Search Results (86)

It is crucial to clarify the grassland carrying capacity (CC) and the balance between grass and livestock under different scenarios for ecological protection and sustainable development in the Three River Headwaters Region (TRHR). This study focused on the TRHR and used livestock data, MODIS Net Primary Productivity (NPP) data, and artificial supplementary feeding data to analyze grassland CC and explore changes in the grass–livestock balance across various scenarios. The results showed that the theoretical CC of edible forage under complete grazing conditions was much lower than that of crude protein under nutritional carrying conditions. Furthermore, without increasing the grazing intensity of natural grasslands, artificial supplementary feeding reduced overstocking areas by 21%. These results suggest that supplementary feeding effectively addresses the imbalance between forage supply and demand, serving as a key measure for achieving sustainable grassland livestock husbandry. Despite the effective mitigation of grassland degradation in the TRHR due to strict grass–livestock balance policies and ecological restoration projects, the actual livestock CC exceeded the theoretical capacity, leading to overgrazing in some areas. To achieve desired objectives, more effective grassland management strategies must be implemented in the future to minimize spatiotemporal conflicts between grasses and livestock and ensure the health and stability of grassland ecosystems. Full article

Sediment fingerprinting was utilized to identify potential hotspots of soil erosion and sediment transport pathways in the Nile Nyabarongo Upper Catchment (NNYU) in Rwanda, where rivers and reservoirs are suffering from alarmingly high levels of sedimentation. Sediment fingerprinting is a practical approach used to identify erosional hotspots and sediment transport processes in highly mountainous regions undergoing swift land use transformation. This technique involves a statistical comparison of the elemental composition of suspended sediments in river water with the elemental composition of soils belonging to different geological formations present in the catchment, thereby determining the sources of the suspended sediment. Suspended sediments were sampled five times over dry and wet seasons in all major headwater tributaries, as well as the main river channel, and compared with soils from respective delineated watersheds. Elemental composition was obtained using laser ablation inductively coupled plasma mass spectrometry, and elements were chosen that could reliably distinguish between the various geological types. The final results indicate different levels of sediment contribution from different geological types. A three-level intervention priority system was devised, with Level 1 indicating the areas with the most serious erosion. Potential sources were located on an administrative map, with the highest likely erosion over the study period (Level 1) occurring in Kabuga cell in the Mwogo sub-catchment, Nganzo and Nyamirama cells in the Nyagako sub-catchment and Kanyana cell in the NNYU downstream sub-catchment. This map enables the pinpointing of site visits in an extensive and rugged terrain to verify the areas and causes of erosion and the pathways of sediment transport. Sediment concentrations (mg L⁻¹) were the highest in the Secoko and Satinsyi tributaries. The composition of suspended sediment was seen to be temporally and spatially dynamic at each sampling point, suggesting the need for an adequate number of sampling locations to identify erosion hotspots in a large mountainous watershed. Apart from prioritizing rehabilitation locations, the detailed understanding of critical zone soil–land cover–climate processes is an important input for developing region-specific watershed management and policy guidelines. Full article

Grassland productivity is crucial for sustainable alpine livestock farming, yet the combined effects of climate change and topography remain unclear. Using long-term time series data of grassland NPP derived from Landsat imagery, along with meteorological and DEM data, this study employed correlation analysis and SEM to quantify climate-driven grassland NPP dynamics and topography-mediated regulatory effects in the Three-River Headwaters Region between 1990 and 2020. Significant spatiotemporal dynamics of grassland NPP were found in response to climate change over the past thirty years. Grassland NPP declined before 1994 and then grew significantly after 1995 at an average rate of 0.88 gC·m⁻²·a⁻¹ (p < 0.01). Spatially, NPP increased in 69% of the region, with significant and highly significant growth in 9.5% (p < 0.05) and 35.7% (p < 0.01), mainly in the southeast. Driven by general warming and wetting, topographic modulation of hydrothermal conditions had intensified a mismatch in both time and space between grassland NPP and climate change, particularly in temperature sensitivity. The positive effect of temperature on NPP shifted to higher elevations (4000–5000 m) and lower slopes (5–25°), with NPP at higher elevations exhibiting greater sensitivity to temperature changes. However, the most substantial contributions to the overall rise in NPP occurred at altitudes of 3000–4000 m and slopes of 0–25°. The key mechanism is that NPP growth above 4000 m was constrained by precipitation scarcity despite thermal limitation alleviation from warming. Overall, the direct effects of climate change outweighed those of various topographic factors, with both showing slight declines since 2010. These findings highlight the need for differentiated governance, restoration, and adaptive management of grasslands across diverse topographic gradients. Full article

Remote sensing plays an important role in understanding the degradation and restoration processes of alpine grasslands. However, the extreme climatic conditions of the region pose difficulties in collecting field spectral data on which remote sensing is based. Thus, in-depth knowledge of the spectral characteristics of alpine grasslands and an accurate assessment of their restoration status are still lacking. In this study, we collected the canopy hyperspectral data of plant communities in the growing season from severely degraded grasslands and actively restored grasslands of different ages in 13 counties of the “Three-River Headwaters Region” and determined the absorption characteristics in the red-light region as well as the trends of red-light parameters. We generated a model for estimating the crude protein content of plant communities in different grasslands based on the screened spectral characteristic covariates. Our results revealed that (1) the raw reflectance parameters of the near-infrared band spectra can distinguish alpine Kobresia meadow from extremely degraded and actively restored grasslands; (2) the wavelength value red-edge position (REP), corresponding to the highest point of the first derivative (FD) spectral reflectance (680–750 nm), can identify the extremely degraded grassland invaded by Artemisia frigida; and (3) the red valley reflectance (Rrw) parameter of the continuum removal (CR) spectral curve (550–750 nm) can discriminate among actively restored grasslands of different ages. In comparison with the Kobresia meadow, the predictive model for the actively restored grassland was more accurate, reaching an accuracy of over 60%. In conclusion, the predictive modeling of forage crude protein content for actively restored grasslands is beneficial for grassland management and sustainable development policies. Full article

Noxious weeds (NWs) are increasingly recognized as a significant threat to the native alpine grassland ecosystems of the Qinghai–Tibetan Plateau (QTP). However, large-scale quantification of their continuous fractional cover remains challenging. This study proposes a pixel-level estimation framework utilizing time-series Sentinel-2 imagery. A Dynamic Mask Non-Stationary Transformer (DMNST) model was developed and trained using multi-temporal multispectral data to map the spatial distribution of NWs in the Three-River Headwaters Region. The model was calibrated and validated using field data collected from 170 plots (1530 quadrats). The results demonstrated that both the dynamic masking module and the non-stationary normalization significantly enhanced the prediction accuracy and robustness, particularly when applied jointly. The model performance varied across different combinations of spectral bands and temporal inputs, with the optimal configurations achieving a test R² of 0.770, MSE of 0.009, and RMSE of 0.096. These findings underscore the critical role of the input configuration and architectural enhancements in accurately modeling the fractional cover of NWs. This study confirms the applicability of Sentinel-2 time-series imagery for modeling the continuous fractional cover of NWs and provides a scalable tool for invasive species monitoring and ecological risk assessment in alpine ecosystems. Full article

The High Andean region of Peru, characterized by a complex orography, has unique and highly biodiverse ecosystems. This region has several headwater basins that play a critical role in the hydrological cycle, providing diverse ecosystem services essential to sustain biodiversity and supply water to human communities. Despite the importance of this region, it faces significant human intervention, particularly mining activities, which affect basin headwaters and jeopardize water security. This study aimed to calibrate the Biological Monitoring Working Party (BMWP) index to evaluate water quality in High Andean rivers in Peru affected by mining activities, using aquatic macroinvertebrates as bioindicators. We used a 15-year dataset (2008 to 2023) from three headwater basins in the High Andean region; this dataset included physicochemical water quality parameters, trace metals, and aquatic macroinvertebrates. The BMWP was calibrated for the High Andean region of Peru with this dataset (BMWP/PeIAZIM); afterward, it was validated to assess water quality in an area influenced by mining activities in this region. The results allowed us to differentiate between aquatic macroinvertebrate families tolerant to mining pollution and highly sensitive families. The sites heavily affected by mining activity returned very low BMWP/PeIAZIM scores; sites with no mining impact had the highest scores. These findings indicate that the calibrated index can be used for water resource management in the High Andean region, contributing to the conservation of its ecosystems. Full article

To preserve ecological integrity and promote sustainable progress in the Three-River Headwaters Region (TRHR), it is vital to understand the vegetation alteration patterns and the sensitivity of these patterns to climatic and anthropogenic influences. In this study, we retrieved the fractional vegetation cover (FVC) through the dimidiate pixel model, driven by MODIS reflectance data from 2001 to 2022, and analyzed its spatiotemporal variations and responses to climate variation and human activities via partial correlation and residual analyses. The results indicated that the FVC retrieval accuracy reached 84.2%. From 2001 to 2022, the growing season FVC displayed a fluctuating yet overall increasing trend, with an average growth rate of 0.23% per year (p < 0.01). The vegetation significantly improved in 50.72% of the TRHR, with the Yellow River source area exhibiting the most notable improvement. However, 67.42% of the TRHR experienced a transition from improvement to degradation in vegetation, indicating a pessimistic outlook for future changes. Partial correlation analysis revealed that temperature had a pronounced influence on the southwestern Yellow River Basin and the southern Yangtze River Basin, whereas precipitation had a substantial effect on the southwestern and northeastern sections of the Yellow River Basin. Additionally, residual analysis revealed that climate change served as the predominant factor behind the changes in the FVC, whereas the anthropogenic intervention contributed substantially to vegetation improvements in the northeastern and western portions of the Yellow River Basin. Our study provides scientific support for the construction of ecological security barriers and the harmonious development of humans and nature in the TRHR. Full article

The vulnerability of Social–Ecological Systems (SES) is a frontier research topic in the field of geography. Research on drought vulnerability has emerged as a key area of focus in the study of SES vulnerability, and it has increasingly been recognized as a critical step in formulating policies for drought prevention and mitigation. In this study, the indicator system for drought vulnerability evaluation of SES in the Three-River Headwaters Region (TRHR) was established. This paper revealed the drought vulnerability evolution process and characteristics, and key driving indicators of SES at county-town-village spatial scales in six time periods of 1990, 2000, 2010, 2015, 2020, and 2023, and predicted the drought vulnerability of SES in 2050 under two scenarios. Results indicate that the average drought vulnerability in the TRHR decreased from 0.526 in 1990 to 0.444 in 2023. Compared to 1990, among the 82 selected towns, 85.37% experienced a decline in 2023, and among the 152 selected villages, 95.39% showed a reduction in 2023. Hot spots of drought vulnerability were concentrated in the southeast of the TRHR, while cold spots were in the northwest. From 1990 to 2000, the drought vulnerability of counties and towns in the TRHR increased, but it decreased between 2000 and 2023. In 1990, Henan County exhibited the highest drought vulnerability at the county level. Waeryi Town in Jiuzhi County had the highest vulnerability among towns, while Suojia Town in Zhidoi County had the lowest. Of the 152 selected villages, 41.45% exhibited relatively high or high levels of drought vulnerability, while 23.68% showed relatively low levels. In 2023, Jiuzhi County became the most vulnerable county, with Baiyu Town in Jiuzhi County ranking highest among towns and Suojia Town in Zhidoi County remaining the least vulnerable. At the village level, 22.37% exhibited relatively high or high vulnerability, whereas 42.11% showed relatively low or low levels. Drought disaster records, the proportion of agricultural and animal husbandry output value, the proportion of grassland, the proportion of large livestock, and the per capita disposable income surface are the key factors influencing drought vulnerability in the TRHR. By 2050, under the first scenario, the average drought vulnerability of the TRHR is projected to be 0.428, indicating a medium level, while the second scenario predicts a further reduction to 0.350, representing a relatively low level. The adaptive governance strategies to mitigate drought vulnerability in the TRHR include developing an integrated drought management system; establishing an ecological management, protection, and financial support model; and so on. Overall, this paper can provide scientific references and policy recommendations for policymakers and researchers on the aspects of drought vulnerability and sustainable development of SES. Full article

Examining the effects of climate change (CC) and anthropogenic activities (AAs) on vegetation dynamics is essential for ecosystem management. However, the time lag and accumulation effects of climate change on plant growth are often overlooked, resulting in an underestimation of CC impacts. Combined with the kernel normalized difference vegetation index (kNDVI), climate data during the growing season from 2000 to 2023 in the Three Rivers Source Region (TRSR) and trend and correlation analyses were employed to assess kNDVI dynamics. Furthermore, time lag and accumulation effect analyses and an upgraded residual analysis were applied to explore how climatic and human drivers jointly influence vegetation. The results show the following: (1) The kNDVI showed a fluctuating but overall increasing trend, indicating an overall improvement in vegetation growth. Although future vegetation is likely to continue improving, certain areas—such as the east of the western Yangtze River basin, south of the Yellow River basin, and parts of the Lancang River basin—will remain at risk of deterioration. (2) Overall, both precipitation and temperature were positively correlated with the kNDVI, with temperature acting as the dominant factor affecting plant growth. The predominant temporal effects of precipitation on the kNDVI were a 0-month lag and a 1-month accumulation, while temperature primarily showed a 2–3-month lag and a 0–1-month accumulation. The main category of the overall climatic temporal effects were precipitation accumulation and temperature time lag effects (PA_TL), which accounted for 70.93% of the TRSR. (3) Together, CC and AA drove vegetation dynamics, with contributions of 35.73% and 64.27%, respectively, indicating that AA played a dominant role. Furthermore, incorporating combined time lag and accumulation effects enhanced the explanatory ability of climatic factors for vegetation growth. Full article

Climate variations and human activities, as two major driving forces, have profound impacts on alpine ecosystems. The Three-River Headwaters Region (TRHR) is located in the alpine region and is the source of three major rivers flowing to eastern China and Southeast Asia. Grassland is the dominant vegetation type in the TRHR and is fragile and sensitive to climate variations and human activities due to the alpine environment. Different types of grassland may have varying coping mechanisms with disturbances due to their unique environments and physiological functions. However, there is limited quantitative research on the response of different grassland types to climate variations and human activities in the TRHR. Therefore, the Carnegie–Ames–Stanford approach (CASA) was selected to simulate the net primary productivity (NPP) affected by climate (NPP_C) and the actual NPP (NPP_A) of steppes and meadows in the TRHR from 2001 to 2022, and the NPP affected by human activities (NPP_H) was calculated by subtracting the NPP_A from the NPP_C. Results showed that the NPP_A increased by 0.53 gC/m²/a during the study period, with the NPP_A of steppes and meadows increasing by 0.55 gC/m²/a and 0.51 gC/m²/a, respectively. The regions dominated by climate variations, human activities, and the combined impact of the two accounted for 22.01%, 29.42%, and 48.57% of the NPP_A changes. In terms of climate change, the impact of temperature and soil moisture on the NPP is equally important. It is worth noting that the alpine meadows (67.60%) contributed more to the increases in the NPP_A than the steppes (32.40%). In addition, climate variations and human activities contributed more to the increased total NPP_A of the meadows (20.54 GgC and 36.41 GgC) than that of the steppes (14.35 GgC and 10.20 GgC). The results clarify the quantitative evaluation system for the impact of human activities and climate change on different types of grasslands in the TRHR, providing guidance for the protection and management of these grasslands. Full article

Prior to implementing watershed-wide projects to reduce the impacts of agriculture on regional streams and rivers, stream habitats and benthic aquatic macroinvertebrate communities were assessed at 15 sites on the South Branch Root River and its major tributaries in southeastern Minnesota, USA. Triplicate kick-net samples were collected from each site during three time periods (1998, 1999, 2006/2008) and stream habitats were inventoried within 150 m long sections at each site. In total, 26,760 invertebrates representing 84 taxa were collected and used to rate stream sites using a regional multi-metric benthic index of biotic integrity (BIBI). BIBI scores were significantly correlated with total invertebrate taxa richness. BIBI ratings improved from poor and very poor at headwater sites in channelized stream sections draining agricultural lands to fair to good to excellent in downstream sections flowing through natural channels in largely forested lands. Fifty percent of samples rated stream sites as poor or very poor. Over 85% of stream habitat assessments indicated the presence of fair to good habitats, although stream sites were relatively wide and shallow and dominated by fine sediments that also embedded coarser substrates. BIBI metrics and scores were strongly positively correlated with pool area, riffle spacing-to-stream width ratios, and silt-free substrate, and negatively correlated with width-to-depth ratios. Most stream sites had few Ephemeroptera, Plecoptera, Trichoptera, and Diptera taxa and too few intolerant taxa. It is expected that benthic invertebrate communities should improve as more riparian buffers are added along all streams. However, on-going channel maintenance activities in headwater stream sections, mandated to encourage drainage of adjacent agricultural fields, will continue to negatively impact headwater habitats and biotic communities. Full article

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R² of 0.44 and an RMSE of 2.56 n/m² on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management. Full article

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems. Full article

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the “Water Tower of China”. We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world. Full article