Physically-Based Models for Estimating Rainfall Triggering Debris Flows in Campania (Southern Italy)

Abstract. On 15–16 December 1999, heavy rainfall severely stroke Campania region (southern Italy), triggering numerous debris flows on the slopes of the San Martino Valle Caudina-Cervinara area. Soil slips originated within the weathered volcaniclastic mantle of soil cover overlying the carbonate skeleton of the massif. Debris slides turned into fast flowing mixtures of matrix and large blocks, downslope eroding the soil cover and increasing their original volume. At the base of the slopes, debris flows impacted on the urban areas, causing victims and severe destruction (Vittori et al., 2000). Starting from a recent study on landslide risk conditions in Campania, carried out by the Regional Authority (PAI –Hydrogeological setting plan, in press), an evaluation of the debris-flow susceptibility has been performed for selected areas of the above mentioned villages. According to that study, such zones would be in fact characterised by the highest risk levels within the administrative boundaries of the same villages ("HR-zones"). Our susceptibility analysis has been performed by applying SCIDDICA S3–hex – a hexagonal Cellular Automata model (von Neumann, 1966), specifically developed for simulating the spatial evolution of debris flows (Iovine et al., 2002). In order to apply the model to a given study area, detailed topographic data and a map of the erodable soil cover overlying the bedrock of the massif must be provided (as input matrices); moreover, extent and location of landslide source must also be given. Real landslides, selected among those triggered on winter 1999, have first been utilised for calibrating SCIDDICA S3–hex and for defining "optimal" values for parameters. Calibration has been carried out with a GIS tool, by quantitatively comparing simulations with actual cases: optimal values correspond to best simulations. Through geological evaluations, source locations of new phenomena have then been hypothesised within the HR-zones. Initial volume for these new cases has been estimated by considering the actual statistics of the 1999 landslides. Finally, by merging the results of simulations, a deterministic susceptibility zonation of the considered area has been obtained. In this paper, aiming at illustrating the potential for debris-flow hazard analyses of the model SCIDDICA S3–hex, a methodological example of susceptibility zonation of the Vallicelle HR-zone is presented.

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Performance of the dynamically dimensioned search algorithm: influence of parameter initialization strategy when calibrating a physically based hydrological model

Hydrology Research ◽

10.2166/nh.2017.139 ◽

2017 ◽

Vol 49 (4) ◽

pp. 971-988 ◽

Cited By ~ 5

Author(s):

Franck Lespinas ◽

Ashu Dastoor ◽

Vincent Fortin

Keyword(s):

Search Algorithm ◽

Low Cost ◽

Initial Parameter ◽

Cost Strategy ◽

Physically Based ◽

Physically Based Models ◽

Calibration Solution ◽

Low Cost Strategy ◽

Parameter Values ◽

Initialization Strategy

Abstract This study presents an evaluation of the performance of the dynamically dimensioned search (DDS) algorithm when calibrating the hydrological component of the Visualizing Ecosystems for Land Management Assessments (VELMA) ecohydrological model. Two calibration strategies were tested for the initial parameter values: (1) a ‘high-cost strategy’, where 100 sets of initial parameter values were randomly chosen within the overall parameter space, and (2) a ‘low-cost strategy’, where a unique set of initial parameter values was derived from the available field data. Both strategies were tested for six different values of the maximum number of model evaluations ranging between 100 and 10,000. Results revealed that DDS is able to converge rapidly to a good parameter calibration solution of the VELMA hydrological component regardless of the parameter initialization strategy used. The accuracy and convergence efficiency of the DDS algorithm were, however, slightly better for the low-cost strategy. This study suggests that initializing the parameter values of complex physically based models using information on the watershed characteristics can increase the efficiency of the automatic calibration procedures.

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Evaluating performances of simplified physically based models for landslide susceptibility

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-13217-2015 ◽

2015 ◽

Vol 12 (12) ◽

pp. 13217-13256 ◽

Cited By ~ 5

Author(s):

G. Formetta ◽

G. Capparelli ◽

P. Versace

Keyword(s):

Landslide Susceptibility ◽

Goodness Of Fit ◽

Shallow Landslides ◽

Model Parameters ◽

Test Case ◽

Susceptibility Analysis ◽

Physically Based ◽

Private And Public ◽

Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

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A New Approach to Mapping Landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA - Research Data

HydroShare Resources ◽

10.4211/hs.6d8c3c46f4c8422796f28584eb9bdfaa ◽

2019 ◽

Cited By ~ 1

Keyword(s):

Research Data ◽

North Cascades ◽

New Approach ◽

The North ◽

Landslide Hazards ◽

Physically Based ◽

Physically Based Models

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Uncertainty in debris flow rainfall thresholds

10.5194/egusphere-egu21-15175 ◽

2021 ◽

Author(s):

Matteo Berti ◽

Alessandro Simoni

Keyword(s):

Debris Flow ◽

Debris Flows ◽

Measurement Errors ◽

Critical Conditions ◽

Model Parameters ◽

Average Intensity ◽

Rainfall Thresholds ◽

Sources Of Uncertainty ◽

Time Space ◽

Physically Based

Rainfall is the most significant factor for debris flows triggering. Water is needed to saturate the soil, initiate the sediment motion (regardless of the mobilization mechanism) and transform the solid debris into a fluid mass that can move rapidly downslope. This water is commonly provided by rainfall or rainfall and snowmelt. Consequently, most warning systems rely on the use of rainfall thresholds to predict debris flow occurrence. Debris flows thresholds are usually empirically-derived from the rainfall records that caused past debris flows in a certain area, using a combination of selected precipitation measurements (such as event rainfall P, duration D, or average intensity I) that describe critical rainfall conditions. Recent years have also seen a growing interest in the use of coupled hydrological and slope stability models to derive physically-based thresholds for shallow landslide initiation.In both cases, rainfall thresholds are affected by significant uncertainty. Sources of uncertainty include: measurement errors; spatial variability of the rainfall field; incomplete or uncertain debris flow inventory; subjective definition of the &#8220;rainfall event&#8221;; use of subjective criteria to define the critical conditions; uncertainty in model parameters (for physically-based approaches). Rainfall measurement is widely recognized as a main source of uncertainty due to the extreme time-space variability that characterize intense rainfall events in mountain areas. However, significant errors can also arise by inaccurate information reported in landslide inventories on the timing of debris flows, or by the criterion used to define triggering intensities.This study analyzes the common sources of uncertainty associated to rainfall thresholds for debris flow occurrence and discusses different methods to quantify them. First, we give an overview of the various approaches used in the literature to measure the uncertainty caused by random errors or procedural defects. These approaches are then applied to debris flows using real data collected in the Dolomites (Northen Alps, Itay), in order to estimate the variabilty of each single factor (precipitation, triggering timing, triggering intensity..). Individual uncertainties are then combined to obtain the overall uncertain of the rainfall threshold, which can be calculated using the classical method of &#8220;summation in quadrature&#8221; or a more effective approach based on Monte Carlo simulations. The uncertainty budget allows to identify the biggest contributors to the final variability and it is also useful to understand if this variability can be reduced to make our thresholds more precise.&#160;

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Establishment of IDF-curves for precipitation in the tropical area of Central Africa - comparison of techniques and results

Natural Hazards and Earth System Science ◽

10.5194/nhess-4-375-2004 ◽

2004 ◽

Vol 4 (3) ◽

pp. 375-387 ◽

Cited By ~ 21

Author(s):

B. Mohymont ◽

G. R. Demarée ◽

D. N. Faka

Keyword(s):

Return Period ◽

Central Africa ◽

Tropical Region ◽

Data Sets ◽

Physically Based ◽

Physically Based Models ◽

Comparison Of Techniques ◽

Idf Curves ◽

Intensity Duration Frequency ◽

Technique Comparison

Abstract. The establishment of Intensity-Duration-Frequency (IDF) curves for precipitation remains a powerful tool in the risk analysis of natural hazards. Indeed the IDF-curves allow for the estimation of the return period of an observed rainfall event or conversely of the rainfall amount corresponding to a given return period for different aggregation times. There is a high need for IDF-curves in the tropical region of Central Africa but unfortunately the adequate long-term data sets are frequently not available. The present paper assesses IDF-curves for precipitation for three stations in Central Africa. More physically based models for the IDF-curves are proposed. The methodology used here has been advanced by Koutsoyiannis et al. (1998) and an inter-station and inter-technique comparison is being carried out. The IDF-curves for tropical Central Africa are an interesting tool to be used in sewer system design to combat the frequently occurring inundations in semi-urbanized and urbanized areas of the Kinshasa megapolis.

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At-Site Assessment of a Regional Design Criterium for Water-Demand Peak Factor Evaluation

Water ◽

10.3390/w11010024 ◽

2018 ◽

Vol 11 (1) ◽

pp. 24 ◽

Cited By ~ 3

Author(s):

Gabriella Balacco ◽

Andrea Gioia ◽

Vito Iacobellis ◽

Alberto Piccinni

Keyword(s):

Water Demand ◽

Southern Italy ◽

Regional Distribution ◽

Gumbel Distribution ◽

Site Assessment ◽

Peak Factor ◽

Physically Based ◽

Distribution Parameters ◽

Factor Evaluation ◽

Regional Design

In this study an analysis of the water supply variability for three towns in Puglia (Southern Italy), Roccaforzata, Palagianello and Palagiano, was carried out, based on time series continuously recorded over two years. The towns’ population ranges between 1800 and 16,000 inhabitants and the flow data, collected with time steps of 10 min, are referred to drinking water in an urban environment. The frequency analysis was conducted on the hourly and instantaneous peak factors and confirmed that the Gumbel distribution is able to represent the stochastic behavior of the peak water demand. A physically based formulation of the distribution parameters was exploited in order to investigate the regional distribution of the peak factor for towns with a different population.

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Calibration of physically based models: back to basics?

Journal of Hydroinformatics ◽

10.2166/hydro.2003.0020 ◽

2003 ◽

Vol 5 (4) ◽

pp. 233-244 ◽

Cited By ~ 25

Author(s):

Vincent Guinot ◽

Philippe Gourbesville

Keyword(s):

Hydrological Model ◽

Flash Flood ◽

Model Parameters ◽

Model Structure ◽

Ungauged Catchments ◽

Model Response ◽

Physically Based ◽

Physically Based Models ◽

Extreme Hydrological Events ◽

Main Factor

The modelling of extreme hydrological events often suffers from a lack of available data. Physically based models are the best available modelling option in such situations, as they can in principle provide answers about the behaviour of ungauged catchments provided that the geometry and the forcings are known with sufficient accuracy. The need for calibration is therefore limited. In some situations, calibration (seen as adjusting the model parameters so that they fit the calculation as closely to the measurements as possible) is impossible. This paper presents such a situation. The MIKE SHE physically based hydrological model is used to model a flash flood over a medium-sized catchment of the Mediterranean Alps (2820 km2). An examination of a number of modelling alternatives shows that the main factor of uncertainty in the model response is the model structure (what are the dominant processes). The second most important factor is the accuracy with which the catchment geometry is represented in the model. The model results exhibit very little sensitivity to the model parameters, and therefore calibration of these parameters is found to be useless.

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Picturing and modeling catchments by representative hillslopes

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1225-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 1225-1249 ◽

Cited By ~ 19

Author(s):

Ralf Loritz ◽

Sibylle K. Hassler ◽

Conrad Jackisch ◽

Niklas Allroggen ◽

Loes van Schaik ◽

...

Keyword(s):

Expert Knowledge ◽

Water Dynamics ◽

Rainfall Runoff ◽

Data Set ◽

Physically Based Model ◽

Optimal Representation ◽

Physically Based ◽

Physically Based Models ◽

Streamflow Data ◽

Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

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