scholarly journals Comparison of catchment grouping methods for flow duration curve estimation at ungauged sites in France

2011 ◽  
Vol 15 (8) ◽  
pp. 2421-2435 ◽  
Author(s):  
E. Sauquet ◽  
C. Catalogne
Keyword(s):  
Regression Models ◽  
Empirical Orthogonal Functions ◽  
Parametric Models ◽  
The Other ◽  
Orthogonal Functions ◽  
Flow Duration ◽  
Ungauged Sites ◽  
Two Parameters ◽  
Homogeneous Regions ◽  
The Impact

Abstract. The study aims at estimating flow duration curves (FDC) at ungauged sites in France and quantifying the associated uncertainties using a large dataset of 1080 FDCs. The interpolation procedure focuses here on 15 percentiles standardised by the mean annual flow, which is assumed to be known at each site. In particular, this paper discusses the impact of different catchment grouping procedures on the estimation of percentiles by regional regression models. In a first step, five parsimonious FDC parametric models are tested to approximate FDCs at gauged sites. The results show that the model based on the expansion of Empirical Orthogonal Functions (EOF) outperforms the other tested models. In the EOF model, each FDC is interpreted as a linear combination of regional amplitude functions with spatially variable weighting factors corresponding to the parameters of the model. In this approach, only one amplitude function is required to obtain a satisfactory fit with most of the observed curves. Thus, the considered model requires only two parameters to be applicable at ungauged locations. Secondly, homogeneous regions are derived according to hydrological response, on the one hand, and geological, climatic and topographic characteristics on the other hand. Hydrological similarity is assessed through two simple indicators: the concavity index (IC) representing the shape of the dimensionless FDC and the seasonality ratio (SR), which is the ratio of summer and winter median flows. These variables are used as homogeneity criteria in three different methods for grouping catchments: (i) according to an a priori classification of French Hydro-EcoRegions (HERs), (ii) by applying regression tree clustering and (iii) by using neighbourhoods obtained by canonical correlation analysis. Finally, considering all the data, and subsequently for each group obtained through the tested grouping techniques, we derive regression models between physiographic and/or climatic variables and the two parameters of the EOF model. Results on percentile estimation in cross validation show that a significant benefit is obtained by defining homogeneous regions before developing regressions, particularly when grouping methods make use of hydrogeological information.

scholarly journals Comparison of catchment grouping methods for flow duration curve estimation at ungauged sites in France

2011 ◽  
Vol 8 (2) ◽  
pp. 3233-3269 ◽  
Author(s):  
E. Sauquet ◽  
C. Catalogne
Keyword(s):  
Regression Models ◽  
Regression Tree ◽  
Empirical Orthogonal Functions ◽  
Parametric Models ◽  
The Other ◽  
Orthogonal Functions ◽  
Flow Duration ◽  
Ungauged Sites ◽  
Two Parameters ◽  
Homogeneous Regions

Abstract. The study aims at estimating flow duration curves (FDC) at ungauged sites in France and quantifying the associated uncertainties using a large dataset of 1080 FDCs. The interpolation procedure focuses here on 15 percentiles standardised by the mean annual flow, which is supposed to be known at each site. In particular, this paper discusses the relevance of different catchments grouping procedures on percentiles estimation by regional regression models. First, five parsimonious FDC parametric models were tested to approximate FDCs at gauged sites. The results show that the model based on Empirical Orthogonal Functions (EOF) expansion outperforms the other ones. In this model each FDC is interpreted as a linear combination of regional amplitude functions with weights – the parameters of the model – varying in space. Here, only one amplitude function was found sufficient to fit well most of the observed curves. Thus the considered model requires only two parameters to be estimated at ungauged locations. Second, homogeneous regions were derived according to hydrological response on one hand, and geological, climatic and topographic characteristics on the other hand. Hydrological similarity was assessed through two simple indicators: the concavity index (IC) that represents the shape of the standardized FDC and the seasonality ratio (SR) which is the ratio of summer and winter median flows. These variables were used as homogeneity criteria in three different methods for grouping catchments: (i) according to their membership in one of an a priori French classification into Hydro-Eco-Regions (HERs), (ii) by applying a regression tree clustering and (iii) by using hydrological neighbourhood obtained by canonical correlation analysis. Finally, regression models between physiographic and/or climatic variables and the two parameters of the EOF model were derived considering all the data and thereafter for each group obtained through the tested grouping techniques. Results on percentiles estimation in cross validation show a significant benefit to form homogeneous regions before developing regressions, particularly when grouping methods use hydrogeological information.

scholarly journals Heterogeneity measures in hydrological frequency analysis: review and new developments

2017 ◽  
Vol 21 (3) ◽  
pp. 1651-1668 ◽  
Author(s):  
Ana I. Requena ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Basic Step ◽  
New Developments ◽  
Ungauged Sites ◽  
Index Flood ◽  
Step Procedure ◽  
Homogeneous Regions ◽  
The Impact ◽  
Region Size ◽  
Degree Of Heterogeneity ◽  
Illustrative Application

Abstract. Some regional procedures to estimate hydrological quantiles at ungauged sites, such as the index-flood method, require the delineation of homogeneous regions as a basic step for their application. The homogeneity of these delineated regions is usually tested providing a yes/no decision. However, complementary measures that are able to quantify the degree of heterogeneity of a region are needed to compare regions, evaluate the impact of particular sites, and rank the performance of different delineating methods. Well-known existing heterogeneity measures are not well-defined for ranking regions, as they entail drawbacks such as assuming a given probability distribution, providing negative values and being affected by the region size. Therefore, a framework for defining and assessing desirable properties of a heterogeneity measure in the regional hydrological context is needed. In the present study, such a framework is proposed through a four-step procedure based on Monte Carlo simulations. Several heterogeneity measures, some of which commonly known and others which are derived from recent approaches or adapted from other fields, are presented and developed to be assessed. The assumption-free Gini index applied on the at-site L-variation coefficient (L-CV) over a region led to the best results. The measure of the percentage of sites for which the regional L-CV is outside the confidence interval of the at-site L-CV is also found to be relevant, as it leads to more stable results regardless of the regional L-CV value. An illustrative application is also presented for didactical purposes, through which the subjectivity of commonly used criteria to assess the performance of different delineation methods is underlined.

The Concept of Explanatory Adequacy

2016 ◽  
pp. 96-113 ◽  
Author(s):  
Luigi Rizzi
Keyword(s):  
Large Scale ◽  
Generative Grammar ◽  
Language Learner ◽  
Minimalist Program ◽  
Parametric Models ◽  
The Other ◽  
History Of ◽  
Explanatory Adequacy ◽  
The Impact ◽  
Linguistic Phenomenon

This chapter illustrates the technical notion of ‘explanatory adequacy’ in the context of the other forms of empirical adequacy envisaged in the history of generative grammar: an analysis of a linguistic phenomenon is said to meet ‘explanatory adequacy’ when it comes with a reasonable account of how the phenomenon is acquired by the language learner. It discusses the relevance of arguments from the poverty of the stimulus, which bear on the complexity of the task that every language learner successfully accomplishes, and therefore define critical cases for evaluating the explanatory adequacy of a linguistic analysis. After illustrating the impact that parametric models had on the possibility of achieving explanatory adequacy on a large scale, the chapter addresses the role that explanatory adequacy plays in the context of the Minimalist Program, and the interplay that the concept has with the further explanation ‘beyond explanatory adequacy’ that minimalist analysis seeks.

Statistical description of the East Australian Current low-frequency variability from the WOCE PCM3 array

2006 ◽  
Vol 57 (3) ◽  
pp. 273 ◽  
Author(s):  
Mauricio M. Mata ◽  
Susan Wijffels ◽  
John A. Church ◽  
Matthias Tomczak
Keyword(s):  
Ocean Circulation ◽  
World Ocean ◽  
Low Frequency ◽  
General Rule ◽  
Empirical Orthogonal Functions ◽  
East Australian Current ◽  
Orthogonal Functions ◽  
The Pacific ◽  
Strong Surface ◽  
The Impact

The in situ dataset used in the current study consists of the Pacific Current Meter 3 (PCM3) array, which was a significant part of the Australian contribution to the World Ocean Circulation Experiment to study the variability of the East Australian Current (EAC), and was operational between September 1991 and March 1994. Area-preserving spectral analysis has been used to investigate the typical time scales observed by the current meters. As a general rule, the spectra from the top layers of the shallow (1, 2 and 3) and the deep (4, 5 and 6) moorings have a distinct peak in the temporal mesoscale band (periods between 70 and 170 days), with a general redistribution of energy towards the higher-frequencies near the ocean floor. This peak has been linked with eddy variability of the EAC system, which influences the fluctuations of the current main jet. The vertical modes of the velocity profile show that the strong surface-intensified baroclinic signal of the EAC dominated the variability at mooring 4 location. Further offshore the predominant configuration resembles more closely the barotropic mode. Ultimately, spatial empirical orthogonal functions (EOF) analysis point out the impact of the presence/absence of the EAC jet in the array.

scholarly journals Investigating the Impact of CO2 on Low-Frequency Variability of the AMOC in HadCM3

2017 ◽  
Vol 30 (19) ◽  
pp. 7863-7883 ◽  
Author(s):  
Edward Armstrong ◽  
Paul Valdes ◽  
Jo House ◽  
Joy Singarayer
Keyword(s):  
Climate Model ◽  
Singular Spectrum Analysis ◽  
Empirical Orthogonal Functions ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Positive Density ◽  
Quasi Equilibrium ◽  
Ekman Pumping ◽  
Orthogonal Functions ◽  
The Impact

Abstract This study investigates the impact of CO2 on the amplitude, frequency, and mechanisms of Atlantic meridional overturning circulation (AMOC) variability in millennial simulations of the HadCM3 coupled climate model. Multichannel singular spectrum analysis (MSSA) and empirical orthogonal functions (EOFs) are applied to the AMOC at four quasi-equilibrium CO2 forcings. The amount of variance explained by the first and second eigenmodes appears to be small (i.e., 11.19%); however, the results indicate that both AMOC strength and variability weaken at higher CO2 concentrations. This accompanies an apparent shift from a predominant 100–125-yr cycle at 350 ppm to 160 yr at 1400 ppm. Changes in amplitude are shown to feed back onto the atmosphere. Variability may be linked to salinity-driven density changes in the Greenland–Iceland–Norwegian Seas, fueled by advection of anomalies predominantly from the Arctic and Caribbean regions. A positive density anomaly accompanies a decrease in stratification and an increase in convection and Ekman pumping, generating a strong phase of the AMOC (and vice versa). Arctic anomalies may be generated via an internal ocean mode that may be key in driving variability and are shown to weaken at higher CO2, possibly driving the overall reduction in amplitude. Tropical anomalies may play a secondary role in modulating variability and are thought to be more influential at higher CO2, possibly due to an increased residence time in the subtropical gyre and/or increased surface runoff driven by simulated dieback of the Amazon rain forest. These results indicate that CO2 may not only weaken AMOC strength but also alter the mechanisms that drive variability, both of which have implications for climate change on multicentury time scales.

scholarly journals Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256610
Author(s):  
Xingpei Yan ◽  
Zheng Zhu
Keyword(s):  
Traffic Safety ◽  
Traffic Congestion ◽  
Regression Models ◽  
Travel Demand ◽  
The Other ◽  
Explanatory Variables ◽  
Safety Issues ◽  
Country Level ◽  
The Impact ◽  
Cluster 2

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China’s e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

scholarly journals Modes of the BiOS-driven Adriatic Sea thermohaline variability

2021 ◽  
Author(s):  
Cléa Lumina Denamiel ◽  
Iva Tojčić ◽  
Petra Pranić ◽  
Ivica Vilibić
Keyword(s):  
Time Series ◽  
Thermohaline Circulation ◽  
Climate Model ◽  
Adriatic Sea ◽  
Decadal Variability ◽  
Current Speed ◽  
Empirical Orthogonal Functions ◽  
Climate Simulation ◽  
Orthogonal Functions ◽  
The Impact

Abstract In this study the impact of the Adriatic-Ionian Bimodal Oscillating System (BiOS) on the interannual to decadal variability of the Adriatic Sea thermohaline circulation is quantified during the 1987-2017 period with the numerical results of the Adriatic Sea and Coast (AdriSC) historical kilometer-scale climate simulation. The time series associated with the first five Empirical Orthogonal Functions (EOFs) computed from the salinity, temperature and current speed monthly detrended anomalies at 1-km resolution are correlated to the BiOS signal. First, it is found that the AdriSC climate model is capable to reproduce the BiOS-driven phases derived from in-situ observations along a long-term monitoring transect in the middle Adriatic. Then, for the entire Adriatic basin, high correlations to the 2-year delayed BiOS signal are obtained for the salinity and current speed first two EOF time series at 100 m depth and the sea-bottom Finally, the physical interpretation of the EOF spatial patterns reveals that Adriatic bottom temperatures are more influenced by the dense water circulation than the BiOS. These findings confirmed and generalized the known dynamics derived previously from observations, and the AdriSC climate model can thus be used to better understand the past and future BiOS-driven physical processes in the Adriatic Sea.

Estimating mean monthly runoff at ungauged locations: an application to France

2008 ◽  
Vol 39 (5-6) ◽  
pp. 403-423 ◽  
Author(s):  
Eric Sauquet ◽  
Lars Gottschalk ◽  
Irina Krasovskaia
Keyword(s):  
Statistical Parameter ◽  
River Regulation ◽  
Empirical Orthogonal Functions ◽  
Observation Data ◽  
Interpolation Scheme ◽  
Orthogonal Functions ◽  
Ungauged Sites ◽  
Stochastic Interpolation ◽  
Monthly Runoff ◽  
Statistical Laws

An approach for estimating mean monthly runoff at ungauged sites is presented. Special attention is paid to include effects of local features such as karst and river regulation by reservoirs. The developments introduced conform with hydrostochastic concepts in that simple physical and statistical laws are inherent in the methods used for mapping. Hence, the approach developed here is consistent with the water balance along the river network. The suggested method combines an application of empirical orthogonal functions and an adapted stochastic interpolation scheme to match the runoff data. The observation data are handled in the frame of a hydrological information system. This allows the display of results either in the form of the change in a statistical parameter along the river branches towards the basin outlet or as a map of the variation of the parameters across the basin or region space. The approach is demonstrated for France.

Genesis of Hurricane Julia (2010) within an African Easterly Wave: Sensitivity Analyses of WRF-LETKF Ensemble Forecasts

2014 ◽  
Vol 71 (9) ◽  
pp. 3180-3201 ◽  
Author(s):  
Stefan F. Cecelski ◽  
Da-Lin Zhang
Keyword(s):  
Deep Convection ◽  
Empirical Orthogonal Functions ◽  
Sensitivity Analyses ◽  
Tropical Cyclogenesis ◽  
Tropospheric Temperature ◽  
Orthogonal Functions ◽  
Ensemble Forecasts ◽  
Low Level ◽  
Modes Of Variability ◽  
Two Parameters

Abstract In this study, the predictability of tropical cyclogenesis (TCG) is explored by conducting ensemble sensitivity analyses on the TCG of Hurricane Julia (2010). Using empirical orthogonal functions (EOFs), the dominant patterns of ensemble disagreements are revealed for various meteorological parameters such as mean sea level pressure (MSLP) and upper-tropospheric temperature. Using the principal components of the EOF patterns, ensemble sensitivities are generated to elucidate which mechanisms drive the parametric ensemble differences. The dominant pattern of MSLP ensemble spread is associated with the intensity of the pre–tropical depression (pre-TD), explaining nearly half of the total variance at each respective time. Similar modes of variance are found for the low-level absolute vorticity, though the patterns explain substantially less variance. Additionally, the largest modes of variability associated with upper-level temperature anomalies closely resemble the patterns of MSLP variance, suggesting interconnectedness between the two parameters. Sensitivity analyses at both the pre-TD and TCG stages reveal that the MSLP disturbance is strongly correlated to upper-tropospheric temperature and, to a lesser degree, surface latent heat flux anomalies. Further sensitivity analyses uncover a statistically significant correlation between upper-tropospheric temperature and convective anomalies, consistent with the notion that deep convection is important for augmenting the upper-tropospheric warmth during TCG. Overall, the ensemble forecast differences for the TCG of Julia are strongly related to the processes responsible for MSLP falls and low-level cyclonic vorticity growth, including the growth of upper-tropospheric warming and persistent deep convection.

scholarly journals Influence of synoptic patterns on surface ozone variability over the eastern United States from 1980 to 2012

2015 ◽  
Vol 15 (19) ◽  
pp. 10925-10938 ◽  
Author(s):  
L. Shen ◽  
L. J. Mickley ◽  
A. P. K. Tai
Keyword(s):  
United States ◽  
Atmospheric Chemistry ◽  
Surface Ozone ◽  
Empirical Orthogonal Functions ◽  
Eastern United States ◽  
Orthogonal Functions ◽  
Synoptic Patterns ◽  
Time Period ◽  
Relative Standard ◽  
The Impact

Abstract. We investigate the effect of synoptic-scale weather patterns on observed maximum daily 8-hour average (MDA8) surface ozone over the eastern United States during 1980–2012 in summer (June–August, JJA). Zonally averaged, the relative standard deviation (SD) of daily MDA8 JJA ozone shows a bimodal structure, with peaks at 28–32 and 40–45° N, and we show that those regions are most influenced by the variability in daily weather. We apply empirical orthogonal functions (EOFs) to understand the causes of this structure. The first three leading EOF patterns explain 53 % of the total variance in detrended surface ozone, displaying (1) a widespread response of ozone in the eastern United States associated with north–south movement of jet wind latitude, (2) a north–south pattern linked to the Bermuda High system when its west boundary is located along the east coast, and (3) an east–west pattern characteristic of a westward extension of the Bermuda High and an enhanced Great Plains low level jet (GPLLJ). The northern peak of ozone relative SD can be explained by polar jet activity, while the southern peak appears related to variability in the Bermuda High and GPLLJ. We define a new metric polar jet frequency as the total number of days the jet traverses the Midwest and northeast each summer. In the Midwest and northeast, we find that the correlation coefficient r between detrended mean JJA MDA8 ozone and the polar jet frequency ranges between −0.76 and −0.93 over 1980–2012 depending on the time period selected, suggesting that polar jet frequency could provide a simple metric to predict ozone variability in future climate regimes. In the southeast, the influence of the Bermuda High on mean JJA MDA8 ozone depends on the location of its west edge. For those summers when the average position of the west edge is located west of ~ 85.4° W, a westward shift in the Bermuda High west edge increases ozone in the southeast by ~ 1 ppbv deg−1 in longitude. For all summers, a northward shift in the Bermuda High west edge increases ozone over the entire eastern United States by 1–2 ppbv deg−1 in latitude. None of the synoptic patterns identified in this study show a significant trend from 1980 to 2012, confirming that the observed ozone decrease over the eastern United States during this time period is mainly caused by emission controls. Our work underscores the impact of synoptic patterns on ozone variability and suggests that a combination of changing local and synoptic meteorology together with trends in background ozone will determine the influence of climate change on US ozone air quality in future decades. The observed relationships of US surface ozone and synoptic circulations in this study can also be used to validate models of atmospheric chemistry.

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