@article{GhasemizadeBaroniAbbaspouretal.2017,
  author    = {Ghasemizade, Mehdi and Baroni, Gabriele and Abbaspour, Karim and Schirmer, Mario},
  title     = {Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model},
  series = {Environmental modelling \& software with environment data news},
  volume    = {88},
  journal   = {Environmental modelling \& software with environment data news},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1364-8152},
  doi       = {10.1016/j.envsoft.2016.10.011},
  pages     = {22 -- 34},
  year      = {2017},
  abstract  = {Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol' and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior.},
  language  = {en}
}
@article{KlausZeheElsneretal.2014,
  author    = {Klaus, Julian and Zehe, Erwin and Elsner, Martin and Palm, Juliane and Schneider, Dorothee and Schroeder, Boris and Steinbeiss, Sibylle and van Schaik, Loes and West, Stephanie},
  title     = {Controls of event-based pesticide leaching in natural soils: A systematic study based on replicated field scale irrigation experiments},
  series = {Journal of hydrology},
  volume    = {512},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2014.03.020},
  pages     = {528 -- 539},
  year      = {2014},
  abstract  = {Tile drains strongly influence the water cycle in agricultural catchment in terms of water quantity and quality. The connectivity of preferential flow to tile drains can create shortcuts for rapid transport of solutes into surface waters. The leaching of pesticides can be linked to a set of main factors including, rainfall characteristics, soil moisture, chemical properties of the pesticides, soil properties, and preferential flow paths. The connectivity of the macropore system to the tile drain is crucial for pesticide leaching. Concurring influences of the main factors, threshold responses and the role of flow paths are still poorly understood. The objective of this study is to investigate these influences by a replica series of three irrigation experiments on a tile drain field site using natural and artificial tracers together with applied pesticides. We found a clear threshold behavior in the initialization of pesticide transport that was different between the replica experiments. Pre-event soil water contributed significantly to the tile drain flow, and creates a flow path for stored pesticides from the soil matrix to the tile drain. This threshold is controlled by antecedent soil moisture and precipitation characteristics, and the interaction between the soil matrix and preferential flow system. Fast transport of pesticides without retardation and the remobilization could be attributed to this threshold and the interaction between the soil matrix and the preferential flow system. Thus, understanding of the detailed preferential flow processes clearly enhances the understanding of pesticide leaching on event and long term scale, and can further improve risk assessment and modeling approaches. (C) 2014 Elsevier B.V. All rights reserved.},
  language  = {en}
}