In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km². The scale of this hazard underscores a critical need for long-term stability assessment of the affected slopes. While previous studies have primarily used optical remote sensing to map landslide distributions, quantitative evaluation of post-failure movement dynamics remains limited. This study developed an integrated monitoring framework that combines time-series SBAS-InSAR displacement measurements (using Sentinel-1 data from August 2024 to September 2025) with deep learning-based optical interpretation, rainfall analysis, and geological data. Our approach enables the quantitative, region-scale stability assessment of the Zixing landslide cluster one year after the initial event. Experimental results reveal sustained surface displacement with rates ranging from −30 to 30 mm/year, and localized displacements exceeding 40 mm/year. Notably, over 48% of the mapped landslides are classified as active or critically active, indicating widespread, ongoing instability. Correlation analysis further establishes precipitation as a key driver of accelerated movement. Beyond the Zixing case, this work provides a transferable methodology for assessing long-term post-disaster landslide behavior, offering direct value for regional hazard management and early-warning systems.

Radar echo extrapolation (REE) plays a crucial role in convective nowcasting. Existing deep learning (DL)-based methods for REE are predominantly based on the analysis of echo composite reflectivity (CR). However, CR product solely offer single-layered echo intensity information, thereby losing vertical details of convective systems such as echo top heights, resulting in lower accuracy in REE. To address these limitations, this article proposes a multi-product fusion network (i.e., MPFNet) for REE. First, residual convolutional encoders (RCEs) are designed, which adopt the ResNet to reuse features and combine attention mechanisms to improve focus on convective features. In addition, to leverage the correlations and complementarities among multi-product features, a multi-product fusion module (MPFM) that adopts multi-head attention for modeling the interrelations among multi-product features and depthwise separable convolution (DSC) for feature fusion is proposed. Finally, a residual decoder (RD) is designed instead of a conventional deconvolution decoder to aggregate fused features for the restoration of predicted echo sequences. The proposed MPFNet is verified by convective nowcasting experiments, and the experimental results demonstrate that it can effectively utilize multiple radar products for guiding REE. And it significantly outperforms the state-of-the-art (SOTA) methods such as Earthformer and PreDiff. Compared to the previously best-performing Earthformer, MPFNet achieves an average improvement of 1.3% and 2.4% in CSI and HSS, respectively, in convective nowcasting experiments on the SWAN dataset, and an average improvement of 1.2% and 3.2% in CSI and HSS on the MeteoNet dataset.

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.

Global warming might expand crop growth areas for the prevailing single and double rice cropping systems in Southern China. Based on historical weather and crop data from 1981 to 2015, we evaluated the economic benefit and environmental cost for single and double rice cropping systems (SRCS and DRCS) in areas that are sensitive to climate variability in the middle and lower reaches of the Yangtze River. The five chosen indices were: net profit, agronomic nitrogen use efficiency (ANUE), water use efficiency (WUE), total amount, and global warming potential (GWP) of greenhouse gas (GHG). The goal of this study is to provide scientific evidence for local policymakers to use in selecting the most suitable rice cropping systems to maximize economic profits while adapting to climate change. The results showed that net profit was $171.4 per hectare higher for DRCS than for SRCS in the study region. In addition, output per unit nitrogen usage was $0.25 per kg N higher for DRCS than for SRCS. Net profit would increase if DRCS replaced SRCS, and the maximum amplitude of increase in net profit for this replacement occurred under the settings of 150 kg ha⁻¹ nitrogen fertilizer level and continuous irrigation when the paddy water layer started to fade. On the other hand, annual variation in net profit for SRCS was consistently smaller than DRCS, regardless of changes in nitrogen fertilizer level and irrigation regime settings. SRCS showed better WUE than DRCS in both rainfed and irrigated situations, as well as lower seasonal CH₄ and N₂O emissions during the study period. Therefore, we conclude that SRCS is superior to DRCS for the sake of maximizing economic profit while maintaining sustainable agriculture in areas that are sensitive to climate variability in the middle and lower reaches of the Yangtze River.

Weather radar echo extrapolation is one of the essential means for weather nowcasting. It has been considerably inspired over the last decade by deep learning. However, the internal similarity of the echo evolution process has little been exploited. To investigate this merit, a deep contrastive model with an encoder–projector structure is proposed in this letter, which projects the subsequences sampled from the same evolution process into the neighborhood of latent space by contrastive learning. Thus, the internal evolution similarity of the input echo sequence itself can be discovered and exploited for promoting prediction. To make the training smoother, we also adopt a cumulative sampling strategy that follows a simple-to-hard manner. Experimental results on two real-world radar datasets demonstrate the superiority of our model in comparison to state-of-the-art. The effectiveness of the sampling strategy and extrapolation ability on limited input is also analyzed and verified. Training code and pretrained models are available at https://github.com/tolearnmuch/ESCL .

In 2022, southern China experienced an unprecedented summer-autumn extreme heatwave and drought, resulting in significant impacts on agricultural and forestry production. Camellia oleifera, a woody edible-oil-bearing species endemic to China and one of the world’s four major woody oil crops, is primarily cultivated in subtropical low mountain and hilly regions, with extensive distribution across 15 major production provinces in southern China. The period from July to October represents a critical stage for fruit enlargement and oil accumulation in C. oleifera. This study aims to assess the impact of the 2022 extreme heatwave and prolonged drought on C. oleifera production in China and to explore disaster thresholds for heat and drought affecting C. oleifera.The study focuses on 15 major C. oleifera-producing provinces in China, utilizing meteorological data from the Tianqing Big Data Center (daily precipitation and maximum temperature from 1,307 weather stations) and identifying representative years significantly affected by summer-autumn heat and drought (2022, 2021, and 2013). We investigated the mortality rates of C. oleifera plants and flower buds in 2022, and collected data from three representative years including yield, affected areas, fruit oil content, plant phenotypic traits, and phenological phases. Meteorological disaster indices used in this study include the cumulative number of days with daily maximum temperature ≥35°C (DTm35) and ≥39°C (DTm39), extreme maximum temperature (Tmax), the longest consecutive days with maximum temperature ≥35°C (CDTm35) and without effective rainfall (CDnr).Results indicate that, in 2022, most regions within the C. oleifera-producing areas experienced DTm35 ≥ 37d with localized DTm39 ≥ 20 d and Tmax reaching 45.0°C. From July 21 to December 3, some areas experienced CDnr ≥ 61 d. These conditions led to severe water deficits, with fresh fruit yields in severely affected areas decreased by more than 90%, flower bud mortality rates ranging from 10.6% to 37.2%, and fruit set rates in some regions dropping to below 1% in the following year. In extreme heat zones, fresh fruit oil content was reduced by more than 40%. New plantations experienced mortality rates of 71.0%–74.5%, young forests 15.2%–70.4%, and mature forests 9.2%–14.7% due to compounded heat and drought stress. Comprehensive analysis of the three representative years reveals that during the oil accumulation phase, CDTm35 ≥9 d combined with DTm39 ≥3 d leads to significant reductions in oil content, while CDnr ≥46 d during the flower bud maturation phase significantly increases flower bud mortality. Furthermore, CDTm35 ≥18 d and CDnr ≥31 d are critical thresholds for abnormal fruit drop and substantial reductions in fresh fruit yield. When CDTm35 ≥15 d and CDnr ≥31 d, the mortality rate of young forests increases significantly. These findings provide valuable insights into the disaster thresholds for heat and drought impacts on C. oleifera and highlight the vulnerability of its production system to climate extremes.