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The South Asian high (SAH) location and intensity are linked with the latent heating of the Tibetan Plateau (TP) and Yangtze River basin. The relationship between SAH variability and its impact on South Asian monsoon (SAM) onset is rarely linked with TP soil moisture. This study uses remotely sensed soil moisture and reanalysis products to quantify the relationship between the TP spring (April–June) soil moisture with SAH and SAM onset during 1988–2008. The results show that the TP spring soil moisture and monsoon onset indices are negatively correlated (R < −0.65), whereas the SAH exhibits a significant positive correlation (R ≥ 0.70) with TP soil moisture. The monsoon onset shows a difference of 20–25 days between the early- and late-onset composites. Significant positive (negative) soil moisture anomalies persist over the TP during the early (late) onset followed by positive (negative) LH (SH) anomalies during early (late)-onset composites. The TP thermal forcing exhibited positive anomalies during the early (late)-onset composites implying significant soil moisture control over the diabatic heating, which favors vertical ascent over the eastern plateau. Such a pattern leads to an earlier formation and movement of the SAH toward the Bay of Bengal (BOB) and southwestward of the TP. Before the early and late monsoon onset composites, the SAH pentad evolution drives the lower-tropospheric westerlies/easterlies toward continental SA. In the Indian Ocean the wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent affecting the onset timing over the Arabian Sea and continental SA with less influence over the BOB monsoon onset. Significance Statement: The Tibetan Plateau heating is one of the key drivers of the Asian monsoon precipitation in the surrounding regions, which has been previously studied in detail. This study explored the Tibetan Plateau spring soil moisture's effect on South Asian monsoon onset timing. The monsoon onset timing is calculated using changes in wind direction, atmospheric temperature, and relative precipitation magnitude. Results found that the spring soil moisture substantially affects the TP thermal heating and the SA monsoon onset timing and highlights the physical processes leading to changes in the monsoon onset timing. The inclusion of soil moisture in estimating the monsoon onset timing can provide a tangible way of improving our understanding of the monsoon and associated water resources management practices. [ABSTRACT FROM AUTHOR]

Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05–0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations. [ABSTRACT FROM AUTHOR]

The ability of soil moisture (SM) to affect precipitation (P) is a vital part of the water-energy cycles. Accurately quantifying this coupling enhances the ability to predict hydroclimatic extremes like floods and droughts. In this study, the ability of soil moisture to affect precipitation (SM-P) is characterized by two parts: the influence of soil moisture on evapotranspiration (SM-ET), and the influence of evapotranspiration on precipitation (ET-P). We determined localized ET-P by incorporating the coupling between latent heat flux (LH) and LCL height, to optimize the estimation of the SM-P. This approach links SM more closely to P by considering the influence of surface fluxes. The results indicate that CMIP6 models exhibited the anticipated hotspot patterns for the three coupling metrics in transition regions. However, we observed that climate models generally exhibit weaker SM-P coupling compared to reanalysis models. Both SM-ET and SM-P showcase higher values wherein wet climate regions during dry years, and the converse occurs in dry regions. Due to sensitivity to climate change, the ET-P exhibits a more pronounced upward trend in the future. This study helps understand P's response to SM shifts in climate models, crucial for predicting hydrological extremes and coupled global warming impact. [ABSTRACT FROM AUTHOR]

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change. [ABSTRACT FROM AUTHOR]

Soil moisture as a key variable of land processes greatly influences the weather and climate. This study investigates the observed linkage between Tibetan Plateau (TP) spring soil moisture and summer precipitation and floods in the Yangtze River basin during 1988–2008 using satellite and in‐situ observations. A significant (p = 1%) proportion of interannual variations of summer precipitation (about 25%) in the Yangtze River basin can be attributed to spring TP soil moisture anomalies which show a dipole pattern. When spring soil moisture anomalies are positive (negative) over eastern (western) TP, there is more summer precipitation and consequently river discharge in the Yangtze River basin, or vice versa. The possible mechanisms can be explained from the perspectives of surface energy balance and atmospheric thermodynamics. More (less) spring soil moisture over the eastern (western) plateau enhances summer diabatic heating, which may be related to soil moisture memory. The enhanced summer diabatic heating stimulates vigorous ascending motions over TP, which diverge in the upper troposphere (200 hPa) and descend over the western Pacific. This is conducive to the enhancement and mutual proximity of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH). As a result, the warm and humid air flows from the Bay of Bengal and the western Pacific and the cold and dry air flows from the boreal continent converge in the Yangtze River basin, causing excessive summer precipitation. Therefore, TP spring soil moisture can be considered a seasonal predictor of summer precipitation and possible subsequent floods in the Yangtze River basin. Plain Language Summary: Spring soil moisture over the Tibetan Plateau (TP) explains a significant proportion of interannual variations of summer precipitation (about 25%) in the Yangtze River basin. When spring soil moisture anomalies are positive (negative) over the eastern (western) TP, there is more summer precipitation and consequently river discharge in the Yangtze River basin, or vice versa. The possible mechanisms can be attributed to the perspectives of surface energy balance and atmospheric thermodynamics. More (less) spring soil moisture over the eastern (western) plateau enhances summer diabatic heating. The diabatic heating controls the atmospheric circulation changes over the TP and surrounding regions associated with the enhancement and eastward (westward) stretching of the South Asian High (Western Pacific Subtropical High), favoring the convergence of the warm and humid (cold and dry) air flows from Tropical Ocean (boreal continent) to the Yangtze River basin, resulting in excessive summer precipitation. Key Points: Spring soil moisture over the Tibetan Plateau (TP) explains about 25% of interannual variations of summer precipitation in the Yangtze River basinPositive (negative) soil moisture anomalies over eastern (western) TP lead to more precipitation and discharge in the Yangtze River basinMore precipitation is due to moisture‐rich airflow convergence linked to South Asian High and Western Pacific Subtropical High affected by soil moisture‐induced diabatic heating [ABSTRACT FROM AUTHOR]

The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions. [ABSTRACT FROM AUTHOR]

Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with R2 of 0.806 and root-mean-square error of 0.043 m3·m−3, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions. [ABSTRACT FROM AUTHOR]

This study develops a data-driven modification scheme for a commonly used soil moisture retrieval algorithm by introducing a vegetation density-related single scattering albedo based on in situ and Fengyun-3B passive microwave observations. The Jiangxi province in China's mainland is one of the most challenging regions for soil moisture retrievals due to its complex topography, open water, and vegetation conditions. However, it has a very dense in situ soil moisture observation network which makes it a suitable test-bed to examine the performance of the modification scheme. The development of this new scheme consists of two steps. In a first step, the model is initialized using the most recently developed algorithm configuration. In the second step, these initial outcomes are used as input to determine the vegetation density related single scattering albedo which is solely based on observational data and used as the final algorithm configuration over our study area. We start by comparing the two most recent algorithm configurations against the in situ soil moisture network and demonstrate an overall improvement in terms of correlations coefficient for the most recent version. Then, the observational data- driven modification scheme was proposed and evaluated against the in situ soil moisture network with further improvements after its implementation. We furthermore applied the vegetation density-based scattering albedo in soil moisture retrievals over all grid cells in Jiangxi, and found that soil moisture data with the newly developed configuration significantly improved (up to 30%) compared to the preceding algorithm configurations. The two existing algorithm configurations were also evaluated over all grid cells and all results indicate consistent improvements between the successive algorithm versions. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in terrestrial water cycle, playing a key role in the exchange of water and energy in the land-atmosphere interface. The spatiotemporal variations of soil moisture from multiple sources during 1988–2010 are evaluated against in situ observations in the Yellow River basin, China, including the Essential Climate Variable satellite’s passive microwave product (SMECV), ERA-Interim reanalysis (SMERA), the National Centers for Environmental Prediction/Department of Energy’s Reanalysis-2 (SMNCEP), and the Variable Infiltration Capacity model products (SMVIC). The seasonal soil moisture dynamics of SMECV and SMVIC appear to be consistent with SMin  situ, with significant soil drying in spring and wetting in summer. SMERA and SMNCEP, however, fail to capture the soil drying before rainy seasons. Remarkably, SMECV shows large agreement with SMin  situ in terms of the interannual variations and the long-term drying trends. SMVIC captures the interannual variations but fails to have the long-term trends in SMin  situ. As for SMERA and SMNCEP, they fail to capture both the interannual variations and the long-term soil drying trends in SMin  situ. [ABSTRACT FROM AUTHOR]

The availability of reliable long-term and large scale soil moisture observation datasets over the Tibetan Plateau has been of more concern in recent years. It is therefore necessary to verify any such data to have a better guide to their usage. In this study, we have compared satellite soil moisture retrievals of the Special Sensor Microwave Imager (SSM/I), with ERA-Interim/Land and Global Land Data Assimilation System (GLDAS-1) soil moisture products over the Tibetan Plateau. They are found to be largely different from each other regarding the soil moisture magnitudes in spring, summer and autumn, with those of the ERA-Interim/Land particularly higher than the other datasets in the eastern plateau. The SSM/I and the ERA-Interim/Land data are in good agreement considering their seasonal dynamics, which are however considerably different from the GLDAS-1 models (CLM, Noah, Mosaic and VIC). For interannual variations, it is striking to find strong negative correlations (R < − 0.6) between the SSM/I and the ERA-Interim/Land data in spring and autumn, especially over the central plateau. The SSM/I data is found to have also strong negative correlations (R < − 0.4) with data of the four GLDAS-1 models in the eastern plateau in spring; and such negative correlations appear in central and western plateau in autumn. In summer, the SSM/I data appears to be positively correlated with the ERA-Interim/Land and all the GLDAS-1 data over the plateau. The negative correlations indicate these data have quite different temporal dynamics for their interannual variations, especially in spring and autumn. A look at the spatial variations revealed that the SSM/I data shows a north-south seesaw pattern in all seasons. The GLDAS-1 Mosaic, Noah and CLM data are able to represent this seesaw pattern principally, which however, can hardly be seen in the ERA-Interim/Land and the GLDAS-1 VIC data. Our results have indicated significant uncertainties in the soil moisture data from remote sensing and reanalysis sources; and we have to use such data with great care. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing has become one newest technology for deriving soil moisture at large scales. Using a radiative transfer algorithm, we have derived soil moisture over the Tibetan Plateau from the brightness temperature of the microwave radiometer imager (MWRI) onboard China’s Fengyun 3B (FY3B) satellite. The derived FY3B soil moisture data are evaluated with in situ observations, the ERA-Interim reanalysis and the retrievals from microwave imager (TMI) onboard the Tropical Rainforest Measuring Mission (TRMM). The FY3B and the TMI data are found to have both overestimated the soil moisture magnitudes against in situ observations. The FY3B data significantly outperform the TMI retrievals and particularly the ERA-Interim data with respect to their temporal dynamics, which is more important in soil moisture applications. This finding suggests the promising potential for using FY3B microwave brightness temperature to derive soil moisture over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Based on in situ soil moisture and river runoff records in the Tarim River basin, usability of the long term Essential Climate Variable (ECV) soil moisture dataset is validated in the arid climatic region of China. The spatio-temporal variation of soil moisture and its possible influencing factors in the 1988–2013 is also preliminary analyzed in the current paper. Results reveal that the ECV soil moisture can capture the large scale dynamics of regional water cycle quite satisfactorily, showing good agreement with in situ observations in their seasonal and interannual variability. In the period of 1988–2013, the ECV soil moisture shows obvious increasing trends in the northwest and the southwest parts of the Tarim River basin, particularly in spring (March–May) and autumn (September–November). Statistical analysis further suggests that the variations of soil moisture in the Tarim River basin are more controlled by precipitation, and temperature is less effective in controlling of soil moisture variations. [ABSTRACT FROM AUTHOR]

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China's Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records. [ABSTRACT FROM AUTHOR]

Abstract: The presence of data gaps is always a concern in geophysical records, creating not only difficulty in interpretation but, more importantly, also a large source of uncertainty in data analysis. Filling the data gaps is a necessity for use in statistical modeling. There are numerous approaches for this purpose. However, particularly challenging are the increasing number of very large spatio-temporal datasets such as those from Earth observations satellites. Here we introduce an efficient three-dimensional method based on discrete cosine transforms, which explicitly utilizes information from both time and space to predict the missing values. To analyze its performance, the method was applied to a global soil moisture product derived from satellite images. We also executed a validation by introducing synthetic gaps. It is shown this method is capable of filling data gaps in the global soil moisture dataset with very high accuracy. [Copyright &y& Elsevier]

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency's essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration's (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China's Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B's contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter in land surface processes, which can control the surface energy and water budgets and thus affect the air temperature. Studying the coupling between soil moisture and air temperature is of vital importance for forecasting climate change. This study evaluates this coupling over China from 1980–2013 by using an energy-based diagnostic method, which represents the momentum, heat, and water conservation equations in the atmosphere, while the contributions of soil moisture are treated as external forcing. The results showed that the soil moisture–temperature coupling is strongest in the transitional climate zones between wet and dry climates, which here includes Northeast China and part of the Tibetan Plateau from a viewpoint of annual average. Furthermore, the soil moisture–temperature coupling was found to be stronger in spring than in the other seasons over China, and over different typical climatic zones, it also varied greatly in different seasons. We conducted two case studies (the heatwaves of 2013 in Southeast China and 2009 in North China) to understand the impact of soil moisture–temperature coupling during heatwaves. The results indicated that over areas with soil moisture deficit and temperature anomalies, the coupling strength intensified. This suggests that soil moisture deficits could lead to enhanced heat anomalies, and thus, result in enhanced soil moisture coupling with temperature. This demonstrates the importance of soil moisture and the need to thoroughly study it and its role within the land–atmosphere interaction and the climate on the whole. [ABSTRACT FROM AUTHOR]

Soil moisture is an important factor in land-atmosphere interactions and other land processes. Improved estimates from climate models have, in the last two decades, become an important alternate source of information. In this study, we extend the evaluation of soil moisture anomalies of different generations of three families of model datasets (the European Center for Medium-Range Weather Forecasts' (ECMWF) reanalysis, the Modern Era Retrospective Analysis for Research and Applications of NASA, and the Global Land Data Assimilation System of theNational Oceanic and Atmospheric Administration (NOAA)) in recent studies to the People's Republic of China. Two validation techniques, namely, root-mean-square error (RMSE) from triple collocation analysis (TCA) and correlations (R) with ground observations, were used. The study confirmed the results of previous studies that focused on other regions and showed that the newer generations of each modeling family generally had better skill than the older generations with higher correlations and lower RMSEs. A cross-validation of the results from the two techniques for the newer products showed that the higher correlations and lower RMSEs from the TCA were found over regions with moderate vegetation cover, while regions with less vegetation cover had lower correlations and larger RMSEs (ECMWF (R: −0.93), NASA (R: −0.73), and NOAA (R: −0.61)), indicating that these two techniques complement each other to fairly validate the products. [ABSTRACT FROM AUTHOR]

Understanding the reasons for past changes in soil moisture conditions is key to improving forecasts of this extremely important variable coupling land and atmosphere. To contribute to this understanding, we analyzed the variability of soil moisture and its relationship to regional climate features. In this study we focused on the Poyang Lake basin, which is located on the southern bank of the Yangtze River at the junction of its middle and lower reaches in Jiangxi Province. Using in situ soil moisture observations from Nankang and Hukou stations for the period from 1998 to 2012, we tested the regional applicability of the high-resolution long-term Essential Climate Variable (ECV) soil moisture (SM) dataset (1978–2013) by means of trend and correlation analyses. We only used the ECV SM product here that is based on passive microwave technology. Both observed and ECV SM data show the same variations on monthly and inter-annual time scales including identical trend direction and strong correlations. To investigate the relationship between soil moisture conditions in the Poyang Lake basin and the Western Pacific Subtropical High (WPSH), correlation coefficients were calculated between the ECV SM data, the strength index ( I S ), and the northern limit index ( I N ) of the WPSH. The I S of the last autumn and winter shows a strong positive correlation with soil moisture during the subsequent rainy season. A strong WPSH in the preceding autumn and winter could result in more humid climate conditions in the Poyang lake basin during the following rainy season. The I N shows a strong negative correlation with concurrent soil moisture from July to September. The further the WPSH extends towards the north, the drier the area. [ABSTRACT FROM AUTHOR]

Abstract Grassland recovery from degradation is increasingly occurring worldwide. Diverse managements have been considered as effective ways to restore degraded grassland, but it remains unclear how semi-arid grasslands respond to long-term grazing exclusion and fenced mowing. Here, a study was conducted under open grazing, grazing exclusion and fenced mowing in a semi-arid grassland on the Loess Plateau. We measured plant species composition and diversity, plant production, surface litter and soil water and carbon content. Shifts in grassland management led to significant divergence in plant community composition. Long-term grazing exclusion (35 years) significantly increased plant biomass, surface litter, soil water and carbon storage, but suppressed plant diversity compared to open grazing. Conversely, fenced mowing significantly increased plant diversity accompanying with a weak effect on soil carbon. Moreover, mowing significantly reduced surface litter and soil moisture, which have strong implications for nutrient depletion and soil drying. Our results suggest that introducing disturbances are necessary to safeguard biodiversity, and continuous mowing (5 years) belongs to over exploitation of the long-term protected grassland. Therefore, it is essential to optimize management with dual objectives of biodiversity and soil carbon sequestration in the future. Highlights • Three representative management-related steppe conditions were evaluated. • Long-term grazing exclusion replenished SOC but decreased plant diversity. • Fenced mowing safeguarded plant diversity but induced soil drying. [ABSTRACT FROM AUTHOR]