Improvement of model evaluation by incorporating  prediction and measurement uncertainty

Chen, Lei; Li, Shuang; Zhong, Yulong; Shen, Zhenyao

doi:10.5194/hess-22-4145-2018

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…However, this approach is suitable for situations where there are less data, and these intervals may not always be feasible when more data can be collected or when a continuous and random data distribution can be assumed. The cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) are commonly used methods for model evaluation in uncertainty frameworks [103]. Nevertheless, due to limited knowledge and natural randomness, a fixed PDF or error range cannot be found.…”

Section: Parameter Uncertaintymentioning

confidence: 99%

Review of Nonpoint Source Pollution Models: Current Status and Future Direction

Wang,

Chen,

et al. 2022

Water

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Modelling tools are commonly used for predicting non-point source (NPS) pollutants and it is timely to review progress that has been made in terms of the development of NPS models. This paper: (1) proposes a systematic description of model framework and generalizes some commonly used models; (2) identifies the common challenges in model structure and applications; (3) summarizes the future directions of NPS models. Challenges in model construction and application are based on the following: (1) limitations in understanding specific NPS pollution processes; (2) model expansion to different scales; (3) data scarcity and its impacts on model performance; (4) prediction uncertainty due to model input, parameter and model structure; (5) insufficient accuracy for decision-making. Finally, this paper proposes future directions for model development, including: (1) a source–flow–sink framework for model development; (2) standardization for model input and parameter; (3) reliable decision support for environmental management. The findings of this review provide helps in the accurate prediction and management of NPS pollution around the world.

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Section: Parameter Uncertaintymentioning

confidence: 99%

Review of Nonpoint Source Pollution Models: Current Status and Future Direction

Wang,

Chen,

et al. 2022

Water

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“…Arabi et al 2007;Shen et al 2010). Regarding the uncertainties arising from the data used for modeling, previous studies have focused on the influences of the spatial resolutions of the digital elevation models (DEMs) (Xu et al 2016), forcing precipitation data (Schurz et al 2019), and streamflow and water quality data used for model calibration (Chen et al 2018). The pollution load in a river system comes from different sources.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the influence of hydrological condition on the phosphorus loads in an agricultural river basin using the SWAT model

et al. 2021

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Excessive phosphorus is an important cause of eutrophication. For river basin management, source identification and control of nonpoint source (NPS) pollution are difficult. In this study, to explore influences of hydrological conditions on phosphorus, the Soil and Water Assessment Tool (SWAT) model is applied to the Luanhe River basin in North China. Moreover, influences of the spatial scale of the livestock and poultry amount data on estimations of phosphorus loads are also discussed. The results show that applying town-level livestock and poultry amount data allows the model to perform better when estimating phosphorus loads, indicating that using data at a finer administrative level is necessary. For the typical wet year, the estimated annual phosphorus load was 2.6 times that in the typical dry year. Meanwhile, the contribution of pollution in summer to the annual load is greater in the wet year than that in the dry year. The spatial distributions of subbasins with high unit loads of phosphorus differ under different hydrological conditions, meaning that critical areas for pollution control vary with the wetness of each year. All these findings indicate that for pollution control at basin scale, considering the seasonal and interannual variabilities in hydrological conditions is highly demanded.

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“…In some extreme situations, the total error can be up to more than 30% (Dotto et al, 2014). Improper handling of input uncertainty in model calibration may yield grossly misleading and biased parameter estimates, which cannot represent the real catchment (Chen et al 2018). The bias in parameter estimations can lead to that the SWMM model calibrated using historical record cannot predict future response correctly and thus restricts the application of the SWMM https://doi.org/10.5194/hess-2020-367 Preprint.…”

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confidence: 99%

A Framework for Automatic Calibration of SWMM Considering Input Uncertainty

Gao

Yang

Han

et al. 2020

Preprint

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Abstract. A new Bayesian framework for automatic calibration of SWMM, which simultaneously considers both parameter uncertainty and input uncertainty, was developed. The framework coupled an optimization algorithm DREAM and the Storm Water Management Model (SWMM) by newly developed API functions used to obtain and adjust parameter values of the model. Besides, a rainfall error model was integrated into the framework to consider systematic rainfall errors. A case study in Guangzhou, China was conducted to demonstrate the use of the framework. The calibration capability of the framework was tested and the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries were identified in the study area. the results show that calibration considering both parameter uncertainty and rainfall uncertainty captures peak flow much better and is more robust in terms of the Nash Sutcliffe index than that only considering parameter uncertainty.

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Improvement of model evaluation by incorporating prediction and measurement uncertainty

Cited by 9 publications

References 33 publications

Review of Nonpoint Source Pollution Models: Current Status and Future Direction

Review of Nonpoint Source Pollution Models: Current Status and Future Direction

Evaluating the influence of hydrological condition on the phosphorus loads in an agricultural river basin using the SWAT model

A Framework for Automatic Calibration of SWMM Considering Input Uncertainty

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