Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

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Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

10.32920/ryerson.14643702.v1 ◽

2021 ◽

Author(s):

Harry R. Manson

Keyword(s):

Sensitivity Analysis ◽

Curve Number ◽

Monte Carlo Analysis ◽

Model Parameters ◽

Rainfall Runoff ◽

Lumped Model ◽

Daily Average ◽

Rainfall Runoff Model ◽

The Impact ◽

Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

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Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

10.32920/ryerson.14643702 ◽

2021 ◽

Author(s):

Harry R. Manson

Keyword(s):

Sensitivity Analysis ◽

Curve Number ◽

Monte Carlo Analysis ◽

Model Parameters ◽

Rainfall Runoff ◽

Lumped Model ◽

Daily Average ◽

Rainfall Runoff Model ◽

The Impact ◽

Runoff Model

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The Effects of Rainfall Intensities and Duration on SCS-CN Model Parameters under Simulated Rainfall

Water ◽

10.3390/w12061595 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1595

Author(s):

Xiaoxian Wang ◽

Huaxing Bi

Keyword(s):

Rainfall Intensity ◽

Model Fitting ◽

Curve Number ◽

Model Parameters ◽

Event Analysis ◽

Simulated Rainfall ◽

Rainfall Runoff ◽

Analysis Method ◽

Initial Abstraction ◽

Scs Cn

The Soil Conservation Service curve number (SCS-CN) method has been widely used in rainfall-runoff modelling; however, the direct effects of rainfall intensities and duration, which are major factors in hydrological engineering design, on its parameters (initial abstraction ratio (λ) and potential maximum storage (S), the latter is transformed into curve number (CN)) have seldom been studied. In this study, we chose simulated rainfall experiments on runoff plots (30 mm/h, 180 min; 60 mm/h, 90 min; 90 mm/h, 60 min) to obtain synchronized rainfall-runoff data and calculated the parameters using general model fitting and event analysis methods every ten minutes throughout the duration. The results indicate that the parameters changed with rainfall intensities and duration. S decreased as the rainfall intensities increased. Under the same rainfall intensity, the S increased with the duration overall. The corresponding λ changed with rainfall intensities and duration, and has no obvious relationship under different rainfall intensities. Initial abstraction in the event analysis method is the same as the actual situation and we chose these parameters obtained with the event analysis method as our selected parameters. These selected parameters and the parameters obtained using Fu et al.’s method which was based on the standardized procedure in NEH-630 were evaluated by the Nash–Sutcliffe efficiency (NSE), the percentage deviation coefficient (PBIAS), and the ratio of the root mean square error to the standard deviation of measured data (RSR). The results show that the statistics for the selected parameters satisfied the evaluating standard, and have a better value, while the statistics for the parameters obtained by Fu et al.’s method declined as the rainfall intensity increased, and PBAIS was out of the standard range under 90 mm/h rainfall intensity condition. This indicates that the rainfall intensities and duration have important effects on the parameters of the model, and the parameters vary dynamically with the rainfall intensity and duration. These results could be useful for hydrological design in which engineers consider the return period (i.e., rainfall intensities and duration).

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

10.5772/intechopen.98211 ◽

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.

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Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4041622 ◽

2019 ◽

Vol 14 (2) ◽

Cited By ~ 1

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.

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Climatic and basin factors affecting the flood frequency curve: PART II – A full sensitivity analysis based on the continuous simulation approach combined with a factorial experimental design

Hydrology and Earth System Sciences ◽

10.5194/hess-4-483-2000 ◽

2000 ◽

Vol 4 (3) ◽

pp. 483-498 ◽

Cited By ~ 6

Author(s):

M. Franchini ◽

A. M. Hashemi ◽

P. E. O’Connell

Keyword(s):

Sensitivity Analysis ◽

Soil Moisture ◽

Experimental Design ◽

Probability Distribution ◽

Flood Frequency ◽

Rainfall Regime ◽

Model Parameters ◽

Rainfall Runoff ◽

Factorial Experimental Design ◽

Frequency Curve

Abstract. The sensitivity analysis described in Hashemi et al. (2000) is based on one-at-a-time perturbations to the model parameters. This type of analysis cannot highlight the presence of parameter interactions which might indeed affect the characteristics of the flood frequency curve (ffc) even more than the individual parameters. For this reason, the effects of the parameters of the rainfall, rainfall runoff models and of the potential evapotranspiration demand on the ffc are investigated here through an analysis of the results obtained from a factorial experimental design, where all the parameters are allowed to vary simultaneously. This latter, more complex, analysis confirms the results obtained in Hashemi et al. (2000) thus making the conclusions drawn there of wider validity and not related strictly to the reference set selected. However, it is shown that two-factor interactions are present not only between different pairs of parameters of an individual model, but also between pairs of parameters of different models, such as rainfall and rainfall-runoff models, thus demonstrating the complex interaction between climate and basin characteristics affecting the ffc and in particular its curvature. Furthermore, the wider range of climatic regime behaviour produced within the factorial experimental design shows that the probability distribution of soil moisture content at the storm arrival time is no longer sufficient to explain the link between the perturbations to the parameters and their effects on the ffc, as was suggested in Hashemi et al. (2000). Other factors have to be considered, such as the probability distribution of the soil moisture capacity, and the rainfall regime, expressed through the annual maximum rainfalls over different durations. Keywords: Monte Carlo simulation; factorial experimental design; analysis of variance (ANOVA)

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

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-2567-2011 ◽

2011 ◽

Vol 11 (9) ◽

pp. 2567-2582 ◽

Cited By ~ 41

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.

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Using SCS-CN and Earth Observation for the Comparative Assessment of the Hydrological Effect of Gradual and Abrupt Spatiotemporal Land Cover Changes

Water ◽

10.3390/w12051386 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1386 ◽

Cited By ~ 6

Author(s):

Emmanouil Psomiadis ◽

Konstantinos X. Soulis ◽

Nikolaos Efthimiou

Keyword(s):

Land Cover ◽

Forest Fires ◽

Earth Observation ◽

Comparative Assessment ◽

Combined Effect ◽

Population Increase ◽

Urbanization Process ◽

Direct Runoff ◽

The Impact ◽

Scs Cn

In this study a comparative assessment of the impacts of urbanization and of forest fires as well as their combined effect on runoff response is investigated using earth observation and the Soil Conservation Service Curve Number (SCS-CN) direct runoff estimation method in a Mediterranean peri-urban watershed in Attica, Greece. The study area underwent a significant population increase and a rapid increase of urban land uses, especially from the 1980s to the early 2000s. The urbanization process in the studied watershed caused a considerable increase of direct runoff response. A key observation of this study is that the impact of forest fires is much more prominent in rural watersheds than in urbanized watersheds. However, the increments of runoff response are important during the postfire conditions in all cases. Generally, runoff increments due to urbanization seem to be higher than runoff increments due to forest fires affecting the associated hydrological risks. It should also be considered that the effect of urbanization is lasting, and therefore, the possibility of an intense storm to take place is higher than in the case of forest fires that have an abrupt but temporal impact on runoff response. It should be noted though that the combined effect of urbanization and forest fires results in even higher runoff responses. The SCS-CN method, proved to be a valuable tool in this study, allowing the determination of the direct runoff response for each soil, land cover and land management complex in a simple but efficient way. The analysis of the evolution of the urbanization process and the runoff response in the studied watershed may provide a better insight for the design and implementation of flood risk management plans.

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Criticality Characteristics and Sensitivity Analysis of Fukushima Debris Beds Containing MCCI Products

Journal of Nuclear Engineering and Radiation Science ◽

10.1115/1.4047094 ◽

2020 ◽

Vol 6 (4) ◽

Author(s):

María Freiría López ◽

Michael Buck ◽

Jörg Starflinger

Keyword(s):

Sensitivity Analysis ◽

Nuclear Power ◽

Realistic Model ◽

Power Station ◽

Wide Range ◽

Core Meltdown ◽

Erosion Factor ◽

The Impact ◽

Control Rods ◽

Core Concrete

Abstract This study investigates the criticality characteristics of debris beds that may have been formed through the molten–core–concrete-interaction (MCCI) at the pedestal floor of the damaged reactors in Fukushima Daiichi Nuclear Power Station. These were modeled as UO2-concrete systems submerged in water. First, a conservative model was used to evaluate the impact that the presence of concrete has on the neutron multiplication factor (keff) of debris beds. The good moderation capacities of concrete were proved, and it was found that recriticality would be possible under the considered conservative assumptions. Second, a more realistic model was used to perform an uncertainty and sensitivity analysis of a wide range of debris parameters (debris porosity, core meltdown grade, debris size, debris composition, concrete erosion factor, etc.). In this case, the results indicate that the probability of a recriticality event is very remote. It was also found that the presence of boron (B4C) from the control rods within debris has by far the highest influence on keff.

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Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

Energies ◽

10.3390/en12173322 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3322 ◽

Cited By ~ 4

Author(s):

Marieline Senave ◽

Staf Roels ◽

Stijn Verbeke ◽

Evi Lambie ◽

Dirk Saelens

Keyword(s):

Sensitivity Analysis ◽

Steady State ◽

Data Analysis ◽

Heat Loss ◽

Input Data ◽

Loss Coefficient ◽

Wide Range ◽

Board Monitoring ◽

The Impact

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

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