Simulation of Rainfall-Runoff in the Deep Hollow Lake Watershed Using an Integrated Surface-Subsurface Flow Model

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

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Analysis of parameter uncertainty of a flow and quality stormwater model

Water Science & Technology ◽

10.2166/wst.2009.434 ◽

2009 ◽

Vol 60 (3) ◽

pp. 717-725 ◽

Cited By ~ 18

Author(s):

C. B. S. Dotto ◽

A. Deletic ◽

T. D. Fletcher

Keyword(s):

Water Quality ◽

Bayesian Methods ◽

Flow Model ◽

Model Parameters ◽

Rainfall Runoff ◽

Sources Of Uncertainty ◽

Music Model ◽

Rainfall Runoff Model ◽

Water Quality Models ◽

Runoff Model

Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.

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A coupled stream flow and depth-integrated subsurface flow model for catchment hydrology

Journal of Hydrology ◽

10.1016/j.jhydrol.2015.09.044 ◽

2015 ◽

Vol 530 ◽

pp. 66-78 ◽

Cited By ~ 9

Author(s):

Yi Pan ◽

Sylvain Weill ◽

Philippe Ackerer ◽

Frederick Delay

Keyword(s):

Stream Flow ◽

Flow Model ◽

Subsurface Flow ◽

Catchment Hydrology

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An Experimental Study on the Rainfall-Runoff Progress of Wheat under Different Slope Angle

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.912-914.1986 ◽

2014 ◽

Vol 912-914 ◽

pp. 1986-1994

Author(s):

Na Na Zhao ◽

Fu Liang Yu ◽

Chuan Zhe Li ◽

Jia Liu ◽

Hao Wang

Keyword(s):

Regression Analysis ◽

Soil Moisture ◽

Power Function ◽

Surface Runoff ◽

Rainfall Intensity ◽

Slope Angle ◽

Subsurface Flow ◽

Rainfall Runoff ◽

Determination Coefficient ◽

Initial Soil

Rainfall-runoff process plays an important role in hydrological cycle, and the study on the rainfall-runoff will provide foundation and basis for research on basin hydrology and flood forecasting. In this paper, the surface runoff and subsurface flow of wheat were observed in the laboratory by artificial rainfall, and analyzed the cumulated surface runoff and recession process of subsurface flow by regression analysis. In addition, the factors affected the runoff and response of soil moisture on the runoff coefficients was also discussed. Results showed that the rainfall intensity, soil coverage and slope had important influence on the surface runoff generation, and the surface runoff was observed when the total rainfall amount exceeded 32mm and 13mm for 5°and 15° slope respectively. The cumulative surface runoff could be expressed as a power function, which had higher determination coefficient R2 (0.92~0.999). The subsurface flow was only observed at the ripening period and wheat stubble treatment, and mainly affected by slope angle and initial soil moisture, whereas rainfall intensity showed little impact. The recession curve of subsurface flow can be described as a simple exponential expression or power function, which the determination coefficient was 0.88 and 0.94 by regression analysis, respectively. Moreover, there was an obvious threshold (approximately 30%) between the average initial soil moisture and runoff coefficients, which the runoff increased significantly as above the threshold.

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