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 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.

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.

Analysis and projection of runoff variation in three Chinese rivers

2016 ◽  
Vol 48 (5) ◽  
pp. 1296-1310 ◽  
Author(s):  
Lingqi Li ◽  
Irina Krasovskaia ◽  
Lihua Xiong ◽  
Lei Yan
Keyword(s):  
Annual Runoff ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Flow Duration ◽  
Runoff Variation ◽  
Variation Patterns ◽  
Runoff Series ◽  
Runoff Variability ◽  
Flow Duration Curves ◽  
Wei River

Runoff variability is investigated separately for the Wei, the Bei, and the Qing Rivers in China with a focus on their respective differences in monthly flow patterns and flow duration curves (FDCs) between years with and without annual runoff deficit. The number of deficit runoff years increased in the Wei River and changed slightly in the Bei and Qing Rivers, respectively. Monthly flow variation patterns and FDCs differ between deficit and non-deficit years. The deficit years generally demonstrate earlier and more dispersed flow maxima. Deficit runoff years are contingent with the negative phase of the Polar-Eurasian Oscillation and vice versa, while generally they show contingency with the positive phase of the SST (Niño 3.4) and vice versa. The correlation between the human activity factors and the weights obtained by decomposing the runoff series into empirical orthogonal functions indicated that the human impact on the runoff variation was detectable: 22–25% in the Wei River, 28% in the Bei River, and negligible in the Qing River. We projected FDCs by weighting the distinctly different FDCs for deficit/non-deficit years according to several precipitation scenarios.

Empirical Methods in Short-Term Climate Prediction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Climate Prediction ◽  
Empirical Orthogonal Functions ◽  
Data Sets ◽  
Seasonal Forecasts ◽  
Short Term ◽  
Orthogonal Functions ◽  
Ocean Atmosphere Interaction ◽  
Global Coverage ◽  
Atmosphere Interaction ◽  
Global Data

This clear and accessible text describes the methods underlying short-term climate prediction at time scales of 2 weeks to a year. Although a difficult range to forecast accurately, there have been several important advances in the last ten years, most notably in understanding ocean-atmosphere interaction (El Nino for example), the release of global coverage data sets, and in prediction methods themselves. With an emphasis on the empirical approach, the text covers in detail empirical wave propagation, teleconnections, empirical orthogonal functions, and constructed analogue. It also provides a detailed description of nearly all methods used operationally in long-lead seasonal forecasts, with new examples and illustrations. The challenges of making a real time forecast are discussed, including protocol, format, and perceptions about users. Based where possible on global data sets, illustrations are not limited to the Northern Hemisphere, but include several examples from the Southern Hemisphere.

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

Identifying VIV Vibration Modes by Use of the Empirical Orthogonal Functions Technique

2002 ◽  
Author(s):  
Gudmund Kleiven
Keyword(s):  
Model Test ◽  
Vibration Mode ◽  
Multivariate Data ◽  
Empirical Orthogonal Functions ◽  
Mode Shapes ◽  
Data Sets ◽  
Vibration Modes ◽  
Ocean Engineering ◽  
Orthogonal Functions ◽  
Related Technique

The Empirical Orthogonal Functions (EOF) technique has widely being used by oceanographers and meteorologists, while the Singular Value Decomposition (SVD being a related technique is frequently used in the statistics community. Another related technique called Principal Component Analysis (PCA) is observed being used for instance in pattern recognition. The predominant applications of these techniques are data compression of multivariate data sets which also facilitates subsequent statistical analysis of such data sets. Within Ocean Engineering the EOF technique is not yet widely in use, although there are several areas where multivariate data sets occur and where the EOF technique could represent a supplementary analysis technique. Examples are oceanographic data, in particular current data. Furthermore data sets of model- or full-scale data of loads and responses of slender bodies, such as pipelines and risers are relevant examples. One attractive property of the EOF technique is that it does not require any a priori information on the physical system by which the data is generated. In the present paper a description of the EOF technique is given. Thereafter an example on use of the EOF technique is presented. The example is analysis of response data from a model test of a pipeline in a long free span exposed to current. The model test program was carried out in order to identify the occurrence of multi-mode vibrations and vibration mode amplitudes. In the present example the EOF technique demonstrates the capability of identifying predominant vibration modes of inline as well as cross-flow vibrations. Vibration mode shapes together with mode amplitudes and frequencies are also estimated. Although the present example is not sufficient for concluding on the applicability of the EOF technique on a general basis, the results of the present example demonstrate some of the potential of the technique.

Sign in / Sign up

Export Citation Format

Share Document