Source Region Of The Yellow River Research Articles

High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

Soil moisture (SM) is a critical component of the land surface hydrological cycle, significantly impacting various sectors such as hydrology, meteorology, and agriculture. Accurate, high-resolution SM data are essential for effective flood forecasting, water resource management, and understanding the soil freeze-thaw processes in cold regions. This study aims to generate 1 km resolution liquid surface SM (SSM) data with a twice-daily update frequency by downscaling SMAP Level-4 SSM data using random forest (RF) and multiple linear regression (MLR) in the source region of the Yellow River (SRYR), by considering the differences in SM changes between freezing and thawing periods. To obtain the SSM data, 16 downscaling schemes of both RF and MLR were designed for each of the three scenarios. In each downscaling process, both land surface temperature (LST) and normalized difference vegetation index (NDVI) were utilized in MLR and RF models, alongside various combinations of additional variables such as albedo, elevation, leaf area index (LAI), soil texture. Results showed that during the freezing period, RF produced superior SSM estimates when supplemented with NDVI, LST, albedo, elevation, LAI, and soil texture. MLR was more effective during the thawing period when paired with NDVI, LST, elevation, LAI, and soil texture. During the freezing period, the downscaled SMAP SSM exhibited average R, RMSE, ubRMSE of 0.76, 0.029 m3·m-3, and 0.023 m3·m-3, respectively, when compared with in-situ measurements. During the thawing period, the average R, MAE, RMSE, and ubRMSE between the downscaled SMAP SSM and in-situ measurements were 0.52, 0.057 m3·m-3, 0.067 m3·m-3, and 0.054 m3·m-3, respectively, compared to 0.45, 0.070 m3·m-3, 0.083 m3·m-3, and 0.060 m3·m-3 for the original SMAP SSM. Thus, the research significantly enhances both the accuracy and spatial resolution of SMAP SSM estimations, underscoring its vital role in advancing hydrological studies within the SRYR.

Quantitative analysis of the contribution of moisture recycling to precipitation in the cold region

This study quantitatively analyzed the contribution rate of recycled moisture to precipitation in the basin based on the Craig-Gordon model and the three-end-member mixing model through selecting 456 precipitation sample data collected from six sampling points in the source region of the Yellow River from September 2019 to August 2021. The results showed that: the contribution rate of moisture recycling to precipitation during the growing season is 40 %, and the total contribution to local moisture recycling is equivalent to 41 mm of precipitation. The contribution rate of evaporation and transpiration has obvious seasonal variation characteristics, showing a trend of decreasing first and then increasing in the source region of the Yellow River. Spatially, the contribution rate of evaporation and transpiration showed an increasing trend from south to north. It is assumed that all the precipitation generated by moisture recycling produces runoff, and the water yield is about 51 × 108 m3, which is 25 % of the total annual average runoff. In addition, the proportion of local moisture recirculation is mainly related to altitude, topography, vegetation coverage, and meteorological factors. Moisture recirculation is one of the important sources of precipitation in the source region of the Yellow River.

Climate change impacts on seasonal runoff in the source region of the Yellow River: Insights from CORDEX experiments with uncertainty analysis

The source region of the Yellow River (SRYR) plays a crucial role in water resources to downstream areas. This study investigates the potential impacts of climate change on seasonal runoff in the SRYR using an ensemble of Regional Climate Models (RCMs) from two CORDEX experiments under RCP2.6 and RCP8.5 scenarios coupled with the WaSiM hydrological model. Two versions of the WaSiM hydrological model (WaSiM-PM and WaSiM-Hamon) are employed to validate the historical runoff, and WaSiM-PM’s superior performance make it the preferred choice for projecting climate change impacts on runoff. After bias-correction, RCM outputs show significant improvements, with correlation coefficients above 0.99 for temperature and 0.95 for precipitation. Under RCP2.6, temperature is projected to increase gradually (0.01–0.12 °C/decade), while under RCP8.5, warming is more pronounced (0.49–0.74 °C/decade). Precipitation generally shows an increasing trend, with variations between scenarios and experiments. Runoff projections indicate consistent peak flows in July across all scenarios. By the 2090 s under RCP8.5, substantial decreases in runoff are projected, especially in autumn and winter (approaching 40 %), with annual mean runoff decreasing by 30.3 %-31.5 %. Uncertainty analysis reveals that GCMs are the primary source of uncertainty (37–47 %) in the 2040 s, while emission scenarios dominate (43–58 %) in the 2090 s. Hydrological models show seasonal variability in uncertainty contribution, and downscaling experiments also play a significant role. This study highlights the potential for significant decreases in water resources in the SRYR, particularly under high emission scenarios, emphasizing the need for robust adaptation strategies in water resource management.

Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

Stronger influences of grassland growth than grassland area on hydrological processes in the source region of the Yellow River

Concerns about regional hydrological responses to land use cover changes have arisen worldwide. However, the separation and quantification of the effects of changes in vegetation area and growth status on hydrological processes remains unclear. Here, we applied a distributed hydrology soil vegetation model (DHSVM) to analyze how the grassland growth state and surface area influence hydrological processes in the source region of the Yellow River (SRYR). The results showed that although the grassland surface area decreased slightly from 1981 to 2020, grassland growth (represented by the leaf area index) tended to increase from 2000. Grassland changes (including area and growth status) increased soil moisture storage and evapotranspiration to an overall extent of 2.03 % and 5.66 %, respectively; and most of the increase occurred after 2000 by 2.90 % and 9.40 %, respectively. These processes under grassland change led to an overall decrease in runoff of 2.41 %, with a much stronger decrease after 2000 of 4.3 %. Changes in grassland growth accounted for ∼95 % of the runoff variations caused by grassland changes in the SRYR during the 40-year study period, compared to changes in grassland surface area. Simulations assuming different extents of grassland degradation suggest that such degradation can reduce evapotranspiration and soil moisture storage, thereby increasing runoff, whereas grassland restoration has the opposite effect. Our results highlighted the sensitivity of hydrological processes in the SRYR to grassland growth and the need for appropriate interventions to maintain and restore grasslands.

Legacy and alternative perfluoroalkyl acids in the Yellow River on the Qinghai-Tibet Plateau: Levels, spatiotemporal characteristics, and multimedia transport processes

The source region of the Yellow River (SRYR) located in the northeast of the Qinghai-Tibetan Plateau is not only the largest runoff-producing area in the Yellow River Basin, but also the most important freshwater-supply ecological function area in China. In this study, the short-term spatiotemporal distribution of selected legacy and alternative perfluoroalkyl acids (PFAAs) in the SRYR was first investigated in multiple environmental media. Total PFAA concentrations were in the range of 1.16-14.3 ng/L, 4.25-42.1 pg/L, and 0.21-13.0 pg/g dw in rainwater, surface water, and sediment, respectively. C4-C7 PFAAs were predominant in various environmental matrices. Spatiotemporal characteristics were observed in the concentrations and composition profiles. Particularly, the spatial distribution of rainwater and the temporal distribution of surface water exhibited highly significant differences (p<0.01). Indian monsoon, westerly air masses, and local mountain-valley breeze were the driving factors that contributed to the change of rainwater. Rainwater, meltwater runoff, and precursor degradation were important sources of PFAA pollution in surface water. Organic carbon content was a major factor influencing PFAA distribution in sediment. These results provide a theoretical basis for revealing the regional transport and fate of PFAAs, and are also important prerequisites for effectively protecting the freshwater resource and aquatic environment of the Qinghai-Tibetan Plateau.

Cyclical ‘wetting’ phenomenon in the source region of Yellow River under long-term trends from 1956 to 2022

Study regionthe source region of the Yellow River (SRYB) Study focusDue to environmental changes, the drying and wetting processes have become increasingly intricate, and it is uncertain whether the wetting phenomenon in the SRYB indicates a long-term trend or a temporary cyclic event. This study utilized monthly streamflow and precipitation records from 1956 to 2022 to calculate hydrological and meteorological-dryness/wetness, represented by the standardized streamflow and precipitation indices, respectively. This study used detrended, wavelet, and cross-wavelet methods to investigate dry-wet variability and identified the atmospheric circulation phenomena driving the dryness/wetness and cyclical changes in the region. New hydro-meteorological insights for the SRYB(i) The SRYB showed an overall fluctuating wetting trend, which intensified after 2000. (ii) There were notable oscillatory cycles at 3–5 years, 10–12 years, and approximately 20–25 years in both hydrological and meteorological dry-wet variability. (iii) Transitions from dry to wet conditions had no significant link to short-term cycles (e.g., 3–5 years), but were linked to decadal cycles (e.g., 20–25 years). (iv) Spring and winter precipitation and streamflow dominated the interannual dry-wet variability. (v) The ENSO, AMO, and PDO influenced short-term cyclic fluctuations, while the AO and PDO influenced decadal cyclic fluctuations. This indicates that the wetting trend follows a cyclic pattern, and the intensification since 2000 is consistent with a decadal cyclic signal.

Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p &lt; 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p &lt; 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p &lt; 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p &lt; 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.

An Assessment of the Coupled Weather Research and Forecasting Hydrological Model on Streamflow Simulations over the Source Region of the Yellow River

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.

Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years

As a climate-sensitive region of the Tibetan Plateau, the source regions of the Yangtze and Yellow Rivers (SRYYRs) urgently require an analysis of land cover change (LUCCs) over a long period, high temporal resolution, and high spatial resolution. This study utilizes nearly 40 years of land cover, the Normalized Difference Vegetation Index (NDVI), climate, and geomorphological data, applying methods including a land transfer matrix, slope trend analysis, correlation analysis, and landscape pattern indices to analyze the spatial and temporal changes, composition, layout, and quality of the local land cover and the factors. The findings reveal that (1) the land cover area change rate was 8.96% over the past 40 years, the unutilized land area decreased by 24.49%, and the grassland area increased by 6.37%. The changes were obvious at the junction of the two source regions and the southeast side of the source region of the Yellow River. (2) the landscape pattern was more centralized and diversified. The number of low-cover grassland patches increased by 12.92%. (3) The region is still dominated by medium- and low-cover vegetation, with the mean annual NDVI increasing at a rate of 0.006/10a, and the rate of change after 2000 is three times higher than previously. (4) The degree of land cover change is greater in the middle altitudes, semisunny aspects, steepest slopes, and middle-relief mountains. Additionally, 76.8% of the region’s vegetation growth is dominated by mean annual temperatures. This study provides fundamental data and theory for understanding LUCCs and the driving factors in alpine plateau regions.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Regional climate response to global warming in the source region of the Yellow River and its impact on runoff

Regional climate response to global warming in the source region of the Yellow River and its impact on runoff

Influence of water conservancy project on runoff in the source region of the Yellow River and wetland changes in the Lakeside Zone, China

Influence of water conservancy project on runoff in the source region of the Yellow River and wetland changes in the Lakeside Zone, China

Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.

Simulation and prediction of changes in maximum freeze depth in the source region of the Yellow River under climate change

The source region of the Yellow River (SRYR) is located at the edge of the Qinghai-Tibet Plateau (QTP), which is completely covered by frozen ground. Due to relatively higher temperatures, the frozen ground in the SRYR is particularly fragile and susceptible to the impacts of global climate change. This study discusses the maximum freeze depth (MFD) of frozen ground in the SRYR, including analysis of measured data at the stations, comparison of simulation models, and projection of future changes. The MFD of frozen ground recorded at nine meteorological stations within the SRYR ranged from a few tens of centimeters to more than two meters. The decreasing trend of MFD was recorded except for a few stations from 1997 to 2017, with a maximum rate of −22.8 cm/10a. The decreasing rate of MFD for the whole SRYR from 1997 to 2017 is −10.8 cm/10a. Furthermore, we assessed the performance of three simulation methods: Stefan equation, multiple linear regression, and BP neural network predicting the MFD using the measured data. The Stefan equation exhibited limited accuracy in simulating the MFD, while the BP neural network demonstrated remarkable performance, with a correlation coefficient R of 0.949. In addition, we evaluated the applicability of different global climate models (GCMs) in the SRYR, identified the optimal model, and combined it with the BP neural network model to predict future MFD change. Among the five climate models, the BCC-CSM2-MR model and ensemble model fit the measured precipitation and air temperature well. The projected results based on the BCC-CSM2-MR model and ensemble model indicate that the MFD of different stations in the SRYR and the whole region will still tend to decrease in the future. Our results contribute to understanding the response of cold region frozen ground to climate change and provide available data.

Establishing a quantitative assessment methodology framework of water conservation based on the water balance method under spatiotemporal and different discontinuous ecosystem scales

Water conservation (WC) is an essential terrestrial ecosystem service that mitigates surface runoff and replenishes groundwater, which has received considerable attention under the dual pressures of climate change and human activity. However, there is insufficient understanding of the trends in WC changes on temporal (annual, monthly, daily), spatial, and ecosystem scales. This study proposed a quantitative assessment methodology framework (QAMF) for analyzing the spatiotemporal variation of WC under different discontinuous ecosystems. The QAMF mainly used models and methods such as the hydrological model (SWAT), calibration and uncertainty program (SWAT-CUP), WC calculation formula (water balance method), and spatial analysis method (empirical orthogonal function and wavelet analysis). It was applied to the source region of the Yellow River (SRYR), where the ecological landscape pattern underwent varying degrees of degradation, and WC capacity decreased. The results show that: Firstly, the constructed SWAT in the SRYR had high accuracy, and the proposed formula for calculating WC was suitable for multi-temporal scale analysis of WC in spatially distributed discontinuous basins. Secondly, the annual and monthly WC were respectively 81.00–184.13 mm and −28.58–107.64 mm, and daily WC was positive during extreme precipitation periods and negative during dry periods. The regulating effect of WC was fully reflected on the daily scale, partially reflected on the monthly scale, and absent on the annual scale. Third, the crucial WC area was mainly distributed in the southeast, and there was a significant primary yearly cycle of WC in the SRYR. Finally, different ecosystems exhibited different WC capabilities, and protecting the diversity of ecosystems played an essential role in maintaining and improving the WC function in the SRYR. This project has great scientific significance and technological support for scientifically evaluating the WC capacity in the SRYR.

High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) &gt; Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) &gt; Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) &gt; Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging

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.