Fluorescent particle tracers for surface hydrology

Surface water processes control downstream runoff phenomena, waste and pollutant diffusion, erosion mechanics, and sediment transport. However, current observational methodologies do not allow for the identification and kinematic characterization of the physical processes contributing to catchment dynamics. Traditional methodologies are not capable to cope with extreme in-situ conditions, including practical logistic challenges as well as inherent flow complexity. In addition, available observational techniques are non-exhaustive for describing multiscale hydrological processes. This research addresses the need for novel observations of the hydrological community by developing pioneer flow characterization approaches that rely on the mutual integration of traditional tracing techniques and state-of-the-art image-based sensing procedures. These novel methodologies enable the in-situ direct observation of surface water processes through remote and unsupervised procedures, thus paving the way to the development of distributed networks of sensing platforms for catchment-scale environmental sensing. More specifically, the proposed flow characterization methodology is a low-cost measurement system that can be applied to a variety of real-world settings spanning from few centimeters rills in natural catchments to riverine ecosystems. The technique is based on the use of in-house synthesized environmentally-friendly fluorescent particle tracers through digital cameras for direct flow measurement and travel time estimations. Automated image analysis-based procedures are developed for real-time flow characterization based on image manipulation, template-based correlation, particle image velocimetry, and dimensionality reduction methodologies. The feasibility of the approach is assessed through laboratory-designed experiments, where the accuracy of the methodology is investigated with respect to well-established flow visualization techniques. Further, the transition of the proposed flow characterization approach to natural settings is studied through paradigmatic observations of natural stream flows in small scale channel and riverine settings and overland flows in hillslope environments. The integration of the proposed flow sensing system in a stand-alone, remote, and mobile platform is explored through the design, development, and testing of a miniature aerial vehicle for environmental monitoring through video acquisition and processing

Fluorescent eco-particles for surface flow physics analysis

In this letter, we describe a novel methodology for fabricating inexpensive environmentally-friendly fluorescent microparticles for quantitative surface flow visualization. Particles are synthesized from natural white beeswax and a highly diluted solution of a nontoxic fluorescent red dye. Bead fluorescence exhibits a long lifetime in adverse conditions, such as exposure to weathering agents, and is enhanced by Ultra Violet radiation. The fluorescent eco-particles are integrated in a particle image velocimetry study of circular hydraulic jump to demonstrate their feasibility in tracing complex surface flows

Chilean glacial lake outburst flood impacts on dam construction

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Includes bibliographical references (leaves 80-82).Four Glacial Lake Outburst Floods (GLOF) occurred in the Colonia Glacier (Northern Patagonia Icefield, Chile) from April 2008 to March 2009. Lago Cachet 2 emptied four times producing a maximum excess discharge in the downstream Rio Baker of about 2,500 m3/s. These events have occurred at the same time as the proposal by HidroAysen to install two dams on the Rio Baker to produce hydropower. The aim of this thesis is to investigate the GLOF mechanisms and to estimate the magnitude of outburst flows to better understand their effect on the feasibility of the HidroAysen project. A temperature balance model for Lago Cachet 2 is developed to estimate the lake temperature before an outburst. These temperatures become inputs for the modified Clarke's model that predicts peak discharge of the lake given its geometry. The temperature model gave a lake temperature in January equal to 7.4 °C degrees that produces a peak discharge of approximately 2,000 m3/s, somewhat lower than the one registered at the confluence of the Rio Colonia with the Rio Baker (the station registered a peak discharge of 2,500 m3/s). A sensitivity analysis of the model to the various inputs suggests that model accuracy could be improved with more information about the geometry of Lago Cachet 2 and meteorological data. The study also illustrates how air temperature influences the melting of the Colonia Glacier and how the temperature trend is responsible for the GLOF frequency. A possible future scenario is proposed for Lago Cachet 2.by Flavia Tauro.M.Eng

PTV-Stream: A simplified particle tracking velocimetry framework for stream surface flow monitoring

Abstract Particle tracking velocimetry (PTV) is a promising image-based approach for remote streamflow measurements in natural environments. However, most PTV approaches require highly-defined round-shaped tracers, which are often difficult to observe outdoors. PTV-Stream offers a versatile alternative to cross-correlation-based PTV by affording the identification and tracking of features of any shape transiting in the field of view. This nearest-neighbor algorithm is inherently thought for estimating surface flow velocity of streams in outdoor conditions. The procedure allows for reconstructing and filtering the trajectories of features that are more likely to pertain to actual objects transiting in the field of view rather than to water reflections. The procedure is computationally efficient and is demonstrated to yield accurate measurements even in case of downsampled image sequences

Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

: The study of non-perennial streams requires extensive experimental data on the temporal evolution of surface flow presence across different nodes of channel networks. However, the consistency and homogeneity of available datasets is threatened by the empirical burden required to map stream network expansions and contractions. Here, we developed a data-driven, graph-theory framework aimed at representing the hierarchical structuring of channel network dynamics (i.e., the order of node activation/deactivation during network expansion/retraction) through a directed acyclic graph. The method enables the estimation of the configuration of the active portion of the network based on a limited number of observed nodes, and can be utilized to combine datasets with different temporal resolutions and spatial coverage. A proof-of-concept application to a seasonally-dry catchment in central Italy demonstrated the ability of the approach to reduce the empirical effort required for monitoring network dynamics and efficiently extrapolate experimental observations in space and time

Optical sensing for stream flow observations: a review

Images are revolutionizing the way we sense and characterize the environment by offering higher spatial and temporal coverage in ungauged environments at competitive costs. In this review, we illustrate the major image-based approaches that have been lately adopted within the hydrological research community. Although many among such methodologies have been developed some decades ago, recent efforts have been devoted to their transition from laboratories to operational outdoor settings. Sample applications of image-based techniques include flow discharge estimation in riverine environments, clogging dynamics in irrigation systems, and flow diagnostics in engineering infrastructures. The potential of such image-based approaches towards fully remote observations is also illustrated through a simple experiment with an unmanned aerial vehicle

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge of physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to distinguish in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios

UAV-based LiDAR for high-throughput determination of plant height and above‐ground biomass of the bioenergy grass arundo donax

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.</jats:p