Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments

The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

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A framework to regionalize conceptual model parameters for global hydrological modeling

10.5194/hess-2020-127 ◽

2020 ◽

Author(s):

Wenyan Qi ◽

Jie Chen ◽

Lu Li ◽

Chong-yu Xu ◽

Jingjing Li ◽

...

Keyword(s):

Water Resources ◽

Conceptual Model ◽

Hydrological Modeling ◽

Global Scale ◽

Spatial Proximity ◽

Model Parameters ◽

Hydrological Models ◽

Physical Similarity ◽

Similarity Method ◽

Global Water

Abstract. To provide an accurate estimate of global water resources and help to formulate water allocation policies, global hydrological models (GHMs) have been developed. However, it is difficult to obtain parameter values for GHMs, which results in large uncertainty in estimation of the global water balance components. In this study, a framework is developed for building GHMs based on parameter regionalization of catchment scale conceptual hydrological models. That is, using appropriate global scale regionalization scheme (GSRS) and conceptual hydrological models to simulate runoff at the grid scale globally and the Network Response Routing (NRF) method to converge the grid runoff to catchment streamflow. To achieve this, five regionalization methods (i.e. the global mean method, the spatial proximity method, the physical similarity method, the physical similarity method considering distance, and the regression method) are first tested for four conceptual hydrological models over thousands medium-sized catchments (2500–50000 km2) around the world to find the appropriate global scale regionalization scheme. The selected GSRS is then used to regionalize conceptual model parameters for global land grids with 0.5°×0.5° resolution on latitude and longitude. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting (IDW) method and the output average option (SPI-OUT) offers the best regionalization solution, and the greatest gains of the SPI-OUT method were achieved with mean distance between the donor catchments and the target catchment is no more than 1500 km. (2) It was found the Kling-Gupta efficiency (KGE) value of 0.5 is a good threshold value to select donor catchments. And (3) Four different GHMs established based on framework were able to produce reliable streamflow simulations. Overall, the proposal framework can be used with any conceptual hydrological model for estimating global water resources, even though uncertainty exists in terms of using difference conceptual models.

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Regionalization of hydrological model parameters using data depth

Hydrology Research ◽

10.2166/nh.2011.031 ◽

2011 ◽

Vol 42 (5) ◽

pp. 356-371 ◽

Cited By ~ 7

Author(s):

András Bárdossy ◽

Shailesh Kumar Singh

Keyword(s):

Hydrological Model ◽

Data Depth ◽

Model Parameters ◽

Hydrological Models ◽

Rainfall Runoff ◽

Catchment Characteristics ◽

Rainfall Runoff Model ◽

Using Data ◽

Runoff Model

The parameters of hydrological models with no or short discharge records can only be estimated using regional information. We can assume that catchments with similar characteristics show a similar hydrological behaviour. A regionalization of hydrological model parameters on the basis of catchment characteristics is therefore plausible. However, due to the non-uniqueness of the rainfall/runoff model parameters (equifinality), a procedure of a regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper, a different procedure based on the depth function and convex combinations of model parameters is introduced. Catchment characteristics to be used for regionalization can be identified by the same procedure. Regionalization is then performed using different approaches: multiple linear regression using the deepest parameter sets and convex combinations. The assessment of the quality of the regionalized models is also discussed. An example of 28 British catchments illustrates the methodology.

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Towards improving the Pareto frontier in environmental models using multi-dataset calibrations coupled with metaheuristic methods

10.5194/egusphere-egu21-11996 ◽

2021 ◽

Author(s):

David C. Finger ◽

Anna E. Sikorska-Senoner

Keyword(s):

Water Quality ◽

Water Resources ◽

Model Calibration ◽

Pareto Frontier ◽

Model Performance ◽

Model Parameters ◽

Hydrological Models ◽

Quality Models ◽

Environmental Models ◽

Water Quality Models

Environmental models, such as hydrological models or water quality models, are incorporate numerical algorithms that describe either empirically or physical-based the large variety of natural processes that govern the flow of water (or other variables) and its components. The purposes of these models range from improving our understanding of the principles of hydrological processes at a catchment scale to making predictions about how anthropogenic activities will influence future water resources. To be applicable, these models require calibration with observed output data, which is most often streamflow for hydrological models. Yet, the complex nature of hydrological processes, on the one hand, and the limited observed data to inform model parameters, on the other hand, evoke the unavoidable equifinality issue in the calibration of these models. This equifinality issue is expressed with the presence of several optimal model parameters that have different values but lead to similar model performance. One way of dealing with this issue is through providing a parameter ensemble with optimal solutions instead of a single parameter set, reported often as parametric model uncertainty.However, this equifinality issue is far from being solved, as also highlighted by one of 23 Unsolved Problems in Hydrology (UPH). This is particularly the case if more variables than only streamflow are of interest. Our hypothesis is that using more than one dataset for calibrating any environmental model helps reducing the equifinality issue during model calibration and thus improves the identifiability of model parameters. In this review-based study, we present recent examples of hydrological (and water quality) models from literature that have been calibrated within a multiple dataset framework to reduce the equifinality issue. We demonstrate that a multi-dataset calibration yields a better model performance regardless of the complexity of the model. Finally, we show that coupling a multi-dataset model calibration with metaheuristics (such as Monte Carlo or Genetic Algorithm) can help reducing the equifinality of model parameters and improving the Pareto frontier. At the bottom of this study, we outline how such a multi-dataset calibration can lead to better model predictions and how it can help emerging water resources problems due to an emerging climate crisis.This work contributes to one of the seven major themes of 23 UPH, i.e., Modelling methods. It paths a way forward towards reducing parameter uncertainty in hydrological predictions (UPH question #20) and thus towards improving modelling of hydrologic responses in the extrapolation phase, i.e., under changed catchment conditions (UPH question #19).

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Influences of calibration data length and data period on model parameterization and quantification of terrestrial ecosystem carbon dynamics

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-6835-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 6835-6865 ◽

Cited By ~ 2

Author(s):

Q. Zhu ◽

Q. Zhuang

Keyword(s):

Climate Variability ◽

Terrestrial Ecosystem ◽

Carbon Dynamics ◽

Series Data ◽

Model Parameters ◽

Calibration Data ◽

Climate Conditions ◽

Model Parameterization ◽

Data Period ◽

Data Length

Abstract. Reliability of terrestrial ecosystem models highly depends on the quantity and quality of the data that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes are abundant. However, the knowledge of how much data (data length) and which subset of the time series data (data period) should be used to effectively calibrate the model is still lacking. In this study we use the AmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) using an adjoint based data assimilation technique for five different ecosystem types including deciduous broadleaf forest, coniferous forest, grassland, shrubland and boreal forest. We hypothesize that calibration data covering various climate conditions for the ecosystems (e.g. drought and wet; high and low air temperature) can reduce the uncertainty of the model parameter space. Here parameterization is conducted to explore the impact of both data length and data period on the uncertainty reduction of the posterior model parameters and the quantification of site and regional carbon dynamics. We find that: (1) the model is better constrained when it uses two-year data comparing to using one-year data. Further, two-year data is long enough in calibrating TEM's carbon dynamics, since using three-year data could only marginally improve the model performance at our study sites; (2) the model is better constrained with the data that have a higher "climate variability" than that with a lower one. The climate variability is used to measure the overall possibility of the ecosystem to experience various climate conditions including drought and extreme air temperatures and radiation; (3) the US regional simulations indicate that the effect of calibration data length on carbon dynamics is amplified at regional and temporal scales, leading to large discrepancies among different parameterization experiments, especially in July and August. This study shall help the eddy flux observation community in conducting field observations. The study shall also benefit the ecosystem modeling community in using multiple-year data to improve model parameterization and predictability.

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Particle Swarm Optimization and Shuffle Complex Evolution for Calibrating Xinanjiang Model Parameters

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i10.1202 ◽

2013 ◽

Vol 10 (10) ◽

pp. 2036-2048

Author(s):

OKelvin Kuok ◽

Po Chan Chiu

Keyword(s):

Particle Swarm Optimization ◽

Model Calibration ◽

Hydrological Model ◽

Particle Swarm ◽

Daily Rainfall ◽

Model Parameters ◽

Hydrological Models ◽

Swarm Optimization ◽

Xinanjiang Model ◽

Conceptual Hydrological Model

Xinanjiang model, a conceptual hydrological model, is well known and widely used in China since 1970s. Xinanjiang model consists of large number of parameters that cannot be directly obtained from measurable quantities of catchment characteristics, but only through model calibration. Parameter optimization is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfallâ€“runoff processes. This study presents newly developed Particle Swarm Optimization (PSO) and compared with famous Shuffle Complex Evolution (SCE) to auto-calibrate Xinanjiang model parameters. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, Malaysia. Input data used for model calibration are daily rainfall data Year 2001, and validated with data Year 1990, 1992, 2000, 2002 and 2003. Simulation results are measured with Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E2). Results show that the performance of PSO is comparable with the famous SCE algorithm. For model calibration, the best R andE2 obtained are 0.775 and 0.715 respectively, compared to R=0.664 and E2=0.677 for SCE. For model validation, the average R=0.859 and average E2=0.892 are obtained for PSO, compared to average R=0.572 and average E2 =0.631 obtained for SCE.Â

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Estimating parameters of the variable infiltration capacity model using ant colony optimization

Water Science & Technology ◽

10.2166/wst.2016.282 ◽

2016 ◽

Vol 74 (4) ◽

pp. 985-993

Author(s):

JiaJia Yue ◽

Bo Pang ◽

ZongXue Xu

Keyword(s):

Evolutionary Algorithm ◽

Ant Colony Optimization ◽

River Basin ◽

Model Calibration ◽

Heihe River ◽

Model Parameters ◽

Hydrological Models ◽

Variable Infiltration Capacity ◽

Parameter Calibration ◽

Infiltration Capacity

Because hydrological models are so important for addressing environmental problems, parameter calibration is a fundamental task for applying them. A broadly used method for obtaining model parameters for the past 20 years is the evolutionary algorithm. This approach can estimate a set of unknown model parameters by simulating the evolution process. The ant colony optimization (ACO) algorithm is a type of evolutionary algorithm that has shown a strong ability in tackling combinatorial problems and is suitable for hydrological model calibration. In this study, an ACO based on the grid partitioning strategy was applied to the parameter calibration of the variable infiltration capacity (VIC) model for the Upper Heihe River basin and Xitiaoxi River basin, China. The shuffled complex evolution (SCE-UA) algorithm was used to test the applicability of the ACO. The results show that ACO is capable of model calibration of the VIC model; the Nash–Sutcliffe coefficient of efficiency is 0.62 and 0.81 in calibration and 0.65 and 0.86 in validation for the Upper Heihe River basin and Xitiaoxi River basin respectively, which are similar to the SCE-UA results. Despite the encouraging results obtained thus far, further studies could still be performed on the parameter optimization of an ACO to enlarge its applicability to more distributed hydrological models.

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Calibration of hydrological model parameters for ungauged catchments

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-1105-2006 ◽

2006 ◽

Vol 3 (3) ◽

pp. 1105-1124 ◽

Cited By ~ 8

Author(s):

A. Bárdossy

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Model Parameters ◽

Hydrological Models ◽

Rainfall Runoff ◽

Regional Function ◽

Ungauged Catchments ◽

Rainfall Runoff Model ◽

Climate Statistics ◽

Runoff Model

Abstract. The parameters of hydrological models for catchments with few or no discharge records can only 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. Transferable parameter sets are identified using regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics.

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Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria

Hydrology and Earth System Sciences ◽

10.5194/hess-13-259-2009 ◽

2009 ◽

Vol 13 (2) ◽

pp. 259-271 ◽

Cited By ~ 49

Author(s):

J. Parajka ◽

V. Naeimi ◽

G. Blöschl ◽

J. Komma

Keyword(s):

Soil Moisture ◽

Model Calibration ◽

Winter Season ◽

Hydrologic Model ◽

Model Parameters ◽

Sources Of Information ◽

Average Correlation ◽

Robust Model ◽

Spatial Coverage ◽

Top Soil

Abstract. This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991–2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information.

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Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-3313-2008 ◽

2008 ◽

Vol 5 (6) ◽

pp. 3313-3353 ◽

Cited By ~ 2

Author(s):

J. Parajka ◽

V. Naeimi ◽

G. Blöschl ◽

J. Komma

Keyword(s):

Soil Moisture ◽

Model Calibration ◽

Winter Season ◽

Hydrologic Model ◽

Model Parameters ◽

Sources Of Information ◽

Average Correlation ◽

Robust Model ◽

Spatial Coverage ◽

Top Soil

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Optimal Calibration of Evaporation Models against Penman–Monteith Equation

Water ◽

10.3390/w13111484 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1484

Author(s):

Dagmar Dlouhá ◽

Viktor Dubovský ◽

Lukáš Pospíšil

Keyword(s):

Model Calibration ◽

Heat Storage ◽

Free Water ◽

Complex Model ◽

Model Parameters ◽

Net Radiation ◽

Statistical Measures ◽

Pit Lakes ◽

Evaporation Models ◽

Monteith Equation

We present an approach for the calibration of simplified evaporation model parameters based on the optimization of parameters against the most complex model for evaporation estimation, i.e., the Penman–Monteith equation. This model computes the evaporation from several input quantities, such as air temperature, wind speed, heat storage, net radiation etc. However, sometimes all these values are not available, therefore we must use simplified models. Our interest in free water surface evaporation is given by the need for ongoing hydric reclamation of the former Ležáky–Most quarry, i.e., the ongoing restoration of the land that has been mined to a natural and economically usable state. For emerging pit lakes, the prediction of evaporation and the level of water plays a crucial role. We examine the methodology on several popular models and standard statistical measures. The presented approach can be applied in a general model calibration process subject to any theoretical or measured evaporation.

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