Spatial variability of soil erodibility factor using geo-statistics method in Duhok dam watershed, Kurdistan region, Iraq

<p>Sediments deposition derived from the erosion in upstream areas can lead to river siltation or canals downstream irrigation. According to the complexity of erosion problem at Keuliling reservoir, it is essential that topography, hydrology, soil type and land use to be analyzed comprehensively. Software used to analyze is AVSWAT 2000 (Arc View Soil and Water Assessment Tools-2000), one of the additional tool of ArcView program. The results obtained are the watershed delineation map, soil type map to produce soil erodibility factor (K) which indicates the resistance of soil particles toward exfoliation, land use map to produce crop management factor (C) and soil conservation and its management factors (P). Hydrology analysis includes soil type, land use and utility for the erosion rate analysis through Hydrologic Response Unit (HRU). The biggest HRU value of sub-basin is on area 5 and the lowest one is on area 10. All four HRU in sub-basin area 5 are potentially donating high value for HRU. In short, this area has the longest slope length so that it has a large LS factor. About 50% of the land was covered by bushes which gain higher C factor rather than forest. Moreover, it has contour crop conservation technique with 9-20 % declivity resulting in having dominant factor of P. Soil type is dominated by Meucampli Formation which has soil erodibility factor with high level of vulnerable toward the rainfall kinetic energy. All in all, the vast majority of HRU parameters in this sub-basin area obtain the highest HRU value. Hydrology analysis, soil type, and use-land are useful for land area analysis that is susceptible to erosion which was identified through Hydrologic Response Unit (HRU) using GIS. As the matter of fact, spatially studies constructed with GIS can facilitate the agency to determine critical areas which are needed to be aware or fully rehabilitated.</p>

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Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽

10.3390/land11010134 ◽

2022 ◽

Vol 11 (1) ◽

pp. 134

Author(s):

Xiaofang Huang ◽

Lirong Lin ◽

Shuwen Ding ◽

Zhengchao Tian ◽

Xinyuan Zhu ◽

...

Keyword(s):

Land Use ◽

Soil Erosion ◽

Spatial Variability ◽

Influencing Factors ◽

Soil Type ◽

Soil Survey ◽

Purple Soil ◽

Soil Erodibility ◽

K Value ◽

K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

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Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

Remote Sensing ◽

10.3390/rs12183103 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3103

Author(s):

Qinghu Jiang ◽

Yiyun Chen ◽

Jialiang Hu ◽

Feng Liu

Keyword(s):

Land Use ◽

Spectral Data ◽

Near Infrared ◽

Nir Spectroscopy ◽

Soil Erodibility ◽

Infrared Reflectance ◽

Cultivated Land ◽

Near Infrared Reflectance ◽

Natural Land ◽

Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

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