scholarly journals Using SWAT to Evaluate Streamflow and Lake Sediment Loading in the Xinjiang River Basin with Limited Data

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 39 ◽  
Author(s):  
Lifeng Yuan ◽  
Kenneth J. Forshay
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Lake Sediment ◽  
Sediment Yield ◽  
Sediment Load ◽  
Poyang Lake ◽  
Sediment Source ◽  
Sediment Loading ◽  
Source Areas ◽  
Annual Sediment

Soil erosion and lake sediment loading are primary concerns of watershed managers around the world. In the Xinjiang River Basin of China, severe soil erosion occurs primarily during monsoon periods, resulting in sediment flow into Poyang Lake and subsequently causing lake water quality deterioration. Here, we identified high-risk soil erosion areas and conditions that drive sediment yield in a watershed system with limited available data to guide localized soil erosion control measures intended to support reduced sediment load into Poyang Lake. We used the Soil and Water Assessment Tool (SWAT) model to simulate monthly and annual sediment yield based on a calibrated SWAT streamflow model, identified where sediment originated, and determined what geographic factors drove the loading within the watershed. We applied monthly and daily streamflow discharge (1985–2009) and monthly suspended sediment load data (1985–2001) to Meigang station to conduct parameter sensitivity analysis, calibration, validation, and uncertainty analysis of the model. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE -observation’s standard deviation ratio (RSR) values of the monthly sediment load were 0.63, 0.62, 3.8%, and 0.61 during calibration, respectively. Spatially, the annual sediment yield rate ranged from 3 ton ha−1year−1 on riparian lowlands of the Xinjiang main channel to 33 ton ha−1year−1 on mountain highlands, with a basin-wide mean of 19 ton ha−1year−1. The study showed that 99.9% of the total land area suffered soil loss (greater than 5 ton ha−1year−1). More sediment originated from the southern mountain highlands than from the northern mountain highlands of the Xinjiang river channel. These results suggest that specific land use types and geographic conditions can be identified as hotspots of sediment source with relatively scarce data; in this case, orchards, barren lands, and mountain highlands with slopes greater than 25° were the primary sediment source areas. This study developed a reliable, physically-based streamflow model and illustrates critical source areas and conditions that influence sediment yield.

scholarly journals Assessment of Hydrology and Sediment Yield in the Mekong River Basin Using SWAT Model

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3503
Author(s):  
Ty Sok ◽  
Chantha Oeurng ◽  
Ilan Ich ◽  
Sabine Sauvage ◽  
José Miguel Sánchez-Pérez
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Sediment Yield ◽  
Large Scale ◽  
Sediment Load ◽  
Swat Model ◽  
Mekong River ◽  
Scale Model ◽  
Time Step ◽  
Mekong River Basin

The Mekong River Basin (MRB) in Southeast Asia is among the world’s ten largest rivers, both in terms of its discharge and sediment load. The spatial and temporal resolution to accurately determine the sediment load/yield from tributaries and sub-basin that enters the Mekong mainstream still lacks from the large-scale model. In this study, the SWAT model was applied to the MRB to assess long-term basin hydrology and to quantify the sediment load and spatial sediment yield in the MRB. The model was calibrated and validated (1985–2016) at a monthly time step. The overall proportions of streamflow in the Mekong River were 34% from surface runoff, 21% from lateral flow, 45% from groundwater contribution. The average annual sediments yield presented 1295 t/km2/year in the upper part of the basin, 218 t/km2/year in the middle, 78 t/km2/year in the intensive agricultural area and 138 t/km2/year in the highland area in the lower part. The annual average sediment yield for the Mekong River was 310 t/km2/year from upper 80% of the total MRB before entering the delta. The derived sediment yield and a spatial soil erosion map can explicitly illustrate the identification and prioritization of the critical soil erosion-prone areas of the MR sub-basins.

Suspended sediment source areas and future climate impact on soil erosion and sediment yield in a New York City water supply watershed, USA

Geomorphology ◽  
2013 ◽  
Vol 183 ◽  
pp. 110-119 ◽  
Author(s):  
Rajith Mukundan ◽  
Soni M. Pradhanang ◽  
Elliot M. Schneiderman ◽  
Donald C. Pierson ◽  
Aavudai Anandhi ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Soil Erosion ◽  
Water Supply ◽  
Sediment Yield ◽  
York City ◽  
Climate Impact ◽  
Sediment Source ◽  
Source Areas ◽  
City Water

scholarly journals Effects of Climate Change and Human Activities on Soil Erosion in the Xihe River Basin, China

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1085 ◽  
Author(s):  
Shanshan Guo ◽  
Zhengru Zhu ◽  
Leting Lyu
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
River Basin ◽  
Air Temperature ◽  
Human Activities ◽  
Sediment Load ◽  
Sudden Increase ◽  
Validation Period ◽  
Total Change ◽  
Calibration Period

Climate change and human activities are the major factors affecting runoff and sediment load. We analyzed the inter-annual variation trend of the average rainfall, air temperature, runoff and sediment load in the Xihe River Basin from 1969–2015. Pettitt’s test and the Soil and Water Assessment Tool (SWAT) model were used to detect sudden change in hydro-meteorological variables and simulate the basin hydrological cycle, respectively. According to the simulation results, we explored spatial distribution of soil erosion in the watershed by utilizing ArcGIS10.0, analyzed the average erosion modulus by different type of land use, and quantified the contributions of climate change and human activities to runoff and sediment load in changes. The results showed that: (1) From 1969–2015, both rainfall and air temperature increased, and air temperature increased significantly (p < 0.01) at 0.326 °C/10 a (annual). Runoff and sediment load decreased, and sediment load decreased significantly (p < 0.01) at 1.63 × 105 t/10 a. In 1988, air temperature experienced a sudden increase and sediment load decreased. (2) For runoff, R2 and Nash and Sutcliffe efficiency coefficient (Ens) were 0.92 and 0.91 during the calibration period and 0.90 and 0.87 during the validation period, for sediment load, R2 and Ens were 0.60 and 0.55 during the calibration period and 0.70 and 0.69 during the validation period, meeting the model’s applicability requirements. (3) Soil erosion was worse in the upper basin than other regions, and highest in cultivated land. Climate change exacerbates runoff and sediment load with overall contribution to the total change of −26.54% and −8.8%, respectively. Human activities decreased runoff and sediment load with overall contribution to the total change of 126.54% and 108.8% respectively. Runoff and sediment load change in the Xihe River Basin are largely caused by human activities.

scholarly journals Sediment Load and Suspended Sediment Concentration Prediction

2013 ◽  
Vol 1 (No. 1) ◽  
pp. 23-31 ◽  
Author(s):  
Bečvář Martin
Keyword(s):  
Suspended Sediment ◽  
Sediment Yield ◽  
Suspended Sediment Concentration ◽  
Sediment Load ◽  
Sediment Concentration ◽  
River Basins ◽  
River Systems ◽  
Sediment Yields ◽  
Suspended Sediment Concentrations ◽  
Annual Sediment

Sediment is a natural component of riverine environments and its presence in river systems is essential. However, in many ways and many places river systems and the landscape have been strongly affected by human activities which have destroyed naturally balanced sediment supply and sediment transport within catchments. As a consequence a number of severe environmental problems and failures have been identified, in particular the link between sediments and chemicals is crucial and has become a subject of major scientific interest. Sediment load and sediment concentration are therefore highly important variables that may play a key role in environment quality assessment and help to evaluate the extent of potential adverse impacts. This paper introduces a methodology to predict sediment loads and suspended sediment concentrations (SSC) in large European river basins. The methodology was developed within an MSc research study that was conducted in order to improve sediment modelling in the GREAT-ER point source pollution river modelling package. Currently GREAT-ER uses suspended sediment concentration of 15 mg/l for all rivers in Europe which is an obvious oversimplification. The basic principle of the methodology to predict sediment concentration is to estimate annual sediment load at the point of interest and the amount of water that transports it. The amount of transported material is then redistributed in that corresponding water volume (using the flow characteristic) which determines sediment concentrations. Across the continent, 44 river basins belonging to major European rivers were investigated. Suspended sediment concentration data were collected from various European basins in order to obtain observed sediment yields. These were then compared against the traditional empiric sediment yield estimators. Three good approaches for sediment yield prediction were introduced based on the comparison. The three approaches were applied to predict annual sediment yields which were consequently translated into suspended sediment concentrations. SSC were predicted at 47 locations widely distributed around Europe. The verification of the methodology was carried out using data from the Czech Republic. Observed SSC were compared against the predicted ones which validated the methodology for SSC prediction.

Land use, soil erosion, and sediment yield at Pinto Lake, California: comparison of a simplified USLE model with the lake sediment record

2011 ◽  
Vol 45 (2) ◽  
pp. 199-212 ◽  
Author(s):  
John F. Boyle ◽  
Andy J. Plater ◽  
Claire Mayers ◽  
Simon D. Turner ◽  
Rob W. Stroud ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Lake Sediment ◽  
Sediment Yield ◽  
Sediment Record

scholarly journals Assessment of uncertainties in soil erosion and sediment yield estiamtes at ungauged basins: an application to the Garra River basin, India

2017 ◽  
Author(s):  
Somil Swarnkar ◽  
Anshu Malini ◽  
Shivam Tripathi ◽  
Rajiv Sinha
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Sediment Yield ◽  
Uncertainty Propagation ◽  
River Basins ◽  
Large River ◽  
Delivery Ratio ◽  
Mountainous Region ◽  
Ungauged Basins ◽  
Sediment Delivery

Abstract. High soil erosion and excessive sediment load are serious problems in several Himalayan River basins. To apply mitigation procedures, precise estimation of soil erosion and sediment yield with associated uncertainties are needed. Here, Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) equations are used to estimate the spatial pattern of soil erosion (SE) and sediment yield (SY) in the Garra River basin, a small Himalayan tributary of River Ganga. A methodology is proposed for quantifying and propagating uncertainties in SE, SDR and SY estimates. Expressions for uncertainty propagation are derived by first-order uncertainty analysis, making the method viable even for large river basins. The methodology is applied to investigate the relative importance of different RUSLE factors in estimating the magnitude and uncertainties of SE over two distinct morpho-climatic regimes of the Garra River basin, namely, upper mountainous region &amp; lower alluvial plains. The results suggest that average SE in the basin falls in very high category (20.4 ± 4.1 t/ha/y) with higher values in the upper mountainous region (84.4 ± 13.9 t/ha/y) than in the lower alluvial plains (17.7 ± 3.6 t/ha/y). Furthermore, the topographic steepness (LS) and crop practice (CP) factors exhibit higher uncertainties than other RUSLE factors. The annual average SY is estimated at two locations in the basin – Nanak Sagar dam (NSD) for the period 1962–2008 and Husepur gauging station (HGS) for 1987–2002. The SY at NSD and HGS are estimated to be 8.0 ± 1.4 × 105 t/y and 7.9 ± 1.7 ×106 t/y, respectively, and the estimated 90 % confidence interval contains the observed values 6.4 × 105 t/y and 7.2 × 106 t/y. The study demonstrated the usefulness of the proposed methodology for quantifying uncertainty in SE and SY estimates at ungauged basins.

scholarly journals A New Indicator to Better Represent the Impact of Landscape Pattern Change on Basin Soil Erosion and Sediment Yield in the Upper Reach of Ganjiang, China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 990
Author(s):  
Yongfen Zhang ◽  
Nong Wang ◽  
Chongjun Tang ◽  
Shiqiang Zhang ◽  
Yuejun Song ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
River Basin ◽  
Landscape Metrics ◽  
Landscape Pattern ◽  
Sediment Yield ◽  
River Basins ◽  
Landscape Patterns ◽  
Soil Erodibility ◽  
Land Use Pattern

Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.

scholarly journals Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 613-618
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Tectonic Activity ◽  
Precipitation Seasonality ◽  
Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

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