Craig Ulrich

Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

The value of remote sensing and surface geophysical data for characterizing the spatial variability and relationships between land-surface and subsurface properties was explored in an Alaska (USA) coastal plain ecosystem. At this site, a nested suite of measurements was collected within a region where the land surface was dominated by polygons, including: LiDAR data; ground-penetrating radar, electromagnetic, and electrical-resistance tomography data; active-layer depth, soil temperature, soil-moisture content, soil texture, soil carbon and nitrogen content; and pore-fluid cations. LiDAR data were used to extract geomorphic metrics, which potentially indicate drainage potential. Geophysical data were used to characterize active-layer depth, soil-moisture content, and permafrost variability. Cluster analysis of the LiDAR and geophysical attributes revealed the presence of three spatial zones, which had unique distributions of geomorphic, hydrological, thermal, and geochemical properties. The correspondence between the LiDAR-based geomorphic zonation and the geophysics-based active-layer and permafrost zonation highlights the significant linkage between these ecosystem compartments. This study suggests the potential of combining LiDAR and surface geophysical measurements for providing high-resolution information about land-surface and subsurface properties as well as their spatial variations and linkages, all of which are important for quantifying terrestrial-ecosystem evolution and feedbacks to climate.ResuméLa portée de la télédétection et des données géophysique de surface pour caractériser la variabilité spatiale et les relations entre la surface du terrain et les propriétés de la subsurface a été étudiée sous tous ses aspects dans l’écosystème de la plaine côtière d’Alaska (USA). Dans cette région, sur un site où la surface du sol est dominée par des polygones, une série de données se recoupant a été collectée, incluant : données LiDAR; géoradar, tomographie électromagnétique et résistivité; profondeur de la couche aquifère, température, teneur en humidité, texture, teneur en carbone et en azote du sol; et cations du fluide des pores. Les données Lidar ont été utilisées pour établir les cotes géomorphiques, qui peuvent indiquer un drainage potentiel. Des données géophysiques ont été utilisées pour déterminer la profondeur de la couche aquifère, la teneur en humidité du sol, et la variabilité du pergélisol. L’analyse par agglomérat des données LiDAR et des attributs géophysiques ont révélé la présence de trois zones spatiales ayant une distribution similaire des propriétés géomorphiques, hydrogéologiques, thermales et géochimiques. La correspondance entre la zonation géomorphique basée sur LiDAR, la couche aquifère selon la géophysique et la zonation permafrost, met en lumière la relation significative entre ces compartiments de l’écosystème. Cette étude montre le potentiel d’une combinaison des mesures LiDAR et des mesures géophysiques de surface pour fournir une information haute résolution sur les propriétés de surface et de subsurface du sol aussi bien que sur leur variations spatiales et liens, toutes étant importantes pour quantifier l’évolution de l’écosystème terrestre et les réponses au climat.ResumenSe exploró el valor de los sensores remotos y de los datos geofísicos de superficie para caracterizar la variabilidad espacial y las relaciones entre la superficie y las propiedades subsuperficiales en un ecosistema de planicie costera en Alaska (EEUU). En este sitio, un conjunto anidado de medidas fue colectado dentro de una región donde la superficie estaba dominada por polígonos, incluyendo: datos LiDAR; datos de radar, electromagnéticos, y tomografías de resistividad eléctrica; profundidad de la capa activa, temperatura del suelo, contenido de humedad del suelo, textura del suelo, contenido de carbono y nitrógeno en suelo; y cationes del fluido de poros. Los datos LiDAR fueron usados para extraer los indicadores geomórficos, que posiblemente indican un drenaje potencial. Los datos geofísicos fueron para caracterizar la profundidad de la capa activa, el contenido de humedad del suelo y la variabilidad del permafrost. En análisis de cluster de los LiDAR y los atributos geofísicos revelaron la presencia espacial de tres zonas, que tenían una única distribución de propiedades geomórficas, hidrológicas, térmicas y geoquímicas. La correspondencia entre la zonación geomórfica basada en LiDAR y la capa activa basada en geofísica y la zonación del permafrost destaca la vinculación significativa entre estos compartimentos del ecosistema. Este estudio sugiere el potencial de la combinación LiDAR y las mediciones geofísicas de superficie para proveer información de alta resolución acerca de las propiedades de la superficie y de la subsuperficie así como su variación espacial y su articulación, todos los cuales son importantes para cuantificar la evolución del ecosistema terrestre y las reacciones con el clima.摘要用来描述地表和地下性质的空间变异性和两者之间的关系的遥感和地面地球物理数据已在阿拉斯加(美国)的一个沿海平原生态系统进行了探讨。在本次研究场地的一个表面呈多边形的区域收集到了一套测量数据,包括激光雷达数据;探地雷达数据,电磁和电阻断层扫描数据;活性层深度,土壤温度,土壤水气含量,土壤质地,土壤碳和氮的含量;以及孔隙流体阳离子数据。激光雷达数据用来提取地貌指标,这可能指示出潜在的排泄。地球物理数据用来刻画活性层的深度,土壤水气含量和永久冻土的变化特征。通过对激光雷达和地球物理数据属性的聚类分析发现了三个在地形,水文,热和地球化学性质分布上存在异常的空间区域。基于激光雷达测量的地貌分区与基于地球物理数据的活性层和永久冻土分区之间的对应关系突出了这些生态系统分区间的紧密联系。本次研究表明可以通过结合激光雷达和地表地球物理测量来为地表和地下的性质以及它们在空间上的变化和关系提供高分辨率的信息,所有这些对于量化陆地生态系统的演化和对气候变化的反应都是非常重要的。ResumoNum ecossistema da planície costeira do Alaska (EUA) foi explorado o valor da deteção remota e de dados de geofísica de superfície para caracterizar a variabilidade espacial e as relações entre propriedades da superfície do terreno e da subsuperfície. Neste local, inserido numa região onde o terreno superficial é dominado por polígonos, foi recolhido um conjunto agregado de medições, incluindo: dados de LiDAR; dados de geoadar, eletromagnéticos e de tomografia de resistência elétrica; profundidade da camada ativa, temperatura do solo, teor de água no solo, textura do solo, teor de carbono e azoto no solo; e catiões no fluido poroso. Os dados LiDAR foram usados para extrair dimensões geomórficas que potencialmente indicam o potencial de drenagem. Os dados geofísicos foram usados para caracterizar a profundidade da camada ativa, o teor de humidade no solo e a variabilidade no permafrost. A análise grupal de atributos LiDAR e geofísicos revelou a presença de três zonas espaciais que tinham distribuições únicas de propriedades geomórficas, hidrológicas, térmicas e geoquímicas. A correspondência entre o zonamento geomórfico baseado no LiDAR e a zonação da camada activa baseada na geofísica e do permafrost, demonstra a significativa conexão entre estes compartimentos do ecossistema. Este estudo sugere o potencial da combinação de medições de LiDAR e de geofísica de superfície para fornecer informação de alta resolução acerca das propriedades da superfície do terreno e da subsuperfície, assim como sobre as variações espaciais e conexões, sendo todas elas importantes para a quantificação da evolução do ecossistema terrestre e as retroações com o clima.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Identifying multiscale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem

We develop a multiscale zonation approach to characterize the spatial variability of Arctic polygonal ground geomorphology and to assess the relative controls of these elements on land surface and subsurface properties and carbon fluxes. Working within an ice wedge polygonal region near Barrow, Alaska, we consider two scales of zonation: polygon features (troughs, centers, and rims of polygons) that are nested within different polygon types (high, flat, and low centered). In this study, we first delineated polygons using a digital elevation map and clustered the polygons into four types along two transects, using geophysical and kite-based landscape-imaging data sets. We extrapolated those data-defined polygon types to all the polygons over the study site, using the polygon statistics extracted from the digital elevation map. Based on the point measurements, we characterized the distribution of vegetation, hydrological, thermal, and geochemical properties, as well as carbon fluxes, all as a function of polygon types and polygon features. Results show that nested polygon geomorphic zonation—polygon types and polygon features—can be used to represent distinct distributions of carbon fluxes and associated properties, as well as covariability among those properties. Importantly, the results indicate that polygon types have more power to explain the variations in those properties than polygon features. The approach is expected to be useful for improved system understanding, site characterization, and parameterization of numerical models aimed at predicting ecosystem feedbacks to the climate.

Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study

Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity (VS) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse VS imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the VS models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise.

Simulating bioclogging effects on dynamic riverbed permeability and infiltration

Bioclogging in rivers can detrimentally impact aquifer recharge. This is particularly so in dry regions, where losing rivers are common, and where disconnection between surface water and groundwater (leading to the development of an unsaturated zone) can occur. Reduction in riverbed permeability due to biomass growth is a time-variable parameter that is often neglected, yet permeability reduction from bioclogging can introduce order of magnitude changes in seepage fluxes from rivers over short (i.e., monthly) timescales. To address the combined effects of bioclogging and disconnection on infiltration, we developed numerical representations of bioclogging processes within a one-dimensional, variably saturated flow model representing losing-connected and losing-disconnected rivers. We tested these formulations using a synthetic case study informed with biological data obtained from the Russian River, California, USA. Our findings show that modeled biomass growth reduced seepage for losing-connected and losing-disconnected rivers. However, for rivers undergoing disconnection, infiltration declines occurred only after the system was fully disconnected. Before full disconnection, biologically induced permeability declines were not significant enough to offset the infiltration gains introduced by disconnection. The two effects combine to lead to a characteristic infiltration curve where peak infiltration magnitude and timing is controlled by permeability declines relative to hydraulic gradient gains. Biomass growth was found to hasten the onset of full disconnection; a condition we term ‘effective disconnection’. Our results show that river infiltration can respond dynamically to bioclogging and subsequent permeability declines that are highly dependent on river connection status.

Coincident aboveground and belowground autonomous monitoring to quantify covariability in permafrost, soil, and vegetation properties in Arctic tundra

Author(s): Dafflon, B; Oktem, R; Peterson, J; Ulrich, C; Tran, AP; Romanovsky, V; Hubbard, SS | Abstract: ©2017. American Geophysical Union. All Rights Reserved. Coincident monitoring of the spatiotemporal distribution of and interactions between land, soil, and permafrost properties is important for advancing our understanding of ecosystem dynamics. In this study, a novel monitoring strategy was developed to quantify complex Arctic ecosystem responses to the seasonal freeze-thaw-growing season conditions. The strategy exploited autonomous measurements obtained through electrical resistivity tomography to monitor soil properties, pole-mounted optical cameras to monitor vegetation dynamics, point probes to measure soil temperature, and periodic manual measurements of thaw layer thickness, snow thickness, and soil dielectric permittivity. The spatially and temporally dense monitoring data sets revealed several insights about tundra system behavior at a site located near Barrow, AK. In the active layer, the soil electrical conductivity (a proxy for soil water content) indicated an increasing positive correlation with the green chromatic coordinate (a proxy for vegetation vigor) over the growing season, with the strongest correlation (R = 0.89) near the typical peak of the growing season. Soil conductivity and green chromatic coordinate also showed significant positive correlations with thaw depth, which is influenced by soil and surface properties. In the permafrost, soil electrical conductivity revealed annual variations in solute concentration and unfrozen water content, even at temperatures well below 0°C in saline permafrost. These conditions may contribute to an acceleration of long-term thaw in Coastal permafrost regions. Demonstration of this first aboveground and belowground geophysical monitoring approach within an Arctic ecosystem illustrates its significant potential to remotely “visualize” permafrost, soil, and vegetation ecosystem codynamics in high resolution over field relevant scales.

Landscape topography structures the soil microbiome in arctic polygonal tundra

In the Arctic, environmental factors governing microbial degradation of soil carbon (C) in active layer and permafrost are poorly understood. Here we determined the functional potential of soil microbiomes horizontally and vertically across a cryoperturbed polygonal landscape in Alaska. With comparative metagenomics, genome binning of novel microbes, and gas flux measurements we show that microbial greenhouse gas (GHG) production is strongly correlated to landscape topography. Active layer and permafrost harbor contrasting microbiomes, with increasing amounts of Actinobacteria correlating with decreasing soil C in permafrost. While microbial functions such as fermentation and methanogenesis were dominant in wetter polygons, in drier polygons genes for C mineralization and CH4 oxidation were abundant. The active layer microbiome was poised to assimilate N and not to release N2O, reflecting low N2O flux measurements. These results provide mechanistic links of microbial metabolism to GHG fluxes that are needed for the refinement of model predictions.The role of ecosystem structure in microbial activity related to greenhouse gas production is poorly understood. Here, Taş and colleagues show that microbial communities and ecosystem function vary across fine-scale topography in a polygonal tundra.

Quantification of Arctic Soil and Permafrost Properties Using Ground-Penetrating Radar and Electrical Resistivity Tomography Datasets

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-to-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

Permafrost Degradation and Subsidence Observations during a Controlled Warming Experiment

Global climate change has resulted in a warmer Arctic, with projections indicating accelerated modifications to permafrost in the near future. The thermal, hydrological, and mechanical physics of permafrost thaw have been hypothesized to couple in a complex fashion but data collection efforts to study these feedbacks in the field have been limited. As a result, laboratory and numerical models have largely outpaced field calibration datasets. We present the design, execution, and initial results from the first decameter-scale controlled thawing experiment, targeting coupled thermal/mechanical response, particularly the temporal sequence of surface subsidence relative to permafrost degradation at depth. The warming test was conducted in Fairbanks, AK, and utilized an array of in-ground heaters to induce thaw of a ~11 × 13 × 1.5 m soil volume over 63 days. The 4-D temperature evolution demonstrated that the depth to permafrost lowered 1 m during the experiment. The resulting thaw-induced surface deformation was ~10 cm as observed using a combination of measurement techniques. Surface deformation occurred over a smaller spatial domain than the full thawed volume, suggesting that gradients in cryotexture and ice content were significant. Our experiment provides the first large field calibration dataset for multiphysics thaw models.

Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO2 and N2

Author(s): Newcomer, ME; Hubbard, SS; Fleckenstein, JH; Maier, U; Schmidt, C; Thullner, M; Ulrich, C; Flipo, N; Rubin, Y | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Rivers in climatic zones characterized by dry and wet seasons often experience periodic transitions between losing and gaining conditions across the river-aquifer continuum. Infiltration shifts can stimulate hyporheic microbial biomass growth and cycling of riverine carbon and nitrogen leading to major exports of biogenic CO2 and N2 to rivers. In this study, we develop and test a numerical model that simulates biological-physical feedback in the hyporheic zone. We used the model to explore different initial conditions in terms of dissolved organic carbon availability, sediment characteristics, and stochastic variability in aerobic and anaerobic conditions from water table fluctuations. Our results show that while highly losing rivers have greater hyporheic CO2 and N2 production, gaining rivers allowed the greatest fraction of CO2 and N2 production to return to the river. Hyporheic aerobic respiration and denitrification contributed 0.1–2 g/m2/d of CO2 and 0.01–0.2 g/m2/d of N2; however, the suite of potential microbial behaviors varied greatly among sediment characteristics. We found that losing rivers that consistently lacked an exit pathway can store up to 100% of the entering C/N as subsurface biomass and dissolved gas. Our results demonstrate the importance of subsurface feedbacks whereby microbes and hydrology jointly control fate of C and N and are strongly linked to wet-season control of initial sediment conditions and hydrologic control of seepage direction. These results provide a new understanding of hydrobiological and sediment-based controls on hyporheic zone respiration, including a new explanation for the occurrence of anoxic microzones and large denitrification rates in gravelly riverbeds.