Modelling the effects of multiple stressors on respiration and microbial biomass in the hyporheic zone using decision trees

Mori, Nataša; Debeljak, Barbara; Škerjanec, Mateja; Simčić, Tatjana; Kanduč, Tjaša; Brancelj, Anton

doi:10.1016/j.watres.2018.10.093

Cited by 24 publications

(6 citation statements)

References 43 publications

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“…3 of 22 While physically based numerical models can represent explicit mechanisms and simulate HZ denitrification at varying spatial and temporal scales, these models are computationally expensive (Ren et al, 2021) and require various data sources for model calibration (Chen et al, 2021). As an alternative, machine learning approaches show high performance with limited data and capture complex relationships between inputs and outputs (Mori et al, 2019). In some cases, both approaches can be combined to gain further insight and predictability.…”

Section: 1029/2021wr031131mentioning

confidence: 99%

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Son

Fang

Byun

2022

Water Resources Research

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Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

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Section: 1029/2021wr031131mentioning

confidence: 99%

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Son

Fang

Byun

2022

Water Resources Research

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“…For example, Mori et al interpreted ML models to provide evidence-based information on how stressors and ecologically important environmental factors interact and drive ecological processes and microbial biomass. 98 Such information cannot be obtained without performing model interpretation. Similarly, there are several attempts to interpret ML models to correlate factors with the chemical activity of EDCs, including unveiling features that make EDCs chemically active, determining the type of activity on the ERα or the AR, and how these features exert their functions.…”

Section: Current Status Of ML Applications In Esementioning

confidence: 99%

“…This process may also yield new knowledge. For example, Mori et al interpreted ML models to provide evidence-based information on how stressors and ecologically important environmental factors interact and drive ecological processes and microbial biomass . Such information cannot be obtained without performing model interpretation.…”

Section: Current Status Of ML Applications In Esementioning

confidence: 99%

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Zhong

Zhang

Bagheri

et al. 2021

Environ. Sci. Technol.

278

254

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The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

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“…An integrated surface and subsurface model (Amanzi-ATS) was developed to compute aerobic respiration and denitrification in the HZ at the watershed scale (Jan et al, 2021), but this study is still limited to demonstrating the capability of the watershed model to While physically based numerical models can represent explicit mechanisms and simulate HZ denitrification at varying spatial and temporal scales, these models are computationally expensive (Ren et al, 2021) and require various data sources for model calibration (Chen et al, 2021). As an alternative, machine learning approaches show high performance with limited data and capture complex relationships between inputs and outputs (Mori et al, 2019). In some cases, both approaches can be combined to gain further insight and predictability.…”

Section: Introductionmentioning

confidence: 99%

Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin

Son

Fang

Gomez‐Velez

et al. 2022

Preprint

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Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach-scale and low-order stream watersheds. We developed a basin-scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC-related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

show abstract

Modelling the effects of multiple stressors on respiration and microbial biomass in the hyporheic zone using decision trees

Cited by 24 publications

References 43 publications

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin

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