Pollack Periodica
Volume/Issue:
Accepted Manuscript / Online First
Rainfall duration and parameter sensitivity on flash-flood at a steep watershed
Authors:
Gergely ÁmonDepartment of Transport Infrastructure and Water Resource Engineering, Faculty of Architecture, Civil and Transportation Engineering, Széchenyi István University, Győr, Hungary

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Katalin BeneDepartment of Transport Infrastructure and Water Resource Engineering, Faculty of Architecture, Civil and Transportation Engineering, Széchenyi István University, Győr, Hungary

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Online Publication Date:
14 Mar 2023
Publication Date:
14 Mar 2023
Article Category:
Research Article
DOI:
https://doi.org/10.1556/606.2022.00713
Keywords:
numerical modeling; watershed hydrology; parameter sensitivity; flash-flood; sensitivity study
Open access

Abstract

The common feature of streams in steep sloping watersheds is that there is a significant change from base-flow to flash-flood; sometimes two or three orders of magnitude. In Hungary, these streams are usually ungauged, with lack of available data, and models. The watershed features both urban and natural land use conditions, but the main area is quite homogenic.

This paper evaluates the impact of different model parameterizations, and rainfall duration on flash-flood events in the Morgó-creek watershed. The goal is to find the main parameters that can represent the uncertainty of a flash-flood sensitive area, and how the calibrated and determined parameters take effect on a model if these values are shifted on given intervals.

Abstract

The common feature of streams in steep sloping watersheds is that there is a significant change from base-flow to flash-flood; sometimes two or three orders of magnitude. In Hungary, these streams are usually ungauged, with lack of available data, and models. The watershed features both urban and natural land use conditions, but the main area is quite homogenic.

This paper evaluates the impact of different model parameterizations, and rainfall duration on flash-flood events in the Morgó-creek watershed. The goal is to find the main parameters that can represent the uncertainty of a flash-flood sensitive area, and how the calibrated and determined parameters take effect on a model if these values are shifted on given intervals.

1 Introduction

Flash-flood events are a considerable natural hazard that will intensify in the future due to climate change and expansion of the built environment. In the last 10–15 years, these rainfall events have become more frequent throughout Hungary, with higher peak intensities [1]. This trend in rainfall patterns would induce flash-flood events in certain areas of Hungary. They are most likely to occur in watersheds with relatively steep slopes, low infiltration capacity, and high levels of antecedent soil moisture. To forecast a flash-flood, hydrologists may use different numerical models, and then compare their predictions to field performance data. Unfortunately, the watersheds within Hungary that are prone to flash-flooding are generally un-gauged [2, 3].

Hydrologic models that predict stream flow out of a watershed may consist of a set of parameters that are either lumped or spatially distributed over the watershed or rely heavily on previous performance data from similar watersheds. Lumped models require less data input and computational effort; however, they require more modification or fine tuning to improve their predictive capability [4]. Distributed models require more data at high temporal and spatial resolution. The initial data gathering effort results in more accurate predictions with some analyses capable of making predictions in real-time [4]. The choice of model may also depend on ease of use, available data, and past experiences. This study used semi-distributed and lumped models, where uncertainties are reduced through sensitivity studies and comparisons to other models.

Flash-floods often have a serious environmental impact on the watershed and models do exist to evaluate the damage [5], peak flow was chosen as the predictive indicator.

2 Available data and the watershed description

The Morgó Creek watershed is in northern Hungary about 60 km directly north of Budapest. The creek flows directly into the left bank of the Danube River at about rkm 1,689, near the town of Kismaros, Fig. 1. The watershed area is 52.63 km2. Land use is mostly woodlands (∼70%) with agriculture use in the southern region (∼25%), and 4–5% urban area near the outlet, the soil is volcanic, Fig. 2. The watershed has a high average slope with upper regions 4.6–9.1%; conducive for flash-floods. Urban areas along lower regions average 0.5–1%. The only existing data came from a previous study where outlet flow from a 50-year return period (2% frequency) rainfall was Q2% = 53.3 cm. Since then, no further high-water data was measured or calculated on the creek [6].

Fig. 1.
Fig. 1.

Watershed location at the northern-Hungarian region

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

Fig. 2.
Fig. 2.

Hungarian surface parent rock map, M30: dacite-pyroclastite; subvolcanic dacite, andesite, M29: andesite, -pyroclastics, M27: shallow-marine foraminiferal, mollusc-bearing clay marl (Source: https://map.mbfsz.gov.hu)

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

3 Model development

The HEC-HMS modeling software [7, 8], analyzed the system of 21 sub-watersheds with similar terrain usage, soil, and slope parameters [6]. The event-based runoff process starts with precipitation, then reaches ground surface. At the ground surface, if infiltration and surface storage capacities are exceeded, runoff is generated. The runoff of the event-based model structure is shown in Fig. 3.

Fig. 3.
Fig. 3.

HEC-HMS modules for the flash-flood model

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

The surface storage module was left out because the number of uncertain parameters. The module defines an amount of stored water on the surface, and is recommended for only continuous simulations [7]. Base-flow was left out because the watershed initially is dry.

The Green and Ampt method [9–11] was selected to calculate infiltration. The method is a combination of mass conservation and the unsaturated form of Darcy's law,
ft=K(1+(φ−θi)SfFt),
where ft is the loss during time interval t; K is the saturated hydraulic conductivity; φ is the soil porosity; θi is the volumetric moisture content at time interval; φ-θi is the moisture deficit; Sf is the wetting front suction; and Ft is the cumulative loss at time t. Relation between K and Sf is showed in Fig. 4.
Fig. 4.
Fig. 4.

Relation between wetting front suction and hydraulic conductivity in HEC-HMS [7]

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

The Green and Ampt model also includes an initial removal that represents interception in the canopy or surface depressions not otherwise included in the model [7]. This interception is separate from the time to ponding that is an integral part of the model. The Green and Ampt method uses an initial loss parameter, and it is defined as the moisture deficit of the soil. For the sensitivity analyses, 75% saturation was assumed, moisture deficit was 25% given as volumetric ratio. Based on land use conditions, 10% impervious area was assumed.

The Clark-Unit hydrograph model was used to determine surface runoff [12, 13]:
AtA=f(x)={1.414(ttc)1.5,t≤tc2,1−1.414(1−ttc)1.5,x≥0,
where At is the cumulative watershed area contributing at time; A is the total watershed area and tc is the time of concentration. Concentration was determined using the equations suggested by the HEC-HMS manual [7]:
tc=1.54⋅L0.875⋅S−0.181; R=16.4⋅L0.342⋅S−0.79,
where tc is the time of concentration; R is the storage coefficient; L is the longest route on the watershed; S is the average slope.

4 Model parameters, and parameter calibration

Some initial analyses were run to ensure the model was performing within acceptable limits. Since the only field verification was an estimate of peak flow from a rainfall event with 2% frequency of occurrence, it was adopted for a calibration analysis. The intensity/duration corresponded to a 2% event with uniform intensity and 1 h duration. Further input for the calibration check came from topographic and soil maps, as well as ortho-photos to estimate slope, infiltration, and roughness factor. The soil parameters were estimated via soil texture information and HEC-HMS Technical Reference Manual Tables 5–2 in [7]. It was assumed that the volcanic soil in Fig. 2 corresponds to sandy loam soil characteristics. The results of the calibration analysis are shown in Fig. 5 together with the field estimate of peak flow [6].

Fig. 5.
Fig. 5.

Flow vs. time from calibration analysis compared to field estimate

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

5 Sensitivity analyses

5.1 Rainfall duration

To better quantify the impact of uncertainty from input parameters, a sensitivity analysis of the watershed was conducted. A model rainfall was set to reflect a 1% frequency of occurrence and event durations from 1 to 12 h. Storm duration and time of concentration has a high impact on the magnitude of the flood peak, usually the highest floods occur due to storm duration, that are closest to the time of concentration. For the sensitivity analyses storm duration were selected that are close the calibrated around 2 h time of concentration of each sub-watershed. 1% frequency event was chosen to better represent possible impacts of climate change and comply with Hungarian design standards [14, 15]. The intensity vs. time function was defined with a triangular distribution. The resulting intensity function increased linearly to a peak that is double the uniform intensity value. The peak was set to 0.375 times the event duration [16]. A summary of rainfall parameters is included in Table 1.

Table 1.

Scenario for calibration and modeling, where i is the uniform rainfall event intensity; Peak is the triangular peak rainfall intensity; Tp is the elapsed time to peak intensity; h is the total cumulative rainfall; Sf is the wetting suction front rom Fig. 4 and Imp is the impervious area

Eventi [mm h−1]Peak [mm h−1]Tp [min]h [mm]Sf [cm]Imp [%]
2%, 1h51.48102.9622.551.481910
1%, 1h60.48120.9622.560.481910
1%, 2h35.7271.4445.071.441910
1%, 4h21.0942.1890.084.361910
1%, 6h15.5031.00135.093.001910
1%, 8h12.4624.92180.099.681910
1%, 12h9.1518.30270.0109.801910

For comparison, the six different 1% frequency events are shown with cumulative rainfall vs. time in Fig. 6. While the 1 h event shows the highest intensity, longer events generate more cumulative rainfall and may generate higher outflows from the watershed.

Fig. 6.
Fig. 6.

Cumulative rainfall vs. time 1% frequency event triangular intensity 1–12 h duration

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

For the sensitivity studies two types of result values was chosen to compare scenarios: 1) peak outflow, 2) runoff ratio: runoff flow volume/rainfall event volume. To serve as a baseline for comparison, calibrated watershed parameters were set to the values shown in the second column of Table 2. The six different rainfall events were applied to the baseline configuration, generating outflow vs. time plots as shown in Fig. 7.

Table 2.

Peak flow, precipitation and outflow volumes, and runoff ratio for baseline watershed and six rainfall events

Rainfall eventQmax (m3 s−1)Volume Precipitation (1,000 m3)Volume Outflow (1,000 m3)Ratio Runoff Flow/Precipitation
1%, 1 h93.353160.37934.2729.56%
1%, 2 h43.683758.76478.0412.72%
1%, 4 h30.914439.26444.1810.01%
1%, 6 h27.424894.50489.5310.00%
1%, 8 h24.545461.84524.489.60%
1%, 12 h20.365778.66578.0110.00%
Fig. 7.
Fig. 7.

Outflow vs. time for baseline scenario and six different rainfall events

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

The figure clearly shows the effects of the very high peak rainfall intensity for the 1 h event. Listed in Table 2 is a summary of further results from the baseline scenario and the six chosen rainfall events.

Table 2 shows the dominant influence of rainfall duration on flow values for this watershed. Peak flow, runoff volume, and ratio are more than double for the 1 h rainfall compared to longer durations. The lowest outflow volume was produced by the 4 h event, and then slowly increased for longer duration rainfalls. The watershed is mostly covered with woodland areas which, according to Hungarian standards, should produce a runoff ratio around 10%. Table 2 shows that all model results are close to 10% value except for the 1 h event it can be suggested that the behavior of the watershed is not only a function of rainfall intensity and duration.

5.2 Watershed characteristics

The first parameter is hydraulic conductivity (K). During sensitivity analyses sandy loam, and loamy sand soils were investigated. Once the saturated hydraulic conductivity was determined for the watershed, the wetting front suction was adjusted, based on the relationship determined in Fig. 4. The other one is the parameter which defines the ratio of the watershed area which is impervious. Table 3 shows the intervals of the sensitivity calculations.

Table 3.

Baseline and varied parameters for sensitivity analysis

ParameterValueChange [%]
Hydraulic conductivity, K (mm h−1)35−75−50+50+75
Impervious area [%]10−100−50+50+100
The sensitivity analyses compared the Percentage Change in RUNoff ratio (PCRUN) and the Percentage Change of Peak flow (PCQP) where
PC=Sensitivity value−BaselineBaseline·100%.

5.3 Results of sensitivity analysis

In each group, only one parameter was changed, while the other two remained set to the baseline values shown in Table 3 within the group six rainfall events were analyzed as it is summarized in Table 2.

K had a significant influence on both output quantities as shown in Fig. 8. A higher K value decreased runoff ratio, while a lower K value significantly increased runoff ratio (Fig. 8a). In a similar manner, higher K values reduced peak flow for only the shortest duration events while lower K values significantly increased peak flow for all events (Fig. 8b).

Fig. 8.
Fig. 8.

Effect of hydraulic conductivity, K, on a) runoff ratio and b) peak flow

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

Changes are not symmetric when comparing higher vs. lower K values. This is partially due to the baseline K values that drain faster than average soils. Analyzing a higher K will not allow for much more infiltration; it is already high and will only affect the short duration/high intensity events (1 h, 2 h duration). However, as K is reduced, infiltration rates become significantly less, so even the lower intensity/longer duration events will surpass infiltration rate capacity and produce significantly more runoff. In nature, conductivity values may vary between 0.001 and 100 + mm h−1. Even smaller K values possible, but at those values they are essentially impervious from the perspective of runoff prediction during rainfall events. To get a more detailed appreciation for the effect of K, runoff ratios and peak flow values are listed in Table 4. The runoff ratio values increase significantly for most of the events as peak flow as well. An additional factor that influences both results is the change in soil suction values that automatically occur when K changes. As K is reduced, HEC-HMS interprets this as a change in soil type; therefore, wetting front suction values change. Increasing K did not trigger a change in soil type and suction value. This contributed to the asymmetrical results as well.

Table 4.

Runoff ratio and peak flow values for reduced conductivity, K, for all events

Rainf. eventRunoff Ratio (%)Qmax (m3 s−1)
K = 35 mm h−1K = 17.5 mm h−1K = 8.25 mm h−1K = 35 mm h−1K = 17.5 mm h−1K = 8.25 mm h−1
1%, 1 h29.5647.661.293.35146187
1%, 2 h12.7232.350.243.68113172
1%, 4 h10.0117.737.930.9163135
1%, 6 h10.0010.529.127.4229100
1%, 8 h9.609.621.624.542673
1%, 12 h10.0010.013.720.362037

The effects of impervious areas on runoff ratio and peak flow are shown in Fig. 9. Note that the x-axis is percentage change of impervious area from a baseline of 10%. This means the full range of percent impervious area shown on the x-axis ranges 0%–20%. On both Figs 9a and 9b, the results for 4, 6, 8, and 12 h events give the same results. Changes in runoff ratio and peak flow in absolute values are symmetrical, which means increasing or decreasing the impervious area produces a proportional change in runoff ratio or peak flow. Increasing impervious area increases both results and decreasing area decreases both. Changes to impervious area impacts results from the 1 h duration rainfall event the least (27% difference, for 100% change). For events longer than 2 h duration rainfall, the impact is the same: for a 100% impervious area change PCRUN and PCQP are both 100%. This indicates 4–12 h rainfall events are more sensitive to change in impervious area.

Fig. 9.
Fig. 9.

Effect of changing impervious area on a) runoff ratio and b) peak flow

Citation: Pollack Periodica 2023; 10.1556/606.2022.00713

6 Summary

The results of the sensitivity analyses are shown in Table 5.

Table 5.

Summary of results

ParameterResults of sensitivity study
Runoff ratioPeak flow
Hydraulic conductivityHighly sensitive if under-calibrated on grainy soil (asymmetrical)Highly sensitive if under-calibrated on grainy soil (asymmetrical)
Ratio of impervious areaSymmetrically sensitiveSymmetrically sensitive highly

Runoff ratio, and peak flow became more sensitive to hydraulic conductivity, as the values were lowered. Although in this study the hydraulic conductivity was changed in a way that they became different soil types, underestimation of hydraulic conductivity has the risk of resulting much higher peak flow than will occur.

The percentage of impervious area shows a symmetrical, similar percent of change both ways for each sensitivity study. The change is higher at longer duration events for both runoff ratio, and peak flow.

7 Conclusion

The result of the sensitivity analyses show that the peak flow of the 1 h event is much greater than the other peak flows on the investigated stream. Possible reasons for this:

  1. the effect of infiltration rate is significantly smaller than the rainfall intensity;

  2. the higher slopes;

  3. size and shape of the watershed. In the interval between 1 and 2 h rainfall duration the infiltration capacity of the soil starts to increase thereby significantly decreasing the peak flow.

For events longer than 2 h, runoff ratio, and runoff volume did not change very much. The runoff ratio significantly decreased after the 1-h event and stabilized around 10%. Also the 1 h rainfall causes higher peak flow than expected.

Change in percentage of impervious area had an inversely proportional effect on the peak flow and runoff ratio. The impervious area during flash-flood events is defined as constant, but it's possible that the area changes during short duration rainfall events since small depressions and ponds on a watershed could temporarily increase the impervious area. Further research connecting hydrologic and hydro-dynamical models can help to determine how this parameter affects the surface flow processes.

References

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    L. Varga, K. Buzás, and M. Honti, “New rainfall-maximum functions” (in Hungarian), Hidrológiai Közlöny, vol. 96, no. 2, pp. 64–69, 2016.

    • Search Google Scholar
    • Export Citation
  • [2]↑

    F. Alsilibe and K. Bene, “Watershed subdivision and weather input effect on streamflow simulation using SWAT model,” Pollack Period., vol. 17, no. 1, pp. 88–93, 2021.

    • Search Google Scholar
    • Export Citation
  • [3]↑

    K. Mátyás and K. Bene, “Using numerical modeling error analysis methods to indicate changes in a watershed,” Pollack Period., vol. 13, no. 3, pp. 175–186, 2018.

    • Search Google Scholar
    • Export Citation
  • [4]↑

    H. A. P. Hapuarachi, Q. J. Wang, and T. C. Pagano, “A review of advances in flash-flood forecasting,” Hydrol. Process., vol. 25, no. 18, pp. 2771–2784, 2011.

    • Search Google Scholar
    • Export Citation
  • [5]↑

    J. A. B. Cánovas, M. Eguibar, J. M. Bodoque, A. Díez-Herrero, M. Stoffel, and I. Gutiérrez-Pérez, “Estimating flash-flood discharge in an ungauged mountain catchment with 2D hydraulic models and dendrogeomorphic palaeostage indicators,” Hydrol. Process., vol. 25, no. 6, pp. 970–979, 2011.

    • Search Google Scholar
    • Export Citation
  • [6]↑

    G. Ámon and I. Dukay, “Water resources and ecologycal planning project on Morgó-creek” (in Hungarian), Stream Engineering Ltd. and Renatur 2005 Ltd., 2019. [Online]. Available: shorturl.at/efgvw. Accessed: April 30, 2021.

    • Search Google Scholar
    • Export Citation
  • [7]↑

    M. Bartles, T Brauer, D. Ho, M. Fleming, G. Karlovits, J. Pak, N. Van, and J. Willis, Hydrologic Modeling System HEC-HMS, User's Manual. California: US Army Corps of Engineering, Hydrologic Engineering Center, 2021.

    • Search Google Scholar
    • Export Citation
  • [8]↑

    G. W. Brunner, HEC-RAS, River Analysis System, Hydraulic Reference Manual. California: US Army Corps of Engineering. Hydrologic Engineering Center, 2016.

    • Search Google Scholar
    • Export Citation
  • [9]↑

    A. Rinaldi and R. Febrina, “Green-ampt and Horton equation,” 2015. [Online]. Available: https://doi.org/10.13140/RG.2.1.1041.7527. Accessed: April 30, 2021.

    • Search Google Scholar
    • Export Citation
  • [10]

    C. Tzimopoulos, G. Papaevangelou, K. Papadopoulos, and C. Evangelides, “New explicit form of green and Ampt model for cumulative infiltration estimation,” Res. J. Environ. Sci., vol. 14, no. 1, pp. 30–41, 2020.

    • Search Google Scholar
    • Export Citation
  • [11]↑

    A. Akhtar, W. J. Zhang, and J. Li, “Field scale ponding infiltration assessment using modified Green-Ampt approach,” Pakistan J. Agric. Sci., vol. 57, no. 5, pp. 1459–1468, 2020.

    • Search Google Scholar
    • Export Citation
  • [12]↑

    J. Szilágyi, “On the Clark unit hydrograph model of HEC-HMS,” Periodica Polytechnica, Civil Eng., vol. 62, no. 1, pp. 277–279, 2018.

    • Search Google Scholar
    • Export Citation
  • [13]↑

    M. Salarijaz, K. Ghorbani, and M. Abdolhosseini, “Estimation of Clark's instantaneous unit hydrograph parameters,” 2016. [Online]. Available: https://www.researchgate.net/publication/336989940. Accessed: December 17, 2022.

    • Search Google Scholar
    • Export Citation
  • [14]↑

    E. D. Nagy, J. Szilágyi, and P. Torma, “Assessment of dimension-reduction and grouping methods for catchment response time estimation in Hungary,” J. Hydrol. Reg. Stud., vol. 38, 2021, Paper no. 100971.

    • Search Google Scholar
    • Export Citation
  • [15]↑

    E. D. Nagy and J. Szilágyi, “Revision of the Wisnovszky-equation on Hungarian watersheds with examination of the time of concentration and numerous morphological preferences” (in Hungarian), Hungarian J. Hydrol., vol. 101, no. 1, pp. 19–32, 2021.

    • Search Google Scholar
    • Export Citation
  • [16]↑

    G. Ámon and K. Bene, “Adaptive data parameterization of base-flow and flashflood models of an ungagged watershed,” in 15th Miklos Ivanyi International PhD-DLA Symposium, Pecs, Hungary, October 28–29, 2019, Paper no. P–67.

    • Search Google Scholar
    • Export Citation
  • [1]

    L. Varga, K. Buzás, and M. Honti, “New rainfall-maximum functions” (in Hungarian), Hidrológiai Közlöny, vol. 96, no. 2, pp. 64–69, 2016.

    • Search Google Scholar
    • Export Citation
  • [2]

    F. Alsilibe and K. Bene, “Watershed subdivision and weather input effect on streamflow simulation using SWAT model,” Pollack Period., vol. 17, no. 1, pp. 88–93, 2021.

    • Search Google Scholar
    • Export Citation
  • [3]

    K. Mátyás and K. Bene, “Using numerical modeling error analysis methods to indicate changes in a watershed,” Pollack Period., vol. 13, no. 3, pp. 175–186, 2018.

    • Search Google Scholar
    • Export Citation
  • [4]

    H. A. P. Hapuarachi, Q. J. Wang, and T. C. Pagano, “A review of advances in flash-flood forecasting,” Hydrol. Process., vol. 25, no. 18, pp. 2771–2784, 2011.

    • Search Google Scholar
    • Export Citation
  • [5]

    J. A. B. Cánovas, M. Eguibar, J. M. Bodoque, A. Díez-Herrero, M. Stoffel, and I. Gutiérrez-Pérez, “Estimating flash-flood discharge in an ungauged mountain catchment with 2D hydraulic models and dendrogeomorphic palaeostage indicators,” Hydrol. Process., vol. 25, no. 6, pp. 970–979, 2011.

    • Search Google Scholar
    • Export Citation
  • [6]

    G. Ámon and I. Dukay, “Water resources and ecologycal planning project on Morgó-creek” (in Hungarian), Stream Engineering Ltd. and Renatur 2005 Ltd., 2019. [Online]. Available: shorturl.at/efgvw. Accessed: April 30, 2021.

    • Search Google Scholar
    • Export Citation
  • [7]

    M. Bartles, T Brauer, D. Ho, M. Fleming, G. Karlovits, J. Pak, N. Van, and J. Willis, Hydrologic Modeling System HEC-HMS, User's Manual. California: US Army Corps of Engineering, Hydrologic Engineering Center, 2021.

    • Search Google Scholar
    • Export Citation
  • [8]

    G. W. Brunner, HEC-RAS, River Analysis System, Hydraulic Reference Manual. California: US Army Corps of Engineering. Hydrologic Engineering Center, 2016.

    • Search Google Scholar
    • Export Citation
  • [9]

    A. Rinaldi and R. Febrina, “Green-ampt and Horton equation,” 2015. [Online]. Available: https://doi.org/10.13140/RG.2.1.1041.7527. Accessed: April 30, 2021.

    • Search Google Scholar
    • Export Citation
  • [10]

    C. Tzimopoulos, G. Papaevangelou, K. Papadopoulos, and C. Evangelides, “New explicit form of green and Ampt model for cumulative infiltration estimation,” Res. J. Environ. Sci., vol. 14, no. 1, pp. 30–41, 2020.

    • Search Google Scholar
    • Export Citation
  • [11]

    A. Akhtar, W. J. Zhang, and J. Li, “Field scale ponding infiltration assessment using modified Green-Ampt approach,” Pakistan J. Agric. Sci., vol. 57, no. 5, pp. 1459–1468, 2020.

    • Search Google Scholar
    • Export Citation
  • [12]

    J. Szilágyi, “On the Clark unit hydrograph model of HEC-HMS,” Periodica Polytechnica, Civil Eng., vol. 62, no. 1, pp. 277–279, 2018.

    • Search Google Scholar
    • Export Citation
  • [13]

    M. Salarijaz, K. Ghorbani, and M. Abdolhosseini, “Estimation of Clark's instantaneous unit hydrograph parameters,” 2016. [Online]. Available: https://www.researchgate.net/publication/336989940. Accessed: December 17, 2022.

    • Search Google Scholar
    • Export Citation
  • [14]

    E. D. Nagy, J. Szilágyi, and P. Torma, “Assessment of dimension-reduction and grouping methods for catchment response time estimation in Hungary,” J. Hydrol. Reg. Stud., vol. 38, 2021, Paper no. 100971.

    • Search Google Scholar
    • Export Citation
  • [15]

    E. D. Nagy and J. Szilágyi, “Revision of the Wisnovszky-equation on Hungarian watersheds with examination of the time of concentration and numerous morphological preferences” (in Hungarian), Hungarian J. Hydrol., vol. 101, no. 1, pp. 19–32, 2021.

    • Search Google Scholar
    • Export Citation
  • [16]

    G. Ámon and K. Bene, “Adaptive data parameterization of base-flow and flashflood models of an ungagged watershed,” in 15th Miklos Ivanyi International PhD-DLA Symposium, Pecs, Hungary, October 28–29, 2019, Paper no. P–67.

    • Search Google Scholar
    • Export Citation
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  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2021  
Web of Science  
Total Cites
WoS		 not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed
Impact Factor
without
Journal Self Cites		 not indexed
5 Year
Impact Factor		 not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed
Scimago  
Scimago
H-index		 12
Scimago
Journal Rank		 0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score		 1,5
Scopus
CIte Score Rank		 Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP		 0,613

2020  
Scimago
H-index		 11
Scimago
Journal Rank		 0,257
Scimago
Quartile Score		 Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score		 340/243=1,4
Scopus
Cite Score Rank		 Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP		 1,09
Scopus
Cites		 321
Scopus
Documents		 67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate		 48%

 

2019  
Scimago
H-index		 10
Scimago
Journal Rank		 0,262
Scimago
Quartile Score		 Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score		 269/220=1,2
Scopus
Cite Score Rank		 Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP		 0,933
Scopus
Cites		 290
Scopus
Documents		 68
Acceptance
Rate		 67%

 

Pollack Periodica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
Print + online subscription: 405 EUR / 492 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

 

Pollack Periodica
Language English
Size A4
Year of
Foundation		 2006
Volumes
per Year		 1
Issues
per Year		 3
Founder Akadémiai Kiadó
Founder's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Oct 2022 0 0 0
Nov 2022 0 0 0
Dec 2022 0 0 0
Jan 2023 0 0 0
Feb 2023 0 0 0
Mar 2023 0 69 38
Apr 2023 0 0 0