Using meteorological data for reconstruction of annual runoff series over an ungauged area: Empirical orthogonal function approach to Moldova–Southwest Ukraine region

Loboda, N. S.; Глушков, А. В.; Khokhlov, V. N.

doi:10.1016/j.atmosres.2004.10.020

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…To some extent, this rule assures the leading EOFs with certain physical meanings. The EOF analysis yields not only spatial patterns, but also their temporal processes correspondingly, which can be used for periodic analysis, interpolation and forecasting [10] , though we did not discuss about it in this article.…”

Section: Output Of Eof Analysismentioning

confidence: 99%

Modelling the stream flow change in a poorly gauged mountainous watershed, southern Tianshan Mountain, using multi-source remote sensing data

2009

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Hydrological predictions in ungauged or poorly gauged basin are crucial for sustainable water management and environmental changes study induced by climate change. Application of remote sensing technology has retrieved lots of spatio-temporal dataset during the past decades for references. In this study, TRMM/PR and MODIS LST data were introduced to get spatial patterns of precipitation and temperature changes by Empirical Orthogonal Function (EOF) technique in a mountainous watershed, southern Tianshan. An input variable group was attempted to be constructed for the Artificial Neural Networks (ANN) to model the stream flow change based on the patterns achieved above. The results indicate that the spatial variability patterns of meteorology can be well recognized from the remote sensing data by EOF analysis. The stream flow process can be satisfyingly simulated with input variables captured from the leading modes during the study period. While, since the probabilistic model was not based on full physical mechanisms, and often times, also limited by the amount of input data, uncertainties often implicated in the output. As an example, it is discussed through the rapidly glaciers melting phenomena induced by climate warming, which is expected to cause change in the flow generation mechanism.

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Section: Output Of Eof Analysismentioning

confidence: 99%

Modelling the stream flow change in a poorly gauged mountainous watershed, southern Tianshan Mountain, using multi-source remote sensing data

2009

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“…Simple correlation coefficients of flood discharges between rivers have been used to describe the frequency distribution of historic flood series. With gauge data, a non-parametric multivariate empirical orthogonal function model was used to address the relationship between flood events of rivers (Obled and Creutin, 1986;Rao and Hsieh, 1991; Hisdal and Tveito, 1993;Loboda et al, 2005). We defined the flood time coefficient matrix (Q) as the product of a spatial matrix and a temporal matrix.…”

Section: Flood Frequency Analysis-correlation Coefficient Matrixmentioning

confidence: 99%

Flood frequency and routing processes at a confluence of the middle Yellow River in China

Zhou

et al. 2007

River Research & Apps

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Floods cause environmental hazards and influence on socio-economic activities. In this study, we evaluated the historic flood frequency at a confluence in the middle Yellow River, China. A non-parametric, multivariate, empirical, orthogonal function matrix model, which consists of time correlation coefficients of flood discharge at different gauge stations and flood events was used for the analysis of flood frequency. The model addresses the characteristics of confluent floods such as frequency and the probability in multiple tributary rivers. Flood frequency analysis is often coupled with studies of hydrological routing processes that reduce the flood capacity of the rivers. Flood routing to the confluence were simulated using kinematic wave theory. Results of this flood frequency analysis showed that flooding frequency has intensified in the past 500 years, especially during the 19th century. Flooding in streams above the confluence was more frequent than in streams below the confluence. Over the last 2000 years, concurrent flooding in multiple tributary rivers accounted for 67.5% of the total flooding in the middle Yellow River. Simulation of flood routing processes shows that the decreased flooding capacity and elevated river bed of the shrunken main channel leads to an increased flood wave propagation time (24-52.3 h) in the study area after 1995. The model indicates that human activities, such as constructions of the Sanmenxia Dam, have changed flood routing boundary conditions and have contributed to the increased flood frequency at the confluence.

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“…These flood indices are effective in relating flood dynamics to precipitation. Simple correlation coefficients of flood discharges between rivers have been used to describe the frequency distribution of historic flood series (Obled and Creutin, 1986;Rao and Hsieh, 1991;Hisdal and Tveito, 1993;Loboda et al, 2005;He et al, 2006;Alpert et al, 2008). With gauge data, non-parametric multivariate empirical orthogonal function model is efficient to address the relationship between flood events of rivers (He et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Confluent flow impacts of flood extremes in the middle Yellow River

Tian

et al. 2015

Quaternary International

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Using meteorological data for reconstruction of annual runoff series over an ungauged area: Empirical orthogonal function approach to Moldova–Southwest Ukraine region

Cited by 14 publications

References 16 publications

Modelling the stream flow change in a poorly gauged mountainous watershed, southern Tianshan Mountain, using multi-source remote sensing data

Modelling the stream flow change in a poorly gauged mountainous watershed, southern Tianshan Mountain, using multi-source remote sensing data

Flood frequency and routing processes at a confluence of the middle Yellow River in China

Confluent flow impacts of flood extremes in the middle Yellow River

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