Yanhui Zheng

Global warming enhances soil moisture (SM)‐temperature (SM‐T) coupling, exacerbating extreme climates. However, the mechanisms and future trends of this enhancement remain inadequately understood. Analysis of multiple reanalysis data sets shows that during summer seasons of 1981–2020, approximately 45.6% of global lands exhibit a significant intensifying SM‐T coupling trend, with hotspots in Northeast Asia, Central Asia, and southwestern North America. Ridge regression reveals the drying SM (contribution: 50.2%) dominates the enhanced SM‐T coupling, followed by greening vegetation (28.3%) and decreasing cloud cover (21.5%). Furthermore, pre‐summer vegetation greening has the effect of intensifying the summer SM‐T coupling. Despite discrepancies in climate models' simulations of SM‐T coupling trend, the optimal multi‐model ensemble mean well reproduces SM‐T coupling's spatiotemporal characteristics. Based on this multi‐model ensemble mean, the enhancement of SM‐T coupling is attributed to anthropogenic forcings and projected to further intensify under future emission scenarios.

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies.

In the year 2019, the middle and lower reaches of the Yangtze River (MLRYR) experienced an unprecedented summer‐autumn drought (SAD) driven by dry‐hot conditions [high near‐surface air temperatures (T) and low precipitation (P)], causing substantial agricultural and economic losses. However, the influence of anthropogenic climate change (ACC) on these dry‐hot conditions and their impacts on SAD occurrences remains uncertain. Here, both observations and simulations show that an ACC‐driven T increase led to the greater likelihood of dry‐hot conditions from August to November 1901–2020 in MLRYR. Using the self‐calibrating Palmer drought index (scPDSI) to assess SAD severity, we find an increasing likelihood of SAD occurrence (from 33.3% in 1901–2000 to 85.7% in 2001–2020) in MLRYR associated with more frequent dry‐hot conditions. Under a business‐as‐usual scenario, future dry‐hot association is projected to be stronger, with exceptional dry‐hot conditions to increase by +10% per century. ACC‐induced increase in dry‐hot conditions would elevate the likelihood of SAD events like the 2019 event from 1.59% (1961–2020) to 17.82% (2041–2100). Therefore, effective measures are needed in MLRYR to adapt to increasing dry‐hot conditions and associated SAD occurrences under anthropogenic warming.

Dry‐ and wet‐bulb temperature (T_d and T_w) are usually to define heatwaves (HWs) which have been enhanced under anthropogenic climate change (ACC) and urbanization. However, responses of various types of HWs (i.e., dry HWs, only high T_d; humid HWs, only high T_w; hybrid HWs, both high T_d and T_w; total HWs, high T_d or T_w), to ACC and urbanization remain unknown. In this study, both observations and simulations show significantly increasing occurrence probability of total HWs over China during 1971–2020, whereas this increase is mainly reflected in hybrid HWs, followed by dry HWs and humid HWs. 68.2%–93.0% of the observed increases in the above four types of HWs can be attributed to ACC; on the other hand, urbanization tends to suppress humid HWs but enhance dry HWs, as a result of contributing to the increase of hybrid HWs by 10.9%. Under future ACC, total HWs are projected to be more frequent as expected, which is mainly sourced from the increasing hybrid HWs because dry/humid HWs are projected to be steady/downward. As a consequence, urban population exposure to ACC‐induced total HWs would remarkably increase to 83.55 billion person‐days by the 2090s, 89.5% of which can be attributed to hybrid HWs. Urbanization would amplify this population exposure of ACC‐induced hybrid HWs from 74.79 billion person‐days to 110.9 billion person‐days. Our results underscore the importance of improving understanding of hybrid HWs in urban areas and developing targeted adaptation planning on a warmer planet.

In July 2021, Typhoon In‐Fa produced record‐breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region‐averaged Rx5day (summer maximum 5‐day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long‐term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions.

A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.