Reprint of: Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

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Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

10.20944/preprints201808.0209.v1 ◽

2018 ◽

Author(s):

Mehmet Cüneyd Demirel ◽

Julian Koch ◽

Gorka Mendiguren ◽

Simon Stisen

Keyword(s):

Sensitivity Analysis ◽

Spatial Variability ◽

Spatial Pattern ◽

Spatial Patterns ◽

Performance Measures ◽

Sampling Strategy ◽

Behavioral Model ◽

Hydrologic Model ◽

Actual Evapotranspiration ◽

Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

10.5194/hess-2019-163 ◽

2019 ◽

Cited By ~ 1

Author(s):

Daniel Erdal ◽

Olaf A. Cirpka

Keyword(s):

Sensitivity Analysis ◽

Flow Model ◽

Global Sensitivity Analysis ◽

Subsurface Flow ◽

Full Model ◽

Stochastic Sampling ◽

Global Sensitivity ◽

Parameter Combination ◽

Active Subspaces ◽

Active Subspace

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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Sensitivity analysis using a diffuse pollution hydrologic model to assess factors affecting pesticide concentrations in river water

Water Science & Technology ◽

10.2166/wst.2010.540 ◽

2010 ◽

Vol 62 (11) ◽

pp. 2579-2589 ◽

Cited By ~ 3

Author(s):

Koji Tani ◽

Yoshihiko Matsui ◽

Kentaro Narita ◽

Koichi Ohno ◽

Taku Matsushita

Keyword(s):

Sensitivity Analysis ◽

River Water ◽

Rate Constant ◽

Degradation Rate ◽

Hydrologic Model ◽

Adsorption Coefficient ◽

Pesticide Concentration ◽

Artificial Drainage ◽

Degradation Rate Constant ◽

Intermittent Irrigation

We quantitatively evaluated the factors that affect the concentrations of rice-farming pesticides (an herbicide and a fungicide) in river water by a sensitivity analysis using a diffuse pollution hydrologic model. Pesticide degradation and adsorption in paddy soil affected concentrations of the herbicide pretilachlor but did not affect concentrations of the fungicide isoprothiolane. We attributed this difference to the timing of pesticide application in relation to irrigation and drainage of the rice paddy fields. The herbicide was applied more than a month before water drainage of the fields and runoff was gradual over a long period of time, whereas the fungicide was applied shortly before drainage and runoff was rapid. However, the effects of degradability-in-water on the herbicide and fungicide concentrations were similar, with concentrations decreasing only when the rate constant of degradation in water was large. We also evaluated the effects of intermittent irrigation methods (irrigation/artificial drainage or irrigation/percolation) on pesticide concentrations in river water. The runoff of the fungicide, which is applied near or in the period of intermittent irrigation, notably decreased when the method of irrigation/artificial drainage was changed to irrigation/percolation. In a sensitivity analysis evaluating the synergy effect of degradation and adsorbability in soil, the degradation rate constant in soil greatly affected pesticide concentration when the adsorption coefficient was small but did not affect pesticide concentration when the adsorption coefficient was large. The pesticide concentration in the river water substantially decreased when either or both the degradation rate constant in soil and adsorption coefficient was large.

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