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Understanding the Tibetan Plateau (TP) thermal processes is of utmost significance in changing climate. This study investigates the effect of soil moisture in changing the TP thermal profile and consequently summer precipitation in South Asia (SA). Soil moisture from Special Sensor Microwave Imager (SSM/I) developed from the F-08, F-11, and F-13 fundamental climate data record and atmospheric reanalysis from ERA-Interim, MERRA-2, and NCEP/CFSR during 1988–2008 are used. A generalized linear method that assesses the reciprocal forcing between two connected fields, named the coupled manifold technique (CMT), is applied to TP soil moisture and SA summer precipitation. It is revealed that interannual variations of SA precipitation are significantly (confidence level = 99%) impacted by TP soil moisture and the explained ratio of variance in SA is 0.3–0.4. Composite analysis indicates that SA summer precipitation has positive anomalies in response to dry TP soil moisture in the previous spring and vice versa. For understanding the possible mechanism, thermal processes, relative humidity, wind components, and moisture flux anomalies were calculated for dry and wet TP soil moisture and summer precipitation. The results suggested that TP soil moisture is likely to regulate near-surface energy balance and diabatic heating profile over TP. As a result, the surrounding lower-level westerlies (easterlies) (at 850 hPa) converge (diverge), associated with divergence (convergence) at the upper troposphere (200 hPa). The westerlies (easterlies) are usually moisture-rich (moisture-deficient) and thus cause more (less) precipitation in western (eastern) SA. It is thus suggested that the spring soil moisture may affect the thermal profile of TP, affecting the summer precipitation in SA as a consequence.

We project drought losses in China under global temperature increase of 1.5 °C and 2.0 °C, based on the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI), a cluster analysis method, and “intensity-loss rate” function. In contrast to earlier studies, to project the drought losses, we predict the regional gross domestic product under shared socioeconomic pathways instead of using a static socioeconomic scenario. We identify increasing precipitation and evapotranspiration pattern for the 1.5 °C and 2.0 °C global warming above the preindustrial at 2020–2039 and 2040–2059, respectively. With increasing drought intensity and areal coverage across China, drought losses will soar. The estimated loss in a sustainable development pathway at the 1.5 °C warming level increases 10-fold in comparison with the reference period 1986–2005 and nearly threefold relative to the interval 2006–2015. However, limiting the temperature increase to 1.5 °C can reduce the annual drought losses in China by several tens of billions of US dollars, compared with the 2.0 °C warming.

The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.

The increase in surface air temperature in China has been faster than the global rate, and more high temperature spells are expected to occur in future. Here we assess the annual heat-related mortality in densely populated cities of China at 1.5 °C and 2.0 °C global warming. For this, the urban population is projected under five SSPs, and 31 GCM runs as well as temperature-mortality relation curves are applied. The annual heat-related mortality is projected to increase from 32.1 per million inhabitants annually in 1986–2005 to 48.8–67.1 per million for the 1.5 °C warming and to 59.2–81.3 per million for the 2.0 °C warming, taking improved adaptation capacity into account. Without improved adaptation capacity, heat-related mortality will increase even stronger. If all 831 million urban inhabitants in China are considered, the additional warming from 1.5 °C to 2 °C will lead to more than 27.9 thousand additional heat-related deaths, annually. Heatwaves are expected to increase under climate change, and so are the associated deaths. Here the authors determine the regional high temperature thresholds for 27 metropolises in China and analyze the changes to heat-related mortality, showing that the additional global-warming temperature increase of 0.5°C, from 1.5°C to 2.0°C, will lead to tens of thousands of additional deaths, annually.

In recent years, the advances in light‐responsive soft materials with fascinating properties and functions have attracted tremendous attention, which are also enlightening when attempting to achieve the goals of complex deformations, motions, or attractive applications by precise regulation. Attractively, light is not only a clean and inexhaustible energy but also can be controlled remotely, quickly and accurately in a non‐contact way. Moreover, light‐responsive soft materials are capable of amplifying photo‐triggered molecular changes at the microscopic scale into macroscopic deformations, that is, directly converting the input light energy into the output mechanical work, therefore enabling potential applications in the field of actuators and functional devices. To date, some wonderful reviews have reported the progress in photo‐driven soft materials. However, the research progress in ultraviolet, visible (Vis) and near‐infrared (NIR) light‐driven soft materials containing azobenzene or other non‐azobenzene moieties has not been reported yet. In this review, we summarize recent progress in light‐responsive soft materials in terms of preparation methods, response wavelengths and potential applications. Firstly, the preparation methods of photoresponsive soft materials are introduced. Subsequently, photoinduced macroscopic deformations or motions are summarized, in which Vis and NIR light‐responsive behaviors are especially highlighted. Finally, the potential applications of photoresponsive soft materials are classified. To guide the future work for researchers, the existing problems and future development prospects of light‐responsive soft materials are proposed. The research status of light‐responsive soft materials is summarized. Firstly, the preparation methods of photoresponsive soft materials are introduced. Subsequently, ultraviolet, visible, and near‐infrared light‐induced deformations or motions are systematically summarized. Then the potential applications of photo‐deformable soft materials are separately described. Finally, the current existing problems and future development prospects of photo‐responsive soft materials are proposed.

Climate projections are essential for decision-making but contain non-negligible uncertainty. To reduce projection uncertainty over Asia, where half the world’s population resides, we develop emergent constraint relationships between simulated temperature (1970–2014) and precipitation (2015–2100) growth rates using 27 CMIP6 models under four Shared Socioeconomic Pathways. Here we show that, with uncertainty successfully narrowed by 12.1–31.0%, constrained future precipitation growth rates are 0.39 ± 0.18 mm year −1 (29.36 mm °C −1 , SSP126), 0.70 ± 0.22 mm year −1 (20.03 mm °C −1 , SSP245), 1.10 ± 0.33 mm year −1 (17.96 mm °C −1 , SSP370) and 1.42 ± 0.35 mm year −1 (17.28 mm °C −1 , SSP585), indicating overestimates of 6.0–14.0% by the raw CMIP6 models. Accordingly, future temperature and total evaporation growth rates are also overestimated by 3.4–11.6% and −2.1–13.0%, respectively. The slower warming implies a lower snow cover loss rate by 10.5–40.2%. Overall, we find the projected increase in future water availability is overestimated by CMIP6 over Asia. Reduction of uncertainty in climate projections is a key issue for climate change adaptation. Here the authors show that an applying an emergent constraint effectively reduces projection uncertainty across Asia, and reveals less warming and a slower increase in water availability than previously estimated.

The construction of expressways in China has produced diverse habitats along slopes characterized by steep gradients, uneven water distribution, poor soil conditions, and no routine maintenance. Manually planting beneficial species is an essential method of effectively improving slope soils to prevent soil erosion. However, few studies have evaluated the reclamation effects and plant community composition and structure used to restore slopes along expressways. This study focused on the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao Expressway. A total of 10 representative plant communities were evaluated using the analytic hierarchy process (AHP)–fuzzy integrated evaluation method. The sites were divided into four layers, namely, plant communities, soil nutrients, soil physical properties, and other ecological factors, and 14 indicators were assessed. The evaluation results showed that four of these plant communities (PCs) were excellent, three PCs were good, one PC was normal, two PCs were poor. The four excellent PCs had high Shannon-Wiener index, pielou index, richness index or community productivity. It is worth noting that most excellent plant community structures were tree + shrub + herb. Based on these results, we recommend that fill slopes should be restored using a combination of trees, herbs, and shrubs; also, the vegetation should include native plants, such as B . papyrifera , U . pumila , A . fruticosa , and Cynodon dactylon (L.). This study could provide ideas for plant community composition and structure of new highway slopes in similar climate environment, and provide theoretical support for plant community composition and structure and soil improvement for the existing slope.

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides.

The present paper examines flood risk (composed of hazard, exposure, and vulnerability) in a range of spatial perspectives – from the global to the local scale. It deals with observed records, noting that flood damage has been increasing. It also tackles projections for the future, related to flood hazard and flood losses. There are multiple factors driving flood hazard and flood risk and there is a considerable uncertainty in our assessments, and particularly in projections for the future. Further, this paper analyses options for flood risk reduction in several spatial dimensions, from global framework to regional to local scales. It is necessary to continue examination of the updated records of flood-related indices, trying to search for changes that influence flood hazard and flood risk in river basins.

Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.