An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model

Moreau, Pierre; Viaud, Valérie; Parnaudeau, Virginie; Salmon-Monviola, Jordy; Durand, Patrick

doi:10.1016/j.envsoft.2013.04.006

Cited by 53 publications

(42 citation statements)

References 61 publications

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“…Compared to other sensitivity analysis methods, ANOVA was found to yield the most robust results without much computational efforts (Tang et al, 2007). ANOVA is based on the assumption that the 25 uncertainty of an environmental system can be explained by the output variance generated by different effects, and has already been used to assess uncertainty, for instance, in climate impact projections (Addor et al, 2014;Bosshard et al, 2013;Köplin et al, 2013) and agro-hydrological applications (Moreau et al, 2013). ANOVA helps to clarify the question of how much of the available expert knowledge is worth feeding into a hydrological classification, given the unavoidable uncertainty linked with the input data.…”

Section: Experimental Designmentioning

confidence: 99%

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Antonetti

Zappa

2017

Preprint

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Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, 10 applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test to which extent expert knowledge can 15 improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the Inverse Distance Weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with 20 different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down setup relying on parameter and process constraints, and an experimentalists' setup based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed. 25The simulation results showed that: (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up setup performed better than the top-down one when simulating short-duration events, but similarly to the top-down setup when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up

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Section: Experimental Designmentioning

confidence: 99%

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Antonetti

Zappa

2017

Preprint

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“…tion algorithms have been developed to address this problem (e.g., Beven and Binley, 1992;Duan et al, 1992;Vrugt et al, 2003Vrugt et al, , 2005Hill and Tiedeman, 2007;Abebe et al, 2010;Aster et al, 2013;Moreau et al, 2013;Sen and Stoffa, 2013), but it is often not feasible or necessary to include all of the model parameters in the calibration process to achieve efficient optimization. For example, over-parameterization is another well-known problem in rainfall-runoff modeling (van Griensven et al, 2006).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

470

273

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s u m m a r y Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.

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“…In that case, nitrogen related soil parameters that have a high influence on crop productivity [33] are not taken into account to extract SWHC parameters. Indeed, fertilizer inputs and soil organic matter mineralization are derived from "a-priori" knowledge and used as input parameters but large uncertainties remain.…”

Section: Limitations Of This Virtual Experimentsmentioning

confidence: 99%

“…A previous global sensitivity analysis of the TNT2 model to spatial inputs and parameters was performed in an agricultural catchment in Brittany, West France, to explore the sensitivity of combined parameters on multiple output variables [33]. Among other results, it demonstrates that yields are sensitive to soil depth and parameters related to nitrogen cycle.…”

Section: Introductionmentioning

confidence: 99%

“…A spatial calibration procedure using the four soil parameters (T 0 , m, depth and retention porosity) can be set up at the catchment scale, but this study focuses only on few crop fields in upstream areas that are described in the next section. The choice of these soil parameters was justified by a sensitivity analysis of the simulated yield to soil depth performed in [33]. This previous study also identified a high sensitivity of crop productivity to parameters related to nitrogen cycle (mineralization rates and initial organic matter content).…”

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confidence: 99%

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Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series

et al. 2016

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Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHC year_I,pixel_j , corresponding to minimal difference between observed and synthetic LAX year_I,pixel_j , is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAX year_I . Results show that simulated and synthetic LAX year_I,allpixels obtained from SWHC year_I,allpixels are close and accurately fit the observed LAX year_I,allpixels (RMSE = 0.05 m 2 /m 2 to 0. SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m 2 /m 2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

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An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model

Cited by 53 publications

References 61 publications

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series

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