scholarly journals Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

2018 ◽  
Author(s):  
Anqi Wang ◽  
Dimitri P. Solomatine
Keyword(s):  
Sensitivity Analysis ◽  
Comparative Analysis ◽  
Practical Experience ◽  
Ease Of Use ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Sensitivity Indices ◽  
Kolmogorov Smirnov ◽  
Better Than

Abstract. Sensitivity Analysis (SA) and Uncertainty Analysis (UA) are important steps for better understanding and evaluation of hydrological models. The aim of this paper is to briefly review main classes of SA methods, and to presents the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST, Morris, LH-OAT, RSA and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV) applied to the case study of Bagmati basin (Nepal), and also initially tested on the case of Dapoling-Wangjiaba catchment in China. The methods are compared with respect to effectiveness, efficiency and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, thus can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability which we think are due to the ways CDFs are built, and using Kolmogorov-Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, as well as availability of software.

scholarly journals Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1062 ◽  
Author(s):  
Anqi Wang ◽  
Dimitri P. Solomatine
Keyword(s):  
Sensitivity Analysis ◽  
Practical Experience ◽  
Distribution Functions ◽  
Ease Of Use ◽  
Cumulative Distribution ◽  
Performance Criteria ◽  
Hydrological Models ◽  
Sensitivity Indices ◽  
Kolmogorov Smirnov

Currently, practically no modeling study is expected to be carried out without some form of Sensitivity Analysis (SA). At the same time, there is a large number of various methods and it is not always easy for practitioners to choose one. The aim of this paper is to briefly review main classes of SA methods, and to present the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST (extended Fourier Amplitude Sensitivity Test), Morris, LH-OAT, RSA (Regionalized Sensitivity Analysis), and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod, and HBV) applied to the case study of Bagmati basin (Nepal). The methods are compared with respect to effectiveness, efficiency, and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, and thus it can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability, which we think are due to the ways Cumulative Distribution Functions (CDFs) are built, and using Kolmogorov–Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, and availability of software.

scholarly journals Analysis of Problems Related to the Calculation of Flood Frequency Using Rainfall-Runoff Models: A Case Study in Poland

2020 ◽  
Vol 12 (17) ◽  
pp. 7187
Author(s):  
Dariusz Młyński
Keyword(s):  
Sensitivity Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
High Sensitivity ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Model Quality ◽  
Retention Capacity

This work aimed to quantify how the different parameters of the Snyder model influence the errors in design flows. The study was conducted for the Kamienica Nowojowska catchment (Poland). The analysis was carried out according to the following stages: determination of design precipitation, determination of design hyetograph, sensitivity analysis of the Snyder model, and quality assessment of the Snyder model. Based on the conducted research, it was found that the Snyder model did not show high sensitivity to the assumed precipitation distribution. The parameters depending on the retention capacity of the catchment had much greater impact on the obtained flow values. The verification of the model quality showed a significant disproportion in the calculated maximum flow values with the assumed return period.

On runoff generation and the distribution of storage deficits

2013 ◽  
Vol 44 (4) ◽  
pp. 673-689
Author(s):  
A. Wood ◽  
K. J. Beven
Keyword(s):  
Distribution Function ◽  
Beta Function ◽  
Water Levels ◽  
Distribution Model ◽  
Slow Pathway ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Small Advantage ◽  
Better Than

A number of hydrological models use a distribution function to develop the non-linear rainfall–runoff catchment response. In this study the beta function is applied to represent a distribution of soil moisture storages in conjunction with a fast and slow pathway routing. The BETA3 and BETA4 modules, presented in this paper, have a distribution of discrete storage elements that have variable and redistributed water levels at each timestep. The PDM-BETA5 is an analytical solution with a similar structure to the commonly used probability distribution model (PDM). Model testing was performed on three catchments in the Northern Pennine region in England. The performances of the BETA models were compared with a commonly used formulation of the PDM. The BETA models performed marginally better than the PDM in calibration and parameter estimation was better with the BETA models than for the PDM. The BETA models had a small advantage in validation on the hydrologically fast responding test catchments.

scholarly journals Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective

2021 ◽  
Vol 25 (7) ◽  
pp. 3937-3973
Author(s):  
Paul C. Astagneau ◽  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
Joseph H. A. Guillaume ◽  
Juraj Parajka ◽  
...  
Keyword(s):  
Technical Note ◽  
Ease Of Use ◽  
Data Availability ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
User Perspective ◽  
R Packages ◽  
Spatial Discretisation ◽  
Technical Features ◽  
Selection Of

Abstract. Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.

Sensitivity analysis of surface runoff parameters for hydrological modeling of periurban ungauged basin  

2021 ◽  
Author(s):  
Irene Di Cicco ◽  
Carlo Giudicianni ◽  
Armando Di Nardo ◽  
Roberto Greco
Keyword(s):  
Land Use ◽  
Sensitivity Analysis ◽  
Water Resources ◽  
Surface Runoff ◽  
Model Structure ◽  
Hydrological Models ◽  
Ungauged Basins ◽  
Model Simplification ◽  
Sensitivity Indices ◽  
Input Parameters

<p>Rapid human-induced changes, such as climate change, population growth and rapid urbanization, are putting enormous stress on water resources. An accurate estimate of available water resources is a prerequisite for sustainable water resources planning and management. For gauged basins, historical records of hydrological observations are available, but for ungauged basins, the assessment of water availability is a challenging task. Therefore, the major focus of studies in ungauged basins is the development of appropriate tools that can accurately quantify hydrologic responses under various land use and climatic conditions. The reduction of the number of unknown parameters to be estimated is a key aspect in the development of hydrological models for ungauged basins.</p><p>This work is part of these issues and proposes an approach to reduce the complexity of hydrological models that include substantial uncertainties about the input data, initial and boundary conditions, model structure and parameters, owing to lack of data (i.e. for ungauged basins) and poor knowledge of hydrological response mechanisms. The case study of a basin of the District of Licola, located in the territory of the municipality of Giugliano, a city near Naples (southern Italy) is analyzed. Originally devoted to agriculture and grazing, it has been affected in the last decades by intense urbanization, which caused an increase in the impermeability of the soil cover. The increase in residential, commercial and production buildings has changed the functioning of the drainage network canals, compared to the original conditions, causing an increase in the frequency of flooding in the area. The semi-distributed hydrological model SWMM is adopted, which allows the subdivision of the basin in sub-basins according to land use and soil data.</p><p>Sensitivity Analysis (SA) is an effective approach to model simplification, providing an assessment of how much each input / parameter contributes to the output uncertainty. In general, SA is an essential part of model development, reducing uncertainties that have negative effects on the accuracy and reliability of simulated results. Specifically, in this study the SA is carried out with a method based on the decomposition of the variance of the peak flow and runoff volume, to quantitatively evaluate the contributions of single uncertain inputs/parameters that characterize the surface runoff with respect to different rainfall events, for both pervious and impervious areas. To this aim, the Fourier Amplitude Sensitivity Test (FAST) is implemented. This method allows quantifying not only the “main effect” of variance, but also provides the Total Sensitivity Indices (TSI), defined as the sum of all the sensitivity indices for each parameter (including the effects of the interaction with other uncertain parameters).</p><p>The research objectives aims at: (i) increased understanding of the relationships between input and output variables in a complex hydrological system; (ii) reduction of model uncertainty, through the identification of input parameters mostly contributing to output variability and should therefore be the focus of sensitivity analysis; (iii) model simplification, fixing  the values of input parameters that have little effect on the output, and identifying and removing redundant parts of the model structure.</p>

scholarly journals Supporting M5 model trees with sensitivity information derived from conceptual hydrological models

2015 ◽  
Vol 17 (6) ◽  
pp. 943-958 ◽  
Author(s):  
Carolina Massmann
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Parameter Sensitivity ◽  
Data Driven ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Trees ◽  
Sensitivity Indices ◽  
M5 Model ◽  
Process Based Models

The main objective of this paper is assessing the usefulness of parameter sensitivity information from conceptual hydrological models for data-driven models, an approach which might allow us to take advantage of the strengths of both data-based and process-based models. This study uses the parameter sensitivity of three widely used conceptual hydrological models (GR4J, Hymod and SAC-SMA) and combines them with M5 model trees. The study was carried out for three case studies dealing with different problems to which model trees are applied: one using model trees as error correctors and two case studies in which model trees were used as rainfall–runoff models and which differ in how the sensitivity information is used. The results show that sensitivity time series can improve the predictions of M5 model trees, especially when they do not include the time series of previous discharge as predictor variables. The use of parameter sensitivity information for clustering the time series resulted in model trees that had a structure consistent with the hydrological processes that were taking place in the considered cluster, indicating that the use of sensitivity indices could be a viable way of introducing hydrological knowledge into data-based models.

scholarly journals Comparative analysis of the effectiveness of the conceptual rainfall-runoff hydrological models on the selected rivers in Odra and Vistula basins

2018 ◽  
Vol 23 ◽  
pp. 00025
Author(s):  
Robert Mańko ◽  
Norbert Laskowski
Keyword(s):  
Mathematical Model ◽  
Comparative Analysis ◽  
Mathematical Description ◽  
Conceptual Models ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Reservoir Model ◽  
Physical Processes ◽  
Linear Reservoir ◽  
Comparison Of Effectiveness

Identification of physical processes occurred in the watershed is one of the main tasks in hydrology. Currently the most efficient hydrological processes describing and forecasting tool are mathematical models. They can be defined as a mathematical description of relations between specified attributes of analysed object. It can be presented by: graphs, arrays, equations describing functioning of the object etc. With reference to watershed a mathematical model is commonly defined as a mathematical and logical relations, which evaluate quantitative dependencies between runoff characteristics and factors, which create it. Many rainfall-runoff linear reservoirs conceptual models have been developed over the years. The comparison of effectiveness of Single Linear Reservoir model, Nash model, Diskin model and Wackermann model is presented in this article.

scholarly journals Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis

2016 ◽  
Vol 48 (5) ◽  
pp. 1192-1213 ◽  
Author(s):  
Mun-Ju Shin ◽  
Chung-Soo Kim
Keyword(s):  
Sensitivity Analysis ◽  
Model Performance ◽  
Quantitative Measure ◽  
Structural Uncertainty ◽  
Rainfall Runoff ◽  
Comprehensive Method ◽  
Sensitivity Indices ◽  
Suitability Index ◽  
Performance Statistics ◽  
Hydrologic Responses

Conceptual rainfall–runoff models are widely used to understand the hydrologic responses of catchments of interest. Modellers calculate the model performance statistics for the calibration and validation periods to investigate whether these models serve as satisfactory representations of the natural hydrologic phenomenon. Another useful method to investigate model suitability is sensitivity analysis (SA), which investigates structural uncertainty in the models. However, a comprehensive method is needed, which led us to develop a model suitability index (MSI) by combining the results of model performance statistics and SA. Here, we assessed and compared the suitability of three rainfall–runoff models (GR4J, IHACRES and Sacramento model) for seven Korean catchments using MSI. MSI showed that the GR4J and IHACRES models are suitable, having more than 0.5 MSI, whereas the Sacramento has less than 0.5 MSI, representing unsuitability for most of the Korean catchments. The MSI developed in this study is a quantitative measure that can be used for the comparison of rainfall–runoff models for different catchments. It uses the results of existing model performance statistics and sensitivity indices; hence, users can easily apply this index to their models and catchments to investigate suitability.

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