Linking biofilm spatial structure to real-time microscopic oxygen decay imaging

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Biofouling on 2018, available online at: http://www.tandfonline.com/10.1080/08927014.2017.1423474Two non-destructive techniques, confocal laser scanning microscopy (CLSM) and planar optode (VisiSens imaging), were combined to relate the fine-scale spatial structure of biofilm components to real-time images of oxygen decay in aquatic biofilms. Both techniques were applied to biofilms grown for seven days at contrasting light and temperature (10/20°C) conditions. The geo-statistical analyses of CLSM images indicated that biofilm structures consisted of small (~100 µm) and middle sized (~101 µm) irregular aggregates. Cyanobacteria and EPS (extracellular polymeric substances) showed larger aggregate sizes in dark grown biofilms while, for algae, aggregates were larger in light-20°C conditions. Light-20°C biofilms were most dense while 10°C biofilms showed a sparser structure and lower respiration rates. There was a positive relationship between the number of pixels occupied and the oxygen decay rate. The combination of optodes and CLMS, taking advantage of geo-statistics, is a promising way to relate biofilm architecture and metabolism at the micrometric scale.Peer ReviewedPostprint (author's final draft

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

Turbulence accelerates the growth of drinking water biofilms

Biofilms are found at the inner surfaces of drinking water pipes and, therefore, it is essential to understand biofilm processes to control their formation. Hydrodynamics play a crucial role in shaping biofilms. Thus, knowing how biofilms form, develop and disperse under different flow conditions is critical in the successful management of these systems. Here, the development of biofilms after 4 weeks, the initial formation of biofilms within 10 h and finally, the response of already established biofilms within 24-h intervals in which the flow regime was changed, were studied using a rotating annular reactor under three different flow regimes: turbulent, transition and laminar. Using fluorescence microscopy, information about the number of microcolonies on the reactor slides, the surface area of biofilms and of extracellular polymeric substances and the biofilm structures was acquired. Gravimetric measurements were conducted to characterise the thickness and density of biofilms, and spatial statistics were used to characterise the heterogeneity and spatial correlation of biofilm structures. Contrary to the prevailing view, it was shown that turbulent flow did not correlate with a reduction in biofilms; turbulence was found to enhance both the initial formation and the development of biofilms on the accessible surfaces. Additionally, after 24-h changes of the flow regime it was indicated that biofilms responded to the quick changes of the flow regime. Overall, this work suggests that different flow conditions can cause substantial changes in biofilm morphology and growth and specifically that turbulent flow can accelerate biofilm growth in drinking water

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations

Vertical mixing in atmospheric tracer transport models: error characterization and propagation

International audienceImperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixed layer heights and mixed layer heights derived from ECMWF meteorological data during May?June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixed layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that transport models driven by current generation data assimilation for meteorological fields is by far not sufficient for inversions of continental mixing ratio data. As a solution we suggest the use of better, higher resolution atmospheric models, and a modification of the overall sampling strategy

Vegetation and Topographic Control on Spatial Variability of Soil Organic Carbon

Soil organic carbon (SOC) is one of the most important parameters affecting the hydraulic characteristics of natural soils. Despite being rather easy to measure, SOC is known to be highly variable in space. In this study, vegetation, climate, and morphology factors were used to reproduce the spatial distribution of SOC in the mineral horizons of forest and grassland areas in north-western Italy and the feasibility of the approach was evaluated. When the overall sample (114 samples) was analyzed, average annual rainfall and elevation were significant descriptors of the SOC variability. However, a large part of the variability remains unexplained. Two stratification criteria were then adopted, based on vegetation and topographic properties. We obtained an improvement of the quality of the estimates, particularly for grasslands and forests in the absence of local curvatures. These results indicate that the spatial variability of soil organic matter is scarcely reproducible at the regional scale, unless an a-priori reduction of the heterogeneity is applied. A discussion on the feasibility of applying stratification criteria to deal with heterogeneous samples closes the pape

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity

Sampling soil organic carbon to detect change over time

This research describes a generic monitoring design that could be widely applied to detect temporal changes in soil organic carbon stocks (SOC) across a carbon estimation area (CEA) with no prior knowledge of the spatial or temporal variance of SOC within the CEA. The report includes information on: Bases for designing SOC stock sampling for detecting change Monitoring SOC change to verify the effects of land use or management practicesStatistical rationale for monitoring SOC changeQuality measure and constraints for monitoring SOC changeDesign-based optimisation of sample sizesModel-based optimisation of sample sizesHypothesis testingStatistical model for monitoring SOC changeUsing available data and its variability to guide initial sampling designUncertainty in outcomes of monitoring designsSummary and conclusions

Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling

With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail