Yanjuan Wu

Extreme precipitation is generally expected to intensify in a warmer climate, posing growing risks to ecosystems and human societies. In this study, we aim to examine the precipitation‐temperature relationships in the history and future over eastern China. We first assessed the performance of CMIP6 Multi‐Model Ensemble (MME) and ERA5 reanalysis datasets in simulating mean and extreme precipitation across temperature bins during the historical period (1971–2000) in eastern China. Distinct regional biases are identified: ERA5 overestimates precipitation at moderate temperatures (−10°C to 27°C) and underestimates it at low and high temperatures, while the CMIP6 MME consistently underestimates mean and extreme precipitation across all temperature bins. Both datasets reproduce the observed increasing peak structures, where precipitation intensities increase with temperature up to a peak temperature, and then decline at higher temperatures, closely associated with reduced relative humidity. Observed scaling rates range from 3.90%/°C to 5.89%/°C in northeast China and 3.64%/°C–5.63%/°C in southeast China. ERA5 overestimates these rates, approaching the CC rate (7%/°C), while MME underestimates them, aligning with sub‐CC rates. Scaling rates for extreme precipitation exceed those for mean precipitation, with higher sensitivity for more extreme events. Future projections (2071–2100) under SSP2‐4.5 and SSP5‐8.5 scenarios show similar increasing peak structures, with scaling rates exceeding MME historical values. In northeast China, rates range from 5.12%/°C to 7.03%/°C, significantly higher than northeast China's 2.15%/°C–3.96%/°C. Scaling rates for extreme precipitation are projected to be even higher, indicating increased sensitivity to climate change. These findings underscore the need for improved climate models and reanalysis datasets to capture precipitation dynamics and address rising flood risks in eastern China.

Freshwater insect biodiversity is under global threat from anthropogenic stressors, yet the roles of natural and anthropogenic drivers remain unclear. Here, we examine 783 river basins to map the global diversity and explore the effects of sampling bias, natural, and anthropogenic factors on four major taxa: Ephemeroptera, Plecoptera, Trichoptera and Odonata (EPTO). About 62% of basins were under-sampled, especially in the Global South. Standardized diversity metrics (especially Shannon- and Simpson-based) better captured global diversity patterns of insect than observed diversity. Standardized taxonomic and functional diversity showed two latitudinal peaks, with a minor peak at the equator and a higher peak at ~40°N. Landscape factors had the highest relative impact on taxonomic diversity, while both climatic and landscape factors were primary drivers of functional diversity. By controlling for sampling biases, we provide more accurate insights into the drivers of freshwater insect diversity to inform effective stream conservation and management.

The thermodynamic and convective characteristics of the seasonal progression of the monsoon over eastern China are examined, with the aim of understanding why regional heavy rainfall events (RHREs) over the Yangtze River Basin (YRB) are more frequent and intense during the monsoon progression from mid-June to mid-July than during the retreat from mid-August to mid-September. During the monsoon progression, the southerly monsoon flow at low and mid-levels intensifies and moves northward, while the Western Pacific subtropical high shifts northeastward. These changes result in enhanced moisture advection over eastern China. The stronger southerly flow brings warmer and moist air into the YRB, leading to a higher equivalent potential temperature (θE) and favouring convection. Conversely, during the monsoon retreat, the northerly flow becomes stronger and drier, causing lower θE air over the YRB. Additionally, as the Western Pacific subtropical anticyclone retreats, easterly prevailing winds prevail over eastern China, causing reduced specific humidity. The monsoon progression period exhibits higher convective available potential energy but also higher convective inhibition, which is overcome by the presence of the monsoon front providing sufficient dynamical uplift. Understanding the severity of RHREs, particularly over the YRB, is crucial for improving forecasting capabilities and reducing societal vulnerability.

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China’s Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall’s rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions.

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ET_SEBAL) was compared against the daily reference ET (ET₀) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ET_MOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ET_VIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ET_SEBAL correlated very well with the ET₀, ET_MOD16, and ET_VIC, and bias between the ET_SEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ET_SEBAL was explored. The variation of ET_SEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ET_SEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ET_SEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771–1800 and 1871–1900 relative to the 1971–2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070–2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.

As an important part of resource and environment carrying capacity (RECC), land resource carrying capacity (LRCC) has focused on the limiting factors on regional development from the scale of land resources for more than two hundred years. After a brief review of the origin and early development of LRCC, the progress of LRCC is summarized as a progression from land resource population carrying capacity (LPCC) to land resource comprehensive carrying capacity (LCCC), and the hot issues of LRCC have changed from the simple human–grain relationship to more complex social and economic activities–land resources relationships. The corresponding research methods also evolved, from static research to dynamic research, and the assessment factor have evolved from a single factor to a comprehensive index system. Agro-ecological zoning (AEZ), system dynamics (SD), ecological footprint (EF), and comprehensive evaluation methods have come to be viewed as the most representative methods of LRCC in recent years....