Separation of Structural and Measurement Uncertainties in Watershed Hydrological Models

2021 ◽  
Author(s):  
Abhinav Gupta ◽  
Ganeshchandra Mallya ◽  
Rao Govindaraju
Keyword(s):  
Time Series ◽  
Measurement Uncertainty ◽  
Parametric Uncertainty ◽  
Curve Analysis ◽  
Runoff Coefficient ◽  
Rating Curve ◽  
Residual Time ◽  
Additional Information ◽  
Structural Uncertainties ◽  
Measurement Uncertainties

<p>A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. Measurement uncertainty exists due to errors in the measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in the mathematical representation of hydrological processes. Parametric uncertainty is a consequence of limited data available to calibrate the model, and measurement and structural uncertainties.</p><p>Recently, separation of structural and measurement uncertainties was identified as one of the twenty-three unsolved problems in hydrology. The information about measurement and structural uncertainties is typically available in the form of residual time-series, that is, the difference between observed and simulated streamflow time-series. The residual time-series, however, provides only an aggregate measure of measurement and structural uncertainties. Thus, the measurement and structural uncertainties are inseparable without additional information. In this study, we used random forest (RF) algorithm to gather additional information about measurement uncertainties using hydrological data across several watersheds. Subsequently, the uncertainty bounds obtained by RF were compared against the uncertainty bounds obtained by two other methods: rating-curve analysis and recently proposed runoff-coefficient method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff-coefficient yields uncertainty in both rainfall and streamflow measurements. The results of the study are promising in terms of using data across different watersheds for the construction of measurement uncertainty bounds. The preliminary results of this study will be presented in the meeting.</p>

scholarly journals Techniques for analyses of trends in GRUAN data

2015 ◽  
Vol 8 (4) ◽  
pp. 1673-1684 ◽  
Author(s):  
G. E. Bodeker ◽  
S. Kremser
Keyword(s):  
Measurement Uncertainty ◽  
Trend Analysis ◽  
Global Climate ◽  
Regional Climate ◽  
Added Value ◽  
Least Squares Regression ◽  
Target Uncertainty ◽  
Reference Quality ◽  
Measurement Uncertainties

Abstract. The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterized and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterized uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Analysis of sampling frequency in groundwater quality monitoring systems: a case study

1994 ◽  
Vol 30 (10) ◽  
pp. 73-78 ◽  
Author(s):  
Andrea Szucs ◽  
Gyözö Jordan
Keyword(s):  
Time Series ◽  
Groundwater Quality ◽  
Sampling Frequency ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Additional Information ◽  
Alarm Systems ◽  
Cross Correlation Analysis ◽  
Two Samples ◽  
Groundwater Quality Monitoring

Sampling frequency is one of the most crucial factors in the design of groundwater quality monitoring systems. Monitoring systems in general have two major objectives: (1) to describe natural processes and long-term changes and (2) to serve as alarm-systems and detect single pollution events. A comparison between two data sequences of different sampling frequency - weekly and monthly - is made through an example of the groundwater quality monitoring system in the karstic region of the Transdanubian Mountains in Hungary. Hydrogeochemical time series were first decomposed into their components: trend, periodicity, autocorrelation, and rough in succession. In order to identify outliers within the rough, Exploratory Data Analysis (EDA) was applied. Optimal sampling frequency was determined based on the analysis of the above components. Results have shown that: (1) seasons shorter than two months do exist in the studied time series which cannot be captured by monthly sampling; (2) for monitoring seasonal processes samples should be collected at the Nyquist frequency (at least two samples per period); for pollution detection autocorrelation lag-time (or semi-variogram range in time) should determine the sampling distance; in the lack of autocorrelation property the analysis of outliers should guide the sampling design; (3) cross-correlation analysis between precipitation and the observed parameters indicative of pollutant travel time yields valuable additional information on the pollution sensitivity of the hydrogeological system.

scholarly journals Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign

2017 ◽  
Vol 10 (1) ◽  
pp. 247-264 ◽  
Author(s):  
Aditya Choukulkar ◽  
W. Alan Brewer ◽  
Scott P. Sandberg ◽  
Ann Weickmann ◽  
Timothy A. Bonin ◽  
...  
Keyword(s):  
Measurement Uncertainty ◽  
Measurement Techniques ◽  
Vertical Plane ◽  
Complex Flow ◽  
Field Campaign ◽  
Velocity Statistics ◽  
Spatial Coverage ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Measurement Uncertainties

Abstract. Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

scholarly journals Measurement Uncertainties Encountered During Gas Turbine Driven Compressor Field Testing

1998 ◽  
Author(s):  
Klaus Brun ◽  
Rainer Kurz
Keyword(s):  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Field Test ◽  
Equations Of State ◽  
Accurate Determination ◽  
Field Testing ◽  
Significant Implication ◽  
Test Results ◽  
Measurement Uncertainties

Field testing of gas turbine compressor packages requires the accurate determination of efficiency, capacity, head, power and fuel flow in sometimes less than ideal working environments. Nonetheless, field test results have significant implication for the compressor and gas turbine manufacturers and their customers. Economic considerations demand that the performance and efficiency of an installation are verified to assure a project’s return on investment. Thus, for the compressor and gas turbine manufacturers, as well as for the end-user, an accurate determination of the field performance is of vital interest. This paper describes an analytic method to predict the measurement uncertainty and, thus, the accuracy, of field test results for gas turbine driven compressors. Namely, a method is presented which can be employed to verify the validity of field test performance results. The equations governing the compressor and gas turbine performance uncertainties are rigorously derived and results are numerically compared to actual field test data. Typical field test measurement uncertainties are presented for different sets of instrumentation. Test parameters that correlate to the most significant influence on the performance uncertainties are identified and suggestions are provided on how to minimize their measurement errors. The effect of different equations of state on the calculated performance is also discussed. Results show that compressor efficiency uncertainties can be unacceptably high when some basic rules for accurate testing are violated. However, by following some simple measurement rules and maintaining commonality of the gas equations of state, the overall compressor package performance measurement uncertainty can be limited and meaningful results can be achieved.

Causes for Large-Scale Hydrographic Changes at the Hawaii Ocean Time Series Station

2008 ◽  
Vol 38 (9) ◽  
pp. 1931-1948 ◽  
Author(s):  
D. Stammer ◽  
S. Park ◽  
A. Köhl ◽  
R. Lukas ◽  
F. Santiago-Mandujano
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Global Ocean ◽  
Modeling Framework ◽  
Decadal Time Scale ◽  
Quasi Biennial Oscillation ◽  
Salinity Field ◽  
Additional Information ◽  
Independent Observations ◽  
Station Aloha

Abstract Results from Estimating the Circulation and Climate of the Ocean (ECCO)–Scripps Institution of Oceanography (SIO) global ocean state estimate, available over the 11-yr period 1992 through 2002, are compared with independent observations available at the Hawaii Ocean time series station ALOHA. The comparison shows that at this position, the estimated temporal variability has some skill in simulating observed ocean variability and that the quality of future syntheses could benefit from additional information available from the Argo network and from the time series observations themselves. On a decadal time scale, the influence radius of the station ALOHA T–S time series covers large parts of the tropical and subtropical Pacific Ocean and reaches even into the Indian Ocean through the Indonesian Throughflow. Estimated changes in sea surface height (SSH) result largely from thermosteric changes; however, nonsteric (barotropic) variations on the order of 1–2 cm also contribute to SSH changes at station ALOHA. Moreover, changes of similar magnitude can be caused by changes in the salinity field because of a quasi-biennial oscillation in the horizontal flow structure and heaving of the mean salinity structure on seasonal and interannual time scales. The adjoint modeling framework confirms westward-propagating Rossby waves (due to wind forcing) and subduction of water-mass anomalies (due to surface buoyancy forcing) as the primary mechanisms leading to observed changes of T–S structures at station ALOHA. Specifically, the analysis identifies surface freshwater fluxes along the wintertime outcrop of intermediate waters as a primary cause for salinity changes at station ALOHA and wind stress forcing east of the station position as another forcing mechanism of salinity variations around the Hawaiian Archipelago.

scholarly journals Estimating uncertainties in hydraulicallymodelled rating curves for discharge time series assessment

2018 ◽  
Vol 40 ◽  
pp. 06013
Author(s):  
Valentin Mansanarez ◽  
Ida K. Westerberg ◽  
Steve W. Lyon ◽  
Norris Lam
Keyword(s):  
Time Series ◽  
Hydraulic Model ◽  
Observation Data ◽  
Rating Curve ◽  
Hydraulic Modelling ◽  
Discharge Time ◽  
High Flows ◽  
Rating Curves ◽  
Hydrometric Station ◽  
The Impact

Establishing a reliable stage-discharge (SD) rating curve for calculating discharge at a hydrological gauging station normally takes years of data collection. Estimation of high flows is particularly difficult as they occur rarely and are often difficult to gauge in practice. At a minimum, hydraulicallymodelled rating curves could be derived with as few as two concurrent SD and water-surface slope measurements at different flow conditions. This means that a reliable rating curve can, potentially, be developed much faster via hydraulic modelling than using a traditional rating curve approach based on numerous stage-discharge gaugings. In this study, we use an uncertainty framework based on Bayesian inference and hydraulic modelling for developing SD rating curves and estimating their uncertainties. The framework incorporates information from both the hydraulic configuration (bed slope, roughness, vegetation) using hydraulic modelling and the information available in the SD observation data (gaugings). Discharge time series are estimated by propagating stage records through the posterior rating curve results. Here we apply this novel framework to a Swedish hydrometric station, accounting for uncertainties in the gaugings and the parameters of the hydraulic model. The aim of this study was to assess the impact of using only three gaugings for calibrating the hydraulic model on resultant uncertainty estimations within our framework. The results were compared to prior knowledge, discharge measurements and official discharge estimations and showed the potential of hydraulically-modelled rating curves for assessing uncertainty at high and medium flows, while uncertainty at low flows remained high. Uncertainty results estimated using only three gaugings for the studied site were smaller than ±15% for medium and high flows and reduced the prior uncertainty by a factor of ten on average and were estimated with only 3 gaugings.

A Treatise on Isoattribute Curve Analysis, Consumer Induction Factor, and Country Brand Value

2022 ◽  
pp. 125-162
Author(s):  
Debasish Roy
Keyword(s):  
Time Series ◽  
Curve Analysis ◽  
Working Environment ◽  
Utility Analysis ◽  
Environment Factors ◽  
The Third ◽  
Brand Values ◽  
Final Segment ◽  
Country Brand ◽  
Series Study

This research has endeavored to focus on three major issues that are yet to be explored as per the existing literature on marketing. The first issue focuses on the Isoattribute curve analysis, rooted in the theory of conjoint utility analysis. In other words, the first segment concentrates on the derivation of the Isoattribute curve model which helps to attain the consumer equilibrium condition in a two-commodity world (brand or non-brand products). The second segment of the chapter has transitioned from the microeconomic model to the macroeconomic perspective based on a ‘single-country' approach, i.e., USA, based on a derivation of consumer induction factor (CIF). Finally, the third and final segment of the chapter extends its horizon at a larger scale by conducting a cross-country time-series study of 10 years (2009 – 2018) which redefines branding in an absolutely new dimension where the ‘brand values' of seven sample countries are estimated by inculcating the socio-economic, political, and working environment factors as the major dimensions.

Experimental and Measurement Uncertainty Associated With Characterizing Slurry Mixing Performance of Pulsating Jets at Multiple Scales

2015 ◽  
Author(s):  
Judith Ann Bamberger ◽  
Greg F. Piepel ◽  
Carl W. Enderlin ◽  
Brett G. Amidan ◽  
Alejandro Heredia-Langner
Keyword(s):  
Measurement Uncertainty ◽  
Multiple Scales ◽  
Experimental Tests ◽  
Experimental Results ◽  
Cloud Height ◽  
Solids Concentration ◽  
Relative Standard ◽  
Measurement Uncertainties ◽  
Standard Deviations ◽  
Pulsating Jets

Understanding how uncertainty manifests itself in complex experiments is important for developing the testing protocol and interpreting the experimental results. This paper describes experimental and measurement uncertainties, and how they can depend on the order of performing experimental tests. Experiments with pulse-jet mixers in tanks at three scales were conducted to characterize the performance of transient-developing periodic flows in Newtonian slurries. Other test parameters included the simulant, solids concentration, and nozzle exit velocity. Critical suspension velocity and cloud height were the metrics used to characterize Newtonian slurry flow associated with mobilization and mixing. During testing, near-replicate and near-repeat tests were conducted. The experimental results were used to quantify the combined experimental and measurement uncertainties using standard deviations and percent relative standard deviations (%RSD) The uncertainties in critical suspension velocity and cloud height tend to increase with the values of these responses. Hence, the %RSD values are the more appropriate summary measure of near-replicate testing and measurement uncertainty.

scholarly journals Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1034 ◽  
Author(s):  
David Cuesta-Frau ◽  
Pradeepa H. Dakappa ◽  
Chakrapani Mahabala ◽  
Arjun R. Gupta
Keyword(s):  
Time Series ◽  
Body Temperature ◽  
Matthews Correlation Coefficient ◽  
Input Parameter ◽  
Temporal Variations ◽  
Temperature Time Series ◽  
Additional Information ◽  
Continuous Body ◽  
The Subject ◽  
Temperature Time

Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.

scholarly journals Time-Series Laplacian Semi-Supervised Learning for Indoor Localization †

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3867 ◽  
Author(s):  
Jaehyun Yoo
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Unlabeled Data ◽  
Training Data ◽  
Practical Implementation ◽  
Additional Information ◽  
Localization Scheme ◽  
The Impact

Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan.

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