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Due to thermal carriers generated by a narrow mid-infrared energy gap, cooling is always necessary to achieve ideal photodetection. In quantum dot (QD), the electron thermal generation should be reduced with quantum confinement in all three dimensions. As a result, there would be a great potential to realize high-operating-temperature (HOT) QD mid-IR photodetectors, though not yet achieved. Taking the advantages of colloidal nanocrystals’ solution processability and precise doping control by surface dipoles, this work demonstrates a HOT mid-infrared photodetector with a QD gradient homojunction. The detector achieves background-limited performance with D *  = 2.7 × 10 11 Jones on 4.2 μm at 80 K, above 10 11 Jones until 200 K, above 10 10 Jones until 280 K, and 7.6 × 10 9 Jones on 3.5 μm at 300 K. The external quantum efficiency also achieves more than 77% with responsivity 2.7 A/W at zero bias. The applications such as spectrometers, chemical sensors, and thermal cameras, are also approved, which motivate interest in low-cost, solution-processed and high-performance mid-infrared photodetection beyond epitaxial growth bulk photodetectors. Colloidal quantum dot gradient homojunction would effectively improve the detectivity of mid-infrared photodetector at high-operating temperatures, motivating interest in low-cost, solution-processed and high-performance mid-infrared photodetection.

Memristors, as transformative electronic devices designed to transcend the von Neumann architecture, enable the physical unification of information storage and computation, thereby offering a foundational hardware pathway toward energy-efficient, brain-inspired computing. Their intrinsic analog resistive switching, non-volatility, and history-dependent learning capabilities allow them to natively implement in-memory computing and emulate synaptic plasticity, addressing the critical bottlenecks of energy and speed in conventional systems. Notably, the evolution from electrically controlled memristors to optoelectronic memristors marks a paradigm shift from pure computing to integrated sensing-processing, opening new dimensions for high-speed, parallel, and adaptive signal processing. In recent years, significant progress has been made in the development of memristor-based neuromorphic vision and tactile systems, on-chip signal processors, and dynamic trajectory trackers, demonstrating their potential in edge intelligence, adaptive robotics, and real-time perceptual tasks. This review systematically summarizes the latest advances in memristor technology, providing a comprehensive analysis of their operating mechanisms, material and structural innovations, and cutting-edge applications in neuromorphic perception and computing. Furthermore, it discusses the key challenges and future directions for the development and integration of memristor-based systems in the post-von Neumann era.

Evapotranspiration (ET) is a key link between atmospheric processes and land surface hydrological processes. With the impact of global warming and human activities, research on ET has become a hot topic. Supported by a total of 1,222 Chinese and English literatures from China National Knowledge Infrastructure and the Web of Science Core Collection from 2013 to 2022, this paper adopts the bibliometric visualization method to review the current research progress and future trend of ET with respect to the time of publication, countries, institutions, journals, and research hotspots. The results show that the number of related research articles is increasing rapidly and the journals with high citations are Journal of Hydrology, Agricultural and Forest Meteorology and Agricultural Water Management. The research hotspots have been focused on prototype observation, remote sensing inversion, mechanism equation, model simulation, spatial–temporal analysis, and attribution identification. In the future, there is an urgent need to integrate algorithms such as machine learning and artificial intelligence, to develop higher resolution remote sensing products, to improve the mechanism equations based on precise observations, and to clarify the impact of synergistic effects on ET among the driving factors.

Flexible upconversion (UC) devices, owing to their unique combination of high–efficiency optical energy conversion and mechanical flexibility, have attracted increasing attention in the fields of optoelectronics, wearable devices, flexible displays, and biomedical applications. However, significant challenges remain in balancing optical performance, mechanical adaptability, long–term stability, and scalable fabrication, which limit their practical deployment. This review systematically introduces five representative upconversion mechanisms—excited–state absorption (ESA), energy transfer upconversion (ETU), energy migration upconversion (EMU), triplet–triplet annihilation upconversion (TTA–UC), and photon avalanche (PA)—highlighting their energy conversion principles, performance characteristics, and applicable scenarios. The article further delves into the flexible transition of upconversion devices, detailing not only the evolution of the luminescent layer from bulk crystals and nanoparticles to polymer composites and hybrid systems, but also the optimization of electrodes from rigid metal films to metal grids, carbon–based materials, and stretchable polymers. These developments significantly enhance the stability and reliability of flexible upconversion devices under bending, stretching, and complex mechanical deformation. Finally, emerging research directions are outlined, including multi–mechanism synergistic design, precise nanostructure engineering, interface optimization, and the construction of high–performance composite systems, emphasizing the broad potential of flexible UC devices in flexible displays, wearable health monitoring, solar energy harvesting, flexible optical communications, and biomedical photonic applications. This work provides critical insights for the design and application of high–performance flexible optoelectronic devices.

This study investigated the annual and seasonal rainfall variability at five selected stations of Amhara Regional State, by using the innovative trend analysis method (ITAM), Mann-Kendall (MK) and Sen’s slope estimator test. The result showed that the trend of annual rainfall was increasing in Gondar (Z = 1.69), Motta (Z = 0.93), and Bahir Dar (Z = 0.07) stations. However, the trends in Dangla (Z = −0.37) and Adet (Z = −0.32) stations showed a decreasing trend. As far as monthly and seasonal variability of rainfall are concerned, all the stations exhibited sensitivity of change. The trend of rainfall in May, June, July, August, and September was increasing. However, the trend on the rest of other months showed a decreasing trend. The increase in rainfall during Kiremt season, along with the decrease in number of rainy days, leads to an increase of extreme rainfall events over the region during 1980–2016. The consistency in rainfall trends over the study region confirms the robustness of the change in trends. Innovative trend analysis method is very crucial method for detecting the trends in rainfall time series data due to its potential to present the results in graphical format as well. The findings of this paper could help researchers to understand the annual and seasonal variability of rainfall over the study region and become a foundation for further studies.

Determining the precise placement of hydrological stations on a simulated digital river network is crucial for constructing hydrological models applicable to process simulation, water resource management, and flood forecasting endeavors. To solve this problem, we categorized and scrutinized deviations between the simulated and their actual station locations, and proposed a novel automatic hydrological station relocation algorithm (ASRA). The algorithm was first validated in the Amazon Basin using Global Runoff Data Centre (GRDC) hydrological stations and 90 m × 90 m Shuttle Radar Topography Mission (SRTM) data, successfully correcting the spatial position and corresponding catchment area (CCA) of each station. Findings revealed that CCA inaccuracies were notably decreased, transitioning from an initial 7.62% when employing a conventional 5‐km search radius to 5.43% after adopting an iteratively optimized, objective, and rational 8‐km search radius. The ASRA method was subsequently applied to GRDC stations within the HDMA and HydroSHEDS data sets, successfully repositioning 8,339 and 8,026 stations respectively, all with catchment area deviations of less than 5%, thus either exceeding or at least equaling the precision of prior research efforts. A Python program was developed and incorporated into an ArcGIS toolbox that features user‐friendly attributes, enabling swift computation and accurate rectification, as a result of building upon our method. In short, our study presents a fresh approach and a robust tool for tackling the inconsistencies of hydrological station locations. The updated global GRDC hydrological station locations specifically tailored for both HDMA and HydroSHEDS data sets, together with the toolbox developed, were accessible for download on the figshare platform. Plain Language Summary As a result of the inherent limitations posed by DEM resolution, hydrological stations’ actual positions frequently diverge from their corresponding simulated locations on the digital river network derived from DEM data. These discrepancies can vary across different DEM data sets. While traditional manual methods have been reliable in terms of accuracy, they suffer from inefficiency, while automatic approaches, conversely, have offered speed but at the cost of precision. Hence, this study introduces an automated method and ArcGIS‐based toolbox that combines both high accuracy and efficiency, exemplifying its application in the Amazon basin as well as globally. Key Points The deviation of hydrological station locations on a digital river network were categorized into three types according to the catchment area and location A new ASRA was developed to deal with all deviation types with the advantages of convenience, efficiency, and easy integration with ArcGIS This method provides an objective and reasonable search radius to decrease the catchment area deviation based on an empirical search radius

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

The West Liaohe River Basin, a core arid region in Northeast China, faces a significant evaporation–precipitation imbalance and exhibits fragmented land systems, epitomized by the Horqin Sandy Land. Integrating three decades of land use/land cover (LULC) data with meteorological, ecological, and socioeconomic variables, we employed obstacle diagnosis and structural equation modeling (SEM) to elucidate the spatiotemporal dynamics and drivers of LULC transformations. The results demonstrate the following: (1) Land use exhibited a spatially heterogeneous pattern, with forests, shrubs, and grasslands predominantly concentrated in the northwest and southwest. (2) Vegetation coverage significantly increased from 53.15% in 1990 to 61.32% in 2020, whereas cropland and sandy land areas declined. While the overall basin landscape underwent a marked increase in fragmentation. (3) Human activities were the dominant contributor of LULC changes, particularly for cropland conversion, with key determinants such as population and GDP showing negative path coefficients of −0.59 and −0.77, respectively. Climate change was a secondary contributor, with precipitation exerting a strong positive path coefficient (0.63) that was particularly pronounced during the conversion of grassland to forest. These findings offer a scientific basis for land management, ecological restoration strategies, and water resource utilization in the basin.

To address the limitations of process‐driven models in characterizing physical mechanisms and the interpretability challenges of data‐driven models in flood forecasting, this study proposes a distributed hybrid flood modeling (DHFM) framework that integrates physical mechanisms with deep learning. Differentiable diffusion wave (DW) and convolutional neural network (CNN) routing methods are introduced, which can be seamlessly integrated into the DHFM framework. A differentiable Muskingum (MK) routing method is also implemented as a benchmark. The Mishui Basin in China is selected as a case study to systematically evaluate the performance and interpretability of these three routing methods under both gauged and ungauged scenarios. Results show that the DHFM framework can effectively achieve physical parameterization across different sub‐basins. Compared to the lumped Xin'anjiang hydrological model, it achieve s higher accuracy in both daily streamflow and flood simulations, while also demonstrating favorable interpretability of the embedded neural network. Under gauged scenarios, the differentiable CNN method slightly outperforms DW in terms of performance and efficiency, and significantly surpasses MK. As the number of training stations increases, model performance tends to stabilize or decline. In ungauged scenarios, CNN performs well with sufficient training data (>2 stations) but is sensitive to station selection, exhibiting a substantial performance drop with only one station. In contrast, DW and MK show greater stability. The differentiable CNN method shows potential for adaptively learning unit hydrographs based on channel attributes. The proposed DHFM framework not only enhances flood simulation accuracy but also provides novel perspectives for understanding the physical mechanisms underlying flood processes.

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions.