This study characterizes meteorological drought in the Potosí River Basin, northeastern Mexico, using the Standardized Precipitation Index (SPI), evaluates its relationship with El Niño-Southern Oscillation (ENSO), and examines its relevance to the 2021–2023 Nuevo León water crisis. Daily precipitation from 13 stations (1995–2024) was homogenized using Climatol, and the 3-month SPI (SPI3) was related to the Oceanic Niño Index (ONI) through lagged correlation and phase comparisons accounting for temporal dependence. Of 1326 drought events identified, selected episodes were cross-corroborated against dendrochronological and tropical cyclone records. All 13 stations showed a positive SPI3-ONI association (r = 0.13–0.36), though only 4 retained significance under the primary, autocorrelation-adjusted criterion. Under conventional tests, SPI3 was lower during La Niña than during Neutral or El Niño at all 13 stations; these contrasts held at 10 and 9 stations under the Relative Oceanic Niño Index (RONI), and dependence-preserving bootstrap supported them at 12 and 10, while El Niño-Neutral was not robust. The network-average 12-month SPI (SPI-12) reached −1.11 in July 2022, and 26 of 30 months (August 2020 to January 2023) were La Niña. ENSO contributes a spatially widespread but modest component of drought variability, mainly through La Niña-related drying; the water crisis coincided with unfavorable climatic conditions but is not attributable to ENSO alone.
Climate extremes in the Jinsha River Basin have changed during a period of regional warming and cascade hydropower development, but the climatic contribution of reservoir impoundment remains uncertain. We analyzed 20 indices derived from daily observations at 15 meteorological stations during 1980–2022. Regional and station-level trends were assessed, and reservoir associations were explored using matched before–after–control–impact (BACI) contrasts, distance–trend analyses. The station aggregates showed a consistent warming pattern: summer days (SU) and warm-day frequency (TX90p) increased by 0.50 and 0.72 reference-period standard deviations per decade, respectively, whereas frost days (FDs) and cold-night frequency (TN10p) decreased by 0.24 and 0.33. The five compound dry–hot indices increased by 0.24–0.43 standard deviations per decade, while the joint warm–dry severity index (SCEI) declined by 0.34, indicating more severe monthly warm–dry conditions. Precipitation trends were less spatially coherent. Autocorrelation-aware tests retained the full-network trends, although station-level support for maximum event duration weakened. BACI contrasts were mixed and uncertain, and the full-period distance gradient was absent in the common pre-impoundment period. These analyses describe the sampled station network but do not establish a basin-wide causal effect of reservoir impoundment.
The urban heat island (UHI) effect in Arctic cities remains poorly understood, and its dependence on weather conditions has never been systematically analyzed for this region. This study examines the extent to which large-scale meteorology can explain UHI variability and whether a universal, weather-driven index representing such dependencies can be developed. Using data from the UHIARC observational network and ERA5 reanalysis for seven Russian Arctic cities, we evaluated several classical semi-empirical parameterizations (Wind Factor, Weather Factor, Cloud Factor, Diurnal Factor) alongside a suite of machine learning models, namely linear regression, MLP, LSTM, and CatBoost (version 1.2.10). The empirical baselines showed near-zero predictive skill, whereas CatBoost with explicit temporal features representing delayed connections captured up to 64% of the local UHI variance. A universal index was then constructed by training CatBoost on standardized UHI data from four cities and deriving local statistics (mean and standard deviation) from a 30-year ERA5 climatology. This index had an average R2 = 0.49 on the training cities, generalized successfully to unseen cities, and substantially outperformed the empirical baselines. Our results demonstrate the feasibility of a universal UHI index that generalizes across diverse Arctic settings, enabling climatological assessments of urban heat potential and supporting territorial planning from regional to pan-Arctic scales, including future climate projections.
Mission-oriented transitions require governments to shape markets and co-create value with private actors, yet municipal and private actors assess climate action through different categories. Cities and regions use climate action plans, greenhouse-gas (GHG) inventories and reporting platforms; companies and investors use environmental, social and governance (ESG) frameworks, including the European Sustainability Reporting Standards (ESRS), the EU Taxonomy, the Sustainable Finance Disclosure Regulation (SFDR), and bond principles. This thematic review analyzes key frameworks and maps their subcategories across emissions domains, climate risk, investment and financing, and social benefits. It uses transition design as an organizing lens by mapping alignment and relationships among sectoral actions, finance, governance and social outcomes at a systemic level. Responding to needs emerging from the Mission Cities, this paper proposes a project-level shared language structured as a boundary object to enhance municipal–private sector mutual understanding, thereby supporting capacity building, decision making and collaboration. The analysis identifies where standards, including the ESRS, the EU Taxonomy and the SFDR, can be transferred or translated to the emissions domains of energy, buildings, mobility and hazards, and where more integration work is needed, notably on social benefits.
Annual land surface temperature (LST) products are widely used to characterize urban heat, but absolute temperatures combine local surface variation with broader differences among cities and years. This study evaluates AlphaEarth annual satellite embeddings for predicting absolute LST and city-year median-centered thermal anomalies across seven cities from 2017 through 2025. A predictor comparison evaluated the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), their combination, Landsat surface reflectance, and AlphaEarth embeddings using Random Forest regression. AlphaEarth achieved R2 = 0.812 and RMSE = 2.99 °C for absolute LST prediction and R2 = 0.648 for median-centered anomalies, outperforming conventional predictors. Spatial and temporal validation demonstrated strong recovery of local thermal structure, although transfer to previously unseen cities was weaker. Fixed-location analysis showed strong agreement in multiyear mean anomalies (r = 0.914) but limited recovery of interannual variability (r = 0.096). Geographic reconstruction across seven cities further demonstrated the ability to represent persistent spatial thermal patterns, with greater agreement for mean anomalies than warm-decile recurrence. A supplementary Local Climate Zone (LCZ) analysis associated compact built environments with higher relative surface temperatures and greater hot-tail prevalence, while vegetated environments exhibited cooler conditions. These findings demonstrate the value of AlphaEarth embeddings for characterizing urban surface thermal structure while identifying limitations in temporal variability and geographic transfer.
