Vegetation plays a vital role in maintaining ecological stability, carbon cycling, and food security. However, vegetation dynamics are strongly influenced by large-scale climate oscillations, particularly the El Niño–Southern Oscillation (ENSO). Understanding vegetation responses to different ENSO phases is essential for assessing ecosystem resilience and supporting agricultural planning in monsoon-dependent regions. In this study, MODIS-derived vegetation products Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Fraction of Absorbed Photosynthetically Active Radiation (FPAR) and Gross Primary Productivity (GPP) for the period 2010–2021 were analyzed. Principal Component Analysis (PCA) was applied to integrate these indices into a single Vegetation Activity Component (VAC). Lagged Spearman correlations (ρ) were then calculated to examine the relationship between VAC and the Multivariate ENSO Index (MEI) across four agro-climatic zones within the Middle Gangetic Plain: Tarai, Eastern, North-Eastern, and Vindhyan Plains. The first principal component (PC1) explained 80% of the total variance, with NDVI emerging as the most sensitive variable of vegetation greenness. Lagged correlation spatial analysis showed that La Niña phases were associated with strong positive vegetation responses (ρ = 0.25–0.40), suggesting improved rainfall and soil moisture availability, especially in the Tarai and Eastern Plains. Conversely, El Niño phases resulted in weak to negative correlations (ρ = −0.1 to −0.2), indicating drought stress and delayed vegetation recovery in the Vindhyan and North-Eastern regions. These findings identify distinct ENSO-sensitive hotspots characterized by phase-specific vegetation stress and recovery patterns. The integration of PCA and remote sensing-derived vegetation indices offers a robust and scalable framework for monitoring ENSO-induced vegetation variability, supporting adaptive land and water resource management in monsoon-dependent ecosystems.
Abstract Sea-level rise (SLR) poses escalating risks to the U.S. Gulf Coast, where critical industries, infrastructure, and populations are concentrated in low-lying areas. This study estimates the short- and long-run economic effects of SLR using a panel autoregressive distributed lag-pooled mean group (ARDL-PMG) framework applied to county-level data from 26 coastal counties across five Gulf Coast states from 2005 to 2021. Over this period, mean sea level increased by approximately 11 cm on average across counties, with cumulative changes ranging from about 4 cm to over 24 cm and substantial cross-county variation, providing a meaningful empirical setting for identifying economic adjustment. We analyze sectoral outcomes in employment, business establishments, gross domestic product, and wages. Long-run estimates suggest persistent economic declines associated with rising sea levels, particularly in leisure and hospitality, natural resources and mining, and trade, transportation, and utilities. Construction exhibits mixed dynamics, with short-run disruptions followed by increases in establishments consistent with adaptation-related investment. Although some short-run gains emerge following disaster-related activity, these effects do not offset longer-term structural contraction. Wetland coverage is associated with more heterogeneous longer-run adjustment, suggesting that coastal land-cover conditions may shape economic vulnerability. Descriptive evidence indicates more variable exposure and less stable economic performance in disadvantaged and disaster-prone counties. These findings underscore the need for regionally tailored adaptation strategies that integrate ecosystem preservation with climate-resilient infrastructure and long-run economic planning.
Abstract Floods are a recurrent phenomenon in the Indian state of Assam, with nearly 39% land classified as flood-prone. This increases the vulnerability of the agriculture sector that supports around 70% of the state’s population either directly or indirectly. While extensive research exists on the effects of floods and climate change on the agriculture sector, only a few explain their direct long-run consequences and the micro-level adaptive responses. We fill this gap by analysing the impact of floods and climate variability on the primary sector by considering Assam’s agricultural gross value added (GVA), that embodies the sector’s real economic performance in comparison to the aggregate gross domestic product, for the period 1980–2023. We found statistically significant bidirectional relationship between floods and GVA, along with interactions between floods and local climatic conditions. The findings demonstrate that environmental shocks directly affect agricultural performance. We also observe that adaptive capacity and resilience of the agriculture system are impacted indirectly. Therefore, there is a need to understand how adaptation policies need to be designed so that the resilience of the sector improves. Qualitative analysis of survey data collected from a sample of 436 farm households across 16 villages in the Brahmaputra valley, capturing local perceptions, and coping strategies help us in this direction. The study underscore mediating role of adaptive capacity, a key contribution of the study. Results emphasizes the need for a pro-active disaster risk reduction programme. Overall, the study informs policymakers that they should develop targeted strategies to further lower the adverse effects of floods and climate variability on the agricultural sector.
Abstract Despite the critical roles of boundary layer turbulent processes in tropical cyclone (TC) development, the distinct contributions of the vertical extent and peak magnitude of vertical eddy diffusivity ( K m ) to TC intensification remain insufficiently understood. Using a series of Global-to-Regional Integrated Forecast System simulations of Typhoon Lekima (2019), this study investigates how the vertical extent and magnitude parameters, embedded within the eddy-diffusivity mass-flux planetary boundary layer scheme, modulate turbulent processes and influence TC intensification. The findings suggest that the vertical extent parameter impacts both TC early spin-up efficiency and rapid intensification (RI) physics, whereas the magnitude parameter primarily affects RI. Specifically, during vortex spin-up, a reduced vertical extent enhances the turbulent moisture flux gradient and diminishes vertical diffusion, thereby promoting feedback in the wind-induced surface heat exchange (WISHE). As RI begins, a reduced vertical extent weakens downward mixing of azimuthal momentum, amplifying boundary layer gradient imbalance. In contrast, the weakening of turbulent mixing resulting from the reduction in peak magnitude becomes pronounced before RI onset. This ensuing boundary layer imbalance thus facilitates RI initiation. These findings establish a unified framework, demonstrating how the vertical extent parameter modulates early WISHE-driven spin-up, while both parameters influence gradient wind imbalance during RI, thereby linking turbulent processes to the stage-dependent dominance of TC intensification mechanisms.
Abstract. Atmospheric total peroxy radicals RO2* (RO2∗=HO2+∑RO2) play central roles in tropospheric chemistry, governing the formation of ozone and secondary aerosols. However, due to their extremely low concentrations and high reactivity, direct observation of RO2* remains challenging. In this study, a compact instrument for in-situ measurement of RO2* was developed by combining the Peroxy Radical Chemical Amplification (PERCA) technique with Cavity-Enhanced Absorption Spectroscopy (CEAS). Using a pure HO2 standard for calibration, the system can achieve a chemical chain length of 30 and an optimal detection limit of 0.38 pptv (1σ, 3 min), enabling highly sensitive measurements of ambient peroxy radicals. The self-constructed PERCA–CEAS system was successfully deployed in a field campaign during autumn in Zhuhai to observe ambient RO2*. During the observation period, the mean daytime RO2* was 31.11 ± 18.87 pptv, which resulted in an average P(O3) of 14.41 ± 17.04 ppbv h−1. The comparison of O3 variation and derived P(O3) indicates that the daytime ozone enhancement in Zhuhai was primarily driven by local photochemical production, while regional transport acted mainly as an export effect. Our results demonstrate that a compact PERCA–CEAS system is capable of ambient RO2* measurements and suggest the need of diagnosing O3 formation pattern with the constraint of high time-resolution RO2* concentration.
Biomass burning organic aerosol (BBOA) is a major source of atmospheric brown carbon (BrC), which contributes to climate through solar radiation absorption. Chemical aging (bleaching) of BrC diminishes light absorption over time. Although recent modeling studies have emphasized the need to account for bleaching when assessing BrC's climate impacts, a parameterization representing the entire BBOA matrix has been lacking. Here, we develop a bleaching parameterization for the whole BrC in BBOA based on laboratory experiments that systematically varied temperature and relative humidity. The bleaching timescale increases under low-humidity and low-temperature conditions, likely due to enhanced aerosol viscosity. Implementing this parameterization in a global model increases the simulated direct radiative effect (DRE) of BrC by 1.5 to 2 times relative to previous estimates, with associated uncertainties exceeding 10% of the total organic aerosol DRE. The present scheme particularly shows elevated fresh BrC concentrations in boreal regions, suggesting that bleaching dynamics may influence not only radiative forcing but also snow darkening effects.
Abstract Barrier layers in the upper ocean suppress the upward entrainment of cold thermocline water, trapping heat and momentum near the surface and thereby influencing tropical air-sea interaction. However, the mechanisms governing their spatial and temporal variability are not fully understood. This study highlights the importance of salinity-induced vertical stratification in shaping the climatology and variability of the barrier layer in the Pacific Ocean on subannual and interannual timescales. Compared to observational and reanalysis data, coupled ocean-atmosphere models simulate a less eastward-extending warm pool, along with a thin barrier layer bias. This bias is linked to a saltier upper western Pacific and can be attributed to weaker precipitation and stronger easterly winds along the equator. Consistently, models with a more eastward-extending warm pool tend to exhibit a thicker barrier layer and lower salinity over the western Pacific. On interannual timescales, models agree with observational and reanalysis data that anomalous westerly winds and increased precipitation develop 10–13 months before the peak eastward shift of the warm pool eastern edge (WPEE), accompanied by an upper-ocean freshening. These anomalies peak with the WPEE shift and persist for another 9–10 months. Subannual variations exhibit more complex temporal patterns. Anomalous westerly winds and increased precipitation emerge 3–4 months prior to the WPEE peak extension and reverse rapidly one month after the peak. While models capture the timing and magnitude of wind and precipitation changes, they fail to reproduce subannual salinity variations. Improving salinity climatology and subannual variability in models remains essential for simulating barrier layers.
Abstract. The Observatoire Haute Provence (OHP) station is one of the few long-term measuring stations for vertical ozone profiles in southern Europe. Since 1991, vertical ozone distribution has been monitored by the OHP weekly electrochemical concentration cell (ECC) ozonesonde. In this study, we have corrected the ECC datasets for the period 2002–2007. The correction of the ECC has been carried out using comparisons with other ozone-measuring instruments at the same station (stratospheric lidar and Système d'Analyse par Observation Zénithale (SAOZ) photometer) and with collocated satellite observations of the ozone vertical profile by Aura-Microwave Limb Sounder (MLS). Median ozone concentration of the ECC for the period 2002–2007 was −3.4 % lower than that of the stratospheric lidar and MLS. The ECC internal pump temperature showed a sudden drop of 16 K at 25 km for the period 2002–2007 compared to the period 1991–2001. Considering the long-term trends of the ECC current and stratospheric lidar ozone concentration at 25 km as well as the ECC pump flow rate trend, we show that the recorded ECC pump temperature between 2002 and 2007 is too low by 10 K at 25 km. The ECC pump temperature for the period 2002–2007 has been corrected using a linear altitude-dependent increasing rate of 0.33 K km−1. As a result, the corresponding ECC ozone bias at 25 km with the stratospheric lidar and MLS have been reduced to −0.5 %. The corrected OHP ECC data are available at https://doi.org/10.25326/855 (Ancellet and Godin-Beekmann, 2025).
Abstract. Satellite retrievals of atmospheric greenhouse gas columns are used to obtain information about greenhouse gas sources and sinks by inverse modeling. Such an application requires high accuracy, as even small biases of the retrieved concentrations may result in large errors of the inferred rates of surface emissions (source) and deposition, surface uptake or removal in the atmosphere (sinks). For example, for the upcoming satellite mission dedicated to carbon dioxide monitoring (CO2M), co-funded by ESA and the European Commission for the Copernicus Programme, the accuracy of the dry-air column-averaged CO2 mole fraction (XCO2) is required to be better than 0.5 ppm. Here we investigate a potentially important systematic error source, namely XCO2 biases due to correlated sub-pixel variability of surface reflectance (albedo) and altitude. To minimize this error source we propose the use of an albedo-weighted surface altitude which better represents the satellite’s spatial sample than the unweighted average by using a linearized theoretical analysis. We use Copernicus Sentinel-2 data combined with Copernicus Digital Elevation Model (DEM) data and the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm and create a variety of self-consistent experiments to test this theory. First, we conduct experiments with defined conditions and second, we apply the methodology to some real-world examples: the Bełchatów power plant in Poland, the Black Forest in Germany, the region around Mont Blanc in the European Alps and the whole country of Germany. In all these examples, we find that using the albedo-weighted average of the surface altitude reduces biases at locations with heterogeneous surface structure to values below the requirements for future satellite missions. In addition, we developed a possible post-processing equation to account for this process, because high-resolution albedo currently is not measured simultaneously with satellite instruments. We also find that filter parameters connected to the surface roughness might be relaxed when using the albedo-weighted surface altitude in the retrievals. Furthermore, we examine the dependence of the XCO2 errors on the size of the spatial samples and find that the errors become larger with larger spatial samples, but are generally smaller by more than a factor of four when using the albedo-weighted instead of the unweighted average of the surface altitude. In conclusion, we show that the use of the albedo-weighted surface altitude in the retrieval process results in significant reduction of the XCO2 bias compared to the use of the unweighted mean altitude, as currently used in most retrieval schemes.
Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H2O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions.
Abstract. Nitrogen (N) is a critical nutrient, that controls photosynthesis and decomposition processes. It is important to include the N cycle in the land component of climate models to improve the exchange fluxes of CO2 between land and atmosphere. We present here the implementation of the N cycle in the CNRM land surface model, namely ISBA. We evaluate the model on two Free-Air Enrichment (FACE), experiments sites: Duke and Oak Ridge. In particular, the response to elevated CO2 is studied. We compare the reference version without the N cycle (C) and the new version in which it is included (CN). A comparison to a multi model analysis shows encouraging results, since the computed NPP and N assimilation flux fall in the inter model range. The CN version performs better than the C version for NPP. Next, we focus on the carbon cycle by comparing simulation results to observations. The CN version improves the carbon stocks, largely overestimated by the C version. In particular, at elevated CO2, in the CN version, photosynthesis is downregulated by the N limitation. This yields a reduction of C accumulation in soil and biomass in comparison to the C version. In the literature, diverging strategies are observed to overcome N limitation. The model reproduces well the main features but fails to represent some sites characteristics. Finally, a detailed analysis of the simulated N dynamics is presented.
Abstract. Using 14 wind profiler radars and hourly records at 3000 weather stations during 2016–2020, 226 warm-sector heavy rainfall (WSHR) events were identified in Guangdong, South China. Five indices reflecting the vertical structure of the atmosphere were analyzed as precursor dynamical signals of WSHR occurrence – the Low-Level Jet Index (LLJI), Vertical Wind Shear (VWS), Atmospheric Lifting Intensity (ALI), and Boundary Layer Height (BLH). Notable fluctuations in signals were found 1–4 h before precipitation onset, and the sensitivity and occurrence mechanisms of WSHR in the three regions showed marked regional differences. In western Guangdong, LLJI increased sharply 1–2 h before onset and, although not linearly correlated with rainfall intensity, was systematically higher in strong-rainfall events. LLJI was also significantly correlated with upper-level (3–5 km) ascent, indicating that the strengthening of the low-level jet can directly enhance upward motion and thereby increase precipitation intensity. In central Guangdong, VWS at 0.5–1.5 km was significantly positively correlated with precipitation intensity across three consecutive pre-onset periods, which may reflect frictional deceleration of the low-level jet and the resulting enhancement of low-level convergence. In eastern Guangdong, anomalously weak upper-level ascent and higher convective available potential energy appeared before strong-rainfall events, indicating that suppressed mid-to-upper-level ascent allowed unstable energy to accumulate. Strong events were significantly warmer at 925 hPa, but showed no significant differences in large-scale vertical velocity or low-level relative humidity, suggesting that the instability accumulation was driven mainly by boundary layer warming; the anomalously high BLH is a manifestation of this thermal instability build-up.
Abstract Outgoing longwave radiation (OLR) from satellite observations is a key indicator of tropical deep convection and a valuable proxy for monitoring El Niño-Southern Oscillation (ENSO) evolution. This study evaluates the performance of OLR retrievals from Fengyun-3C (FY-3C), a Chinese polar-orbiting meteorological satellite, against the interpolated OLR product from the National Oceanic and Atmospheric Administration (NOAA) over the tropical Pacific Ocean for 2014–2019. The analysis focuses on distinguishing coupled and uncoupled El Niño events. To quantify the convection–sea surface temperature (SST) relationship, we developed a Coupling Strength Index (CSI) based on FY-3C OLR and Niño-3.4 SST anomalies. Temporal correlations between FY-3C and NOAA OLR exceed 0.98, confirming high overall consistency; FY-3C OLR faithfully captures convective responses to Niño-3.4 SST anomalies, especially during the strong 2015–2016 El Niño and 2017–2018 La Niña. Spatial correlations range from 0.76 to 0.97, with root-mean-square errors of 4.04–8.36 W m −2 , further demonstrating strong agreement with NOAA OLR, albeit with localized biases emerging during rapid convective transitions. CSI analysis reveals robust ocean–atmosphere coupling during events such as the 2015–2016 El Niño (CSI < −5 W m −2 °C −1 ) and identifies uncoupled El Niños characterized by SST warming without matching tropical convection (e.g., late 2014 and late 2018). Notably, CSI enables early detection of convection–SST decoupling, signaling OLR reversals roughly one month in advance. These results demonstrate that FY-3C OLR combined with the CSI provides a concise quantitative metric for ENSO air–sea coupling strength and improves operational monitoring of ENSO phase transitions.
High-resolution mapping of urban surface CO2 is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO2 measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO2 patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO2 accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO2, the CSF-processed target substantially improved prediction performance. The best validation R2 across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO2 areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO2 accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO2 mapping and refined low-carbon governance.
Abstract Urban areas are a significant source of methane emissions, a potent greenhouse gas with a global warming potential 28-34 times stronger than carbon dioxide over a 100-year timeframe. Urban areas are complex environments where multiple types of collocated methane sources exist, many of which have not been extensively measured. In this work, we address a lack of measurement data from two biogenic urban methane sources: sewers and urban water bodies, using five years of mobile surveying data to guide on-site direct measurements. We analyze data from 56 vehicle-based measurement surveys conducted over five years in the Greater Toronto Area (GTA - Canada) from a cluster of high-emitting sewer covers and an urban engineered water canal (i.e., the Keating Channel), in addition to direct measurements from other urban water bodies (i..e, ponds and rivers). We use a static chamber methodology to directly measure methane emissions from 20 sewer covers and perform 150 methane flux measurements from rivers, ponds, and the Keating Channel. We recorded the highest methane emission rate ever directly measured from a sewer at 89 g h-1 of methane, which was located amongst a cluster of 13 sewer covers that collectively emitted 320 g h-1 of methane. We found that average methane emission fluxes from urban water bodies were highest from the Keating Channel at 14,000 (95% c.i.: 8,300 to 19,000) mg d-1 m-2 and urban ponds at 1,780 (95% c.i.: 40 to 3,500) mg d-1 m-2, both of which were characterized by high fluxes from ebullition rather than diffusion. The combined emissions from urban water bodies and sewers account for 6% (95% c.i.: 0 to 13%) of total methane emissions from the GTA. This work provides valuable empirical measurement data to better characterize the upper tail of emission rates from sewers and urban water bodies
Abstract PM 2.5 , as a key atmospheric pollutant severely threatening human health and sustainable development, has drawn increasing attention regarding the influence of urban structure on its concentration. This study focuses on 260 cities in China from 2011 to 2021. By integrating multi-source remote sensing data, reanalysis data, and statistical records, a high-resolution PM 2.5 concentration dataset based on AOD was constructed. By coupling the LightGBM machine learning model with the Geographically and Temporally Weighted Regression (GTWR) model, the study systematically reveals the influence mechanisms and spatiotemporal heterogeneity of urban spatial, economic, and social structures on PM 2.5 concentrations. The results indicate that population density (POP) in the social structure is the most critical factor influencing PM₂.₅, contributing 23.2%, with a notable threshold effect (586 persons/km²). Air pollutant emission (APE) and the night light index (NL) exhibit significant positive and negative influences, contributing 17.65% and 14.18%, respectively. An increase in the separation degree of construction land (IJI) contributes to reducing PM 2.5 concentrations. The GTWR model further reveals significant spatiotemporal heterogeneity among the influencing factors. The impact intensities of POP and IJI show a weakening trend over time, while those of APE and NL display fluctuating characteristics of first decreasing and then increasing. Spatially, the influences of POP and APE exhibit a pattern of increasing intensity from south to north and from central regions toward coastal and northeastern areas.
Inferring fault locking depth from interseismic geodetic observations is important to assessing seismic hazard. Interseismic deformation changes with time due to Earth’s viscoelastic rheology, but elastic models commonly employed for estimating locking depths assume time-invariant deformation. Consequently, the apparent locking depth depends on the time when the deformation is observed. In this study, with a focus on large strike-slip faults, we propose replacing the elastic models with an existing analytical viscoelastic model that incorporates time-dependent deformation while maintaining operational simplicity. Using InSAR observations around the Altyn Tagh Fault as illustrative examples, we explain the application of this approach and discuss how to address practical complications of real faults: (1) The use of this model requires knowledge of the earthquake recurrence interval and the time since the last earthquake in terms of the viscoelastic relaxation time. We show a trade-off relation between these two parameters that can be used to assess the ambiguity of inferred locking depth due to their uncertainties. (2) A stiffness contrast across the fault causes asymmetric deformation. We provide an empirical scaling relationship to allow the application of the laterally homogeneous viscoelastic model. (3) Shallow creep of the fault causes discontinuity of surface deformation. We explain that the rate of steady shallow creep without afterslip and slow slip events is controlled by the ratio of the widths (i.e., depth ranges) of the creep zone and locked zone, and we accordingly design a procedure to incorporate shallow creep. (4) Postseismic transients from nearby earthquakes can cause underestimation of locking depth. We demonstrate that modelling the postseismic effect using the same viscoelastic model can enable an effective correction.
Abstract Gray-zone tropical cyclone (TC) simulations, in which convection is partially resolved and partially parameterized at grid spacings of approximately 3 to 15 km, are sensitive to how grid spacing changes across adaptive meshes. This study evaluates three scale-aware convective parameterization schemes (CPS), the Grell-Freitas (GF), Multi-Scale Kain-Fritsch (MSKF), and New Tiedtke (NTDK), across regional (WRF) and global (Model for Prediction Across Scales, MPAS; ClusterTech Platform for Atmospheric Simulation, CPAS) models for TC simulation over the South China Sea. Twenty-seven simulations spanning three TC cases (Category 2 to 5 intensity, including single-TC and binary interaction scenarios) are verified against observations. While domain-averaged precipitation differs by only 10 to 20% across models, the Convective Rain Ratio (CRR, the fraction of total precipitation produced by the CPS) shows that models achieve similar rainfall through different convective partitioning. Across the 27 simulations, CPS choice accounts for 44% of CRR variance, compared with 29% for the model framework and 16% for the selected TC case. CRR differences are largest in the gray zone, ranging from 30% to over 90% across schemes. Both global models exhibit mid-level drying relative to WRF regardless of CPS choice. This deficit may partly reflect differences in large-scale moisture constraints between regional and global model frameworks. CPAS’s adaptive mesh partially restores outer rainband precipitation relative to MPAS but introduces discontinuities where grid spacing changes sharply. Identical CPS produce different CRR patterns across models, differing by more than 20% for individual TC cases. Current scale-aware CPS adjust their behavior based on local grid spacing, but do not account for how rapidly that spacing changes across the mesh. Future schemes should explicitly account for local gradients in grid spacing.
Accurately forecasting waypoint-level tropical cyclone (TC) intensity, defined as the local wind speed at specific maritime route waypoints under TC influence, is crucial for navigation safety and voyage planning. Conventional studies mainly focus on the central intensity of TC systems and underutilize the complementary value of multimodal meteorological data with inconsistent sampling intervals. To address these challenges, this study proposes a multimodal-augmented conditional diffusion model (MADiff) for waypoint-level TC intensity prediction. To exploit the potential of multimodal inputs, we first design a temporal-adaptive dynamic convolution module (TDConv) to capture multi-timescale features, mitigating multimodal sampling discrepancies without rigid temporal alignment. Second, we develop a discriminative cross-fusion module (DisCF) to aggregate multi-timescale features across diverse modalities, quantifying multimodal heterogeneity and integrating valuable modality-specific features while suppressing noise interference. Fused features are fed into a diffusion model with physics-informed regularization to generate final intensity forecasts. Extensive experiments on four Western North Pacific datasets show that MADiff achieves average MAE and RMSE values of 2.08 kt and 2.37 kt, respectively, for 12 h intensity forecasting. Compared with the state-of-the-art baseline (TC-Clouds-DP), MADiff yields substantial performance improvements, reducing MAE by 16.3% and RMSE by 10.6% on average. This study provides an effective framework for fine-grained TC intensity forecasting, offering valuable insights for extreme marine weather early warning and intelligent navigation decision-making.
Satellite data have revealed an abrupt drop in aquifers’ ability to recharge after the 2020–2022 drought. More real-time monitoring could spot the issue before it arises again.
Vehicular traffic is a major source of air pollution; however, the contribution of remotely acquired traffic information to local machine-learning (ML) air-pollution models remains insufficiently characterised. This study evaluates four interpretable tree-based ML models (Random Forest, Extra Trees, LightGBM, and XGBoost) under six predictor scenarios combining progressively larger predictor sets, ranging from remotely acquired traffic, meteorological, and temporal variables alone to the inclusion of measurements from one and four neighbouring monitoring stations, to estimate NO2, PM10, PM2.5, and O3 concentrations across several sites in London. ML model performance was compared with a ridge linear regression model as a baseline, with spatial interpolation methods and with a cross-site validation experiment. When modelling without data from neighbouring stations, the RMSE for NO2 ranged from 9.73 to 11.66 μg/m3 without traffic information, compared with 8.72 to 11.52 μg/m3 when traffic information was included. Additionally, for NO2, SHAP analyses indicate that traffic-related variables can contribute at levels comparable to pollutant measurements from neighbouring monitoring stations in traffic-dominated environments.
Freshwater scarcity can weaken water governance when hydrological pressure interacts with intensive agricultural demand, regulatory weakness, and political conflict. This research evaluates whether Irrigation District 005 (IR 005) in Chihuahua, northern Mexico, demonstrates local water-governance fragility across three domains: public security, the rule of law, and the ability to sustain water access and food production. A mixed-methods approach integrates legal and human rights documentation, institutional records, published studies, and a structured media review with hydrological, agricultural, climatic, and reservoir data. Water balances were analysed for 1998–2023, precipitation trends for 1980–2020, and crop water requirements were estimated using the Food and Agriculture Organization’s Irrigation and Drainage Paper No. 56 (FAO-56) Penman–Monteith framework, the crop coefficient (Kc), the water-stress coefficient (Ks), the United States Soil Conservation Service (SCS) Curve Number method, and application-efficiency assumptions. The 2020 water conflict resulted in fatalities, injuries, arrests, and documented human rights violations. Rule-of-law capacity was further diminished by unauthorised withdrawals, cultivation beyond authorised irrigation plans, and limited enforcement. The annual water balance shifted to persistent deficits after 2016, reaching an estimated deficit of 2268 cubic hectometres (hm3) in 2020. Annual precipitation did not exhibit a statistically significant monotonic decline during 1980–2020 (Mann–Kendall Z = −0.79, τ = −0.0878, p = 0.4251; Sen’s slope = −1.1628 mm yr−1; Mann–Whitney p = 0.5313), indicating that recent stress is more closely linked to production scale, crop mix, governance conditions, and irrigation efficiency than to a long-term reduction in rainfall. Sensitivity analysis revealed that ±15% changes in Kc and Ks altered gross water requirements by approximately ±16–17%, while equivalent changes in effective precipitation produced changes of only 1–3%. These results demonstrate heightened water-governance fragility resulting from mutually reinforcing hydrological, institutional, and conflict-related pressures. Future research should refine locally calibrated water-demand parameters and develop reproducible monitoring systems that combine hydrological, institutional, satellite, and participatory data to support anticipatory, transparent, and rights-based water governance.
Abstract. Prediction of shallow landslides at the regional scale generally relies on statistical analyses of landslide inventories. Rainfall-duration thresholds and susceptibility maps are among the most common approaches to anticipate future landslide occurrences. However, the outputs and reliability of these approaches can be strongly affected by the representativeness of the landslides included in the inventory. This study specifically investigates the impact of landslides triggered by an extreme rainfall event on the determination of rainfall-duration thresholds and susceptibility maps. We consider the case of Storm Alex, a millennial return period rainfall event, which hit the Alpes-Maritimes region (France) on 2 October 2020. The analysis is based on an inventory of 5383 shallow landslides, including 1656 landslides triggered by Storm Alex. Cumulative rainfall and rainfall duration associated with each landslide were computed following the process of the CTRL-T algorithm. Landslides sharing identical cumulative rainfall and rainfall durations were aggregated into a single point to avoid over-representing rainfall events that triggered many spatially clustered landslides. Then, a 5 % quantile regression was used to compute statistical rainfall-duration thresholds with and without the inclusion of Storm Alex landslides. A Random Forest approach was used to produce susceptibility maps under the same two configurations, which were subsequently compared. Results show that: (a) Including Storm Alex landslides increased the rainfall–duration thresholds by a factor of 1.1 to 1.5, depending on the assumed landslide occurrence time; (b) the exceptional rainfall intensity triggered landslides in areas having an initial lower susceptibility; and (c) including these events in susceptibility modelling alters the spatial distribution of susceptibility values. This study provides a quantitative analysis of the impact of landslides triggered by extreme rainfall events on statistical prediction models.
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