scholarly journals Effects of rainfall data resolution on watershed-scale model performance in predicting runoff

2014 ◽  
Vol 6 (2) ◽  
pp. 227-240 ◽  
Author(s):  
Huiliang Wang ◽  
Xuyong Li ◽  
Shaonan Hao
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Resolution ◽  
Model Performance ◽  
Rainfall Data ◽  
Scale Model ◽  
Model Parameters ◽  
Model Accuracy ◽  
Hspf Model ◽  
Data Resolution ◽  
Dynamic Sensitivity Analysis

The hydrologic simulation program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources, but its effectiveness is limited by predictive uncertainties associated with model input data. This study evaluated the effect of rainfall data resolution on the model performance when predicting runoff. We examined hourly, 3-hourly, 12-hourly, and 24-hourly temporal resolutions, and spatial resolutions from seven to one rain gauges. We used a statistical sensitivity analysis to test the effect of resolution on model accuracy, and a dynamic sensitivity analysis to test the effect on model parameters. Our results indicate that the model performance reduces when using a coarser rainfall resolution. The model used the corresponding parameters to absorb the effect of various resolution changes and reduced their impact on the runoff simulations. We used the paired-samples t-test to examine the significance of the rainfall data resolution to the model parameters, which revealed that the model accuracy was more sensitive to the temporal resolution. Our statistical analysis of the dispersion examined the parameter values. It showed that one parameter was sensitive to temporal resolution and three parameters were sensitive to spatial resolution. This study provided useful information for determining HSPF model parameters using rainfall data at different resolutions.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

Analysis of uncertainty propagation through model parameters and structure

2010 ◽  
Vol 62 (6) ◽  
pp. 1230-1239 ◽  
Author(s):  
Abhijit Patil ◽  
Zhi-Qiang Deng
Keyword(s):  
Sensitivity Analysis ◽  
Standard Deviation ◽  
Dissolved Oxygen ◽  
Error Propagation ◽  
Uncertainty Propagation ◽  
Sensitivity Coefficient ◽  
Model Parameters ◽  
Hydrologic Simulation ◽  
Hspf Model ◽  
Forcing Function

Estimation of uncertainty propagation in watershed models is challenging but useful to total maximum daily load (TMDL) calculations. This paper presents an effective approach, involving the combined application of Rosenblueth method and sensitivity analysis, to the determination of uncertainty propagation through the parameters and structure of the HSPF (Hydrologic Simulation Program-FORTRAN) model. The sensitivity analysis indicates that the temperature is a major forcing function in the DO-BOD balance and controls the overall dissolved oxygen concentration. The mean and standard deviation from the descriptive statistics of dissolved oxygen data obtained using the HSPF model are compared to those estimated using Rosenblueth's method. The difference is defined as the error propagated from water temperature through dissolved oxygen. The error propagation, while considering the second order sensitivity coefficient in Rosenblueth's method, is observed to have a mean of 0.281 mg/l and a standard deviation of 0.099 mg/l. A relative low error propagation value is attributed to low skewness of dependent and independent variables. The results provide new insights into the uncertainty propagation in the HSPF model commonly used for TMDL development.

scholarly journals Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models

2009 ◽  
Vol 13 (1) ◽  
pp. 41-55 ◽  
Author(s):  
A. P. Jacquin ◽  
A. Y. Shamseldin
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Fuzzy Models ◽  
Volumetric Errors ◽  
Points Of View ◽  
Takagi Sugeno ◽  
Geographical Locations

Abstract. This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.

PS Energy Imaging Condition for Microseismic Data - Part 2: Sensitivity Analysis in 3D Anisotropic Media

Geophysics ◽  
2020 ◽  
pp. 1-112
Author(s):  
Can Oren ◽  
Jeffrey Shragge
Keyword(s):  
Sensitivity Analysis ◽  
Symmetry Axis ◽  
Moment Tensor ◽  
Transversely Isotropic ◽  
Earth Model ◽  
Model Parameters ◽  
Elastic Model ◽  
Model Accuracy ◽  
Image Domain ◽  
Imaging Condition

In microseismic monitoring, obtaining reliable information about the event properties, such as the location, origin time, and moment tensor components, is critical for evaluating the success of the fluid injection programs. Elastic wavefield-based migration approaches can robustly image microseismic sources by extrapolating data through an earth model and evaluating an imaging condition. The success of these imaging methods, though, primarily depends on the elastic model accuracy. The previously developed extended PS energy imaging condition can provide valuable information about the accuracy of the elastic model parameters including vertical P- and S-wave velocities as well as anisotropy coefficients. Using the SEAM Barrett Unconventional model, we assess the influence of errors in the anisotropy parameters by conducting a sensitivity analysis in three types of 3D models: VTI (transversely isotropic with a vertical symmetry axis), HTI (transversely isotropic with a horizontal symmetry axis), and ORT (orthorhombic) media. Our analysis on zero-lag and extended PS energy images computed with perturbed anisotropy models shows that event images exhibit different moveout patterns of misfocused energy with respect to the distorted Thomsen parameters e and d; however, for this model, the ? parameters have almost no influence on images regardless of the applied perturbations, which are reflected in minimal traveltime differences in the data. The dependence of microseismic source images on these parameters provides essential insights into anisotropic model accuracy, and suggests that misfocused energy on extended image gathers may be used as a criterion for updating earth models through anisotropic elastic image-domain inversion.

scholarly journals Continuous Modeling of the Mkurumudzi River Catchment in Kenya Using the HEC-HMS Conceptual Model: Calibration, Validation, Model Performance Evaluation and Sensitivity Analysis

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 44 ◽  
Author(s):  
Wendso Ouédraogo ◽  
James Raude ◽  
John Gathenya
Keyword(s):  
Sensitivity Analysis ◽  
Stream Flow ◽  
Model Performance ◽  
Base Flow ◽  
Low Flow ◽  
Coefficient Of Determination ◽  
Flow Index ◽  
Model Parameters ◽  
Time Step ◽  
Continuous Modeling

The Mkurumudzi River originates in the Shimba hills and runs through Kwale County on the Kenyan Coast. Study on this river has been informed by the many economic activities that the river supports, which include sugarcane plantations, mining, tourism and subsistence farming. The main objective of this study was to use the soil moisture accounting (SMA) model specified in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) settings for the continuous modeling of stream flow in the Mkurumudzi catchment. Data from past years were compared with observed stream flow data in order to evaluate whether the model can be used for further prediction. The calibration was performed using data from 1988 to 1991 and validation for the period from 1992 to 1995 at a daily time step. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydrographs. For the calibration period of the continuous modeling, the performance of the model was very good, with a coefficient of determination R2 = 0.80, Nash-Sutcliffe Efficiency NSE = 0.80, index of agreement d = 0.94, and a Root Mean Squared Error (RMSE)/observations’ standard deviation ratio—RSR = 0.46. Similarly, the continuous model performance for the validation period was good, with R2 = 0.67, NSE = 0.65, RSR = 0.62 and d = 0.88. Based on these performance results, the SMA model in the HEC-HMS was found to give a satisfactory prediction of stream flow in the Mkurumudzi Catchment. The sensitivity analysis of the model parameters was performed, and the different parameters were ranked according to their sensitivity in terms of percent change in simulated runoff volume, peaks, Nash-Efficiency, seven-day low flow and base flow index. Sensitivity analysis helped to understand the relationships between the key model parameters and the variables.

Parametric Sensitivity Analysis and Model Tuning Applied to Vapor Compression Systems

2005 ◽  
Author(s):  
Michael C. Keir ◽  
Bryan P. Rasmussen ◽  
Andrew G. Alleyne
Keyword(s):  
Sensitivity Analysis ◽  
System Dynamics ◽  
Model Parameters ◽  
Vapor Compression ◽  
Parameter Sensitivity Analysis ◽  
Model Accuracy ◽  
Vapor Compression System ◽  
Physical Insight ◽  
Model Tuning ◽  
Insight Into

This paper presents a parameter sensitivity analysis for a low-order control-oriented dynamic model of a subcritical vapor compression system. The results are used to tune immeasurable model parameters, account for the unmodeled system dynamics, and are applied to fault detection residual design. The models are validated against data taken from an experimental test stand, and the sensitivity based model tuning is shown to improve the model accuracy while providing enhanced physical insight into system dynamics.

scholarly journals Erosion Potential Method (Gavrilović method) sensitivity analysis

2017 ◽  
Vol 12 (No. 1) ◽  
pp. 51-59 ◽  
Author(s):  
N. Dragičević ◽  
B. Karleuša ◽  
N. Ožanić
Keyword(s):  
Sensitivity Analysis ◽  
Model Performance ◽  
Potential Method ◽  
Future Research ◽  
Model Parameters ◽  
Model Concept ◽  
Individual Parameter ◽  
Sediment Production ◽  
Erosion Intensity

In recent decades, various methods for erosion intensity and sediment production assessment have been developed. The necessity for better model performance has led to the more frequent application of the method sensitivity and uncertainty assessments in order to decrease errors that arise from the model concept and its main assumptions. The analysis presented in this paper refers to the application of the Gavrilović method (Erosion Potential Method), an empirical and semi-quantitative method that can estimate the amount of sediment production and sediment transport as well as the erosion intensity and indicate the areas potentially threatened by erosion. The emphasis in this paper is given upon the method sensitivity analysis that has not previously been conducted for the Gavrilović method. The sensitivity analysis was conducted for fourteen different parameters included in the method, all in relation to different model outputs. Each parameter was perceived and discussed individually in relation to its effect upon the method outputs, and ranked into categories depending on their influence on one or more model outputs. The objective of the analysis was to explore the constraints of the Gavrilović method and the method response to changes deriving from the each individual parameter in an attempt to provide a better understanding of the method, the weight and the contribution of each parameter in the overall method. The parameters that could potentially be used in future research, for method modification and calibration in areas with different catchment characteristics (e.g. climate, geological, etc.) were identified. The most sensitive model parameters resulting from conducted sensitivity analysis for the Gavrilović method are also those considered to be significant in the scientific literature on erosion. The Gavrilović method sensitivity analysis has been done on a case study for the Dubracina catchment area, Croatia.

scholarly journals Sensitivity analysis of infectious disease models: methods, advances and their application

2013 ◽  
Vol 10 (86) ◽  
pp. 20121018 ◽  
Author(s):  
Jianyong Wu ◽  
Radhika Dhingra ◽  
Manoj Gambhir ◽  
Justin V. Remais
Keyword(s):  
Infectious Disease ◽  
Sensitivity Analysis ◽  
Model Performance ◽  
Rank Correlation ◽  
Quantitative Relationship ◽  
Latin Hypercube Sampling ◽  
Transmission Model ◽  
Model Parameters ◽  
State Variables ◽  
Heat Map

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design.

scholarly journals Analysis of the Relative Importance of Model Parameters in Watersheds with Different Hydrological Regimes

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2376 ◽  
Author(s):  
Yelena Medina ◽  
Enrique Muñoz
Keyword(s):  
Sensitivity Analysis ◽  
Model Performance ◽  
Hydrological Regime ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Process ◽  
Relative Importance ◽  
Watershed Scale ◽  
Hbv Model ◽  
Hydrological Regimes

Depending on the purpose of the study, aggregated hydrological models are preferred over distributed models because they provide acceptable results in terms of precision and are easy to run, especially in data scarcity scenarios. To obtain acceptable results in terms of hydrological process representativeness, it is necessary to understand and assess the models. In this study, the relative importance of the parameters of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model is analyzed using sensitivity analysis to detect if the simulated processes represent the predominant hydrological processes at watershed scale. As a case study, four watersheds with different hydrological regimes (glacial and pluvial) and therefore different dominant processes are analyzed. The results show that in the case of the rivers with a glacial regime, the model performance depends highly on the snow module parameters, while in the case of the rivers with a pluvial regime, the model is sensitive to the soil and evapotranspiration modules. The results are directly related to the hydrological regime, which indicates that the HBV model, complemented by sensitivity analysis, is capable of both detecting and representing hydrological processes at watershed scale.

scholarly journals Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff\\ fuzzy models

2008 ◽  
Vol 5 (4) ◽  
pp. 1967-2003
Author(s):  
A. P. Jacquin ◽  
A. Y. Shamseldin
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Fuzzy Models ◽  
Volumetric Errors ◽  
Points Of View ◽  
Takagi Sugeno ◽  
Geographical Locations

Abstract. This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.

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