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Hydrologic controls on carbon processing and export are a critical feature of wetland ecosystems. Hydrologic response to climate variability has important implications for carbon‐climate feedbacks, aquatic metabolism, and water quality. Little is known about how hydrologic processes along the terrestrial‐aquatic interface in low‐relief, depressional wetland catchments influence carbon dynamics, particularly regarding soil‐derived dissolved organic matter (DOM) transport and transformation. To understand the role of different soil horizons as potential sources of DOM to wetland systems, we measured water‐soluble organic matter (WSOM) concentration and composition in soils collected from upland to wetland transects at four Delmarva Bay wetlands in the eastern United States. Spectral metrics indicated that WSOM in shallow organic horizons had increased aromaticity, higher molecular weight, and plant‐like signatures. In contrast, WSOM from deeper, mineral horizons had lower aromaticity, lower molecular weights, and microbial‐like signatures. Organic soil horizons had the highest concentrations of WSOM, and WSOM decreased with increasing soil depth. WSOM concentrations also decreased from the upland to the wetland, suggesting that continuous soil saturation reduces WSOM concentrations. Despite wetland soils having lower WSOM, these horizons are thicker and continuously hydrologically connected to wetland surface and groundwater, leading to wetland soils representing the largest potential source of soil‐derived DOM to the Delmarva Bay wetland system. Knowledge of which soil horizons are most biogeochemically significant for DOM transport in wetland ecosystems will become increasingly important as climate change is expected to alter hydrologic regimes of wetland soils and their resulting carbon contributions from the landscape.

Wetlands provide valuable hydrological, ecological, and biogeochemical functions, both alone and in combination with other elements comprising the wetlandscape. Understanding the processes and mechanisms that drive wetlandscape functions, as well as their sensitivity to natural and man‐made alterations, requires a sound physical understanding of wetland hydrodynamics. Here, we develop and apply a single reservoir hydrologic model to a low‐relief karst wetlandscape in southwest Florida (≈10³ km²of Big Cypress National Preserve) using precipitationPand potential evapotranspirationPETas climatic drivers. This simple approach captures the dynamics of storage for individual wetlands across the entire wetlandscape and accurately predicts landscape discharge. Key model insights are the importance of depth‐dependent extinction of evapotranspirationETand the negligible effects of depth‐dependent specific yield, the effects of which are diluted by landscape relief. We identify three phases of the wetlandscape hydrological regime: dry, wet‐stagnant, and wet‐flowing. The model allowed a simple steady‐state analysis, which demonstrated the sudden seasonal shift between wet‐stagnant and wet‐flowing states, indicating a consistent threshold atP ≈ PET. Notably, stage data from any single wetland appears sufficient for accurate whole‐landscape discharge prediction because of the relative homogeneity in timing and duration of local wetland hydrologic connectivity in this landscape. We also show that this method will be transferable to other wetlandscapes, where individual storage elements respond hydrologically synchronously, whereas model performance is expected to deteriorate for hydrologically more heterogeneous wetlandscapes.

Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.