Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model

Stahn, Philipp; Busch, Stefanie; Salzmann, Thomas; Eichler‐Löbermann, Bettina; Miegel, Konrad

doi:10.1007/s12665-017-6701-y

Cited by 13 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, this approach originally focused on the effects of different parameters on the model response, that is, parameter uncertainty. More recently, however, these strategies have also been extended to account for other sources of uncertainty (Baroni & Tarantola, 2014;Lilburne & Tarantola, 2009;Savage et al, 2016;Shoaib et al, 2016;Stahn et al, 2017). Thus, different model structures could be also evaluated in the assessment, especially when combined with flexible modeling frameworks (e.g., Clark et al, 2008Clark et al, , 2015Kneis, 2015).…”

Section: 1029/2018wr022813mentioning

confidence: 99%

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

Francke

Baroni

Brosinsky

et al. 2018

Water Resources Research

View full text Add to dashboard Cite

Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro‐sedimentological model WASA‐SED for the Mediterranean‐mountainous Isábena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.

show abstract

Section: 1029/2018wr022813mentioning

confidence: 99%

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

Francke

Baroni

Brosinsky

et al. 2018

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…A modified-MGA based module was developed for global inverse parameter estimation [5]. Stahn, P. evaluated the agricultural hydrological exchange model for simulating soil water balance of different monocropping and mixed crops on the basis of hydrological and phenological observations by using SOBOL global sensitivity method and multi-objective algorithm combination [6]. Singh, V. adopted the parameterization-based sensitivity analysis method to identify the parameters that had the greatest influence on model calibration [7] .…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the Physical Hydrological Model CASC2D-SED in Arid Area

Zhang¹,

Ma²,

Zhang³

et al. 2023

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Sensitivity analysis of model parameters and inputs is an important research method to improve model accuracy and calibration efficiency. The purpose of this paper is to explore the sensitivity of model parameters and model inputs in arid areas, and then to study the linear or nonlinear relationship of model sensitivity taking Erjiama small watershed in Jungar banner, Ordos, Inner Mongolia, China as the research object. Based on the calibration of CASC2D-SED model, four model parameters, including hydraulic conductivity, Manning coefficient, Suction head and vegetation interception, and one model input-river network were perturbed at a variation rate of 25%. The hydrological process is simulated by using the combination of 20 model parameters and model inputs after perturbation, and then the sensitivity analysis of model parameters and inputs is studied. The results show that: 1) The sensitivity of Manning coefficient to peak discharge and peak arrival time is non-linear, while the sensitivity of hydraulic conductivity, Suction head and vegetation interception to peak discharge, peak arrival time, simulated total discharge and infiltration amount is linear; 2) With the increase of hydraulic conductivity, Suction head and vegetation interception, the peak discharge and simulated total discharge decrease gradually, while the peak arrival time and infiltration amount increase gradually; 3) With the increase of Manning coefficient, the simulated total flow decreases gradually, while the infiltration rate increases gradually; 4) With the increase of the number of tributaries, the peak discharge, the simulated total discharge and the total amount of infiltration gradually decrease, while the flood peak arrival time presents a U-shaped change; 5) With the increase of Manning coefficient, hydraulic conductivity, Interception and Suction head, the simulated sediment flow decreases. Manning coefficient is more sensitive to the simulated amount of clay than the other three model parameters. Sensitivity analysis of parameters and inputs of CASC2D-SED model plays a guiding role in model

show abstract

“…Among others, in the last decades frameworks of global sensitivity analysis (GSA) have been identified as important tools for model assessment and improvement (e.g., Baroni & Tarantola, 2014;Savage et al, 2016). Specifically, by means of GSA, it is possible to investigate the importance of different uncertain model elements and their interaction (Günther et al, 2019;Savage et al, 2016;Stahn et al, 2017). Complementary frameworks of identifiability analyses have been used to identify adequate model configurations and parametrizations (e.g., Ajami et al, 2007;Coxon et al, 2014;Herman et al, 2013;Mustafa et al, 2020;Wagener et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures

Pilz

Francke

Baroni

et al. 2020

Water Resources Research

View full text Add to dashboard Cite

In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process‐based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro‐meteorological boundary conditions. For instance, we tested the Penman‐Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in‐depth analysis of process implementations and catchment functioning.

show abstract

Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model

Cited by 13 publications

References 46 publications

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

Sensitivity Analysis of the Physical Hydrological Model CASC2D-SED in Arid Area

How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures

Contact Info

Product

Resources

About