Search

Understanding the vegetation phenology and its variations in the Tibetan Plateau is critical to the study of ecological responses to global climate change. In this study, several pre-processed methods or techniques were applied to filter the Global Inventory Modelling and Mapping Study's Normalized Difference Vegetation Index (GIMMS NDVI) data from 1982 to 2006, and construct the daily NDVI series. Firstly, vegetation and non-vegetation were determined by NDVI quantity contour, and cloud-covered pixels were also eliminated by NDVI change characteristics in a year. Then, the NDVI series were filtered by three-standard deviation and Savitzky-Golay method. Finally, the Savitzky-Golay method was employed to fit and construct the daily NDVI series. These methods guarantee a more reliable subsequent calculation of subsequent vegetation phenology. The vegetation phenology parameters including the start of growth season (SOG), the end of growth season (EOG), the lengths of growth season (LOG) and the absolute increase in vegetation (AIV), defined as the difference between the maximum NDVI and the NDVI for SOG in a year, were derived from the daily NDVI series based on the maximum ratio threshold method and their variations were analysed. The results showed that the SOGs were gradually delayed from the southeast to the northwest of the Tibetan Plateau, but the distribution pattern of the EOGs was opposite to that of the SOGs. From 1982 to 2006, SOGs were advanced approximately 3-18 days and EOGs delayed around 0-24 days in the southeast, whereas AIVs decreased around 0-0.3. In the northwest, these phenology parameters followed inverse trends compared with those of the southeast. Over the 25-year period, LOG changes had no constructive or active effects on the vegetation absolute increase. These complex phenological shifts were mainly due to the spatial differences in the environmental changes. However, in some extent, they might be related to the vegetation itself, such as its fractional cover. These findings may help to understand the alpine vegetation responds to climate change in the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The eco-environment in the source region of the Yellow River in western China has been experiencing deterioration in the past decades. Vegetation affected by climate variables and anthropogenic activities is indicative of eco-environment well-being. To quantify temporal and spatial variations of vegetation coverage and analyse potential causes for the variations, we analysed the normalized difference vegetation index (NDVI), temperature and precipitation data from 2000 to 2007. We found that altitude and topographic aspects have a strong influence on vegetation coverage. Altitudes between 4500 and 4800 m and shady aspects provide more favourable environments for vegetation growth. Data show stronger vegetation growth within the temperature range of 4.5–5.5°C. Vegetation growth generally increases with precipitation. At higher elevations of 4800–5200 m, however, despite high precipitation rates, lower temperatures restrict growth. Local hydrology conditions are found to directly influence vegetation variations. Vegetation degradation increases with distance from surface water boundaries up to 4 km, but groundwater might serve as a reliable source for preventing vegetation from degrading. Finally, we found that the percentage of degradation decreases with increasing distance from residential loci up to 24 km, which suggests that overgrazing can be a lead cause for localized vegetation degradation. Findings of this study may have a broad implication in assessing vegetation variation and grassland restoration. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin. [ABSTRACT FROM AUTHOR]

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management. [ABSTRACT FROM AUTHOR]

Thermal conductivity is a basic parameter of soil heat transferring, playing an important role in many fields including groundwater withdrawal, ground source heat pump, and heat storage in soils. However, it usually requires a lot of time and efforts to obtain soil thermal conductivity. To conveniently obtain accurate soil thermal conductivity, a new model describes the relationship between soil thermal conductivity (λ) and degree of saturation (Sr) was proposed in this study. Dry soil thermal conductivity (λdry) and saturated soil thermal conductivity (λsat) were described using a linear expression and a geometric mean model, respectively. A quadratic function with one constant was added to calculate λ beyond the lower λdry and upper λsat limit conditions. The proposed model is compared with five other frequently used models and measured data for 51 soil samples ranging from sand to silty clay loam. Results show that the proposed model match the measured data well. The proposed model can be used to determine soil thermal conductivity of a variety of soil textures over a wide range of water content. [ABSTRACT FROM AUTHOR]

Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km2 in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata's spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km2 to 9.02 km2 between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation. [ABSTRACT FROM AUTHOR]

• The variation characteristics of permafrost for 36 years has been identified. • The relationship between n in the Budyko framework and DDF is quantified. • Permafrost degradation contributes more than 20% to runoff change. To quantitatively investigate the permafrost degradation and its effects on hydrological processes in the upper reaches of Heihe River, a surface frost number model was firstly constructed to simulate the permafrost distribution and degradation from 1980 to 2015. The variation characteristics of the DDF, permafrost area, low limit of permafrost, and active layer thickness were then determined in the study area. Furthermore, the Budyko framework was applied to quantify the contribution of permafrost degradation to runoff. The results indicated that the permafrost degradation was significant in the past 36 years, with an average decreasing rate of 80.4 km2/a in the permafrost area, an average increasing rate of 13 m/a and 0.61 cm/a in the low limit elevation and in the active layer thickness, respectively. It is also found that basin characteristic parameter in the Budyko framework is closely related to permafrost degradation in cold regions, 40% of whose change is contributed by permafrost degradation. In addition, it is revealed that permafrost degradation contributed more than 20% to runoff change in study area. This investigation also indicated that the runoff response to permafrost degradation has a 3 months delayed effect. This study deepens the understanding of the impact of permafrost degradation on hydrological processes. [ABSTRACT FROM AUTHOR]

Glaciers on the Tibetan Plateau play an important role in the local hydrological cycle. However, there are only few studies on groundwater in the alpine basins in the Tibetan Plateau which considered the effects of glaciers. Glaciers are extensively distributed in the Dongkemadi River Basin, which is a representative alpine basin in the Yangtze River source region. This study focuses on building a numerical groundwater flow model with glaciations using HydroGeoSphere (HGS) to simulate subglacial meltwater recharge to groundwater in the Dongkemadi River Basin in response to future climate changes. Effects of hydraulic conductivity, precipitation, and temperature on subglacial meltwater recharge to groundwater were discussed. Glacier changes in the future 50 years were predicted under different climate change scenarios. Results show that: (1) the average thickness of the glacier will change significantly; (2) the simulated rate of annual mean subglacial meltwater recharge to groundwater is 4.58 mm, which accounts for 6.33% of total groundwater recharge; and (3) hydraulic conductivity has the largest influence on subglacial meltwater recharge to groundwater, followed by temperature and precipitation. Results of this study are also important to sustainable water resource usage in the Yangtze River source region. Article Impact Statement: Investigate the subglacial meltwater recharge to groundwater in the Dongkemadi River Basin in response to future climate change. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the important components of the global hydrologic cycle, energy exchange, and carbon cycle. However, basin scale actual ET (hereafter ETa) is difficult to estimate accurately. We present an evaluation of four actual ET products (hereafter ETp) in seven sub-basins in the Tibetan Plateau. The actual ET calculated by the water balance method (hereafter ETref) was used as the reference for correction of the different ETp. The ETref and ETp show obvious seasonal cycles, but the ETp overestimated or underestimated the ET of the sub-basins in the Tibetan Plateau. A simple and effective method was proposed to correct the basin-scale ETp. The method was referred to as ratio bias correction, and it can effectively remove nearly all biases of the ETp. The proposed method is simpler and more effective in correcting the four ETp compared with the gamma distribution bias correction method. The reliability of the ETp is significantly increased after the ratio bias correction. The ratio bias correction method was used to correct the ETp in the seven sub-basins in the Tibetan Plateau, and regional ET was significantly improved. The results may help improve estimation of the ET of the Tibetan Plateau and thereby contribute to a better understanding of the hydrologic cycle of the plateau. [ABSTRACT FROM AUTHOR]