Remote sensing-based evapotranspiration (ET) provides spatially continuous information on basin water consumption, but its use in water resources management requires careful consideration of scale, attribution assumptions, and uncertainty. This narrative review synthesizes ET models and products, climatic and human controls, attribution approaches, and hydrological evaluation. Product performance varies across regions and scales, and agreement among products sharing inputs cannot replace independent evaluation. Statistical associations, model-conditioned contributions, and intervention effects answer different questions; climate–human interactions require explicit reference states and decomposition rules. We propose three checks for ET-change claims: whether the uncertainty interval supports a defined decision threshold, whether the attribution method supports the intended interpretation, and whether independent hydrological evidence constrains the conclusion. A workflow links these checks to irrigation, water-rights, groundwater, drought-restriction, and ecological-restoration applications. In a worked example from a Kansas groundwater management area, a published pumping-reduction interval meets the program threshold, and a hypothetical calculation shows that error correlation between periods can change whether an ET-based indicator meets a threshold. The framework offers a transparent basis for screening and conditional assessment, while accounting and causal claims require evidence matched to the quantity, domain, and period.
Large-scale, slowly propagating wave-like structures have been observed in the atmosphere of Venus, but their dynamical origin remains uncertain. In laboratory experiments we investigated whether analogous structures can develop through baroclinic instability under conditions imitating the Venusian lower-to-middle cloud region. A differentially heated rotating annulus set-up was designed to conceptually imitate the planetary setting in terms of the relevant geometrical and dynamical similarity parameters, applying meridional and zonal temperature contrast as thermal forcing. Temperature time series analysis, infrared thermography, and feature tracking velocimetry were used to characterize the flow in the system. Slowly propagating, billow-like patterns were observed, similar in shape to the vortices of Venus. Wave activity was found to emerge above a critical meridional temperature contrast, but became substantially reduced when the ratio of the imposed zonal and meridional temperature contrasts exceeded approximately 1.6. The experiments indicate that the relative magnitude of meridional and day-night thermal forcing may play an important role in determining the occurrence of such wave-like atmospheric structures.
Global ozone forecasts commonly depend on an initialized chemical state. We present O3CastNet, a diagnostic neural network that estimates ozone from two consecutive atmospheric states and their timestamps, without ozone input. The diagnostic operates on weather model outputs and can in principle be adapted to different medium range forecast systems. Here, FuXi supplies atmospheric predictors from ECMWF operational analyses and remains fixed while a single diagnostic model is trained for all leads. The ozone output covers nine pressure levels from 250 to 1000 hPa. Training spans 2003 to 2022, validation uses 2023, and testing covers all initializations in the 2024 test set at leads from 6 to 120 h. Verification against EAC4 shows lower root mean square error than CAMS throughout the forecast range at 500 to 1000 hPa. At 250 and 300 hPa, CAMS retains an advantage at longer leads. Spatial diagnostics reveal broad regional improvements and positive temporal anomaly correlation, with substantial geographical variation. These results demonstrate useful ozone prediction from forecast meteorology and time information without chemical initialization. They establish the performance of the FuXi implementation, while transfer to other weather models remains to be evaluated.
To investigate the temporal responses of vegetation to climate extremes and the drivers of vegetation change across coastal mainland China, this study analyzed monthly NDVI, meteorological, and land-cover data from 2000 to 2024. Spatiotemporal NDVI changes and the lagged and cumulative response windows for 15 climate indices were examined. Partial least squares regression and modified residual analysis were then used to estimate the contributions of climate change and non-climatic factors to NDVI trends. The results showed the following: (1) The multiyear mean NDVI was 0.772, and 83.15% of valid pixels exhibited an increasing trend, including 64.31% with a significant increase, with localized declines occurring in densely urbanized areas and some coastal plains. (2) Cumulative effects dominated the NDVI responses to climate indices, with a mean lag of 0.41 months and a mean cumulative window of 2.64 months. Response windows were generally shorter along the northern coast than along the southeastern and southern coasts. (3) Mean air temperature, monthly maximum of daily minimum air temperature, and diurnal temperature range had relatively high importance in the partial least squares model; the proportions of samples with VIP ≥ 1 were 80.28%, 72.77%, and 62.91%, respectively. (4) From 2000–2024, climatic factors contributed 0.001333 year−1 (33.78%) to the increasing annual-mean NDVI trend, while the non-climatic residual—not a direct estimate of human activity—contributed 0.002613 year−1 (66.22%). Temperature was the principal component of the regional climatic contribution, but the composition of contributions varied substantially among vegetation types and across latitude and longitude bands.
Aligning climate exposures with crop calendars is essential for credible climate-risk assessment in double-cropping systems. We quantified crop-season-matched precipitation and temperature effects on wheat and maize yields across 18 cities in Henan Province, China’s leading grain province, using a balanced panel of 522 city-year observations (1996–2024) and two-way fixed-effects models. Because winter wheat is harvested in early June and summer maize is sown in late June, wheat is modelled only against spring (March–May) climate and maize only against summer (June–August) climate. Spring precipitation significantly reduced wheat yield (elasticity −0.0299), and spring warming was associated with lower yield by 3.4% per °C once trends and province-wide shocks were removed; the apparent positive raw temperature correlation (+0.283) proved artefactual. Summer climate effects on maize were insignificant at the seasonal scale, consistent with irrigation buffering, but July heat and August rainfall revealed emerging vulnerabilities. Three documented extremes—2003 summer flood, July 2021 extreme rainfall, and 2023 harvest rain—were consistent with the panel mechanisms, with city-level yields falling 52–70%, up to 64%, and 11–16% below trend, respectively. K-means clustering identified three agro-climatic zones (robust to excluding yield variables), supporting spatially differentiated adaptation. Sensitivity analyses confirm that results hold when the wheat window is extended to March–June or when growing degree days replace mean temperature. Climate-risk assessment must be crop-, season-, and zone-specific.
