Spatial Variability Of Soil Moisture Research Articles

Precipitation Sensitivity to the Uncertainty of Terrestrial Water Flow in WRF-Hydro: An Ensemble Analysis for Central Europe

Abstract Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science.

IMPACT OF LAND USE ON SOIL WATER CONTENT IN A HILLY RAINFED AGROSYSTEM: A CASE STUDY IN THE CAP BON PENINSULA IN TUNISIA

Understanding the temporal and spatial variability of soil moisture is fundamentalfor improving the management of water and soil resources on rainfed agrosystems.The present study focused on the soil moisture (SM) patterns observed in a hillyrainfed agrosystem. We analyzed five datasets from measurement at 15 sites duringvarious crop growth cycles under the common cereals/legumes/pasture croppingsystems within the Kamech catchment in Tunisia. Results indicated a strongseasonality in the precipitation and evapotranspiration dynamics that stronglyinfluences soil moisture patterns, with a strong reduction in the water availabilityduring summer (average SM = 0.20 m3/m3) as compared to winter (average SM =0.40 m3/m3). The data of two consecutive years showed that the spatial variability,expressed through the coefficient of variation of soil moisture, was at its highestpoint during spring. The averaged soil moisture of the two years yielded areasonable significant linear relation (R²=0.67**), indicating temporal stability ofthe spatial pattern. Actual evapotranspiration was found to be the predominantfactor influencing the dynamics of soil moisture.

The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau

The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by performing comparisons among SMAP standard products, in-situ observations and Community Land Model (CLM) simulations driven by high-resolution meteorological forcing. At local scales, the enhanced SMAP products, the standard products and CLM simulations all generally compare well with the in-situ observations. The SMAP products show stronger correlations (0.64–0.88) but slightly larger unbiased root mean square errors (ubRMSE, ~0.06) relative to the CLM simulations (0.58–0.79 and 0.037–0.047, for correlation and ubRMSE, respectively). At the regional scale, both SMAP products show similar spatial distributions of SM on the TP (Tibetan Plateau), although, as expected, the enhanced product provides more fine details. The SMAP enhanced product is in good agreement with model simulations with respect to temporal and spatial variations in SM over most of the TP. Regions with low correlation between SMAP enhanced products and model simulations are mainly located in the northwestern TP and regions of complex topography, where meteorological stations are sparse and non-existent or elevation is highly variable. In such remote regions, CLM simulations may be problematic due to inaccurate land cover maps and/or uncertainties in meteorological forcing. The independent, high-resolution observations provided by SMAP could help to constrain the model simulation and, ultimately, improve the skill of models in these problematic regions.

A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution.

Assessing the environmental impacts of soil compaction in Life Cycle Assessment

Maintaining biotic capacity is of key importance with regard to global food and biomass provision. One reason for productivity loss is soil compaction. In this paper, we use a statistical empirical model to assess long-term yield losses through soil compaction in a regionalized manner, with global coverage and for different agricultural production systems. To facilitate the application of the model, we provide an extensive dataset including crop production data (with 81 crops and corresponding production systems), related machinery application, as well as regionalized soil texture and soil moisture data. Yield loss is modeled for different levels of soil depth (0–25cm, 25–40cm and >40cm depth). This is of particular relevance since compaction in topsoil is classified as reversible in the short term (approximately four years), while recovery of subsoil layers takes much longer. We derive characterization factors quantifying the future average annual yield loss as a fraction of the current yield for 100years and applicable in Life Cycle Assessment studies of agricultural production. The results show that crops requiring enhanced machinery inputs, such as potatoes, have a major influence on soil compaction and yield losses, while differences between mechanized production systems (organic and integrated production) are small. The spatial variations of soil moisture and clay content are reflected in the results showing global hotspot regions especially susceptible to soil compaction, e.g. the South of Brazil, the Caribbean Islands, Central Africa, and the Maharashtra district of India. The impacts of soil compaction can be substantial, with highest annual yield losses in the range of 0.5% (95% percentile) due to one year of potato production (cumulated over 100y this corresponds to a one-time loss of 50% of the present yield). These modeling results demonstrate the necessity for including soil compaction effects in Life Cycle Impact Assessment.

Spatial Variability of Soil Moisture and the Scale Issue: A Geostatistical Approach

AbstractWe study the standard deviation of water saturation SDS as function of the mean saturation by a stochastic model of unsaturated flow, which is based on the first‐order solution of the three‐dimensional Richards equation. The model assumes spatially variable soil properties, following a given geostatistical description, and it explicitly accounts for the different scales involved in the determination of the spatial properties of saturation: the extent L, i.e., the domain size, the spacing Δ among measurements, and the dimension associated to the sampling measurement. It is found that the interplay between those scales and the correlation scale I of the hydraulic properties rules the spatial variability of saturation. A “scale effect” manifests for small to intermediate L/I, for which SDS increase with the extent L. This nonergodic effect depends on the structural and hydraulic parameters as well as the scales of the problem, and it is consistent with a similar effect found in field experiments. In turn, the influence of the scale is to decrease the saturation variability and increase its spatial correlation. Although the solution focuses on the medium heterogeneity as the main driver for the spatial variability of saturation, neglecting other important components, it explicitly links the spatial variation of saturation to the hydraulic properties of the soil, their spatial variability, and the sampling schemes; it can provide a useful tool to assess the impact of scales on the saturation variability, also in view of the several applications that involve the saturation variability.

Responses of soil moisture to vegetation restoration type and slope length on the loess hillslope

Soil moisture, a critical variable in the hydrologic cycle, is highly influenced by vegetation restoration type. However, the relationship between spatial variation of soil moisture, vegetation restoration type and slope length is controversial. Therefore, soil moisture across soil layers (0-400 cm depth) was measured before and after the rainy season in severe drought (2015) and normal hydrological year (2016) in three vegetation restoration areas (artificial forestland, natural forestland and grassland), on the hillslopes of the Caijiachuan Catchment in the Loess area, China. The results showed that artificial forestland had the lowest soil moisture and most severe water deficit in 100-200 cm soil layers. Water depletion was higher in artificial and natural forestlands than in natural grassland. Moreover, soil moisture in the shallow soil layers (0-100 cm) under the three vegetation restoration types did not significantly vary with slope length, but a significant increase with slope length was observed in deep soil layers (below 100 cm). In 2015, a severe drought hydrological year, higher water depletion was observed at lower slope positions under three vegetation restoration types due to higher transpiration and evapotranspiration and unlikely recharge from upslope runoff. However, in 2016, a normal hydrological year, there was lower water depletion, even infiltration recharge at lower slope positions, indicating receiving a large amount of water from upslope. Vegetation restoration type, precipitation, slope length and soil depth during a rainy season, in descending order of influence, had significant effects on soil moisture. Generally, natural grassland is more beneficial for vegetation restoration than natural and artificial forestlands, and the results can provide useful information for understanding hydrological processes and improving vegetation restoration practices on the Loess Plateau

Uniformity and spatial variability of soil moisture and irrigation distribution on natural turfgrass sports fields

Emerging technology that couples spatial irrigation distribution data with spatial plant and soil property data may provide a more robust assessment of irrigation system performance than conventional methods. Research was conducted on native soil and sand capped sports fields to (1) compare the lower quarter distribution uniformity (DU_lq) and spatial variability of soil moisture (volumetric water content [VWC]) and irrigation distribution (using the catch can method); (2) investigate the influence of catch can amount, soil compaction (penetration resistance), and turfgrass vigor (normalized difference vegetation index [NDVI]) on VWC; and (3) delineate and compare site-specific management units (SSMUs) for VWC and catch can amount to generate more informed irrigation-based management decisions. Volumetric water content DU_lq was much higher than catch can DU_lq on the native soil field, but the DU_lqs were similar on the sand capped field. Spatial maps of VWC and catch can amount indicated that the spatial variability of VWC was not reflected in the spatial variability of catch can amount on either field. Penetration resistance and NDVI were significant at predicting VWC on the native soil field (<i>p</i> &lt; 0.001 and <i>p</i> &lt; 0.01, respectively), and their spatial maps resembled the VWC map. Only catch can amount was significant at predicting VWC on the sand capped field (<i>p</i> &lt; 0.001), but their spatial maps were dissimilar. Volumetric water content SSMUs would be useful for site-specific irrigation, soil moisture sensor placement, or aerification. Catch can amount SSMUs would be useful to visually identify and then correct deficiencies in an irrigation system9s water distribution. Implementation of suggested management strategies may result in the conservation of water by improving overall irrigation efficiency.

Возможности бистатической радиолокации пространственных вариаций влажности и рельефа поверхности почвенного покрова на основе сигналов ГЛОНАСС и GPS

Возможности бистатической радиолокации пространственных вариаций влажности и рельефа поверхности почвенного покрова на основе сигналов ГЛОНАСС и GPS

Downscaling of SMAP Soil Moisture Using Land Surface Temperature and Vegetation Data

Core Ideas We present a soil moisture downscaling algorithm based on thermal inertia theory. Vegetation modulates the soil moisture–surface temperature difference relationship. This algorithm better characterized soil moisture spatial and temporal variability. Remotely sensed soil moisture retrieved by the Soil Moisture Active and Passive (SMAP) sensor is currently provided at a 9‐km grid resolution. Although valuable, some applications in weather, agriculture, ecology, and watershed hydrology require soil moisture at a higher spatial resolution. In this study, a passive microwave soil moisture downscaling algorithm based on thermal inertia theory was improved for use with SMAP and applied to a data set collected at a field experiment. This algorithm utilizes a normalized difference vegetation index (NDVI) modulated relationship between daytime soil moisture and daily temperature change modeled using output variables from the land surface model of the North American Land Data Assimilation System (NLDAS) and remote sensing data from the Moderate‐Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). The reference component of the algorithm was developed at the NLDAS grid size (12.5 km) to downscale the SMAP Level 3 radiometer‐based 9‐km soil moisture to 1 km. The downscaled results were validated using data acquired in Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15) that included in situ soil moisture and Passive Active L‐band System (PALS) airborne instrument observations. The resulting downscaled SMAP estimates better characterize soil moisture spatial and temporal variability and have better overall validation metrics than the original SMAP soil moisture estimates. Additionally, the overall accuracy of the downscaled SMAP soil moisture is comparable to the PALS high spatial resolution soil moisture retrievals. The method demonstrated in this study downscales satellite soil moisture to produce a 1‐km product that is not site specific and could be applied to other regions of the world using the publicly available NLDAS/Global Land Data Assimilation System data.

Selection of a semivariogram model in the study of spatial distribution of soil moisture

AbstractThe paper presents a selection of a semivariogram model in the study of spatial variability of soil moisture in a loess agricultural catchment. Soil moisture tests were carried out in the Moszenki village, 15 km northwest of Lublin. Soil moisture measurements were performed at two dates at 104 points, located on a rectangular surface measuring 700 × 1200 m. These points were laid out in the corners of a grid of squares with sides 100 m. In addition, 6 measurements were made at a distance of less than 100 m from the nearest points. Soil moisture was measured in the soil surface (0–5 cm). ArcGis software with Geostatistical Analyst extension was used for modelling semivariograms. In both terms, five models of semivariograms were used: stable, circular, spherical, exponential and Gaussian. Kriging was used for the estimation of soil moisture values. Among the semivariogram models analyzed in this study, the largest errors in the determined values of soil moisture relative to the empirical data were observed for the exponential model, and the smallest for the Gaussian model. However, it should be emphasized that the values of the analysed errors for the individual semivariogram models were similar. Application of the ordinary kriging method for interpolation of spatial distribution of soil moisture yields good results, but it has to be kept in mind that the final shape of the spatial distribution is influenced by the choice of the semivariance function model.

Factors Controlling Temporal Stability of Surface Soil Moisture: A Watershed‐Scale Modeling Study

Core Ideas Soil moisture data at a watershed scale can be derived from distributed land surface modeling. Factors controlling soil moisture temporal stability can be studied using model simulations. Soil moisture estimation accuracy is improved when dominant factors are considered a priori. The minimum number of sampling points and the shortest sampling periods were investigated. The spatial variability of soil moisture makes it difficult to represent watershed‐scale soil moisture using traditional point‐scale soil moisture sensors. In the temporal stability method, the spatial pattern of soil moisture is assumed to persist with time. Hence, measurements at a representative point can be used to represent the mean soil moisture. We investigated the factors that determine temporal stability and attempted to locate these representative points. Long‐term simulated high‐resolution soil moisture data, at a watershed scale, were used. Results showed that locations with a dominant vegetation cover and a low topographic wetness index (TI) can provide reasonable mean soil moisture estimates. Using vegetation cover and TI information, we minimized the number of the sampling locations needed for identifying the best estimate of the true watershed‐scale mean. The sampling period duration is also a key factor. Using random combination tests, the minimum number of required sampling points and shortest sampling time were estimated. When 10 sampling points were used, a sampling period of 10 mo was required for accurately determining the representative point. Our study results will help apply the temporal stability method to the estimation of areal soil moisture and the calibration and validation of remote sensing data.

Coupling Stable Isotope Analysis with Gas Push‐Pull Tests to Derive In Situ Values for the Fractionation Factor αox Associated with the Microbial Oxidation of Methane in Soils

Core Ideas: Methane CH4 isotopologue fraction was measured in situ for the first time. αox varied strongly with oxidation rate. αox varied between landfills and between individual points on the same landfill. αox varied with spatial and temporal variability of soil properties. Our findings limit application of stable isotopes for CH4 oxidation quantification. A prerequisite for the application of stable isotope fractionation for the quantification of the methane (CH4) oxidation efficiency of landfill covers is that the fractionation factor αox is known or can be estimated with adequate accuracy. So far, αox has only been determined in laboratory experiments. In this study, αox was determined under in situ conditions in the field by coupling two independent methods, gas push‐pull tests and stable isotope analysis, to assess biological fractionation of CH4 isotopologues in landfill cover soils. On six landfills with nine points of investigation, 22 measurements were performed, covering a wide range of environmental conditions, such as soil temperature and moisture and observed oxidation rates. Values for αox varied between near 1, indicating little fractionation, and 1.151. Correlation of αox with the CH4 oxidation rate found by gas push‐pull tests revealed a clear asymptotic relationship, with low rates being associated with high values for αox and high rates resulting in little fractionation. Values for αox varied between the different landfills and between the individual points of investigation on the same landfill. The latter is assumed to reflect the spatial variability of methanotrophic activity due to spatial variability in soil moisture and hence air‐filled porosity as well as the spatial variability of gas fluxes. Significant variation of αox was observed for the same sampling point, presumably reflecting the temporal variability of factors influencing methanotrophic activity. These effects could include seasonally changing environmental conditions (e.g., soil temperature and moisture) but also the temporal variability of gas fluxes through the landfill soil cover, changing exposure of methanotrophs to CH4 and oxygen and hence their activity. The quantification of the CH4 oxidation efficiency using fractionation of stable isotopes is very sensitive to αox. Assuming a value constant in time and space and transferring this value from laboratory experiments to field settings entails significant uncertainty regarding the quantification of CH4 oxidation.

Spatial patterns of soil moisture from two regional monitoring networks in the United States

Understanding soil moisture spatial variability (SMSV) at regional scales is of great value for various purposes; however, relevant studies are still limited and have yielded inconsistent findings about the primary controls on regional SMSV. To further address this issue, long-term soil moisture data were retrieved from two large scale monitoring networks located in the continental United States, including the Michigan Automated Weather Network and the Oklahoma Mesonet. To evaluate different controls on SMSV, supporting datasets, which contained data on climate, soil, topography, and vegetation, were also compiled from various sources. Based on temporal stability analysis, the results showed that the mean relative difference (MRD) of soil moisture was more correlated with soil texture (e.g., negative correlations between MRD and sand fraction, and positive ones between MRD and silt and clay fractions) than with meteorological forcings in both regions, which differed from the traditional notion that meteorological forcings were the dominant controls on regional SMSV. Moreover, the results revealed that contrary to the previous conjecture, the use of soil moisture temporal anomaly did not reduce the impacts of static properties (e.g., soil properties) on soil moisture temporal dynamics. Instead, it was found that the magnitude of soil moisture temporal anomaly was mainly negatively correlated with sand fraction and positively with silt and clay fractions in both regions. Finally, the relationship between the spatial average and standard deviation of soil moisture as well as soil moisture temporal anomaly was investigated using the data from both networks. The field data showed that the relationship for both soil moisture and soil moisture temporal anomaly was more affected by soil texture than by climatic conditions (e.g., precipitation). The results of this study provided strong field evidence that local factors (e.g., soil properties) might outweigh regional factors (e.g., meteorological forcings) in controlling regional SMSV.

Evaluating climate and soil effects on regional soil moisture spatial variability using EOFs

AbstractSoil moisture is an important state variable in terrestrial water cycles; however, only few studies are available on regional soil moisture spatial variability (SMSV), which yielded inconsistent findings about regional controls on SMSV. Here, long‐term soil moisture data were obtained from the Automated Weather Data Network and Soil Climate Analysis Network in three regions with different climate regimes across the continental U.S. Comprehensive data sets were compiled to examine regional controls on SMSV using the method of Empirical Orthogonal Function. One dominant spatial structure (EOF1) of soil moisture was found in the study regions, which explained over 75%, 67%, and 86% of the spatial variance in soil moisture in Nebraska, Utah, and the Southeast U.S., respectively. Despite the significant spatial variability in precipitation and potential evapotranspiration in all the study regions, the results showed that meteorological forcings had limited effects on regional SMSV in those regions with different climatic conditions, which differed from the traditional notion that SMSV is mainly controlled by meteorological forcings at the scale from 50 to 400 km. Instead, local factors related to soil (e.g., sand and clay fractions) were found to have significant correlations with EOF1, although the effects of other local factors (e.g., topography and vegetation) were generally negligible. This study provides strong field evidence that soil can exert much stronger impacts on regional SMSV than previously thought, which can override the effects of meteorological forcings. Future studies are still needed to elaborate on the relative roles of climate and soil in affecting regional SMSV.

Evaluating a sensor setup with respect to near-surface soil water monitoring and determination of in-situ water retention functions

Monitoring water status near the soil surface is a prerequisite for studying hydrological processes at the soil-atmosphere boundary and an option for calibrating remotely sensed water content data, for instance. As the water status of the uppermost soil layer is highly variable in space and time, adequate sensors are required to enable accurate measurements. Therefore, a sensor setup was tested and evaluated in the laboratory and in the field for such a purpose. The arrangement included Hydra Probe and MPS-2 sensors to measure water content and matric potential, respectively.Performance of the MPS-2 was validated in the laboratory by comparing sensor readings with the water potential of a soil, drained to equilibrium for certain pressure steps inside a pressure plate apparatus. Afterwards, six Hydra Probes and twelve MPS-2 sensors were installed in bare soil at a small field plot of about 9m2. The measurements represented soil water status to a depth of 6cm from surface. Core samples were repeatedly excavated around the measurement spots. Their water content was determined and the samples were further utilized to analyze water retention characteristics.The tested setup properly reflected changes of near-surface soil water status due to rainfall and evaporation. However, some shortcomings weakened the potential of the chosen arrangement. Site-specific calibration of the Hydra Probes – implemented by relating sensor readings to the water content values of the core samples – confirmed the applicability of the recommended standard calibration parameters for the respective soil texture. The derived user calibration enabled a measurement accuracy of 0.02cm3·cm−3. Further improvement was restrained by the spatial variability of soil moisture. In this context, spots that were permanently drier or wetter than the others were discovered by means of a temporal stability approach. Performance of MPS-2 sensors was more critical with respect to the objectives. Sensor-to-sensor variation was small at the applied pressure steps of −20, −50, and −100kPa, but the respective averaged readings were −18, −37, and −57kPa. At matric potentials of −200 and −300kPa, the MPS-2 revealed substantial sensor-to-sensor variation. The large deviation of the sensor readings in the field confirmed that the calibration of the MPS-2 should be improved. However, in spite of this inaccuracy, the wide measuring range of the MPS-2 offers suitability to a wide range of potential applications. As an example, water retention functions were calculated from the in-situ data and compared to retention data from the core samples.

Prediction of soil moisture scarcity using sequential Gaussian simulation in an arid region of China

Soil moisture plays a vital role in maintaining the sustainability of dryland ecosystems. Accurately predicting soil moisture scarcity (SMS) has an important interest of guidance to soil and water conservation. In this study, we gathered a time series of soil moisture measurements throughout the growing season (from April to October) in an area of approximately 100km2 in a desert oasis of northwestern China. Sequential Gaussian simulation was applied to investigate the spatial variability and scarcity of soil moisture across multiple land use types. Soil moisture exhibited considerable spatial heterogeneity with different magnitudes of spatial dependence at different times. Two hundred simulated realizations depicted the possible spatial variations of soil moisture in the geographic space. SMS was characterized as the natural event that occurred when the spatial probability of soil moisture not exceeding 0.15cm3cm−3 was greater than a critical threshold. With the increasing of probability thresholds, the proportion of SMS locations in each land use decreased at different rates. Given the spatial probability threshold of 0.6, 1.3–3.8% of the cultivated land, 2.6–5.2% of the forest land, 3.2–4.6% of the grassland, and 2.7–7.4% of the shrub land were of SMS during the measuring period. The newly cultivated land and the ecotone of desert and oasis were the major regions SMS occurred. Some soil moisture conservation measures such as precision irrigation should be taken to prevent the probable land degradation and agricultural disasters in these areas. The prediction of SMS using stochastic simulation contributes to improving soil water management in the oasis and provides a methodology reference for similar studies in risk analysis.

A physically based hydrological connectivity algorithm for describing spatial patterns of soil moisture in the unsaturated zone

AbstractHydrologic connectivity has been proposed as an important concept for understanding local processes in the context of catchment hydrology. It can be useful for characterizing the soil moisture variability in complex heterogeneous landscapes. The current land surface models (e.g., Community Land Model, CLM) could not completely account for flow path continuity and connected patterns of subsurface properties in the unsaturated zone. In this study, we developed a physically based hydrologic connectivity algorithm based on dominant physical controls (e.g., topography, soil texture, and vegetation) to better understand the spatially distributed subsurface flow and improve the parameterization of soil hydraulic properties in hydrological modeling. We investigated the effects of mixed physical controls on soil moisture spatial variability and developed hydrologic connectivity using various thresholds. The connectivity was used for identifying the soil moisture variability and applied in a distributed land surface model (CLM) for calibrating soil hydraulic properties and improving model performance for estimating spatially distributed soil moisture. The proposed concept was tested in two watersheds (Little Washita in Oklahoma and Upper South Skunk in Iowa) comparing estimated soil moisture with the airborne remote sensing data (Electronically Scanning Thinned Array Radiometer and Polarimetric Scanning Radiometer). Our finding demonstrated that the spatial variations of soil moisture could be described well using physically based hydrologic connectivity, and the land surface model performance was improved by using the calibrated (distributed) soil hydraulic parameters. In addition, we found that the calibrated soil hydraulic parameters significantly affect model outputs not only on the water cycle but also on surface energy budgets.

Field-Scale Spatial Variability of Soil Moisture and L-Band Brightness Temperature from Land Surface Modeling

Abstract Although soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada’s Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using grid points corresponding to measurement sites. SPS brightness temperature fields compare well with remote sensing data in terms of spatial variability. It is shown that during drier periods, factors other than soil texture become important with respect to soil moisture spatial variability. However, during periods with plenty of precipitation, soil texture seems essential in improving simulated soil moisture spatial variability at high resolutions. These results support the conclusion that SPS could provide very high–resolution soil moisture products for research and operational purposes if high-resolution soil texture and vegetation products are made available on a larger scale.

Contribution of Environmental Factors to Soil Moisture and Their Scale Effect on the LOESS Plateau, China

Environmental factors have great impact on soil moisture. Despite their great contribution to the spatial distribution and variability of soil moisture, how they changes over the scales remains poorly understood. Here we examined the contribution of eight environmental factors to the soil moisture at three scales (slope scale, small watershed scale and regional scale) on the Loess Plateau, China. Our study showed that as the scale increased from slope to regional scale, the contribution of climatic factors generally increased, whereas that of topographical factors generally decreased. Consequently, the information provided by slope position (Slpo) slowly turned to that of slope gradient (Slga), mean annual precipitation (Pma), mean annual temperature (Tma) and other climatic factors. The shifting of the information in our study could also be associated with the changes of determinant and main limiting factors among the different scales. These results suggested that the temporal and spatial variability of soil moisture as well as its dominant factors are different over time and space. They are determined by scale effect of environmental factors that influence soil moisture.