Sediment Yield in Mountain Basins, Analysis, and Management: The SMART-SED Project

Brambilla, Davide; Papini, Monica; Ivanov, Vladislav Ivov; Bonaventura, Luca; Abbate, Andrea; Longoni, Laura

doi:10.1007/978-3-030-43953-8_3

Cited by 6 publications

(16 citation statements)

References 26 publications

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“…The hydrological part of CRHyME follows a standard implementation, where each cell of the terrain domain is considered as a tank that communicates in cascade to the others (Brambilla et al, 2020;Roo et al, 1996;Sutanudjaja et al, 2018) following the downstream river network. The hydrological cycle that normally takes place in the first 2 m of soil layers is described considering the interaction of the rainfall with the surface through the canopy interception, infiltration, and percolation processes and also snow accumulation and melting are simulated.…”

Section: Hydrological Module and Equationsmentioning

confidence: 99%

“…▪ The sediment transport in terms of the bed-load process has been described considering the Erosion Potential Method (EPM) (Longoni et al, 2016;Brambilla et al, 2020;Milanesi et al, 2015;Ivanov et al, 2020a) for simulating erosion processes and the stream power laws available in the literature for defining the transport capacity of the rivers (Vetsch et al, 2018).…”

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confidence: 99%

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CRHyME (Climatic Rainfall Hydrogeological Model Experiment): a new model for geo-hydrological hazard assessment at the basin scale

Abbate¹,

Mancusi²,

Frigerio³

et al. 2023

Preprint

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Abstract. This work presents the new model called CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment), a tool for the geo-hydrological hazard evaluation. CRHyME is a physically based and spatially distributed model written in Python language and represents an extension of the classic hydrological models that simulate inflows-outflows at the basin scale. A series of routines have been integrated to describe the phenomena of geo-hydrological instabilities such as the 10 triggering of shallow landslides as well as debris flows, catchment erosion, and sediment transport into the river. These phenomena are generally decoupled with respect to the continuous hydrological simulation while in CRHyME they are quantitatively and simultaneously evaluated through a multi-hazard approach. CRHyME has been tested on some case studies located in Italian basins. Valtellina and Emilia's areas were considered for the calibration and validation procedures of the model thanks also to the availability of literature data concerning past occurred 15 geo-hydrological instability phenomena. Calibration and validation of the model conducted on presented case studies have been assessed through some hydrological indexes such as NSE (Nash–Sutcliffe Efficiency) and RMSE (Root Mean Square Error) while for landslide phenomena the ROC (Receiver Operating Characteristic) methodology was applied. CHRyME has been able to: 1) reconstruct the surface runoff at the reference hydrometric stations located at the outlets of the basins, 2) estimate the solid transport at some hydropower reservoirs compared to the reference data, and 3) evaluate the triggering of 20 shallow landslides and debris flows compared to those recorded in the literature. The ranking has shown a rather good performance of the model in terms of numerical conservativity of water and solid balances, revealing suitable not only for back-analysis studies but also as an efficient tool for Civil Protection multi-hazard assessment.

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Section: Hydrological Module and Equationsmentioning

confidence: 99%

mentioning

confidence: 99%

CRHyME (Climatic Rainfall Hydrogeological Model Experiment): a new model for geo-hydrological hazard assessment at the basin scale

Abbate¹,

Mancusi²,

Frigerio³

et al. 2023

Preprint

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“…Depending on the type of model adopted, PE can be expressed using different formulations. According to [57,92], in LUME, the PE depends on time-delay coefficients, as reported in Equations ( 8)- (11). In Equation (8), the PE is a product of three terms: PE dynamic , PE cloud , and PE fallout .…”

Section: Estimating Microphysical τ C and Fallout τ F Time-delay Coef...mentioning

confidence: 99%

“…They constitute a serious threat to infrastructures, cities, and populations since they can evolve rapidly and can affect entire catchments, causing extensive injuries and loss of lives [1,6,8,9]. From an emergency management viewpoint, it is extremely important to try to predict or anticipate the possible triggering of those threats to reduce the associated risk to the population [10][11][12]. One of the main strategies to forecast these hydrogeological hazards involves the study of triggering rainfall [1,3,4,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

Abbate

Papini

Longoni

2022

Water

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Critical hydrometeorological events are generally triggered by heavy precipitation. In complex terrain, precipitation may be perturbed by the upslope raising of the incoming humid airflow, causing in some cases extreme rainfall. In this work, the application of LUME—Linear Upslope Model Extension—to a group of extreme events that occurred across mountainous areas of the Central Alps and Apennines in Italy is presented. Based on the previous version, the model has been “extended” in some aspects, proposing a methodology for physically estimating the time-delay coefficients as a function of precipitation efficiency. The outcomes of LUME are encouraging for the cases studied, revealing the intensification of precipitation due to the orographic effect. A comparison between the reference rain gauge data and the results of the simulations showed good agreement. Since extreme precipitation is expected to increase due to climate change, especially across the Mediterranean region, LUME represents an effective tool to investigate more closely how these extreme phenomena originate and evolve in mountainous areas that are subject to potential hydrometeorological risks.

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“…In the former case, systems of partial differential equations (PDE) are used to model the dynamics of the flow and to consistently assess sediment transport (see, e.g., Vetsch et al 2017;Bonaventura et al 2021). In this case, field data can be used to calibrate the PDE, both in terms of providing sensible input parameters (e.g., Bakke et al 2017;Gatti et al 2020) and to validate the model outputs (e.g., Brambilla et al 2020). Critical points of this class of methods typically lie in the numerical complexity of solving the PDEs, in the data assimilation process, and in the uncertainty quantification of the model, which often require the development of ad hoc techniques.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream

et al. 2022

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The problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy is addressed. This study is based on a large set of measurements collected from real pebbles, traced along the stream through radio-frequency identification tags after precipitation events. Two classes of data-driven models based on machine learning and functional geostatistics approaches are proposed and evaluated to predict the probability of movement of single pebbles within the stream. The first class built upon gradient-boosting decision trees allows one to estimate the probability of movement of a pebble based on the pebbles’ geometrical features, river flow rate, location, and subdomain types. The second class is built upon functional kriging, a recent geostatistical technique that allows one to predict a functional profile—that is, the movement probability of a pebble, as a function of the pebbles’ geometrical features or the stream’s flow rate—at unsampled locations in the study area. Although grounded in different perspectives, both models aim to account for two main sources of uncertainty, namely, (1) the complexity of a river’s morphological structure and (2) the highly nonlinear dependence between probability of movement, pebble size and shape, and the stream’s flow rate. The performance of the two methods is extensively compared in terms of classification accuracy. The analyses show that despite the different perspectives, the overall performance is adequate and consistent, which suggests that both approaches can provide modeling frameworks for sediment transport. These data-driven approaches are also compared with physics-based ones that are classically used in the hydrological literature. Finally, the use of the developed models in a bottom-up strategy, which starts with the prediction/classification of a single pebble and then integrates the results into a forecast of the grain-size distribution of mobilized sediments, is discussed.

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Sediment Yield in Mountain Basins, Analysis, and Management: The SMART-SED Project

Cited by 6 publications

References 26 publications

CRHyME (Climatic Rainfall Hydrogeological Model Experiment): a new model for geo-hydrological hazard assessment at the basin scale

CRHyME (Climatic Rainfall Hydrogeological Model Experiment): a new model for geo-hydrological hazard assessment at the basin scale

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream

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