Soil Moisture Patterns Research Articles

Drip irrigation wetting patterns and nitrate distribution: comparison between electrical resistivity (ERI), dye tracer, and 2D soil–water modelling approaches

Despite the widespread use of drip irrigation and fertigation, there is limited research on soil wetting patterns and nutrient distribution from drip emitters in agricultural soils. We compared the use of electrical resistivity imaging (ERI) with dye tracer patterns, measured soil nitrate distribution, and modelled changes in soil moisture and nitrate distribution, in a commercial apple orchard in southern Tasmania. Time-lapse two-dimensional ERI revealed wetting plumes beneath the emitters ranged from nil infiltration, to infiltration beyond 1.0 m depth. Lack of infiltration beneath some emitters was attributed to runoff caused by either water repellence or the surface leaf mulch, whilst infiltration to 1.0 m depth was attributed to preferential flow processes. The uncalibrated ERT was unable to discern the separate contributions from of solute concentration, moisture content and temperature to the change in electrical resistivity. The dye tracer study revealed the majority of the fertigated nitrate was retained in the A1 horizon, whilst the fertigated water infiltrated much deeper into the A2 and B2 horizons. The 2D modelling was not able to replicate the variations in infiltration demonstrated by the ERT, principally due to the lumped nature of the soil parameterisation, and inability of the model to simulate preferential flow. As such, use of modelling tools and texture-based guidelines for design and management of drip emitters is not recommended for the texture contrast soils.

The impacts of soil freeze/thaw dynamics on soil water transfer and spring phenology in the Tibetan Plateau

ABSTRACT Climate warming has induced significant changes in permafrost and seasonally frozen ground (SFG) in the Tibetan Plateau, which have complex influences on local environments. A better understanding of the impacts of soil freeze/thaw (F/T) dynamics on soil water transfer and vegetation growth is important to explore related eco-hydrological influences. We investigated soil F/T dynamics and their impacts with in-situ and satellite-based observations. Our results showed the contrasting F/T dynamics between SFG and permafrost areas. In permafrost areas, soil froze downward from the ground surface and upward from the active layer bottom with a distinct freezing zero-curtain, and minimum soil moisture occurred in the intermediate layer, but the thawing process was unidirectional. However, the vertical F/T directions were contrary in SFG areas, where soil moisture generally increased with depth and the thawing zero-curtain was distinct. The spring onset showed a positive correlation with thaw onset in permafrost areas, but such a correlation was variable in SFG areas likely depending on soil-moisture level. Our results implied that the different soil-moisture patterns and the varying vegetation response might be related to the spatially contrasting F/T dynamics, which may have different impacts on soil water transfer, and further affect the zero-curtain and vegetation phenology.

Impact of Plant Functional Types on Coherence Between Precipitation and Soil Moisture: A Wavelet Analysis

AbstractMapping the spatiotemporal patterns of soil moisture within heterogeneous landscapes is important for resource management and for the understanding of hydrological processes. A critical challenge in this mapping is comparing remotely sensed or in situ observations from areas with different vegetation cover but subject to the same precipitation regime. We address this challenge by wavelet analysis of multiyear observations of soil moisture profiles from adjacent areas with contrasting plant functional types (grassland, woodland, and encroached) and precipitation. The analysis reveals the differing soil moisture patterns and dynamics between plant functional types. The coherence at high‐frequency periodicities between precipitation and soil moisture generally decreases with depth but this is much more pronounced under woodland compared to grassland. Wavelet analysis provides new insights on soil moisture dynamics across plant functional types and is useful for assessing differences and similarities in landscapes with heterogeneous vegetation cover.

Downscaling near-surface soil moisture from field to plot scale: A comparative analysis under different environmental conditions

Indirect measurements of field-scale (hectometer grid-size) spatial-average near-surface soil moisture are becoming increasingly available by exploiting new-generation ground-based and satellite sensors. Nonetheless, modeling applications for water resources management require knowledge of plot-scale (1–5 m grid-size) soil moisture by using measurements through spatially-distributed sensor network systems. Since efforts to fulfill such requirements are not always possible due to time and budget constraints, alternative approaches are desirable. In this study, we explore the feasibility of determining spatial-average soil moisture and soil moisture patterns given the knowledge of long-term records of climate forcing data and topographic attributes. A downscaling approach is proposed that couples two different models: the Eco-Hydrological Bucket and Equilibrium Moisture from Topography. This approach helps identify the relative importance of two compound topographic indexes in explaining the spatial variation of soil moisture patterns, indicating valley- and hillslope-dependence controlled by lateral flow and radiative processes, respectively. The integrated model also detects temporal instability if the dominant type of topographic dependence changes with spatial-average soil moisture. Model application was carried out at three sites in different parts of Italy, each characterized by different environmental conditions. Prior calibration was performed by using sparse and sporadic soil moisture values measured by portable time domain reflectometry devices. Cross-site comparisons offer different interpretations in the explained spatial variation of soil moisture patterns, with time-invariant valley-dependence (site in northern Italy) and hillslope-dependence (site in southern Italy). The sources of soil moisture spatial variation at the site in central Italy are time-variant within the year and the seasonal change of topographic dependence can be conveniently correlated to a climate indicator such as the aridity index.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Spatial Distribution of Soil Hydrological Properties in the Kilombero Floodplain, Tanzania

Analysis and interpretation of soil properties dynamics is a keystone in understanding the hydrologic responses and yield potential of floodplain wetlands. This study characterizes the distribution and spatial trends of selected soil physical properties in the Kilombero floodplain, Tanzania. A total of 76 composite soil samples were taken from 0 to 20 cm and 20 to 40 cm depth in a regular grid design across three hydrological zones, related to flooding intensity defined as fringe, middle, and riparian during the rainy season of 2015. The samples were analyzed for soil texture, bulk density, organic carbon, and saturated hydraulic conductivity. Seasonal soil moisture content was monitored at depths of 10, 20, 30, and 40 cm, using 17 frequency domain reflectometry profile probes type PR2, installed at each hydrological zone for 18 months (March 2015–August 2016). Data were subjected to classical statistical and geostatistical analyses. Results showed significant (p < 0.05) differences in bulk density, texture, soil organic carbon (SOC), and saturated hydraulic conductivity (Ksat) across the hydrological zones. Bulk density showed a clear increasing trend towards the fringe zone. Mean Ksat was highest at the riparian zone (69.15 cm·d−1), and clay was higher in the riparian (20.3%) and middle (28.7%) zones, whereas fringe had the highest percentage of sand (33.7–35.9%). Geostatistical spatial results indicated that bulk density, silt, and SOC at 0–20 cm had intermediate dependence, whereas other soil properties at both depths had high spatial dependence. Soil moisture content showed a significant (p < 0.05) difference across the hydrological zones. The riparian zone retained the highest soil moisture content compared to the middle and fringe zone. The temporal soil moisture pattern corresponded to rainfall seasonality and at the riparian zone, soil moisture exhibited a convex shape of sloping curve, whereas a concave sloping curve for topsoil and for the middle zone at the subsoil was observed during the start of the dry season. Our results are seen to contribute to a better understanding of the spatial distribution of soil properties and as a reference for soil and water management planning in the floodplain.

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT) method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM) for the soil layer and the Michigan microwave canopy scattering (MIMICS) model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD) of 0.053 m3/m3, a mean average deviation (MAD) of 0.034 m3/m3, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF), and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

Regional Climate Variability Under Model Simulations of Solar Geoengineering

AbstractSolar geoengineering has been shown in modeling studies to successfully mitigate global mean surface temperature changes from greenhouse warming. Changes in land surface hydrology are complicated by the direct effect of carbon dioxide (CO2) on vegetation, which alters the flux of water from the land surface to the atmosphere. Here we investigate changes in boreal summer climate variability under solar geoengineering using multiple ensembles of model simulations. We find that spatially uniform solar geoengineering creates a strong meridional gradient in the Northern Hemisphere temperature response, with less consistent patterns in precipitation, evapotranspiration, and soil moisture. Using regional summertime temperature and precipitation results across 31‐member ensembles, we show a decrease in the frequency of heat waves and consecutive dry days under solar geoengineering relative to a high‐CO2 world. However in some regions solar geoengineering of this amount does not completely reduce summer heat extremes relative to present day climate. In western Russia and Siberia, an increase in heat waves is connected to a decrease in surface soil moisture that favors persistent high temperatures. Heat waves decrease in the central United States and the Sahel, while the hydrologic response increases terrestrial water storage. Regional changes in soil moisture exhibit trends over time as the model adjusts to solar geoengineering, particularly in Siberia and the Sahel, leading to robust shifts in climate variance. These results suggest potential benefits and complications of large‐scale uniform climate intervention schemes.

Similarity of the temporal pattern of soil moisture across soil profile in karst catchments of southwestern China

Knowledge of dynamics of profile soil moisture is of importance for soil moisture prediction and modeling. However, information on the relationships between soil moisture at the surface and subsurface layers is relatively inadequate. The objective of this study was to examine the similarities in the overall and scale specific temporal patterns of soil moisture across soil profile. Soil moisture was measured at five soil depths using time-domain reflectometry in two sampling plots over 242 days in a karst depression. Spearman rank correlation and wavelet coherency analyzes were used to determine overall and scale-dependent similarities of temporal patterns of soil moisture, respectively. Results showed that soil moisture in farmland was higher than grassland especially in 10–50 cm depths. The surface depth had higher variability for both farmland and grassland. Spearman rank correlation coefficients between any two soil depths were significant, indicating strong depth persistence in the overall temporal pattern of soil moisture. The similarity decreased as the distance between depths increased. Wavelet coherency analysis indicated that soil moisture temporal pattern had a higher depth persistence in the farmland than grassland. Soil depth had a larger influence than land uses on the similarity in soil moisture temporal pattern, and the relative humidity and precipitation had larger effect than air temperature controlling the change of soil moisture at larger scales. These scale specific similarity in the temporal pattern of soil moisture can be used to guide estimating deeper soil moisture and to improve sampling strategies for soil moisture prediction with a higher prediction accuracy.

Estimating Soil Moisture Patterns with Remote Sensing and Terrain Data at the Small Catchment Scale

Core Ideas The FCM SEA was used for sampling and spatial estimation of soil moisture patterns. Multispectral remote sensing and terrain data were combined to guide the sampling and estimation. Selected vegetation patterns and terrain data provided reasonable estimates of soil moisture. The FCM SEA was stable to explain about 50% of the total observed variance. The FCM SEA was superior to an approach driven solely by terrain data. Detailed information on the temporal and spatial evolution of soil moisture patterns is of fundamental importance to improve runoff prediction, optimize irrigation management and to enhance crop forecasting. However, obtaining representative soil moisture measurements at the catchment scale is challenging because of the dynamic spatial and temporal behavior of soil moisture. High‐resolution remote sensing data provide detailed spatial information about catchment characteristics (e.g., terrain and land use) that can be used as proxies to estimate soil moisture. We assessed the potential use of combined multitemporal multispectral remote sensing (RS) and terrain data for estimating spatial soil moisture patterns at the small catchment scale. The fuzzy c‐means sampling and estimation approach (FCM SEA) was applied to conduct a sensor (proxy) directed (guided) sampling and to reconstruct multitemporal soil moisture patterns based on time domain reflectometry measurements. A comprehensive soil moisture database for the Schäfertal catchment, located in central Germany, was used to test, validate, and compare the FCM SEA performances of the combined remote sensing data with those of a benchmark approach driven solely by terrain data. Results from the study show that a FCM SEA model that integrates bi‐temporal RS imagery and terrain data was more effective in estimating spatial soil moisture patterns relative to the benchmark model. It outperformed the benchmark model in 58% of the cases and was stable to explain about 50% of the total observed variance for a range of different catchment moisture conditions. This was achieved with only a small sample size (n = 30). The results of this study are promising because they highlight the importance of considering multitemporal RS and terrain data and demonstrate how in situ sensors can be optimally placed to enable cost‐efficient monitoring and prediction of spatial soil moisture patterns at the small catchment scale.

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.

Role of co-occurring competition and facilitation in plant spacing hydrodynamics in water-limited environments.

Plant performance (i.e., fecundity, growth, survival) depends on an individual's access to space and resources. At the community level, plant performance is reflected in observable vegetation patterning (i.e., spacing distance, density) often controlled by limiting resources. Resource availability is, in turn, strongly dependent on plant patterning mediated by competitive and facilitative plant-plant interactions. Co-occurring competition and facilitation has never been specifically investigated from a hydrodynamic perspective. To address this knowledge gap, and to overcome limitations of field studies, three intermediate-scale laboratory experiments were conducted using a climate-controlled wind tunnel-porous media test facility to simulate the soil-plant-atmosphere continuum. The spacing between two synthetic plants, a design consideration introduced by the authors in a recent publication, was varied between experiments; edaphic and mean atmospheric conditions were held constant. The strength of the above- and belowground plant-plant interactions changed with spacing distance, allowing the creation of a hydrodynamic conceptual model based on established ecological theories. Greatest soil water loss was observed for the experiment with the smallest spacing where competition dominated. Facilitation dominated at the intermediate spacing; little to no interactions were observed for the largest plant spacing. Results suggest that there exists an optimal spacing distance range that lowers plant environmental stress, thus improving plant performance through reduced atmospheric demand and conservation of available soil water. These findings may provide a foundation for improving our understanding of many climatological, ecohydrological, and hydrological problems pertaining to the hydrodynamics of water-limited environments where plant-plant interactions and community self-organization are important.

Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices

The quantification of soil moisture under different soils and crops at regional scale is a challenging task. Hence, such studies are limited by the availability of ground based measurements. The current study evaluates the spatial and temporal patterns of daily soil moisture simulated by the Soil and Water Assessment Tool (SWAT) for the upper 30cm of the soil profile with indirect soil moisture estimates from Landsat for 2016. The Thermal Vegetation Difference Index (TVDI), was calculated based on the Normalized Difference Vegetation Index (NDVI) and the brightness temperature (BT) using Landsat images, from which regression models were trained by using field measurements from Time Domain Reflectometer (TDR) to calculate soil moisture. Two agricultural catchments namely, Gerdau and Wipperau in Germany were satisfactorily calibrated using SWAT for daily streamflow (1975–2000) with NSE (Nash-Sutcliffe-Efficiency) >0.55 and PBIAS (Percent bias) <5.5%. The parameter uncertainty assessment during the irrigation season (Mar–Sept, 2016) for soil moisture revealed that the uncertainty band is narrow (p-factor=0.57–0.83; r-factor=0.52−1.3). Spatial and temporal patterns of soil moisture from Landsat and SWAT were evaluated by using boxplots and absolute soil moisture difference maps. Results revealed that the overall spatial and temporal patterns of boxplots matched better for the dry period (correlation, r≥0.90) compared to the wet period (r≥0.57). The mean absolute difference between soil moisture from Landsat and SWAT ranged between 0.9–10% for most soils. In addition to it, the soil map was refined to match soil moisture patterns shown in Landsat images for one sandy soil, which further improved the mean absolute difference (1.06–6%). The current study provides an approach to use remotely sensed soil moisture for verifying hydrological modeling results and for optimizing the parameterization of soils, which may bridge the gap between global, regional and field studies in agricultural water management.

Hydrological Responses and Flow Pathways in an Acrisol on a Forested Hillslope with a Monsoonal Subtropical Climate

The nature of subsurface flow depends largely on hydraulic conductivity of the vadoze zone, permeability of the underlying bedrock, existence of soil layers differing in hydraulic properties and macropore content, soil depth, and slope angle. Quantification of flow pathways on forested hillslopes is essential to understand hydrological dynamics and solute transport patterns. Acrisols, with their argic Bt horizons, are challenging in this respect. To further elucidate flow pathways of water and short-term variability of soil moisture patterns in Acrisols, a field study was conducted on a forested hillslope in a sub-catchment of the Tie Shan Ping (TSP) watershed, 25 km northeast of Chongqing City, China. This catchment is covered by a mixed secondary forest dominated by Masson pine (Pinus massoniana). Soil saturated hydraulic conductivity (Ksat) was significantly reduced at the interface between the AB and Bt horizons (2.6 × 10−5 vs. 1.2 × 10−6 m s−1), which led to that the flow volume generated in the Bt horizon was of little quantitative importance compared to that in the AB horizon. There was a marked decrease in porosity between the OA and AB horizons, with a further decrease deeper in the mineral subsoil. Especially, the content of soil pores > 300 μm was higher in the AB horizon (14.3%) than in the Bt horizon (6.5%). This explained the difference in soil Ksat values. This study showed that Bt horizon had limited water transport capability, forcing part of the infiltrated rainwater as interflow through the OA and AB horizons. Thus, the topsoil responded quickly to rainfall events, causing frequent cycles of saturation and aeration of soil pores.

Experimental Warming Aggravates Degradation‐Induced Topsoil Drought in Alpine Meadows of the Qinghai–Tibetan Plateau

AbstractClimatic warming is presumed to cause topsoil drought by increasing evapotranspiration and water infiltration, and by progressively inducing land degradation in alpine meadows of the Qinghai–Tibetan Plateau. However, how soil moisture and temperature patterns of degraded alpine meadows respond to climate warming remains unclear. A 6‐year continuous warming experiment was carried out in both degraded and undegraded alpine meadows in the source region of the Yangtze River. The goal was to identify the effects of climatic warming and land degradation on soil moisture (θ), soil surface temperature (Tsfc), and soil temperature (Ts). In the present study, land degradation significantly reduced θ by 4·5–6·1% at a depth of 0–100 cm (p &lt; 0·001) and increased the annual mean Tsfc by 0·8 °C. Warming with an infrared heater (radiation output of 150 W m−2) significantly increased the annual mean Tsfc by 2·5 °C (p &lt; 0·001) and significantly increased θ by 4·7% at a depth of 40–60 cm. Experimental warming in degraded land reversed the positive effects of the infrared heater and caused the yearly average θ to decrease significantly by 3·7–8·1% at a depth of 0–100 cm. Our research reveals that land degradation caused a significant water deficit near the soil surface. Experimental warming aggravated topsoil drought caused by land degradation, intensified the magnitude of degradation, and caused a positive feedback in the degraded alpine meadow ecosystem. Therefore, an immediate need exists to restore degraded alpine meadow grasslands in the Qinghai–Tibetan Plateau in anticipation of a warmer future. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Temporal stability of soil water storage and its influencing factors on a forestland hillslope during the rainy season in China’s Loess Plateau

Large-scale vegetation restoration in China’s Loess Plateau has been initiated by the central government to control soil and water losses since 1999. Knowledge of the spatio-temporal distribution of soil water storage (SWS) is critical to fully understand hydrological and ecological processes. This study analysed the temporal stability of the SWS pattern during the rainy season on a hillslope covered with Chinese pine (Pinus tabulaeformis Carr.). The soil water content in eight soil layers was obtained at 21 locations during the rainy season in 2014 and 2015. The results showed that the SWS at the 21 locations followed a normal distribution, which indicated moderate variability with the coefficients of variation ranging from 14 to 33%. The mean SWS was lowest in the middle slope. The spatial pattern of SWS displayed strong temporal stability, and the Spearman correlation coefficient ranged from 0.42 to 0.99 (p < 0.05). There were significant differences in the temporal stability of SWS among different soil layers (p < 0.01). The spatial patterns of SWS distribution showed small differences in different periods. The best representative locations of SWS were found at different soil depths. The maximum RMSE and MAE at 0–1.6 m soil depth for the rainy season were 4.27 and 3.54 mm, respectively. The best representative locations determined during a short period (13 days) can be used to estimate the mean SWS well for the same rainy season, but not for the next rainy season. Samples of SWS collected over a fortnight during the rainy season were able to capture the spatial patterns of soil moisture. Roots were the main factor affecting the temporal stability of SWS. Rainfall increased the temporal stability of the soil water distribution pattern. In conclusion, the SWS during the rainy season had a strong temporal stability on the forestland hillslope.

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.

Relation between precipitation location and antecedent/subsequent soil moisture spatial patterns

AbstractRecent evidence has shown that relations between soil moisture and precipitation at spatial and temporal aspect are contrary to each other: afternoon precipitation tends to occur at times in which conditions are overall wet and heterogeneous in the morning, but preferentially over those patches that are relatively drier than the surroundings. This study expands the notion of soil moisture‐precipitation spatial coupling by analyzing the preferred precipitation location over a range of different soil moisture patterns. Using global observations of precipitation and observationally constrained evaporative stress estimates, we confirm that relatively drier patches have more chances of receiving rain, but the preference is weakened under wetter soil conditions. During extremely wet times, wet patches have more chances of receiving rain. Moreover, the preference of precipitation to occur on drier soils is stronger when soil moisture conditions are heterogeneous. Such results indicate that the positive feedback mechanism becomes more positive as soil wetness increases and the negative feedback mechanism becomes more negative as soils become drier and more heterogeneous. The strength of these two feedback mechanisms jointly affects preferential precipitation location. Counterintuitively, analysis from 1 day after‐event soil moisture pattern shows that negative soil moisture‐precipitation coupling may in turn further heterogenize soil moisture patterns, because dry patch gets extremely wet with no or less rain in surrounding. Although results here do not necessarily imply a causal relationship, this work contributes to enhancing our understanding of soil moisture‐precipitation spatial coupling and exposes the complex nuances of these land‐atmosphere interactions.

Pattern similarity based soil moisture analysis for three seasons on a steep hillslope

Soil moisture is an important factor for understanding hydrological and solute transport processes at the hillslope scale. The selection of representative points for soil moisture measurement has been explored to investigate temporal variation of average soil moisture with minimum costs and maximum stability. The optimal selection of soil moisture monitoring points has been reevaluated to address hillslope hydrological processes and the impacts of seasonal differences. An alternative method to select soil moisture measurement points was developed to adequately represent immediate soil moisture response patterns to sequential rainfall events. To address the seasonal features of rainfall events and their impacts on soil moisture redistribution along the hillslope, field soil moisture data were collected at 49 points for three seasons over periods of 25days with bi-hourly monitoring intervals. For effective characterization of soil moisture variation, soil moisture datasets were classified using cluster analysis based on Euclidean similarity. Points delineated using the proposed method not only provide better stability of average soil moistures but also adequately represent the response patterns of soil moisture to rainfall events on the hillslope.

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.