Sediment Fingerprinting for Calibrating a Soil Erosion and Sediment-Yield Model in Mixed Land-Use Watersheds

Author(s):  
James F. Fox ◽  
Darren K. Martin
2002 ◽  
Vol 37 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Davood Nikkami ◽  
Maria Elektorowicz ◽  
Guy R. Mehuys

Abstract To reduce the environmental and economical impact of soil erosion resulting from improper management of land-use activities, a study was initiated by the Iranian Ministry of Construction on Syahrood, one of the sub-basins of the Damavand watershed in Iran. Land-use optimization is one of the appropriate strategies for soil conservation. It can empower the decision maker or watershed manager to choose from different land-use scenarios to reach the best decision with the different combinations of variables. The output results of the sediment yield model, including the integration of the Modified Universal Soil Loss Equation (MUSLE) with Spatial Analysis System-Geographic Information System (SPANS-GIS), along with the net income of each land use were used as input in the land-use optimization model for minimizing the sediment yield and maximizing farm production of each land use. The multi-objective linear programming simplex method of Steuer (1995) was used to solve the problem. The optimization process allocated dryland farming areas to rangelands if no changes were made to the current supporting practice system. The expected annual sediment yield from the entire sub-basin was reduced by 2420 tonnes/year (or by 5%) and the annual net farm income was increased by 3.99 billion Iranian Rial/year (or by 134%).


2014 ◽  
Vol 18 (9) ◽  
pp. 3763-3775 ◽  
Author(s):  
K. Meusburger ◽  
G. Leitinger ◽  
L. Mabit ◽  
M. H. Mueller ◽  
A. Walter ◽  
...  

Abstract. Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959–2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha−1 yr−1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha−1 yr−1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.


2012 ◽  
Vol 16 (5) ◽  
pp. 1321-1334 ◽  
Author(s):  
L. C. Alatorre ◽  
S. Beguería ◽  
N. Lana-Renault ◽  
A. Navas ◽  
J. M. García-Ruiz

Abstract. Soil erosion and sediment yield are strongly affected by land use/land cover (LULC). Spatially distributed erosion models are of great interest to assess the expected effect of LULC changes on soil erosion and sediment yield. However, they can only be applied if spatially distributed data is available for their calibration. In this study the soil erosion and sediment delivery model WATEM/SEDEM was applied to a small (2.84 km2) experimental catchment in the Central Spanish Pyrenees. Model calibration was performed based on a dataset of soil redistribution rates derived from point 137Cs inventories, allowing capture differences per land use in the main model parameters. Model calibration showed a good convergence to a global optimum in the parameter space, which was not possible to attain if only external (not spatially distributed) sediment yield data were available. Validation of the model results against seven years of recorded sediment yield at the catchment outlet was satisfactory. Two LULC scenarios were then modeled to reproduce land use at the beginning of the twentieth century and a hypothetic future scenario, and to compare the simulation results to the current LULC situation. The results show a reduction of about one order of magnitude in gross erosion (3180 to 350 Mg yr−1) and sediment delivery (11.2 to 1.2 Mg yr−1 ha−1) during the last decades as a result of the abandonment of traditional land uses (mostly agriculture) and subsequent vegetation recolonization. The simulation also allowed assessing differences in the sediment sources and sinks within the catchment.


2012 ◽  
Vol 26 (23) ◽  
pp. 3579-3592 ◽  
Author(s):  
Guoqiang Wang ◽  
Hong Jiang ◽  
Zongxue Xu ◽  
Lijing Wang ◽  
Weifeng Yue

2011 ◽  
Vol 45 (2) ◽  
pp. 199-212 ◽  
Author(s):  
John F. Boyle ◽  
Andy J. Plater ◽  
Claire Mayers ◽  
Simon D. Turner ◽  
Rob W. Stroud ◽  
...  

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 990
Author(s):  
Yongfen Zhang ◽  
Nong Wang ◽  
Chongjun Tang ◽  
Shiqiang Zhang ◽  
Yuejun Song ◽  
...  

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.


Heliyon ◽  
2019 ◽  
Vol 5 (12) ◽  
pp. e02981 ◽  
Author(s):  
Moges Kidane ◽  
Alemu Bezie ◽  
Nega Kesete ◽  
Terefe Tolessa

2021 ◽  
Author(s):  
Kunihito Mihara ◽  
Kanta Kuramochi ◽  
Ryusuke Hatano

&lt;p&gt;Introduction&lt;/p&gt;&lt;p&gt;Accelerated erosion by human activities leads to degradation of soil ecosystem services and aquatic environment. It is unavoidable issue in Japan because it holds many sloped agricultural lands. Tokoro river watershed, TRW, in eastern Hokkaido, Japan has unique climate characterized with the least precipitation in Japan and cold winter with little snow which induces soil freezing. Frozen subsoil forms impermeable layers to increase surface runoff in early spring. The objectives of this study were i) to understand the spatial and seasonal variation of water and sediment movement in TRW using Soil and Water Assessment Tool, SWAT which is a process-based hydrological model and ii) to evaluate the impact of agricultural activities, topography of agricultural lands, and runoff characteristics on soil erosion through identification of highly erosive areas and seasons based on the simulation output.&lt;/p&gt;&lt;p&gt;Materials and methods&lt;/p&gt;&lt;p&gt;Water and sediment movement in TRW was simulated from 2011/1/1 to 2015/12/31. SWAT calculates water and sediment movement processes using spatial and temporal information of topography, land use, soil, weather, and land management in watershed. TRW was delineated into 17 subbasins based on topographic information and further divided into 764 HRUs which had homogenous combination of slope class, soil type, and land use in each subbasin. On-land processes were calculated in each HRU. After water and sediment yield from HRUs were summed in each subbasin, stream routing processes were calculated. Model parameters were calibrated so that the estimated stream flow and sediment load at the outlet would fit the measurements. From the simulation by the calibrated model, outputs were extracted as follows: 1) Contribution to the gross sediment yield and erosion rate of each land use; 2) Erosion rate of each subbasin; 3) Erosion rate of whole watershed on each month; and 4) Surface runoff and percentage of surface runoff in water yield in each month.&lt;/p&gt;&lt;p&gt;Results and Discussions&lt;/p&gt;&lt;p&gt;Calibrated SWAT reproduced well the fluctuation of stream flow and sediment load at the outlet of TRW. Although the model underestimated sediment load during large flood events with the average estimation error of -16.1&amp;#177;5.4% on peak-discharge months, it showed satisfactory performance with coefficient of determination: R&lt;sup&gt;2&lt;/sup&gt;=0.88, Nash-Sutcliffe efficiency coefficient: Ens=0.86, and percentage of bias: PBIAS=0.34% for monthly sediment load estimation. Agricultural lands which covered 17.6% of the watershed were considered as the primary sediment sources contributing to 68.5% of estimated gross sediment yield of the watershed. Spatial variation of estimated erosion rate showed high sediment yield in the middle- and down-stream area of TRW where agricultural activities were intensive, and higher sediment yield particularly in the area where more agricultural lands had steep slopes (more than 51 t km&lt;sup&gt;-2&lt;/sup&gt; yr&lt;sup&gt;-1&lt;/sup&gt;). Monthly erosion rate estimation indicated that the most severe erosion occurred on March and April (6.9&amp;#177;1.4 and 7.3&amp;#177;1.9 t km&lt;sup&gt;-2&lt;/sup&gt; mon&lt;sup&gt;-1&lt;/sup&gt; respectively). On March, average percentage of surface runoff was estimated as 90.5&amp;#177;6.5%. Therefore, surface runoff in early snowmelt season when the frozen subsoil prevented infiltration was considered as an important driver of soil erosion.&lt;/p&gt;


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