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Flash droughts feature rapid onsets of soil moisture drought events and result in severe impacts and damages, especially on agricultural and ecological systems. How the flash drought regime across China varies on multitemporal scales with climate change is not fully clear yet. In this study, we extended the flash drought definition to apply to arid regions by adding an absolute soil moisture variation criterion. Then, we detected flash drought events across China during 1981–2021 and characterized their frequency, duration, and affected area changes on seasonal, annual, and decadal scales, using soil moisture data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land. Results show that flash drought hotspots appeared in North China and the Yangtze River Basin. During 1981–2021, the hotspots, even nationwide, underwent significant increases in frequencies, durations, and affected areas of flash droughts. The increases held in the extremely high values of the frequencies and durations in the decadal comparisons. Especially, North China saw the most extensive and rapid increases. Seasonally, flash drought frequencies and durations intensified more during spring and autumn, and seasonal hotspots in eastern China shifted in phase with spatial patterns of soil moisture loss balanced by precipitation and evapotranspiration. Thus, flash droughts tended to amplify atmospheric aridity. These findings on the hotspot regions and the spatiotemporal evolutions of flash droughts across China would pinpoint soil moisture responses to climate change and prepare for climate change impacts on local ecosystems. [ABSTRACT FROM AUTHOR]

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts' Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China. [ABSTRACT FROM AUTHOR]

• Heatwaves in CA are becoming earlier, more frequent, more intense, more persistent. • Heatwaves are more frequently observed in the west but less in the central CA. • Soil moisture depletion is the main factor in formulating heatwaves in CA. Heatwaves pose increasing threats on water resources for ecosystems and human consumption especially in arid region, such as Central Asia (CA). As the hinterland of Eurasia, water scarcity and land degradation in CA have increased its vulnerability to heatwaves. While the frequency of heatwaves has been examined, more heatwave metrics such as timing are less known, possible influences of soil moisture on heatwave changes in this region are not fully understood. Using reanalysis and observational datasets, here we examine the spatial–temporal changes of heatwaves in CA during 1981–2020 and the role of various associated factors including soil moisture. The results show that the extreme heatwaves occurred more frequently in northwestern Kazakhstan. Temporally, heatwaves have been intensifying with significant increases in frequency (0.10 events/year), intensity (0.81 °C/year), and duration (0.08 days/year) since the 1980 s, and these trends have accelerated since 1997. The first yearly heatwave event tended to occur earlier in more recent decades, indicating an advanced heatwave season. We also found that the heatwave occurrences were significantly correlated with hydro-meteorological factors. In northwestern of CA, extreme heatwaves are exacerbated by a rapid decrease in soil moisture resulting from reduced precipitation and increased solar net radiation in this area. Our findings provide a scientific basis for understanding the mechanisms and predictions of heatwave in arid regions from the climate change perspective. [ABSTRACT FROM AUTHOR]

• An intercomparison of five RS SM products with in situ measurements was carried out. • Sixteen different European ecoregions were considered as test areas. • Product performances were evaluated in zones with diverse flora, fauna and climate. • SMAPL4 and ESA CCI provided the best performance as complementary products. • ASCAT products furnished the best results among the non-blended/modeled products. Several satellite microwave-based soil moisture (SM) products have become available in recent years, making possible to produce different datasets at continental and/or a global scale. Numerous variables, including geographic heterogeneity, weather and climate, and land cover, have an impact on these products' accuracy. In this study, we account for all these variables by using the ecoregions as the scale of analysis and conducting a thorough comparison of satellite products with ground-based SM measurements. The whole Europe has been selected as study area, considering it as the aggregation of several ecoregions, each of them classified as an homogeneous zones, in terms of climate, vegetation and potentially investigable soil cover. Selected SM satellite data includes: i) the National Aeronautics and Space Administration (NASA) SMAP L4 V5; ii) the European Space Agency (ESA) SMOS-IC V2.0; iii) the H115 and H116 SM products in time-series format generated by analyzing data collected by the Advanced Scatterometer (ASCAT) aboard MetOp satellites; iv) the CGLS SSM 1 km obtained by the radar onboard ESA's SENTINEL-1 platforms; and v) the European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) "COMBINED" ESA CCI SM v06.1 product, created by merging satellite-based passive/active microwave measurements. Such a variety, in terms of technologies and main features, of publicly available online SM products (in their up-to-date version), allowed for a comprehensive intercomparison against in situ measurements of the International Soil Moisture Network (ISMN), that spreads across all of Europe within the above-mentioned terrestrial ecoregion, using four different metrics, i.e., r, bias, ubRMSE and r/r ANOM. Overall the intercomparison has underlined how SMAP L4 and ESA CCI show the best performance regardless of the considered metric. ASCAT has achieved best performances among non-modeled/blended products, while SMOS-IC has showed slightly better performance than the CGLS SSM 1 km. Using ecoregions allowed to further characterize the differences of each satellite products and identify the areas where all these products are more reliable (e.g., group (1) made by ecoregions as the Cantabrian Mixed forests or the Balkan Mixed forests; or group 2 made by ecoregions which belong to Mediterranean area) or less performing (e.g., the Central European mixed forests and the Pontic steppe). Hence, these classes have been further analyzed exploiting the ERA5-Land data, in terms of the SM, air temperature, precipitation, evaporation and Leaf area index. The research has provided helpful insights into how the performance of particular SM satellite products changes based on both the characteristics of the product under consideration and the area under investigation (i.e., one ecoregion compared to another). [ABSTRACT FROM AUTHOR]

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (∼10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ub RMSE (0.07(±0.02) m3/m3 for both SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively. • A newly developed 9 km global SMAP-IB SM product was evaluated. • SMAP-IB and SMAP-E SM had comparable accuracy with ERA5-Land over non-forest regions. • Difference in correlation between remote sensing and model SM decreased over time. [ABSTRACT FROM AUTHOR]

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP-L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i.e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m3/m3, respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m3/m3, respectively) performed better than ERA5-Land (R = 0.46; ubRMSE = 0.03 m3/m3). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs. • SSM and RZSM products are evaluated over the permafrost region of QTP • Uncertainties of SSM and RZSM products are analyzed • SSM products generally overestimate the in-situ measurements • Large errors were found in RZSM for both satellite-based and reanalysis products [ABSTRACT FROM AUTHOR]