Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

Effects of planting patterns plastic film mulching on soil temperature,moisture, functional bacteria and yield of winter wheat in the Loess Plateau of China

The yield of winter wheat is hindered by drought and low temperature in the Loess Plateau of China.Two common mulching methods to conserve soil moisture,ridge furrows with plastic film mulching (RP) and flat soil surfaces with plastic film mulching (FP) are helpful for wheat production.Our previous study indicated that FP could improve wheat yield more effectively than RP,but the reason remains unclear.The effect of mulching method on functional bacteria also needs to be further studied.In this study,winter wheat was employed to evaluate the impacts of mulching method on soil temperature,moisture content,microorganisms and grain yield.The results showed that FP had a warming effect when the soil temperature was low and a cooling effect when the temperature was too high.However,the ability to regulate soil temperature in the RP method was unstable and varied with year.The lowest negative accumulated soil temperature was found in the FP treatment,which was 20–89 and 43–99%lower than that of the RP and flat sowing with non-film mulching control (NP) treatments,respectively.Deep soil moisture was better transferred to topsoil for wheat growth in the FP and RP treatments than the NP treatment,which made the topsoil moisture in the two treatments (especially FP) more sufficient than that in the NP treatment during the early growing stage of wheat.However,due to the limited water resources in the study area,there was almost no difference between treatments in topsoil water storage during the later stage.The wheat yield in the FP treatment was significantly higher,by 12–16and 23–56%,respectively,than in the RP and NP treatments.Significant positive correlations were observed among the negative accumulated soil temperature,spike number and wheat yield.The Chao1 and Shannon indices in the RP treatment were 17 and 3.9%higher than those in the NP treatment,respectively.However,according to network relationship analysis,the interspecific relationships of bacteria were weakened in the RP treatment.Phosphorus solubilizing,ammonification and nitrification bacteria were more active in the RP than in the FP treatment,and microbes with nitrate reduction ability and plant pathogens were inhibited in the RP treatment,which improved nutrient availability and habitat for wheat.

Enhanced Seasonal Predictability of Spring Soil Moisture over the Indo-China Peninsula for Eastern China Summer Precipitation under Non-ENSO Conditions

Seasonal prediction of summer precipitation over eastern China is closely linked to the East Asian monsoon circulation,which is largely affected by the El Niño-Southern Oscillation(ENSO).In this study,results show that spring soil moisture(SM)over the Indo-China peninsula(ICP)could be a reliable seasonal predictor for eastern China summer precipitation under non-ENSO conditions.When springtime SM anomalies are present over the ICP,they trigger a structured response in summertime precipitation over most of eastern China.The resultant south-to-north,tri-polar configuration of precipitation anomalies has a tendency to yield increased(decreased)precipitation in the Yangtze River basin and decreased(increased)in South and North China with a drier(wetter)spring soil condition in the ICP.The analyses show that ENSO exerts a powerful control on the East Asian circulation system in the ENSO-decaying summer.In the case of ENSO forcing,the seasonal predictability of the ICP spring SM for eastern China summer precipitation is suppressed.However,in the absence of the influence of ENSO sea surface temperature anomalies from the preceding winter,the SM anomalies over the ICP induce abnormal local heating and a consequent geopotential height response owing to its sustained control on local temperature,which could,in turn,lead to abnormal eastern China summer precipitation by affecting the East Asian summer monsoon circulation.The present findings provide a better understanding of the complexity of summer climate predictability over eastern China,which is of potential significance for improving the livelihood of the people.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China

Climate change impacts on grasslands that cover a quarter of the global land area, have become unprecedented during the 21~(st) century. One of the important ecological realms, arid grasslands of northern China, which occupy more than 70% of the region's land area. However, the impact of climate change on vegetation growth in these arid grasslands is not consistent and lacks corresponding quantitative research. In this study, NDVI(normalized difference vegetation index) and climate factors including temperature, precipitation, solar radiation, soil moisture, and meteorological drought were analyzed to explore the determinants of changes in grassland greenness in Inner Mongolia Autonomous Region(northern China) during 1982–2016. The results showed that grasslands in Inner Mongolia witnessed an obvious trend of seasonal greening during the study period. Two prominent climatic factors,precipitation and soil moisture accounted for approximately 33% and 27% of grassland NDVI trends in the region based on multiple linear regression and boosted regression tree methods. This finding highlights the impact of water constraints to vegetation growth in Inner Mongolia's grasslands. The dominant role of precipitation in regulating grassland NDVI trends in Inner Mongolia significantly weakened from 1982 to 1996, and the role of soil moisture strengthened after 1996. Our findings emphasize the enhanced importance of soil moisture in driving vegetation growth in arid grasslands of Inner Mongolia, which should be thoroughly investigated in the future.

The lateral pressure coefficient at rest of expansive soils in landfill at various vertical stresses and moisture contents

When expansive soils in the original location are artificially transferred to landfill in different seasons,and subject to engineering activities afterwards,the corresponding deformation and stability of retaining structures become unpredictable.This necessitates the determination of lateral pressure coefficient at rest(k_(0) value)for expansive soils in landfill.Considering compaction,excavation of expansive soils,as well as construction of landfill in different seasons,series of stepwise loading and unloading consolidation tests at various moisture contents were carried out in this work to explore the evolution characteristics of k_(0) value and assess the dependence of k_(0) value on vertical stress and moisture content.Besides,scanning electron microscope(SEM)was used to track the change in microstructural features with vertical stresses.The results indicated that the k_(0) value of expansive soil shows a pronounced nonlinearity and is inextricably linked with vertical stress and moisture content,based on which a prediction formula to estimate the variation in k_(0) value with vertical stress during loading stage was proposed;there is a significant exponential increase in k_(0) value with overconsolidation ratio(OCR)during unloading stage,and OCR dominates the release of horizontal stress of expansive soil;SEM results revealed that with an increase in vertical stress,the anisotropy of expansive soil microstructure increases dramatically,causing a significant directional readjustment,which is macroscopically manifested as an initially rapid increase in k_(0) value;but when vertical stress increases to a critical value,the anisotropy of microstructure increases marginally,indicating a stable orientation occurring in the soil microstructure,which causes the k_(0) value to maintain a relatively stable value.

Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite Data

For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R2) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R2 of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.

An overview of soil moisture drought research in China:Progress and perspective

论文回顾了中国土壤湿度干旱(SMD)历史重建和季节预测研究进展,并对未来研究进行了展望,自1950s年代以来,全国整体干旱频率增加,持续时间延长,且有明显区域特征.SMD预测多是利用土壤湿度与气候变量之间的统计关系,而少量基于动力学方法的干旱预测研究强调了初始条件和大气强迫数据对季节尺度干旱预测的重要性,本论文提出:1)加强多时间尺度,跨区域的SMD研究;2)联合气候预测系统,陆面模式和多源土壤湿度数据研制SMD预测系统。

Variability of Soil Moisture and Its Relationship with Surface Albedo and Soil Thermal Parameters over the Loess Plateau

Data from July 2006 to June 2008 observed at SACOL （Semi-Arid Climate and Environment Observatory of Lanzhou University, 35.946°N, 104.137°E, elev. 1961 m）, a semi-arid site in Northwest China, are used to study seasonal variability of soil moisture, along with surface albedo and other soil thermal parameters, such as heat capacity, thermal conductivity and thermal diffusivity, and their relationships to soil moisture content. The results indicate that surface albedo decreases with increases in soil moisture content, showing a typical exponential relation between the surface albedo and the soil moisture. The heat capacity, the soil thermal diffusivity, and soil thermal conductivity show large variations between Julian day 90-212 and 450-578. The soil thermal conductivity is found to increase as a power function of soil moisture. Soil heat capacity and soil thermal diffusivity increase with increases in soil moisture. The SACOL observed soil moisture are also used to validate the AMSR-E/AQUA retrieved soil moisture and there is good agreement between them. The analysis of the relationship between satellite retrieved soil moisture and precipitation suggests that the variability of soil moisture depends on the variation of precipitation over the Loess Plateau.

Relationships Between Surface Albedo,Soil Thermal Parameters and Soil Moisture in the Semi-arid Area of Tongyu,Northeastern China

Continuous observation data collected over the whole year of 2004 on a cropland surtace m Tongyu, a senti-arid area of northeastern China （44°25＇N, 122°52＇E）, have been used to investigate the variations of surface albedo and soil thermal parameters, including heat capacity, thermal conductivity and thermal diffusivity, and their relationships to soil moisture. The diurnal variation of surface albedo appears as a U shape curve on sunny days. Surface albedo decreases with the increase of solar elevation angle, and it tends to be a constant when solar elevation angle is larger than 40°. So the daily average surface albedo was computed using the data when solar elevation angle is larger than 40° Mean daily surface albedo is found to decrease with the increase of soil moisture, showing an exponential dependence on soil moisture. The variations of soil heat capacity are small during Julian days 90 300. Compared with the heat capacity, soil thermal conductivity has very gentle variations during this period, but the soil thermal diffusivity has wide variations during the same period. The soil thermal conductivity is found to increase as a power function of soil moisture. The soil thermal diffusivity increases firstly and then decreases with the increase of soil moisture.

Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF)

The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kalman filter （EnKF） assimilation scheme, including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The ＂true＂ soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d^-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

Interactive effects of soil temperature and moisture on soil N mineralization in a Stipa krylovii grassland in Inner Mongolia, China

Determining soil N mineralization response to soil temperature and moisture changes is challenging in the field due to complicated effects from other factors. In the laboratory, N mineralization is highly dependent on temperature, moisture and sample size. In this study, a laboratory incubation experiment was carefully designed and conducted under controlled conditions to examine the effects of soil temperature and moisture on soil N mineralization using soil samples obtained from the Stipa krylovii grassland in Inner Mongolia, China. Five temperature（i.e. 9℃, 14℃, 22℃, 30℃ and 40℃） and five moisture levels（i.e. 20%, 40%, 60%, 80% and 100% WHC, where WHC is the soil water holding capacity） were included in a full-factorial design. During the 71-day incubation period, microbial biomass carbon（MBC）, ammonium nitrogen（NH4 ^＋-N） and nitrate nitrogen（NO3^--N） were measured approximately every 18 days; soil basal respiration for qCO2 index was measured once every 2 days（once a week near the end of the incubation period）. The results showed that the mineral N production and net N mineralization rates were positively correlated with temperature; the strongest correlation was observed for temperatures between 30℃ and 40℃. The relationships between moisture levels and both the mineral N production and net N mineralization rates were quadratic. The interaction between soil temperature and moisture was significant on N mineralization, i.e. increasing temperatures（moisture） enhanced the sensitivity of N mineralization to moisture（temperature）. Our results also showed a positive correlation between the net nitrification rate and temperature, while the correlation between the NH4 ^＋-N content and temperature was insignificant. The net nitrification rate was negatively correlated with high NH4 ^＋-N contents at 80%–100% WHC, suggesting an active denitrification in moist conditions. Moreover, qCO2 index was positively correlated with temperature, especially at 80% WHC. With a low net nitrification rate and high soil basal respiration rate, it was likely that the denitrification concealed the microbial gross mineralization activity; therefore, active soil N mineralization occurred in 60%–80% WHC conditions.

Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets

Based on station observations, The European Centre for Medium-Range Weather Forecasts reanalysis （ERA40）, the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR） reanalysis and Princeton University＇s global meteorological forcing data set （Princeton）, four atmospheric forcing fields were constructed for use in driving the Community Land Model version 3.5 （CLM3.5）. Simulated soil moisture content throughout the period 1951-2000 in the Yellow River basin was validated via comparison with corresponding observations in the upper, middle, and lower reaches. The results show that CLM3.5 is capable of reproducing not only the characteristics of intra-annual and annual variations of soil moisture, but also long-term variation trends, with different statistical significance in the correlations between the observations and simulations from different forcing fields in various reaches. The simulations modeled with station-based atmospheric forcing fields are the most consistent with observed soil moisture, and the simulations based on the Princeton data set are the second best, on average. The simulations from ERA40 and NCEP/NCAR are close to each other in quality, but comparatively worse to the other sources of forcing information that were evaluated. Regionally, simulations are most consistent with observations in the lower reaches and less so in the upper reaches, with the middle reaches in between. In addition, the soil moisture simulated by CLM3.5 is systematically greater than the observations in the Yellow River basin. Comparisons between the simulations by CLM3.5 and CLM3.0 indicate that simulation errors are primarily caused by deficiencies within CLM3.5 and are also associated with the quality of atmospheric forcing field applied.

Soil Moisture Retrieval from Satellite Images and Its Application to Heavy Rainfall Simulation in Eastern China

The soil water index （SWI） from satellite remote sensing and the observational soil moisture from agricultural meteorological stations in eastern China are used to retrieve soil moisture. The analysis of correlation coefficient （CORR）, root-mean-square-error （RMSE） and bias （BIAS） shows that the retrieved soil moisture is convincible and close to the observation. The method can overcome the difficulties in soil moisture observation on a large scale and the retrieved soil moisture may reflect the distribution of the real soil moisture objectively. The retrieved soil moisture is used as an initial scheme to replace initial conditions of soil moisture （NCEP） in the model MM5V3 to simulate the heavy rainfall in 1998. Three heavy rainfall processes during 13-14 June, 18-22 June, and 21-26 July 1998 in the Yangtze River valley are analyzed. The first two processes show that the intensity and location of simulated precipitation from SWI are better than those from NCEP and closer to the observed values. The simulated heavy rainfall for 21-26 July shows that the update of soil moisture initial conditions can improve the model＇s performance. The relationship between soil moisture and rainfall may explain that the stronger rainfall intensity for SWI in the Yangtze River valley is the result of the greater simulated soil moisture from SWI prior to the heavy rainfall date than that from NCEP, and leads to the decline of temperature in the corresponding area in the heavy rainfall days. Detailed analysis of the heavy rainfall on 13-14 June shows that both land-atmosphere interactions and atmospheric circulation were responsible for the heavy ralnfall, and it shows how the SWI simulation improves the simulation. The development of mesoscale systems plays an important role in the simulation regarding the change of initial soil moisture for SWI.

Trends and Scales of Observed Soil Moisture Variations in China

A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.

The Soil Moisture of China in a High Resolution Climate-Vegetation Model

The spatial distribution of soil moisture, especially the temporal variation at seasonal and interannual scales, is difficult for many land surface models （LSMs） to capture partly due to the deficiencies of the LSMs and the highly spatial variability of soil moisture, which makes it problematic to simulate the moisture for climate studies. However the soil moisture plays an important role in influencing the energy and hydrological cycles between the land and air, so it should be considered in land surface models. In this paper, a soil moisture simulation in China with a T213 resolution （about 0.5625°× 0.5625°） is compared to the observational data, and its relationship to precipitation is explored. The soil moisture distribution agrees roughly with the observations, and the soil moisture pattern reflects the variation and intensity of the precipitation. In particular, for the 1998 summer catastrophic floods along the Yangtze River, the soil moisture remains high in this region from July to August and represents the flood well. The seasonal cycle of soil moisture is roughly consistent with the observed data, which is a good calibration for the ground simulation capacity of the Atmosphere-Vegetation Interaction Model （AVIM） with respect to this tough problem for land surface models.

Effects of soil moisture and light intensity on ecophysiological characteristics of Amorpha fruticosa seedlings

We investigated the combined effects of soil moisture and light intensity on the growth, development and ecophysiological characteristics of one-year old Amorpha fruticosa seedlings. Soil moisture and light intensity influenced the ecophysiological characteristics of Amorpha fruticosa seedlings. Soil moisture resulted in the decreases of growth rate, individual size, net photosynthetic rate, transpiration rate, leaf water loss rate （WLR）, and biomass accumulation of plant parts, and led to increased leaf water saturation deficit （WSD）. Under water stress, more photosynthetic products were allocated to root growth. With decreasing light intensity, net photosynthetic rate, transpiration rate, chla/b, water saturation deficit, water use efficiency, water loss rate and biomass accumulation declined, while Chla, Chlb, Chla＋b and carotenoids （Car） increased and more photosynthetic products were allocated to stem and leaf growth. Maximum growth vigor, net photosynthetic rate and total biomass accumulation in Amorpha fruticosa seedlings was recorded at 75 80% soil water-holding capacity and 100% light density in greenhouse environments.

Use of a Land Surface Model to Evaluate the Observed Soil Moisture of Grassland at the Tongyu Reference Site

A land surface model driven by the continuous three-year observed meteorological data with a time interval of 30 minutes at the Tongyu station, a reference site of the Coordinated Enhanced Observing Period （CEOP）, was used to evaluate the observation bias of soil moisture （SM） data and analyze the variation of SM at different time scales. The saline-alkaline soil of the grassland at the Tongyu site makes the measured SM too high, especially in boreal summer of 2003-05. The simulated annual mean SM has the lowest value in 2004 and its three-year variation corresponds to the change of precipitation, whereas the observation shows the increasing trend from 2003 to 2005. Compared to the variation range between -60% and 40% for the anomaly percentage of the simulated daily mean SM during May-October of 2004, the measured data show the higher values more than 40%. The magnitude of the variation trend of the observed daily mean SM in 2003 and 2005 is generally consistent with the simulation. The largest deficiency for the soil moisture observation of the grassland is the overestimated value in the drought year with less precipitation. The simulated monthly mean SM has the lowest value in March due to the large contribution of evaporation relative to precipitation and this phenomenon can not be reproduced in the observation.

Influence of Soil Moisture in Eastern China on the East Asian Summer Monsoon

The sensitivity of the East Asian summer monsoon to soil moisture anomalies over China was investigated based on ensembles of seasonal simulations （March-September） using the NCEP GCM coupled with the Simplified Simple Biosphere Model （NCEP GCM/SSiB）. After a control experiment with free-running soil moisture, two ensembles were performed in which the soil moisture over the vast region from the lower and middle reaches of the Yangtze River valley to North China （YRNC） was double and half that in the control, with the maximum less than the field capacity. The simulation results showed significant sensitivity of the East Asian summer monsoon to wet soil in YRNC. The wetter soil was associated with increased surface latent heat flux and reduced surface sensible heat flux. In turn, these changes resulted in a wetter and colder local land surface and reduced land-sea temperature gradients, corresponding to a weakened East Asian monsoon circulation in an anomalous anticyclone over southeastern China, and a strengthened East Asian trough southward over Northeast China. Consequently, less precipitation appeared over southeastern China and North China and more rainfall over Northeast China. The weakened monsoon circulation and strengthened East Asian trough was accompanied by the convergence of abnormal northerly and southerly flow over the Yangtze River valley, resulting in more rainfall in this region. In the drier soil experiments, less precipitation appeared over YRNC. The East Asian monsoon circulation seems to show little sensitivity to dry soil anomalies in NCEP GCM/SSiB.

Effects of reduced nitrogen and suitable soil moisture on wheat(Triticum aestivum L.) rhizosphere soil microbiological,biochemical properties and yield in the Huanghuai Plain,China

Soil management practices affect rhizosphere microorganisms and enzyme activities, which in turn influence soil ecosystem processes. The objective of this study was to explore the effects of different nitrogen application rates on wheat(Triticum aestivum L.) rhizosphere soil microorganisms and enzyme activities, and their temporal variations in relation to soil fertility under supplemental irrigation conditions in a fluvo-aquic region. For this, we established a split-plot experiment for two consecutive years(2014–2015 and 2015–2016) in the field with three levels of soil moisture: water deficit to no irrigation(W1), medium irrigation to(70±5)% of soil relative moisture after jointing stage(W2), and adequate irrigation to(80±5)% of soil relative moisture after jointing stage(W3);and three levels of nitrogen: 0 kg ha^–1(N1), 195 kg ha^–1(N2) and 270 kg ha^–1(N3). Results showed that irrigation and nitrogen application significantly increased rhizosphere microorganisms and enzyme activities. Soil microbiological properties showed different trends in response to N level;the highest values of bacteria, protease, catalase and phosphatase appeared in N2, while the highest levels of actinobacteria, fungi and urease were observed in N3. In addition, these items performed best under medium irrigation(W2) relative to W1 and W3;particularly the maximum microorganism(bacteria, actinobacteria and fungi) amounts appeared at W2, 5.37×10^7 and 6.35×10^7 CFUs g^–1 higher than those at W3 in 2014–2015 and 2015–2016, respectively;and these changes were similar in both growing seasons. Microbe-related parameters fluctuated over time but their seasonality did not hamper the irrigation and fertilization-induced effects. Further, the highest grain yields of 13 309.2 and 12 885.7 kg ha^–1 were both obtained at W2 N2 in 2014–2015 and 2015–2016, respectively. The selected properties, soil microorganisms and enzymes, were significantly correlated with wheat yield and proved to be valuable indicators of soil quality. These results clearly demonstrated that the combined treatment(W2 N2) significantly improved soil microbiological properties, soil fertility and wheat yield on the Huanghuai Plain, China.