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Quantifying the uncertainty of a conceptual herbicide transport model with time‐dependent, stochastic parameters
Ammann, L., Stamm, C., Fenicia, F., & Reichert, P. (2021). Quantifying the uncertainty of a conceptual herbicide transport model with time‐dependent, stochastic parameters. Water Resources Research, 57(8), e2020WR028311 (27 pp.). https://doi.org/10.1029/2020WR028311
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Potential and challenges of investigating intrinsic uncertainty of hydrological models with stochastic, time‐dependent parameters
Reichert, P., Ammann, L., & Fenicia, F. (2021). Potential and challenges of investigating intrinsic uncertainty of hydrological models with stochastic, time‐dependent parameters. Water Resources Research, 57(3), e2020WR028400 (28 pp.). https://doi.org/10.1029/2020WR028400
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Characterizing fast herbicide transport in a small agricultural catchment with conceptual models
Ammann, L., Doppler, T., Stamm, C., Reichert, P., & Fenicia, F. (2020). Characterizing fast herbicide transport in a small agricultural catchment with conceptual models. Journal of Hydrology, 586, 124812 (15 pp.). https://doi.org/10.1016/j.jhydrol.2020.124812
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A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation
Ammann, L., Fenicia, F., & Reichert, P. (2019). A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation. Hydrology and Earth System Sciences, 23(4), 2147-2172. https://doi.org/10.5194/hess-23-2147-2019
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