly sophisticated representations within transient storage models (TSMs). However, TSM parameter estimation is prone to issues with insensitivity and equifinality, which grow as parameters are added to model formulations. Currently, it is unclear whether (or not) observations from different tracers may lead to greater process inference and reduced parameter uncertainty in the context of TSM. Herein, we aim to unravel the role of in‐stream processes alongside metabolically active (MATS) and inactive storage zones (MITS) using variable TSM formulations. Models with one (1SZ) and two storage zones (2SZ) and with and without reactivity were applied to simulate conservative and smart tracer observations obtained experimentally for two reaches with differing morphologies. As we show, smart tracers are unsurprisingly superior to conservative tracers when it comes to partitioning MITS and MATS. However, when transient storage is lumped within a 1SZ formulation, little improvement in parameter uncertainty is gained by using a smart tracer, suggesting the addition of observations should scale with model complexity. Importantly, our work identifies several inconsistencies and open questions related to reconciling time scales of tracer observation with conceptual processes (parameters) estimated within TSM. Approaching TSM with multiple models and tracer observations may be key to gaining improved insight into transient storage simulation as well as advancing feedback loops between models and observations within hydrologic science.