Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change.
Methodology
Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020–2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors.
Findings
The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk.
Conclusion
COVID-19 has a complex effect on conflict risk worldwide under climate change.
Implications
Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS-Flood). SatVITS-Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time-series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing-season integral anomaly (TFA-GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil-adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High-Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS-Flood in three regions on different continents with 40-year-long, systematic, reliable gauge data. Our results indicate that SatVITS-Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 ? 106 m3 event?1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS-Flood proved useful for detecting historical floods and may provide valuable long-term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Vertical green-living walls (VGWs) are a promising solution for sustainable building design. However, their effectiveness in improving indoor air quality and reducing energy consumption in real-world settings still needs to be studied. Here we aim to contribute to this understanding by examining six indoor plant species (Peperomia obtusifolia, Tradescantia spathacea, Chlorophytum comosum, Spathiphyllum wallisii, Aeschynanthus radicans, and Philodendron hederaceum) in a 15 m2 Patrick Blanc's VGW system established in a shared office space (∼140 m3 volume). Carbon dioxide (CO2) assimilation, transpiration, and stomatal conductance were measured under varying light conditions and CO2 levels. In addition, numerous sensors were placed in the room to assess impacts on the indoor environment. Results indicate that all species but one (Philodendron) were equally effective in reducing CO2. Tradescantia had the highest cooling effect via transpiration. All species except Tradescantia had a very low light compensation point (<5 μmol m−2 s−1 PPFD), indicating their efficiency at reducing CO2 levels even under low light conditions. The net cooling effect of the VGW was 2.5°C–4.5 °C when the ventilation system was on and 1.2°C–3.6 °C when it was off. There was also a positive effect on indoor air quality, with an average CO2 reduction of 5% and sometimes up to 50%. By conducting controlled CO2 enrichment experiments, we estimated a 20% energy consumption savings from reduced air ventilation, equivalent to 1400 kWh/year. These results suggest that VGWs can improve indoor environments and thermal comfort in workplace settings and highlight the importance of choosing appropriate plant species.

Carbonate rocks are highly reactive and presumably have higher ratios of chemical weathering to total denudation relative to most other rock types. Their chemical reactivity affects the first-order morphology of carbonate-dominated landscapes and their climate sensitivity. However, there have been few efforts to quantify the partitioning of denudation into mechanical erosion and chemical weathering in carbonate landscapes such that their sensitivity to changing climatic and tectonic conditions remains elusive. Here, we compile bedrock and catchment-average cosmogenic calcite-³⁶Cl denudation rates and compare them to weathering rates from the same regions. Local bedrock denudation and weathering rates are comparable, ~20–40 mm/ka, whereas catchment-average denudation rates are ~2.7 times higher. This discrepancy is 5 times lower compared to silicate-rich rocks illustrating that elevated weathering rates make denudation more spatially uniform in carbonate-dominated landscapes. Catchment-average denudation rates correlate well with topographic relief and hillslope gradient, and moderate correlations with runoff can be explained by concurrent increases in weathering rate. Comparing denudation rates with weathering rates shows that mechanical erosion processes contribute ~50 % of denudation in southern France and ~70 % in Greece and Israel. Our results indicate that the partitioning between largely slope-independent chemical weathering and slope-dependent mechanical erosion varies based on climate and tectonics and impacts the landscape morphology. In humid, slowly uplifting regions, carbonates are associated with low-lying, flat topography because slope-independent chemical weathering dominates denudation. In contrast, in arid climates with rapid rock uplift rates, carbonate rocks form steep mountains that facilitate rapid, slope-dependent mechanical erosion required to compensate for inefficient chemical weathering and runoff loss to groundwater systems. This result suggests that carbonates represent an end-member for interactions between climate, tectonics, and earth materials.  10.5194/esurf-11-247-2023

Root systems form a significant part of tree biomass and function. Yet, roots are hidden from our eyes, making it difficult to track the belowground processes. By contrast, our capacity to detect aboveground changes in trees has been continuously improving using optical methods. Here, we tested two fundamental questions: (1) To what extent can we detect aboveground responses to mechanical damage of the root system? (2) To what extent are roots redundant? We applied three different non-destructive remote sensing means: (1) optical means to derive leaf greenness, (2) infrared means to detect the changes in leaf surface temperature and (3) spectral means to derive five vegetation indices (i.e. the photochemical reflectance index (PRI), the chlorophyll photosynthesis index (CIRed-edge), the anthocyanin reflectance index 1, the structure insensitive pigment index and the normalized difference water index (NDWI)). We recorded the above metrics for hours and days and up to a month following induced root damage in three key Mediterranean tree species: Aleppo pine (Pinus halepensis Mill.), Palestine oak (Quercus calliprinos Webb.) and Carob (Ceratonia siliqua L.). To induce root damage, we removed 25, 50 and 75 percent of the root system in each species and compared it with control saplings. Tree aboveground (canopy) responses to root damage increased over time and with damage level. Leaf warming (up to 3°C) and decreased PRI were the most significant and rapid responses, with temperature differences being visible as early as 2 days following root damage. NDWI and greenness were the least sensitive, with responses detectable only at 75 percent root damage and as late as 14 or 30 days following root damage. Responses varied vastly among species, with carob being the most sensitive and pine being the least. Changes in leaf temperature and PRI indicated that leaf transpiration and photosynthesis were impaired by root damage. Although trees build roots in excess, mechanical damage will eventually decrease transpiration and photosynthesis across tree species.

Abstract The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (Tleaf) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and Tleaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO2]) and drought. Here we established an [CO2] enrichment experiment in which three wheat genotypes were grown at ambient (400?ppm) and elevated (550?ppm) [CO2] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (Tr) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at ~ $\unicode{x0007E}$1.5?m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air (? $\unicode{x02206}$Tleaf?air). We found significant PRI and RUE and ? $\unicode{x02206}$Tleaf?air and Tr correlations, with no significant differences among the genotypes. A PRI?RUE decoupling was observed under drought at ambient [CO2] but not at elevated [CO2], likely due to changes in photorespiration. For a LE range of 350?W?m?2, the ?Tleaf?air range was ~ $\unicode{x0007E}$10°C at ambient [CO2] and only ~ $\unicode{x0007E}$4°C at elevated [CO2]. Thicker leaves in plants grown at elevated [CO2] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO2] conditions. In general, Tleaf was maintained closer to the ambient temperature at elevated [CO2], even under drought. PRI, RUE, ?Tleaf?air, and Tr decreased linearly with canopy depth, displaying a single PRI-RUE and ?Tleaf?air Tr model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

SUMMARY Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 dpi (days post inoculation) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplast-targeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions and reflected the mislocalization of chl-roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox to chlorophyll fluorescence imaging showed that infected leaf areas which exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching (NPQ) and enhanced quantum PSII yield (ΦPSII) compared to the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection and demonstrate how it can be utilized for nondestructive monitoring of the disease biotrophic stage using whole-plant redox imaging.

Herbivorous insects play important roles in agriculture as pests or as weed biological control agents. Predicting the timing of herbivore insect population development can thus be of paramount importance for agricultural planning and sustainable land management. Numerical simulation models driven by temperature are often used to predict insect pest population build-up in agriculture. Such simulation models intend to use station-derived temperatures to drive the development of the target insect, although this temperature may differ substantially from that experienced by the insect on the plant. To improve the estimations, it has been suggested to replace air temperature in the model by land surface temperature (LST) data. Here, we use a numerical simulation model of insect population dynamics driven by either air temperature (combined with atmospheric temperature soundings) or land surface temperature derived from satellites to predict the population trends of the leaf beetle Ophraella communa, a potential biological control agent of Ambrosia artemisiifolia in Europe. For this, we conducted an extensive field experiment that included caged O. communa populations at five sites along an altitudinal gradient (125–1250 m a.s.l.) in Northern Italy during 2015 and 2016. We compared our model predictions using air or land surface temperature with observed beetle population build-up. Model predictions with both air and land surface temperatures predicted a similar phenology to observed populations but overestimated the abundance of the observed populations. When taking into consideration the error of the two measurement methods, the predictions of the model were in overlapping timeframes. Therefore, the current model driven by LST can be used as a proxy for herbivore impact, which is a novel tool for weed biocontrol.

Abstract Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long-term hydrological effects, such as total water yield and baseflow, of this large-scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970?2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid-1990s induced a significant decrease in runoff for the Weihe River Basin in semi-arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long-term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region-specific afforestation policy should be considered to deal with forestation-water quantity trade-off. Conclusions from this study can help improve decision-making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000–2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Responses of terrestrial ecosystems to climate change have been explored in many regions worldwide. While continued drying and warming may alter process rates and deteriorate the state and performance of ecosystems, it could also lead to more fundamental changes in the mechanisms governing ecosystem functioning. Here we argue that climate change will induce unprecedented shifts in these mechanisms in historically wetter climatic zones, towards mechanisms currently prevalent in dry regions, which we refer to as ‘dryland mechanisms’. We discuss 12 dryland mechanisms affecting multiple processes of ecosystem functioning, including vegetation development, water flow, energy budget, carbon and nutrient cycling, plant production and organic matter decomposition. We then examine mostly rare examples of the operation of these mechanisms in non-dryland regions where they have been considered irrelevant at present. Current and future climate trends could force microclimatic conditions across thresholds and lead to the emergence of dryland mechanisms and their increasing control over ecosystem functioning in many biomes on Earth.

SummaryAlthough numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia’s underlying mechanisms at the regional scale.

Vertical green living walls (VGWs)—growing plants on vertical walls inside or outside buildings—have been suggested as a nature-based solution to improve air quality and comfort in modern cities. However, as with other greenery systems (e.g., agriculture), managing VGW systems requires adequate temporal and spatial monitoring of the plants as well as the surrounding environment. Remote sensing cameras and small, low-cost sensors have become increasingly valuable for conventional vegetation monitoring; nevertheless, they have rarely been used in VGWs. In this descriptive paper, we present a first-of-its-kind remote sensing high-throughput monitoring system in a VGW workplace. The system includes low- and high-cost sensors, thermal and hyperspectral remote sensing cameras, and in situ gas-exchange measurements. In addition, air temperature, relative humidity, and carbon dioxide concentrations are constantly monitored in the operating workplace room (scientific computer lab) where the VGW is established, while data are continuously streamed online to an analytical and visualization web application. Artificial Intelligence is used to automatically monitor changes across the living wall. Preliminary results of our unique monitoring system are presented under actual working room conditions while discussing future directions and potential applications of such a high-throughput remote sensing VGW system.

Future atmospheric carbon-dioxide concentration ([CO2]) rise is expected to increase the grain yield of C3 crops like wheat even higher under drought. This expectation is based on small-scale experiments and model simulations based on such observations. However, this combined effect has never been confirmed through actual observations at the nationwide or regional scale. We present the first evidence that warming and drought in the world’s leading wheat-producing countries offset the benefits of increasing [CO2] to wheat yield in the last six decades. Using country-level wheat yield census observations, [CO2] records, and gridded climate data in a statistical model based on a well-established methodology, we show that a [CO2] rise of ~ 98 μmol mol−1 increased the yield by 7% in the area of the top-twelve wheat-producing countries, while warming of 1.2 °C and water depletion of ~ 29 mm m−2 reduced the wheat grain yield by ~ 3% and ~ 1%, respectively, in the last six decades (1961–2019). Our statistical model corroborated the beneficial effect of [CO2] but contrasted the expected increase of grain yield under drought. Moreover, the increase in [CO2] barely offsets the adverse impacts of warming and drought in countries like Germany and France, with a net yield loss of 3.1% and no gain, respectively, at the end of the sampling period relative to the 1961–1965 baseline. In China and the wheat-growing areas of the former Soviet Union—two of the three largest wheat-producing regions—yields were ~ 5.5% less than expected from current [CO2] levels. Our results suggest shifting our efforts towards more experimental studies set in currently warm and dry areas and combining these with statistical and numerical modeling to improve our understanding of future impacts of a warmer and drier world with higher [CO2].

Abstract The combination of a future rise in atmospheric carbon dioxide concentration ([CO2]) and drought will significantly impact wheat production and quality. Genotype phenology is likely to play an essential role in such an effect. Yet, its response to elevated [CO2] and drought has not been studied before. Here we conducted a temperature-controlled glasshouse [CO2] enrichment experiment in which two wheat cultivars with differing maturity timings and life cycle lengths were grown under ambient (aCO2 approximately 400??mol?mol?1) and elevated (eCO2 approximately 550??mol?mol?1) [CO2]. The two cultivars, bred under dry and warm Mediterranean conditions, were well-watered or exposed to drought at 40% pot holding capacity. We aimed to explore water???[CO2]???genotype interaction in terms of phenology, physiology, and agronomic trait response. Our results show that eCO2 had a significant effect on plants grown under drought. eCO2 boosted the booting stage of the late-maturing genotype (cv. Ruta), thereby prolonging its booting-to-anthesis period by approximately 3 days (p?<?0.05) while unaffecting the phenological timing of the early-maturing genotype (cv. Zahir). The prolonged period resulted in a much higher carbon assimilation rate, particularly during pre-anthesis (+87% for Ruta vs. +22% for Zahir under eCO2). Surprisingly, there was no eCO2 effect on transpiration rate and grain protein content in both cultivars and under both water conditions. The higher photosynthesis (and transpiration efficiency) of Ruta was not translated into higher aboveground biomass or grain yield, whereas both cultivars showed a similar increase of approximately 20% in these two traits at eCO2 under drought. Overall, Zahir, the cultivar that responded the least to eCO2, had a more efficient source-to-sink balance with a lower sink limitation than Ruta. The complex water???[CO2]???genotype interaction found in this study implies that future projections should account for multifactor interactive effects in modeling wheat response to future climate.

Global climate change, expected to be one of the most severe challenges that human beings have ever encountered, has had far-reaching impacts on ecosystems and humans, among which the potentially increasing chance of violent conflict has raised attention recently. However, several years of research have produced no consensus regarding whether climate variability affects the risk of armed conflict and how it may affect conflict. In this study, we built a geographically disaggregated method to explore the relationship between climate variability from normal climate conditions and armed conflicts both on a local and regional scale. With the 10,993 conflict records acquired in 25 African countries over 16 years from 2000 to 2015, we estimated the effects of temperature and wet day variability on conflicts in agricultural and non-agricultural areas, respectively, on gridded 1° resolution. The results showed that deviations from the normal climate have a systematical impact on the risk of conflict: The risk of violence rises with increasing deviations from the temperature norms in both non-agricultural and agricultural areas. Regarding the rainfall variability, in non-agricultural areas, the risk of violence grows with increasing anomalous wet days, either more or fewer days than the annual average, while in agricultural areas, increases in violence risk only exhibit under the impact of fewer wet days than the annual average. We expect these findings would provide empirical support for policymakers and relevant organizations who need to prepare additional law enforcement and/or peacekeeping resources when climatic anomalies are detected.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose Lime-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models. (C) 2020 Published by Elsevier B.V.

Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R-2 = 0.93, Root Mean Square Error (RMSE) = 2.7 degrees C, Mean Absolute Error (MAE) = 2.1 degrees C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson's r.

Climate change has a significant impact on the runoff of basins in cold, dry areas. The quantification of regional ecohydrological responses to climate change such as warming and drought is essential for establishing proper water resource management schemes. We propose a simple and novel method based on the Budyko framework to evaluate the hydrologic resilience of 16 basins that conform the Asian Water Tower in the Tibetan Plateau (TP). Our method defines two metrics within the Budyko domain – tolerance (ψ) and plasticity (φ) – that characterize the hydrologic resilience of a basin. Based on an ecohydrological point of view, a basin is considered hydrologically resilient if ψ and φ are both greater than 1 or its φ is negative and ψ is greater than 1. Our results show that ψ varies between 0.27 and 0.74, with an average value of 0.45 and φ varies between 2 and 16.33, with an average value of 6.90, for 14 out of the 16 basins. Only two basins – Taohe and Datonghe – had negative φ (-11.67 and -8.11, respectively) and ψ greater than 1 (2.26 and 19.58, respectively), suggesting that these two are the only basins with a hydrologic resilience to climatic warming/drying in the TP. Within the non-resilient basins, we found vegetation to play a key role in the level of tolerance and plasticity indicating that basins with a larger vegetation cover display a lower capability to adapt to adverse climatic changes. Following these results, we call for afforestation efforts to be carefully considered in cold, dry areas. The proposed method and conclusions drawn by this study may help predict the hydrologic responses to future adverse climatic conditions.