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Many nodes of a complex network in the real world belong to multiple communities. Research on the detection of overlapping communities with common nodes can help us understand the topology of network and extract information hidden in the network. Considering the problems of many existing overlapping community detection methods, such as high time complexity or poor division results, we propose an overlapping community detection method based on local influence and similarity (ISOCD). The ISOCD method selects seed nodes based on local influence, follows a greedy strategy, takes the fitness function of similarity as the optimization goal, and finally optimizes and merges the detected communities. In experiments, the method we propose has good community division effects both on the real-world and synthetic networks, and has low time complexity. [ABSTRACT FROM AUTHOR]

• A new multi-sensor remote sensing based index is developed for drought monitoring. • The new drought index compares favorably with other commonly used drought indices. • The new drought index has a good performance for drought monitoring across different climate regions. • The performance of the new drought index is insensitive to environmental factors. Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available. [ABSTRACT FROM AUTHOR]

Highlights • A new drought index which integrated multi-sensor remote sensing data was developed. • The new drought index performances better than most existing integrated drought indices and single drought indices. • The new drought index does not require site information and local expert knowledge for good performance. Abstract Drought is one of the most expensive but least understood natural disasters. Remote sensing based integrated drought indices have the potential to describe drought conditions comprehensively, and multi-criteria combination analysis is increasingly used to support drought assessment. However, conventional multi-criteria combination methods and most existing integrated drought indices fail to adequately represent spatial variability. An index that can be widely used for drought monitoring across all climate regions would be of great value for ecosystem management. To this end, we proposed a framework for generating a new integrated drought index applicable across diverse climate regions. In this new framework, a local ordered weighted averaging (OWA) model was used to combine the Temperature Condition Index (TCI) from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Vegetation Condition Index (VCI) developed using the Vegetation Index based on Universal Pattern Decomposition method (VIUPD), the Soil Moisture Condition Index (SMCI) derived from the Advanced Microwave Scanning Radiometer–Earth Observation System (AMSR-E), and the Precipitation Condition Index (PCI) derived from the Tropical Rainfall Measuring Mission (TRMM). This new index, which we call the "Geographically Independent Integrated Drought Index (GIIDI)," was validated in diverse climate divisions across the continental United States. Results showed that GIIDI was better correlated with in-situ PDSI, Z-index, SPI-1, SPI-3 and SPEI-6 (overall r-value = 0.701, 0.794, 0.811, 0.733, 0.628; RMSE = 1.979, 0.810, 0.729, 1.049 and 1.071, respectively) when compared to the Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Scaled Drought Condition Index (SDCI), PCI, TCI, SMCI, and VCI. GIIDI also performed well in most climate divisions for both short-term and long-term drought monitoring. Because of the superior performance of GIIDI across diverse temporal and spatial scales, GIIDI has considerable potential for improving our ability to monitor drought across a range of biomes and climates. [ABSTRACT FROM AUTHOR]

Numerous drought indices have been developed to monitor drought conditions. However, different drought indices have differing characteristics, and are suitable for specific environments. The aim of this study was to compare the occurrence of drought, as detected by remote sensing across the Continental United States (CONUS). We used drought events during 2011 and 2012 to compare various indices developed for the study of drought phenomena. Three in situ drought indices, the Palmer drought severity index (PDSI), Z -index and standardized precipitation indices (SPI) with different time scales were used to evaluate drought conditions in different climate divisions. The drought indices compared in this study include the vegetation condition index (VCI), the temperature condition index (TCI), the perpendicular drought index (PDI) and modified PDI (MPDI) derived from moderate-resolution imaging spectroradiometer (MODIS) data, the precipitation condition index (PCI) derived from tropical rainfall measuring mission (TRMM) data, and the soil moisture condition index (SMCI) derived from advanced microwave scanning radiometer - earth observing system (AMSR-E). Other synthesized drought indices, which combine indices such as VCI, TCI, SMCI and PCI, were also compared in this study. These included the vegetation health index (VHI), temperature vegetation dryness index (TVDI), scaled drought condition index (SDCI), the microwave integrated drought index (MIDI), the synthesized drought index (SDI), the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI). The results include a wide variety of drought conditions based on different drought indices. Meteorological drought indices indicated that more regions were under severe drought than agricultural drought indices did. Drought indices appear to be more similar to standard drought indices than PDI, MPDI and TVDI. The results also indicate that different indices have strengths and weaknesses in different climates across CONUS. SMCI has a good correlation with short-term SPI, and the sensitivity of SMCI is strongly dependent on terrain, as it performs worse in regions with heavy tree cover than in regions with a low density of vegetation. TCI, VCI, PDI and MPDI are more similar to 3-month SPI data, but correlate weakly with station-based indices located in areas of high precipitation, higher soil permeability, large-scale agriculture and forests. PCI is more strongly correlated with short-term drought conditions in almost all climate divisions than other single indices. VCI would be more reliable if there were only red and NIR surface reflectance bands available, while VHI would be a better choice if only NDVI and LST data were available. Condition index-based drought indices (PCI, TCI, VCI, SMCI, VHI, SDCI, SDCI, MIDI, OVDI, OMDI) performed better than other categories of drought indices, and the use of time series analysis may be a contributing factor to this difference in performance. [ABSTRACT FROM AUTHOR]

Surface temperatures are mechanistically linked to vegetation biophysical and physiological processes. Although remote sensing in the thermal infrared (TIR) domain can offer novel insights into the impacts of changing surface temperatures on vegetation, the transformative potential of remote sensing for plant ecology has not yet been realized.Remotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time. Plant species, traits and structural characteristics can be evaluated with high spectral resolution TIR emissivity.Beyond canopy scales, thermal remote sensing can enhance the inferences obtained from manipulative experiments and empirical evidence, providing unique insight into shifts in species ranges and phenology with changing climate conditions.Scaling leaf traits, canopy structure and regional patterns require an integrated understanding of both process and technology. Theory linking surface temperatures to vegetation dynamics is summarized from an energy balance perspective. We outline scaling considerations including the impacts of morphology on leaf energy balance, canopy structure influences on convective heat exchange and potential confounding impacts of non‐vegetated surfaces.Synthesis. We introduce a unifying framework to link leaf to globe through thermal remote sensing. Recent and emerging advances in sensors, data availability and analytics, together with synergies between TIR remote sensing and other data sources, present a timely opportunity for ecologists to advance our understanding of plant physiology, ecology and biogeography with thermal remote sensing. [ABSTRACT FROM AUTHOR]

• A comprehensive assessment of the three major droughts' impacts on Amazon forests. • The 2015/16 drought is the most severe meteorological drought since 2000. • Drought in 2015/16 showed lagging impacts on photosynthesis, greenness, and leaf area. • Photosynthesis, greenness, and leaf area had similar suppression in 2010 and 2015/16. • Canopy water content exhibited more widespread and severe drought impacts in 2015/16. The Amazon forests play an integral role in the global carbon cycle and have a substantial impact on Earth's climate. However, it is increasingly susceptible to the effects of prolonged droughts, exacerbated by climate change and human activities. This vulnerability underscores the importance of understanding the forests' reaction to such environmental stressors. Despite their significance, comprehensive cross-comparisons of the climate and vegetation responses during the 2005, 2010, and 2015/2016 drought episodes are not well-established. Here we utilize a range of gridded vegetation and climate datasets—including leaf area index (LAI), solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), vegetation optical depth (VOD), self-calibrating Palmer drought severity index (scPDSI), precipitation (P), land surface temperature (LST), and photosynthetically active radiation (PAR)—to thoroughly assess the climate and vegetation response to these three drought events. Our findings reveal that the extent of drought inhibition in the Amazon forests was 74.7 % in 2015, increasing to 81.3 % in 2016, a significant escalation from 49.6 % in 2005 and 57.7 % in 2010. The effects of these three droughts on vegetation varied in both physiological and structural aspects. The Amazon forests' photosynthetic activity, greenness, and leaf area experienced comparable suppression in 2010 and 2015/2016 droughts. However, canopy water content exhibited more extensive and severe impacts during the 2015/2016 drought. Our findings indicate that varying sensitivities to water deficit and solar radiation lead to diverse spatial patterns and intensities of vegetation response, highlighting the complex dynamics of the Amazon forests under drought stress. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic networks. As a result, they have garnered significant attention from researchers.However, real-world data often face challenges like data sparsity and missing labels, which can hinder the performance and generalization ability of graph convolutional neural networks. To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. We propose an innovative model called Adaptive Feature and Topology Graph Convolutional Neural Network (AAGCN). By incorporating an adaptive layer, our model preprocesses the data and integrates the hidden features and topological information with the original data’s features and structure. These fused features are then utilized in the convolutional layer for training, significantly enhancing the expressive power of graph convolutional neural networks.To evaluate the effectiveness of the adaptive layer in the AAGCN model, we conducted node classification experiments on real datasets. The results validate its ability to address data sparsity and improve the classification performance of graph convolutional neural networks.In conclusion, our research primarily focuses on addressing data sparsity and missing labels in graph convolutional neural networks. The proposed AAGCN model, which incorporates an adaptive layer, effectively extracts hidden features and topological information, thereby enhancing the expressive power and classification performance of these networks. [ABSTRACT FROM AUTHOR]

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future. • Multi-sensor observations provide advantages in addressing knowledge gaps. • Drought related remote sensing datasets are identified for multi-sensor studies. • Key drought phenomena and mechanisms benefiting from multi-sensors are highlighted. • Recent advances for multi-sensor drought monitoring models are synthesized. • Challenges and opportunities using multi-sensors for drought studies are provided. [ABSTRACT FROM AUTHOR]

• 17O-excess was used to distinguish dew equilibrium or kinetic fractionation. • Dew underwent kinetic fractionation associated with evaporation in Gobabeb. • Dew with temperature over 14.7 °C was influenced by evaporation in Indianapolis. • Dew did not experience significant evaporation in Nice. • Relative humidity is a main factor explaining different dew evaporation processes. With increasing aridity in many regions, dew is likely to play an increasingly important role in the ecohydrological processes in many ecosystems, especially in arid and semiarid regions. Few studies investigated the role of evaporation during dew formation and how it varies under different climate settings. 17O-excess, as a new tracer, could be used to extract information of evaporation dynamics from natural water samples (e.g., precipitation, river, and lake). Therefore, to fill the knowledge gap in evaporation mechanisms during dew formation, we report the isotopic variation (δ2H, δ18O, δ17O, and 17O-excess) of dew and precipitation from three distinct climatic regions (i.e., Gobabeb in the central Namib Desert, Nice in France with Mediterranean climate, and Indianapolis in the central United States with humid continental climate). We examined whether dew formed in different climate settings was affected by different degree of evaporation using observed isotopic values and evaporation models during the formation processes, and modeled the effects of key meteorological variables (i.e., temperature and relative humidity) on 17O-excess variations. The results showed that dew in Gobabeb experienced kinetic fractionation associated with evaporation under non-steady state conditions during dew formation with enriched δ18O and low 17O-excess values. Dew formations with temperatures over 14.7 °C in Indianapolis were also influenced by evaporation under non-steady state conditions. However, dew formation in Nice did not experience significant evaporation. Evaporation processes (equilibrium or kinetic fractionation) occurring during nights with heavy dew under three climate settings were mainly related to the variation of atmosphere relative humidity. The 17O-excess tracer provides a new method to distinguish the different evaporation processes (equilibrium or kinetic fractionation) during dew formation and our result provides an improved understanding of dew formation. [ABSTRACT FROM AUTHOR]

• SOS NDVI dates occurred much earlier than SOS dates from EVI, LSWI, GPP, and SIF. • SOS LSWI dates were more linearly consistent with SOS GPP and SOS SIF than EVI and NDVI. • Temperature analysis showed that early SOS NDVI dates were more likely affected by snowmelt. Accurate phenology characterization is of great importance for measuring ecosystem dynamics, especially for carbon and water exchange between land and the atmosphere. Vegetation indices (VIs), calculated from land surface reflectance, are widely used to estimate phenology from the leaf and canopy structure perspective. Gross Primary Production (GPP) and solar-induced chlorophyll fluorescence (SIF) are used to estimate phenology from the canopy functional (physiological) perspective. To what degree are the spring phenology estimated from these different perspectives consistent with each other? In this study, we evaluated the consistency of the start of the growing season (SOS) in spring for snow-covered evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) using three vegetation indices, in-situ GPP data from the eddy covariance flux towers (GPP EC), GPP data from the Vegetation Photosynthesis Model (GPP VPM), and SIF data from the GOME-2. Results showed that SOS NDVI dates were much earlier than SOS dates from EVI (SOS EVI), land surface water index (LSWI) (SOS LSWI), GPP (SOS GPP ; SOS GPP-EC , SOS GPP_VPM) and SIF (SOS SIF) for both snow-covered evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF). SOS LSWI dates were more linearly correlated with SOS GPP and SOS SIF than SOS dates from NDVI and EVI. At ENF sites, SOS LSWI dates were 17 (± 27) days later and SOS EVI were 25 (± 34) days later than SOS GPP_EC dates. At DBF sites, SOS LSWI and SOS EVI dates were 1-week (± 13 days) later than SOS GPP_EC dates. In the snow-covered regions at mid- to high-latitude in the Northern Hemisphere, SOS LSWI dates were 2˜3 weeks (± 5 days) later than those of SOS GPP_VPM and SOS SIF for both ENF and DBF. Our results clearly highlight the need for further investigation of NDVI-based SOS dates, which were likely affected by snowmelt in snow-covered forests, and the potential of LSWI for tracking the effects of snow on SOS dates. Estimations of SOS dates in snow-covered forests should consider the effects of both snow cover and temperature on leaf emergence (green-up) and gross primary production. [ABSTRACT FROM AUTHOR]

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with thein situobservations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr–1. [ABSTRACT FROM AUTHOR]

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982–2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle. [ABSTRACT FROM AUTHOR]

Flash drought is an extreme phenomenon, characterized by unusually rapid intensification of drought severity, with strong impacts on plant growth especially for crops. However, it's unclear how hydrometeorological changes contribute to flash drought and how vegetation physiology, greenness, and productivity respond to flash drought. In this study, we developed an multivariate integrated framework for flash drought identification using a regression model based on principal-components analysis (PCA): the PCA regression flash drought (PRFD) model. Three criteria are specified to emphasize the rapid intensification of drought and its impacts on vegetation growth and water resources. We applied our new model in two geographical units with different climates and hydrology: one is dominated by agriculture and subject to natural drought (the Loess Plateau) and the other, a natural region with infrequent drought (the Qinling Mountains). We found that high frequency of flash droughts is most likely to occur in the eastern and central Loess Plateau and part of the Qinling Mountains. However, in relatively humid areas, flash drought shows strong intensity such as the Qinling Mountains and western and eastern edges of the Loess Plateau. Trend analysis indicates that PRFD has increased frequency, longer duration, and stronger intensity since the 1990s in both regions. From an ecological perspective, PRFD also showed a spatial pattern consistent with values of vegetation-related proxies that were below the long-term average, demonstrating vegetation transpiration, normalized-difference vegetation index (NDVI), gross primary productivity have obvious feedback on flash drought events. The Loess Plateau's NDVI responded immediately to flash drought, versus a 1-month lag in the Qinling Mountains. Because our proposed framework integrates multiple aspects of drought information, it can be applied in areas outside the study region according to regional hydrometeorological conditions. This has significant implications for improving agricultural management and forecasting future severe impacts of flash drought on plant growth. • A new integrated framework is proposed to identify flash drought (FD) events. • FD with greater frequency, longer pentads, and stronger intensity since 1990s. • Vegetation structure and physiological function respond rapidly to FD. • The response time of plant activity to FD is different in diverse climate-water regions. [ABSTRACT FROM AUTHOR]

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]

Fog is an important water source for many ecosystems, especially in drylands. Most fog‐vegetation studies focus on individual plant scale; the relationship between fog and vegetation function at larger spatial scales remains unclear. This hinders an accurate prediction of climate change impacts on dryland ecosystems. To this end, we examined the effect of fog on vegetation utilizing both optical and microwave remote sensing‐derived vegetation proxies and fog observations from two locations at Gobabeb and Marble Koppie within the fog‐dominated zone of the Namib Desert. Significantly positive relationships were found between fog and vegetation attributes from optical data at both locations. The positive relationship was also observed for microwave data at Gobabeb. Fog can explain about 10%–30% of variability in vegetation proxies. These findings suggested that fog impacts on vegetation can be quantitatively evaluated from space using remote sensing data, opening a new window for research on fog‐vegetation interactions. Plain Language Summary: This study presents the first observed relationships between fog and vegetation function using satellite remote sensing data. Significant correlations were found between fog and satellite‐derived vegetation indices at two study sites within the Namib Desert. These findings suggested that satellite data can be used to assess and quantify the impacts of fog on vegetation at large spatial scales. Key Points: The positive impact of fog on vegetation is first observed using satellite remote sensing dataFog can explain 10%–30% of variability in vegetation proxiesOptical and microwave remote sensing data showed continuous positive impact of fog on vegetation [ABSTRACT FROM AUTHOR]

Compared to tap water δ2H and δ18O, tap water 17O-excess preserves additional information about source water dynamics. In this study, we provide the first report of 17O-excess variations of tap water across China (652 samples). Annual 17O-excess of tap waters at the national scale did not show obvious spatial pattern, and was almost unaffected by local environmental factors except in the Qinghai-Tibet Plateau region with a strong latitudinal trend. The mean 17O-excess values in different seasons were not significantly different. The isotopic compositions of most of the tap waters at the annual and seasonal scale were likely influenced by the equilibrium fractionation effect (δ′18O-δ′17O slope ranged from 0.5277 to 0.5301), except for the northwest region in the summer (slope = 0.5264) influenced by kinetic fractionation associated with re-evaporation effect. Based on the information of tap water source distribution, site aridity index and the known precipitation δ18O values, a subset of the tap water can be considered as precipitation proxy. Different from the obvious spatial characteristics of precipitation δ18O, precipitation 17O-excess did not show a clear spatial pattern. But it revealed much detailed precipitation formation mechanisms related to different climate regions and geographical conditions. The lower 17O-excess values of the precipitation-sourced tap waters were caused by kinetic fractionation associated with supersaturation process in snow or glacier formation and re-evaporation effect in some arid regions. The higher 17O-excess values of the precipitation-sourced tap waters in the inland were caused by continental moisture recycling, while likely caused by multiple factors in the southeast coastal region including short transport from ocean source and the humid local environment. Overall, this study provides a unique tap water 17O-excess dataset across China, and probes the precipitation formation mechanisms using tap waters. [ABSTRACT FROM AUTHOR]

• Forest age was mapped at 30 m resolution using GEDI-derived forest height maps. • Climate and topographic data accounted for 23–52% in forest age mapping. • Logistic equation was the best in mapping forest age among stand growth equations. • Random forest was applicable to map forest age without classifying tree species. Forest age is a key parameter for estimating forest growth and carbon uptake and for forest management. Remote sensing provides indirect but useful information for mapping forest age at large scales. However, existing regional and global forest age products were generated at low spatial resolutions (often 1000 m) and are not useful for most forest stands in China that are smaller than 1000 m. This study aims to map forest age at the 30 m resolution based on forest height maps mainly derived from the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, and analyze the roles of auxiliary data including temperature, precipitation, slope, and aspect in forest age mapping. Forest age is defined as the average age of dominant tree species within a pixel. Five commonly-used stand growth equations and twelve machine learning methods were tested for their suitability for mapping forest age of different tree species. We found that the Logistic equation performed the best among the tested stand growth equations and the Random Forest (RF) was the best among the tested machine learning methods. According to RF, forest height contributed predominantly to the variance in forest age mapping, while temperature, precipitation, slope, and aspect also had an overall non-negligible and variable contribution among different tree species. By integrating the climate and topographical variables, RF was applicable for forest age mapping without classifying the tree species. These results show that forest height maps derived from space-borne lidar data such as GEDI and ICESat-2 data are highly useful for mapping forest stand age, and the methodology developed in this study highlights a perspective for generating national and global forest age products at a high spatial resolution. [ABSTRACT FROM AUTHOR]

• A modified isotope-based method was developed to estimate the evapotranspiration partitioning, and it can be applied on various time scales. • The modified method and the traditional method are equivalent at a 15-minute time scale. • The modified method is capable of partitioning evapotranspiration at high-frequency and distributed the high sensitivity parameter δ ET into other three less sensitive parameters. • The high-frequency method is advantageous in avoiding the extrapolation of the keeling plot regression line, and enabling the calculation of confidence interval of evapotranspiration partition. • The high-frequency method might be useful when investigating evapotranspiration partitioning under short-term extreme weather events and flush agricultural irrigation. To better understand water and energy cycles, numerous efforts to partition evapotranspiration (ET) into evaporation (E) and transpiration (T) have been made over the recent half century. One of the analytical methods is the isotopic approach. The isotopic composition of ET (δ ET) is a crucial parameter in the traditional isotope-based ET partitioning model, which however, has considerable uncertainty and high sensitivity. Here we proposed a modified T fraction in total ET (F T) calculation using Keeling plot slope (k), the atmospheric vapor concentration (C v), and the isotopic composition of atmospheric vapor (δ v), to avoid the direct use of δ ET. Following the traditional method, we used the Craig-Gordon model for the isotopic composition of evaporation (δ E) and chamber method for the isotopic composition of transpiration (δ T) in our modified method. The modified F T calculation method (F T (m)) can be applied at a 15-min time scale using the average values (F Ti (mp)) and at a 1 Hz time scale for high-frequency method (F Ti). The modified method was verified by both theoretical derivations and field observations. F Ti (mp) was equivalent to those using the traditional isotopic method at a 15-min time scale. However, F Ti eliminated the highly sensitive parameter δ ET , and redistributed the sensitivity of δ ET into three less sensitive parameters. Additionally, F Ti has two main advantages. First, the high-frequency method avoids the extrapolation of the Keeling plot regression line intercept. Second, the high-frequency method can produce a 95% confidence interval of F T in a measurement cycle (e.g., 15 min). The calculated confidence interval was different from that of traditional uncertainty analysis. The high-frequency method might be useful when investigating evapotranspiration partitioning under short-term extreme weather events and flush agricultural irrigation. [ABSTRACT FROM AUTHOR]

• Optimum air temperature of photosynthesis from GPP EC agree well with those from MODIS EVI. • Optimum air temperature of photosynthesis from GPP EC and MODIS EVI differed substantially from those that are currently used in satellite-based models. • Use of optimum air temperature of photosynthesis from site-year specific or biome-specific approaches can affects global GPP estimates substantially. Gross primary productivity (GPP) of vegetation is affected by air temperature. Biogeochemical models use the optimum air temperature (T opt) parameter, which comes from biome-specific look-up tables (T opt − b − LT). Many studies have shown that plants have the capacity to adapt to changes in environmental conditions over time, which suggests that the static T opt − b − LT parameters in the biogeochemical models may poorly represent actual T opt and induce uncertainty in GPP estimates. Here, we estimated biome-specific, site-year-specific, and site-specific optimum air temperature using GPP data from eddy covariance (EC) flux tower sites (GPP EC) (T opt − b − EC , T opt − sy − EC , T opt − s − EC), the Enhanced Vegetation Index (EVI) from MODIS images (T opt − b − EVI , T opt − sy − EVI , T opt − s − EVI), and mean daytime air temperature (T DT). We evaluated the consistency among the four T opt parameters (T opt − b , T opt − sy , T opt − s and T opt − b − LT), and assessed how they affect satellite-based GPP estimates. We find that T opt parameters from MODIS EVI agree well with those from GPP EC , which indicates that EVI can be used as a variable to estimate T opt at individual pixels over large spatial domains. T opt − b , T opt − sy , and T opt − s differed significantly from T opt − b − LT. GPP estimates using T opt − b and T opt − sy were more consistent with GPP EC than when using T opt − b − LT for all the land cover types. Our use of T opt − sy substantially improved 8-day and annual GPP estimates across biomess (from 1% to 34%), especially for cropland, grassland, and open shrubland. Our simple calculation shows that global GPP estimates differ by up to 10 Pg C/yr when using our suggested T opt − sy − EVI instead of using the static T opt − b − LT. Our new approach on estimating T opt has the potential to improve estimates of GPP from satellite-based models, which could lead to better understanding of carbon-climate interactions. [ABSTRACT FROM AUTHOR]

Drought is a complex and poorly understood natural hazard in complex terrain and plains lie in foothills of Hindukush-Himalaya-Karakoram region of Central and South Asia. Few research studied climate change scenarios in the transboundary Chitral Kabul River Basin (CKRB) despite its vulnerability to global warming and importance as a region inhabited with more than 10 million people where no treaty on use of water exists between Afghanistan and Pakistan. This study examines the meteorological and agricultural drought between 2000 and 2018 and their future trends from 2020 to 2030 in the CKRB. To study meteorological and agricultural drought comprehensively, various single drought indices such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI), and combined drought indices such as Scaled Drought Condition Index (SDCI) and Microwave Integrated Drought Index (MIDI) were utilized. As non-microwave data were used in MIDI, this index was given a new name as Non-Microwave Integrated Drought Index (NMIDI). Our research has found that 2000 was the driest year in the monsoon season followed by 2004 that experienced both meteorological and agricultural drought between 2000 and 2018. Results also indicate that though there exists spatial variation in the agricultural and meteorological drought, but temporally there has been a decreasing trend observed from 2000 to 2018 for both types of droughts. This trend is projected to continue in the future drought projections between 2020 and 2030. The overall study results indicate that drought can be properly assessed by integration of different data sources and therefore management plans can be developed to address the risk and signing new treaties. [ABSTRACT FROM AUTHOR]