Contrasting sources and fate of nitrogen compounds in different groundwater systems in the Central Yangtze River Basin

The significant spatial heterogeneity among river basin ecosystems makes it difficult for local governments to carry out comprehensive governance for different river basins in a special administrative region spanning multi-river basins. However, there are few studies on the construction of a comprehensive governance mechanism for multi-river basins at the provincial level. To fill this gap, this paper took Henan Province of China, which straddles four river basins, as the study region. The chord diagram, overlay analysis, and carbon emission models were applied to the remote sensing data of land use to analyze the temporal and spatial patterns of carbon storage caused by land-use changes in Henan Province from 1990 to 2018 to reflect the heterogeneity of the contribution of the four basins to human activities and economic development. The results revealed that food security land in the four basins decreased, while production and living land increased. Ecological conservation land was increased over time in the Yangtze River Basin. In addition, the conversion from food security land to production and living land was the common characteristic for the four basins. Carbon emission in Henan increased from 134.46 million tons in 1990 to 553.58 million tons in 2018, while its carbon absorption was relatively stable (1.67–1.69 million tons between 1990 and 2018). The carbon emitted in the Huai River Basin was the main contributor to Henan Province’s total carbon emission. The carbon absorption in Yellow River Basin and Yangtze River Basin had an obvious spatial agglomeration effect. Finally, considering the current need of land spatial planning in China and the goal of carbon neutrality by 2060 set by the Chinese government, we suggested that carbon sequestration capacity should be further strengthened in Yellow River Basin and Yangtze River Basin based on their respective ecological resource advantages. For future development in Hai River Basin and Huai River Basin, coordinating the spatial allocation of urban scale and urban green space to build an ecological city is a key direction to embark upon.

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Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽

10.3390/f12060731 ◽

2021 ◽

Vol 12 (6) ◽

pp. 731

Author(s):

Zhuoqing Hao ◽

Jixia Huang ◽

Yantao Zhou ◽

Guofei Fang

Keyword(s):

River Basin ◽

Yangtze River ◽

Yangtze River Basin ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Spatiotemporal Pattern ◽

Pinewood Nematode ◽

The Yangtze River ◽

Pine Wilt ◽

The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

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Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-1087-x ◽

2021 ◽

Cited By ~ 1

Author(s):

Philip E. Bett ◽

Gill M. Martin ◽

Nick Dunstone ◽

Adam A. Scaife ◽

Hazel E. Thornton ◽

...

Keyword(s):

Linear Regression ◽

River Basin ◽

Yangtze River ◽

Asian Summer Monsoon ◽

Yangtze River Basin ◽

Early Summer ◽

Lead Times ◽

Seasonal Forecasts ◽

June July ◽

The Impact

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

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Impacts of land use change on thermodynamic and dynamic changes of precipitation for the Yangtze River Basin, China

International Journal of Climatology ◽

10.1002/joc.7037 ◽

2021 ◽

Author(s):

Qian Lin ◽

Jie Chen ◽

Wei Li ◽

Kailin Huang ◽

Xuezhi Tan ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

River Basin ◽

Yangtze River ◽

Yangtze River Basin ◽

The Yangtze River ◽

Dynamic Changes ◽

The Yangtze River Basin

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Assessing the adequacy of bias corrected IMERG satellite precipitation estimates using extended mixture distribution mapping method over Yangtze River basin

MATEC Web of Conferences ◽

10.1051/matecconf/201824601096 ◽

2018 ◽

Vol 246 ◽

pp. 01096

Author(s):

Qiumei Ma ◽

Lihua Xiong ◽

Chong-Yu Xu ◽

Shenglian Guo

Keyword(s):

River Basin ◽

Yangtze River ◽

Extreme Values ◽

Yangtze River Basin ◽

Mixture Distribution ◽

Mapping Method ◽

Generalized Pareto ◽

Precipitation Estimates ◽

Distribution Mapping ◽

Emd Method

Satellite precipitation estimates (SPE) product with high spatiotemporal resolution is a potential alternative to traditional ground-based gauge precipitation. However, SPE is frequently biased due to its indirect measurement, and thus bias correction is necessary before applying to a specific region. An improved distribution mapping method, i.e., Extended Mixture Distribution (EMD) of censored Gamma and generalized Pareto distributions, was established. The advantage of EMD method is that it describes both moderate and extreme values well and carries on the traditional censored, shifted Gamma distribution to combine the precipitation occurrence/non-occurrence events together. Then the EMD method was applied to the Integrated Multi-satellitE Retrievals for GPM product (IMERG) as statistical post-processing over Yangtze River basin. The Version-2 Gridded dataset of daily Surface Precipitation from China Meteorological Administration (GSP-CMA) was taken as reference. The adequacy of bias corrected IMERG precipitation was assessed and the results showed that (1) the Root Mean Squared Error and Relative Bias between bias-corrected IMERG precipitation and reference are significantly reduced relative to the raw IMERG estimates; (2) the performance of extreme values of IMERG in Yangtze River basin is enhanced since both the under- and over-estimation of the raw IMERG are compromised, due to the generalized Pareto distribution introduced in EMD which is enable to describe the extreme value distribution. This highlights the improved distribution mapping method, EMD is flexible and robust to bias correct the IMERG precipitation to obtain higher accuracy of SPE despite the coarse resolution of reference.

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Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

Remote Sensing ◽

10.3390/rs13153023 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3023

Author(s):

Jinghua Xiong ◽

Shenglian Guo ◽

Jiabo Yin ◽

Lei Gu ◽

Feng Xiong

Keyword(s):

River Basin ◽

Hydrodynamic Model ◽

Yangtze River ◽

Large Scale ◽

Yangtze River Basin ◽

The Yangtze River ◽

Ecosystem Monitoring ◽

Catastrophic Flood ◽

Basin Scale ◽

The Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

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