2012
DOI: 10.1080/02626667.2011.637494
|View full text |Cite
|
Sign up to set email alerts
|

Corrected prediction intervals for change detection in paired watershed studies

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
13
0

Year Published

2012
2012
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 10 publications
(13 citation statements)
references
References 25 publications
0
13
0
Order By: Relevance
“…To assist in assessing the significance of the treatment effects, 95% prediction intervals were computed. While 1242 S. M. GUENTHER, T. GOMI AND R. D. MOORE Som et al (2012) derived an algebraic expression for computing prediction limits in the presence of autocorrelated error terms, we generated them using Monte Carlo simulation (Leach et al, 2012). To account for uncertainty in the estimates of the parameters r 1 , b 0 , etc., separate parameter sets were generated for each of 1000 realizations using the estimated variance-covariance matrix for the parameter estimates in the rmvnorm() function in the R programming language (R Development Core Team, 2011), which generates multivariate normal random numbers.…”
Section: Paired-catchment Analysismentioning
confidence: 99%
“…To assist in assessing the significance of the treatment effects, 95% prediction intervals were computed. While 1242 S. M. GUENTHER, T. GOMI AND R. D. MOORE Som et al (2012) derived an algebraic expression for computing prediction limits in the presence of autocorrelated error terms, we generated them using Monte Carlo simulation (Leach et al, 2012). To account for uncertainty in the estimates of the parameters r 1 , b 0 , etc., separate parameter sets were generated for each of 1000 realizations using the estimated variance-covariance matrix for the parameter estimates in the rmvnorm() function in the R programming language (R Development Core Team, 2011), which generates multivariate normal random numbers.…”
Section: Paired-catchment Analysismentioning
confidence: 99%
“…Som et al. [34] demonstrate that use of classical ordinary least squares (OLS) approach to detect statistically significant treatment effects may not account for temporal autocorrelation of hydrologic data and thus may give incorrect prediction confidence intervals. Work by [35] highlights the limitation of the approach to quantifying effects of urbanization on components of the hydrologic cycle.…”
Section: Introductionmentioning
confidence: 99%
“…Since these random disturbances are independent, 95% confidence limits could be estimated as ± 1.96 σ ( M r ). As suggested by Watson et al () and Som et al (), if more than 5% of the values of M r exceeded the 95% confidence limits, a statistically significant manipulation of a hydrometeorological characteristic of the lake was implied. When the data of any parameter did not adhere to the assumptions of this method, analysis of variance (ANOVA) and a paired t ‐test were used to evaluate if there was a difference in observations and predicted post‐manipulation values.…”
Section: Methodsmentioning
confidence: 81%
“…A common approach in paired catchment studies (Watson et al ; Moore et al 2005; Gomi et al 2006; Som et al ) to detecting change due to a manipulation includes three steps. The first step is to derive a relationship between the entire dataset of control and pre‐manipulation responses, which in this study could be of the form: x626,t=mx·x373,t+bx+εx where x ′ 626, t is a predicted value of x 626, t using x 373, t where x is a hydrometeorological characteristic of the lake (i.e., DOC concentration, light extinction, surface temperature, evaporation rate) on day t .…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation