Chapter Two Good Modelling Practice

Crout, N.M.J.; Kokkonen, Teemu; Jakeman, Anthony J.; Norton, J.P.; Newham, Lachlan; Anderson, Robert C.; Assaf, Hamed; Croke, Barry; Gaber, Noha; Holzworth, Dean; Myšiak, Jaroslav; Reichl, J. P. C.; Seppelt, Ralf; Wagener, Thorsten; Whitfield, Paul H.

doi:10.1016/s1574-101x(08)00602-9

Cited by 29 publications

(30 citation statements)

References 19 publications

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“…Hence, the selection of an appropriate model should not only depend on statistical considerations [105,120,121]. Overall, good modelling practices include: (i) the formulation of a clear purpose and research question; (ii) making adequate assumptions and considering their impact on the results; and (iii) an appropriate model evaluation [30]. Modelling results should be easy to present to different stakeholders and should be widely applicable [122].…”

Section: Strengths-weaknesses-opportunities-threats Analysismentioning

confidence: 99%

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Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Nguyen

Everaert

Boets

et al. 2018

Water

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Abstract:We critically analyzed a set of ecological models that are used to assess the impact of hydropower dams on water quality and habitat suitability for biological communities. After a literature search, we developed an integrated conceptual model that illustrates the linkages between the main input variables, model approaches, the output variables and biotic-abiotic interactions in the ecosystems related to hydropower dams. We found that variations in water flow and water depth coupled with increased nutrient availability are major variables that contribute to structural and functional ecosystem changes. We also found that ecological models are an important tool to assess the impact of hydropower dams. For instance, model simulation of different scenarios (e.g., with and without the dam, different operation methods) can analyze and predict the related ecosystem shifts. However, one of the remaining shortcomings of these models is the limited capacity to separate dam-related impacts from other anthropogenic influences (e.g., agriculture, urbanization). Moreover, collecting sufficient high-quality data to increase the statistical power remains a challenge. The severely altered conditions (e.g., generation of very deep lakes) also lead to difficulties for standardized data collection. We see future opportunities in the integration of models to improve the understanding of the different processes affected by hydropower dam development and operation, as well as the use of remote sensing methods for data collection.

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Section: Strengths-weaknesses-opportunities-threats Analysismentioning

confidence: 99%

“…Models are being increasingly used in environmental assessment, planning and management [30]. However, the type of model implemented, the variables included and the parameterization used strongly differ between different studies.…”

Section: Introductionmentioning

confidence: 99%

Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Nguyen

Everaert

Boets

et al. 2018

Water

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“…Además, un uso consistente y común de una terminología es necesario para la comunicación de avances en el desarrollo de modelos y en su evaluación. Terminología nueva o confusa debería ser claramente definida, y cualquier redefinición de terminología ya en uso debería tener un propósito claro (Crout et al 2008). Como mucha de la teoría de modelización tiene sus orígenes en literatura estadística, los modelizadores deberían en lo posible utilizar la terminología original (ver el glosario en Ripley 1996).…”

Section: Buenas Prácticas En Modelización Ecológicaunclassified

“…Estos problemas conducen a un bajo grado de fiabilidad de las predicciones del modelo (Van Waveren et al 1999). Crout et al (2008) aseveran que el principal requisito de un modelo es que se especifique claramente su propósito. ¿Para qué es el modelo?…”

Section: Buenas Prácticas En Modelización Ecológicaunclassified

Modelos ecológicos: descripción, explicación y predicción

Blanco

2013

ECOS

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“…The Pearson's correlation coefficient ranges between -1 and +1, depending on whether a negative or a positive relationship exists between the simulated and the observed values, while an r = 0 shows non-existent of relationship between the variables. Moreover, graphical visualisation techniques were also used on time-series data, to examine the capability of the model to represent the 260 peak and the base flow events (Moriasi et al, 2007;Crout et al, 2008). These evaluation criteria were used in other applications of the HYPE model (Strömqvist et al, 2012;Jomaa et al, 2016;Yin et al, 2016).…”

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confidence: 99%

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

Namugize

Jewitt

Clark

et al. 2017

Preprint

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Abstract. Most studies considering water quality pollution in the upper reaches of the uMngeni Catchment have relied on the 10 physical grab sampling of water and the subsequent laboratory analysis of chemical determinants. However, this provides limited spatial and temporal information. Thus, the objectives of this study are to assess the capability of the Hydrological Predictions for the Environment (HYPE) model in simulating streamflow, dissolved inorganic nitrogen (DIN) and total phosphorus (TP), in the fast developing uMngeni Catchment in KwaZulu-Natal province, South Africa. The model was set up and calibrated, following a stepwise approach and then validated. Results indicated that the simulation of discharge is most 15 sensitive to the parameters related to evapotranspiration and the water-holding capacity of the soil, while DIN and TP are affected by plant uptake and initial pools of nutrients. DIN is also affected by denitrification. Runoff was captured well during the calibration (1989)(1990)(1991)(1992)(1993)(1994)(1995) and validation periods , with a Nash-Sutcliffe efficiency (NSE) greater than 0.0 in eight of the nine stations and a Pearson's correlation coefficient (r) of > 0.5 at all the sub-catchments. High streamflow events were represented well, low streamflows were over-simulated. The accumulative streamflows were over-predicted in the 20 downstream sub-catchments, with an absolute percentage of bias (PBIAS) of > 25 %. The transport and dynamics of DIN and TP vary differently and they are driven by hydrological and biochemical processes. The concentration of TP follows the pattern of the streamflow, whereas DIN shows an inconsistent variation. The values of DIN decrease from upstream to downstream, while the TP values increase from the headwaters to the outlet of the catchment. Agricultural activities were found to be the largest source of DIN, while the TP is mainly ascribed to the point sources of pollution. 25

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Chapter Two Good Modelling Practice

Cited by 29 publications

References 19 publications

Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Modelos ecológicos: descripción, explicación y predicción

Assessment of the Hype Model for Simulation of Water and Nutrients in the Upper uMngeni River Catchment in South Africa

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