International Soil and Water Conservation Research最新文献

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.

Sediment fingerprinting technology is widely used to differentiate sediment sources. However, despite its long-recognized benefits, there it has been seldom applied to assess the variability of sediment sources during storm events. In this study, sediment fingerprinting is used for four storm events to determine the dynamic changes in sediment sources throughout them in the black soil region in Northeast China. Three potential sediment sources—cultivated land, unpaved roads, and gullies—were effectively differentiated using four geochemical tracers (As, Be, Cs, and Cu), with an accuracy of 100%. The relative sediment contribution from each source was determined using linear and Bayesian mixing models. The mean absolute fit (MAF) values of the linear mixing model (MAF_mean = 0.976–0.949) were higher than those of the Bayesian mixing model (MAF_mean = 0.921–0.992), indicating that the first performed better. Cultivated land was the primary source of the sediment load, accounting for 59.03% of it (load-weighted mean = 68.29%), followed by the gullies (37.15%, load-weighted mean = 28.09%), and unpaved roads (3.90%, load-weighted mean = 3.69%) for the four storm events. In addition, a high variability in sediment source contribution was observed during the storm events. Cultivated land was the dominant sediment source during storm events with higher sediment concentrations (logarithmic function, r² = 0.878, p < 0.01), discharge (linear function, r² = 0.452, p < 0.05), and sediment flux (logarithmic function, r² = 0.857, p < 0.01), whereas the reverse was observed for gullies. Contrastingly, the contribution of sediment from unpaved roads remained relatively stable during rainfall events. This provides a potential means to assess dynamic changes in sediment contributions from different erosion units. Moreover, it provides data support for exploring soil erosion mechanisms and effective erosion control in the black soil region in Northeast China.

The quality of drinking water for the Boston Metropolitan Area, supplied by the Quabbin-Wachusett system, is impacted by environmental trends. The objectives of this study are to increase understanding of the role that small streams may play in degradation of reservoir quality by characterizing seasonal constituent patterns from 1998 to 2020 in the Wachusett Reservoir watershed and by developing enhanced modeling frameworks. Previous monitoring (1998–2012) exhibited increased loads due to increasing flows despite declining solute concentration. This present study analyzed seasonal nitrate (NO₃) and total phosphorus (TP) concentration and load trends from 2012 to 2020 across 11 tributaries. Specific conductivity (SC) was also assessed to evaluate the impacts of road salt application. From 2012 to 2020, statistical results for mean nutrient concentrations suggest static or declining temporal trends, while SC in all tributaries exhibited increasing trends. Land use data suggest association with altered drainage landscapes as potential sources of increased constituent transport. Subbasins with the highest concentrations of TP, NO₃, and SC have the largest percentage of impervious and cultivated areas, two to three times greater than other subbasins. Daily flows were modeled using the airGR hydrological model, subsequently used to calculate loads. Overall, flow magnitude was a more important load driver than long-term nutrient concentrations, thus, showing that stream discharge controlled load variability. On the other hand, persistently high SC levels controlled the increasing SC load trends. Finally, many nutrient reduction management strategies demonstrated an important impact from 1998 to 2020. Despite watershed programs aimed at reducing salt applications, concentrations in streams are increasing, indicating a long-term legacy of salt accumulation. Although smaller tributaries represent a modest portion of the system, addressing these sources has the potential to further reduce the long-term ecological impacts of reservoir constituent loading.

Suspended particulate matter (SPM) in lakes exerts strong impact on light propagation, aquatic ecosystem productivity, which co-varies with nutrients, heavy metal and micro-pollutant in waters. In lakes, SPM exerts strong absorption and backscattering, ultimately affects water leaving signals that can be detected by satellite sensors. Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy. There are still rooms for model accuracy improvements, and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters. We assembled more than 16,400 in situ measured SPM in lakes from six continents (excluding the Antarctica continent), of which 9640 samples were matched with Landsat overpasses within ±7 days. Seven machine learning algorithms and two simple regression methods (linear and partial least squares models) were used to estimate SPM in lakes and the performance were compared. To overcome the problem of imbalance datasets in regression, a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise (SMOGN) was adopted in this study. Through comparison, we found that gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) models demonstrated good spatiotemporal transferability with SMOGN processed dataset, and has potential to map SPM at different year with good quality of Landsat land surface reflectance images. In all the tested modeling approaches, the GBDT model has accurate calibration (n = 6428, R² = 0.95, MAPE = 29.8%) from SPM collected in 2235 lakes across the world, and the validation (n = 3214, R² = 0.84, MAPE = 38.8%) also exhibited stable performance. Further, the good performances were also exhibited by RF model with calibration (R² = 0.93) and validation (R² = 0.86, MAPE = 24.2%) datasets. We applied GBDT and RF models to map SPM of typical lakes, and satisfactory result was obtained. In addition, the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, and L8-OLI), thus the model has the potential to map SPM of lakes for monitoring temporal variations, and tracks lake water SPM dynamics in approximately the past four decades (1984–2021) since Landsat-5/TM was launched in 1984.

The Yihe River Basin is a key area for water conservation and soil erosion control in northern China. The excessive development of land resources is a major factor causing soil erosion and ecological degradation. However, the impacts of land use change on soil erosion in the basin are not yet clearly. Understanding the complex relationship between land use and soil erosion is an important way to promote the development of land resources utilization and ecological construction from cognition to decision-making. This study simulated the temporal-spatial changes of soil erosion in the basin from 1956 to 2020 using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and evaluated the changes of soil erosion under different land use scenarios from 2020 to 2050 using Future Land Use Simulation (FLUS) model. From 1956 to 2020, the overall soil erosion intensity showed a slight decreasing trend, and the average annual soil erosion modulus was 38.21 t/ha/year. Soil erosion intensity was higher in the central and northern mountainous areas, while it was lower in the flat alluvial plains in the south. Arable land (4.07 t/ha/year) was the largest contributor to the amount of soil erosion, and land use changes caused the soil erosion intensity to fluctuate and decrease after 1995. From 2020 to 2050, soil erosion varied widely under different land use scenarios, and the land use pattern targeting ecological priority development would effectively mitigate soil erosion. Therefore, optimizing land use patterns and structures are critical initiatives to prevent soil erosion.

Soil erosion and sedimentation are among the most serious global environmental problems. Soil and water conservation measures have been proven effective ways to reduce soil loss. The objective of this study was to evaluate the impact of three approaches of soil and water conservation measures (soil management, vegetative measures, and structural practices) on soil erosion and water balance of two paired agricultural watersheds located in Southern Brazil. Streamflow and sediment monitoring was carried out from 2016 to 2019 in the two small paired agricultural watersheds; called North watershed (NW) and South watershed (SW). Modeling using Soil & Water Assessment Tool (SWAT) was performed to simulate individual (nine scenarios) and combined (four scenarios) best management practices (BMPs), by including the three approaches. Among the nine individual BMP scenarios, the most effective in reducing soil erosion was crop rotation and cover crop (sediment yield, SY, reduction of 38.4 for NW, and 28.8% for SW). Among the four combined scenarios, the association of all conservation approaches was the most effective in reducing soil erosion (SY reduction of 46 for NW, and 41.5% for SW), followed by the vegetative measures scenario (SY reduction of 43.5 and 34.1% for NW and SW). All combined scenarios increased infiltration and subsurface water components, and decreased surface runoff. The findings of this study can help farmers and policymakers choosing appropriate BMPs to reduce current soil erosion problems and promote water and soil conservation.

Millions of dollars are being spent on gully rehabilitation to help reduce excess fine sediment delivery to the Great Barrier Reef (GBR). There is an urgent need for (i) prioritisation of active gullies for rehabilitation and (ii) the development of methodologies to inform the effectiveness of remediation. In this study we analyse DEMs of Difference derived from 0.5 m resolution 2–3 year interval multi-temporal LiDAR data collected pre and post rehabilitation at three variable gully morphologies in the Burdekin catchment. Our analysis indicates that the highest annual average fine sediment erosion rates for the untreated control gullies occur at the linear gully (53.38 t ha⁻¹ y⁻¹) followed by linear-alluvial gully (34.24 t ha⁻¹ y⁻¹) and least at the alluvial gully (14.41 t ha⁻¹ y⁻¹). The proportional loss or export of fine sediment from the gullies in their un-treated condition ranges from ∼68 to 90% of what is eroded, and when the gullies are treated the proportion of fine sediment that is retained in the gully proportional to what is eroded increases to ∼60% at all sites. Without pre-treatment baseline erosion rates, and additional post treatment LiDAR captures, it is difficult to quantify the treatment effectiveness. Our results offer insights in the erosion mechanisms within different geomorphic gully morphologies and rehabilitation effects in these erosional landforms. This study provides crucial knowledge of gully dynamics that can be coupled with other lines of evidence for better prioritisation of rehabilitation in the GBR catchments.