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In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The classification of floods may be a supporting tool for decision-makers in regard to water management, including flood protection. The main objective of this work is the classification of flood generation mechanisms in 28 catchments of the upper Vistula basin. A significant innovation in this study lies in the utilization of decision trees for flood classification. The methodology has so far been applied in the Alpine region. The analysis reveals that peak daily precipitation in the catchments mainly occurs in summer, particularly from June to August. Maximal daily snowmelt typically happens at the end of winter (March to April) and occasionally in November. Winter peaks are observed in March to April and, in some areas, in November to December, while summer peaks occur in May and, in specific catchments, in October. Higher peak flows for annual floods are noted in March to April and June to August. Most annual floods in the Upper Vistula basin are classified as Rain-on-Snow Floods (RoSFs) or Lowland River Floods (LRFs). LRFs contribute from 19% to almost 72%, while RoSFs range from 18% to 75%. In Season 1 (summer), most seasonal floods are identified as LRFs (51%-100%), with very few as RoSFs (0%-46.9%). In Season 2 (winter), the opposite pattern is observed, with most RoSFs (48.4%-97.9%) and fewer LRFs (0%-20.6%). While there are changes in flood patterns, they are not statistically significant. Conducted studies and obtained results can be useful for the preparation of flood prevention documentation and for flood management in general., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Hydrology and meteorological data from relatively undisturbed watersheds aid in identifying effects on ecosystem services, tracking hydroclimatic trends, and reducing model uncertainties. Sustainable forest, water, and infrastructure management depends on assessing the impacts of extreme events and land use change on flooding, droughts, and biogeochemical processes. For example, global climate models predict more frequent high-intensity storms and longer dry periods for the southeastern USA. We summarized 17 years (2005–2021) of hydrometeorological data recorded in the 52 km2, third-order Turkey Creek watershed at the Santee Experimental Forest (SEF), Southeastern Coastal Plain, USA. This is a non-tidal headwater system of the Charleston Harbor estuary. The study period included a wide range of weather conditions; annual precipitation (P) and potential evapotranspiration (PET) ranged from 994 mm and 1212 mm in 2007 to 2243 mm and 1063 in 2015, respectively. The annual runoff coefficient (ROC) varied from 0.09 in 2007 (with water table (WT) as deep as 2.4 m below surface) to 0.52 in 2015 (with frequently ponded WT conditions), with an average of 0.22. Although the average P (1470 mm) was 11% higher than the historic 1964–1976 average (1320 mm), no significant (α= 0.05) trend was found in the annual P (p = 0.11), ROC (p = 0.17) or runoff (p = 0.27). Runoff occurred on 76.4% of all days in the study period, exceeding 20 mm/day for 1.25% of all days, mostly due to intense storms in the summer and lower ET demand in the winter. No-flow conditions were common during most of the summer growing season. WT recharge occurred during water-surplus conditions, and storm-event base flow contributed 23–47% of the total runoff as estimated using a hydrograph separation method. Storm-event peak discharge in the Turkey Creek was dominated by shallow subsurface runoff and was correlated with 48 h precipitation totals. Estimated precipitation intensity–duration–frequency and flood frequency relationships were found to be larger than those found by NOAA for the 1893–2002 period (for durations ≥ 3 h), and by USGS regional frequencies (for ≥10-year return intervals), respectively, for the same location. We recommend an integrated analysis of these data together with available water quality data to (1) assess the impacts of rising tides on the hydroperiod and biogeochemical processes in riparian forests of the estuary headwaters, (2) validate rainfall–runoff models including watershed scale models to assess land use and climate change on hydrology and water quality, and (3) inform watershed restoration goals, strategies, and infrastructure design in coastal watersheds. [ABSTRACT FROM AUTHOR]

The study evaluated the applicability of Event-Based Approach for Small and Ungauged Basins (EBA4SUB) for calculating annual peak flows with a specific return period (QT) in southern Poland. Data used in the calculations in a form of observation series of annual peak flows were derived from the Institute of Meteorology and Water Management in Warsaw and covered a multi-year period 1971-2015. The data were statistically verified for their homogeneity, significance of monotonic trends, outliers and equality of variances. Peak flows with a given return period were estimated by a statistical method of Pearson Type III distribution, and by EBA4SUB model. The analysis showed that QT for the investigated catchments was the most accurately matching the values derived from the statistical method when EBA4SUB model was employed. This was evidenced by the values of average relative errors that reached 34% for EBA4SUB model (with beta hyetograph). The results of the study demonstrated usefulness of EBA4SUB model for the estimation of QT quantiles in catchments of the upper Vistula water region. [ABSTRACT FROM AUTHOR]

Comparison of quality of Snyder's model for determination flood waves was examination in this work. Model parameters were calibrated based on objective functions: percentage error in peak flow ( PEPF), percentage error in volume ( PEV), peak-weighted root mean square error ( PWRMSE), sum of absolute residuals ( SAR) and sum of squared residuals ( SSR). Quality of model was calculating by Nash-Sutcliffe efficiency coefficient E. Additionally sensitivity of a model was characterized by its flexibility. The analyses were performed in the watershed of Grabinka. It has been found that the use PWRMSE as the objective function allows to obtain the best quality results of simulation. Furthermore, Snyder's model is flexible to the change of Cp coefficient. [ABSTRACT FROM AUTHOR]

The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment. The aim of this paper was to determine the influence of a hydrological model structure and rainfall- -related data on flood parameters obtained from a simulation. The study included an upland river Stobnica, right tributary of the Wisłok. The following assumptions were investigated: (i) the greater number of rainfall stations, the more accurate a flood description, i.e. the resulting hydrograph much better describes the actual flood, (ii) a distributed parameter model provides a more precise description of a catchment response to rainfall than a lumped parameter model. All calculations were performed using HEC-HMS 3.4 software. The analyses showed that increasing the number of rainfall stations slightly improved the model performance (by on average 4.1%). Furthermore, it was showed that in the catchment characterized by low topographical variability and stable land use, more reliable flood simulation results were obtained in the lumped parameter model than in the distributed parameter model. Considering the calibration process slightly improved the model performance, irrespective of its structure and the number of rainfall stations. Multivariate analysis of variance (MANOVA) revealed that the resulting differences in the model efficiency for individual variants were not significant. Considering limited empirical evidence on rainfall-runoff episodes, uncertainty of these results is probably high and thus they should be treated as a starting point for further studies. [ABSTRACT FROM AUTHOR]

The paper compares the results of automatic calibration of Clark's synthetic unit hydrographs. Optimal values of model parameters were determined by the objective functions: percentage error in peak flow (PEPF), percentage error in volume (PEV), peak-weighted root mean square error (PWRMSE), sum of absolute residuals (SAR) and sum of squared residuals (SSR). The last part of the analysis assesses the flexibility of studied model. The research was performed in the upland watershed of Grabinka -- left tributary of the Wisłoka river located in Southern Poland. The analysis reveals that the smallest differences between the maximum flow in the observed and calculated flood culmination were obtained when applying PWRMSE function. This paper also indicates, that Clark's model was efficient for describing the analyzed floods. [ABSTRACT FROM AUTHOR]

The aim of the work was to develop a new empirical model for calculating the peak annual flows of a given frequency of occurrence (QT) in the ungauged catchments of the upper Vistula basin in Poland. The approach to the regionalization of the catchment and the selection of the optimal form of the empirical model are indicated as a novelty of the proposed research. The research was carried out on the basis of observation series of peak annual flows (Qmax) for 41 catchments. The analysis was performed in the following steps: statistical verification of data; estimation of Qmax flows using kernel density estimation; determination of physiographic and meteorological characteristics affecting the Qmax flow volume; determination of the value of dimensionless quantiles for QT flow calculation in the upper Vistula basin; verification of the determined correlation for the calculation of QT flows in the upper Vistula basin. Based on the research we conducted, we found that the following factors have the greatest impact on the formation of flood flows in the upper Vistula basin: the size of catchment area; the height difference in the catchment area; the density of the river network; the soil imperviousness index; and the volume of normal annual precipitation. The verification procedure that we performed made it possible to conclude that the developed empirical model functions correctly. [ABSTRACT FROM AUTHOR]