Exploring Tracer Information and Model Framework Trade‐Offs to Improve Estimation of Stream Transient Storage Processes

TLDR

Novel smart tracer observation techniques are improving the characterization of stream transport and transformation, enabling more sophisticated transient storage models, but parameter estimation in these models remains sensitive to equifinality as complexity increases. The study seeks to determine whether different tracer observations can enhance process inference and reduce parameter uncertainty in transient storage modeling. Using one- and two-storage-zone transient storage models, with and without reactivity, the authors simulated conservative and smart tracer data from two reaches of differing morphology. Smart tracers outperform conservative tracers in partitioning metabolically active and inactive storage, yet when storage is lumped into a single zone, added tracer data yields little uncertainty reduction, highlighting the need for observation scaling with model complexity and revealing inconsistencies in reconciling tracer time scales with model parameters.

Abstract

Abstract 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.