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Identifying the origins of storm fluvial particulate organic carbon (POC) provides information about the hydrological connectivity within the river corridor and the roles of the land-stream interface in the carbon cycle. However, current understanding of storm-induced POC source dynamics is constrained by observations limited in space and time. This study presents a unique approach integrating higher spatial and temporal resolution sampling with a multi-biomarker analysis to better understand POC source dynamics across scales. Storm POC samples were collected at ~2 h intervals at three locations along the flow trajectory of an agricultural stream during six storm events with varied storm characteristics and seasonality, and characterized for their concentrations, C and N contents, stable C isotopes, and biomarker contents. Our results showed a source transition from in-stream algal production during early storm stages to surface soils with vascular plant signatures during peak precipitation and discharge across events and stations. Biomarkers further resolved the terrestrial signature into one likely from bank vegetation and another from row crop soils. This additional separation appeared conditionally, with the magnitude and sequence influenced by environmental factors such as storm trajectory, antecedent conditions, and management/vegetation cover. Source transitions were less distinctive in the lower reaches due to the greater integration of inputs, although one storm with localized precipitation showed the opposite pattern. Both scenarios align with the expected lower hydrological connectivity downstream. With the employed approach, the evolution of the storm pulse POC as it responds to river corridor processes could be visualized both temporally and spatially.
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Emulation of the Saint Venant Equations Enables Rapid and Accurate Predictions of Infiltration and Overland Flow Velocity on Spatially Heterogeneous Surfaces
Abstract The interleaving of impermeable and permeable surfaces along a runoff flow path controls the hillslope hydrograph, the spatial pattern of infiltration, and the distribution of flow velocities in landscapes dominated by overland flow. Predictions of the relationship between the pattern of (im)permeable surfaces and hydrological outcomes tend to fall into two categories: (i) generalized metrics of landscape pattern, often referred to as connectivity metrics, and (ii) direct simulation of specific hillslopes. Unfortunately, the success of using connectivity metrics for prediction is mixed, while direct simulation approaches are computationally expensive and hard to generalize. Here we present a new approach for prediction based on emulation of a coupled Saint Venant equation‐Richards equation model with random forest regression. The emulation model predicts infiltration and peak flow velocities for every location on a hillslope with an arbitrary spatial pattern of impermeable and permeable surfaces but fixed soil, slope, and storm properties. It provides excellent fidelity to the physically based model predictions and is generalizable to novel spatial patterns. The spatial pattern features that explain most of the hydrological variability are not stable across different soils, slopes, and storms, potentially explaining some of the difficulties associated with direct use of spatial metrics for predicting landscape function. Although the current emulator relies on strong assumptions, including smooth topography, binary permeability fields, and only a small collection of soils, slope, and storm scenarios, it offers a promising way forward for applications in dryland and urban settings and in supporting the development of potential connectivity indices.
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- Award ID(s):
- 1632494
- PAR ID:
- 10455522
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 55
- Issue:
- 8
- ISSN:
- 0043-1397
- Page Range / eLocation ID:
- p. 7108-7129
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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