Trend of Annual Maximum Rainfall in Campania Region (Southern Italy)

Extreme rainfall events are increasing in both number and intensity at global scale; however, it is hard to quantify the impact of climate change at local scale given the strong temporal and spatial heterogeneity of this process. Moreover, limited data availability and its spatial variability requires significant effort to identify specific trends at the regional level. In this study, we attempt to construct a detailed description of rainfall patterns and trends over the Campania Region, southern Italy. For this reason, the dataset of rainfall annual maxima in pre-assigned durations was constructed using all available records and extended using interpolation methods such as Inverse Distance Weighting and Ordinary Kriging methods. The rainfall dataset allowed actual trends over the region to be quantified using the Mann-Kendall trend test and the record-breaking analysis. The trend test reveals that most of the rainfall stations display no statistically significant trend, however, an increasing trend of extreme rainfall for short durations, in specific portions of the region, is observed.

How to cite: Avino, A., S. Manfreda, L. Cimorelli, and D. Pianese, Trend of Annual Maximum Rainfall in Campania Region (Southern Italy), Hydrological Processes, (doi:10.1002/hyp.14447), 2021. [pdf]

In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

How to cite: Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; Su, Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sens. 202113, 4893. [pdf]

Delineation of flood-prone areas in cliffed coastal regions through a procedure based on the geomorphic flood index

The geomorphic flood index (GFI) method provides a good representation of flood-prone areas. However, the method does not account for floodwater transfers in undefined interbasins (UIBs), which represent intercluded small basins along the coastline likely to be flooded by adjacent major rivers. The present work addresses this shortcoming by complementing the GFI approach with an iterative procedure that considers UIBs and water transfers between basins. The methodology was tested on a coastal basin in southern Italy and the outcome was compared with a flood map obtained by a two-dimensional hydraulic simulation. GFI performance as a morphological descriptor improved from 74% (standard method) to 94% with the addition of the iterative procedure. The proposed methodology, with the same parameterization, was applied on a second adjacent coastal basin obtaining improvements both in terms of true positive (from 56 to 79%) and false negative rates (from 44 to 21%). Finally, a sensitivity analysis to the flood return periods highlighted a strong influence on model parameterization for return periods below 20 years. This achievement represents a new development in the application of the GFI method, which can help stakeholders in a more time- and cost-effective flood risk management in hazard-prone areas.

How to cite: Albertini, C., D. Miglino, V. Iacobellis, F. De Paola, S. Manfreda, Flood-prone areas delineation in coastal regions using the Geomorphic Flood IndexJournal of Flood Risk Management, e12766,(https://doi.org/10.1111/jfr3.12766) 2021.

A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.

How to cite: Ljubičić, R., Strelnikova, D., Perks, M. T., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P., and Manfreda, S.: A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations, Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, 2021. [pdf]

Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis

Ecosystem complexity is among the important drivers of biodiversity and ecosystem functioning, and unmanned aerial systems (UASs) are becoming an important tool for characterizing vegetation patterns and processes. The variety of UASs applications is immense, and so are the procedures to process UASs data described in the literature. Optimizing the workflow is still a matter of discussion. Here, we present a comprehensive synthesis aiming to identify common rules that shape workflows applied in UAS-based studies facing complexity in ecosystems. Analysing the studies, we found similarities irrespective of the ecosystem, according to the character of the property addressed, such as species composition (biodiversity), ecosystem structure (stand volume/complexity), plant status (phenology and stress levels), and dynamics (disturbances and regeneration). We propose a general framework allowing to design UAS-based vegetation surveys according to its purpose and the component of ecosystem complexity addressed. We support the framework by detailed schemes as well as examples of best practices of UAS studies covering each of the vegetation properties (i.e. composition, structure, status and dynamics) and related applications. For an efficient UAS survey, the following points are crucial: knowledge of the phenomenon, choice of platform, sensor, resolution (temporal, spatial and spectral), model and classification algorithm according to the phenomenon, as well as careful interpretation of the results. The simpler the procedure, the more robust, repeatable, applicable and cost effective it is. Therefore, the proper design can minimize the efforts while maximizing the quality of the results.

How to cite: Müllerová J. , X. Gago, M. Bučas,J. Company, J. Estrany, J. Fortesa, S. Manfreda, A. Michez, M. Mokroš, G. Paulus, E. Tiškus, M. A. Tsiafouli, R. Kent, Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis, Ecological Indicators, Volume 131, November 2021, 108156. [pdf]

Recent Advancements and Perspectives in UAS-Based Image Velocimetry

Videos acquired from Unmanned Aerial Systems (UAS) allow for monitoring river systems at high spatial and temporal resolutions providing unprecedented datasets for hydrological and hydraulic applications. The cost-effectiveness of these measurement methods stimulated the diffusion of image-based frameworks and approaches at scientific and operational levels. Moreover, their application in different environmental contexts gives us the opportunity to explore their reliability, potentialities and limitations, and future perspectives and developments. This paper analyses the recent progress on this topic, with a special focus on the main challenges to foster future research studies.

How to cite: Dal Sasso, S.F.; Pizarro, A.; Manfreda, S. Recent Advancements and Perspectives in UAS-Based Image VelocimetryDrones5, 81, 2021. [pdf]

Impact of detention dams on the probability distribution of floods

Detention dams are one of the most effective practices for flood mitigation. Therefore, the impact of these structures on the basin hydrological response is critical for flood management and the design of flood control structures. With the aim of providing a mathematical framework to interpret the effect of flow control systems on river basin dynamics, the functional relationship between inflows and outflows is investigated and derived in a closed form. This allowed the definition of a theoretically derived probability distribution of the peak outflows from in-line detention basins. The model has been derived assuming a rectangular hydrograph shape with a fixed duration and a random flood peak. In the present study, the undisturbed flood peaks are assumed to be Gumbel distributed, but the proposed mathematical formulation can be extended to any other flood-peak probability distribution. A sensitivity analysis of parameters highlighted the influence of detention basin capacity and rainfall event duration on flood mitigation on the probability distribution of the peak outflows. The mathematical framework has been tested using for comparison a Monte Carlo simulation where most of the simplified assumptions used to describe the dam behaviours are removed. This allowed demonstrating that the proposed formulation is reliable for small river basins characterized by an impulsive response. The new approach for the quantification of flood peaks in river basins characterized by the presence of artificial detention basins can be used to improve existing flood mitigation practices and support the design of flood control systems and flood risk analyses.

How to cite: Manfreda, S., D. Miglino, and C. Albertini, Impact of detention dams on the probability distribution of floods, Hydrol. Earth Syst. Sci., 25, 4231–4242, https://doi.org/10.5194/hess-25-4231-2021, 2021 [pdf]

Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

How to cite: Francos, N.; Romano, N.; Nasta, P.; Zeng, Y.; Szabó, B.; Manfreda, S.; Ciraolo, G.; Mészáros, J.; Zhuang, R.; Su, B.; Ben-Dor, E.  Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: a Case Study of AlentoItalyRemote Sensing13, 2606, (doi: 10.3390/rs13132606) 2021. [pdf]

Increasing LSPIV performances by exploiting the seeding distribution index

Image-based approaches for surface velocity estimations are becoming increasingly popular because of the increasing need for low-cost river flow monitoring methods. In this context, seeding characteristics and dynamics along the video footage represent one of the key variables influencing image velocimetry results. Recent studies highlight the need to identify parameter settings based on local flow conditions and environmental factors apriori, making the use of image velocimetry approaches hard to automatise for continuous monitoring. The seeding distribution index (SDI) – recently introduced by the authors – identifies the best frame window length of a video to analyse, reducing the computational loads and improving image velocimetry performance. In this work, we propose a method based on an average SDI time series threshold with noise filtering. This method was tested on three case studies in Italy and validated on one in UK, where a relatively high number of measurements is available. Following this method, we observed an error reduction of 20-39% with respect to the analysis of the full video. This beneficial effect appears even more evident when the optimisation is applied at sub-sector scales, in cases where SDI shows a marked variability along the cross-section. Finally, an empirical parameter t was proposed, calibrated, and validated for practical uses to define the SDI threshold. tshowed relatively stable values in the different contexts where it has been applied. Application of the seeding index to image-based velocimetry for surface flow velocity estimates is likely to enhance measurement accuracy in future studies.

Keywords: Image Velocimetry, UAS, river flow monitoring, LSPIV, seeding metrics, Seeding Distribution Index, frame footage.

How to cite: Dal Sasso, S.F., A. Pizarro, S. Pearce, I. Maddock, S. Manfreda, Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales, Journal of Hydrology, 2021. [pdf]


A comparison of tools and techniques for stabilising UAS imagery for surface flow observations

This research presents an investigation of different strategies and tools for digital image stabilisation for image velocimetry purposes. Basic aspects of image stabilisation and transformation are presented, and their applicability is discussed in terms of image velocimetry. Seven free-to-use open-source tools (six community-developed and one off-the-shelf) are described and compared according to their stabilisation accuracy, robustness in different flight and ground conditions, computational complexity, ease of use, and other capabilities. A novel approach for fast stabilisation accuracy analysis is also developed, presented, and applied to the stabilised image sequences. Based on the obtained results, some general guidelines for choosing a suitable tool for specific image velocimetry tasks have been obtained. This research also aims to provide a basis for further development or improvement of digital image stabilisation tools, as well as for the analyses of stabilisation impact on image velocimetry results.

How to cite: Ljubičić, R., D. Strelnikova, M. T. Perks, A. Eltner, S. Peña-Haro, A. Pizarro, S. F. Dal Sasso, U. Scherling, P. Vuono, and S. Manfreda, A comparison of tools and techniques for stabilising UAS imagery for surface flow observations, Hydrology and Earth System Sciences, 2021. [pdf]