Scenario-based impact assessment of global and regional change on the semi-natural flow regime
(Sprache: Englisch)
Globally, freshwater ecosystems are considered to be under severe threat from human pressure and climate change (Vörösmarty et al., 2010). Malmqvist and Rundle (2002) suggest that running water is the most impacted upon ecosystem on Earth due to being...
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Globally, freshwater ecosystems are considered to be under severe threat from human pressure and climate change (Vörösmarty et al., 2010). Malmqvist and Rundle (2002) suggest that running water is the most impacted upon ecosystem on Earth due to being surrounded by dense human settlements and exploited for domestic and industrial water supply, irrigation, electricity generation and waste disposal. For example, the progressive over-exploitation of surface water resources for irrigation and urban uses in the Colorado River Basin has resulted most years in no runoff reaching the river s delta (Gleick, 2003). [ ]Hereafter, natural and anthropogenic driving forces will be referred to as global and regional driving forces, respectively. The future effects of these forces up to the 2050s will be assessed in quantitative scenarios implemented in a hydrological model. It is believed that using this nomenclature (i.e. global and regional instead of natural and anthropogenic) better reflects considered environmental stressors, since global-scale driving forces will include not only climatic change but also changes in CO2, atmospheric carbon dioxide and plant physiological parameters, whereas regional-scale driving forces will include changes in land use, agriculture development and agricultural water management. Hence, the difference is that the first group of driving forces acts globally and independently on the study area, whereas the second group includes factors that are specific to the study area. Furthermore, in order to expand on the title of this thesis, impacts in the present study will be assessed not only on the flow regime as such, but also on its ecological functions, i.e. on the environmental flow regime. This is motivated mainly by the semi-natural character of the study area, that is unique in Poland and in Europe, but it also underlines the novelty of this thesis, as going beyond the pure impacts on the flow regime in a scenario-modelling framework is rare
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in hydrological science, if achieved at all.
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Chapter 3, Materials and Methods:In this chapter the materials used and methods applied to reach the objectives outlined previously are described. Section 3.1 presents basic features of the SWAT model, as well as the model s set-up and calibration approaches. The following two sections describe global change scenarios (section 3.2) and regional changes (section 3.3). In section 3.4 an environmental flow method based on the building block approach is outlined, which is then followed in section 3.5 by an introduction to state and impact indicators measuring different aspects of providing sufficient amounts of water to the environment.
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3.1.3 Auto-calibration tools
Piniewski and Okruszko (2011) performed a spatially distributed flow calibration using SWAT2005 Auto-calibration Tools incorporated in ArcSWAT, including a sensitivity analysis tool and an auto-calibration tool. Parameter sensitivities were estimated using the Latin Hypercube-One-factor-At-a-Time (LH-OAT) technique (van Griensven et al., 2006b). Automatic calibration was carried out using the Para- Sol tool (van Griensven and Meixner, 2007), which is based on the Shuffled Complex Evolution-University of Arizona (SCE-UA) optimisation algorithm (Duan et al., 1992). The conclusion was that although these tools proved to be effective, the amount of time needed to perform auto-calibration at multiple sites was great. Indeed, with eight years of daily data from 11 stations, 8D parameter space, 151 subbasins and 1,131 HRUs and most of the SCE-UA parameters kept as defaults, the total amount of time used for auto-calibration yielded approximately 85 days of computer work (pure model runs) on an Intel Core2 Duo 3GHz processor using a Windows XP OS. Hence, a more time-efficient tool would be more desirable. The purpose of this part of thesis was to recalibrate the updated model using experience gained during the previous study, a new auto-calibration tool and a new approach towards spatial calibration and the
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validation of SWAT. SWAT-CUP software (Abbaspour et al., 2007a; Abbaspour, 2008; Rostamian et al., 2008), i.e. SWAT - Calibration and Uncertainty Programs3, was applied under version 3.1.3. SWAT-CUP is a public domain interface which links five different optimisation algorithms (SUFI-2, GLUE, ParaSol, MCMC and PSO) to SWAT. The main function of an interface is to provide a link between the input/output of a calibration program and the model. Hence, SWAT-CUP facilitates sensitivity analysis, calibration, validation and uncertainty analysis of the SWAT model. In the current study two algorithms were applied: SUFI-2 and PSO. The SUFI-2, Sequential Uncertainty Fitting Version 2, combines optimization with uncertainty analysis. Abbaspour et al. (2007b) and Abbaspour (2008) provide a detailed description of this algorithm, which is summarised below. The user has to select a number of model parameters to be included in the procedure, usually after assessing their sensitivities, and defines their ranges. The user also has to input the measured data time series, select the objective function and define several internal parameters. Furthermore, the user can choose from among seven types of objective functions described in Abbaspour (2008), two of which are particularly popular among hydrological modellers (Moriasi et al., 2007): the Nash-Sutcliffe Efficiency coefficient (NSE) and the coefficient of determination R2.The program first performs Latin Hypercube sampling across the defined parameter space, assuming uniform distribution, leading to n parameter combinations, where n is the number of desired simulations. Next, SWAT is run n times and saves n time series of simulated output variables (in this case river flow). In the last step, measures employed to assess uncertainties are calculated. Since SUFI-2 is a stochastic procedure, it does not evaluate standard goodness-of-fit measures based on two signals; instead, it calculates the degree to which all uncertainties (input,
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Bibliographische Angaben
- Autor: Mikolaj Piniewski
- 2014, Erstauflage, 212 Seiten, 53 Abbildungen, Maße: 15,5 x 22 cm, Kartoniert (TB), Englisch
- Verlag: Anchor Academic Publishing
- ISBN-10: 395489274X
- ISBN-13: 9783954892747
Sprache:
Englisch
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