International Soil and Water Conservation Research最新文献

Dust emission caused by wind erosion of soil is an important surface process in arid and semi-arid regions. However, existing dust emission models pay insufficient attention to the impacts of aerodynamic entrainment of particles. In addition, studies of wind erosion processes do not adequately account for the dynamics of wind erosion rates and dust emission fluxes, or for the impact of soil texture on dust emission. Our wind tunnel simulations of wind erosion and dust emission showed that the soil texture, wind erosion duration, and shear velocity are major factors that affect the dynamics of wind erosion and dust emission. Because of the limited supply of surface sand and the change in surface erosion resistance caused by surface coarsening during erosion, the wind erosion rate and the flux of particles smaller than 10 μm (PM₁₀) caused by aerodynamic entrainment decreased rapidly with increasing erosion duration, which suggests that surface wind erosion and dust emission occur primarily during the initial stage of wind erosion. The PM₁₀ emission efficiency decreased with increasing shear velocity following a power function, and finer textured sandy loam soils had greater PM₁₀ emission efficiency than loamy sand soils.

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area. The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length. The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length, and to evaluate its predictive capability at different resolution DEMs. Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero. Using the multiple flow direction algorithm united with the transfer grid (MFDTG), the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths. The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China. The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5% showed that the relationship between surface-runoff intensity and slope length was a negative power function. Estimated by the proposed modified L-equation ((al_x^(b+1)/22.13)^m), the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1, 5, 25 and 90 m resolutions respectively. This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes. Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Predicting and allocating surface water resources are becoming increasingly important tasks for addressing the risk of water shortages and challenges of climate change, especially in reservoir basins. However, surface water resource management includes many systematic uncertainties and complexities that are difficult to address. Thus, advanced models must be developed to support predictive simulations and optimal allocations of surface water resources, which are urgently required to ensure regional water supply security and sustainable socioeconomic development. In this study, a soil and water assessment tool-based interval linear multi-objective programming (SWAT-ILMP) model was developed and integrated with climate change scenarios, SWAT, interval parameter programming, and multi-objective programming. The developed model was applied to the Xinfengjiang Reservoir basin in South China and was able to identify optimal allocation schemes for water resources under different climate change scenarios. In the forecast year 2025, the optimal water quantity for power generation would be the highest and account for ∼60% of all water resources, the optimal water quantity for water supply would account for ∼35%, while the optimal surplus water released from the reservoir would be the lowest at ≤5%. In addition, climate change and reservoir initial storage would greatly affect the surplus water quantity but not the power generation or water supply quantity. In general, the SWAT-ILMP model is applicable and effective for water resource prediction and management systems. The results from different scenarios can provide multiple alternatives to support rational water resource allocation in the study area.

Magnetic powder is regarded as an effective and economical tracer for estimating soil erosion. However, the principle and application for using magnetic powder to estimate soil erosion are still not fully developed. In this study, magnetic powders with mean diameters of both 30 and 1 μm were mixed into three soils at different mass proportion. The relationship between magnetic susceptibility and the mass proportion of the introduced magnetic powder in the tagged soil, and the binding ability of magnetic powder to soil particles after both dry and wet sieving were investigated. The accuracy of tracking soil loss by using magnetic powder as a tracer was assessed. The results showed that there was a significant linear relationship between the magnetic susceptibility and the mass proportion of the introduced magnetic powder in the tagged soil. The relationship between the amount of soil captured by a magnet and the mass proportion of magnetic powder in the tagged soil indicated that soils were readily magnetized by magnetic powder, especially fine fractions. The magnetic susceptibility of magnetic powder in different sizes of soil aggregates was variable. A majority of magnetic powder of both 30 and 1 μm diameters was strongly bound with fine particles <0.05 mm in dry and wet sieving. Using the estimated tracer mass proportions, the relative errors between measured and estimated soil losses with enrichment correction factor were less than 18.3% under the simulated rain events. This study not only reveal the principle of Fe₃O₄ powder in soil erosion, but also improve its estimated precision of soil loss, which can make the tracing method by Fe₃O₄ magnetic powder more useable in future.

Gully erosion can lead to the destruction of farmland and the reduction in crop yield. Gully mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control measures. The narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images challenging. To implement gully erosion mapping, we developed a small training samples-oriented lightweight deep leaning model, called asymmetric non-local LinkNet (ASNL-LinkNet). The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted features. Experiment results show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning methods. The quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to 0.72. This study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.

Gully erosion is one of the most severe types of land degradation, hindering food production and sustainable agricultural development. However, the historical evolution process and the impact of land use change on gully erosion remain unclear. To address this issue, we conducted a field investigation on gully erosion in 2018 and interpreted land use and gullies using historical remote sensing images in 1968 and 1978 over an area of 84.48 km². The study found that from 1968 to 1978 to 2018, all gully morphological parameters including gully length density and gully areal density increased significantly. The main origin of gully erosion found was from dry farmland. The annual soil loss rate induced by gully erosion was 1.46 mm during 1968–2018. Gully erosion rates were higher during 1968–1978 than during 1978–2018. Furthermore, the length, areal and volumetric erosion rates in gullies formed by multiple gullies merging was greater than that of newly formed gullies (NFG) and gullies developing continuously from a single pre-existing gully, while the widening rate of NFG was highest. The susceptibility of land use types to gully erosion was in the order of woodland < dry farmland < degraded land. The annual average increase in gully area was 871.09 m² km^-2 year^-1 for parcels that were converted from woodland to dry farmland, which was 5.56 times and 1.78 times greater than that of woodland and dry farmland maintenance, respectively. Therefore, urgent implementation of ecological land use plans and gully erosion control practices is suggested for this region.

Soil erosion is a complex process involving multiple natural and anthropic agents, causing the deterioration of multiple components comprising soil health. Here, we provide an estimate of the spatial patterns of cropland susceptibility to erosion by sheet and rill, gully, wind, tillage, and root crops harvesting and report the co-occurrence of these processes using a multi-model approach. In addition, to give a global overview of potential future changes, we identify the locations where these multiple concurrent soil erosion processes may be expected to intersect with projected dry/wet climate changes by 2070. Of a modelled 1.48 billion hectares (B ha) of global cropland, our results indicate that 0.56 B ha (∼36% of the total area) are highly susceptible (classes 4 and 5) to a single erosion process, 0.27 B ha (∼18% of the total area) to two processes and 0.02 B ha (1.4% of the total area) to three or more processes. An estimated 0.82 B ha of croplands are susceptible to possible increases in water (0.68 B ha) and wind (0.14 B ha) erosion. We contend that the presented set of estimates represents a basis for enhancing our foundational knowledge on the geography of soil erosion at the global scale. The generated insight on multiple erosion processes can be a useful starting point for decision-makers working with ex-post and ex-ante policy evaluation of the UN Sustainable Development Goal 15 (Life on Land) activities. Scientifically, this work provides the hitherto most comprehensive assessment of soil erosion risks at the global scale, based on state-of-the-art models.

Soil infiltration properties (SIPs) of infiltration rate and saturated hydraulic conductivity significantly affect hydrological and erosion processes, thus, knowledge of SIPs under different land use/cover are vital for land use management to control soil erosion for realizing the sustainable development of the small agricultural watershed. Nevertheless, few studies have been carried out to investigate the differences in SIPs and their dominant influencing factors between different land use/cover in the black soil region of Northeast China. Therefore, eight typical land use/cover were selected to clarify the variations in SIPs between different land use/cover and further identify their dominant influencing factors. SIPs of initial infiltration rate (IIR), steady infiltration rate (SIR), and saturated hydraulic conductivity (Ks) were determined under eight typical land use/cover (forestland, shrub land, grassland, longitudinal shelterbelt, transverse shelterbelt, agricultural road, and cropland of Zea mays L. and Glycine max (Linn.) Merr) using a tension disc infiltrometer with three pressure heads of −3, −1.5, and 0 cm. The results of one-way ANOVA analysis showed that SIPs varied greatly between different land use/cover. Shelterbelt plant with Populus L. had the maximum IIR, SIR, and Ks, and then followed by shrub land, agricultural road, cropland, grassland, and forestland. Spearman correlation analysis indicated that SIPs were significantly correlated with soil and vegetation properties. Redundancy analysis revealed that differences in SIPs between different land use/cover were dominantly attributed to the differences in soil texture, field capacity, and plant root mass density, which explained 79.36% of the total variation in SIPs. Among these dominant influencing factors, the results of structural equation model indicated that the indirect effects of plant root and soil texture played the most important role in variations of SIPs via affecting soil texture and pore characteristics. These results have significant implications for the precise prediction of watershed hydrological and erosion processes, also provide a scientific basis for guiding the distribution pattern of land use in the cultivated watershed.

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.