Assessment of soil erosion risk and its response to climate change in the mid-Yarlung Tsangpo River region

Understanding the mechanism of gully erosion in the alpine region through an interpretable machine learning approach

In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.

Cover crop performance under a changing climate in continuous corn system over Nebraska

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha- 1 ) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Assessment of sediment transport in Luxiapuqu watershed using RUSLE-TLSD and InSAR techniques: Yarlung Tsangpo River, China

The Yarlung Tsangpo River Basin is characterized by its intricate topography and a significant presence of erosive materials. These often coincide with heavy localized precipitation, resulting in pronounced hydraulic erosion and geological hazards in mountainous regions. To tackle this challenge, we integrated the RUSLE-TLSD (Revised Universal Soil Loss Equation-Transportation-limited sediment delivery) model with InSAR (Interferometric Synthetic Aperture Radar) data, aiming to explore the sediment transport process and pinpoint hazard-prone sites within mountainous small watershed. The RUSLE-TLSD model aids in evaluating multi-year sediment transport dynamics in mountainous zones. And, the InSAR data precisely delineates changes in sediment scouring and siltation at sites vulnerable to hazards. Our research estimates that the potential average soil erosion within the watershed stands at 52.33 t/(hm2 a), with a net soil erosion of 0.69 t/(hm2 a), the sediment transport pathways manifest within the watershed's gullies and channels. Around 4.32% of the watershed area undergoes sedimentation, predominantly at the base of slopes and within channels. Notably, areas (d) and (e) emerge as the most susceptible to disasters within the watershed. Further analysis of the InSAR data highlighted four regions in the typical area (e) from 2017 that are either sedimentation- or erosion-prone, referred to as "hotspots." Among them, R1 exhibits a strong interplay between water and sediment, rendering it highly sensitive to environmental factors. In contrast, R4, characterized by a sharp bend in siltation, remains relatively impervious to external elements. The NDVI (normalized difference vegetation index) stands out as the pivotal determinant influencing sediment transport within the watershed, exerting a pronounced impact on the outlet section, especially in spring. By employing this approach, we gained a deeper understanding of sediment transport mechanisms and potential hazards in small watershed in uninformative mountainous areas. This study furnishes a robust scientific framework beneficial for erosion mitigation and disaster surveillance in mountainous watersheds.

Improvement of sediment yield index model through incorporating rainfall erosivity

Soil erosion and sediment yield in watersheds are comprehensively affected by land use/cover changes and climatic factors. The current sediment yield index (SYI) model incorporates parameters of area (A), delivery ratio (DR), and curve number (CN), which reflect the character of underlying surface conditions, while the impact of rainfall intensity on sediment yield could not be properly considered. This study aims to improve the current SYI model by introducing rainfall-related factors such as rainfall erosivity (R) and applying it to estimate the sediment yield of river basin. Taking the Dongjiang River basin, South China, as a case study, the performances of the improved simplified SYI model (SYI-CN + R) were compared and demonstrated at multi-spatiotemporal scales. The results showed that (1) compared with the SYI model which only has the parameter CN (SYI-CN), the model SYI-CN + R achieves better simulation performances at yearly (the efficiency coefficient (CE) is 81% in the whole basin and 62% in the sub-basin) and half-month (CE is 69% in the whole basin and 57% in the sub-basin) time scales. (2) On the basin scale, the simulation performance in the whole basin is better overall compared to that in the sub-basin, and the model SYI-CN + R at the half-month time scale is more suitable for the sediment yield simulation in the Dongjiang River basin, with higher value of correlation coefficient (CC) of 87% and 83% for the whole basin and the sub-basin, respectively. And (3) the values of CN and R have an obvious spatial gradient in the whole basin, showing an increasing trend from northeast to southwest as a whole, with larger values concentrated in the lower reaches and smaller values in the middle and upper reaches. This study extends the application and improves the performance of the SYI model, and provides a basis for soil and water conservation in a river basin with fewer observation data.

Effects of land-use patterns on the biogeography of the sediment bacteria in the Yarlung Tsangpo River

Bacteria communities, as key drivers of energy flow and nutrient recycling in rivers, usually consist of a few abundant taxa and many rare taxa. During the last decades, rivers on the Tibetan Plateau have experienced dramatic land surface changes under climate change and anthropogenic disturbances. However, the responses of abundant and rare taxa to such changes and disturbances still remains unclear. In this study, we explored the biogeography and drivers of the abundant and rare bacteria in Yarlung Tsangpo River sediments on the Tibetan Plateau. Our study demonstrated that changes in surrounding land-use patterns, especially in forest land, bare land and cropland, had profound influences on the distribution of the abundant and rare sediment bacteria in the Yarlung Tsangpo River. Although both communities exhibited significant distance-decay patterns, dispersal limitation was the dominant process in the abundant community, while the rare community was mainly driven by heterogeneous selection. Our results also revealed that the abundant bacteria exhibited stronger adaptation across environmental gradients than the rare bacteria. The similar biogeographic patterns but contrasting assembly processes in abundant and rare communities may result from the differences in their environmental adaptation processes. This work provides valuable insights into the importance of land surface changes in influencing the biogeographic patterns of bacteria in fluvial sediments, which helps to predict their activities and patterns in Tibetan rivers under future global climate change and anthropogenic disturbances.

Frequency analysis of storm-scale soil erosion and characterization of extreme erosive events by linking the DWEPP model and a stochastic rainfall generator

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m^-2 [1.1 t ha^-1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > ~60 mm h^-1) and high rainfall depth (>~200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha^-1 y^-1. The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha^-1 y^-1 and 4.1 t ha^-1 y^-1, respectively. They were followed by agriculture fields with 1.31 t ha^-1 y^-1 and 1.33 t ha^-1 y^-1. The lowest results were located in the residential areas with 0.61 t ha^-1 y^-1 and 0.63 t ha^-1 y^-1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by - 0.24 t ha^-1 y^-1 (2050), and - 0.18 t ha^-1 y^-1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

Pattern of Turbidity Change in the Middle Reaches of the Yarlung Zangbo River, Southern Tibetan Plateau, from 2007 to 2017

Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau.

Real Time Sensing of Soil Potassium Levels Using Zinc Oxide-Multiwall Carbon Nanotube-Based Sensors

There is a significant interest in the detection and monitoring of nutrient levels in agriculture farms. In this article, we report the fabrication of Zinc oxide (ZnO) modified multi-walled Carbon nanotube (F-MWCNT) sensor specifically developed for soil nutrient sensing. A thin layer of Valinomycin membrane was grown on the top of the F-MWCNT/ZnO nanocomposite active layer. The resulting composite structure Al/F-MWCNT/ZnO/Valinomycin was found to have a proportional impedance change with soil Potassium (K⁺) levels. The performance of the sensor was investigated in the 1- 5 kHz range. The impedance magnitude was found to linearly decrease from 2.5± 0.23 to [Formula: see text] range for K⁺ concentrations from 5 to 25 mM displaying a sensitivity of [Formula: see text]/mM with a correlation coefficient (R²) of 0.95744.

Suspended Sediment Transport Responses to Increasing Human Activities in a High-Altitude River: A Case Study in a Typical Sub-Catchment of the Yarlung Tsangpo River

The Yarlung Tsangpo River is one of the highest major rivers in the world. The river is known for its pristine landscape. However, in recent years, increasing human activities, such as inhabitation, afforestation, and infrastructure projects, have significantly disturbed this fluvial system, while their impacts are not fully known. In this study, the water and sediment transport processes in the Nugesha–Yangcun (N–Y) reach of the Yarlung Tsangpo River, as well as the impact of human activity, are investigated. The N–Y sub-catchment consists of two parts, i.e., the Lhasa River catchment and the mainstream catchment. Riverine discharge, suspended sediment concentration (SSC), and precipitation data are acquired, and a detailed investigation is conducted. The water yield has not changed considerably in recent years, while the sediment yield has exhibited a sharp decline, from ~5 Mt to ~1 Mt. The sediment decrease is mainly caused by the reduced sediment source, which is considered highly related to afforestation. In addition, the dominant sediment contributor has changed from the mainstream catchment to the tributary catchment (while the sediment yield in the mainstream catchment has decreased to almost zero). An anomalously enhanced SSC occurred in the Lhasa River in two consecutive years from 2015, with the SSC value increasing sharply from 0.2 kg/m3 to 0.8 kg/m3, and maintaining a high level for approximately three months. This phenomenon is considered to be related to infrastructure projects in the same period, with the SSC recovered after road construction ended. The increasing human activities have had significant impacts on the sediment regimes in the Yarlung Tsangpo River; hence, more attention should be paid to river basin management.