Integral assessment of climate impact on the transboundary Mesta River flow formation in Bulgaria

Diadovski, I.; Atanassova, M.; Ivanov, I.

doi:10.1007/s10661-006-9287-5

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…In the present work, an integral approach to evaluating the level of climate impact on the river flow formation is applied following earlier studies of Diadovski et al [12,21]. With respect to the risk assessment of flood or drought events specific integral indicators are introduced, which are based on the ratios between the highest water discharge for the year Q max,i to the multi-annual-average value of the maximum water discharge Q max,0 and on the ratio between the minimum water discharge Q min,i to the multi-annual-average value of the minimum water discharge Q min,0 .…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we believe that difficult for evaluation risk events due to climate changes at regional and global level could be assessed or even predicted if appropriate indicators are introduced. In previous papers the authors investigate the dynamics of the module coefficients K i as a background for integral assessment of the climatic impact on the river flow formation [12]. Usually, these module coefficients are applied for calculation of the coefficient of variation of the flow for a certain period.…”

Section: Introductionmentioning

confidence: 99%

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Risk Assessment of Extreme Events along a River Flow

Diadovski¹,

Atanassova²,

Simeonov³

2010

JWARP

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The present work considers the Struma River water flow on Bulgarian territory as a starting point for evalua-tion of maximum and minimum water flow using an original integral method. The risk assessment is deter-mined by specific indices like the index Mmax,i for the deviation of the maximum water flow from the calcu-lated norm of the maximum flow Qmax,0 and the index Mmin,i for the deviation of the minimum water flow from calculated norm of the minimum flow Qmin,0. The new integral approach introducing specific indicators for risk assessment like the indices Mmin,i and Mmax,i has been checked at three sampling locations of the Na-tional monitoring net along the Struma River: Pernik (in the beginning), Krupnik (in the middle) and Marino pole (at the border in Greece) for the period 1948-2006. A significant trend towards decreasing of Mmax,i in-dex is outlined for the three points. On contrary, a significant trend towards increasing of Mmin,i index is found at Krupnik and Marno pole sampling points

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Extreme Events along a River Flow

Diadovski¹,

Atanassova²,

Simeonov³

2010

JWARP

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“…Nestos flows into the Thracian Sea, containing a network of 25 affluents (Arkoudorema, Diavolorema, Kousdorema, etc.) [35]. The physical location is depicted in Figure 1.…”

Section: Area Under Study-data Descriptionmentioning

confidence: 99%

Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

et al. 2022

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Understanding the underlying processes and extracting detailed characteristics of rivers is critical and has not yet been fully developed. The purpose of this study was to examine the performance of non-linear time series methods on environmental data. Specifically, we performed an analysis of water level measurements, extracted from sensors, located on specified stations along the Nestos River (Greece), with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA) methods. A more detailed inspection with the sliding windows (epoqs) method was applied on the Recurrence Rate, Average Diagonal Line and Trapping Time parameters, with results showing phase transitions providing useful information about the dynamics of the system. The suggested method seems to be promising for the detection of the dynamical transitions that can characterize distinct time windows of the time series and reveals information about the changes in state within the whole time series. The results will be useful for designing the energy policy investments of producers and also will be helpful for dam management assessment as well as government energy policy.

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“…Using them one considers the environmental system as a multivariate object and treats it respectively. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Usually, the studies performed try to assess the river water quality or to optimize the monitoring procedure by multivariate statiscs aim:…”

Section: Introductionmentioning

confidence: 99%

Environmetric data interpretation to assess the water quality of Maritsa River catchment

Papazova

Simeonova

2013

Journal of Environmental Science and Health, Part A

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Maritsa River is one of the largest rivers flowing on Bulgarian territory. The quality of its waters is of substantial importance for irrigation, industrial, recreation and domestic use. Besides, part of the river is flowing on Turkish territory and the control and management of the Maritsa catchment is of mutual interst for the neighboring countires. Thus, performing interpretation and modeling of the river water quality is a major environmetric problem. Two multivariate statstical methods (Cluster analysis/CA/and Principal components analysis/PCA/) were applied for model assessment of the water quality of Maritsa River on Bulgarian territory. The study used long-term monitoring data from 21 sampling sites characterized by 8 surface water quality indicators. The application of CA to the indicators results in 3 significant clusters showing the impact of biological, anthropogenic and eutrophication sources. For further assessment of the monitoring data, PCA was implemented, which identified, again,three latent factors confirming, in principle, the clustering output. The latent factors were conditionally named "biologic", "anthropogenic" and "eutrophication" source. Their identification coinside correctly to the location of real pollution sources along the Maritsa River catchment. The linkage of the sampling sites along the river flow by CA identified four special patterns separated by specific tracers levels: biological and anthropogenic major impact for pattern 1, euthrophication major impact for pattern 2, background levels for pattern 3 and eutrophication and agricultural major impact for pattern 4. The apportionment models of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters. Thus, a better risk management of the surface water quality is achieved both on local and national level.

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Integral assessment of climate impact on the transboundary Mesta River flow formation in Bulgaria

Cited by 7 publications

References 2 publications

Risk Assessment of Extreme Events along a River Flow

Risk Assessment of Extreme Events along a River Flow

Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

Environmetric data interpretation to assess the water quality of Maritsa River catchment

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