International Soil and Water Conservation Research最新文献

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.

Establish a reliable rainfall-runoff relation capable of predicting runoff in ungauged basins is a matter of interest across the world for a long time and has been taking importance during the past decades. Regionalization approaches, hydrological models and machine learning techniques have been used to estimate runoff. However, returning some simplicity to the predictions might be necessary for practical uses. In this paper, we re-introduce C. E. Grunsky approach, developed in the early 1900s to predict runoff from values of precipitation on a two-equations system. Here, we analyze the Grunsky generalized method applied for 716 Brazilian catchments, on an interannual and monthly scales. First, we established the best method to find the rainfall-runoff relation coefficient for each catchment. Then, we evaluate the performance of the method on a local scale, i.e., catchment by catchment. Lastly, we analyze the method of regionalization, by grouping the catchments into six hydrologically similar classes. For local scale, the Kling-Gupta Efficiency (KGE) values range from 0.87 to 0.93 on the interannual scale and is greater than 0.50 on the monthly scale. For the regionalized approach, KGE varies from 0.60 to 0.84 on an interannual scale. We also found suitable KGE values on a monthly scale, with more than 22% of catchments with KGE greater than 0.50, being the best performances in the Non-seasonal and Extremely-wet groups, and the worst performance in the Dry group. Our findings indicate that Grunsky approach is suitable to predict streamflow for Brazilian catchments on interannual and monthly scales. This simple and easy-to-use equation presents a reliable alternative to more complex methods to compute runoff by only using rainfall data.

Marginalization and abandonment of paddy terraces are widespread, but their effects on the sustainability of subsequent agricultural production are still unknown. Hani Paddy Terraces, included in Globally Important Agriculture Heritage Systems, are threatened by paddy fields drainage. Here, changes in terrace structure, the productivity of topsoil (0–20 cm), and soil water holding capacity at 0–70 cm depth were determined in a case study of Hani Paddy Terraces in Amengkong River Basin in Yuanyang County in Southwestern China, which had been converted into dryland terraces for 2–14 years. Our results showed that: (1) The degree of terrace structures degradation exhibited a U-shaped curve with increasing time since draining, with those drained for 5–9 years having the best structure; (2) Soil productivity index decreased first and then increased with time after conversion; (3) Maximum water holding capacity at 0–70 cm soil depth dramatically decreased after conversion and such trend became increasingly obvious with increasing time since conversion. Our study revealed that drainage of paddy terraces along with associated changes in crop and field management led to an increase in soil productivity, but degradation of terrace structures and a decrease in water holding capacity will inhibit restoration to paddy terraces. These findings enhance the understanding of the biophysical changes due to marginalization in paddy terraces.

Accurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity. This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency. However, simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions. Here, we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data. We selected four study areas representing typical loess landforms to test the performance of our method. The proposed method can achieve satisfactory mapping results, with the F1 score, mean Intersection-over-Union (mIoU), and overall accuracy of 90.5%, 85.3%, and 92.3%, respectively. In addition, the proposed model also showed significant accuracy improvement by inputting additional topographic information (especially the slope of slope). Compared with existing algorithms (Random forests, original DeepLabV3+, and Unet), the proposed approach in this study achieved a better accuracy-efficiency trade-off. Overall, the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.