scholarly journals A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

2011 ◽  
Vol 11 (9) ◽  
pp. 2567-2582 ◽  
Author(s):  
H. Roux ◽  
D. Labat ◽  
P.-A. Garambois ◽  
M.-M. Maubourguet ◽  
J. Chorda ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Soil Properties ◽  
Overland Flow ◽  
Hydrological Model ◽  
Extreme Event ◽  
Model Parameters ◽  
Peak Time ◽  
Efficiency Coefficient ◽  
Soil Saturation ◽  
The Impact

Abstract. A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures.

scholarly journals Parameter Dependencies of a Biomechanical Cervical Spine FSU - The Process of Finding Optimal Model Parameters by Sensitivity Analysis

2021 ◽  
Author(s):  
Sabine Bauer ◽  
Ivanna Kramer
Keyword(s):  
Sensitivity Analysis ◽  
Intervertebral Discs ◽  
Model Parameters ◽  
Biomechanical Behavior ◽  
Functional Spinal Unit ◽  
Spine Model ◽  
Biomechanical Parameters ◽  
Multi Body ◽  
The Impact

The knowledge about the impact of structure-specific parameters on the biomechanical behavior of a computer model has an essential meaning for the realistic modeling and system improving. Especially the biomechanical parameters of the intervertebral discs, the ligamentous structures and the facet joints are seen in the literature as significant components of a spine model, which define the quality of the model. Therefore, it is important to understand how the variations of input parameters for these components affect the entire model and its individual structures. Sensitivity analysis can be used to gain the required knowledge about the correlation of the input and output variables in a complex spinal model. The present study analyses the influence of the biomechanical parameters of the intervertebral disc using different sensitivity analysis methods to optimize the spine model parameters. The analysis is performed with a multi-body simulation model of the cervical functional spinal unit C6-C7.

Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Souransu Nandi ◽  
Tarunraj Singh
Keyword(s):  
Sensitivity Analysis ◽  
Linear Systems ◽  
Global Sensitivity Analysis ◽  
Benchmark Problems ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Time Invariant ◽  
The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

scholarly journals The Lituya Bay landslide-generated mega-tsunami. Numerical simulation and sensitivity analysis

2018 ◽  
Author(s):  
José Manuel González-Vida ◽  
Jorge Macías ◽  
Manuel Jesús Castro ◽  
Carlos Sánchez-Linares ◽  
Marc de la Asunción ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Sensitivity Analysis ◽  
Numerical Model ◽  
Shallow Water ◽  
Extreme Event ◽  
Fluid Model ◽  
Model Parameters ◽  
Water Type ◽  
Bathymetric Data ◽  
Run Up

Abstract. The 1958 Lituya Bay landslide-generated mega-tsunami is simulated using the Landslide-HySEA model, a recently developed finite volume Savage-Hutter Shallow Water coupled numerical model. Two factors are crucial if the main objective of the numerical simulation is to reproduce the maximal run-up, with an accurate simulation of the inundated area and a precise re-creation of the known trimline of the 1958 mega-tsunami of Lituya Bay. First, the accurate reconstruction of the initial slide. Then, the choice of a suitable coupled landslide-fluid model able to reproduce how the energy released by the landslide is transmitted to the water and then propagated. Given the numerical model, the choice of parameters appears to be a point of major importance, this leads us to perform a sensitivity analysis. Based on public domain topo-bathymetric data, and on information extracted from the work of Miller (1960), an approximation of Gilbert Inlet topo-bathymetry was set up and used for the numerical simulation of the mega-event. Once optimal model parameters were set, comparisons with observational data were performed in order to validate the numerical results. In the present work, we demonstrate that a shallow water type of model is able to accurately reproduce such an extreme event as the Lituya Bay mega-tsunami. The resulting numerical simulation is one of the first successful attempts (if not the first) at numerically reproducing in detail the main features of this event in a realistic 3D basin geometry, where no smoothing or other stabilizing factors in the bathymetric data are applied.

scholarly journals Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

2020 ◽  
Vol 163 ◽  
pp. 01001
Author(s):  
Georgy Ayzel ◽  
Liubov Kurochkina ◽  
Eduard Kazakov ◽  
Sergei Zhuravlev
Keyword(s):  
Deep Learning ◽  
Hydrological Model ◽  
State Of The Art ◽  
Flash Flood ◽  
Early Warning Systems ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Basin Scale ◽  
The Impact

Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.

Sensitivity analysis of an integrated numerical flow model output to key parameters used in common qualitative vulnerability assessment methods

2020 ◽  
Author(s):  
Joanna Doummar ◽  
Assaad H. Kassem
Keyword(s):  
Sensitivity Analysis ◽  
Vulnerability Assessment ◽  
Groundwater Vulnerability ◽  
Assessment Methods ◽  
Qualitative Assessment ◽  
Model Parameters ◽  
Karst Aquifers ◽  
Model Output ◽  
Local Sensitivity ◽  
The Impact

<p>Qualitative vulnerability assessment methods applied in karst aquifers rely on key factors in the hydrological compartments usually assigned different weights according to their estimated impact on groundwater vulnerability. Based on an integrated numerical groundwater model on a snow-governed karst catchment area (Assal Spring- Lebanon), the aim of this work is to quantify the importance of the most influential parameters on recharge and spring discharge and outline potential parameters that are not accounted for in standard methods, when in fact they do play a role in the intrinsic vulnerability of a system. The assessment of the model sensitivity and the ranking of parameters are conducted using an automatic calibration tool for local sensitivity analysis in addition to a variance-based local sensitivity assessment of model output time series (recharge and discharge)  for two consecutive years (2016-2017) to various model parameters. The impact of each parameter was normalized to estimate standardized weights for each of the process based key-controlling parameters. Parameters to which model was sensitive were factors related to soil, 2) fast infiltration (bypass function) typical of karst aquifers, 3) climatic parameters (melting temperature and degree day coefficient) and 4) aquifer hydraulic properties that play a major role in groundwater vulnerability inducing a temporal effect and varied recession. Other less important parameters play different roles according to different assigned weights proportional to their ranking. Additionally, the effect of slope/geomorphology (e.g., dolines) was further investigated.  In general, this study shows that the weighting coefficients assigned to key vulnerability factors in the qualitative assessment methods can be reevaluated based on this process-based approach.</p><p> </p><p> </p><p> </p>

scholarly journals Impact of Urbanization on Stormwater Runoff from a Small Urban Catchment: Gdańsk Małomiejska Basin Case Study

2014 ◽  
Vol 61 (3-4) ◽  
pp. 141-162 ◽  
Author(s):  
Borys Olechnowicz ◽  
Katarzyna Weinerowska-Bords
Keyword(s):  
Stormwater Management ◽  
Overland Flow ◽  
System Development ◽  
Drainage System ◽  
Wave Model ◽  
Green Roofs ◽  
Army Corps ◽  
Peak Time ◽  
Runoff Reduction ◽  
The Impact

AbstractThis paper deals with the impact of different forms of urbanization on the basin outflow. The influence of changes in land cover/use, drainage system development, reservoirs, and alternative ways of stormwater management (green roofs, permeable pavements) on basin runoff was presented in the case of a small urban basin in Gdansk (Poland). Seven variants of area development (in the period of 2000-2012) - three historical and four hypothetical - were analyzed. In each case, runoff calculations for three rainfall scenarios were carried out by means of the Hydrologic Modeling System designed by Hydrologic Engineering Center of the U.S. Army Corps of Engineers (HEC-HMS). The Soil Conservation Service (SCS) Curve Number (CN) method was used for calculations of effective rainfall, the kinematic wave model for those of overland flow, and the Muskingum-Cunge model for those of channel routing. The calculations indicated that urban development had resulted in increased peak discharge and runoff volume and in decreased peak time. On the other hand, a significant reduction in peak values was observed for a relatively small decrease in the normal storage level (NSL) in reservoirs or when green roofs on commercial centers were present. The study confirmed a significant increase in runoff as a result of urbanization and a considerable runoff reduction by simple alternative ways of stormwater management.

scholarly journals Application of deterministic distributed hydrological model for large catchment: a case study at Vu Gia Thu Bon catchment, Vietnam

2016 ◽  
Vol 18 (5) ◽  
pp. 885-904 ◽  
Author(s):  
Ngoc Duong Vo ◽  
Philippe Gourbesville
Keyword(s):  
Overland Flow ◽  
Hydrological Model ◽  
Unsaturated Flow ◽  
Correlation Coefficients ◽  
Water Levels ◽  
Distributed Hydrological Model ◽  
Ungauged Catchments ◽  
Quality Of Results ◽  
The Impact

In order to create a tool to help hydrologists and authorities to have good understanding about occurrences in stream flow regime together with its variation in the future under the impact of climate change in the Vu Gia Thu Bon catchment, a deterministic distributed hydrological model has been developed and constructed. This model covers the major processes in the hydrologic cycle including rainfall, evapotranspiration, overland flow, unsaturated flow, groundwater flow, channel flow, and their interactions. The model is calibrated and validated against the daily data recorded at seven stations during 1991–2000 and 2001–2010, respectively. The quality of results is demonstrated by Nash–Sutcliffe and correlation coefficients that reach 0.82 and 0.92, respectively, in discharge comparison. With water levels, the obtained coefficients are lower but the quality of results still remains high; Nash–Sutcliffe and correlation coefficients reach 0.77 and 0.89, respectively, in the upstream part of the catchment. This analysis demonstrates the performance of the deterministic distributed modeling approach in simulating hydrological processes one more time; it also confirms the usefulness of this model with ungauged catchments or large catchments. Additionally, this analysis proves the role of multi-calibration in increasing the accuracy of hydrological models for large catchments.

scholarly journals Sensitivity-guided evaluation of the HBV hydrological model parameterization

2012 ◽  
Vol 15 (3) ◽  
pp. 967-990 ◽  
Author(s):  
M. B. Zelelew ◽  
K. Alfredsen
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Model ◽  
River Basin Management ◽  
Low Flow ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Analysis Method ◽  
Flow Conditions ◽  
Model Parameterization ◽  
Sensitivity Analysis Method

Applying hydrological models for river basin management depends on the availability of the relevant data information to constrain the model residuals. The estimation of reliable parameter values for parameterized models is not guaranteed. Identification of influential model parameters controlling the model response variations either by main or interaction effects is therefore critical for minimizing model parametric dimensions and limiting prediction uncertainty. In this study, the Sobol variance-based sensitivity analysis method was applied to quantify the importance of the HBV conceptual hydrological model parameterization. The analysis was also supplemented by the generalized sensitivity analysis method to assess relative model parameter sensitivities in cases of negative Sobol sensitivity index computations. The study was applied to simulate runoff responses at twelve catchments varying in size. The result showed that varying up to a minimum of four to six influential model parameters for high flow conditions, and up to a minimum of six influential model parameters for low flow conditions can sufficiently capture the catchments' responses characteristics. To the contrary, varying more than nine out of 15 model parameters will not make substantial model performance changes on any of the case studies.

scholarly journals Generalization of parameters in the storage–discharge relation for a low flow based on the hydrological analysis of sensitivity

2015 ◽  
Vol 371 ◽  
pp. 69-73 ◽  
Author(s):  
K. Fujimura ◽  
Y. Iseri ◽  
S. Kanae ◽  
M. Murakami
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Water Resource Management ◽  
Hydrological Model ◽  
Low Flow ◽  
Optimum Parameters ◽  
Proportional Relationship ◽  
Two Parameters ◽  
The Impact ◽  
Different Climates

Abstract. The storage-discharge relations have been widely used for water resource management and have led to reliable estimation of the impact of climate change on water resources. In a previous study, we carried out a sensitivity analysis of the parameters in a discharge-storage relation in the form of a power function and found that the optimum parameters can be characterized by an exponential function (Fujimura et al., 2014). The aim of this study is to extend the previous study to clarify the properties of the parameters in the storage–discharge relations by carrying out a sensitivity analysis of efficiency using a hydrological model. The study basins are four mountainous basins in Japan with different climates and geologies. The results confirm that the two parameters in the storage–discharge relations can be expressed in an inversely proportional relationship. In addition, we can conveniently assume a practical function for the storage–discharge relations where only one parameter is used on the basis of the new relationship between the two parameters.

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