scholarly journals Combining an R-Based Evolutionary Algorithm and Hydrological Model for Effective Parameter Calibration

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1339 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Hydrological Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
Physically Based ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Time Required ◽  
Runoff Model

The hydrological model assessment and development (hydromad) modeling package is an R-based package that can be applied to simulate hydrological models and optimize parameters. As the hydromad package is incompatible with hydrological models outside the package, the parameters of such models cannot be directly optimized. Hence, we proposed a method of optimizing the hydrological-model parameters by combining the executable (EXE) file of the hydrological model with the shuffled complex evolution (SCE) algorithm provided by the hydromad package. A physically based, spatially distributed, grid-based rainfall–runoff model (GRM) was employed. By calibrating the parameters of the GRM, the performance of the model was found to be reasonable. Thus, the hydromad can be used to optimize the hydrological-model parameters outside the package if the EXE file of the hydrological model is available. The time required to optimize the parameters depends on the type of event, even for the same catchment area.

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 Regionalization of hydrological model parameters using data depth

2011 ◽  
Vol 42 (5) ◽  
pp. 356-371 ◽  
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.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2006 ◽  
Vol 3 (3) ◽  
pp. 1105-1124 ◽  
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.

scholarly journals Quantitative analysis of runoff reduction in the Laohahe basin

2012 ◽  
Vol 43 (1-2) ◽  
pp. 38-47 ◽  
Author(s):  
Liliang Ren ◽  
Xiaofan Liu ◽  
Fei Yuan ◽  
Jing Xu ◽  
Wei Liu
Keyword(s):  
Climate Change ◽  
Human Activity ◽  
Hydrological Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Runoff Reduction ◽  
Runoff Series ◽  
Drastic Effect ◽  
Rainfall Runoff Model ◽  
Runoff Model

In order to determine the reason for runoff reduction, daily natural runoff series were restored using a conceptual rainfall–runoff model. The period of 1970–1979 was regarded as a base period with little human activity; model parameters for each subcatchment within the Laohahe basin were calibrated for this period. The effects of human activity and climate change on runoff were quantified by comparing the observed runoff and the natural runoff simulated by the hydrological model. The results show that the observed annual mean runoffs in the 1980s and especially in the 2000s are smaller than those of the 1970s. Although runoff reduction in the 1980s and 2000s is mainly caused by climate change, human activity also plays an important role on the runoff reduction. Taking the 2000 as an example, human activity and climate change are responsible for 45.6 and 54.4% of the runoff reduction in Laohahe basin, respectively. The effect of human activity on runoff reduction in the Laohahe basin is increasingly intensive from the 1980s to the 2000s. Human activity in the Dianzi catchment has the most drastic effect within the Laohahe basin.

scholarly journals Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1641 ◽  
Author(s):  
Huanyu Wang ◽  
Yangbo Chen
Keyword(s):  
Water Content ◽  
Parameter Uncertainty ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Physically Based ◽  
Distributed Hydrological Models

The world has experienced large-scale urbanization in the past century, and this trend is ongoing. Urbanization not only causes land use/cover (LUC) changes but also changes the flood responses of watersheds. Lumped conceptual hydrological models cannot be effectively used for flood forecasting in watersheds that lack long time series of hydrological data to calibrate model parameters. Thus, physically based distributed hydrological models are used instead in these areas, but considerable uncertainty is associated with model parameter derivation. To reduce model parameter uncertainty in physically based distributed hydrological models for flood forecasting in highly urbanized watersheds, a procedure is proposed to control parameter uncertainty. The core concept of this procedure is to identify the key hydrological and flood processes in the highly urbanized watersheds and the sensitive model parameters related to these processes. Then, the sensitive model parameters are adjusted based on local runoff coefficients to reduce the parameter uncertainty. This procedure includes these steps: collecting the latest LUC information or estimating this information using satellite remote sensing images, analyzing LUC spatial patterns and identifying dominant LUC types and their spatial structures, choosing and establishing a distributed hydrological model as the forecasting tool, and determining the initial model parameters and identifying the key hydrological processes and sensitive model parameters based on a parameter sensitivity analysis. A highly urbanized watershed called Shahe Creek in the Pearl River Delta area was selected as a case study. This study finds that the runoff production processes associated with both the ferric luvisol and acric ferralsol soil types and the runoff routing process on urban land are key hydrological processes. Additionally, the soil water content under saturated conditions, the soil water content under field conditions and the roughness of urban land are sensitive parameters.

scholarly journals Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

2016 ◽  
Vol 20 (1) ◽  
pp. 375-392 ◽  
Author(s):  
Y. Chen ◽  
J. Li ◽  
H. Xu
Keyword(s):  
Parameter Optimization ◽  
Hydrological Model ◽  
Pso Algorithm ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Inertia Weight ◽  
Physically Based ◽  
Distributed Hydrological Models

Abstract. Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

Using a multi objective framework for improved calibration and spatial interpolation in hydrological models of the Berg river catchment, South Africa

2020 ◽  
Author(s):  
Andrew Watson ◽  
Jodie Miller ◽  
Sven Kralisch ◽  
Annika Künne ◽  
Manfred Fink
Keyword(s):  
River Flow ◽  
Hydrological Model ◽  
Western Cape ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Multi Objective ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Semi Arid ◽  
Station Density

<p>As understanding river flow regime dynamics is important for future management and conservation of global water resources, the use of hydrological models in ungauged rivers systems has become increasingly common. As the effectiveness of hydrological models to replicate streamflow is limited by the spatial and temporal density of climate stations, it becomes necessary to understand the climate representation of the model at various timesteps. As climate stations are often most dense near cities at low altitude, the importance of having enough stations at different elevation bands impacts the effectiveness of the hydrological model to replicate the sub-basin flow contribution. The use of multi-objective criteria to understand model performance at gauged sub-basins is important during model parameter transfer to ungauged sections. During this study the distributed J2000 rainfall/runoff model was used to understand the impact that climate station density has on model regionalisation and the simulation of hydrological flow components. Furthermore, a station importance factor was used to identify the models station reliance, the maximum station distance for effective hydrological simulation and the relative importance of flow from different sub-basins at the catchment outlet. The rainfall/runoff model was calibrated and validated using multi-objective criteria namely; Nash-Sutcliffe-Efficiency (E1 and E2), Percent Bias (PBIAS) and Kling-Gupta-Efficiency (KGE) coefficients for two gauges, located on the main stem of the river system, to determine a global model parameter dataset which can be used for the model sub-basins. The approach was applied to the Berg River, an inland catchment (7700 km<sup>2</sup>) located in the Western Cape province of South Africa. While the Berg River is an important agricultural area which is dominated by irrigation, it is also the source of large-scale inter-basin transfers to the metropolitan city of Cape Town. The Western Cape has recently (2012-2017) been subject to a crippling drought which had devastating impacts on agricultural production, as well as inter-basin transfers to the city of Cape Town. The results from the hydrological model showed that for precipitation spatial representation, a station density of 1/20 km<sup>2</sup> as well as good mid-altitude (200-300 masl) coverage resulted in good hydrological modelling performance. For the simulation of evaporation, the spatial density of measurements impacted the estimation of potential evaporation, but simulated soil-moisture was the main control and station density did not affect the model results. This study highlights the importance of ensuring that precipitation station coverage is sufficient for effective hydrological simulations from sub-basins, with recommendations of both spatial coverage and elevational representation being provided for semi-arid Southern African conditions. The spatial accounting of micro-climatic variability goes some distance to ensure representative sub-basin flow contributions, improving the ability of hydrological models to replicate river flow regimes in semi-arid heterogenous catchments.</p>

scholarly journals Hydrological modelling under climate change considering nonstationarity and seasonal effects

2015 ◽  
Vol 47 (2) ◽  
pp. 260-273 ◽  
Author(s):  
Kue Bum Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
Hydrological Model ◽  
Hydrological Modelling ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Observation Data ◽  
Rainfall Runoff ◽  
Climate Condition ◽  
The Future ◽  
Rainfall Runoff Model ◽  
Runoff Model

Traditional hydrological modelling assumes that the catchment does not change with time. However, due to changes of climate and catchment conditions, this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions. In this study IHACRES, a conceptual rainfall–runoff model, is applied to a catchment in southwest England. Long observation data (1961–2008) are used and seasonal calibration (only the summer) has been done since there are significant seasonal rainfall patterns. Initially, the calibration is based on changing the model parameters with time by adapting the parameters using the step forward and backward selection schemes. However, in the validation, both models do not work well. The problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. Therefore, a new scheme is explored. Only one parameter is selected for adjustment while the other parameters are set as the fixed and the regression of one optimised parameter is made not only against time but climate condition. The result shows that this nonstationary model works well both in the calibration and validation periods.

scholarly journals Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

2015 ◽  
Vol 12 (10) ◽  
pp. 10603-10649 ◽  
Author(s):  
Y. Chen ◽  
J. Li ◽  
H. Xu
Keyword(s):  
Parameter Optimization ◽  
Hydrological Model ◽  
Pso Algorithm ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Inertia Weight ◽  
Physically Based ◽  
Distributed Hydrological Models

Abstract. Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

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