Methodology for Identifying the Best Equations for Estimating the Time of Concentration of Watersheds in a Particular Region

Abstract

In this paper, a methodology is proposed for identifying the best equations for estimating the time of concentration (ToC) of watersheds located in a specific geographic region. The methodology primarily works on the basis of modifying available equations to minimize their bias for any particular region of interest. In the methodology, for each watershed, the ToC considered as "reference" is determined using the Natural Resource Conservation Service (NRCS) velocity method employed in the TR-55 model and geographic information systems data. Moreover, a ranking-based selection strategy is developed for evaluating and selecting the best performing methods. In a case study, the proposed methodology is applied to 72 watersheds and subwatersheds in Khorasan Razavi province, Iran. According to the study, the California equation (with a correction factor of 1.715), the Kirpich (Tennessee) equation (with a correction factor of 1.721), and the Arizona DOT equation (with a correction factor of 1.126) have the best performance for the selected watersheds.

Effect of the Non-Stationarity of Rainfall Events on the Design of Hydraulic Structures for Runoff Management and Its Applications to a Case Study at Gordo Creek Watershed in Cartagena de Indias, Colombia

“…It is one of the most critical parameters for any hydrological analysis and the subsequent design of a hydraulic structure aimed at managing stormwater. Many methodologies (empirical and semi-empirical) have been proposed for its estimation [24,25]. For this study, the Kirpich method was used (Equation (6)) [26], as the Gordo creek watershed fits some of the morphometric characteristics needed for using this formula [25].…”

“…The goodness-of-fit test of Akaike Information Criterion (AIC), with values of 753.3721 and 747.5103 for stationary and non-stationary conditions respectively, showed that the latter best depicts the increasing rainfall pattern. Values of rainfall were later estimated for different return periods (2,5,10,25, 50, and 100 years) to quantify the increase (non-stationary versus stationary condition), which ranged 6% to 12% for return periods from 5 years to 100 years, and 44% for a 2-year return period. The effect of these findings were tested in the Gordo creek watershed by first calculating the resulting direct surface runoff (DSR) for various return periods, and then modeling the hydraulic behavior of the downstream area (composed of a 178.5-m creek's reach and an existing box-culvert located at the watershed outlet) that undergoes flooding events every year.…”

“…The studies that carry out assessments for stationary (SC) versus non-stationary (NSC) conditions of streamflow and/or rainfall, typically encompass only performing a statistical analysis (of a trend) without quantifying how the two scenarios may affect real life conditions, both hydrologically and hydraulically speaking. In this study, multiannual 24-h maximum rainfall (P 24h-max ) data (from the synoptic weather station located at the Rafael Núñez airport) was used to: (a) assess the trend of the P 24h-max observations over time, specifically for Cartagena de Indias, (b) quantify the rainfall values obtained for several return periods (2,5,10,25,50, and 100 years) via stationary and non-stationary frequency analyses, (c) carry out a hydrological and hydraulic analysis on the Gordo creek watershed under SC and NSC for different return periods in order to understand the recurring floods reported that affect a commercial and industrial area downstream, and (d) point out the importance of accounting for the effect of climate change in the decision making process in runoff management, especially in flood-prone areas.…”

Analysis of a Flash Flood in a Small Basin in Crete

“…Therefore, T c was equal to 3.28 h regarding the Giandotti's formula, whereas it was equal to 1.79 h with respect to the Kirpich's equation. Adjustment factors are superposed by many scientists thus far, in order to bias correct the estimations of T c , due to application of the aforementioned empirical formulas to catchments of different location and geomorphology of the ones originally fitted [42][43][44]. A correction factor of 2.0 may be applied to general overland flow and natural grass channels, whereas a correction factor of 1.0 can be adjusted to overland flow on bare soil or roadside ditches [32].…”

“…Not having observed runoff data at the catchment's outlet, we could not use statistical indices such as Nash-Sutcliffe to calibrate the model. Instead, we collected the most considerable runs (42) in order to better approach the aforementioned calibration and validation criterion. We believe that with the method followed, we approached quite sufficiently the range of uncertainty of the resulted hydrograph.…”