Parameter Regionalization of a Tank Model for Simulating Runoffs from Ungauged Watersheds

강민구,; Lee, Joo-Heon; 박기욱,

doi:10.3741/jkwra.2013.46.5.519

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…where SECP is a soil evaporation compensation parameter, which is positively correlated to K s . The SECP parameter has been reported to vary from 0.001 to 0.1, depending on watershed soil texture [8,40,49]. We adopted the feasible ranges of the Tank model parameters that were set considering the hydrological characteristics of Korean watersheds [8,36] (Table 1).…”

Section: Tank Modelmentioning

confidence: 99%

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Song

Her

Suh

et al. 2019

Water

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Regionalized lumped rainfall-runoff (RR) models have been widely employed as a means of predicting the streamflow of an ungauged watershed because of their simple yet effective simulation strategies. Parameter regionalization techniques relate the parameter values of a model calibrated to the observations of gauged watersheds to the geohydrological characteristics of the watersheds. Thus, the accuracy of regionalized models is dependent on the calibration processes, as well as the structure of the model used and the quality of the measurements. In this study, we have discussed the potentials and limitations of hydrological model parameter regionalization to provide practical guidance for hydrological modeling of ungauged watersheds. This study used a Tank model as an example model and calibrated its parameters to streamflow observed at the outlets of 39 gauged watersheds. Multiple regression analysis identified the statistical relationships between calibrated parameter values and nine watershed characteristics. The newly developed regional models provided acceptable accuracy in predicting streamflow, demonstrating the potential of the parameter regionalization method. However, uncertainty associated with parameter calibration processes was found to be large enough to affect the accuracy of regionalization. This study demonstrated the importance of objective function selection of the RR model regionalization.

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Section: Tank Modelmentioning

confidence: 99%

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Song

Her

Suh

et al. 2019

Water

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“…ere are several ways of predicting the inflow to the systems starting from simplified tank models [24][25][26][27] to unsteady hydrological routing models [28][29][30][31][32]. Literature shows these approaches are clustered into empirical, conceptual, physical, and data-driven models [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

Karunanayake

Gunathilake

Rathnayake

2020

Applied Computational Intelligence and Soft Computing

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Prediction of water resources for future years takes much attention from the water resources planners and relevant authorities. However, traditional computational models like hydrologic models need many data about the catchment itself. Sometimes these important data on catchments are not available due to many reasons. Therefore, artificial neural networks (ANNs) are useful soft computing tools in predicting real-world scenarios, such as forecasting future water availability from a catchment, in the absence of intensive data, which are required for modeling practices in the context of hydrology. These ANNs are capable of building relationships to nonlinear real-world problems using available data and then to use that built relationship to forecast future needs. Even though Sri Lanka has an extensive usage of water resources for many activities, including drinking water supply, irrigation, hydropower development, navigation, and many other recreational purposes, forecasting studies for water resources are not being carried out. Therefore, there is a significant gap in forecasting water availability and water needs in the context of Sri Lanka. Thus, this paper presents an artificial neural network model to forecast the inflows of one of the most important reservoirs in northern Sri Lanka using the upstream catchment’s rainfall. Future rainfall data are extracted using regional climate models for the years 2021–2050 and the inflows of the reservoir are forecasted using the validated neural network model. Several training algorithms including Levenberg–Marquardt (LM), BFGS quasi-Newton (BFG), scaled conjugate gradient (SCG) have been used to find the best fitting training algorithm to the prediction process of the inflows against the measured inflows. Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. In addition, the forecasted results using the projected climate scenarios clearly showcase the benefit of using the forecasting model in solving future water resource management to avoid or to minimize future water scarcity. Therefore, the validated model can effectively be used for proper planning of the proposed drinking water supply scheme to the nearby urban city, Jaffna in northern Sri Lanka.

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“…In general, the model parameters are calibrated for the limited catchments, and then its parameters are regionalized with catchment characteristics so that the model can be applied to the entire basin including ungauged catchments. There have been multiple attempts to establish regressed equations between the geographical characteristics and the model parameters [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Review of Tank Model and Its Applicability to Various Korean Catchment Conditions

Lee

Chegal²,

Lee

2020

Water

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This paper reviews a conceptual rainfall-runoff model called Tank which has been widely used over the last 20 years in Korea as a part of a water resource modelling framework for assessing and developing long-term water resource polices. In order to examine the uncertainty of model predictions and the sensitivity of model’s parameters, Monte Carlos and Markov chain-based approaches are applied to five catchments of various Korean geographical and climatic conditions where the catchment sizes are ranged from 83 to 4786 km2. In addition, three optimization algorithms—dynamically dimensioned search (DDS), robust parameter estimation (ROPE), and shuffled complex evolution (SCE)—are selected to test whether the model parameters can be optimized consistently within a narrower range than the uncertainty bounds. From the uncertainty analysis, it is found that there is limited success in refining the priori distributions of the model parameters, indicating there is a high degree of equifinality for some parameters or at least there are large numbers of parameter combinations leading to good solutions within model’s uncertainty bounds. Out of the three optimization algorithms, SCE meets the criteria of the consistency best. It is also found that there are still some parameters that even the SCE method struggles to refine the priori distributions. It means that their contribution to model results is minimal and can take a value within a reasonable range. It suggests that the model may be reconceptualized to be parsimonious and to rationalize some parameters without affecting model’s capacity to replicate historical flow characteristics. Cross-validation indicates that sensitive parameters to catchment characteristics can be transferred when geophysical similarity exists between two catchments. Regionalization can be further improved by using a regression or geophysical similarity-based approach to transfer model parameters to ungauged catchments. It may be beneficial to categorize the model parameters depending on the level of their sensitivities, and a different approach to each category may be applied to regionalize the calibrated parameters.

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Parameter Regionalization of a Tank Model for Simulating Runoffs from Ungauged Watersheds

Cited by 10 publications

References 9 publications

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

A Review of Tank Model and Its Applicability to Various Korean Catchment Conditions

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