scholarly journals Integrated uncertainty assessment of discharge predictions with a statistical error model

2013 ◽  
Vol 49 (8) ◽  
pp. 4866-4884 ◽  
Author(s):  
M. Honti ◽  
C. Stamm ◽  
P. Reichert
Keyword(s):  
Statistical Error ◽  
Uncertainty Assessment ◽  
Error Model

scholarly journals Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

2012 ◽  
Vol 16 (4) ◽  
pp. 1221-1236 ◽  
Author(s):  
A. E. Sikorska ◽  
A. Scheidegger ◽  
K. Banasik ◽  
J. Rieckermann
Keyword(s):  
Water Cycle ◽  
Uncertainty Assessment ◽  
Error Model ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Reliable Prediction ◽  
Urban Catchment ◽  
Predictions In Ungauged Basins ◽  
Base Data ◽  
Support Decision Making

Abstract. Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

scholarly journals Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata)

2017 ◽  
Vol 64 ◽  
pp. 213-222 ◽  
Author(s):  
H.T. Banks ◽  
Elizabeth Collins ◽  
Kevin Flores ◽  
Prayag Pershad ◽  
Michael Stemkovski ◽  
...  
Keyword(s):  
Green Algae ◽  
Model Comparison ◽  
Statistical Error ◽  
Error Model ◽  
Logistic Growth ◽  
Raphidocelis Subcapitata

A statistical error model for image sensors and its testing

2007 ◽  
Vol 38 (11) ◽  
pp. 1-11
Author(s):  
Hideyuki Ichihara ◽  
Toshimasa Kuchii ◽  
Masaaki Yamadate ◽  
Hideaki Sakaguchi ◽  
Hiroshi Uemura ◽  
...  
Keyword(s):  
Statistical Error ◽  
Image Sensors ◽  
Error Model

scholarly journals Quantifying uncertainty in climatological fields from GPS radio occultation: an empirical-analytical error model

2011 ◽  
Vol 4 (3) ◽  
pp. 2749-2788 ◽  
Author(s):  
B. Scherllin-Pirscher ◽  
G. Kirchengast ◽  
A. K. Steiner ◽  
Y.-H. Kuo ◽  
U. Foelsche
Keyword(s):  
Radio Occultation ◽  
Sampling Error ◽  
Statistical Error ◽  
Error Component ◽  
Error Model ◽  
Atmospheric Parameters ◽  
Bending Angle ◽  
Analytical Error ◽  
Low Latitudes ◽  
Analytical Error Model

Abstract. Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. Single RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10° zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40°. Beyond 40°, this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be 50 % of the sampling error for bending angle and 30 % or less for the other atmospheric parameters. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07 % in refractivity and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at low latitudes below about 10 km.

scholarly journals Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

2011 ◽  
Vol 8 (6) ◽  
pp. 11075-11113 ◽  
Author(s):  
A. E. Sikorska ◽  
A. Scheidegger ◽  
K. Banasik ◽  
J. Rieckermann
Keyword(s):  
Water Cycle ◽  
Uncertainty Assessment ◽  
Error Model ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Reliable Prediction ◽  
Urban Catchment ◽  
Predictions In Ungauged Basins ◽  
Base Data ◽  
Support Decision Making

Abstract. Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced by 150% with Bayesian updating, using only a few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

scholarly journals Quantifying uncertainty in climatological fields from GPS radio occultation: an empirical-analytical error model

2011 ◽  
Vol 4 (9) ◽  
pp. 2019-2034 ◽  
Author(s):  
B. Scherllin-Pirscher ◽  
G. Kirchengast ◽  
A. K. Steiner ◽  
Y.-H. Kuo ◽  
U. Foelsche
Keyword(s):  
Radio Occultation ◽  
Sampling Error ◽  
Total Error ◽  
Statistical Error ◽  
Error Component ◽  
Error Model ◽  
Atmospheric Parameters ◽  
Analytical Error ◽  
Low Latitudes ◽  
Analytical Error Model

Abstract. Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10° zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40°. Beyond 40°, this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be only about 30% of the original one or less. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07% in refractivity and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at altitudes below about 6 km (high latitudes) to 10 km (low latitudes).

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