Compound drought–heatwaves (CDHWs) accelerate the warming and drying of soils, triggering soil compound drought–heatwaves (SCDHWs) that jeopardize the health of soil ecosystems. Nevertheless, the behavior of these events worldwide and their responses to climatic warming are underexplored. Here, we show a global escalation in the frequency, duration, peak intensity, and severity of SCDHWs, as well as an increase in affected land area, from 1980 to 2023. The increasing trends, which are particularly prominent since the early 2000 s, and projected to persist throughout this century, are dominated by summertime SCDHWs and enhanced by El Niño. Intensive soil warming as well as climatologically lower soil temperatures compared to air temperatures lead to localized hotspots of escalating SCDHW severity in northern high latitudes, while prolonged duration causes such hotspots in northern South America. Transformation of natural ecosystems, particularly forests and wetlands, to cropland as well as forest degradation substantially enhance the strength of SCDHWs. Global SCDHWs consistently exhibit higher frequencies, longer durations, greater severities, and faster growth rates than CDHWs in all aspects from 1980 to 2023. They are undergoing a critical transition, with droughts replacing heatwaves as the primary constraint. We emphasize the significant intensification of SCDHWs in northern high latitudes as well as the prolonged duration of SCDHWs in the Southern Hemisphere, posing an underrated threat to achieving carbon neutrality and food security goals.

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement.

The Tibetan Plateau (TP) plays a vital role in Asian and even global atmospheric circulation, through the interactions between land and atmosphere. It has experienced significant climate warming and spatially and temporally variant wetting over the past half century. Because of the importance of land surface status to the interactions, determining the wetting and drying of the TP from individual changes in precipitation (Prep) or temperature is difficult. Soil moisture (SM) is the water synthesis of the surface status. The persistent deficit of SM (SM drought) is more sensitive to climate change than normal SM. This study first explored the climate wetting and drying of the TP from variations in historical SM droughts over 1961–2014, with a focus on spatiotemporal patterns, long-term variations, and climate causes of summer (May–September) SM droughts based on multiple observation and reanalysis data. The results showed comparatively frequent and severe droughts in the central and southern area, particularly in the semiarid and subhumid regions. SM drought exhibited an abrupt and significant (p < 0.05) alleviation in the interior and central-west TP in the middle to late 1990s. The prominent drought alleviation indicated a hydroclimate shift to a wetter plateau, not merely steady trends as given in the literature. We demonstrated that the wetting shift was dominated by Prep over potential evapotranspiration (PET). By contrast, the in-phase trends were combined forces of Prep and PET, with increased forces of PET after the wetting shift. Furthermore, the Prep dominance was largely attributed to a phase transition of the Atlantic multi-decadal oscillation from cold to warm since the mid-1990s. The PET impacts on the wetting trends were likely dominated by solar radiation, wind speed, and vapor pressure deficit. Regionally, the wetting shift was distinct from the arid to semiarid and semiarid to subhumid climate. Such spatiotemporal changes may affect the TP's atmospheric circulation and, subsequently, the Asian monsoon and global circulation, in addition to the fragile ecosystem in the TP.

The spatiotemporal variability of droughts over a region can often be characterized by combining orthogonal climate modes with corresponding temporal coefficients. The temporal characteristics of climate modes have been extensively addressed, while their spatial development remains largely unexplored. Hence, this study investigated the spatiotemporal evolution of meteorological droughts from the perspective of climate modes. Nearly 50 years of monthly precipitation records (1960–2007) from 73 meteorological stations in the Poyang Lake basin, China, were used. The standardized precipitation index at a 1-month scale was applied to quantify meteorological droughts. The dominant modes of the droughts over the basin were identified using principal component analysis, K-means cluster analysis, and analysis of variance. Based on the trajectory migration identification method, the role of the climate modes in spatiotemporal evolution was analyzed. The results showed that four spatial modes of the droughts in the basin were identified. The spatial extents, centroids, and severity of the drought clusters based on the identified modes were significantly consistent with those based on the meteorological stations (R² > 0.8, p < 0.05), which indicated that these modes could mostly match the large areas where serious dry/wet conditions occurred. Moreover, their performances in characterizing the spatiotemporal evolutional features (severity, migration distance, and pattern) of drought events were valid, which indicated that they might be considered as the elementary constituents of the historical meteorological drought events across the basin. The findings might offer some implications to understand drought development and causes through possible connections between the dominant modes and climate variability.

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km²) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement.

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R² = 0.13). In contrast, our derived results strongly correlated with the water level series (R² = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km² in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources.