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.

Assessment of Suitability of Global Reanalysis for Hydrological Applications by Coupling Performance Statistics and Sensitivity Analysis in Kenya

2021 ◽  
Author(s):  
Maureen Wanzala ◽  
Andrea Ficchi ◽  
Hannah Cloke ◽  
Elizabeth Stephens
Keyword(s):  
Sensitivity Analysis ◽  
Early Warning ◽  
Model Performance ◽  
Quantitative Measure ◽  
Poor Performance ◽  
Flood Management ◽  
Develop Model ◽  
Daily Streamflow ◽  
Early Action ◽  
Performance Statistics

<p>Information about monitoring of hydrological extremes, agricultural yields and irrigation may be informed by early warning, forecasts and flood management advice through appropriate modelling skills. However hydrological modelling is a challenging task in poorly gauged catchments, especially in developing countries like Kenya. Open access global precipitation and temperature reanalysis datasets with different spatial and temporal resolutions provide alternative sources in data-scarce regions but, individual reanalysis precipitation datasets have significant uncertainties. Inspired by data scarcity issues, significant spatial and temporal gaps in gauge observations, and poor performance of individual reanalysis in hydrological models, this study assess the performance of five new-era reanalysis datasets (ERA5, ERA-Interim, Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR) and Japanese 55-year Reanalysis Project(JRA55)) to simulate daily streamflow using the GR4J model across the 20 catchments in Kenya. Deviating from the modelling normality of calculating the model performance statistics for the calibration and validation periods to investigate whether a model serves as satisfactory representations of the natural hydrologic phenomenon, we couple with sensitivity analysis (SA) to unveil model structural uncertainty and suitability when forced with the different reanalysis products. In this study we use the reanalysis precipitation, maximum (T max) and minimum (T min) temperatures against the observations from the Climate Hazards group Precipitation (CHIRPS) for 1981–2016 to calculate performance statistics, streamflow simulations and sensitivity analysis. In addition, we develop model suitability index (MSI) by coupling the performance statistics with the sensitivity results across the different reanalysis products for our study catchments. Our results show that ERA5 performs better than other reanalysis products in terms of performance statistics and streamflow simulations at catchments scale. MSI results were suitable with ERA5 and lower in JRA55 across most of the Kenyan catchments, with 0.8 and 0.4 MSI respectively. MSI developed in this study is a quantitative measure that can be used for the comparison of reanalysis products for different catchments, thus useful for application to modelling to assess the suitability of both the modelling tools and catchment response to alternative forcings for early warning and inform early action.</p>

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.

Rainfall runoff modelling of the Upper Ganga and Brahmaputra basins using PERSiST

2015 ◽  
Vol 17 (6) ◽  
pp. 1070-1081 ◽  
Author(s):  
M. N. Futter ◽  
P. G. Whitehead ◽  
S. Sarkar ◽  
H. Rodda ◽  
J. Crossman
Keyword(s):  
Model Performance ◽  
Structural Uncertainty ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

While tensions exist between parsimony and completeness, it appears that structural uncertainty is more important than parameter equifinality in controlling rainfall-runoff model performance.

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.

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 Sensitivity Analysis for Microscopic Crowd Simulation

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 162
Author(s):  
Marion Gödel ◽  
Rainer Fischer ◽  
Gerta Köster
Keyword(s):  
Sensitivity Analysis ◽  
Low Cost ◽  
Input Parameter ◽  
Dimensional Subspace ◽  
Crowd Simulation ◽  
Pedestrian Dynamics ◽  
Sensitivity Indices ◽  
Expensive Problems ◽  
Influential Parameters ◽  
Typical Scenario

Microscopic crowd simulation can help to enhance the safety of pedestrians in situations that range from museum visits to music festivals. To obtain a useful prediction, the input parameters must be chosen carefully. In many cases, a lack of knowledge or limited measurement accuracy add uncertainty to the input. In addition, for meaningful parameter studies, we first need to identify the most influential parameters of our parametric computer models. The field of uncertainty quantification offers standardized and fully automatized methods that we believe to be beneficial for pedestrian dynamics. In addition, many methods come at a comparatively low cost, even for computationally expensive problems. This allows for their application to larger scenarios. We aim to identify and adapt fitting methods to microscopic crowd simulation in order to explore their potential in pedestrian dynamics. In this work, we first perform a variance-based sensitivity analysis using Sobol’ indices and then crosscheck the results by a derivative-based measure, the activity scores. We apply both methods to a typical scenario in crowd simulation, a bottleneck. Because constrictions can lead to high crowd densities and delays in evacuations, several experiments and simulation studies have been conducted for this setting. We show qualitative agreement between the results of both methods. Additionally, we identify a one-dimensional subspace in the input parameter space and discuss its impact on the simulation. Moreover, we analyze and interpret the sensitivity indices with respect to the bottleneck scenario.

scholarly journals SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 872
Author(s):  
Vesna Đukić ◽  
Ranka Erić
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Flash Flood ◽  
Model Performance ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
River Catchment ◽  
Hydrological Variable ◽  
Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

scholarly journals Derivation of S-Curve from Oscillatory Hydrograph Using Digital Filter

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1456
Author(s):  
Kee-Won Seong ◽  
Jang Hyun Sung
Keyword(s):  
Digital Filter ◽  
Model Performance ◽  
Performance Criteria ◽  
Rainfall Runoff ◽  
Small Watershed ◽  
Unit Hydrograph ◽  
Time To Peak ◽  
S Curve ◽  
And Performance ◽  
Smooth Data

An oscillatory S-curve causes unexpected fluctuations in a unit hydrograph (UH) of desired duration or an instantaneous UH (IUH) that may affect the constraints for hydrologic stability. On the other hand, the Savitzky–Golay smoothing and differentiation filter (SG filter) is a digital filter known to smooth data without distorting the signal tendency. The present study proposes a method based on the SG filter to cope with oscillatory S-curves. Compared to previous conventional methods, the application of the SG filter to an S-curve was shown to drastically reduce the oscillation problems on the UH and IUH. In this method, the SG filter parameters are selected to give the minimum influence on smoothing and differentiation. Based on runoff reproduction results and performance criteria, it appears that the SG filter performed both smoothing and differentiation without the remarkable variation of hydrograph properties such as peak or time-to peak. The IUH, UH, and S-curve were estimated using storm data from two watersheds. The reproduced runoffs showed high levels of model performance criteria. In addition, the analyses of two other watersheds revealed that small watershed areas may experience scale problems. The proposed method is believed to be valuable when error-prone data are involved in analyzing the linear rainfall–runoff relationship.

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 2 (3) ◽  
pp. 639-690 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Model Performance ◽  
Source Area ◽  
Subsurface Water ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. Through flux exchanges among the different spatial domains of the REW, surface and subsurface water interactions are fully coupled. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work is the first full application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

Sign in / Sign up

Export Citation Format

Share Document