Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Sediment is one of the important natural resources on the Earth. Information on sediment resources is key to making decisions regarding soil resources management and mitigation during sediment hazard events. Thus, this research analyzed and mapped the benefit-cost ratio (BCR) of sediment in the Ping River using a Geographic Information System (GIS). Furthermore, the benefit of sediment was analyzed using a new application of the Revised Universal Soil Loss Equation (RUSLE) with a spatial resolution of 1 km2. The results reveal that the potential of annual soil loss and sediment deposition in the Ping River Basin (PRB) were approximately 825 and 530 m3/km2·y, respectively. In addition, the results indicated that there was a higher BCR in the upstream area of the PRB where there was greater sediment deposition. The average benefit of sediment in the PRB is USD 4280/km2·y. It is expected that the BCR of the sediment resources map analyzed in this research will help policy-makers for decision-making on the benefits of sediment resources in Thailand.

Rivers draining contrasting landscapes exhibit distinct potentials to emit diffusive methane (CH4)

Methane (CH₄) is the second most important greenhouse gas, contributing approximately 17% of radiative forcing, and CH₄ emissions from river networks due to intensified human activities have become a worldwide issue. However, there is a dearth of information on the CH₄ emission potentials of different rivers, especially those draining contrasting watershed landscapes. Here, we examined the spatial variability of diffusive CH₄ emissions and discerned the roles of environmental factors in influencing CH₄ production in different river reaches (agricultural, urban, forested and mixed-landscape rivers) from the Chaohu Lake Basin in eastern China. According to our results, the urban rivers most frequently exhibited extremely high CH₄ concentrations, with a mean concentration of 5.46 μmol L^-1, equivalent to 4.1, 9.7, and 7.2 times those measured in the agricultural, forested, and mixed-landscape rivers, respectively. The availability of carbon sources and total phosphorus were commonly identified as the most important factors for CH₄ production in agricultural and urban rivers. Dissolved oxygen and oxidation-reduction potential were separately discerned as important factors for the forested and mixed-landscape rivers, respectively. Monte Carlo flux estimations demonstrated that rivers draining contrasting landscapes exhibit distinct potentials to emit CH₄. The urban rivers had the highest CH₄ emissions, with a flux of 9.44 mmol m^-2 d^-1, which was 5.1-10.4 times higher than those of the other river reaches. Overall, our study highlighted that management actions should be specifically targeted at the river reaches with the highest emission potentials and should carefully consider the influences of different riverine environmental conditions as projected by their watershed landscapes.

Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better than the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.