Parameter Uncertainty Analysis of Surface Flow and Sediment Yield in the Huolin Basin, China

Chen, Xue; Chen, Bing; Wu, Hongjing

doi:10.1061/(asce)he.1943-5584.0000909

Cited by 44 publications

(33 citation statements)

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“…Since environmental data are usually not well behaved, input data and parameters are the major sources of uncertainties in risk assessment [15,16]. For example, concentrations of contaminants in the water sample are sometimes below the analytical reporting limit of the laboratory (also called the detection limit).…”

Section: Introductionmentioning

confidence: 99%

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Chen

2014

Toxics

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Abstract:The toxic chemical and heavy metals within wastewater can cause serious adverse impacts on human health. Health risk assessment (HRA) is an effective tool for supporting decision-making and corrective actions in water quality management. HRA can also help people understand the water quality and quantify the adverse effects of pollutants on human health. Due to the imprecision of data, measurement error and limited available information, uncertainty is inevitable in the HRA process. The purpose of this study is to integrate statistical and probabilistic methods to deal with censored and limited numbers of input data to improve the reliability of the non-cancer HRA of dermal contact exposure to contaminated river water by considering uncertainty. A case study in the Kelligrews River in St. John's, Canada, was conducted to demonstrate the feasibility and capacity of the proposed approach. Five heavy metals were selected to evaluate the risk level, including arsenic, molybdenum, zinc, uranium and manganese. The results showed that the probability of the total hazard index of dermal exposure exceeding 1 is very low, and there is no obvious evidence of risk in the study area.

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Section: Introductionmentioning

confidence: 99%

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Chen

2014

Toxics

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“…Furthermore, in the studies conducted in other parts of the world (e.g. Lenhart et al ., ; Muleta and Nicklow, ; Migliaccio and Chaubey, ; Yang et al ., ; Cibin et al ., ; Xue et al ., ), out of the sensitive parameters considered for streamflow simulation, generally, parameters like CN2, ESCO, SOL_AWC and GW_DELAY are common in most studies.…”

Section: Resultsmentioning

confidence: 99%

“…A close perusal of Figures and reveal that in case of SUFI‐2, the posterior distribution of optimal parameters spreads uniformly throughout the entire posterior range, whereas some of the parameters are not uniformly distributed in the posterior distribution in the case of GLUE. This indicates that GLUE provides a more objective distribution for posterior distribution (Xue et al ., ). It is also apparent from Figures and that sometimes SUFI‐2 yields poor results (NSE = zero or negative), but this is not the case with GLUE.…”

Section: Comparison Of Sufi‐2 and Glue Optimization Techniquesmentioning

confidence: 97%

“…() have quantified the uncertainty associated with hydrological modelling in addition to sensitivity analysis using different optimization techniques. In the recent past, a couple of researchers (Yang et al ., ; Singh et al ., ; Xue et al ., ) performed modelling studies focusing on the uncertainty analysis using SWAT. In these studies, a separate tool named SWAT Calibration and Uncertainty Program (SWAT‐CUP, http://www.neprashtechnology.ca/Default.aspx) has been used for analysing the uncertainty in the modelling results.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification and uncertainty analysis for simulating streamflow in a river basin of Eastern India

Uniyal

Jha

Verma

2015

Hydrological Processes

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Uncertainty is inherent in modelling studies. However, the quantification of uncertainties associated with a model is a challenging task, and hence, such studies are somewhat limited. As distributed or semi-distributed hydrological models are being increasingly used these days to simulate hydrological processes, it is vital that these models should be equipped with robust calibration and uncertainty analysis techniques. The goal of the present study was to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for simulating streamflow in a river basin of Eastern India, and to evaluate the performance of salient optimization techniques in quantifying uncertainties. The SWAT model for the study basin was developed and calibrated using Parameter Solution (ParaSol), Sequential Uncertainty Fitting Algorithm (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) optimization techniques. The daily observed streamflow data from 1998 to 2003 were used for model calibration, and those for 2004-2005 were used for model validation. Modelling results indicated that all the three techniques invariably yield better results for the monthly time step than for the daily time step during both calibration and validation. The model performances for the daily streamflow simulation using ParaSol and SUFI-2 during calibration are reasonably good with a Nash-Sutcliffe efficiency and mean absolute error (MAE) of 0.88 and 9.70 m 3 /s for ParaSol, and 0.86 and 10.07 m 3 /s for SUFI-2, respectively. The simulation results of GLUE revealed that the model simulates daily streamflow during calibration with the highest accuracy in the case of GLUE (R 2 = 0.88, MAE = 9.56 m 3 /s and root mean square error = 19.70 m 3 /s). The results of uncertainty analyses by SUFI-2 and GLUE were compared in terms of parameter uncertainty. It was found that SUFI-2 is capable of estimating uncertainties in complex hydrological models like SWAT, but it warrants sound knowledge of the parameters and their effects on the model output. On the other hand, GLUE predicts more reliable uncertainty ranges (R-factor = 0.52 for daily calibration and 0.48 for validation) compared to SUFI-2 (R-factor = 0.59 for daily calibration and 0.55 for validation), though it is computationally demanding. Although both SUFI-2 and GLUE appear to be promising techniques for the uncertainty analysis of modelling results, more and more studies in this direction are required under varying agro-climatic conditions for assessing their generic capability. Figure 11. Ninety-five per cent prediction uncertainty band (shaded area) by GLUE technique during (a) model calibration and (b) model validation for daily streamflow simulation 3763 PARAMETER IDENTIFICATION AND UNCERTAINTY ANALYSIS

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“…These values are often defined as optional input attributes and are generated through model calibration process, which is an important step in the application of a watershed model (Guinot et al 2011, Ye et al 2011). Nevertheless, the identification of these soil properties is a complex non-linear problem because numerous subprocesses are considered or because the interactions between parameters are complex (Shen et al 2010, Xue et al 2014. In this sense, soil properties cannot be identified easily because the calibrated soil properties might be a balanced different parameters, which cannot reflect the hydrological or environmental characteristics of the watershed (Schwen et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Using site-specific soil samples as a substitution for improved hydrological and nonpoint source predictions

Lei

Wang

Zhong

et al. 2016

Environ Sci Pollut Res

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Soil databases are one of the most important inputs for watershed models, and the quality of soil properties affects how well a model performs. The objectives of this study were to (1) quantify the sensitivity of model outputs to soil properties and to (2) use site-specific soil properties as a substitution for more accurate hydrological and nonpoint source (H/NPS) predictions. Soil samples were collected from a typical mountainous watershed in China, and the impacts of soil sample parameters on H/NPS predictions were quantified using the Soil and Water Assessment Tool (SWAT). The most sensitive parameters related to predicting flow, sediment, and total phosphorus (TP) mainly were the soil hydrological, the channel erosion processes, and the initial soil chemical environment, respectively. When the site-specific soil properties were used, the uncertainties (coefficient of variation) related to predicting the hydrology, sediment and TP decreased by 75∼80 %, 75∼84 %, and 46∼61 %, respectively. Based on changes in the Nash-Sutcliff coefficient, the model performance improved by 4.9 and 19.45 % for the hydrological and sediment model, accordingly. However, site-specific soil properties did not contribute to better TP predictions because of the high spatial variability of the soil P concentrations across the large watershed. Thus, although site-specific soil samples can be used to obtain more accurate H/NPS predictions, more sampling sites are required to apply this method in large watersheds.

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Parameter Uncertainty Analysis of Surface Flow and Sediment Yield in the Huolin Basin, China

Cited by 44 publications

References 38 publications

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Parameter identification and uncertainty analysis for simulating streamflow in a river basin of Eastern India

Using site-specific soil samples as a substitution for improved hydrological and nonpoint source predictions

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