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  • 1. Bruce, Louise C
    et al.
    Frassl, Marieke A
    Arhonditsis, George B
    Gal, Gideon
    Hamilton, David P
    Hanson, Paul C
    Hetherington, Amy L
    Melack, John M
    Read, Jordan S
    Rinke, Karsten
    Rigosi, Anna
    Trolle, Dennis
    Winslow, Luke
    Adrian, Rita
    Ayala, Ana I
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Limnology.
    Bocaniov, Serghei A
    Boehrer, Bertram
    Boon, Casper
    Brookes, Justin D
    Bueche, Thomas
    Busch, Brendan D
    Copetti, Diego
    Cortés, Alicia
    de Eyto, Elvira
    Elliott, J Alex
    Gallina, Nicole
    Gilboa, Yael
    Guyennon, Nicolas
    Huang, Lei
    Kerimoglu, Onur
    Lenters, John D
    MacIntyre, Sally
    Makler-Pick, Vardit
    McBride, Chris G
    Moreira, Santiago
    Özkundakci, Deniz
    Pilotti, Marco
    Rueda, Francisco J
    Rusak, James A
    Samal, Nihar R
    Schmid, Martin
    Shatwell, Tom
    Snorthheim, Craig
    Soulignac, Frédéric
    Valerio, Giulia
    van der Linden, Leon
    Vetter, Mark
    Vinçon-Leite, Brigitte
    Wang, Junbo
    Weber, Michael
    Wickramaratne, Chaturangi
    Woolway, R Iestyn
    Yao, Huaxia
    Hipsey, Matthew R
    A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network2018In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 102, p. 274-291Article in journal (Refereed)
    Abstract [en]

    The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required.

  • 2.
    Dimberg, Peter H.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
    Bryhn, Andreas C.
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
    Hytteborn, Julia K.
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
    Probabilities of monthly median chlorophyll-a concentrations in subarctic, temperate and subtropical lakes2013In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 41, p. 199-209Article in journal (Refereed)
    Abstract [en]

    High concentrations of chlorophyll-a (chl-a) during summer are by definition a common problem in eutrophicated lakes. Several models have been designed to predict chl-a concentrations but are unable to estimate the probability of predicted concentrations or concentration spans during subsequent months. Two different methods were developed to compute the probabilities of obtaining a certain chl-a concentration. One method is built on discrete Markov chains and the other method on a direct relationship between median chl-a concentrations from two months. Lake managers may use these methods to detect and counteract the risk of high chl-a concentrations and algal blooms during coming months. Both methods were evaluated and applied along different scenarios to detect the probability to exceed chl-a concentration in different coming months. The procedure of computing probabilities is strictly based on general statistics which means that neither method is constrained for chl-a but can also be used for other variables. A user-friendly software application was developed to facilitate and extend the use of these two methods.

  • 3.
    Gyllenhammar, Andreas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences.
    Gumbricht, Thomas
    WASUBI: A GIS tool for subbasin identification in topographically complex waterscapes2005In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 20, no 6, p. 729-736Article in journal (Refereed)
    Abstract [en]

    Spatially distributed modelling is dependent on segmentation of the underlying geographic data and the model output could be improved if relevant compartments are found and used. Herein, we present a method for identifying subbasins in complex topographic waterscapes. The method first finds local troughs and then expands them over a friction surface derived from bathymetry. Friction surfaces generated by different cost growth functions were tested, and shown to give consistent results. The model is sensitive to the quality of the underlying topographic dataset and its spatial resolution. WASUBI (WAterscape SUBbasin Identification) is written in ArcView's built-in scripting language, Avenue, and distributed as a program extension. The method is easy to use, only requiring a digital elevation model (DEM) and the user to give the number of basins to be generated. The segmentation method was tested against datasets covering the Finnish Archipelago Sea and the Okavango Delta in Botswana. Test results showed that the WASUBI delineated subbasins were more enclosed than subbasins created by a semi-random delineation method. The objectives of this study was to construct and critically test algorithms for subbasin identification, based only on readily available DEM data, for use in geographic information system modelling.

  • 4. Irizar, Ion
    et al.
    Zambrano, Jesús
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Carlsson, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Morrás, Mikel
    Aymerich, Enrique
    Robust tuning of bending-points detection algorithms in batch-operated processes: Application to autothermal thermophilic aerobic digesters2015In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 71, p. 148-158Article in journal (Refereed)
  • 5. Kara, Emily L.
    et al.
    Hanson, Paul
    Hamilton, David
    Hipsey, Matthew R.
    McMahon, Katherine D.
    Read, Jordan S.
    Winslow, Luke
    Dedrick, John
    Rose, Kevin
    Carey, Cayelan C.
    Bertilsson, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Limnology.
    Marques, David da Motta
    Beversdorf, Lucas
    Miller, Todd
    Wu, Chin
    Hsieh, Yi-Fang
    Gaiser, Evelyn
    Kratz, Tim
    Time-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake at temporal scales of hours to months2012In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 35, p. 104-121Article in journal (Refereed)
    Abstract [en]

    We evaluated the predictive ability of a one-dimensional coupled hydrodynamic-biogeochemical model across multiple temporal scales using wavelet analysis and traditional goodness-of-fit metrics. High-frequency in situ automated sensor data and long-term manual observational data from Lake Mendota, Wisconsin, USA, were used to parameterize, calibrate, and evaluate model predictions. We focused specifically on short-term predictions of temperature, dissolved oxygen, and phytoplankton biomass over one season. Traditional goodness-of-fit metrics indicated more accurate prediction of physics than chemical or biological variables in the time domain. This was confirmed by wavelet analysis in both the time and frequency domains. For temperature, predicted and observed global wavelet spectra were closely related, while observed dissolved oxygen and chlorophyll fluorescence spectral characteristics were not reproduced by the model for key time scales, indicating that processes not modeled may be important drivers of the observed signal. Although the magnitude and timing of physical and biological changes were simulated adequately at the seasonal time scale through calibration, time scale-specific dynamics, for example short-term cycles, were difficult to reproduce, and were relatively insensitive to the effects of varying parameters. The use of wavelet analysis is novel to aquatic ecosystem modeling, is complementary to traditional goodness-of-fit metrics, and allows for assessment of variability at specific temporal scales. In this way, the effect of processes operating at distinct temporal scales can be isolated and better understood, both in situ and in silico. Wavelet transforms are particularly well suited for assessment of temporal and spatial heterogeneity when coupled to high-frequency data from automated in situ or remote sensing platforms.

  • 6.
    Kauffeldt, Anna
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
    Wetterhall, F.
    Pappenberger, F.
    Salamon, P.
    Thielen, J.
    Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level2016In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 75, p. 68-76Article, review/survey (Refereed)
    Abstract [en]

    Uncertainty in operational hydrological forecast systems forced with numerical weather predictions is often assessed by quantifying the uncertainty from the inputs only. However, part of the uncertainty in modelled discharge stems from the hydrological model. A multi-model system can account for some of this uncertainty, but there exists a plethora of hydrological models and it is not trivial to select those that fit specific needs and collectively capture a representative spread of model uncertainty. This paper provides a technical review of 24 large-scale models to provide guidance for model selection. Suitability for the European Flood Awareness System (EFAS), as example of an operational continental flood forecasting system, is discussed based on process descriptions, flexibility in resolution, input data requirements, availability of code and more. The model choice is in the end subjective, but this review intends to objectively assist in selecting the most appropriate model for the intended purpose.

  • 7.
    Metcalfe, Peter
    et al.
    Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England..
    Beven, Keith
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England..
    Freer, Jim
    Univ Bristol, Sch Geog, Bristol BS8 1SS, Avon, England..
    Dynamic TOPMODEL: A new implementation in R and its sensitivity to time and space steps2015In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 72, p. 155-172Article in journal (Refereed)
    Abstract [en]

    In 2001, Beven and Freer introduced a "dynamic" variant of TOPMODEL that addressed some of the limitations of the original model whilst retaining its computational and parametric efficiency. The original assumption of a quasi-steady water table was replaced by time-dependent kinematic routing within hydrological similar areas. The new formulation allows a more flexible discretisation, variable upslope drainage areas and spatially variable physical properties. There has, however, never been a freely distributable version of dynamic TOPMODEL. Here, we describe a new, open source, version developed in the R environment. It incorporates handling of geo-referenced spatial data that allows it to integrate with modern GIS. It makes use of data storage and vectorisation features of the language that will allow efficient scaling of the problem domain. The implementation is evaluated with data from a small catchment. The formulation of the model in terms of a flow distribution matrix is described and its use illustrated for treatment of surface and subsurface flow routing. The model uses an improved implicit solution for updating the subsurface storages and fluxes. The paper focuses on the robustness of the predicted output variables to changes in the time and space discretisations.

  • 8.
    Pappenberger, F
    et al.
    Environmental Science, Lancaster University, Lancaster LA1 4YQ, United Kingdom.
    Iorgulescu, I
    Ecole Polytechnique Fédérale de Lausanne, Switzerland.
    Beven, K J
    Environmental Science, Lancaster University, Lancaster LA1 4YQ, United Kingdom.
    Sensitivity analysis based on regional splits and regression trees (SARS-RT)2006In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 21, no 7, p. 976-990Article in journal (Refereed)
    Abstract [en]

    A global sensitivity analysis with regional properties is introduced. This method is demonstrated on two synthetic and one hydraulic example. It can be shown that an uncertainty analysis based on one-dimensional scatter plots and correlation analyses such as the Spearman Rank Correlation coefficient can lead to misinterpretations of any model results. The method which has been proposed in this paper is based on multiple regression trees (so called Random Forests). The splits at each node of the regression tree are sampled from a probability distribution. Several criteria are enforced at each level of splitting to ensure positive information gain and also to distinguish between behavioural and non-behavioural model representations. The latter distinction is applied in the generalized likelihood uncertainty estimation (GLUE) and regional sensitivity analysis (RSA) framework to analyse model results and is used here to derive regression tree (model) structures. Two methods of sensitivity analysis are used: in the first method the total information gain achieved by each parameter is evaluated. In the second method parameters and parameter sets are permuted and an error rate computed. This error rate is compared to values without permutation. This latter method allows the evaluation of the sensitivity of parameter combinations and thus gives an insight into the structure of the response surface. The examples demonstrate the capability of this methodology and stress the importance of the application of sensitivity analysis. (C) 2005 Elsevier Ltd. All rights reserved.

1 - 8 of 8
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