Haruko M. Wainwright

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.

Bayesian hierarchical approach and geophysical data sets for estimation of reactive facies over plume scales

A stochastic model is developed to integrate multiscale geophysical and point data sets for characterizing coupled subsurface physiochemical properties over plume-relevant scales, which is desired for parameterizing reactive transport models. We utilize the concept of reactive facies, which is based on the hypothesis that subsurface units can be identified that have distinct reactive-transport-property distributions. To estimate and spatially distribute reactive facies and their associated properties over plume-relevant scales, we need to (1) document the physiochemical controls on plume behavior and the correspondence between geochemical, hydrogeological, and geophysical measurements; and (2) integrate multisource, multiscale data sets in a consistent manner. To tackle these cross-scale challenges, we develop a hierarchical Bayesian model to jointly invert various wellbore and geophysical data sets that have different resolutions and spatial coverage. We use Markov-chain Monte-Carlo sampling methods to draw many samples from the joint posterior distribution and subsequently estimate the marginal posterior distribution of reactive-facies field and their associated reactive transport properties. Synthetic studies demonstrate that our method can successfully integrate different types of data sets. We tested the framework using the data sets collected at the uranium-contaminated Savannah River Site F-Area, including wellbore lithology, cone penetrometer testing, and crosshole and surface seismic data. Results show that the method can estimate the spatial distribution of reactive facies and their associated reactive-transport properties along a 300 m plume centerline traverse with high resolution (1.2 m by 0.305 m).

Water Table Dynamics and Biogeochemical Cycling in a Shallow, Variably-Saturated Floodplain

Three-dimensional variably saturated flow and multicomponent biogeochemical reactive transport modeling, based on published and newly generated data, is used to better understand the interplay of hydrology, geochemistry, and biology controlling the cycling of carbon, nitrogen, oxygen, iron, sulfur, and uranium in a shallow floodplain. In this system, aerobic respiration generally maintains anoxic groundwater below an oxic vadose zone until seasonal snowmelt-driven water table peaking transports dissolved oxygen (DO) and nitrate from the vadose zone into the alluvial aquifer. The response to this perturbation is localized due to distinct physico-biogeochemical environments and relatively long time scales for transport through the floodplain aquifer and vadose zone. Naturally reduced zones (NRZs) containing sediments higher in organic matter, iron sulfides, and non-crystalline U(IV) rapidly consume DO and nitrate to maintain anoxic conditions, yielding Fe(II) from FeS oxidative dissolution, nitrite from denitrification, and U(VI) from nitrite-promoted U(IV) oxidation. Redox cycling is a key factor for sustaining the observed aquifer behaviors despite continuous oxygen influx and the annual hydrologically induced oxidation event. Depth-dependent activity of fermenters, aerobes, nitrate reducers, sulfate reducers, and chemolithoautotrophs (e.g., oxidizing Fe(II), S compounds, and ammonium) is linked to the presence of DO, which has higher concentrations near the water table.

Reduced order modeling in iTOUGH2

The inverse modeling and uncertainty quantification capabilities of iTOUGH2 are augmented with reduced order models (ROMs) that act as efficient surrogates for computationally expensive high fidelity models (HFMs). The implementation of the ROM capabilities involves integration of three main computational components. The first component is the ROM itself. Two response surface approximations are currently implemented: Gaussian process regression (GPR) and radial basis function (RBF) interpolation. The second component is a multi-output adaptive sampling procedure that determines the sample points used to construct the ROMs. The third component involves defining appropriate error measures for the adaptive sampling procedure, allowing ROMs to be constructed efficiently with limited user intervention. Details in all three components must complement one another to obtain an accurate approximation. The new capability and its integration with other analysis tools within iTOUGH2 are demonstrated in two examples. The results from using the ROMs in an uncertainty quantification analysis and a global sensitivity analysis compare favorably with the results obtained using the HFMs. GPR is more accurate than RBF, but the difference can be small and similar conclusion can be deduced from the analyses. In the second example involving a realistic numerical model for a hypothetical industrial-scale carbon storage project in the Southern San Joaquin Basin, California, USA, significant reduction in computational effort can be achieved when ROMs are used to perform a rigorous global sensitivity analysis.

Identifying key controls on the behavior of an acidic-U(VI) plume in the Savannah River Site using reactive transport modeling.

Acidic low-level waste radioactive waste solutions were discharged to three unlined seepage basins at the F-Area of the Department of Energy (DOE) Savannah River Site (SRS), South Carolina, USA, from 1955 through 1989. Despite many years of active remediation, the groundwater remains acidic and contaminated with significant levels of U(VI) and other radionuclides. Monitored Natural Attenuation (MNA) is a desired closure strategy for the site, based on the premise that regional flow of clean background groundwater will eventually neutralize the groundwater acidity, immobilizing U(VI) through adsorption. An in situ treatment system is currently in place to accelerate this in the downgradient portion of the plume and similar measures could be taken upgradient if necessary. Understanding the long-term pH and U(VI) adsorption behavior at the site is critical to assess feasibility of MNA along with the in-situ remediation treatments. This paper presents a reactive transport (RT) model and uncertainty quantification (UQ) analyses to explore key controls on the U(VI)-plume evolution and long-term mobility at this site. Two-dimensional numerical RT simulations are run including the saturated and unsaturated (vadose) zones, U(VI) and H(+) adsorption (surface complexation) onto sediments, dissolution and precipitation of Al and Fe minerals, and key hydrodynamic processes are considered. UQ techniques are applied using a new open-source tool that is part of the developing ASCEM reactive transport modeling and analysis framework to: (1) identify the complex physical and geochemical processes that control the U(VI) plume migration in the pH range where the plume is highly mobile, (2) evaluate those physical and geochemical parameters that are most controlling, and (3) predict the future plume evolution constrained by historical, chemical and hydrological data. The RT simulation results show a good agreement with the observed historical pH and concentrations of U(VI), nitrates and Al concentrations at multiple locations. Mineral dissolution and precipitation combined with adsorption reactions on goethite and kaolinite (the main minerals present with quartz) could buffer pH at the site for long periods of time. UQ analysis using the Morris one-at-a-time (OAT) method indicates that the model/parameter is most sensitive to the pH of the waste solution, discharge rates, and the reactive surface area available for adsorption. However, as a key finding, UQ analysis also indicates that this model (and parameters) sensitivity evolves in space and time, and its understanding could be crucial to assess the temporal efficiency of a remediation strategy in contaminated sites. Results also indicate that residual U(VI) and H(+) adsorbed in the vadose zone, as well as aquifer permeability, could have a significant impact on the acidic plume long-term mobility.

Mathematical Modelling of Arctic Polygonal Tundra with Ecosys: 1. Microtopography Determines How Active Layer Depths Respond to Changes in Temperature and Precipitation

Author(s): Grant, RF; Mekonnen, ZA; Riley, WJ; Wainwright, HM; Graham, D; Torn, MS | Abstract: ©2017. American Geophysical Union. All Rights Reserved. Microtopographic variation that develops among features (troughs, rims, and centers) within polygonal landforms of coastal arctic tundra strongly affects movement of surface water and snow and thereby affects soil water contents (θ) and active layer depth (ALD). Spatial variation in ALD among these features may exceed interannual variation in ALD caused by changes in climate and so needs to be represented in projections of changes in arctic ALD. In this study, increases in near-surface θ with decreasing surface elevation among polygon features at the Barrow Experimental Observatory (BEO) were modeled from topographic effects on redistribution of surface water and snow and from lateral water exchange with a subsurface water table during a model run from 1981 to 2015. These increases in θ caused increases in thermal conductivity that in turn caused increases in soil heat fluxes and hence in ALD of up to 15 cm with lower versus higher surface elevation which were consistent with increases measured at BEO. The modeled effects of θ caused interannual variation in maximum ALD that compared well with measurements from 1985 to 2015 at the Barrow Circumpolar Active Layer Monitoring (CALM) site (R2 = 0.61, RMSE = 0.03 m). For higher polygon features, interannual variation in ALD was more closely associated with annual precipitation than mean annual temperature, indicating that soil wetting from increases in precipitation may hasten permafrost degradation beyond that caused by soil warming from increases in air temperature. This degradation may be more rapid if increases in precipitation cause sustained wetting in higher features.

A science data gateway for environmental management

Science data gateways are effective in providing complex science data collections to the world‐wide user communities. In this paper we describe a gateway for the Advanced Simulation Capability for Environmental Management (ASCEM) framework. Built on top of established web service technologies, the ASCEM data gateway is specifically designed for environmental modeling applications. Its key distinguishing features include (1) handling of complex spatiotemporal data, (2) offering a variety of selective data access mechanisms, (3) providing state‐of‐the‐art plotting and visualization of spatiotemporal data records, and (4) integrating seamlessly with a distributed workflow system using a RESTful interface. ASCEM project scientists have been using this data gateway since 2011. Copyright

iTOUGH2: A multiphysics simulation-optimization framework for analyzing subsurface systems

Abstract iTOUGH2 is a simulation-optimization framework for the TOUGH suite of nonisothermal multiphase flow models and related simulators of geophysical, geochemical, and geomechanical processes. After appropriate parameterization of subsurface structures and their properties, iTOUGH2 runs simulations for multiple parameter sets and analyzes the resulting output for parameter estimation through automatic model calibration, local and global sensitivity analyses, data-worth analyses, and uncertainty propagation analyses. Development of iTOUGH2 is driven by scientific challenges and user needs, with new capabilities continually added to both the forward simulator and the optimization framework. This review article provides a summary description of methods and features implemented in iTOUGH2, and discusses the usefulness and limitations of an integrated simulation-optimization workflow in support of the characterization and analysis of complex multiphysics subsurface systems.