Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program

The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates

Proximal-sensing-powered modelling of energy-water fluxes in a vineyard: A spatial resolution analysis

Spatial resolution is a key parameter in energy–water surface flux modelling. In this research, scale effects are analyzed on fluxes modelled with the FEST-EWB model, by upscaling both its inputs and outputs separately. The main questions are: (a) if high-resolution remote sensing images are necessary to accurately model a heterogeneous area; and (b) whether and to what extent low-resolution modelling provides worse/better results than the upscaled results of high-resolution modelling. The study area is an experimental vineyard field where proximal sensing images were obtained by an airborne platform and verification fluxes were measured via a flux tower. Modelled fluxes are in line with those from alternative energy-balance models, and quite accurate (NSE = 0.78) with respect to those measured in situ. Field-scale evapotranspiration has resulted in both the tested upscaling approaches (with relative error within ±30%), although fewer pixels available for low-resolution calibration may produce some differences. When working at low resolutions, the model has produced higher relative errors (20% on average), but is still within acceptable bounds. This means that the model can produce high-quality results, partially compensating for the loss in spatial heterogeneity associated with low-resolution images

Application of EMI and FDR sensors to assess the fraction of transpirable soil water over an olive grove

Accurate soil water status measurements across spatial and temporal scales are still a challenging task, specifically at intermediate spatial (0.1-10 ha) and temporal (minutes to days) scales. Consequently, a gap in knowledge limits our understanding of the reliability of the spatial measurements and its practical applicability in agricultural water management. This paper compares the cumulative EM38 (Geonics Ltd., Mississauga, ON, Canada) response collected by placing the sensor above ground with the corresponding soil water content obtained by integrating the values measured with an FDR (frequency domain reflectometry) sensor. In two field areas, characterized by different soil clay content, two Diviner 2000 access tubes (1.2 m) were installed and used to quantify the dimensionless fraction of transpirable soil water (FTSW). After the calibration, the work proposes the combined use of the FDR and electromagnetic induction (EMI) sensors to measure and map FTSW. A strong correlation (R2= 0.86) between FTSW and EM38 bulk electrical conductivity was found. As a result, field changes of FTSW are due to the variability of soil water content and soil texture. As with the data acquired in the field, more structured patterns occurred after a wetting event, indicating the presence of subsurface flow or root water uptake paths. After assessing the relationship between the soil and crop water status, the FTSW domain includes a critical value, estimated around 0.38, below which a strong reduction of relative transpiration can be recognized

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management.

This dissertation examined the carbon sequestration potential of a low C:N soil amendment and its incorporation into the soil over a rolling agricultural field. A segmented planar fit was developed to assess and correct the systematic errors the topography introduces on the carbon dioxide fluxes. The carbon dioxide fluxes were then be partitioned into gross primary productivity and soil respiration to understand the influence of the contrasting management practices, using flux variance partitioning. Concomitant with the partitioning, high resolution temporal and spatial scale remote sensing images were interpolated and standardized to conduct hypothesis testing for treatment effects

Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Assessing hydrometeorological controls on subalpine plant community evapotranspiration and evaluating the METRIC method using high-resolution UAV imagery in the Canadian Rocky Mountains

Subalpine wetlands in the Canadian Rocky Mountains function as buffers for snowmelt runoff towards downstream systems and communities. However, hydrological regimes are changing rapidly in these ecosystems due to climate warming in the region causing earlier snowmelt patterns and extended vegetation growth periods. Response of vegetation health and composition to temperature and precipitation increases can have a significant influence on evapotranspiration (ET), the main component of wetland water balances. Subalpine plant communities are especially sensitive to shading mechanisms over the growing season, which limits ET flux. However, as the composition of plant communities are expected to migrate with climate, it is important to monitor changes in primary water sinks such as ET in vulnerable ecosystems, such as subalpine wetlands. Due to difficult accessibility, few studies have been conducted to monitor these ecosystems. Recent technological advances in unmanned aerial vehicles (UAV) provide opportunities to monitor these ecosystems at a high spatial resolution. This study aims to quantify plant community scale ET, assess the spatial variability and sensitivity of this ET to climate and vegetation health, and evaluate the Mapping Evapotranspiration with Internalized Calibration (METRIC) model for ET estimation in a subalpine wetland. ET was measured in-situ using a dynamic closed chamber method for the plant community scale at Fortress Mountain in Kananaskis, Alberta. Vegetation health, water content, and plant water stress was derived from spectral signatures using vegetation indices. High-resolution imagery with multispectral, thermal, and LiDAR sensors were collected during ground measurements to capture the spatial variability of ET throughout the wetland using the METRIC model. Modelled ET was compared with chamber ET measurements to assess the accuracy and applicability of the METRIC model using UAV imagery in a subalpine wetland. Net radiation and plant community type were the dominant controls on ET at the community scale. Variability in physiological differences between plant communities, such as depth of stomatal openings, cuticle thickness, leaf surface area to volume ratio, and root water uptake rates affect plant response of ET to radiation and temperature. Plant physiology as well as volumetric water content, proximity to surface water, and groundwater connections, also influenced spatial ET trends. METRIC model results had high estimation accuracy when compared to chamber results. METRIC ET had strong relationship with hourly (R2=0.79) and daily (R2=0.82) chamber ET. Taller vegetation (trees and shrubs) had higher estimation accuracy than lower-lying vegetation (ground vegetation and moss). Spatial variability of ET using the local indicators of spatial association (LISA) with METRIC results showed clusters of high ET in the Southern and Western sections of the meadow and low ET in the Northern and Eastern sections of the meadow. The results of this study demonstrate that as plant communities are expected to migrate with changing climate conditions in subalpine ecosystems, METRIC model applications using UAV imagery could be an effective solution to monitoring plant community ET at a high spatial resolution in vulnerable and inaccessible areas

Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions

Aircraft-based measurements for the identification and quantification of sources and sinks in the carbon cycle

Improved quantification of carbon-cycle sources and sinks is an important requirement for determining mitigation strategies and modeling future climate interactions. Analytically robust measurements require high-precision instrumentation and thoughtful experimental design to produce rigorous and reproducible results despite complex and quickly changing meteorological and environmental conditions. Here, an aircraft platform equipped with a high-precision cavity ring-down spectrometer for CO2, CH4 and H2O quantification was used to acquire data from previously un-sampled sources. The aircraft mass-balance technique was used to quantify CH4 emissions from natural gas well pads in the drilling stage, which were 2-3 orders of magnitude higher than previous estimates of emissions from this stage. In addition, the first in-situ flare emission data was collected for natural gas flares in North Dakota, Pennsylvania and Texas. Flare efficiency was high for most flares, higher than assumed efficiency. However, a few flares sampled with lower efficiencies closer to the assumed flare efficiency suggest the need for characterization of operational conditions specific to operators and basins. Finally, eddy-covariance O2 and heat fluxes were measured over three east-coast forests at sites close to and far from surface eddy-covariance towers. Tower data is often used in models to represent a larger heterogeneous region. Aircraft and tower O2 and sensible heat flux agreed well, indicating that for these sites, tower data is a good approximation of the larger region, though significant variability was observed. Aircraft latent heat fluxes were routinely much larger that tower fluxes, most likely due to the influence of advection which is measured by the aircraft eddy-covariance technique, but not by towers