TY  - JOUR
AU  - Mulaomerović-Seta, Ajla
AU  - Blagojević, Borislava
AU  - Mihailović, Vladislava
AU  - Petroselli, Andrea
PY  - 2023
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1419
AB  - Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward's algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking-Wallis and Anderson-Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering.
T2  - Hydrology
T1  - A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions
IS  - 6
VL  - 10
DO  - 10.3390/hydrology10060126
UR  - conv_933
ER  - 

@article{
author = "Mulaomerović-Seta, Ajla and Blagojević, Borislava and Mihailović, Vladislava and Petroselli, Andrea",
year = "2023",
abstract = "Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward's algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking-Wallis and Anderson-Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering.",
journal = "Hydrology",
title = "A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions",
number = "6",
volume = "10",
doi = "10.3390/hydrology10060126",
url = "conv_933"
}

Mulaomerović-Seta, A., Blagojević, B., Mihailović, V.,& Petroselli, A.. (2023). A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions. in Hydrology, 10(6).
https://doi.org/10.3390/hydrology10060126
conv_933

Mulaomerović-Seta A, Blagojević B, Mihailović V, Petroselli A. A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions. in Hydrology. 2023;10(6).
doi:10.3390/hydrology10060126
conv_933 .

Mulaomerović-Seta, Ajla, Blagojević, Borislava, Mihailović, Vladislava, Petroselli, Andrea, "A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions" in Hydrology, 10, no. 6 (2023),
https://doi.org/10.3390/hydrology10060126 .,
conv_933 .

TY  - JOUR
AU  - Blagojević, Borislava
AU  - Mihailović, Vladislava
AU  - Bogojević, Aleksandar
AU  - Plavsić, Jasna
PY  - 2023
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1397
AB  - Changes in the hydrological regime are widely investigated using a variety of approaches. In this study, we assess changes in annual and seasonal flow characteristics based on a probabilistic representation of the seasonal runoff regime at the daily time scale. The probabilistic seasonal runoff pattern is constructed by determining quantiles from marginal distributions of daily flows for each day within the year. By applying Fourier transformation on the statistics of the daily flow partial series, we obtain smooth periodical functions of distribution parameters over the year and consequently of the quantiles. The main findings are based on the comparison of the dry, average, and wet hydrologic condition zones as defined by the daily flow quantiles of selected probabilities. This analysis was conducted for ten catchments in Serbia by considering changes between two 30-year nonoverlapping periods, 1961-1990 and 1991-2020. It was found that the relative change in runoff volume is the most pronounced in the extreme dry condition zone in the winter season (-33% to 34%). The annual time shift is the largest in the dry and average condition zones, ranging from -11 to 12 days. The applied methodology is not only applicable to the detection of hydrologic change, but could also be used in operational hydrology and extreme flow studies via drought indices such as the Standardized Streamflow Index.
T2  - Water
T1  - Detecting Annual and Seasonal Hydrological Change Using Marginal Distributions of Daily Flows
IS  - 16
VL  - 15
DO  - 10.3390/w15162919
UR  - conv_1724
ER  - 

@article{
author = "Blagojević, Borislava and Mihailović, Vladislava and Bogojević, Aleksandar and Plavsić, Jasna",
year = "2023",
abstract = "Changes in the hydrological regime are widely investigated using a variety of approaches. In this study, we assess changes in annual and seasonal flow characteristics based on a probabilistic representation of the seasonal runoff regime at the daily time scale. The probabilistic seasonal runoff pattern is constructed by determining quantiles from marginal distributions of daily flows for each day within the year. By applying Fourier transformation on the statistics of the daily flow partial series, we obtain smooth periodical functions of distribution parameters over the year and consequently of the quantiles. The main findings are based on the comparison of the dry, average, and wet hydrologic condition zones as defined by the daily flow quantiles of selected probabilities. This analysis was conducted for ten catchments in Serbia by considering changes between two 30-year nonoverlapping periods, 1961-1990 and 1991-2020. It was found that the relative change in runoff volume is the most pronounced in the extreme dry condition zone in the winter season (-33% to 34%). The annual time shift is the largest in the dry and average condition zones, ranging from -11 to 12 days. The applied methodology is not only applicable to the detection of hydrologic change, but could also be used in operational hydrology and extreme flow studies via drought indices such as the Standardized Streamflow Index.",
journal = "Water",
title = "Detecting Annual and Seasonal Hydrological Change Using Marginal Distributions of Daily Flows",
number = "16",
volume = "15",
doi = "10.3390/w15162919",
url = "conv_1724"
}

Blagojević, B., Mihailović, V., Bogojević, A.,& Plavsić, J.. (2023). Detecting Annual and Seasonal Hydrological Change Using Marginal Distributions of Daily Flows. in Water, 15(16).
https://doi.org/10.3390/w15162919
conv_1724

Blagojević B, Mihailović V, Bogojević A, Plavsić J. Detecting Annual and Seasonal Hydrological Change Using Marginal Distributions of Daily Flows. in Water. 2023;15(16).
doi:10.3390/w15162919
conv_1724 .

Blagojević, Borislava, Mihailović, Vladislava, Bogojević, Aleksandar, Plavsić, Jasna, "Detecting Annual and Seasonal Hydrological Change Using Marginal Distributions of Daily Flows" in Water, 15, no. 16 (2023),
https://doi.org/10.3390/w15162919 .,
conv_1724 .

TY  - JOUR
AU  - Đokić, Nikola
AU  - Blagojević, Borislava
AU  - Mihailović, Vladislava
PY  - 2021
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1223
AB  - Flood flow frequency analysis (FFA) plays one of the key roles in many fields of hydraulic engineering and water resources management. The reliability of FFA results depends on many factors, an obvious one being the reliability of the input data - datasets of the annual peak flow. In practice, however, engineers often encounter the problem of incomplete datasets (missing data, data gaps and/or broken records) which increases the uncertainty of FFA results. In this paper, we perform at-site focused analysis, and we use a complete dataset of annual peak flows from 1931 to 2016 at the hydrologic station Senta of the Tisa (Tisza) river as the reference dataset. From this original dataset we remove some data and thus we obtain 15 new datasets with one continuous gap of different length and/or location. Each dataset we further subject to FFA by using the USACE HEC-SSP Bulletin 17C analysis, where we apply perception thresholds for missing data representation. We vary perception threshold lower bound for all missing flows in one dataset, so that we create 56 variants of the input HEC-SSP datasets. The flood flow quantiles assessed from the datasets with missing data and different perception thresholds we evaluate by two uncertainty measures. The results indicate acceptable flood quantile estimates are obtained, even for larger return periods, by setting a lower perception threshold bound at the value of the highest peak flow in the available - incomplete dataset.
PB  - Institut za istraživanja i projektovanja u privredi, Beograd
T2  - Journal of Applied Engineering Science
T1  - Missing data representation by perception thresholds in flood flow frequency assessment
EP  - 438
IS  - 2
SP  - 432
VL  - 19
DO  - 10.5937/jaes0-28902
UR  - conv_1940
ER  - 

@article{
author = "Đokić, Nikola and Blagojević, Borislava and Mihailović, Vladislava",
year = "2021",
abstract = "Flood flow frequency analysis (FFA) plays one of the key roles in many fields of hydraulic engineering and water resources management. The reliability of FFA results depends on many factors, an obvious one being the reliability of the input data - datasets of the annual peak flow. In practice, however, engineers often encounter the problem of incomplete datasets (missing data, data gaps and/or broken records) which increases the uncertainty of FFA results. In this paper, we perform at-site focused analysis, and we use a complete dataset of annual peak flows from 1931 to 2016 at the hydrologic station Senta of the Tisa (Tisza) river as the reference dataset. From this original dataset we remove some data and thus we obtain 15 new datasets with one continuous gap of different length and/or location. Each dataset we further subject to FFA by using the USACE HEC-SSP Bulletin 17C analysis, where we apply perception thresholds for missing data representation. We vary perception threshold lower bound for all missing flows in one dataset, so that we create 56 variants of the input HEC-SSP datasets. The flood flow quantiles assessed from the datasets with missing data and different perception thresholds we evaluate by two uncertainty measures. The results indicate acceptable flood quantile estimates are obtained, even for larger return periods, by setting a lower perception threshold bound at the value of the highest peak flow in the available - incomplete dataset.",
publisher = "Institut za istraživanja i projektovanja u privredi, Beograd",
journal = "Journal of Applied Engineering Science",
title = "Missing data representation by perception thresholds in flood flow frequency assessment",
pages = "438-432",
number = "2",
volume = "19",
doi = "10.5937/jaes0-28902",
url = "conv_1940"
}

Đokić, N., Blagojević, B.,& Mihailović, V.. (2021). Missing data representation by perception thresholds in flood flow frequency assessment. in Journal of Applied Engineering Science
Institut za istraživanja i projektovanja u privredi, Beograd., 19(2), 432-438.
https://doi.org/10.5937/jaes0-28902
conv_1940

Đokić N, Blagojević B, Mihailović V. Missing data representation by perception thresholds in flood flow frequency assessment. in Journal of Applied Engineering Science. 2021;19(2):432-438.
doi:10.5937/jaes0-28902
conv_1940 .

Đokić, Nikola, Blagojević, Borislava, Mihailović, Vladislava, "Missing data representation by perception thresholds in flood flow frequency assessment" in Journal of Applied Engineering Science, 19, no. 2 (2021):432-438,
https://doi.org/10.5937/jaes0-28902 .,
conv_1940 .