The Kolmogorov-Smirnov Test for Goodness of Fit

Massey, Frank J.

doi:10.2307/2280095

Cited by 1,034 publications

(253 citation statements)

References 10 publications

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“…For each water year, we combined sediment mass and runoff volume to calculate the average runoff sediment load and divided accumulated sediment mass by drainage area to calculate sediment yield. We processed sediment samples to determine their grain size distributions (see SI) and used two-sample KolmogoroveSmirnov (KeS) tests to compare grain size between sample pairs (Massey, 1951). We used the KozenyeCarman equation (Chapuis, 2012) to calculate saturated vertical hydraulic conductivity (K [L/T]) based on the grain size distribution of individual samples:…”

Section: Sediment Transport and Accumulationmentioning

confidence: 99%

Coupling distributed stormwater collection and managed aquifer recharge: Field application and implications

Beganskas

Fisher

2017

Journal of Environmental Management

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Section: Sediment Transport and Accumulationmentioning

confidence: 99%

Coupling distributed stormwater collection and managed aquifer recharge: Field application and implications

Beganskas

Fisher

2017

Journal of Environmental Management

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“…The goodness-of-fit is evaluated in a two-stage process. Firstly, a K-S test is applied (Massey, 1951). As the K-S test is highly sensitive due to the large sample sizes (Serinaldi, 2008), the null hypothesis (the sample comes from the selected distribution) is rejected in some cases for all of the candidates.…”

Section: Marginal Distribution Estimationmentioning

confidence: 99%

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao

Vogl

Laux

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over-or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer functionbased statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REG-NIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971-1985) and validation (1986-2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10 % (wet bias) to −1 % (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32 % March-April-May (MAM), −15 % June-July-August (JJA), 4 % September-October-November (SON) and 28 % December-January-February (DJF) to 16 % (MAM), −11 % (JJA), −1 % (SON) and −3 % (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

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“…The 2-sample Kolmogorov-Smirnov test (Massey, 1951) is used here to check the agreement between the observed (from the reanalysis) and simulated empirical distributions of each drought characteristics (duration, mean area, total magnitude x-and y-location of the centroid) over the 1958-2008 period. Results show that the H 0 hypothesis (the two samples come from the same distribution) cannot be rejected in any case, i.e.…”

Section: Comparison Of Present-day Distributionsmentioning

confidence: 99%

Evolution of spatio-temporal drought characteristics: validation, projections and effect of adaptation scenarios

Vidal

Martin

Kitova

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, such as mean duration, mean affected area and total magnitude. This paper addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to simulate spatio-temporal characteristics of meteorological and agricultural droughts in France over a presentday period? (2) How such characteristics will evolve over the 21st century? (3) How to use standardized drought indices to represent theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-yr multilevel and multiscale drought reanalysis over France. Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index and the Standardized Soil Wetness Index, respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well simulated by the downscaled hydroclimate projections over a present-day period. All spatiotemporal characteristics of drought events are then found to dramatically increase over the 21st century, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals, either retrospective or prospective. The perceived spatio-temporal characteristics of drought events derived from these theoretical adaptation scenarios show much reduced changes, but they call for more realistic scenarios at both the catchment and national scale in order to accurately assess the combined effect of local-scale adaptation and global-scale mitigation.

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The Kolmogorov-Smirnov Test for Goodness of Fit

Cited by 1,034 publications

References 10 publications

Coupling distributed stormwater collection and managed aquifer recharge: Field application and implications

Coupling distributed stormwater collection and managed aquifer recharge: Field application and implications

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Evolution of spatio-temporal drought characteristics: validation, projections and effect of adaptation scenarios

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