Comparison of regionalization approaches in parameterizing sediment rating curve in ungauged catchments for subsequent instantaneous sediment yield prediction

Heng, Sokchhay; Suetsugi, Tadashi

doi:10.1016/j.jhydrol.2014.03.003

Cited by 37 publications

(10 citation statements)

References 22 publications

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“…Observations of soil erosion and sediment load are lacking for rivers in many parts of the world, particularly in developing countries (Heng & Suetsugi, 2014). Thus, there is a need to determine sediment yield from upland areas through alternative sources of data, in order to estimate catchment total sediment yield (Palazón et al, 2014;Cortizas et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modeling Sediment Yield in Semi‐Arid Pasture Micro‐Catchments, NW Iran

et al. 2016

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Modeling sediment yield is a complex task because of the nonlinearity of natural processes intervening at slope and basin scale. In this study slope steepness, vegetation cover, and soil properties along with sediment yield were studied in 20 pasture micro‐catchments in a semi‐arid region, NW Iran in order to understand and predict sediment yield. The micro‐catchments included only one first‐order gully and drain toward a rock check dam in the outlet. The sediment yield of each micro‐catchment was calculated using the measurement of sediment mass in the check dams for a 16‐year period (1994–2010). Relationships between sediment yield and drainage characteristics were analyzed using correlation matrix and multiple linear regression method. Based on the results, sediment yield in the micro‐catchments varied from 0.29 Mg ha−1 y−1 to 14.81 Mg ha−1 y−1, with an average of 5.04 Mg ha−1 y−1. It was significantly related to slope steepness, vegetation cover, and soil organic matter using a linear regression equation (R2 = 0.87, p < 0.001). The slope, vegetation, and soil organic matter explained about 44%, 23%, and 20% of total variance in sediment yield, respectively. The spatial validation of the model using data from eight different micro‐catchments located nearby showed that the model efficiency is 0.94. Therefore, the model can be used for predicting sediment yield in this and similar study area, with a high degree of accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Modeling Sediment Yield in Semi‐Arid Pasture Micro‐Catchments, NW Iran

et al. 2016

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“…For instance, riverine SS generation and transport capacity vary over multiple timescales based on several factors (Vercruysse et al 2017). Understanding this variability is complicated by the difficulties in installing and maintaining monitoring stations (Heng and Suetsugi 2014), and so rating curves are critical for gap‐filling SSC data that can then be examined for long‐term variability and trends (Jung et al 2020). Consistent with other recent work (Gray et al 2015), results from this study highlight that long‐term assessments of mass flux in riverine systems could be sensitive to the rating curves used for this purpose, and so care should be taking in this choice.…”

Section: Discussionmentioning

confidence: 99%

Inter‐model Comparison of Turbidity‐Discharge Rating Curves and the Implications for Reservoir Operations Management

Wang

Gelda²,

Mukundan³

2021

J American Water Resour Assoc

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This study compares several statistical rating curve techniques to estimate turbidity, a proxy for suspended sediment concentration, in fluvial systems based on available discharge data. Seven models were tested, including variants of quadratic rating curves, quantile regression, local regression, dynamic linear models (DLMs), and Box‐Jenkins models. Two comparisons were conducted in a case study of the Esopus Creek watershed in New York, a major water source for the New York City Water Supply System (NYCWSS). First, the models were tested in their ability to forecast turbidity at 1–7 day lead times assuming perfect forecasts of discharge using two daily datasets of varying record lengths and resolution. Second, the models were used to gap‐fill turbidity data based on available discharge data, and the resulting continuous turbidity time series were used to assess optimal reservoir operations in the NYCWSS to manage water quality. Results suggest that DLMs coupled with additional time series modeling on the residuals produce the most robust forecasts across lead times for both high and low turbidity values. During average conditions, differences between rating curves have little impact on inferred reservoir operations due to the buffering effect of storage. But during extreme events, rating curve differences lead to large differences in inferred operations, suggesting that rating curve choice can play an important role in assessing the risk of reservoir‐based water quality management.

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“…The ISW method [37,38] estimates streamflow values at the target station as the weighted average of the streamflow values at n donor stations. The weights are inversely proportional to the power of Water 2020, 12, 459 9 of 22 physical similarity from the target station.…”

Section: Inverse Similarity Weighted (Isw) Methodsmentioning

confidence: 99%

“…In the literature, the value of the power parameter is commonly chosen as 2, which is known as the inverse distance squared weighted [36]. Alternatively, the inverse similarity weighted (ISW) method [37,38], which is similar to the IDW method, can be applied on the basis of multiple donor stations. Unlike the IDW method, the ISW method uses physical similarity instead of the geographical distance between the target and the donor station.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey

Yilmaz

Önöz

2020

Water

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In this study, a comparative evaluation of the statistical methods for daily streamflow estimation at ungauged basins is presented. The single donor station drainage area ratio (DAR) method, the multiple-donor stations drainage area ratio (MDAR) method, the inverse similarity weighted (ISW) method, and its variations with three different power parameters (1, 2, and 3) are applied to the two main subbasins of the Euphrates Basin in Turkey to estimate daily streamflow data. Each station in each basin is considered in turn as the target station where there are no streamflow data. The donor stations are selected based on the physical similarities between the donor and target stations. Then, streamflow data from the most physically similar donor station(s) is transferred to the target station using the statistical methods. In addition, the effect of data preprocessing on the estimation performance of the statistical methods is investigated. The preprocessing discussed in this study is streamflow data smoothing using the two-sided moving average (MA). Three statistical methods using the smoothed data by the MA, named as DAR-MA, MDAR-MA, and ISW-MA, are proposed. The estimation performance of the statistical methods is compared by using daily streamflow data with preprocessing and without preprocessing. The Nash-Sutcliffe efficiency (NSE), the ratio of the root mean square error (RMSE) to the standard deviation of the observed data (RSR), the percent bias (PBIAS), and the coefficient of determination (R 2 ) are used to evaluate the performance of the statistical methods. The results show that MDAR and ISW give improved performances compared to DAR to estimate daily streamflow for 7 out of 8 target stations in the Middle Euphrates Basin and for 4 out of 7 target stations in the Upper Euphrates Basin. Higher NSE values for both MDAR and ISW are mostly obtained with the three most physically similar donor stations in the Middle Euphrates Basin and with the two most physically similar donor stations in the Upper Euphrates Basin. The best statistical method for each target station exhibits slightly greater NSE when the smoothed data by the MA is used for all target stations in the Middle Euphrates Basin and for 6 out of 7 target stations in the Upper Euphrates Basin.

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Comparison of regionalization approaches in parameterizing sediment rating curve in ungauged catchments for subsequent instantaneous sediment yield prediction

Cited by 37 publications

References 22 publications

Modeling Sediment Yield in Semi‐Arid Pasture Micro‐Catchments, NW Iran

Modeling Sediment Yield in Semi‐Arid Pasture Micro‐Catchments, NW Iran

Inter‐model Comparison of Turbidity‐Discharge Rating Curves and the Implications for Reservoir Operations Management

A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey

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