Fitting a simplified two-parameter gamma distribution function for synthetic sediment graph derivation from ungauged catchments

Singh, Pushpendra Kumar; Jain, Manoj; Mishra, Sumeet

doi:10.1007/s12517-011-0473-6

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…The function used to fit observed FDM data in this study is the two‐parameter beta distribution function, modified for purposes herein. Distribution functions have been widely used for synthetic unit hydrographs ( e.g ., Bhunya et al ., , , ) as well as deriving a synthetic sediment graph (Singh et al ., ). The beta distribution function is noted as the preferred method for deriving a synthetic unit hydrograph due to the flexible nature of the shape and skew (Bhunya et al ., ).…”

Section: Methodsmentioning

confidence: 99%

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Webb

Fassnacht

Gooseff

2017

J American Water Resour Assoc

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Snow is an important component of the hydrologic cycle for many regions worldwide. In addition to vital water resources, snowmelt can be important for forest ecosystem dynamics and flood risk. However, standard design events in the United States lack a design snowmelt event, including only precipitation events, though snowmelt has been shown to be larger than rainfall. In this article, we present a method using hourly snow water equivalent data to develop and test a function for representing the diurnal pattern of snowmelt. A two‐parameter beta distribution function is modified for the purposes of this study and found to fit the pattern of snowmelt well with a root mean squared error of 0.008. Soil moisture sensors were additionally utilized to assess the timing of the snowmelt water outflow from the base of the snowpack that supports the shape of the function, but suggests that the timing of losses recorded on snow pillows lag as much as 3 h. Further testing of the function showed the shape of the function to be accurate. The methods developed and tested in this paper can be applied for design purposes comparing snowmelt and rainfall events or to improve hydrological models investigating processes such as streamflow or groundwater recharge.

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Section: Methodsmentioning

confidence: 99%

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Webb

Fassnacht

Gooseff

2017

J American Water Resour Assoc

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“…In estimating the temporal variation of SS for a storm, the Instantaneous Unit Hydrograph (IUH) has been used to produce Instantaneous Unit Sediment Graph (IUSG) and then provide SG (Banasik and Mitchell 2008;Bhunya et al 2010;Banasik and Hejduk 2014). Other methods, such as regression analysis (Sadeghi and Saeidi 2010), power model (Bhunya et al 2010), two-parameter gamma distribution function (Singh et al 2013), and SCS-CN method with IUSG (Gupta et al 2019), have also been used for deriving the synthetic SG. The SG has been simulated using hydrograph (Saeedi et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Synthetic Sediment Graph Developed using Various Modified Time-Area Methods

Katebikord

Sadeghi

Singh

2022

GES

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Suspended sediment (SS) is an essential indicator for assessing watershed health. However, the temporal variation of SS, called sediment graph (SG) using readily available data, is not always considered, particularly in un-gauged watersheds, which are many in developing countries. Since field measurements of SS are time-consuming and costly, the synthetic SG seems to be a promising alternative. Therefore, it is essential to have reliable SS data for watershed management. This study aimed at simulating SGs through conceptual analysis of soil erosion and sediment yield at the watershed scale. To that end, soil erosion, sediment yield, and sediment routing were modeled using 38 storm events collected during 2011 and 2019 at the Galazchai Watershed in West Azerbaijan Province, Iran. Initially, the Time-Area Method (TAM) was applied, and then two strategies were considered to improve the TAM performance, including RUSLE and sediment delivery ratio (SDR) using gradient ratio and WaTEM/SEDEM methods. Comparing simulated SGs with recorded ones showed that the SDR-based method had the lowest relative error in time to peak and base time, but the peak value had the highest relative error. Results also showed that TAM developed using the spatially distributed travel time method had a better performance than the channel longitudinal profile method. Overall, TAM could not simulate the temporal variation of sediment and needs further research.

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