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High Mountain Asia (HMA), with its high altitude, widely distributed snow and frozen soil, influences the climate of the northern hemisphere and even the world through thermal balance and the water vapor cycle and is also an indicator of global climate change. The influence of HMA snow cover on its surrounding areas has always been a research hotspot. Taking the Yangtze River Basin (YRB) of China as an example, this paper analyzes the relationship between winter snow depth in HMA and drought and flood in spring and summer in the YRB in the recent 40 years by using Singular Value Decomposition (SVD). The results show that the influence of snow cover on drought and flood in spring is inversely different between eastern and western parts of HMA, while the effect in summer is consistent. When the snow depth is larger (smaller) in the east and smaller (larger) in the west in winter, the YRB is drier (wetter) in spring. When the overall snow depth in HMA is larger (smaller) in winter, the northern part of the middle and lower reaches of the YRB is drier (wetter) in summer. The results provide support for understanding the impact of HMA snow cover on the surrounding climate and some important indicators for drought and flood prediction in the YRB. [ABSTRACT FROM AUTHOR]

Global climate change and human activities have increased the frequency and severity of droughts. This has become a critical factor affecting vegetation growth and diversity, resulting in detrimental effects on agricultural production, ecosystem stability, and socioeconomic development. Therefore, assessing the response of vegetation dynamics to drought can offer valuable insights into the physiological mechanisms of terrestrial ecosystems. Here, we applied long-term datasets (2001–2020) of solar-induced chlorophyll fluorescence (SIF) and normalized difference vegetation index (NDVI) to unveil vegetation dynamics and their relationship to meteorological drought (SPEI) across different vegetation types in the Yangtze River Basin (YRB). Linear correlation analysis was conducted to determine the maximum association of SPEI with SIF and NDVI; we then compared their responses to meteorological drought. The improved partial wavelet coherence (PWC) method was utilized to quantitatively assess the influences of large-scale climate patterns and solar activity on the relationship between vegetation and meteorological drought. The results show that: (1) Droughts were frequent in the YRB from 2001 to 2020, and the summer's dry and wet conditions exerted a notable influence on the annual climate. (2) SPEI exhibits a more significant correlation with SIF than with NDVI. (3) NDVI has a longer response time (3–6 months) to meteorological drought than SIF (1–4 months). Both SIF and NDVI respond faster in cropland and grassland but slower in evergreen broadleaf and mixed forests. (4) There exists a significant positive correlation between vegetation and meteorological drought during the 4–16 months period. The teleconnection factors of Pacific Decadal Oscillation (PDO), El Niño Southern Oscillation (ENSO), and sunspots are crucial drivers that affect the interaction between meteorological drought and vegetation, with sunspots having the most significant impact. Generally, our study indicates that drought is an essential environmental stressor that disrupts vegetation growth over the YRB. Additionally, SIF demonstrates great potential in monitoring vegetation response to drought. These findings will be meaningful for drought prevention and ecosystem conservation planning in the YRB. [ABSTRACT FROM AUTHOR]

Locally evaporated water vapor is an important source of precipitation in China. The spatiotemporal variation characteristics of the precipitation recycling ratio (ρ) in the Yangtze River Basin (YRB) in 1979–2020 were calculated and analyzed, and the contribution of internal and external cycling precipitation changes to the total precipitation changes in YRB under climate change was studied. The results show that the annual average ρ in YRB is approximately 10.3%, with the highest value of 21.8% in summer, lower than 10% in spring and autumn, and the lowest in winter, with only approximately 3.5%. Over the past 40 years, the annual average ρ in YRB has shown an increasing trend, with an increased rate of 0.4%/10a, especially in summer, with an increasing rate of 1.2%/10a. In terms of spatial distribution, ρ in YRB shows an obvious difference between the eastern and western regions, with that in the upstream western region being significantly higher than that in the downstream eastern region. The annual average ρ in the upstream region was 15–35% and can reach 20–50% in summer. The annual average ρ in the downstream region is below 10%. In general, precipitation formed by advection moisture accounts for the majority of the total precipitation in YRB. From 1979 to 2020, the annual precipitation in YRB showed an increasing trend. The cumulative increase is about 47.4 mm, of which 68.9% was contributed by local evaporation, and 31.1% was contributed by external moisture. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is a basis of material and energy flows in terrestrial ecosystems, and it is also an important component in the research on carbon cycle and carbon budget. This paper evaluated the spatial distribution pattern and temporal change trends for forest NPP simulated by the LPJ (Lund-Potsdam-Jena) model and NDVI (normalized difference vegetation index) in the Yangtze River basin from 1982 to 2013. The results revealed that: (1) the spatial distribution of the forest NPP and NDVI in the Yangtze River basin has gradually decreased from the southeast coast to the northwest. The forest NPP and NDVI in the mid-lower Yangtze were higher than that of the upper Yangtze; (2) the forest NPP and NDVI in most areas of the Yangtze River basin were positively correlated with the temperature and precipitation. Moreover, the correlations among the temperature with the forest NPP and NDVI were stronger than that of correlations among precipitation with forest NPP and NDVI. Moreover, the extreme drought event in the year of 2004–2005 led the NPP to decrease in the middle and lower Yangtze River basin; (3) human activity such as major ecological projects would have a certain impact on the NPP and NDVI. The increase in forest areas from 2000 to 2010 was larger than that from 1990 to 2000. Moreover, the increasing rate for the NDVI was higher than that of NPP, especially after the year 2000, which indicates that the major ecological projects might have great impacts on the vegetation dynamics. Moreover, more attention should be paid on the joint impacts of human activity and climate change on terrestrial NPP and NDVI. [ABSTRACT FROM AUTHOR]

This paper estimates the likely impacts of future climate change on streamflow, especially the hydrological extremes over the Yangtze River basin. The future climate was projected by the Coordinated Regional Climate Downscaling Experiment in East Asia (CORDEX-EA) initiative for the periods 2020–2049 under two representative concentration pathways (RCP) 4.5 and 8.5 emission scenarios. The bias corrected outputs from five regional climate models (RCMs) were used in conjunction with the variable infiltration capacity (VIC) macroscale hydrological model to produce hydrological projections. For the future climate of the Yangtze River basin, outputs from an ensemble of RCMs indicate that the annual mean temperature will increase for 2020–2049 by 1.81 °C for RCP4.5 and by 2.26 °C for RCP8.5. The annual mean precipitation is projected to increase by 3.62% under RCP4.5 and 7.65% under RCP8.5. Overall, increases in precipitation are amplified in streamflow, and the change in streamflow also shows significant temporal and spatial variations and large divergence between regional climate models. At the same time, the maximum streamflow in different durations are also projected to increase at three mainstream gauging stations based on flood frequency analysis. In particular, larger increases in maximum 1-day streamflow (+14.24% on average) compared to 5-day and 15-day water volumes (+12.79% and +10.24%) indicate that this projected extreme streamflow increase would be primarily due to intense short-period rainfall events. It is necessary to consider the impacts of climate change in future water resource management. [ABSTRACT FROM AUTHOR]

Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis. [ABSTRACT FROM AUTHOR]