Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based ...Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m<sup>2</sup> hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m<sup>2</sup>, 95% CI 10,613 - 23,952 m<sup>2</sup>) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m<sup>2</sup>, 95% CI 13,054 - 20,133 m<sup>2</sup>). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.展开更多
以潮白河为研究区域,探讨了与模型参数及模型模拟性能有关的多参数灵敏度及不确定性分析方法(Multi-Parameter Sensitivity and Uncertainty Analysis,MPSUA)。基于MonteCarlo模拟的多参数灵敏度分析,可以评价模型中多个参数的相对重要...以潮白河为研究区域,探讨了与模型参数及模型模拟性能有关的多参数灵敏度及不确定性分析方法(Multi-Parameter Sensitivity and Uncertainty Analysis,MPSUA)。基于MonteCarlo模拟的多参数灵敏度分析,可以评价模型中多个参数的相对重要性。GLUE不确定性分析则能对模型性能进行量化评估。实例研究表明,通过MPSUA方法,可以减少优化参数的个数。而且,在没有对模型进行参数优化之前,基于MPSUA就可以确定模型的模拟精度。例如同样的模型在潮河可以获得比在白河更高的模拟精度。这种同一模型在不同流域所体现的差异性,一方面是源于模型结构本身的不完善,另一方面则与用于建模的数据误差有关。SCE-UA参数优化结果与MPSUA结果几乎一致,说明本文的参数灵敏度与模型总体性能评估方法比较合理。展开更多
Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bot...Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.展开更多
文摘Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m<sup>2</sup> hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m<sup>2</sup>, 95% CI 10,613 - 23,952 m<sup>2</sup>) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m<sup>2</sup>, 95% CI 13,054 - 20,133 m<sup>2</sup>). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.
文摘以潮白河为研究区域,探讨了与模型参数及模型模拟性能有关的多参数灵敏度及不确定性分析方法(Multi-Parameter Sensitivity and Uncertainty Analysis,MPSUA)。基于MonteCarlo模拟的多参数灵敏度分析,可以评价模型中多个参数的相对重要性。GLUE不确定性分析则能对模型性能进行量化评估。实例研究表明,通过MPSUA方法,可以减少优化参数的个数。而且,在没有对模型进行参数优化之前,基于MPSUA就可以确定模型的模拟精度。例如同样的模型在潮河可以获得比在白河更高的模拟精度。这种同一模型在不同流域所体现的差异性,一方面是源于模型结构本身的不完善,另一方面则与用于建模的数据误差有关。SCE-UA参数优化结果与MPSUA结果几乎一致,说明本文的参数灵敏度与模型总体性能评估方法比较合理。
基金supported by the National Natural Science Foundation of China(40901023)the National Basic Research Program of China (2010CB428403)
文摘Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.
