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 On the selection of precipitation products for the regionalisation of hydrological model parameters

2021 ◽  
Vol 25 (11) ◽  
pp. 5805-5837
Author(s):  
Oscar M. Baez-Villanueva ◽  
Mauricio Zambrano-Bigiarini ◽  
Pablo A. Mendoza ◽  
Ian McNamara ◽  
Hylke E. Beck ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Temporal Distribution ◽  
Hydrological Regime ◽  
Hydrologic Model ◽  
Spatial Proximity ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Water Balance Components ◽  
Feature Similarity

Abstract. Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four P products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each P product. Despite differences in the spatio-temporal distribution of P, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE′s) > 0.77), two independent verification periods (median KGE′s >0.70 and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE′s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between P forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging P products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of P products does not substantially affect the regionalisation performance; (iii) a P product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

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.

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.

scholarly journals On the selection of precipitation products for the regionalisation of hydrological model parameters

2021 ◽  
Author(s):  
Oscar M. Baez-Villanueva ◽  
Mauricio Zambrano-Bigiarini ◽  
Pablo A. Mendoza ◽  
Ian McNamara ◽  
Hylke E. Beck ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Temporal Distribution ◽  
Hydrological Regime ◽  
Spatial Proximity ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Feature Similarity ◽  
Dry Conditions ◽  
Calibration And Verification

Abstract. Over the past years, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects individual catchment calibration and verification, as well as the relative performance of three well-known regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We configured and calibrated a conceptual semi-distributed HBV-like hydrological model for each catchment, using four P products (ERA5, MSWEPv2.8, RF-MEPv2, and CR2MET), and two objective functions. The three regionalisation techniques were applied and evaluated for each combination of P product and objective function, using a leave-one-out cross-validation procedure. Despite differences in the spatio-temporal distribution of P quantities, all P products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation results (with median KGE's for the best method ranging from 0.56 to 0.63). Our results suggest that model calibration is able to compensate, to some extent, differences between forcing datasets, and that the spatial resolution of P products does not substantially affect the regionalisation performance. Overall, feature similarity provided the best results, followed closely by spatial proximity, while parameter regression performed the worst, thus reinforcing the importance of transferring complete parameter sets to ungauged catchments. Our results suggest that: i) merging P products and ground-based measurements does not necessarily translate into an improved hydrological modelling performance; ii) a P product that provides the best individual model performance during calibration and verification does not necessarily provide the best performance in terms of parameter regionalisation; and iii) the hydrological regime affects the performance of regionalisation methods, with rain-dominated catchments with a snow component performing the best over Chile for spatial proximity and feature similarity.

scholarly journals Temporal parameter sensitivity guided verification of process dynamics

2015 ◽  
Vol 12 (2) ◽  
pp. 1729-1764 ◽  
Author(s):  
M. Pfannerstill ◽  
B. Guse ◽  
D. Reusser ◽  
N. Fohrer
Keyword(s):  
Hydrological Model ◽  
Temporal Dynamics ◽  
Parameter Sensitivity ◽  
Temporal Parameter ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Process ◽  
Time Step ◽  
Process Dynamics ◽  
Model Components

Abstract. To ensure reliable results of a hydrological model, it is essential that the model reproduces the hydrological processes adequately. Information about process dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal dynamics of parameter sensitivity are used to describe the dominance of parameters for each time step. The parameter dominance is then related to the corresponding hydrological process, since the temporal parameter sensitivity represents the modelled hydrological process. For a reliable model application it has to be verified that the modelled hydrological processes match the expectations of real-world hydrological processes. We present a framework, which distinguishes between a verification of single model components and of the overall model behaviour. We analyse the temporal dynamics of parameter sensitivity of a modified groundwater component of a hydrological model. The results of the single analysis for the modified component show that the behaviour of the parameters of the modified groundwater component is consistent with the idea of the structural modifications. Additionally, the appropriate simulation of all relevant hydrological processes is verified as the temporal dynamics of parameter sensitivity represent these processes according to the expectations. Thus, we conclude that temporal dynamics of parameter sensitivity are helpful for verifying modifications of hydrological models.

scholarly journals Calibration of a Distributed Hydrological Model in a Data-Scarce Basin Based on GLEAM Datasets

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 897 ◽  
Author(s):  
Xin Jin ◽  
Yanxiang Jin
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Great Effort ◽  
Observation Data ◽  
Hydrological Models

The calibration of hydrological models is often complex in regions with scarce data, and generally only uses site-based streamflow data. However, this approach will yield highly generalised values for all model parameters and hydrological processes. It is therefore necessary to obtain more spatially heterogeneous observation data (e.g., satellite-based evapotranspiration (ET)) to calibrate such hydrological models. Here, soil and water assessment tool (SWAT) models were built to evaluate the advantages of using ET data derived from the Global Land surface Evaporation Amsterdam Methodology (GLEAM) to calibrate the models for the Bayinhe River basin in northwest China, which is a typical data-scarce basin. The result revealed the following: (1) A great effort was required to calibrate the SWAT models for the study area to obtain an improved model performance. (2) The SWAT model performance for simulating the streamflow and water balance was reliable when calibrated with streamflow only, but this method of calibration grouped the hydrological processes together and caused an equifinality issue. (3) The combination of the streamflow and GLEAM-based ET data for calibrating the SWAT model improved the model performance for simulating the streamflow and water balance. However, the equifinality issue remained at the hydrologic response unit (HRU) level.

scholarly journals Process verification of a hydrological model using a temporal parameter sensitivity analysis

2015 ◽  
Vol 19 (10) ◽  
pp. 4365-4376 ◽  
Author(s):  
M. Pfannerstill ◽  
B. Guse ◽  
D. Reusser ◽  
N. Fohrer
Keyword(s):  
Hydrological Model ◽  
Temporal Dynamics ◽  
Parameter Sensitivity ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Process ◽  
Hydrological Models ◽  
Parameter Sensitivity Analysis ◽  
Process Verification ◽  
Process Dynamics

Abstract. To ensure reliable results of hydrological models, it is essential that the models reproduce the hydrological process dynamics adequately. Information about simulated process dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal dynamics of parameter sensitivity are analysed to identify the simulated hydrological processes. Based on these analyses it can be verified if the simulated hydrological processes match the observed processes of the real world. We present a framework that makes use of processes observed in a study catchment to verify simulated hydrological processes. Temporal dynamics of parameter sensitivity of a hydrological model are interpreted to simulated hydrological processes and compared with observed hydrological processes of the study catchment. The results of the analysis show the appropriate simulation of all relevant hydrological processes in relation to processes observed in the catchment. Thus, we conclude that temporal dynamics of parameter sensitivity are helpful for verifying simulated processes of hydrological models.

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.

scholarly journals Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 961 ◽  
Author(s):  
Ajay Bajracharya ◽  
Hervé Awoye ◽  
Tricia Stadnyk ◽  
Masoud Asadzadeh
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Response Surfaces ◽  
Frozen Soil ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Cold Region ◽  
Formidable Challenge ◽  
Short Period ◽  
Event Based

The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.

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.

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