Calibration Validation Research Articles

Using Densely Distributed Soil Moisture Observations for Calibration of a Hydrologic Model

Abstract Calibration is a crucial step in hydrologic modeling that is typically handled by tuning parameters to match an observed hydrograph. In this research, an alternative calibration scheme based on soil moisture was investigated as a means of identifying the potentially heterogeneous calibration needs of a distributed hydrologic model. The National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) was employed to carry out such a calibration, along with concentrated in situ soil moisture observations from the Iowa Flood Studies (IFloodS) field campaign in Iowa’s Turkey River basin. Synthetic, single-pixel experiments were conducted in order to identify parameters relevant to soil moisture dynamics and to test the ability of three calibration procedures (discharge, soil moisture, and hybrid based) to recapture prescribed parameter sets. It was found that three storage parameters of HL-RDHM could be consistently identified using soil moisture RMSE as the objective function and that the addition of discharge-based calibration led to more consistent parameter identification for all 11 storage and release parameters. Expanding to full-basin experiments, these three calibration procedures were applied following an investigation to find the most advantageous method of distributing the point-based calibrations carried out at each pixel collocated with an IFloodS observation site. A method based on pixel similarity was deemed most appropriate for this purpose. Additionally, streamflow simulations calibrated with soil moisture showed improvement in RMSE and Nash–Sutcliffe efficiency (NSE) for all calibration–validation events despite a short calibration period, a promising result when considering calibration of ungauged basins. However, supplementary evaluation metrics show mixed results for streamflow simulations, suggesting further investigation is required.

A fuzzy-stochastic multiscale model for fiber composites: A one-dimensional study

We study mathematical and computational models for computing the deformation of fiber-reinforced polymers due to external forces. A thorough study requires an understanding of both micro-structural effects and uncertainty/variability in the manufacturing process, such as uncertainty in the size and distribution of fibers and variability in material properties and fracture parameters. We first show that the present uncertainties/variabilities, which are of both random and non-random types, cannot be accurately characterized by current stochastic multiscale models based on precise probability theory, such as stationary random fields. Next, we present a new hybrid fuzzy-stochastic model, which can more accurately describe uncertainties/variabilities in fiber composites. The new model, which is referred to as a fuzzy-stationary random field, consists of a random field with fuzzy moments. We then construct a global–local multiscale algorithm in a fuzzy-stochastic framework for efficiently computing output quantities of interest, such as displacements and stresses, in regions of relatively small size, e.g. hot spots. The algorithm utilizes the concept of representative volume elements and homogenization and constructs a global solution to compute a local approximation that captures the microscale features of the problem. The results are based on and backed by real experimental data through a calibration–validation approach.

Workshop 1 – Dosimetry in Research and Test Reactors and in Accelerators

Considerable effort is being expended on the review and documentation of older data. One motivation for this is the need for customers to duplicate the test conditions for work that may have been performed decades ago. Another is the need to properly archive data for the increasing number of facilities that have already been shut down in Europe, the United States and Japan, or are scheduled to be shut down in the near future. Other reasons exist, but the inability of facilities to meet performance requirements imposed after the Fukushima event was identified as a major contributor. Beyond the need to perform extensive review and documentation of experiments already conducted in order to preserve this knowledge for future use, it is important that facilities stopping operation in the near future identify the data that still need to be obtained, and perform the necessary experimental work before the shutdown occurs. Reactors in operation continue to maintain their usual activities with regard to detector calibration and code validation. Although decreasing in number, there still exist reactors with standard fields for calibration and code validation, such as those at SCK-CEN in Belgium and a new critical assembly under construction in Nevada in the United States. The option to seek access to the reactors in China

IN-SITU MONITORING METHOD FOR DIRECTION FINDING ANTENNAS

Antenna arrays for directionnding (DF) are usually designed and tested in controlled environments such as anechoic chambers. However, antenna pattern may change signicantly when antennas are placed in their operational environment. In such perturbing close context, the antennas calibration validity becomes a major issue which can lead to DF performance degradation and costly recalibration process. This paper presents an innovative design and implementation of a non-disturbing solution for quasi-real time antenna monitoring. The proposed system is based on optically modulated scattering (OMS) technique. Its capacity to detect the presence of various types of obstacles, which signicantly perturb the antenna radiation pattern, is evaluated. A relation between monitoring mode and DF mode measurement signals is established. Finally, a design and sizing of the overall system is proposed.

Calibration Techniques for Accurate Measurements by Underwater Camera Systems.

Calibration of a camera system is essential to ensure that image measurements result in accurate estimates of locations and dimensions within the object space. In the underwater environment, the calibration must implicitly or explicitly model and compensate for the refractive effects of waterproof housings and the water medium. This paper reviews the different approaches to the calibration of underwater camera systems in theoretical and practical terms. The accuracy, reliability, validation and stability of underwater camera system calibration are also discussed. Samples of results from published reports are provided to demonstrate the range of possible accuracies for the measurements produced by underwater camera systems.

Calibration of low-level beta-gamma coincidence detector systems for xenon isotope detection

The beta-gamma coincidence detector systems used for the measurement of the CTBT-relevant xenon isotopes (Xe-131m, Xe-133m, Xe-133 and Xe-135) in the International Monitoring System network and in the On-Site Inspection are reviewed. These detectors typically consist of a well-type or bore-through NaI crystal into which a measurement cell, serving also as a sample container, is inserted. This work describes the current calibration procedure for energy, resolution and efficiency, implementation challenges, availability and uncertainties of the specific nuclear decay data and the path forward to full calibration validation using GEANT4.

Determining probability distributions of parameter performances for time-series model calibration: A river system trial

Summary A calibration method is presented that uses a sub-period resampling method to estimate probability distributions of performance for different parameter sets. Where conventional calibration methods implicitly identify the best performing parameterisations on average, the new method looks at the consistency of performance during sub-periods. The method is implemented with the conceptual river reach algorithms within the Australian Water Resources Assessments River (AWRA-R) model in the Murray–Darling Basin, Australia. The new method is tested for 192 reaches in a cross-validation scheme and results are compared to a traditional split-sample calibration–validation implementation. This is done to evaluate the new technique’s ability to predict daily streamflow outside the calibration period. The new calibration method produced parameterisations that performed better in validation periods than optimum calibration parameter sets for 103 reaches and produced the same parameterisations for 35 reaches. The method showed a statistically significant improvement to predictive performance and potentially provides more rational flux terms over traditional split-sample calibration methods. Particular strengths of the proposed calibration method is that it avoids extra weighting towards rare periods of good agreement and also prevents compensating biases through time. The method can be used as a diagnostic tool to evaluate stochasticity of modelled systems and used to determine suitable model structures of different time-series models. Although the method is demonstrated using a hydrological model, the method is not limited to the field of hydrology and could be adopted for many different time-series modelling applications.

Application of the NIR Method to Determine Nutrients in Yerba Mate (Ilex paraguariensis A. St.-Hill) Leaves

Near-infrared spectroscopy (NIR) was used to measure the concentrations of macronutrients [carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg)] and micronutrients [sodium (Na), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn)] in yerba mate leaves. We collected 270 samples of yerba mate, growing in association with eucalyptus, in different cultivation conditions, in the harvest seasons of 2011 and 2012. Calibration models comprised 216 samples for calibration and internal validation of the models and 54 sample for external prediction. The results allowed determination of the mineral elements, providing values of the correlation coefficient (R) higher than 0.90, on calibration models. The predictive power of the models was verified and showed that NIR has potential to successful quantify P (R = 0.62), Fe (R = 0.75), and Cu (R = 0.89) in this study. On the other hand, for C, K, Na, Mn, and Zn the technique was not efficient.

Detecting floodplain inundation based on the upstream–downstream relationship

Summary The rise in river stage (water depth) can lead to disastrous floodplain inundation. On the basis of hydraulic simulation data, this study proposes novel data-analytical methods to infer the threshold river stage and detect floodplain inundation. A quasi-Muskingum model is derived from the classical Muskingum model to characterise the relationship between upstream and downstream river stages. Based on this model, F -test and modified Akaike information criterion AIC c are introduced to test if there is a change of the upstream–downstream relationship. Furthermore, a bootstrap-based calibration–validation experiment is set up to evaluate the performance of the quasi-Muskingum model. The proposed methods are applied to a case study of the 1991 and 2001 floods in the Flinders and Norman Rivers in Northern Australia. The results show that floodplain inundation does change the upstream–downstream relationship as it drastically alters the stage–discharge relationship. To combine the quasi-Muskingum model with F -test and AIC c facilitates an efficient approach to detect the change and infer the threshold river stage. The analytical testing is in concert with visual examination – the time when the river stage becomes higher than the detected threshold coincides with the beginning of floodplain inundation. Despite the change, the quasi-Muskingum model effectively captures the upstream–downstream relationship and requires a small number of samples in calibration. This study highlights the effectiveness of the data-analytical methods in dealing with the change of the upstream–downstream relationship.

On the Feasibility of Characterizing Soil Properties From AVIRIS Data

We evaluate the feasibility of quantifying surface soil properties over large areas and at a fine spatial resolution using high-resolution airborne imaging spectroscopy. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected by the National Aeronautics and Space Administration immediately after the large 2011 Mississippi River flood at the Birds Point New Madrid (BPNM, $\approx 700 \mathrm{km}^{2} $ ) floodway in Missouri, USA, was used in a data mining lasso framework for mapping of soil textural properties such as percentages of sand, silt, clay, soil-organic matter, and many other soil chemicals constituents. The modeling results show that the approach is feasible and provide insights in the accuracy and uncertainty of the approach for both soil textural properties and chemical constituents. These models were further used for a pixel-by-pixel prediction of each the soil constituent, resulting in high-resolution (7.6 m) quantitative spatial maps in the entire floodway. These maps reveal coherent spatial correlations with historical meander patterns of Mississippi River and fine-scale features such as erosional gullies, represented by difference in constituent concentration, e.g., low soil organic matter, with the underlying topography immediately disturbed by the large flooding event. Further, we have argued and established that the independent validation results are better represented as a probability density function as compared with a single calibration–validation set. It is also found that modeled soil constituents are sensitive to NDVI and the calibration sample sizes, and the results improve with stricter (lower) NDVI thresholds and larger calibration sets.

Theoretical foundation, methods, and criteria for calibrating human vibration models using frequency response functions

While simulations of the measured biodynamic responses of the whole human body or body segments to vibration are conventionally interpreted as summaries of biodynamic measurements, and the resulting models are considered quantitative, this study looked at these simulations from a different angle: model calibration. The specific aims of this study are to review and clarify the theoretical basis for model calibration, to help formulate the criteria for calibration validation, and to help appropriately select and apply calibration methods. In addition to established vibration theory, a novel theorem of mechanical vibration is also used to enhance the understanding of the mathematical and physical principles of the calibration. Based on this enhanced understanding, a set of criteria was proposed and used to systematically examine the calibration methods. Besides theoretical analyses, a numerical testing method is also used in the examination. This study identified the basic requirements for each calibration method to obtain a unique calibration solution. This study also confirmed that the solution becomes more robust if more than sufficient calibration references are provided. Practically, however, as more references are used, more inconsistencies can arise among the measured data for representing the biodynamic properties. To help account for the relative reliabilities of the references, a baseline weighting scheme is proposed. The analyses suggest that the best choice of calibration method depends on the modeling purpose, the model structure, and the availability and reliability of representative reference data.

INITIAL CHECKOUT RESULTS OF THE COMPACT INFRARED CAMERA (CIRC) FOR EARTH OBSERVATION

Abstract. Compact Infrared Camera (CIRC) is a technology-demonstration instrument equipped with an uncooled infrared array detector (microbolometer) for space application. CIRC is the first microbolometer sensor without a calibration function in orbit, like a shutter system or an onboard blackbody. The main objective of the CIRC is to detect wildfires, which are major and chronic disasters affecting various countries of Southeast Asia, particularly considering the effects of global warming and climate change. The CIRC achieves a small size (approximately 200 mm), light mass (approximately 3 kg), and low electrical power consumption (<20 W) by employing athermal optics and a shutterless system. The CIRC can be consequently mounted on multiple satellites to enable highfrequency observation. Installation of CIRCs on the ALOS-2 and on the JEM/CALET is expected to increase observation frequency. We present the initial check-out results of the CIRC onboard ALOS-2. Since the initial check-out phase (July 4–14, 2014), the CIRC has acquired the images of Earth. CIRC was demonstrated to function according to its intended design. After the early calibration validation phase, which confirmed the temperature accuracy of observed data, CIRC data has been available to the public January 2015 onward. We also introduce a few observational results about wildfire, volcanoes, and heat-island.

On the Value of Hydrological Models Developed in the Context of Undergraduate Education for Discharge Prediction and Reservoir Management

A conceptual hydrological catchment model called ‘student model’ is introduced which has been interactively developed within an undergraduate course on hydrological modelling at Siegen University (BSc Civil Engineering). After successful model calibration and model validation to a German mesoscale catchment, the ‘student’ model is applied to quantify the impact of regional climate change scenarios on the water budget of the Wiehl drinking water reservoir. The simulation results show that the conceptual ‘student model’ performs well for both calibration and validation periods. It well represents the variability in water flows at different timescales and therefore has the potential to be used for scenario analysis. The ‘student model’ shows a comparable reaction to climate change scenarios as sophisticated hydrological catchment models did which were applied to neighboured catchments in North Rhine-Westphalia based on the same underlying parent scenarios. The magnitude of the simulated climate impacts on seasonal water balances and extreme flows indicates that current reservoir management needs to be adjusted to the projected variability of inflows to the reservoir. Despite its simplicity, the ‘student model’ can have a high value for reservoir management. It well represents the inflow into the reservoir based on available data and provides a reliable estimate of climate change impacts, indicating the necessity of adjustment of the operation rules in the future. At the same time it remains transparent to the model users, offering a simple but realistic training tool for the generation of reservoir inflow time series. It therefore can contribute to the solution of recent hydrological and water management problems. Restrictions of such models are due to the lumped nature and the limited applicability for a priori predictions.

Quantitative Approach to Melamine Detection in Egg White with Surface-Enhanced Raman Spectroscopy

Due to the harmfulness of melamine to human, the quantitative detection of melamine in egg is very necessary. In the present study, the surface enhanced Raman spectra technology combined with chemometric analysis method was used to conduct melamine quantitative detection in egg white. Firstly, the melamine egg sample could be got by the method of artificial feeding hens usingdifferent feeding formulation. Then the surface enhanced Raman spectra of egg white was determined using portable Raman spectroscopy (Opto Trace RamTracer-200) and Raman enhancement reagents, and the melamine content within the white eggs was measured with gas chromatography mass spectrometry technology. The software of Raman Analyzer was used for baseline correction of Raman spectra. The correlation coefficient method was used to choose 320 spectral variables from the surface enhanced Raman spectroscopy as input variables to establish partial least squares quantitative calibration model . And the peaks-decomposition method was used to establish peaks-decomposition quantitative calibration model. Both models selected 90 and 44 samples respectively as calibration sets and validation sets during model establishment, and both models achieved good prediction effect. The determination coefficient between predicted values of partial least squares quantitative calibration model and measured values of gas chromatography mass spectrometry was 0.856, and root mean square error of prediction was 1.547. The determination coefficient was 0.947 and RMSEP was 0.893 for the peaks-decomposition quantitative calibration model. This study demonstrated that the method can effectively quantitatively detect melamine in eggs. Testing a sample only takes 15 minutes, which can provide a new way for the melamine egg detection.

In-Line High-Speed All-Fiber Polarimeter With True Real-Time Acquisition for Sensor Systems Based on Fast Polarization Rotation

We report on the detailed characteristics of an all fiber polarimetric optical detector based on tilted fiber gratings. Our device exhibits an RF bandwidth of 500 MHz and a single-calibration optical bandwidth of over 30 nm. We also show that the device has minimal PDL, PMD, and return loss. Finally, we show that the device may be calibrated using only random unknown input polarizations with ${\rm DOP} = 1$ and constant or measured power. We demonstrate that our polarimeter can use a local metric of calibration validity in place of an absolute comparison to a reference polarimeter. We show that the total measurement time for an accurate calibration can be reduced to under 1 s. We integrated our polarimeter into an acquisition system capable of measuring fast polarization rotation events over long periods of time with 250 MS/s peak sampling rate.

Novel calibration tools and validation concepts for microarray-based platforms used in molecular diagnostics and food safety control.

Commercial platforms consisting of ready-to-use microarrays printed with target-specific DNA probes, a microarray scanner, and software for data analysis are available for different applications in medical diagnostics and food analysis, detecting, e.g., viral and bacteriological DNA sequences. The transfer of these tools from basic research to routine analysis, their broad acceptance in regulated areas, and their use in medical practice requires suitable calibration tools for regular control of instrument performance in addition to internal assay controls. Here, we present the development of a novel assay-adapted calibration slide for a commercialized DNA-based assay platform, consisting of precisely arranged fluorescent areas of various intensities obtained by incorporating different concentrations of a "green" dye and a "red" dye in a polymer matrix. These dyes present "Cy3" and "Cy5" analogues with improved photostability, chosen based upon their spectroscopic properties closely matching those of common labels for the green and red channel of microarray scanners. This simple tool allows to efficiently and regularly assess and control the performance of the microarray scanner provided with the biochip platform and to compare different scanners. It will be eventually used as fluorescence intensity scale for referencing of assays results and to enhance the overall comparability of diagnostic tests.

Impacts of Spatial Extend and Site Location on Calibration of Urban Growth Models

During the last decades, cities in sub-saharan Africa have undergone rapid urban growth due to increased population growth and high economic activities. This research explores the impacts of varying modelling settings including spatial extend and its location for the city of Nairobi using a cellular automata (CA) urban growth model (UGM). Our UGM used multi-temporal satellite-based data for classification of urban land-use of 1986, 2000 and 2010, road data, slope data and exclusion layer. Monte-Carlo technique was used for model calibration and Multi Resolution Validation (MRV) technique for validation. Simulation of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three spatial grid sizes varying in extent and location were applied in the UGM calibration and validation. Thus, this research explored the impacts of varying spatial extent (grid) and location on urban growth modelling and hence can contribute to an improved sustainable planning and development. This is useful for future planning as the Nairobi grows and expands into the peri-urban areas.

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required.

Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

Research on wood technological properties using near infrared (NIR) spectroscopy has shown prom- ising results. The aim of this study was to evaluate the efficiency of NIR spectroscopy for estimating chemical properties of mangium wood (Acacia mangium). NIR spectra were obtained from 150 wood meal samples of mangium trees that were 5-7-years-old. A multivariate data analysis method of partial least squares was used to develop calibration regression models for predicting chemical properties based on NIR spectra. The results showed a good relationship between values derived from laboratory analyses and those predicted by NIR spectros- copy for a-cellulose and hemicellulose content. The cali- bration models had high values for the coefficient of determination (R 2 ( 0.80) and the ratio of performance to deviation (RPD ( 2.0). Meanwhile, lignin and extractive content were poorly predicted; calibration validation revealed R 2 \ 0.60 and RPD = 1.0. This study indicated that NIR spectroscopy analysis on wood meal of A. man- gium could be reliably used to predict a-cellulose and hemicellulose.

Contactless moisture content and density evaluation of sawn timber using microwave transmission

Prediction of basic timber properties like moisture content and density is an important factor for quality control in timber industry. This paper deals with the detection of moisture content and density of spruce (Picea abies). It describes the calibration and validation of the newly developed microwave prototype on the basis of 480 measurements for moisture contents between 8 and 19% and densities between 340 and 600 kg m–3. The calibration and first validation set lead to R2≧92% in both cases. The prediction for a second and third validation decreases as especially the range of moisture content for these pieces was limited. Compared to other moisture and density measurement systems, the prototype showed good performance. With this technology it is possible to detect moisture and density of a piece of timber contact free and non-destructive with high prediction accuracy for both parameters in one pass.