scholarly journals C:N:P stoichiometry in six distinct habitats of a glacier terminus in the Yangtze River Source Area

2021 ◽  
Author(s):  
Ze Ren ◽  
Hongkai Gao ◽  
Wei Luo ◽  
James J. Elser
Keyword(s):  
Yangtze River ◽  
Source Area ◽  
Future Climate Change ◽  
The Yangtze River ◽  
C:N:P Stoichiometry ◽  
Organic C ◽  
Nutrient Contents ◽  
Glacier Terminus ◽  
N:P Ratios ◽  
Successional Stages

Glaciers are among the least explored environments on Earth, especially from a perspective of nutrient stoichiometry. In this study, we documented and compared the nutrient availabilities (concentrations) and composition (stoichiometric ratios) of nutrients (C, N, and P) in six distinct habitats of a glacier terminus in the Yangtze River Source area, including surface ice (SI), basal ice (BI), basal sediment (BaS), newly exposed forefront soil close to glacial terminus (TS), soil at increasing distances from glacier terminus (DS), and forefront soil with well-developed vegetation (VS). The results showed that SI had significantly higher DOC and N concentrations as well as higher C:P and N:P ratios than BI. However, BI had significantly higher SRP than SI. In addition, both SI and BI had very high C:P and N:P ratios, suggesting P-limitation. For sediment/soil in glacier terminus, nitrogen and organic carbon concentrations were significantly lower in BaS, TS, and DS than in VS. Moreover, TP and SRP concentrations were significantly higher in BaS and VS than in TS and DS. These nutrient patterns could be explained by differences in biotic influence in soil development or by changes in soil physical properties. With regard to nutrient limitation, VS had a significantly higher C:N, C:P, and N:P ratios than BaS, TS, and DS, supporting a long-held biogeochemical and ecological paradigm that ecosystem processes during early successional stages are primarily organic C and N limited but are P-limited in later successional stages. Considering that glaciers cover around 10% of the terrestrial landmass and are experiencing severe retreat, documenting and comparing nutrient contents and stoichiometry in glacier terminus can further our understanding of global biogeochemical cycles under future climate change regimes.

scholarly journals Evaluation of hydrological response under future climate change by revising meteorological data in the source region of the Yangtze River, China

2021 ◽  
Author(s):  
Xiaohong Chen ◽  
Haoyu Jin ◽  
Pan Wu ◽  
Wenjun Xia ◽  
Ruida Zhong ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Yangtze River ◽  
Meteorological Data ◽  
Source Region ◽  
Future Climate ◽  
Future Climate Change ◽  
Hydrological Response ◽  
The Yangtze River ◽  
Data Correction

Abstract The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.

scholarly journals Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices

Climate ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 53
Author(s):  
Heng Qian ◽  
Shi-Bin Xu
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate Change ◽  
Climate Indices ◽  
The Yangtze River ◽  
Second Mode ◽  
The Yangtze River Basin

Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change.

scholarly journals Responses of Soil and Microbial C:N:P Stoichiometry to Vegetation Succession in a Karst Region of Southwest China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 755 ◽  
Author(s):  
Min Song ◽  
Wanxia Peng ◽  
Hu Du ◽  
Qingguo Xu
Keyword(s):  
Microbial Biomass ◽  
Vegetation Succession ◽  
Karst Region ◽  
Microbial Biomass N ◽  
Agricultural Abandonment ◽  
Total N ◽  
C:N:P Stoichiometry ◽  
Organic C ◽  
Successional Stages ◽  
Time Substitution

Spontaneous vegetation succession after agricultural abandonment is a general phenomenon in many areas of the world. As important indicators of nutrient status and biogeochemical cycling in ecosystems, the stoichiometry of key elements such as carbon (C), nitrogen (N) and phosphorous (P) in soil and microbial biomass, and their responses to vegetation recolonization and succession after agricultural abandonment remain poorly understood. Here, based on a space-for-time substitution approach, surface soil samples (0–15 cm) were collected from four vegetation types, e.g., tussock grassland, shrubland, secondary forest, and primary forest, which represent four successional stages across this region. All samples were examined C, N and P concentrations and their ratios in soil and microbial biomass. The results showed that soil organic C and total N content increased synchronously but total soil P did not remarkably change along a progressive vegetation succession. Consequently, soil C:P and N:P ratios increased while C:N ratio stayed almost unchanged during vegetation succession. Soil microbial biomass C (SMBC) and microbial biomass N (SMBN) concentrations elevated while SMBP did not significantly change during vegetation succession. Unlike the soil C:N:P stoichiometry, however, microbial C:N and C:P ratios were significantly or marginally significantly greater in grassland than in the other three successional stages, while microbial N:P did not significantly vary across the four successional stages. Overall, the present study demonstrated that soil and microbial stoichiometry responded differently to secondary vegetation succession in a karst region of subtropical China.

Desertification problems in the Yangtze River source area, China

2004 ◽  
Vol 15 (2) ◽  
pp. 177-182 ◽  
Author(s):  
P. Yan ◽  
G. R. Dong ◽  
Z. Z. Su ◽  
D. S. Zhang
Keyword(s):  
Yangtze River ◽  
Source Area ◽  
The Yangtze River

scholarly journals Attribution Analysis of Climate and Anthropic Factors on Runoff and Vegetation Changes in the Source Area of the Yangtze River from 1982 to 2016

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 612
Author(s):  
Guangxing Ji ◽  
Huiyun Song ◽  
Hejie Wei ◽  
Leying Wu
Keyword(s):  
Climate Change ◽  
Yangtze River ◽  
Vegetation Change ◽  
Climatic Factors ◽  
Source Area ◽  
Linear Regression Method ◽  
The Yangtze River ◽  
Potential Evaporation ◽  
Runoff Change ◽  
The Impact

Analyzing the temporal variation of runoff and vegetation and quantifying the impact of anthropic factors and climate change on vegetation and runoff variation in the source area of the Yangtze River (SAYR), is of great significance for the scientific response to the ecological protection of the region. Therefore, the Budyko hypothesis method and multiple linear regression method were used to quantitatively calculate the contribution rates of climate change and anthropic factors to runoff and vegetation change in the SAYR. It was found that: (1) The runoff, NDVI, precipitation, and potential evaporation in the SAYR from 1982 to 2016 all showed an increasing trend. (2) The mutation year of runoff data from 1982 to 2016 in the SAYR is 2004, and the mutation year of NDVI data from 1982 to 2016 in the SAYR is 1998. (3) The contribution rates of precipitation, potential evaporation and anthropic factors to runoff change of the SAYR are 75.98%, −9.35%, and 33.37%, respectively. (4) The contribution rates of climatic factors and anthropic factors to vegetation change of the SAYR are 38.56% and 61.44%, respectively.

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