Integrated imaging of above and below ground properties and their interactions: A case study in East River Watershed,  Colorado

Falco, Nicola; Dafflon, Baptiste; Wainwright, H. M.; Léger, Emmanuel; Haedrich, Caitlin E.; Peterson, John; Williams, Kenneth H.; Hubbard, Susan S.

doi:10.1190/segam2018-2998456.1

Cited by 4 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve our understanding of vegetation distribution and its response to hydrological perturbations, new methods are needed to characterize and monitor plant communities tractably, in high resolution and across large spatial extents. To address this gap, Falco et al (2018) developed an approach to characterize heterogeneous plant functional types using remote‐sensing data and a new data fusion and machine‐learning approach. Their remote‐sensing data fusion framework utilized a spectral‐spatial classification strategy that was based on support vector machine and morphological contextual analysis.…”

Section: Early Scientific Insightsmentioning

confidence: 99%

See 1 more Smart Citation

The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics

Hubbard

Williams

Agarwal

et al. 2018

Vadose Zone Journal

Self Cite

143

154

View full text Add to dashboard Cite

Extreme weather, fires, and land use and climate change are significantly reshaping interactions within watersheds throughout the world. Although hydrological-biogeochemical interactions within watersheds can impact many services valued by society, uncertainty associated with predicting hydrologydriven biogeochemical watershed dynamics remains high. With an aim to reduce this uncertainty, an approximately 300-km 2 mountainous headwater observatory has been developed at the East River, CO, watershed of the Upper Colorado River Basin. The site is being used as a testbed for the Department of Energy supported Watershed Function Project and collaborative efforts. Building on insights gained from research at the "sister" Rifle, CO, site, coordinated studies are underway at the East River site to gain a predictive understanding of how the mountainous watershed retains and releases water, nutrients, carbon, and metals. In particular, the project is exploring how early snowmelt, drought, and other disturbances influence hydrological-biogeochemical watershed dynamics at seasonal to decadal timescales. A system-of-systems perspective and a scale-adaptive simulation approach, involving the combined use of archetypal watershed subsystem "intensive sites" are being tested at the site to inform aggregated watershed predictions of downgradient exports. Complementing intensive site hydrological, geochemical, geophysical, microbiological, geological, and vegetation datasets are long-term, distributed measurement stations and specialized experimental and observational campaigns. Several recent research advances provide insights about the intensive sites as well as aggregated watershed behavior. The East River "community testbed" is currently hosting scientists from more than 30 institutions to advance mountainous watershed methods and understanding.

show abstract

Section: Early Scientific Insightsmentioning

confidence: 99%

“…Estimates of plant functional type distribution across a region that encompasses the hillslope and floodplain intensive sites (left), including the location of the electrical resistance tomography transect shown in Fig. 9a (right) (from Falco et al, 2018). …”

Section: Early Scientific Insightsmentioning

confidence: 99%

The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics

Hubbard

Williams

Agarwal

et al. 2018

Vadose Zone Journal

Self Cite

143

154

View full text Add to dashboard Cite

show abstract

“…Sample distances to this line were also calculated using NNJoin and samples on the downstream side of the line were converted to negative values to indicate upstream and downstream sides of the meander. TPI is computed from the DTM as the difference between the elevation of a center point and the average elevation measured in the neighboring area (3 by 3 m) 59 . To display genome abundances as used in the rlq-fourth corner analysis, filtered abundance values were chi-square transformed in R using the decostand in the vegan package and exported to display in QGIS.…”

Section: Gismentioning

confidence: 99%

Meanders as a scaling motif for understanding of floodplain soil microbiome and biogeochemical potential at the watershed scale

Carnevali

Lavy

Thomas

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

42Biogeochemical exports of C, N, S and H2 from watersheds are modulated by the activity of 43 microorganisms that function over micron scales. This disparity of scales presents a substantial 44 challenge for development of predictive models describing watershed function. Here, we tested 45 the hypothesis that meander-bound regions exhibit patterns of microbial metabolic potential that 46 are broadly predictive of biogeochemical processes in floodplain soils along a river corridor. We 47intensively sampled floodplain soils located in the upper, middle, and lower reaches of the East 48River in Colorado and reconstructed 248 draft quality genomes representative at a sub-species 49level. Approximately one third of the representative genomes were detected across all three 50 locations with similar levels of abundance, and despite the very high microbial diversity and 51 complexity of the soils, ~15% of species were detected in two consecutive years. A core floodplain 52 microbiome was enriched in bacterial capacities for aerobic respiration, aerobic CO oxidation, and 53 thiosulfate oxidation with the formation of elemental sulfur. We did not detect systematic patterns 54 of gene abundance based on sampling position relative to the river. However, at the watershed 55 scale meander-bound floodplains appear to serve as scaling motifs that predict aggregate capacities 56 for biogeochemical transformations in floodplain soils. Given this, we conducted a transcriptomic 57 analysis of the middle site. Overall, the most highly transcribed genes were amoCAB and nxrAB 58 (for nitrification) followed by genes involved in methanol and formate oxidation, and nitrogen and 59 CO2 fixation. Low soil organic carbon correlated with high activity of genes involved in methanol, 60formate, sulfide, hydrogen, and ammonia oxidation, nitrite oxidoreduction, and nitrate and nitrite 61 reduction. Thus, widely represented genetic capacities did not predict in situ activity at one time 62 point, but rather they define a reservoir of biogeochemical potential available as conditions change. 63 64 65Introduction 66Watersheds are geographic areas that capture precipitation that is ultimately discharged 67 into rivers and other larger water bodies. Of particular interest are watersheds in mountainous 68 regions, as these are major sources of freshwater 1, 2 . Within mountainous watersheds, complex 69 interactions among vegetation, hydrology, geochemistry, and geology occur within and across 70 watershed compartments, including across bedrock-soil-vegetation compartments of terrestrial 71 hillslopes, across terrestrial-aquatic interfaces and within the fluvial system itself. Interactions 72 within a reactive watershed typically vary as a function of disturbance as well as landscape position 73 and topography. For example, interactions in an alpine region of a mountainous watershed are 74 likely to be quite different from a lower montane floodplain region 3 . Floodplains, which extend 75 from the river banks to the base of hillslopes, comprise the rip...

show abstract

“…There have been significant advances in quantifying the above/ below-ground watershed compartments over space based on a suite of remote sensing datasets, including plant species distributions and traits (e.g., Madritch et al, 2014;Falco et al, 2019;Chadwick et al, 2020;Falco et al, 2020), soil thickness and soil properties (e.g., Patton et al, 2018;Yan et al, 2020), and bedrock variability (e.g., Parsekian et al, 2015;Uhlemann et al, 2022). The co-variability of these compartments has been documented based on analyzing multiple remote sensing and spatial data layers (e.g., Wainwright et al, 2015;Pelletier et al, 2018;Devadoss et al, 2020;Hermes et al, 2020;Enguehard et al, 2022;Wainwright et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

Wainwright,

Dafflon,

Siirila-Woodburn

et al. 2024

Front. Water

View full text Add to dashboard Cite

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

show abstract

Integrated imaging of above and below ground properties and their interactions: A case study in East River Watershed, Colorado

Cited by 4 publications

References 17 publications

The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics

The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics

Meanders as a scaling motif for understanding of floodplain soil microbiome and biogeochemical potential at the watershed scale

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

Contact Info

Product

Resources

About