The role of physical properties in controlling soil nitrogen cycling across a tundra-forest ecotone of the Colorado Rocky Mountains, U.S.A

Chen, Youchao; Wieder, William R.; Hermes, Anna L.; Hinckley, Eve‐Lyn S.

doi:10.1016/j.catena.2019.104369

Cited by 12 publications

(17 citation statements)

References 41 publications

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“…It is clear that landscape position and physical processes are a key element of N fluxes in this setting. For example, soil moisture has been shown to be a strong control on N cycling on Niwot Ridge (Chen et al, 2020), as has hillslope aspect which functions as a control on residence time in soils (Hinckley et al, 2014). In a setting that is undergoing rapid and complex changes in seasonal cycles, maximum and minimum temperatures and an overall shift in the growing season (Kittel et al, 2016), it is possible that shifting physical controls over water fluxes are a key uncertainty in overall element budgets.…”

Section: Discussionmentioning

confidence: 99%

Long‐Term Trends in Acid Precipitation and Watershed Elemental Export From an Alpine Catchment of the Colorado Rocky Mountains, USA

2020

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Acid deposition associated with precipitation is an ecological problem that has affected watersheds in industrialized parts of North America and Europe, but remote landscapes, such as the Colorado Rocky Mountains, have also been impacted. The deposition of strong acids, including nitric and sulfuric acids, has decreased substantially over the past 30 years at Niwot Ridge, a high‐alpine watershed of Colorado. The pH of precipitation has followed these declines and has increased to ~5.5. Meanwhile, NH4+, another important constituent of acid/base balance, and a fertilizing nutrient, has more than doubled in precipitation between 1984 and 2017. A statistical model of long‐term water chemistry concentrations and loads revealed a variety of trends in watershed export of acids and bases, some of which were unexpected, and remain unexplained. For example, despite declining sulfate deposition, sulfate export has increased. On the other hand, watershed nitrate export has remained constant despite decreases in atmospheric deposition, while watershed NH4+ export has increased, albeit minimally, relative to total nitrogen export. Watershed pH and alkalinity appear to have stabilized in response to decreased acid precipitation, which may be explained by geochemical processes, such as carbonate weathering. Overall, in this high‐alpine watershed, atmospheric deposition is trending toward preindustrial conditions, and there is a need to conduct targeted, process‐based studies to determine the mechanisms underlying these trends. The unexpected watershed responses that we identify here require a new framework for understanding acid precipitation recovery.

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Section: Discussionmentioning

confidence: 99%

Long‐Term Trends in Acid Precipitation and Watershed Elemental Export From an Alpine Catchment of the Colorado Rocky Mountains, USA

2020

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“…Areas classified as the D-MD, D-WM, and W-RD groups (∼40% of the study area) are expected to contribute significantly to water fluxes and the fluxes of solutes stored in snowpack (e.g., inorganic N; Williams et al, 2015), mobilized from shallow soils (e.g., dissolved organic carbon), or produced from carbonic acid weathering (Winnick et al, 2017). As temperatures warm and soil moisture saturation decreases during the growing season, these areas develop optimal conditions for soil respiration (Knowles et al, 2015), soil N transformation rates (Chen et al, 2020), and plant growth (Supplementary Figure 1). We would expect that D-MD, D-WM, and W-RD groups then "prime" soils for mobilization of solutes in response to snowmelt and convective rainstorms later in the season, but future hydrologic modeling studies are needed to test this hypothesis.…”

Section: Implications Of Soil Moisture Patterns For Biogeochemical Anmentioning

confidence: 99%

“…We classified the other half of the study area as the D-D group. Dry areas have high rates of net N mineralization and nitrification early in the summer season (June-July) and again during August-September (Chen et al, 2020). These biogeochemical processes are stimulated by higher soil moisture during early snowmelt and summer rains.…”

Section: Implications Of Soil Moisture Patterns For Biogeochemical Anmentioning

confidence: 99%

“…These are both key factors that complicate hillslope modeling studies (Kirchner, 2016;Guio Blanco et al, 2018) and efforts to quantify chemical export from the CZ (Ward et al, 2019). Furthermore, different hydrologic behaviors may map to important ecosystem control points (Bernhardt et al, 2017)-areas that support continuously or periodically high rates of biogeochemical cycling (Darrouzet-Nardi and Bowman, 2011;Knowles et al, 2015;Chen et al, 2020). Finally, patchscale variability in snowmelt and soil moisture affects plant phenology and physiology (Bjorkman et al, 2018;Winkler et al, 2018) and results in catchment-scale spatial patterns of plant community composition and productivity (Seastedt, 2020) and responses to change (Suding et al, 2015;Winkler et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

et al. 2020

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Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (102->103 m) and microtopography (<100-102 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.

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“…However, the excessive application of nitrogen fertilizers may induce groundwlater and soil pollution [7]. Natural factors and continuous human activities can directly affect the soil nitrogen content in different regions [8]. Due to the high spatial heterogeneity of soil nitrogen at the regional scale, accurately monitoring the spatial distribution of the soil total nitrogen (STN) content is important to balance the nitrogen cycle and for sustainable development in intensive agriculture [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Xiao

Chen

et al. 2020

Sustainability

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Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

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The role of physical properties in controlling soil nitrogen cycling across a tundra-forest ecotone of the Colorado Rocky Mountains, U.S.A

Cited by 12 publications

References 41 publications

Long‐Term Trends in Acid Precipitation and Watershed Elemental Export From an Alpine Catchment of the Colorado Rocky Mountains, USA

Long‐Term Trends in Acid Precipitation and Watershed Elemental Export From an Alpine Catchment of the Colorado Rocky Mountains, USA

From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

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