River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.
Keywords: river monitoring; image velocimetry; LSPIV; PTV; UAS; surface flow velocity; seeding density
How to cite: Dal Sasso, S.F.; Pizarro, A.; Manfreda, S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sens.2020, 12, 1789. [pdf]
Spatial patterns found in vegetated ecosystems exhibit different degrees of organization in stand density that can be interpreted as an indicator of ecosystem health. In semiarid environments, it is possible to observe transitions from over-dispersed individuals (e.g., an ordered lattice) to under-dispersed individuals (e.g., clumped points). These configurations correspond to different strategies of adaptation or optimization, whose understanding may help to predict some of the consequences of environmental changes for both ecosystem services and water resources. For this reason, we have developed a theoretical framework that characterizes the dispersion of individuals through a generalized double Poisson distribution and estimates the landscape-wide statistics using a soil moisture model accounting for tree canopies and root systems overlapping. Considering both the shading effect (light interception) of the canopies and the partitioning of water fluxes due to the presence of multiple individual root systems in one point, the optimum spacing between individuals at a given stand density is determined. This framework allows identifying the climatic boundaries for different landscape patterns in terms of optimal water use and stress. This simple scheme explains well the observed patterns of vegetation in arid and semiarid ecosystems.
How to cite: Manfreda, S., K. K. Caylor, S. Good, An Ecohydrological framework to explain shifts in vegetation organization across climatological gradients, Ecohydrology, 10(3), 1-14, (doi: 10.1002/eco.1809), 2017. [pdf]
Characterizing the spatial dynamics of soil moisture ﬁelds is a key issue in hydrology, oﬀering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superﬁcial layer shows a lower degree of spatial organization and higher temporal variability, which is reﬂected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.
How to cite: Manfreda, S., M. McCabe, E.F. Wood, M. Fiorentino and I. Rodríguez-Iturbe, Spatial Patterns of Soil Moisture from Distributed Modeling, Advances in Water Resources, 30(10), 2145-2150, (doi: 10.1016/j.advwatres.2006.07.009), 2007. [pdf]