Spatio-temporal Patterns Of Soil Moisture Research Articles

Climate overtakes vegetation greening in regulating spatiotemporal patterns of soil moisture in arid Central Asia in recent 35 years

ABSTRACTSatellite datasets have revealed significant greening and soil drying in arid Central Asia. However, the influence mechanism of vegetation and climate on soil moisture dynamics is still unclear. In this study, we investigated the spatiotemporal consistency of vegetation and soil moisture changes, examined the controlling factors of soil moisture under different vegetation indices (normalized differential vegetation index, NDVI; leaf area index, LAI), and explored the time lag and accumulation effects of vegetation greening on the soil moisture in arid Central Asia in the period of 1985 to 2020. The results showed that the co-occurrence of vegetation greening and soil drying in 30.4% (NDVI) and 19.4% (LAI) of the study area spatially. Temporally, the soil moisture increased from 1985 to 1989, but decreased from 1989 or 1990 to 2020. In contrast, vegetation activity increased continuously during 1985–2020. Precipitation accounted for the most variance of the soil moisture. The vegetation greenness demonstrated positive correlations with soil moisture, representing 20.7% (for NDVI) and 39.3% (for LAI) of vegetated areas (areas with long-term mean NDVI ≥ 0.1 and LAI > 0), while negative correlations were observed in only 5.4% (for NDVI) and 3.3% (for LAI) of vegetated areas, respectively. Soil moisture exhibited varying time lag and accumulation effects in response to different vegetation indices, precipitation, and temperature. The main temporal effects of NDVI and temperature were simultaneous changes with no time lag or accumulation effects on soil moisture. In contrast, the main temporal effects of LAI and precipitation included 1-month lag in their influence on soil moisture. Considering the inconsistencies in spatiotemporal patterns between vegetation and soil moisture, as well as the dominance and time lag effects of precipitation on soil moisture variations, we concluded that climate, especially precipitation, rather than vegetation greening, regulates soil moisture in Central Asia. These findings provide a scientific basis for vegetation restoration or regional water resource management in drylands.

Characterising the coincidence of soil moisture – precipitation extremes as a possible precursor to European floods

Studies around the world have shown that increases in extreme precipitation are not directly resulting in reported instances of flooding. Despite the evidence that antecedent soil moisture conditions play a significant role in regulating the runoff response during extreme storm events, fewer works have tried to understand the spatial and temporal dynamics of such coupled interactions. In the present work, we aim to improve our understanding of the dominant spatiotemporal patterns of soil moisture (SM) – precipitation (P) dependence over Europe. Using an event-based, non-parametric Event Coincidence Analysis (ECA) approach, we first quantified the probability of SM preceding P by the precursor coincidence rate. We also examined the statistical precursor relationship between SM-P joint episodes and reported annual flood events using the conditional precursor coincidence rate. The results show that seasonality is a major driving force in the spatiotemporal dynamics of SM-P coupling over Europe. The conditional precursor coincidence rates are in line with previous flood hazard studies over Europe and show the importance of considering such diverse interdependencies in flood risk mitigation. Our work has implications for understanding the occurrence of preconditioned compound events worldwide and provides a vital starting point for characterising the major processes that need to be accounted for in comprehensive state of the art flood risk assessment studies.

Effect of topography on spatiotemporal patterns of soil moisture in a mountainous region of Northwest China

Soil moisture data were obtained from the Watershed Allied Telemetry Experimental Research Network (Waternet), located in a mountainous region of northwest China, to evaluate the impacts of environmental factors on regional soil moisture spatiotemporal patterns. Based on the temporal stability analysis method, the results showed that soil moisture spatial distributions in the study region were more correlated with elevation (a local control) than with precipitation (a regional control), highlighting the importance of local factors in controlling regional soil moisture spatial patterns. Moreover, the spatial variance of the absolute soil moisture content (e.g., the total soil moisture spatial variability) at the Waternet stations was decomposed into time-invariant and time-variant components. The results showed that the spatial variability in the time-invariant component (e.g., the temporal average soil moisture content) was the primary component in the total soil moisture spatial variability. More importantly, the temporal evolutions of the time-variant components and their contributions to the total soil moisture spatial variance were also affected by local factors, particularly by elevation in the study region. Overall, this study provides further observational evidence, which suggests that depending on specific regional conditions, local factors through affecting both time-invariant and time-variant components can outweigh regional factors in controlling soil moisture spatial variability at regional scales.

Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors

Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.

Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems.

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.

Combined soil-terrain stratification for characterizing catchment-scale soil moisture variation

Soil properties and terrain characteristics influence spatiotemporal patterns of soil moisture across a watershed. To improve the predictive power of landscape hydrologic models, it is essential to consider both soil and terrain attributes when stratifying a catchment into similar hydrologic functional units. In this study, we developed and validated a new catchment-scale stratification scheme for the Shale Hills watershed by combining soil and terrain attributes in an attempt to delineate soil-landscape units with similar soil moisture dynamics. Terrain was combined with soils information by first using a Random Forest supervised classification algorithm to predict a detailed soil map using 47 field soil samples and terrain variables derived from 1-m LiDAR. A slope class map generated from the LiDAR-derived digital elevation model (DEM) was overlaid on the predicted soil map to delineate areas of similar slope value across the catchment. We compared the performance of this new stratification scheme with two classical stratification schemes, a soil map developed from detailed field survey and a landform unit map based on the DEM, for estimating soil moisture time-series across the forested watershed. The combined soil-terrain method outperformed classical stratification schemes in estimating soil moisture time-series over a 4-year period. Our results demonstrate that combining soil and terrain attributes can help improve the stratification of a catchment into similar soil hydrologic functional units, which is valuable to distributed hydrology modeling and other applications.

Spatiotemporal Patterns of Soil Matric Potential in the Shale Hills Critical Zone Observatory

Spatiotemporal patterns of soil moisture are important for understanding landscape hydrologic processes. While the spatiotemporal characteristics of soil moisture content (θ) have been frequently studied, those of soil matric potential (ψ) remain sparse. We investigated the spatial variability and temporal stability of ψ at multiple depths (10–100 cm) across a 7.9‐ha forested catchment and their relationships with soil type, terrain, and season. The results from a 5.5‐yr database consisting of 62 sites clearly showed a downward parabolic trend in ψ spatial variability with decreasing spatial mean ψ values (i.e., becoming drier) across all depths. The catchment's overall spatial variability of ψ generally increased with soil depth and was relatively high during summer and fall. Sand and silt contents were significant factors (p < 0.05) influencing ψ in surface layers (0–20 cm), while ψ values in the subsurface (40–100 cm) were highly correlated with elevation. The temporal stability of ψ spatial pattern was generally higher in the surface soil than the subsoil but weaker in the spring than in other seasons. Moreover, relatively dry areas tended to have less evident ψ temporal stability at each depth, attributable to a higher sensitivity of ψ to changes in θ when the soil is dry. Finally, at least one of the representative sites of the catchment mean ψ at each depth was found within a south‐facing concave hillslope at mid‐elevation, and these sites were different from the representative sites of the catchment mean θ. The results of this study have implications for upscaling soil water from point‐based observations to hillslope and catchment scales.

Potentials and limitations of modelling spatio‐temporal patterns of soil moisture in a high mountain catchment using WaSiM‐ETH

AbstractSoil moisture is a highly dynamic ecological variable. This dynamic is especially distinct in high mountains areas, where topography, snow cover, micro‐climate, vegetation, soil properties and land cover vary across short distances. For such areas, soil moisture modelling on a fine scale is therefore particularly challenging. We evaluated the distributed, physically based model WaSiM‐ETH with regard to its ability to model soil moisture in high mountain catchments, including spatio‐temporal patterns. The study was performed in the alpine catchment area of the Lötschen valley (160 km2) in Switzerland, an area ranging from 600 to 4000 m a.s.l. The model was run for the period of 2003–2007 with high temporal (hourly) and spatial (50 m) resolution. Extensive TDR‐ and FD‐measurements conducted in 2006 and 2007 enabled the validation of the simulated soil moisture. A local sensitivity analysis indicated a dependence of soil moisture on skeleton fraction and temperature. The validation results showed that WaSiM‐ETH is able to simulate absolute soil moisture variability across different altitudes and land cover types with only a moderate accuracy (R = 0·47), whereas the soil moisture dynamic is reproduced well (R = 0·69, IoA = 0·66). High spatial variability of skeleton fraction and coarse meteorological data are the main causes for the limitation in soil moisture modelling. Nevertheless, analysing the spatio‐temporal patterns of soil moisture revealed a clear seasonal pattern that is in line with literature. We found soil moisture to be determined by liquid precipitation, snow‐melt and evapotranspiration. Greatest efforts should be laid on the exact determination of fine‐scale meteorological data and spatial skeleton estimation to improve soil moisture modelling in high mountain areas in the future. Copyright © 2010 John Wiley & Sons, Ltd.

Spatial distribution dynamics of topsoil moisture in shrub microenvironment after rain events in arid and semi-arid areas by means of high-resolution maps

Understanding the dynamic patch mosaic of topsoil moisture on arid hillsides has implications for understanding eco-geomorphic processes. The present study used high-resolution maps to investigate the spatial distribution of moisture in the topsoil on the shrub microenvironment scale, and its dynamics during the first days after rain events. These maps were acquired by a thermal infrared (TIR) method, based on close-range TIR imaging and on a model in which spatially consecutive TIR data are converted into soil moisture values. The main results show that, in addition to the general diminishing trend of topsoil moisture content, and in spite of the specific differences between the two research sites, there was, in both arid and semi-arid zones, a general spatio-temporal pattern of topsoil moisture in a shrub microenvironment during the first days after rainfall. This pattern is manifested in the presence of a changing moist patch under the shrub, and diminishing moisture content gradients, from the shrub center towards the bare soil that vary over space and time. An improved understanding of surface water resources is gained from investigations of the spatio-temporal patterns of soil moisture during and following rainfall events.

A terrain and data-based method for generating the spatial distribution of soil moisture

Soil moisture is a critical environmental variable, as it significantly affects infiltration, evapotranspiration and surface and subsurface runoff processes. Measurement of soil moisture, however, has only recently become possible over large scales. In modelling applications, the spatial distribution of soil moisture is typically assumed to mirror that of a terrain attribute such as the wetness index of [Beven KJ, Kirkby MJ. A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 1979;24(1):43–69] (e.g. TOPMODEL [2]. Intense ground-based measurements of the spatial distribution of soil moisture in the root zone have been conducted in Australia and New Zealand, allowing examination of the predictive power of various terrain attributes with respect to soil moisture. Here a method is developed that enables the generation of realistic spatio-temporal patterns of soil moisture based on the changing importance of spatial attributes with average wetness condition θ ¯ t . Maps of terrain and other spatial attributes are combined and weighted according to relationships dependent on θ ¯ t to generate simulated spatial patterns of soil moisture. The method is presently based on field data and requires spatial patterns of soil moisture for a range of θ ¯ t conditions in order to establish relationships between θ ¯ t and the attributes. Observed variability is introduced by adding an error term whose statistics are also a function of θ ¯ t .Ultimately generalised weightings could be derived, based on the literature or subjective assessment by the user.

The Tarrawarra project: high resolution spatial measurement, modelling and analysis of soil moisture and hydrological response

Detailed spatial patterns of soil moisture were measured for 13 dates at the 10·5 ha Tarrawarra catchment in southern Victoria, Australia. Several analyses of the data are summarized. These include: hydrological behaviour, including preferred states, spatial organization and the performance of terrain indices; geostatistical properties of the soil moisture patterns; and remote sensing of the soil moisture patterns. In the second part of the paper, the patterns along with surface runoff and meteorological data are used in applications of the Thales and VIC models at Tarrawarra. Thales is a process-based distributed parameter hydrological model which explicitly simulates the spatio-temporal patterns of soil moisture, while VIC uses a lumped statistical distribution approach to model the spatial variability of soil moisture storage. Both models simulate saturation excess runoff and are forced by rainfall and potential evapotranspiration. VIC was calibrated to observed runoff at the catchment outlet. Limited manual calibration of Thales to runoff and the soil moisture patterns was performed. Internal testing was achieved by comparison of predicted and observed spatial soil moisture patterns for the Thales model and of predicted and observed cumulative distributions of active soil moisture storage for the VIC model. With limited calibration effort, Thales was able to to simulate the seasonal changes in characteristics of the spatial soil moisture patterns. Detailed examination of the errors in the simulated patterns allowed identification of structural problems in the model, including problems with simulating lateral redistribution as the catchment wets in autumn. For the VIC model, time-series of spatially averaged internal state variables (total storage) were consistent with observations. However, the statistical distribution of soil moisture storage assumed in the model differed from that observed. The collection of detailed spatial data for soil moisture patterns provided a basis for testing the internal states relevant to each model formulation (spatially distributed for Thales and statistically lumped for VIC), as well as improving the identification of the dominant runoff processes. Copyright © 1999 John Wiley & Sons, Ltd.