Browsing by Author "Marani, Marco"

At a given time, tidal landforms inhabit one of three alternate elevation-determined stable states: salt marsh, sub-tidal platform, or tidal flat. The balance between soil production and sea level rise controls transitions between states. Due to increasing anthropogenic inputs to the carbon and nitrogen cycles, CO2 and nutrient enrichment rates are rising. What effect will this have on the evolution of the tidal landform? The present thesis recognizes that (1) soil production depends on halophyte biomass, (2) nutrient enrichment promotes a shift in biomass production from below- to aboveground thus increasing potential for sediment trapping, and (3) elevated CO2 causes an increase in total biomass production. Through use of point- and one-dimensional models, the present thesis finds that under constant suspended sediment levels equal to 20 kg/m3, (1) nutrient enrichment decreases accretion and increases suspended sediment requirements necessary to maintain accessibility of the salt marsh state, (2) elevated CO2 increases accretion decreases suspended sediment requirements necessary to maintain accessibility of the salt marsh state, and (3) the increase in accretion affected by CO2 addition is greater in magnitude than the decrease affected by nutrient enrichment. Thus we can infer that in a future scenario including increasing CO2, nutrient enrichment, and decreasing suspended sediment concentration, the enhancement effect of CO2 will dominate and a net increase in accretion will occur.

Coastal ecosystems provide a wide range of services including protecting the mainland from the destructive effects of storms, nutrient cycling, water filtration, nurseries for fish and crustaceans, and carbon sequestration. These zones are threatened by human impacts and climate change through more frequent intense storms and sea level rise with a projected increase of up to 16 mm/yr for the last two decades of the 21st century. However, it is not fully understood what mechanisms control the formation and degradation of these landforms, and determine their resilience to environmental change. In this work, I highlight the role of various physical characteristics and environmental parameters that contribute to the formation and stability of coastal environments.

First, I develop and use remote sensing analyses to quantitatively characterize coastal dune eco-topographic patterns by simultaneously identifying the spatial distribution of topographic elevation and vegetation biomass in order to understand the coupled dynamics of vegetation and coastal dunes. LiDAR-derived leaf area index and hyperspectral-derived normalized difference vegetation index patterns yield vegetation distributions at the whole-system scale which are in agreement with each other and with field observations. LiDAR-derived concurrent quantifications of biomass and topography show that plants more favorably develop on the landward side of the foredune crest and that the foredune crestline marks the position of an ecotone, which is interpreted as the result of a sheltering effect sharply changing local environmental conditions. The findings reveal that the position of the foredune crestline is a chief ecomorphodynamic feature resulting from the two-way interaction between vegetation and topography.

Next, to shed light on the vertical depositional dynamics of salt marshes in response to sea level rise, I investigate the hypothesis that competing effects between biomass production and aeration/decomposition determine an approximately spatially constant contribution of soil organic matter (SOM) to total accretion. I use concurrent observations of SOM and decomposition rates from marshes in North Carolina. The results are coherent with the notion that SOM does not significantly vary in space and suggest that this may be the result of an at least partial compensation of opposing trends in biomass productivity and decomposed organic matter. The analyses show that deeper soil layers are characterized by lower decomposition rates and higher stabilization factors than shallower layers, likely because of differences in inundation duration. However, overall, decomposition processes are sufficiently rapid that the labile material in the fresh biomass is completely decomposed before it can be buried and stabilized. The findings point to the importance of the fraction of initially refractory material and of the stabilization processes in determining the final distribution of SOM within the soil column.

Finally, I develop a process-based model to evaluate the relative role of watershed, estuarine, and oceanic controls on salt marsh depositional/erosional dynamics and define how these factors interact to determine salt marsh resilience to environmental change at the estuary scale. The results show that under some circumstances, vertical depositional dynamics can lead to transitions between salt marsh and tidal flat equilibrium states that occur much more rapidly than marsh/tidal flat boundary erosion or accretion could. Additionally, the analyses reveal that river inputs affect the existence and extent of marsh/tidal flat equilibria by both modulating exchanges with the ocean (by partially “filling” the basin) and by providing suspended sediment.

Abstract

Legal and illegal gold mining in the Madre de Dios area cause several environmental concerns linked with mercury pollution and deforestation, which make hydrological and sediment transport processes central for any assessment of ongoing and future environmental impacts. This thesis addresses the problem of estimating flow and transport processes in the virtually ungauged Madre de Dios basin (Peru) to provide a first basis on which transport of mercury adsorbed on the sediment may be evaluated. The thesis first applies a recent method to derive the probability distribution of liquid and solid discharges to gauged basins in the Tropical Zone (Puerto Rico). This application allows the evaluation of the uncertainty associated with estimates of flow and transport pdf's. Once the iuncertainty is quantified, the same tool is applied to provide the first evaluation of the frequency distributions of discharge and sediment fluxes in the Madre de Dios area.

Environmental sensing is experiencing tremendous development due largely to the advancement of sensor technology and wireless technology/internet that connects them and enable data exchange. Environmental monitoring sensor systems range from satellites that continuously monitor earth surface to miniature wearable devices that track local environment and people's activities. However, transforming these data into knowledge of the underlying physical and/or chemical processes remains a big challenge given the spatial, temporal scale, and heterogeneity of the relevant natural phenomena. This research focuses on the development and application of novel sensing and inference techniques in air and water environments. The overall goal is to infer the state and dynamics of some key environmental variables by building various models: either a sensor system or numerical simulations that capture the physical processes.

This dissertation is divided into five chapters. Chapter 1 introduces the background and motivation of this research. Chapter 2 focuses on the evaluation of different models (physically-based versus empirical) and remote sensing data (multispectral versus hyperspectral) for suspended sediment concentration (SSC) retrieval in shallow water environments. The study site is the Venice lagoon (Italy), where we compare the estimated SSC from various models and datasets against in situ probe measurements. The results showed that the physically-based model provides more robust estimate of SSC compared against empirical models when evaluated using the cross-validation method (leave-one-out). Despite the finer spectral resolution and the choice of optimal combinations of bands, the hyperspectral data is less reliable for SSC retrieval comparing to multispectral data due to its limited amount of historical dataset, information redundancy, and cross-band correlation.

Chapter 3 introduces a multipollutant sensor/sampler system that developed for use on mobile applications including aerostats and unmanned aerial vehicles (UAVs). The system is particularly applicable to open area sources such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed “Kolibri”, consists of low-cost sensors measuring CO2 and CO, and samplers for particulate matter and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller, which can record and transfer data in real time using a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emission monitors (CEMs) and another mobile sampling instrument (the ``Flyer'') that had been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer. The VOC emission factors obtained using the Kolibri are comparable to existing literature values. The Kolibri system can be applied to various open area sampling challenging situations such as fires, lagoons, flares, and landfills.

Chapter 4 evaluates the trade-off between sensor quality and quantity for fenceline monitoring of fugitive emissions. This research is motivated by the new air quality standard that requires continuous monitoring of hazardous air pollutants (HAPs) along the fenceline of oil and gas refineries. Recently, the emergence of low-cost sensors enables the implementation of spatially-dense sensor network that can potentially compensate for the low quality of individual sensors. To quantify sensor inaccuracy and uncertainty of describing gas concentration that is governed by turbulent air flow, a Bayesian approach is applied to probabilistically infer the leak source and strength. Our results show that a dense sensor network can partly compensate for low-sensitivity or high noise of individual sensors. However, the fenceline monitoring approach fails to make an accurate leak detection when sensor/wind bias exists even with a dense sensor network.

Chapter 5 explores the feasibility of applying a mobile sensing approach to estimate fugitive methane emissions in suburban and rural environments. We first compare the mobile approach against a stationary method (OTM33A) proposed by the US EPA using a series of controlled release tests. Analysis shows that the mobile sensing approach can reduce estimation bias and uncertainty compared against the OTM33A method. Then, we apply this mobile sensing approach to quantify fugitive emissions from several ammonia fertilizer plants in rural areas. Significant methane emission was identified from one plant while the other two shows relatively low emissions. Sensitivity analysis of several model parameters shows that the error term in the Bayesian inference is vital for the determination of model uncertainty while others are less influential. Overall, this mobile sensing approach shows promising results for future applications of quantifying fugitive methane emission in suburban and rural environments.

New and old mosquito-borne diseases have emerged and re-emerged in temperate regions over the recent past, but an understanding of mosquito population dynamics, a fundamental step toward disease control, remains elusive. In particular, we are still lacking reliable predictive models of mosquito abundance in temperate areas due to the subtle links between the fluctuation of mosquito population and highly heterogeneous environmental drivers. Hence, this doctoral dissertation presents an interdisciplinary approach towards an improved understanding and prediction of the fluctuations in mosquito abundance in temperate regions. In the first part of this dissertation a hierarchical Gompertz-based model is used to assess the relative importance of endogenous (density dependence) and exogenous (environmental forcings) controls and their interactions in regulating the dynamics of a West Nile Virus vector (Culex pipiens) in the Po River delta in Italy. The results clearly detect the effects of density-dependence in the observed population dynamics for the mosquito species analyzed and highlight the controls exerted by environmental forcings and habitat conditions. Subsequently, the characteristic scales of temporal variability in mosquito populations, and the representativeness of observations at different sampling resolutions, are investigated using a 10 year daily mosquito sample from Brunswick County, North Carolina. The species present in the sample (among which Aedes vexans and Culiseta melanura are addressed in greater detail, as vectors of East Equine Encephalitis and West Nile Virus) are investigated using a combination of time series analysis, individual based simulations, and density-dependent modeling approaches. Significant population fluctuations with characteristic periodicity between 2 days and several years are found in response to different regulation mechanisms. In particular, the observed fast fluctuations are importantly determined by a varying mosquito activity, rather than by reproduction/mortality processes, driven by rapid changes in meteorological conditions. Finally, in the third part of this study, a state space reconstruction (SSR) approach is used to understand how the predictability of mosquito abundance varies with aggregation time scale and with the prediction horizon, and how much can the prediction of mosquito abundance be improved by using daily observations compared to the commonly used once-per-week samples. The results show that the predictability of mosquito abundance decreases as the time scale of the models increases from one week to one month, while the predictability of per capita growth rate increases together with the modeling scale. It is also shown that the prediction of mosquito per capita growth rate can be improved using daily abundance observations. Furthermore, many mosquito models compare the observed and predicted abundance as a measure of model performance. However, my results suggest that short term forecasts of mosquito abundance may appear to have a significant capability due to the positive autocorrelation between abundance in subsequent time steps, even when the model's ability to predict the abundance change is low. Model capability should thus be evaluated comparing observed and modelled per capita rates of change.

Extreme events - that is, intense events so rare to be poorly represented in historical observational records - play a fundamental role in atmospheric processes, and can have far reaching consequences ranging from impacts on society, economy, the environment, as well as on the global water and energy budgets. However, characterizing the statistical properties of such events is a challenging task, as (i) by definition extremes are poorly sampled, and thus studying them often requires extrapolation beyond the range of available observations, and (ii) extremes are often the result of nonlinear and intermittent processes, which determine significant difficulties both in predicting them and in studying their frequency of occurrence beyond the range of observations. This dissertation focuses on new statistical methods specifically aimed at characterizing extremes events in rainfall and boundary layer turbulence, including contributions along three main lines of inquiry:

1) Developing extreme value models able to reduce estimation uncertainty in the case of short rainfall time series. To this end, a non-asymptotic approach is developed which, deviating from traditional extreme value theory, models the entire distribution of rainfall magnitudes and frequency of occurrence. Using compound distributions and the structure of latent-level Bayesian models, this framework accounts for the effects of low-frequency variability of rainfall statistics on the tail decay of their probability distribution.

2) Characterizing the frequency of extreme values from remotely-sensed rainfall estimates. This objective is approached by developing a downscaling technique that allows comparing rainfall statistics across different spatial averaging scales, and by constructing a model of the error so as to permit their validation over poorly gauged locations. The framework developed here now allows for the production of large-scale estimates of the frequency of extreme rainfall based on satellite-derived rainfall datasets and their validation even in data-scarce regions.

3) Investigating the dynamics of scalar quantities transported in the atmospheric boundary layer, with a focus on fluxes of sensible heat and methane. In the case of sensible heat, I studied to what extent the extreme values properties of temperature fluctuations retain information on the turbulence generation mechanism. In the case of methane, I focused instead on an inverse problem: given the observed statistical properties of methane concentration fluctuations, is it possible to infer the spatial intermittency of its source at the ground? In both cases, I found that statistical properties of the scalar, including its extreme value statistics, can be used to improve characterization of the turbulent flow and of its boundary conditions.