scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 12 (6) ◽  
pp. 1273-1283 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

scholarly journals Dynamics of hydrological model parameters: calibration and reliability

2019 ◽  
Author(s):  
Tian Lan ◽  
Kairong Lin ◽  
Xuezhi Tan ◽  
Chong-Yu Xu ◽  
Xiaohong Chen
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Dynamic Parameter ◽  
Model Parameters ◽  
Hydrological Models ◽  
State Variables ◽  
Individual Parameter ◽  
Calibration Scheme ◽  
Response Modes ◽  
Abrupt Shifts

Abstract. It has been demonstrated that the dynamics of hydrological model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the calibration for the dynamization of parameter set involves critical components of hydrological models, including parameters, objective functions, state variables, and fluxes, which usually are ignored. Hence, it is essential to design a reliable calibration scheme regarding these components. In this study, we compared and evaluate five calibration schemes with respect to multi-metric evaluation, dynamized parameter values, fluxes, and state variables. Furthermore, a simple and effective tool was designed to assess the reliability of the dynamized parameter set. The tool evaluates the convergence processes for global optimization algorithms using violin plots (ECP-VP), effectively describes the convergence behaviour in individual parameter spaces. The different types of violin plots can well match to all possible properties of fitness landscapes. The results showed that the reasons for poor model performance included time-invariant parameters oversimplifying the dynamic response modes of the model, the high-dimensionality disaster of parameters, the abrupt shifts of the parameter set, and the complicated correlations among parameters. The proposed calibration scheme overcome these issues, characterized the dynamic behaviour of catchments, and improved the model performance. Additionally, the designed ECP-VP tool effectively assessed the reliability of the dynamic parameter set, providing an indication on recognizing the dominant response modes of hydrological models in different sub-periods or catchments with the distinguishing catchment characteristics.

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.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2007 ◽  
Vol 11 (2) ◽  
pp. 703-710 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Climate Statistics ◽  
Runoff Model ◽  
German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

scholarly journals Testing the Robustness of a Physically-Based Hydrological Model in Two Data Limited Inland Valley Catchments in Dano, Burkina Faso

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 43
Author(s):  
Mouhamed Idrissou ◽  
Bernd Diekkrüger ◽  
Bernhard Tischbein ◽  
Boubacar Ibrahim ◽  
Yacouba Yira ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Burkina Faso ◽  
Hydrological Model ◽  
Model Performance ◽  
Surface Flow ◽  
Small Scale ◽  
Model Parameters ◽  
Inland Valley ◽  
Physically Based

This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.

Evaluating the performance of a multispecies statistical catch-at-age model

2013 ◽  
Vol 70 (3) ◽  
pp. 470-484 ◽  
Author(s):  
Kiersten L. Curti ◽  
Jeremy S. Collie ◽  
Christopher M. Legault ◽  
Jason S. Link
Keyword(s):  
Species Interactions ◽  
Input Data ◽  
Gadus Morhua ◽  
Atlantic Cod ◽  
Model Performance ◽  
Clupea Harengus ◽  
Model Parameters ◽  
Data Types ◽  
Commercial Catch ◽  
Important Species

Predation is a substantial source of mortality that is a function of the abundance of predator and prey species. This source of mortality creates a challenge of incorporating species interactions in statistical catch-at-age models in a way that accounts for the uncertainty in input data, parameters, and results. We developed a statistical, age-structured, multispecies model for three important species in the Georges Bank fish community: Atlantic cod (Gadus morhua), silver hake (Merluccius bilinearis), and Atlantic herring (Clupea harengus). The model was fit to commercial catch, survey, and diet data from 1978 to 2007. The estimated predation rates were high, compared with fishing mortality, and variable with time. The dynamics of the three species can be explained by the interplay between fishing and predation mortality. Monte Carlo simulations were used to evaluate the ability of the model to estimate parameters with known error introduced into each of the data types. The model parameters could be estimated with confidence from input data with error levels similar to those obtained from the model fit to the observed data. This evaluation of model performance should help to move multispecies statistical catch-at-age models from proof of concept to functional tools for ecosystem-based fisheries management.

scholarly journals Investigation of the transferability of hydrological models and a method to improve model calibration

2005 ◽  
Vol 5 ◽  
pp. 83-87 ◽  
Author(s):  
G. Hartmann ◽  
A. Bárdossy
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Calibration Procedure ◽  
Performance Measure ◽  
Distributed Hydrological Model ◽  
Improve Model ◽  
Spatially Distributed ◽  
Measure Of Performance

Abstract. In order to find a model parameterization such that the hydrological model performs well even under different conditions, appropriate model performance measures have to be determined. A common performance measure is the Nash Sutcliffe efficiency. Usually it is calculated comparing observed and modelled daily values. In this paper a modified version is suggested in order to calibrate a model on different time scales simultaneously (days up to years). A spatially distributed hydrological model based on HBV concept was used. The modelling was applied on the Upper Neckar catchment, a mesoscale river in south western Germany with a basin size of about 4000 km2. The observation period 1961-1990 was divided into four different climatic periods, referred to as "warm", "cold", "wet" and "dry". These sub periods were used to assess the transferability of the model calibration and of the measure of performance. In a first step, the hydrological model was calibrated on a certain period and afterwards applied on the same period. Then, a validation was performed on the climatologically opposite period than the calibration, e.g. the model calibrated on the cold period was applied on the warm period. Optimal parameter sets were identified by an automatic calibration procedure based on Simulated Annealing. The results show, that calibrating a hydrological model that is supposed to handle short as well as long term signals becomes an important task. Especially the objective function has to be chosen very carefully.

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 Identification of hydrological model parameters for flood forecasting using data depth measures

2011 ◽  
Vol 8 (2) ◽  
pp. 2423-2476 ◽  
Author(s):  
T. Krauße ◽  
J. Cullmann
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Data Depth ◽  
Mcmc Methods ◽  
Model Parameters ◽  
Robust Parameter ◽  
Robust Parameter Estimation ◽  
Optimisation Algorithms

Abstract. The development of methods for estimating the parameters of hydrological models considering uncertainties has been of high interest in hydrological research over the last years. Besides the very popular Markov Chain Monte Carlo (MCMC) methods which estimate the uncertainty of model parameters in the settings of a Bayesian framework, the development of depth based sampling methods, also entitled robust parameter estimation (ROPE), have attracted an increasing research interest. These methods understand the estimation of model parameters as a geometric search of a set of robust performing parameter vectors by application of the concept of data depth. Recent studies showed that the parameter vectors estimated by depth based sampling perform more robust in validation. One major advantage of this kind of approach over the MCMC methods is that the formulation of a likelihood function within a Bayesian uncertainty framework gets obsolete and arbitrary purpose-oriented performance criteria defined by the user can be integrated without any further complications. In this paper we present an advanced ROPE method entitled the Advanced Robust Parameter Estimation by Monte Carlo algorithm (AROPEMC). The AROPEMC algorithm is a modified version of the original robust parameter estimation algorithm ROPEMC developed by Bárdossy and Singh (2008). AROPEMC performs by merging iterative Monte Carlo simulations, identifying well performing parameter vectors, the sampling of robust parameter vectors according to the principle of data depth and the application of a well-founded stopping criterion applied in supervised machine learning. The principals of the algorithm are illustrated by means of the Rosenbrock's and Rastrigin's function, two well known performance benchmarks for optimisation algorithms. Two case studies demonstrate the advantage of AROPEMC compared to state of the art global optimisation algorithms. A distributed process-oriented hydrological model is calibrated and validated for flood forecasting in a small catchment characterised by extreme process dynamics.

scholarly journals A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics

2020 ◽  
Vol 24 (12) ◽  
pp. 5859-5874
Author(s):  
Tian Lan ◽  
Kairong Lin ◽  
Chong-Yu Xu ◽  
Zhiyong Liu ◽  
Huayang Cai
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Poor Response ◽  
Dynamic Parameter ◽  
Global Optimum ◽  
Seasonal Dynamic ◽  
Model Parameters ◽  
Dynamic Parameters ◽  
Dynamic Changes ◽  
Catchment Characteristics

Abstract. Previous studies have shown that the seasonal dynamics of model parameters can compensate for structural defects of hydrological models and improve the accuracy and robustness of the streamflow forecast to some extent. However, some fundamental issues for improving model performance with seasonal dynamic parameters still need to be addressed. In this regard, this study is dedicated to (1) proposing a novel framework for seasonal variations of hydrological model parameters to improve model performance and (2) expanding the discussion on model results and the response of seasonal dynamic parameters to dynamic characteristics of catchments. The procedure of the framework is developed with (1) extraction of the dynamic catchment characteristics using current data-mining techniques, (2) subperiod calibration operations for seasonal dynamic parameters, considering the effects of the significant correlation between the parameters, the number of multiplying parameters, and the temporal memory in the model states in two adjacent subperiods on calibration operations, and (3) multi-metric assessment of model performance designed for various flow phases. The main finding is that (1) the proposed framework significantly improved the accuracy and robustness of the model; (2) however, there was a generally poor response of the seasonal dynamic parameter set to catchment dynamics. Namely, the dynamic changes in parameters did not follow the dynamics of catchment characteristics. Hence, we deepen the discussion on the poor response in terms of (1) the evolutionary processes of seasonal dynamic parameters optimized by global optimization, considering that the possible failure in finding the global optimum might lead to unreasonable seasonal dynamic parameter values. Moreover, a practical tool for visualizing the evolutionary processes of seasonal dynamic parameters was designed using geometry visualization techniques. (2) We also discuss the strong correlation between parameters considering that dynamic changes in one parameter might be interfered with by other parameters due to their interdependence. Consequently, the poor response of the seasonal dynamic parameter set to dynamic catchment characteristics may be attributed in part to the possible failure in finding the global optimum and strong correlation between parameters. Further analysis also revealed that even though individual parameters cannot respond well to dynamic catchment characteristics, a dynamic parameter set could carry the information extracted from dynamic catchment characteristics and improve the model performance.

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