It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.
How to cite: Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sens.2020, 12, 509. [pdf]
Foundation scour is among the main causes of bridge collapse worldwide, resulting in significant direct and indirect losses. A vast amount of research has been carried out during the last decades on the physics and modelling of this phenomenon. The purpose of this paper is, therefore, to provide an up-to-date, comprehensive, and holistic literature review of the problem of scour at bridge foundations, with a focus on the following topics: (i) sediment particle motion; (ii) physical modelling and controlling dimensionless scour parameters; (iii) scour estimates encompassing empirical models, numerical frameworks, data-driven methods, and non-deterministic approaches; (iv) bridge scour monitoring including successful examples of case studies; (v) current approach for assessment and design of bridges against scour; and, (vi) research needs and future avenues.
How to cite: Pizarro, A.; Manfreda, S.; Tubaldi, E., The Science behind Scour at Bridge Foundations: A Review. Water, 12, 374, (doi: 10.3390/w12020374) 2020. [pdf]
Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s −1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s −1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s −1 of the ADCP measurements, on average.
How to cite: Pearce, S.; Ljubičić, R.; Peña-Haro, S.; Perks, M.; Tauro, F.; Pizarro, A.; Dal Sasso, S.F.; Strelnikova, D.; Grimaldi, S.; Maddock, I.; Paulus, G.; Plavšić, J.; Prodanović, D.; Manfreda, S. An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. Remote Sens., 12, 232, 2020. [pdf]
In this work, the semi-distributed hydrological modeling system GEOframe-NewAge was integrated with a web-based decision support system implemented for the Civil Protection Agency of the Basilicata region, Italy. The aim of this research was to forecast in near real-time the most important hydrological variables at 160 control points distributed over the entire region. The major challenge was to make the system operational in a data-scarce region characterized by a high hydraulic complexity, with several dams and infrastructures. In fact, only six streamflow gauges were available for the calibration of the model parameters. Reliable parameter sets were obtained by simulating the hydrological budget and then calibrating the rainfall-runoff parameters. After the extraction of the flow-rating curves, six sets of parameters were obtained considering the different streamflow components (i.e., the baseflow and surface runoff) and using a multi-site calibration approach. The results show a good agreement between the measured and modeled discharges, with a better agreement in the sections located upstream of the dams. Moreover, the results were validated using the inflows measured at the most important dams (Pertusillo, San Giuliano and Monte Cotugno). For rivers without monitoring points, parameters were assigned using a principle of hydrological similarity in terms of their geology, lithology, and climate.
How to Cite: Bancheri, M., R. Rigon and S. Manfreda, The GEOframe-NewAge modelling system applied in a data scarce environment, Water, 12, 86, 2019. [pdf]
Traditional monitoring approaches are unlikely to provide the level of detail required to advance our understanding and description of the underlying physical processes and mechanisms for both technical and economical limitations (Manfreda and McCabe, 2019). Indeed, our ability to monitor system processes in the face of recent climate and anthropogenic changes is being increasingly compromised by the significant decline in the number of monitoring installations over the last few decades (Shiklomanov et al., 2002). The dynamic nature and inherent variability of many hydrological processes dictates a need for new monitoring technologies and approaches able to increase spatial and temporal resolution of data.
How to cite: Manfreda, S., Dal Sasso, S. F., Pizarro, A., & Tauro, F. New Insights Offered by UAS for River Monitoring. Applications of Small Unmanned Aircraft Systems: Best Practices and Case Studies, 211, 2019.
Since the turn of the 21st Century, image based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe, and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed, equipped with differing image pre-processing, and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis, along with validation data is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate, and describe datasets acquired from six countries across Europe and Asia, consisting of videos that have been subjected to a range of pre-processing, and image velocimetry analysis.We present both the raw footage and processed imagery along with information about the processing parameters used. Validation data is available for 12 of the 13 case studies presented enabling these data to be used for validation and accuracy assessment.
In recent years, the acquisition of data from multiple sources, together with improvements in computational capabilities, has allowed to improve our understanding on natural hazard through new approaches based on machine learning and Big Data analytics. This has given new potential to flood risk mapping, allowing the automatic extraction of flood prone areas using digital elevation model (DEM) based geomorphic approaches. Most of the proposed geomorphic approaches are conceived mainly for the identification of flood extent. In this article, the DEM‐based method based on a geomorphic descriptor—the geomorphic flood index (GFI)—has been further exploited to predict inundation depth, which is useful for quantifying flood induced damages. The new procedure is applied on a case study located in southern Italy, obtaining satisfactory performances. In particular, the inundation depths are very similar to the ones obtained by hydraulic simulations, with a root‐mean‐square error (RMSE) = 0.335 m, in the domain where 2D dynamics prevail. The reduced computational effort and the general availability of the required data make the method suitable for applications over large and data‐sparse areas, opening new horizons for flood risk assessment at national/continental/global scale.
How to cite: Manfreda, S., C. Samela, A DEM-based method for a rapid estimation of flood inundation depth, Journal of Flood Risk Management, 12 (Suppl. 1):e12541, (doi: 10.1111/jfr3.12541) 2019. [pdf]
Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface (0-2 cm) soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigates using passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a 2 year time-scale and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based Soil Moisture Analytical Relationship (SMAR) is calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and is first tested at the Shale Hills experimental catchment in central Pennsylvania. The model meets a root mean square error (RMSE) benchmark of 0.06 cm3 cm-3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model is calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters are generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is < 0.06 cm3 cm-3. Lastly, the 1 km EUS regional RZSM maps are tested with data from Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm-3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same resolution of soil property maps.
How to cite: Baldwin, D., S. Manfreda, H. Lin, and E.A.H. Smithwick, Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model, Remote Sensing, 11, 2013, 2019. [pdf]
The scour at bridge foundations caused by supercritical flows is reviewed and knowledge gaps are analyzed focusing on the flow and scour patterns, on available measuring techniques for laboratory and field, and on physical and advanced numerical modeling techniques. Evidence suggest that scour depth caused by supercritical flows is much smaller than expected, in the order of magnitude of that found in subcritical flows, although the reasons explaining this behavior remain still unclear. Important questions on the interaction of the horseshoe vortex with the detached hydraulic-jump and the wall-jet flow observed in supercritical flows arise, e.g. does the interaction between the flow structures enhance or debilitate the bed shear stresses caused by the horseshoe vortex? What is the effect of the Froude number of the incoming flow on the flow structures around the foundation and on the scour process? Recommendations are provided to develop and adapt research methods used in the subcritical flow regime for the study of the more challenging supercritical flow case.
How to cite: Link, O., E. Mignot; S. Roux; B. Camenen; C. Escauriaza; J. Chauchat; W. Brevis; S. Manfreda, Scour at Bridge Foundations in Supercritical Flows: An Analysis of Knowledge Gaps, Water (MDPI), 11(8), 1656; https://doi.org/10.3390/w11081656, 2019. [pdf]
A detailed delineation of flood-prone areas over large regions represents a challenge that cannot be easily solved with today’s resources. The main limitations lie in algorithms and hardware, but also costs, scarcity and sparsity of data and our incomplete knowledge of how inundation events occur in different riverfloodplains. We showcase the implementation of a data-driven web application for regional analyses and detailed (i.e., tens of meters) mapping of floodplains, based on (a) the synthesis of hydrogeomorphic features into a morphological descriptor and (b) its classification to delineate flood-prone areas. We analysed the skill of the descriptor and the performance of the mapping method for European rivers. The web application can be effectively used for delineating flood-prone areas, reproducing the reference flood maps with a classification skill of 88.59% for the 270 major river basins analysed across Europe and 84.23% for the 64 sub-catchments of the Po River.
How to cite: Tavares da Costa, R., S. Manfreda, V. Luzzi, C. Samela, P. Mazzoli, A. Castellarin, S. Bagli, A web application for hydrogeomorphic flood hazard mapping, Environmental Modelling and Software, Volume 118, August 2019, Pages 172-186 (doi: 10.1016/j.envsoft.2019.04.010) 2019. [pdf]