Vegetation–Atmosphere–Land Interactions Driven by Precipitation Extremes in Northeast China

Linking land–atmosphere processes and hydrological response under prolonged drought in a semi-arid basin

Semi-arid river basins are highly sensitive to hydroclimatic variability, where rainfall seasonality and soil moisture conditions strongly influence vegetation dynamics, surface energy balance, and hydrological response. This study investigates seasonal eco-hydroclimatic variability in the Apodi–Mossoró River Basin (northeastern Brazil), focusing on rainfall–runoff coupling and land–atmosphere interactions under prolonged drought and regulated flow conditions. An integrated framework combined Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), Land Surface Temperature Anomaly (LSTA), and albedo products, potential evapotranspiration (PET) from the TerraClimate reanalysis dataset, observed precipitation and streamflow records, and large-scale climate indices for the 2002–2023 period, together with hydrological modeling and wavelet analyses. Results revealed a strongly seasonal hydroclimatic regime, with precipitation concentrated between February and May (Seasonality Index = 0.908), while water-deficit conditions predominated throughout the year. Higher NDVI and LSWI values occurred during wetter periods, whereas spatial changes in LSTA and albedo were more evident during dry conditions suggesting altered land–atmosphere interactions under prolonged drought conditions. Trend analyses showed generally weak and heterogeneous long-term changes across the basin, while PET exhibited relatively low temporal variability throughout the study period. The Pettitt test identified a statistically significant shift in streamflow around 2013 despite the absence of a significant precipitation change point. Wavelet coherence analyses revealed non-stationary precipitation–streamflow coupling modulated by El Niño–Southern Oscillation and tropical Atlantic variability. The Soil Moisture Accounting Procedure (SMAP) model satisfactorily reproduced streamflow variability during calibration and validation, despite limitations under low-flow conditions. Streamflow dynamics were associated with prolonged drought and hydrological regulation effects. Land–atmosphere interactions revealed strong seasonal coupling among vegetation, moisture, and surface energy dynamics. The SMAP model reproduced streamflow variability under contrasting hydroclimatic conditions. Wavelet coherence revealed significant climate controls on rainfall–runoff coupling across multiple timescales. A significant post-2013 streamflow regime shift occurred without corresponding precipitation changes. Streamflow dynamics were associated with prolonged drought and hydrological regulation effects. Land–atmosphere interactions revealed strong seasonal coupling among vegetation, moisture, and surface energy dynamics. The SMAP model reproduced streamflow variability under contrasting hydroclimatic conditions. Wavelet coherence revealed significant climate controls on rainfall–runoff coupling across multiple timescales. A significant post-2013 streamflow regime shift occurred without corresponding precipitation changes.

Potential Impacts of Soil Moisture on Monthly-Scale Precipitation During the Rainy Season in South China

Land-atmosphere interactions provide a key pathway through which land-surface anomalies shape regional climate variability. Using ERA5 reanalysis data for 1985–2024, we examine the impacts of soil moisture on monthly precipitation during the rainy season in South China (SC) from both local and non-local perspectives. Singular value decomposition (SVD) and correlation-based diagnostics are combined to isolate coupled patterns and to trace their underlying physical processes. Enhanced soil moisture in July increases local evaporation and atmospheric humidity, thereby contributing to greater total rainfall. Concurrently, the augmented latent and sensible heat fluxes lower surface temperatures, reduce boundary layer depth, and boost convective available potential energy. The synergy between these favorable moisture and dynamic conditions underpins the positive soil moisture–precipitation feedback observed at a cross-monthly timescale. Beyond local feedbacks, anomalously wet soils over northern Indochina in July exert a robust positive influence on August rainfall in SC by modifying atmospheric circulation and moisture transport. A comprehensive synthesis indicates a synergistic effect between local soil moisture in SC and that in northern Indochina, collectively promoting increased precipitation in August. These findings shed light on the regional linkage mechanism of soil moisture–precipitation feedback across monthly scales, offering valuable insights for understanding the drivers of rainy-season precipitation in SC and improving short-term climate predictions.

Spatiotemporal Responses of Terrestrial Ecosystems to Climate Forcing and Mitigation Strategies for Cold Regions Engineering

Vegetation dynamics are key indicators of climate change and ecological risk in cold regions. This study investigated vegetation–climate interactions in Heilongjiang Province, China, using an integrated analytical framework that combined the Mann–Kendall test, Pettitt test, Hurst exponent, Continuous Wavelet Transform, and Wavelet Transform Coherence with the Normalized Difference Vegetation Index and meteorological data from 1990 to 2024. The dominant vegetation comprises cold–temperate coniferous forests, mixed broadleaf–conifer forests, meadow steppes, and croplands. Results showed that (1) the climate system experienced asynchronous abrupt changes, with potential evapotranspiration, precipitation, and temperature changing in 2008, 2011, and 2013, respectively. Accordingly, the Normalized Difference Vegetation Index reversed in 2011, with greening rates in the range of 0.009–0.022 yr−1 in the northwestern and central regions and browning rates in the range of −0.010 to −0.025 yr−1 in the southwestern and eastern regions. (2) The Hurst exponent ranged from 0.23 to 0.48 for temperature and potential evapotranspiration, indicating strong anti-persistence and high future ecological vulnerability. (3) Wavelet coherence analysis identified precipitation as the dominant climatic driver at 6–9-year scales, whereas temperature shifted from a short-term positive driver to a long-term stressor, and potential evapotranspiration mainly regulated vegetation at 5–8-year scales. These findings provide scientific support for ecological risk assessment and climate-resilient cold regions engineering.

Evaluating ENSO–vegetation interactions and variability using remote sensing-derived vegetation indices and principal component analysis

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.

Summertime Fog and Dew Impacts on Vegetation Depend on Aridity

Rainfall and vapor pressure deficit (VPD) are well-studied hydrological variables that largely determine aboveground net primary production (ANPP) in most ecosystems. Meanwhile, the impacts of another important part of the hydrologic cycle, non-rainfall water from fog and dew, remain poorly understood at the ecosystem level. To fill this gap, we used meteorological variables measured at weather stations along with satellite-derived vegetation greenness data from surrounding areas to examine how fog and dew frequency affect summer plant growth across the contiguous United States. Our analysis shows that, even after accounting for precipitation, VPD, and land-cover type, fog and, more so, dew enhanced vegetation productivity in water-limited regions. In contrast, non-rainfall water had a neutral or negative impact on plant growth in humid regions, with fog showing the strongest and most widespread negative effects. Taken together, our findings reveal that summertime non-rainfall water has differential effects on vegetation that are largely determined by ecosystem-level water availability. These aridity-dependent effects of fog and dew should be considered in future ecological and agricultural studies and in assessments of projected climate impacts on vegetation.

Spatiotemporal patterns of coupling between atmospheric vapor pressure deficit and vegetation dynamics in East Africa