Soil Moisture Accounting (SMA) based sediment graph models for small watersheds

Gupta, Sushindra Kumar; Tyagi, Jaivir; Singh, Pratap; Sharma, Geeta; Jethoo, Ajay Singh

doi:10.1016/j.jhydrol.2019.04.077

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Due to its popularity, the SCS-CN method has been the object of many studies. These studies focused on application of the method, e.g., in rainfall-runoff modelling [10], analyzing the impact of land use changes such as urbanization on runoff values [11], predicting sediment yield [12,13], as well as on the estimation and verification of the CN parameter [14][15][16][17]. Yet, less attention has been given to the dimensionless initial abstraction ratio (λ), and most often this ratio was assumed equal to 0.20, as it was originally proposed by method's developers [5].…”

Section: Introductionmentioning

confidence: 99%

Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

Krajewski

Sikorska‐Senoner

Hejduk

et al. 2020

Water

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The Curve Number method is one of the most commonly applied methods to describe the relationship between the direct runoff and storm rainfall depth. Due to its popularity and simplicity, it has been studied extensively. Less attention has been given to the dimensionless initial abstraction ratio, which is crucial for an accurate direct runoff estimation with the Curve Number. This ratio is most often assumed to be equal to 0.20, which was originally proposed by the method’s developers. In this work, storm events recorded in the years 2009–2017 in two small Polish catchments of different land use types (urban and agroforested) were analyzed for variability in the initial abstraction ratio across events, seasons, and land use type. Our results showed that: (i) estimated initial abstraction ratios varied between storm events and seasons, and were most often lower than the original value of 0.20; (ii) for large events, the initial abstraction ratio in the catchment approaches a constant value after the rainfall depth exceeds a certain threshold value. Thus, when using the Soil Conservation Service-Curve Number (SCS-CN) method, the initial abstraction ratio should be locally verified, and the conditions for the application of the suggested value of 0.20 should be established.

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Section: Introductionmentioning

confidence: 99%

Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

Krajewski

Sikorska‐Senoner

Hejduk

et al. 2020

Water

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“…Bhunya et al (2003) proposed reliable expressions to derive shape parameter (n s ) from known q ps (1/hr) and t ps (hr): n s = 5.53 s 1.75 + 1.04 IF(0.01 < s <0.35) & n s = 6.29 s 1.998 + 1.157 IF ( s > 0.35) .. (3a-b) Where,  s = q ps × t ps and q ps = Q PS /Q S [(kg/s)/ (hr).(kg/s)]. Using above IUSG concept, Gupta et al (2019) developed four SGMs denoted as SMA-SGM 1-4 (Table 1).…”

Section: Theoretical Considerationsmentioning

confidence: 99%

“…Tyagi et al (2008) developed a time distributed sediment yield model (SYM) utilizing the SCS-CN based infiltration model. More recently, Gupta et al (2019) developed four SGMs based on Nash's IUSG, SCS-CN method and Power law coupled with SMA procedure assuming that if a soil is saturated, rainfall becomes runoff (Michel et al, 2005) adopted in present study to predict SGs.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Accounting Based Sediment Yield Modelling of Arid Drainage Basin of Rajasthan, India

Patil¹,

Mishra²,

Bhardwaj³

et al. 2022

EEC

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This study evaluates the adequacy of soil moisture accounting based sediment graph models (SMA-SGMs) for an arid upland drainage basin of 1520 km2, those are originally evaluated for humid/sub-humid watersheds of size as large as 28 km2. These SGMs fit realistic sediment graphs (SGs) with Nash-Sutcliffe efficiency (NSE) of 92.18 to 95.66%, besides conserving their mass closely. These SGMs are physically more plausible and precise than the conventional regression relations. Thus, proposed models proves their efficacy over arid Jasnagar sub-basin of Luni river, Rajasthan (India) by simulating and validating sediment response with negligible relative error (RE) in total sediment load/volume (QS), peak sediment load (QPS) and time to peak sediment load (tPS) viz. RE(QS)=7.24%, RE(QPS)=0.43% and RE(tPS) =0, respectively.

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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%

“…They found that the sediment production in different parts of the watershed was in proportion to the extent of their connection during the year. Gupta et al (2019) modeled the SG for small watersheds using soil moisture under four different conditions in six watersheds, and the estimated values were compared with observed values and the Bhunya et al (2010) Sediment Graph Model (BSGM). Ruben et al (2020) used Acoustic Sediment Estimation Toolbox (ASET) to calculate SS transport in the Paraná River in Argentina to obtain temporal and spatial variations of SSC.…”

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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Soil Moisture Accounting (SMA) based sediment graph models for small watersheds

Cited by 9 publications

References 31 publications

Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

Soil Moisture Accounting Based Sediment Yield Modelling of Arid Drainage Basin of Rajasthan, India

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

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