A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.

Evaluating effects of Dielectric Models on the surface soil moisture retrieval in the Qinghai-Tibet Plateau

Based on the measurement of L-band ground-based microwave radiometer(ELBARA-III type)in the Qinghai-Tibet Plateau and theτ-ωradiative transfer model,this research evaluated the effects of four soil dielectric models,i.e.,Wang-Schmugge,Mironov,Dobson,and Four-phase,on the L-band microwave brightness temperature simulation and soil moisture retrieval.The results show that with the same vegetation and roughness parameterization scheme,the four soil dielectric models display obvious differences in microwave brightness temperature simulation.When the soil moisture is less than 0.23 m3/m3,the simulated microwave brightness temperature in Wang-Schmugge model is significantly different from that of the other three models,with maximum differences of horizontal polarization and vertical polarization reaching 8.0 K and 4.4 K,respectively;when the soil moisture is greater than 0.23 m3/m3,the simulated microwave brightness temperature of Four-phase significantly exceeds that of the other three models;when the soil moisture is saturated,maximum differences in simulated microwave brightness temperature with horizontal polarization and vertical polarization are 6.1 K and 4.8 K respectively,and the four soil dielectric models are more variable in the microwave brightness temperature simulation with horizontal polarization than that with vertical polarization.As for the soil moisture retrieval based on the four dielectric models,the comparison study shows that,under the condition of horizontal polarization,Wang-Schmugge model can reduce the degree of retrieved soil moisture underestimating the observed soil moisture more effectively than other parameterization schemes,while under the condition of vertical polarization,the Mironov model can reduce the degree of retrieved soil moisture overestimating the observed soil moisture.Finally,based on the Wang-Schmugge model and FengYun-3C observation data,the spatial distribution of soil moisture in the study area is retrieved.

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×10^(8)people,providing important ecosystem services for human survival and development.However,dryland ecosystems are extremely fragile and sensitive to external environmental changes.Land use and land cover(LULC)changes significantly impact soil structure and function,thus affecting the soil multifunctionality(SMF).However,the effect of LULC changes on the SMF in the drylands of China has rarely been reported.In this study,we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center.We explored the drivers of the SMF changes under different LULC types(including forest,grassland,shrubland,and desert)and used structural equation modeling to explore the main driver of the SMF changes.The results showed that the SMF under the four LULC types decreased in the following descending order:forest,grassland,shrubland,and desert.The main driver of the SMF changes under different LULC types was mean annual temperature(MAT).In addition to MAT,pH in forest,soil moisture(SM)and soil biodiversity index in grassland,SM in shrubland,and aridity index in desert are crucial factors for the SMF changes.Therefore,the SMF in the drylands of China is regulated mainly by MAT and pH,and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes.The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Forecast and Analysis of Soil Moisture Based on SIMPEL Model

The forecast of soil moisture lays foundation for water management in farmlands. The change of soil moisture is influenced by multiple meteorological fac- tors. It becomes much significant for improvement of agricultural production and ef- fective use of water to explore the rule of water dynamic at small scale, spatially or temporally. In the research, water dynamic in soil horizons at 0-40 cm in winter wheat belts was simulated by SIMPLE model as per water balance principle. Fur- thermore, ETp in fields was computed according to Haude method （DVWK stan- dards）; retained amount of water in fields and wilting coefficient were calculated based on soil parameters with SPAW （Soil-Plant-Air-Water）. The simulated results of SIMPLE model showed that the correlation of measured and simulated water con- tent in soils was 0.95 and relative error averaged lower than 3.1%, suggesting that the model would make a more precise estimation of water content in root zone in the area.

Soil moisture of different vegetation types on the Loess Plateau

Water stored in deep loess soil is one of the most important resources regulating vegetation growth in the semi-arid area of the Loess Plateau, but planted shrub and forest often disrupt the natural water cycle and in turn influence plant growth. The purpose of this study was to examine the effects of main vegetation types on soil moisture and its inter-annual change. Soil moisture in 0-10 m depth of six vegetation types, i.e., crop, grass, planted shrub of caragana, planted forests of arborvitae, pine and the mixture of pine and arborvitae were measured in 2001,2005 and 2006. Soil moisture in about 0-3 m of cropland and about 0-2 m of other vegetation types varied inter-annually dependent on annual precipitation, but was stable inter-annually below these depths. In 0-2 m, soil moisture of cropland was significantly greater than those of all other vegetation types, and there were no si nificant differences among other vegetation types. In 2-10 m, there was no significant mois- ture difference between cropland and grassland, but the soil moistures under both of them were significantly higher than those of planted shrub and forests. The planted shrub and forests had depleted soil moisture below 2 m to or near permanent wilting point, and there were no significant moisture differences among forest types. The soil moisture of caragana shrub was significantly lower than those of forests, but the absolute difference was very small. The results of this study implicated that the planted shrub and forests had depleted deep soil moisture to the lowest limits to which they could extract and they lived mainly on present year precipitation for transpiration.

A Case Study of the Improvement of Soil Moisture Initialization in IAP-PSSCA

A prediction system is employed to investigate the potential use of a soil moisture initialization scheme in seasonal precipitation prediction through a case study of severe floods in 1998. The results show that driving the model with reasonable initial soil moisture distribution is helpful for precipitation prediction, and the initialization scheme is easy to use in operational prediction.

Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

Probabilistic modeling of soil moisture dynamics in a revegetated desert area

Soil moisture is the key link between land hydrological and ecological processes which plays an important role in the terrestrial water cycle. As extreme weather events have increased in recent years, the stochastic simulation of soil moisture has gradually become the focus of ecohydrology research. Based on continuous monitoring of soil moisture data from 2008 to 2011, and histor- ical precipitation data from 199l to 2011, combined with the Rodriguez-Iturbe soil moisture dynamic stochastic model, soil mois- ture dynamics and its probability density fimction in a revegetated desert area was simulated. Results show that annual soil mois- ture dynamic changes of the revegetated desert area during the growing season complied with rainfall distribution; soil moisture probability presents a single-peak distribution in the plant rhizosphere layer （0-60 cm）. The peak width in the 20 cm topsoil was wider than in other soils, and the distribution presented the strong fluctuations and multiple aggregates. The peak widths of 40 cm and 60 cm soil moisture probability distribution were small, which are in accordance with simulated results of the Rodri- guez-lturbe model. This confrms that the Rodriguez-Imrbe model has good applicability and can well simulate the statistical characteristics of soil moisture in an arid revegetated desert area.

Resilience, reliability and risk analyses of maize, sorghum and sunflower in rain-fed systems using a soil moisture modeling approach

This paper is aimed at examining the applicability of methods for resilience, reliability and risk analyses of rain-fed agricultural systems from modeled continuous soil moisture availability in rain-fed crop lands. The methodology involves integration of soil and climatic data in a simple soil moisture accounting model to assess soil moisture availability, and a risk used as indicator of sustainability of rain-fed agricultural systems. It is also attempted to demonstrate the role of soil moisture modeling in risk analysis and agricultural water management in a semi-arid region in Limpopo Basin where rain-fed agriculture is practiced. For this purpose, a daily-time step soil moisture accounting model is employed to simulate daily soil moisture, evaporation, surface runoff, and deep percolation using 40 years (1961-2000) of agroclimatic data, and cropping cycle data of maize, sorghum and sunflower. Using a sustainability criterion on crop water requirement and soil moisture availability, we determined resilience, risk and reliability as a quantitative measure of sustainability of rain-fed agriculture of these three crops. These soil moisture simulations and the sustainability criteria revealed further confirmation of the relative sensitivity to drought of these crops. Generally it is found that the risk of failure is relatively low for sorghum and relatively high for maize and sunflower in the two sites with some differences of severity of failure owing to the slightly different agroclimatic settings.

The Soil Moisture and Net Primary Production Affected by CO_2 and Climate Change Using a Coupled Model

In this paper, a coupled model was used to estimate the responses of soil moisture and net primary production of vegetation(NPP) to increasing atmospheric CO2 concentration and climate change. The analysis uses three experiments simulated by the second-generation Earth System Model(CanESM2) of the Canadian Centre for Climate Modelling and Analysis(CCCma), which are part of the phase 5 of the Coupled Model Intercomparison Project(CMIP5). The authors focus on the magnitude and evolution of responses in soil moisture and NPP using simulations modeled by CanESM, in which the individual effects of increasing CO2 concentration and climate change and their combined effect are separately accounted for. When considering only the single effect of climate change, the soil moisture and NPP have a linear trend of 0.03 kg m–2 yr–1 and –0.14 gC m–2 yr–2, respectively. However, such a reduction in the global NPP results from the decrease of NPP at lower latitudes and in the Southern Hemisphere, although increased NPP has been shown in high northern latitudes. The largest negative trend is located in the Amazon basin at –1.79 gC m–2 yr–2. For the individual effect of increasing CO2 concentration, both soil moisture and NPP show increases, with an elevated linear trend of 0.02 kg m–2 yr–1 and 0.84 gC m–2 yr–2, respectively. Most regions show an increasing NPP, except Alaska. For the combined effect of increasing atmospheric CO2 and climate change, the increased soil moisture and NPP exhibit a linear trend of 0.04 kg m–2 yr–1 and 0.83 gC m–2 yr–2 at a global scale. In the Amazon basin, the higher reduction in soil moisture is illustrated by the model, with a linear trend of –0.39 kg m–2 yr–1, for the combined effect. Such a change in soil moisture is caused by a weakened Walker circulation simulated by this coupled model, compared with the single effect of increasing CO2 concentration(experiment M2), and a consequence of the reduction in NPP is also shown in this area, with a linear trend of-0.16 gC m-2 yr-2.

‘Initial’ Soil Moisture Effects on the Climate in China——A Regional Climate Model Study

In this study,the effects of ‘initial’ soil moisture(SM) in arid and semi-arid Northwestern China on subsequent climate were investigated with a regional climate model. Besides the control simulations(denoted as CTL),a series of sensitivity experiments were conducted,including the DRY and WET experiments,in which the simulated ‘initial’ SM over the region 30 –50°N,75 –105°E was only 5% and 50%,and up to 150% and 200% of the simulated value in the CTL,respectively. The results show that SM change can modify the subsequent climate in not only the SM-change region proper but also the far downstream regions in Eastern and even Northeastern China. The SM-change effects are generally more prominent in the WET than in the DRY experiments. After the SM is initially increased,the SM in the SM-change region is always higher than that in the CTL,the latent(sensible) heat flux there increases(decreases),and the surface air temperature decreases. Spatially,the most prominent changes in the WET experiments are surface air temperature decrease,geopotential height decrease and corresponding abnormal changes of cyclonic wind vectors at the mid-upper troposphere levels. Generally opposite effects exist in the DRY experiments but with much weaker intensity. In addition,the differences between the results obtained from the two sets of sensitivity experiments and those of the CTL are not always consistent with the variation of the initial SM. Being different from the variation of temperature,the rainfall modifications caused by initial SM change are not so distinct and in fact they show some common features in the WET and DRY experiments. This might imply that SM is only one of the factors that impact the subsequent climate,and its effect is involved in complex processes within the atmosphere,which needs further investigation.

On the Application of Probabilistic Hydrometeorological Simulation of Soil Moisture across Different Stations in India

An application of a proposed hydrometeorological approach for probabilistic simulation of soil moisture is carried out. The time series of in-situ soil moisture and meteorological variables at monthly scale from a few monitoring stations having different soil-hydrologic properties across India are utilized. Preliminary investigation with both precipitation and near-surface air-tempera- ture as meteorological variables to establish that the strength of association between soil moisture and precipitation is more significant as compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in-situ observed soil moisture, CPC model output and with soil moisture data of the Climate Change Initiative (CCI) project. The parameter of the developed model is linked to the soil-hydrologic characteristics through Hydrologic Soil Group (HSG) classification. Higher values of model parameter (dependence parameter (θ) for the selected copula) correspond to HSG A and B having higher soil porosity, whereas, lower values correspond to HSG B and C having lower soil porosity.

Surface Soil Moisture Simulation for a Typical Torrential Event with a Modified Noah LSM Coupling to the NWP Model

Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.

Effects of Moisture Content and Plasticity Index on Duncan-Chang Model Parameters of Hydraulic Fill Soft Soil

In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32.2?-?54.1 for triaxial unconsolidated undrained shear test. The results show that?Cuu,?K?and?n?values all showed a downward trend, and?Rf?variation was not obvious with the increase of moisture content. The variation rule of each parameter is not obvious with the increase of plasticity index. When moisture content is constant,?Cuu?and?n?values do not change much,?K?increases with the increase of plasticity index within the range of 70%?-?80% moisture content, and does not change much with the increase of plasticity index when moisture content is greater than 80%,?Rf?has no obvious rule.?When the plasticity index is constant,?Cuu,?Kand?n?decrease with the increase of moisture content,?Rf?has no obvious rule. The maximum value of?Cuu?is 20.18?kPa, the minimum is 3.72?kPa, and the maximum to minimum ratio is 5.42. The maximum value of?K?is 0.517, the minimum is 0.022, and the maximum to minimum ratio is 23.5. The maximum value of?n?is 1.198, the minimum is 0.150, and the maximum to minimum ratio is 7.99. The maximum value of?Rf?is 0.872, the minimum is 0.679, and the maximum to minimum ratio is 1.28.

Effects of Water-table Depth and Soil Moisture on Plant Biomass, Diversity, and Distribution at a Seasonally Flooded Wetland of Poyang Lake, China

Hydrological regime has been widely recognized as one of the major forces determining vegetation distribution in seasonally flooded wetland. Poyang Lake, the largest freshwater lake in China, has been encountering dramatic changes in hydrological conditions in last decade, which greatly influenced the wetland vegetations. To explore the relationships between hydrology and vegetation distri- bution, water-table depth, soil moisture, species composition, diversity and biomass were measured at a seasonally flooded wetland section at Wucheng National Nature Reserve. Three plant communities, Artemisia capillaris, Phragmites australis and Carex cineras- cens communities, were examined which are zonally distributed from upland to lakeshore with decreasing elevation. Canonical corre- spondence analysis （CCA）, spearmen correlation and logistic regression were adopted to analyze the relationships between vegetation characteristics and hydrological variables of water-table depth and soil moisture. Results show that significant hydrological gradient exist along the wetland transect. Water-table demonstrates a seasonal variation and is consistently deepest in A. capillaris community （ranging from q）.5 m above ground to ＋10.3 m below ground）, intermediate in P. australis community （-2.6 m to ＋7.8 m） and shallow- est in C. cinerascens community （-4.5 m to ＋6.1 m）. Soil moisture is lowest and most variable in A. capillaris community, highest and least variable in P. australis community, and intermediate and moderate variable in C. cinerascens community. The CCA ordination indicated that variables of water-table depth and soil moisture are strongly related to community distribution, which explained 81.7% of the vegetation variations. Species diversity indices are significantly positively correlated with soil moisture and negatively correlated with moisture variability, while above- and belowground biomass are positively correlated with moisture. Above- and belowground biomass present Gaussian models along the gradient of average water-table depth in growing season, while species diversity indices show bimodal patterns. The optimal average water-table depths for above- and belowground biomass are 0.8 m and 0.5 m, respectively, and are 2.2 m and 2.4 m for species richness and Shannon-Wiener indices, respectively. Outcomes of this work improved the under- standings of the relationship between hydrology and vegetation.

Isothermal diffusion of water vapor in unsaturated soils based on Fick’s second law

In arid regions, water vapor diffusion predominates the total water migration in unsaturated soil, which significantly influences agriculture and engineering applications. With the aim of revealing the diffusion mechanism of water vapor in unsaturated soil, a water vapor migration test device was developed to conduct the water vapor migration indoor test. The test results demonstrate that the characteristics of water vapor diffusion in unsaturated soil conformed to Fick’s second law. A mathematical model for water vapor diffusion under isothermal conditions in unsaturated soil was established based on Fick’s law. Factors including the initial moisture content gradient, initial moisture content distribution, soil type and temperature that affect the water vapor diffusion coefficient were analyzed. The results show that there was good agreement between the moisture content calculated by the mathematical model and obtained by the indoor experiment. The vapor diffusion coefficient increased with increasing initial moisture content gradient and temperature. When the initial moisture content gradient is constant, the vapor diffusion coefficient increases with the increase of matrix suction ratio in dry and wet soil section. The effect of soil type on the water vapor diffusion coefficient was complex, as both the moisture content and soil particle sizes affected the water vapor diffusion.

THE EFFECT OF LAND USE CHANGES ON SOIL CONDITIONS IN ARID REGION

Land use change may influence a variety of natural and ecological processes, including soil nutrient, soil moisture, soil erosion, land productivity and biodiversity. In this paper, 9 typical land use patterns sustainable 15－ 20 years have been chosen to study the effect of different land use patterns on soil nutrient, soil erosion, soil moisture, saline and so on. We drew conclusions as follows: Firstly, it is clear of the effect of land use change on soil. Land use change results in the decline of soil nutrient and erosion rate, but the increase of land productivity; secondly, the erosion rate and the rate of vegetation cover is the subtractive correlativity. It reflects the effect of soil erosion on land productivity. It is clear of the positive correlativity between land productivity and soil moisture and explains the role of land surface vegetation to preventing aridity in the northwestern China; lastly, it is feasible to develop animal husbandry properly in arid region. The topgallant land use pattern is the combination of forest and meadow in arid region. The rational land use may prevent or weaken the intensity of soil erosion in a certain extent. Therefore, activities accorded with ecological principal such as readjustment of land use structure, rational reclamation along with adoption of prevention and control measures can reverse land degradation process.

Effects of Plastic Film Mulching of Millet on Soil Moisture and Temperature in Semi-Arid Areas in South Ningxia of China

The effects of film mulching of millet on soil water content were studied in semi-arid areas in the Loess Plateau of South Ningxia, China. Different mulching methods including water micro-collecting farming （WF）, water micro-collecting farming in winter fallow （WW）, hole seeding on film （HF）, hole seeding on film in winter fallow （HW） were compared to determine the effects of mulching methods on soil water collecting and conservation during millet growth periods of 2003-2004, as well as the variation tendency of water content after rainfall, output of millet and water use efficiency （WUE）. The experimental results in the two successive years indicated that water micro-collecting farming had a better function of collecting water after rainfall, and side infiltrated water was stored under the ridges and the top layer 0-40 cm soil water changes were great. WF had obvious role in water collection and preservation of soil moisture. It effectively improved the water supply capacity by about 19.05% in the end of growth seasons. The storage of HW and WW increased by 24.9 and 7.1 mm compared with CK, and output of yield were obviously increased. Film mulching increased the yield of millet and enhanced water use efficiency （WUE）. During different growth periods, WF exhibited better water storage function with lower water consumption, and demonstrated optimal social and ecological benefits.