Source Region Of The Yellow River Research Articles

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 < 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 < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 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) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > 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.

Ground Deformation and Permafrost Degradation in the Source Region of the Yellow River, in the Northeast of the Qinghai-Tibet Plateau

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe’ yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region.

Effects of Climate Change on Surface Runoff and Soil Moisture in the Source Region of the Yellow River

The impact of climate change on surface runoff and soil moisture in the source region of the Yellow River is analyzed, which will provide a scientific basis for the rational use and protection of water resources in the source area. In this paper, the SWAT hydrological model was coupled with the Coupled Model Intercomparison Project (CMIP) to predict future changes in surface runoff and soil moisture in the source region of the Yellow River. The prediction of surface runoff and soil moisture in the Yellow River Basin was analyzed by a linear regression model. The SWAT model rate had a calibration period R2 of 0.876 and a validation period R2 of 0.972. The trend of surface runoff and annual mean temperature in the source region of the Yellow River from 2011 to 2022 showed an overall increasing trend, and soil moisture showed a general decreasing trend. 2011–2022 trends between surface runoff and annual mean temperature in the source region of the Yellow River showed a highly significant difference, indicating that surface runoff flow was significantly influenced by temperature. The difference between the trends in soil moisture and the annual mean temperature was highly significant. The surface runoff fluctuated greatly in different years, and the surface runoff changed greatly in different scenarios of CMIP5 (RCP2.6, RCP4.5, and RCP8.5). For all three climate change scenarios, the surface runoff displayed a downward trend. The surface runoff showed a similar uneven distribution for all scenarios on a yearly cycle. Under the three climate scenarios, the runoff was highest between May and August, with a slowly increasing trend from January to April and a slightly decreasing trend from September to December. The interannual and interannual distribution of soil water was basically consistent with the distribution of surface runoff, and there was an overall trend in the length of all soil water reduction scenarios. Surface runoff and soil moisture are and will be greatly affected by climate change (mainly temperature and precipitation). Under the three climate scenarios, the precipitation increases to some extent, but the surface runoff and soil moisture will both decrease, which may be attributed to the greater evaporation than the precipitation.

Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments

AbstractClimate change has rapidly degraded the cryosphere in alpine headwaters, altering the runoff regime of alpine watersheds and threatening water security worldwide. To precisely simulate runoff response to climate change in alpine catchments, we took the source region of the Yellow River as a case to develop a physics‐informed deep learning (DL) model that tightly hybridizes DL with the physics of dominant hydrological processes, including the neural network‐based coupling of soil freeze‐thaw physics inspired by the Stefan equation. A physics‐informed DL model without soil freeze‐thaw representation was also established as a benchmark. Results demonstrated the former model's superiority in capturing streamflow's inter‐annual dynamics and reproducing baseflow recession properties caused by soil freeze‐thaw when average temperature rises by 1°C. This emphasizes the importance of the physics of dominant hydrological processes for DL to make credible runoff projections under rapid climate change. Furthermore, we comprehensively compared the physics‐informed DL model with a lumped model, physically‐based distributed model, and standard DL model, showing that the physics‐informed DL model performs best in simulating streamflow at multi‐timescales and the impacts of soil freeze‐thaw on runoff, and is more capable of simulating runoff relative change and trend in response to climate change. In summary, our findings demonstrate physics‐informed DL's credibility in runoff projections under drastic climate change when infused with the physics of dominant hydrological processes, and illustrate its superior ability to learn complex hydrological processes, providing a new approach for runoff simulation in permafrost‐affected alpine catchments under climate change.

Trade‐Offs Between Spatial and Temporal Accuracy of Complementary Relationship Models for Evaporation in an Ungauged Basin

AbstractThe complementary relationship (CR) between actual and potential evaporation has undergone rapid development over the past decades. Commonly, the evaluation of CR models does not comprehensively cover spatial and temporal aspects of model performance; thus, the spatial and temporal accuracy and parameter sensitivity of different CR models remain relatively unknown, especially in ungauged basins. We examine the spatial and temporal performance and parameter sensitivity of four CR models with fixed parameters at the monthly scale over the source region of the Yellow River in China. Additionally, two CR models with distributed parameters were selected as comparisons. Because of the lack of evaporation spatiotemporal “true” values, the corrected water‐balance‐derived evaporation was used as the temporal reference, and the ensemble mean of multiple evaporation products was used as the spatial reference. We find that trade‐offs between the spatial and temporal accuracy of CR models with fixed parameters for basin evaporation should be considered in the application. Although four CR models with fixed parameters exhibited different spatial and temporal performances, their overall performance was generally similar. The parameters of CR models had consistent spatial and temporal sensitivities, and the sensitivity of parameter αe was stronger than that of other parameters. This study demonstrates that CR models with distributed parameters have a promising future in estimating basin or regional evaporation. An integrated spatial and temporal evaluation framework can better distinguish the performance of CR models. The methodologies can be used as a guideline for evaluating and calibrating CR models that match an application's needs.

Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River

Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.

Geochemistry of a paleo-oxbow lake sediments and its implications for the late Holocene extreme overbank flooding history of the Yellow River within the Zoige Basin, NE Tibetan Plateau

Paleo-oxbow lake sediments can provide archives to reconstruct paleo-channel evolution and flooding history of the river. Multi-proxy approaches including detailed sediment stratigraphy, sedimentology and geochemistry have been implemented in a high-resolution sedimentary section of paleo-oxbow lake of the Yellow River within the Zoige Basin on the NE Tibetan Plateau, to reconstruct regional environmental changes and extreme overbank flooding history. Our results suggest that not only traditional sedimentological proxies, but also chemical elements can be applied in defining sequences with different genetic types, especially the paleoflood deposits in the paleo-oxbow profile. Two units of late-Holocence extreme overbank flooding deposits (OFDs) are identified in terms of the significantly higher proportions of sand, high contents of SiO2, Na2O, Ba, low contents of Al2O3, Fe2O3, MgO, K2O, Ti, Rb and high values of Zr/Fe, Zr/Rb ratios. These extraordinary flood events within the Zoige Basin were dated back to 2,960 ± 240–2,870 ± 270 a and 1840 ± 200–1700 ± 160 a, in response to the mid-Holocene climatic optimum to the late Holocene and the Dark Age Cold Period (DACP). And the strong rainfall caused by the abnormal atmospheric circulation during the period of climate transition and abrupt change may led to the frequent occurrence of extreme flood events in the source region of the Yellow River. These findings are important for understanding the response of regional fluvial system to high climatic instability and provide a new perspective for us to analyze the risk of flood disasters on the Tibetan Plateau under the background of climate change.

Construction and Optimization of an Ecological Network in the Yellow River Source Region Based on MSPA and MCR Modelling.

The source region of the Yellow River (SRYR) is an important water conservation and farming area in China. Under the dual influence of the natural environment and external pressure, ecological patches in the region are becoming increasingly fragmented, and landscape connectivity is continuously declining, which directly affect the landscape patch pattern and SRYR sustainable development. In the SRYR, morphological spatial pattern analysis (MSPA) and landscape index methods were used to extract ecologically important sources. Based on the minimum cumulative resistance model (MCR), Linkage Mapper was used to generate a potential corridor, and then potential stepped stone patches were identified and extracted by the gravity model and betweenness centrality to build an optimal SRYR ecological network. The distribution of patches in the core area of the SRYR was fragmented, accounting for 80.53% of the total grassland area. The 10 ecological sources based on the landscape connectivity index and 15 important corridors identified based on the MCR model were mainly distributed in the central and eastern regions of the SRYR. Through betweenness centrality, 10 stepped stone patches were added, and 45 planned ecological corridors were obtained to optimize the SRYR ecological network and enhance east and west connectivity. Our research results can provide an important reference for the protection of the SRYR ecosystem, and have important guiding significance and practical value for ecological network construction in ecologically fragmented areas.

Quantifying the Contribution of Climate Change and Human Activities to Runoff Changes in the Source Region of the Yellow River

Quantifying the Contribution of Climate Change and Human Activities to Runoff Changes in the Source Region of the Yellow River