Standard Deviation Of Relative Difference Research Articles

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3.

Machine learning method for predicting the influence of scanning parameters on random measurement error

Measurements of technical objects can be done with contact and non-contact approaches. Contact methods are accurate but slow. On the other hand, non-contact methods deliver rapid point acquisition and are increasingly being used as their precision mounts. However, multiple scanning parameters such as the incident angle, object colour and scanning distance influence the measurement error and uncertainty when capturing the geometry of the object. With the aim of creating a generalised model that considers the influence of the aforementioned scanning parameters with satisfactory accuracy, a model for predicting the random measurement error based on machine learning (ML) is proposed in this study. Data acquired from measurements with varying scanning distances, incident angles and surface colours were used to train ML models. The tested ML methods included linear regression, support vector machine, neural network, k-nearest neighbour, AdaBoost and random forest. The best-performing trained model was the random forest, with a standard deviation of relative differences of 1.46% for the case of red surfaces, and 5.2% for the case of an arbitrarily coloured surface, which is comparable to results achieved with model-based methods. The trained models and the data are available online.

Measurement of CSF core Alzheimer disease biomarkers for routine clinical diagnosis: do fresh vs frozen samples differ?

BackgroundCerebrospinal fluid (CSF) amyloid-beta (Aβ) 42/40 ratio, threonine-181-phosphorylated-tau (p-tau), and total-tau (t-tau) represent core biomarkers of Alzheimer disease (AD). The recent availability of automated platforms has represented a significant achievement for reducing the pre-analytical variability of these determinations in clinical setting. With respect to classical manual ELISAs, these platforms give us also the possibility to measure any single sample and to get the result within approximately 30 min. So far, reference values have been calculated from measurements obtained in frozen samples. In this work, we wanted to check if the values obtained in fresh CSF samples differ from those obtained in frozen samples, since this issue is mandatory in routine diagnostic work.MethodsFifty-eight consecutive CSF samples have been analyzed immediately after lumbar puncture and after 1-month deep freezing (− 80 °C). As an automated platform, we used Lumipulse G600-II (Fujirebio Inc.). Both the fresh and the frozen aliquots were analyzed in their storage tubes.ResultsIn fresh samples, a mean increase of Aβ40 (6%), Aβ42 (2%), p-tau (2%), and t-tau (4%) was observed as compared to frozen samples, whereas a slight decrease was observed for Aβ42/Aβ40 ratio (4%), due to the higher deviation of Aβ40 in fresh samples compared to Aβ42. These differences are significant for Aβ40, Aβ42/Aβ40 ratio, p-tau, and t-tau. Nevertheless, the Aβ42/Aβ40 ratio showed a lower variability (smaller standard deviation of relative differences) with respect to Aβ42. With respect to the AD profile according to the A/T/(N) criteria for AD diagnosis, no significant changes in classification were observed when comparing results obtained in fresh vs frozen samples.ConclusionsSmall but significant differences have been found for Aβ40, Aβ42/Aβ40 ratio, p-tau, and t-tau in fresh vs frozen samples. Importantly, these differences did not imply a modification in the A/T/(N) classification system. In order to know if different cutoffs for fresh and frozen samples are required, larger, multi-center investigations are needed.

Comparison of different lifting analysis tools in estimating lower spinal loads – Evaluation of NIOSH criterion

Excessive loads on the human spine is recognized as a risk factor for back injuries/pain. Various lifting analysis tools such as musculoskeletal models, regression equations and NIOSH (National Institute for Occupational Safety and Health) lifting equation (NLE) have been proposed to evaluate and mitigate associated risks during manual material handling activities. Present study aims to compare predicted spinal loads from 5 different lifting analysis tools as well as to critically evaluate the NIOSH recommended weight limit (RWL). Spinal loads were estimated under different symmetric/asymmetric lifting tasks in which hand-load mass at each task was set based on RWL from NLE. Estimated intradiscal pressures (IDPs) of various tools were also compared with in vivo measurements. We compared RWL by NLE versus our estimations of RWL calculated from our regression equations using biomechanical criteria (compression <3400 N with/without shear <1000, 1250 or 1500 N). Our regression equations followed by OpenSim, AnyBody, simple polynomial and 3DSSPP satisfactorily predicted L4-L5 IDPs. Lifting analysis tools estimated comparable spinal compression forces (mean Pearson’s r = 0.80; standard deviation of relative difference = 26%) while in shear, differences were greater (mean Pearson’s r = 0.68; standard deviation of relative difference = 56%). NLE estimations of RWL were conservative in comparison with our estimations for lean individuals (BMI < 25 kg/m2) when compression <3400 N and shear <1250 N were considered as the biomechanical criteria. For heavier individuals, however, NLE estimations of RWL generated spinal compression >3400 N (NIOSH biomechanical safety threshold) as well as shear >1000 N. Although RWLs estimated by NLE was body weight independent, body weight substantially altered RWLs estimated from our regression equations. For improved estimation of the risk of injury, more accurate failure criteria for spinal segments are essential.

Applicability of temporal stability analysis in predicting field mean of soil moisture in multiple soil depths and different seasons in an irrigated vineyard

Accurately estimating field mean of soil water content (SWC) is important for irrigation management in an agricultural field. Temporal stability (TS) analysis has been widely applied to identify representative locations (RLs) for predicting spatial mean of SWC in natural landscapes. The objectives of the study were to examine TS patterns of SWC at various soil depths in different years, and to evaluate the performance of TS analysis in predicting field mean in an irrigated vineyard. Soil water content was measured regularly at 135 locations in the vineyard at soil depths of 0–20, 20–40, and 40–60 cm in 2012–2014. Temporal stability indices of mean relative difference (MRD), standard deviation of relative difference (SDRD), the comprehensive indicator (CI), and the mean absolute bias error (MABE) were obtained for each location at different depths in different seasons. The range of MRD and standard deviation of MRD (SDMRD) increased with soil depth for the three years. Inter-season correlations of the indices at the same depth were stronger than those between different depths of the same year. The RLs identified by CI and MABE were different between different soil depths and between the three seasons. Temporal stability analysis outperformed the random sampling method greatly, and on average one single RL could achieve RMSE about 2% and the maximum absolute error (MAE) less than 4% at a given sampling day during the validation period when the same year data were used to identify the RL. Estimation error could be reduced by 40% with the number of RLs increased from one to four. For most cases, four RLs identified by 2012 data were good enough to obtain RMSE and MAE less than 3% for the following two years. Soil texture information was found unable to identify RLs that could reliably estimate field mean. Temporal stability analysis is a valuable tool in identifying RLs to estimate field mean in an irrigated agricultural field, while intensive SWC sampling during half an irrigation cycle is needed to identify the RLs.

The temporal stability of soil moisture spatial pattern and its influencing factors in rocky environments

Rock outcrops (ROCs) are widespread across the Earth’s surface. However, little empirical research has addressed the dynamic characteristics of soil water stored in rocky environments, especially for soil moisture spatial pattern (SMSP) and its temporal stability. This study aimed to explore the spatiotemporal characteristics of SMSP in a karst topographical setting and to reveal the relevant factors most influencing it. In three adjacent sampling plots located in Shilin County, Yunnan Province, China corresponding to (1) a forestland, (2) a shrubland, (3) a grassland, their soil moisture was measured monthly at depths of 10, 20, 30, 40, 60, and 100 cm from May 2014 to April 2017, while key environmental parameters—rock coverage, vegetation aboveground biomass, root distribution, and precipitation—were also quantified in each plot. Our results showed that where ROCs emerged, soil moisture displayed higher spatial heterogeneity than reported from other terrestrial ecosystems, but this area also maintained high temporal stability in the SMSP, as indicated by the low standard deviation of relative difference σ(δi) and highly significant Spearman rank correlations (rs). Linear regression analysis demonstrated the feasibility of using a single representative location to estimate limited-scale soil water conditions in karst rocky environments (R2-values ≥ 0.88). More importantly, the spatiotemporal dynamics of SMSP were apparently driven by the joint effects of soil depth, vegetation, and precipitation. The SMSP was markedly homogeneous and stable in the deep soil layer, as well as during the rainy season; however, with the onset of the dry season, the vegetation tended to induce a temporary change in the root-zone SMSP. Our study suggests that the multifactor-influenced soil moisture and high temporal stability of SMSP offer both a challenge and an opportunity for better understanding the hydrological models and dynamics of rocky environments.

Temporal stability of soil water storage in multiple soil layers in high-elevation forests

Understanding soil water storage (SWS) dynamics in soil profiles is important for hydrological modeling and restoration of vegetation in semi-arid areas. Using the temporal stability method, we aimed to investigate the temporal stability of SWS at 0–40, 40–80, and 80–120 cm depth, and to identify representative sites for reliable estimates of the mean SWS in the permanent forest plot in the Qilian Mountains of China. Further, we wanted to identify correlations between temporal stability of SWS and soil, topography, and properties of the vegetation. Soil water content at soil depths 0–120 (for locations 1–40) and 0–70 cm (for locations 41–60) were measured using time-domain reflectometry (TDR) on 52 dates from 2016 to 2017.Results revealed that time-averaged mean SWS for the three layers differed significantly (P < 0.05), and the temporal changes and spatial variations of the mean SWS all decreased with increasing soil depth. Based on either the Spearman correlation coefficient or the standard deviation of relative difference (SDRD) index, the temporal stability of SWSs within the soil profiles under different soil layers were strong, and the number of time-stable locations increased with increasing soil depth, indicating that the SWS intended to be more temporally stable in deeper soil. One time-stable site can be representative of the mean SWS for multiple soil layers for the whole plot, and can accurately estimate the mean SWS for the three soil layers (R2 > 0.86, P < 0.001). Moreover, these time-stable locations should be those having average LAI, SBD or relatively low canopy interception loss compared to the corresponding field means. Soil bulk density, canopy interception rate, and aboveground biomass of mosses can significantly (P < 0.05) affect the stability of SWSs in this high-elevation forests. Such effects, however, differed among the different soil layers due to different conditions of soil characteristics, distribution of root biomass, and freeze–thawing processes. These results suggest that the influence of vegetation properties of the temporal stability of SWS should attract more attention and that both the relative importance of and interactions among different determining factors is helpful for better understanding the mechanistic determinants of SWS temporal stability in these areas.

Temporal stability of soil moisture in an oasis of northwestern China

AbstractTemporal stability of soil moisture has been widely used in hydrological monitoring since it emerged. However, the spatial analysis of temporal stability at the landscape scale is often limited because of insufficient sampling numbers. This work made an effort to investigate the spatial variations of temporal stability of soil moisture in an oasis landscape. The specific objectives of the study were to explore the spatial patterns of temporal stability and to determine the controlling factors of temporal stability in the desert oasis. A time series of soil moisture measurements were gathered on 23 occasions at 118 locations over 3 years in a rectangular transect of approximately 100 km2. The nonparametric Spearman's rank correlation coefficient, standard deviation of relative difference (SDRD), and mean absolute bias error (MABE) were used to quantify the temporal stability of soil moisture. Results showed that the temporal stability of soil moisture was depth dependent and season dependent. The spatial pattern of soil moisture in a deep soil layer and between two same seasons generally had a high temporal stability. SDRD and MABE were spatially autocorrelated and exhibited strong spatial structures in the geographic space. The concept of temporal stability can be extended to describe the time‐stable areas of soil moisture with geostatistics. There were great differences between SDRD and MABE in describing the temporal stability of soil moisture and in identifying the controlling factors of temporal stability. In this case, MABE was a better alternative to estimate the areal mean soil moisture using representative locations than SDRD. Land use type, soil moisture condition, and soil particle composition were the dominant controls of temporal stability in the oasis. These insights could help to better understand the essence of temporal stability of soil moisture in arid regions.

Estimation of spatial mean soil water storage using temporal stability at the hillslope scale in black locust (Robinia pseudoacacia) stands

Black locust (Robinia pseudoacacia) has been extensively planted on the Loess Plateau for soil and water conservation, but afforestation with this non-native tree has produced some negative effects such as soil desiccation. Knowledge of the soil water storage (SWS) within soil profiles in black locust stands is thus critical for optimization of forest management for sustainable production. The objectives of this study were to evaluate the temporal stability of SWS to identify the most time-stable locations (MTSLs) for reliably estimating the mean SWS and to further investigate the factors influencing its temporal stability. The SWS values of various soil layers (0–1, 1–2, and 2–3m) were measured from May 2014 to October 2015 at 70 locations along two 187-m long transects on a hillslope covered with black locust in the Loess Plateau, China. A total of 18 SWS datasets were collected over the period of measurement. Results indicated that the temporal variation of spatial mean SWS decreased and the spatial variation of SWS increased with increasing soil depth. A strong temporal persistence in all soil layers was demonstrated by high Spearman's rank correlation coefficients (P<0.01), with the temporal stability in deep soil layers relatively more stable than in shallow layer. Among four methods (MRD, mean relative difference; SDRD, standard deviation of relative difference; ITS, index of temporal stability; and MABE, mean absolute bias error) used to identify the MTSLs for the estimation of the mean SWS, the ITS provided the best results. The MTSLs identified in diverse layers of the first transect were then used to successfully predict the mean SWS along the second transect (R2>0.91). Elevation, soil saturated hydraulic conductivity, leaf area index, and fine root area density were the major factors influencing the temporal stability of SWS. The results of this study were useful for estimating mean SWS and could improve soil water management and hydrological applications in sloping black locust forested areas of the Loess Plateau.

Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content

The identification of representative soil moisture sampling sites is important for the validation of remotely sensed mean soil moisture in a certain area and ground-based soil moisture measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean soil moisture. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0–20cm) soil moisture data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean soil moisture estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on soil properties and terrain indices (EFs), repeated soil moisture measurements (Theta), EFs plus one-time soil moisture data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and global and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE=0.023m3m−3) than that based on the smallest SDRD (RMSE=0.034m3m−3). The K-means clustering based on EFsTheta (-PCA) was better (RMSE <0.020m3m−3) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the global random method. Forty and 60 sampling sites are needed for stratified sampling and global sampling respectively to make their performances comparable to the best K-means method (EFsTheta-PCA). Overall, TS required only one site, but its accuracy was limited. The best K-means method required <8 sites and yielded high accuracy, but extra soil and terrain information is necessary when using this method. The stratified sampling strategy can only be used if no pre-knowledge about soil moisture variation is available. This information will help in selecting the optimal methods for estimation the area mean soil moisture.

Assessing effects of soil hydraulic properties on the temporal stability of absolute soil moisture content and soil moisture anomaly under different climatic conditions

Due to the complex interactions of soil moisture with its controlling factors, numerical models are more suitable for quantifying different controls on the temporal pattern of soil moisture. Here, the effects of soil hydraulic properties on the temporal stability of absolute soil moisture content (TS SMC) and soil moisture anomaly (TS SMA) under different climatic conditions were assessed using a vadose zone model. To run the model, two soil datasets with van Genuchten parameters were generated and hydrometeorological data from four study sites with a significant precipitation gradient were obtained. The TS SMC analysis revealed that the patterns of mean relative difference (MRD) and standard deviation of relative difference (SDRD) of soil moisture partly depended on climatic conditions. With increasing precipitation, the range of MRD generally decreased; whereas, the range and mean value of SDRD increased, indicating that soil moisture was temporally more stable under drier climatic conditions. Based on the Spearman rank correlation coefficient, the results showed that the dependence of MRD on soil hydraulic parameters was partly determined by climatic conditions. With decreasing precipitation, the effects of residual soil moisture content, saturated hydraulic conductivity, and the parameter l that accounts for pore tortuosity and connectivity strengthened, while the effects of saturated soil moisture content and the parameter n that is a measure of pore size distribution weakened. Finally, according to the Pearson correlation coefficient, the results revealed that the control of soil hydraulic properties was shown to be reduced in the TS SMA analysis, thus confirming the previously made conjecture that the use of soil moisture anomalies in the temporal stability analysis reduces the impacts of static soil properties.

MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

Landscape-scale temporal stability of soil water storage within profiles on the semiarid Loess Plateau of China

Knowledge of the temporal stability of soil water storage (SWS) at landscape scale is scarce. The recognition of landscape-scale temporal evolution of soil water profiles is critical for soil water management and vegetational restoration in semiarid watersheds. Soil moisture was measured with neutron probes to a depth of 3.0 m on 18 sampling dates at 135 locations along a landscape transect from August 2012 to October 2013. Temporal stability of SWS at a landscape scale and a point scale was examined using Spearman's rank correlation analysis and indices of standard deviation of relative difference and mean absolute bias error, respectively. The mean spatial SWS in the shallow soil layer (0-1.0 m) was relatively more variable temporally than in the deeper soil layers (1.0-3.0 m), and the mean SWS in the deep soil layer (2.0-3.0 m) was more variable spatially. The mean Spearman's rank correlation coefficient increased with increasing soil depth and decreased with increasing time lags between measurements for the deeper soil layers (1.0-3.0 m). The number of temporally stable locations and the accuracy of prediction for predicting the mean SWS increased with increasing soil depth. The temporal stability of the SWS patterns was controlled by soil texture, organic carbon content, bulk density, and saturated soil hydraulic conductivity. Aboveground biomass and site elevation (except for the 2.0-3.0-m layer), however, affected the temporal persistence of SWS relatively weakly. This study provides useful information for estimating mean SWS at the landscape scale and may improve the management of soil water on the semiarid Loess Plateau of China.

Effect of vegetation on the temporal stability of soil moisture in grass-stabilized semi-arid sand dunes

Summary Soil moisture is a critical state variable affecting a variety of land surface and subsurface processes. Despite the complex interactions between soil moisture and its controlling factors, the phenomenon of temporal stability of soil moisture (TS SM) has been widely observed under natural conditions. In this study, the control of vegetation on TS SM is investigated by artificially manipulating surface vegetation (e.g., vegetated and de-vegetated plots) in a native grassland-stabilized sand dune area with similar soil texture and topography. Soil moisture data were collected at the depths of 30 cm (within the root zone) and 110 cm (below the root zone) over a period of four years. Using soil moisture data from the de-vegetated plots as a baseline, TS SM within the root zone is shown to be mainly affected by vegetation phenology at the study site. Therefore, the control of vegetation on TS SM varies on both seasonal and annual time scales. The change in the interseasonal patterns of TS SM is tightly related to plant phenology and the control of vegetation on the ranking of mean relative difference (MRD) of soil moisture significantly weakens during non-growing seasons due to diminished root water uptake. It suggests that the timing of sampling schemes (e.g., growing season vs. non-growing season) may alter TS SM patterns. On annual time scales, TS SM is affected by climatic conditions, as the control of vegetation on TS SM becomes stronger under drier conditions. In particular, vegetation tends to create larger contrasts in soil moisture levels between vegetated and de-vegetated plots in drier years. The soil moisture data also provide evidence that vegetation tends to reduce TS SM and increase spatial variability in soil moisture at the study site. The standard deviation of relative difference (SDRD) of soil moisture at the 30 cm depth (within the root zone) is considerably larger in the vegetated plots than those in the de-vegetated plots. As such, the effectiveness of using representative locations for monitoring mean soil moisture conditions in the vegetated plots deteriorates.

Modeling the impacts of soil hydraulic properties on temporal stability of soil moisture under a semi-arid climate

Summary Despite the significant spatiotemporal variability of soil moisture, the phenomenon of temporal stability of soil moisture ( TS SM ) has been widely observed in field studies. However, the lack of understandings of the factors that control TS SM has led to some contradictory findings about TS SM . To resolve this issue, numerical models may offer an alternative way to complement field studies by quantifying different controls on TS SM . In this study, a 1-D vadose zone model was adopted to simulate daily soil moisture contents, which were used to compute the mean relative difference ( MRD ) and standard deviation of relative difference ( SDRD ) of soil moisture. Different from recent modeling studies, a soil dataset was employed with 200 samples of correlated soil hydraulic parameters for sandy soils. Compared to the results of previous modeling studies, more reasonable patterns of MRD and SDRD that resembled field observations were produced. By varying soil hydraulic parameter values, different patterns of MRD and SDRD could also be generated, implying variations in soil hydraulic properties could partly control the patterns of MRD and SDRD . More specifically, the residual soil moisture content ( θ r ) was found to be the primary control on MRD , mainly due to the semi-arid climate that was simulated. By fixing θ r , however, a highly nonlinear relationship emerged between MRD and the shape factor n in the van Genuchten model, which resulted in the positively skewed distributions of MRD widely observed for sandy soils in field experiments. Moreover, both the range and skewness of the distributions of MRD were affected by the range of n . In addition, with increasing n , a positive correlation between MRD and the shape factor l in the van Genuchten model was also found. The simulation results suggested that the control of soil hydraulic properties on MRD might weaken for areas under bare surface conditions or for regions with more humid climates due to elevated soil moisture contents. Therefore, the impacts of soil hydraulic properties on TS SM may vary under different climate regimes.

Temporal stability of soil water storage in three landscapes in the middle reaches of the Heihe River, northwestern China

Temporal stability of soil moisture is important for the sustainability of compound ecosystems where different landscapes coexist and interact with each other. In this study, an arid region composed of desert, cropland and wetland in northwestern China was selected to evaluate the temporal stability of soil water storage (SWS) and identify the representative locations of the spatial mean SWSs in diverse soil layers of each landscape. Soil water storages of 0–1, 1–2 and 2–3 m soil layers were estimated from volumetric water contents measured at fixed intervals in a regular 1 × 1 km grid in an area of 100 km2 from May 2011 to December 2012 using a neutron probe. Spearman’s rank correlation and relative difference analysis both indicated the increasing temporal stability of SWS with depth in the three landscapes. Locations with the lowest standard deviation of relative differences accurately estimated the spatial mean SWSs after providing constant offsets. At the representative locations in the three layers of the desert, the cumulative probabilities for clay, silt, sand and soil organic carbon contents were 0.75 and between 0.5 and 0.75, respectively, and the respective values in the cropland were >0.75, between 0.5 and 0.75, between 0.25 and 0.5 and between 0.5 and 0.75. An a priori approach was then proposed to select the potential representative locations in larger areas of the desert and cropland, from which actual representative locations can be identified after long period measurement. This strategy is economic and labor saving, and can benefit upscaling studies.

Temporal stability analysis for estimating spatial mean soil water storage and deep percolation in irrigated maize crops

The temporal stability of soil moisture in irrigated cropland may have profound implications for precision agriculture in arid regions. This study evaluated the temporal variation of components of the soil water balance in irrigated cropland in northwestern China and identified representative locations to reliably estimate the profiles (0–1 and 1–2m) of spatial mean soil water storage (SWS) and deep percolation below a depth of 2m. Soil water storages were determined with a neutron probe at 48 locations (31 and 17 in the northern and southern croplands, respectively) on a total of 18 occasions in 2011 and 2012. Crop evapotranspiration, SWS variations and deep percolation were analyzed during the maize growing seasons. Soil water storages in the northern and southern croplands were temporally stable. The location with the lowest standard deviation of relative differences could accurately estimate the spatial mean SWS with a high coefficient of determination (R2>0.91, P<0.001) and prediction accuracy (PE>0.76) and near-zero mean absolute relative error (MARE=0). The most representative locations were different for the 0–1 and 1–2m soil layers, but the location for the 0–1m layer could also generally represent SWS in the 1–2m layer in the northern and southern croplands. From 21 May to late September in both years, approximately 39 and 22% of the irrigation and rainwater were lost as deep percolation in the northern and southern croplands, respectively. Deep percolation at the most representative locations of spatial mean SWS for the 0–1m layers could generally estimate the spatial mean deep percolation below a depth of 2m. This study provides an alternative approach for estimating spatial mean SWS and deep percolation, which is essential for the rational management of scarce resources of irrigation water in a large arid region of northwestern China.

Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula

Summary An understanding soil moisture spatio-temporal variability is essential for hydrological and meteorological research. This work aims at evaluating the spatio-temporal variability of near surface soil moisture and assessing dominant meteorological factors that influence spatial variability over the Korean peninsula from May 1 to September 29, 2011. The results of Kolmogorov–Smirnov tests for goodness of fit showed that all applied distributions (normal, log-normal and generalized extreme value: GEV) were appropriate for the datasets and the GEV distribution described best spatial soil moisture patterns. The relationship between the standard deviation and coefficient of variation (CV) of soil moisture with mean soil moisture contents showed an upper convex shape and an exponentially negative pattern, respectively. Skewness exhibited a decreasing pattern with increasing mean soil moisture contents and kurtosis exhibited the U-shaped relationship. In this regional scale (99,720 km2), we found that precipitation indicated temporally stable features through an ANOVA test considering the meteorological (i.e. precipitation, insolation, air temperature, ground temperature and wind speed) and physical (i.e. soil texture, elevation, topography, and land use) factors. Spatial variability of soil moisture affected by the meteorological forcing is shown as result of the relationship between the meteorological factors (precipitation, insolation, air temperature and ground temperature) and the standard deviation of relative difference of soil moisture contents (SDRDt) which implied the spatial variability of soil moisture. The SDRDt showed a positive relationship with the daily mean precipitation, while a negative relationship with insolation, air temperature and ground temperature. The variation of spatial soil moisture pattern is more sensitive to change in ground temperature rather than air temperature changes. Therefore, spatial variability of soil moisture is greatly affected by meteorological factors and each of the meteorological factors has certain duration of effect on soil moisture spatial variability in regional scale. The results provide an insight into the soil moisture spatio-temporal patterns affected by meteorological and physical factors simultaneously, as well as the design criteria of regional soil moisture monitoring network at regional scale.

Temporal stability of surface soil moisture in a desert area of northwestern China

The patterns of soil moisture in arid environments are very important for the conservation and restoration of vegetation but have been rarely studied due to the difficulty of sampling in these environments. The characterization of the temporal stability of soil moisture and its statistical parameters is much needed. In this study, two sub-fields (southern and northern fields) with different topographies and soil properties in a region of the desert area in northwestern China were chosen to evaluate the temporal persistence of surface soil moisture, identify representative locations for reliably estimating the field mean moisture content, and investigate the factors influencing the temporal stability of soil moisture. Surface soil moisture was measured approximately every two weeks from April to October 2012 with a Theta Probe, for a total of 13 sampling campaigns. Soil moisture for the southern, finer-textured field was higher and more variable than for the northern, coarser-textured field. High Spearman's rank correlation coefficients (p < 0.01) indicated a strong temporal stability of spatial patterns for both fields. Tests of relative difference indicated that soil moisture content was more stable under dry than under wet conditions. The representative locations identified for both fields accurately estimated the field mean moisture content, with coefficients of determination (R-2) ranging from 0.981 to 0.998 and root mean square errors (RMSEk) ranging from 0.12% to 0.81% vol/vol. Among the methods for estimating the field mean moisture content using the representative locations, i.e. minimal SDRD (standard deviation of relative differences), ITS (index of temporal stability), MABE (mean absolute bias error), and RMSE (root mean square error), the minimal SDRD with a constant offset provided the best results. Correlation analysis revealed that (in descending order of importance) bulk density, total porosity, soil-particle composition, and soil organic carbon were the main factors influencing the temporal stability of soil moisture in this relatively flat region. Locations with relatively higher sand contents tended to have more pronounced temporal stabilities. This study contributes to our understanding of soil moisture patterns in arid environments, which have important implications for ground sampling design, hydrologic modeling, and sustainable land use. (C) 2013 Published by Elsevier B.V.

Hillslope scale temporal stability of soil water storage in diverse soil layers

Knowledge of the soil water storage (SWS) of soil profiles on the scale of a hillslope is important for the optimal management of soil water and revegetation on sloping land in semi-arid areas. This study aimed to investigate the temporal stability of SWS profiles (0–1.0, 1.0–2.0, and 2.0–3.0 m) and to identify representative sites for reliably estimating the mean SWS on two adjacent hillslopes of the Loess Plateau in China. We used two indices: the standard deviation of relative difference (SDRD) and the mean absolute bias error (MABE). We also endeavored to identify any correlations between temporal stability and soil, topography, or properties of the vegetation. The SWS of the soil layers was measured using neutron probes on 15 occasions at 59 locations arranged on two hillslopes (31 and 28 locations for hillslope A (HA) and hillslope B (HB), respectively) from 2009 to 2011. The time-averaged mean SWS for the three layers differed significantly (P < 0.05) between HA and HB and was greatly affected by topography and vegetation. Temporal–spatial analyses showed that the temporal variation of SWS decreased with increasing soil depth, while the spatial variation increased on both hillslopes. Comparisons of the values for SDRD and MABE and the number of time-stable locations with SDRD and MABE < 5% among various depths indicated that temporal stability increased with an increase in soil depth. The representative sites identified for each hillslope (two on HA and one on HB) accurately estimated the mean SWS for the three soil layers (R2 ⩾ 0.95, P < 0.001). SWS on the scale of a hillslope was strongly time stable, and the temporal–spatial patterns of SWS were highly dependent on sampling depth. The temporal stability of SWS patterns was controlled by soil texture, organic carbon content, elevation, and properties of the vegetation in the study area, which was characterised by diverse or complex terrains and plant cover. Such effects, however, might vary across hillslopes due to different conditions of wetness and patterns of land use. This study provides useful information on the profiles of mean SWS on the scale of a hillslope, which is necessary for improving the management of soil water on sloping land on the Loess Plateau.