Características de los balastos electrónicos

The performance of SF models can be improved through a better understanding of the hydrological systems of a river basin such as analyzing the SF regime (Gautam & Acharya, 2012). The SF model performance can also be significantly improved by selecting the most adequate input and output variables, which mainly depends on Lt estimation; therefore, it is of great significance in several surface water hydrological analyses and models (Bowden et al., 2005; Fang et al., 2008; Yao et al., 2014).

In this research, WL and RF records of the upstream station are used as the input variables of the AI-based model, whereas the SF data from the downstream station are used as the output variables of the AI-based model. Therefore, estimating the Lt between the

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upstream and downstream stations is a fundamental step in determining a potential combination of input and output variables for AI-based models. It is also important in exploring the sensitivity of various combinations of input and output variables to the prediction accuracy of AI-based models.

2.7.4.1 Analysis of Long-Term Variations in Stream Flow Regimes

SF regimes can be described by various parameters such as rate, magnitude, duration, timing and fluctuations over a varied scale of frequencies, including hourly, daily, monthly, yearly, decadal and multi- decadal (Krasovskaia & Gottschalk, 2002; Morán- Tejeda et al., 2011). Investigation of these parameters assists in understanding the whole SF regime and related hydrological phenomena, such as low and high SF events. Furthermore, the long-term variations in a SF regime can be recognized clearly based on the description of SF through these parameters (Poff et al., 1997; Richter, 1996; Yang et al., 2005).

Due to the variations of SF, long-period records of SF are essential to investigate and describe the SF regime. The statistical analyses used in long-term variations in SF regimes should be performed using long-period records (i.e. 50 years or more) as trends resulted from short observations may be part of weather fluctuations or just temporary changes (Gautam & Acharya, 2012; Kundzewicz & Robson, 2004; Opitz-Stapleton & Gangopadhyay, 2011).

There are many reasons why variations in SF appear, for example, climate change, human activities and geomorphic variations, which are possibly the main sources of SF change (Chang et al., 2014; Sang et al., 2013). Usually the changes in SF grow slowly; over the last 100-year period, an apparent decline in yearly SF has been verified in about 25% of the world’s rivers (Descroix et al., 2012; Walling & Fang, 2003; Yang et al., 2005; Yue

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Studying the changes in the SF regime is essential to enhance understanding the river hydrologic system which is necessary for the improvement of accuracy of SF and flood prediction (Gautam & Acharya, 2012; Xu et al., 2012). It is not only important from hydrologic aspects but also from both socioeconomic and natural aspects. As example, ecosystems are highly influenced by variations in SF regime because they are dependent on SF to protect their composition and continuity (Richter, 1996).

In the view of the above discussion, the better analysis of SF regimes is considered a very important step to improve the researches knowledge about the SF process which leading to improve the performance of SF modelling and prediction processes.

2.7.4.2 Lag Time Estimation

The travel time concept is used to estimate the time needed by the flow to move from any location to another within the river basin. This notion is frequently employed in many hydrological applications. Due to developments in hydrologic models and applications, various expressions of travel time have been adopted and often used, such as concentration time and Lt (Green & Nelson, 2002; Honarbakhsh et al., 2012; Thomas et al., 2015).

The travel time concept could be described by two ways: the hydrological (operational) and conceptual (theoretical) definitions. The hydrological definitions are applicable when hydrological data is available (Fang et al., 2005). The conceptual definition for time of concentration is the period taken by a water to move from the hydraulically most distal part of the basin to the outlet or reference point downstream (Fang et al., 2005; Kirpich, 1940; McCuen et al., 1984). The hydraulically most distant point is the point with the longest travel time to the basin outlet, and not necessarily with the longest flow path. The theoretical definition of Lt is the time a water drop takes to travel from an upstream location to a downstream location within river basin (Woodward, 2010).

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Travel time reflects the speed at which the river basin responds to RF events (Pavlovic & Moglen, 2008) and is influenced by several parameters including the slope and length of the flow path, flow path roughness, flow depth, initial soil moisture, and duration and intensity of the effective RF (Green & Nelson, 2002; McCuen, 2009; Singh, 1988). These parameters are very complex and thus render estimation difficult and time consuming. Due to the complexity of describing all physical and hydrological specifications of the entire flow path and other basic parameters affecting travel time, many empirical equations for Lt estimation have been derived based on flow path and basin average parameters to simplify travel time estimation (Green & Nelson, 2002; Singh, 1976). Hydrologically, a perfect estimation of travel time cannot be achieved, as it requires infinite, steady and continuous RF over the river basin, which is an impossible condition in reality (Saghafian & Julien, 1995).

 Definition of Lag Time

In hydrological modelling applications, travel time is generally represented by Lt. The main hydrological definition of Lt is the difference in time between the center of mass of effective RF and the center of mass of direct runoff (i.e. hydrograph) produced by the effective RF (Banasik et al., 2005; Viessman & Lewis, 2003).

Several other hydrological definitions of Lt between the WL upstream station and SF downstream station can be handled, which are reported by (Viessman & Lewis, 2003), (Fang et al., 2005), (Honarbakhsh et al., 2012), (Grimaldi et al., 2012), (Talei & Chua, 2012) and referenced herein:

(1) The time interval from the time of maximum WL rate to the time of the peak of hydrograph of SF station;

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(2) The time interval from the time of the centroid of actual WL excess to the time of the peak of hydrograph;

(3) The time from the end of WL excess to the inflection point on hydrograph; and

(4) The time interval from the beginning of WL excess to the centroid of hydrograph.

The hydrological definitions of Lt between the RF upstream station and SF downstream station are similar with the hydrological definitions between the WL upstream station and SF downstream station and they are based on the same hydrological concepts. It should be noted that for these definitions to be applicable to estimating the Lt between two locations or between two stations within the river basin, they must be hydraulically connected without any significant non-natural barriers (Viessman & Lewis, 2003). Figure 2.13 describes two of definitions of Lt. (1) Time interval from the centroid of the RF to the centroid of hydrograph (t1); and (2) time interval from the centroid of RF to the

peak of hydrograph (t2) (Talei & Chua, 2012).

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Figure 2.13: Schematic illustration of lag time estimation based on two different

definitions (Talei & Chua, 2012)  Lag Time Estimation Methods

In literature, three main approaches have been applied for estimating the Lt, first of which is the estimation using empirical formulas. Huge number of empirical formulas have been used to estimate Lt, Li and Chibber (2008) presented and evaluated about fourteen empirical formulas with different data requirements. In the current research, four empirical formulas were employed to estimate the Lt between upstream stations, and a downstream station.

The second approach is to estimate Lt by calculating the R between WL or RF hourly records of upstream stations and Q records of downstream station. The Lt is thus defined as the lag interval that required in providing the highest R between the upstream and downstream records. This method is not completely in high agreement with the classic definitions of Lt, but can be measured to provide an approximation of the Lt.

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The third approach is to estimate the Lt based on the observed data of WL or EF and SF through one of the hydrological (operational) definitions of Lt. In the current research, the first hydrological definition of Lt was employed to estimate the Lt between upstream stations, and a downstream station.

 Influence of the hydrological parameters on the Lag Time

Lt between two places among the river basin could be different due to many hydrological parameters such as basin, flow path and RF characteristics. The basin parameters that affect the Lt are areal extent, surface topographic, vegetation, and land use. The flow path characteristics that affect the Lt are slope, length, roughness, flow depth and antecedent soil moisture. RF characteristics that affect the Lt are intensity and duration. There are other parameters may be slightly affect the Lt, such as wind speed, relative humidity and climate conditions (Green & Nelson, 2002; McCuen, 2009; Sabzevari et al., 2010; Singh, 1988). These parameters are very complex, thus making it difficult and time consuming to study.

Due to the complexity of description all physical and hydrological characteristics of the entire flow path and other parameters influencing the Lt; many empirical equations and estimation approaches have been derived based on the flow path and basin average parameters to simplify the estimation of the Lt (Green & Nelson, 2002; Singh, 1976).

Although the availability of empirical equations to estimate the Lt (Grimaldi et al., 2012; Li & Chibber, 2008), the influence of the hydrological parameters that are likely affecting the Lt such as RF and SF have not been studied intensively.

The investigation of the influence of the hydrological parameters that are likely affecting the Lt is very important key in SF modelling and detection the times of high SF events. Mostly, the Lt reflects the speed at which the river basin responds to RF events (Pavlovic & Moglen, 2008).

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The RF and SF are considered as the main variables affecting the Lt. To investigate the effect of these parameters on the Lt, RF intensity was represented by two variables, peak rainfall intensity (Rfp) and the average of previous 48 hour rainfall (Rf48) while the SF

was represented by two variables, peak hourly stream flow (Qp) and the average of previous 48 hour stream flow (Q48). Rf48 andQ48 are used to represent the degree of

saturation in the river basin (Simas, 1996).

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