On evaluating density driven groundwater flow in the closed basin

The hydrogeochemical cycle in the Pilot Valley, a closed basin, is subject to climate variability over a wide range of spatial and temporal scales over long period of time. Saturated and Unsaturated Transport Model (SUTRA) is employed in the Pilot Valley to simulate subsurface and density driven groundwater flow under various climatic and geologic conditions. A Maxey-Eakin method with coupled catchment model, aridity index and incomplete beta function for groundwater recharge distribution is integrated into the SUTRA model for various simulations. A Rayleigh number is used to analyze these circulation patterns of flow under variable climate and geologic conditions. The simulation results, under different groundwater recharge rates, indicate the existence or absence of free convection flow and salt nose movement under the playa and towards hinge line. The simulation result for a historical wet period (12 ka) has a narrow salt nose extent and a historical dry period (6 ka) has a wider salt nose extent. High permeability values generate more free convective cells and low permeability values generate less or eliminate free convective cells in the flow domain. This study will help minimize damage from extreme climatic conditions which occur frequently in the study area and also help manage water resources efficiently

Measurement and Model Uncertainty

To fully characterize measured or modeled solar resource data, the data set should beaccompanied by a statement of uncertainty that will help the analyst to correctly apply the information and will provide the necessary context for the reliability of each value. For example, a full characterization of uncertainty provides a basis to assess the predicted output of planned solar conversion systems and is thus a key factor when determining the bankability of the project. Uncertainty can be thought of as the confidence one has in the data. However, it is important to determine the uncertainty using a standard methodology that others also can use and will obtain identical results. The Guide to the Expression of Uncertainty in Measurements (GUM) (ISO 2008) is an example of how to determine the uncertainty in measurements. GUM has been formalized by several organizations, including the International Bureau of Weights and Measurements (French acronym: BIPM), and published by the International Standards Organization (ISO). In this chapter, the uncertainties associated with the measured or modeled solar resource data are discussed along with the validation of physical or empirical models that use such data. Precise methods to measure and model the solar resource are difficult to develop because of the rapidly changing nature of solar irradiance. While instrumentation is improving, the measurement or modeling of incident irradiance can have large uncertainties, depending on circumstances. The GUM methodology for quantifying uncertainty in either measured (Section 6.1) or modeled values (Section 6.2) is discussed in what follows. Note that the uncertainty in modeled data is typically obtained by comparison with reference measurements, which is why this development comes first

Developing a Framework for Reference Cell Standards for PV Resource Applications

Quantifying and predicting electricity production from photovoltaic (PV) systems is based on measured or modeled irradiance data. These solar resource data consist of global horizontal irradiance, global tilted irradiance, direct normal irradiance, and diffuse horizontal irradiance, which are either derived from satellite observations validated against ground-based measurements or directly obtained from ground-based measurements. The ground-based measurements are made using thermopile or photodiode radiometers. For a PV plant, the efficiency of the energy production is verified by comparing measured Output with the modeled production, which is computed using either modeled or measured irradiance data. Thus, plant performance assessments typically include the uncertainty of the Transposition and, in some cases, decomposition of a given irradiance to convert to the appropriate plane-ofarray (POA) irradiance corresponding to the orientation of the PV installation. Additionally, the various types of uncertainty in radiometric measurements or the modeled irradiance and the PV module specifications influence the model results and thereby further increase the uncertainty. An alternative method of assessing the PV system performance has been to use reference cells to measure the “PV resource.” When used to calculate PV performance ratios, there are inherent systematic differences between radiometers and reference cells, such as spectral, directional, temperature, time responses, nonstability, and nonlinearity differences. Reference cells tend to mimic the performance and characteristics of a PV module more closely. In this report, a framework is proposed to develop standards that will better quantify and characterize the use of reference cells for PV resource measurements. The measurement from an appropriate reference cell in the POA correlates closely with the plant performance, reduces the number of modeling steps needed to simulate PV performance, and hence reduces the uncertainties of the comparisons. Because technologically matched reference cells and PV modules respond similarly to each wavelength of light that composes the incident solar radiation, the uncertainty associated with the changing spectral distribution of incident radiation during the day and year can be greatly reduced. This will reduce the overall uncertainty in estimated PV performance. The same can be said for the angle-of-incidence effects because the reference cells are deployed in the same POA as the PV module. At that point, the main sources of uncertainty are in modeling the temperature effect differences between the reference cells and the PV module and accounting for the difference between the short-circuit current monitored by the reference cells and max power point current and voltage at which the PV module operates. These sources of uncertainty are also associated with typical irradiance measurements made by pyranometers and pyrheliometers; however, typical irradiance measurements also include uncertainties associated with the spectral mismatch between thermopile or photodiode pyranometers and the PV module, which are minimized with the use of reference cells. To enhance the use of reference cells for resource assessment, we identify the necessary data, characteristics, and calibration methodologies of reference cells and how to standardize the use of these data and methods. Further, as we develop this framework, the classification of reference cells will be essential to provide guidance for selecting PV reference cells appropriate for a specific application. As we address the calibration and sources of uncertainties of reference cells, classification schemes and expected limits of performance with respect to certain Parameters become important. Moreover, this report discusses possible technical and analytical challenges that might be encountered as these methodologies are developed. The development of these methodologies is also needed for other initiatives, such as the use of reference cell measurements directly in performance or economic models. It should be possible to meet various application needs with reference cells through the verification, acceptance, and implementation of reference cells for resource assessments

Vergleichende Staats- und Verwaltungsanalyse Bundesrepublik Deutschland - Italien Endbericht des Forschungsprojekts

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