Spatio-temporal Variability Of Soil Moisture Research Articles

The puzzle of soil Moisture: Unravelling its dynamics in a watershed with complex physiographic features

Periodic in-situ soil moisture observations are essential for managing agriculture, water resources and validating satellite-based soil moisture products. To facilitate this, a comprehensive soil moisture monitoring network is required that provides an optimal balance between the accuracy of measurement and the associated costs. Therefore, the present study attempts to investigate the spatio-temporal variability of soil moisture over a period of 57 weeks in a watershed spanning 422 km2 with elevation ranging from 739 m to 2914 m. The watershed presents complex physiographic features such as extreme seasonal variability, differential elevations and diverse land use patterns (agricultural, forest and grasslands). The study follows a systematic sampling strategy, using a 2 km x 2 km spatial resolution across 104 grids over the entire watershed area. The soil moisture data is then used to estimate the number of optimal locations from a spatial and temporal perspective. The number of spatial optimal locations and temporal optimality are quantified using the student’s t-distribution test and the random combination method, respectively. Moreover, empirical orthogonal functions (EOFs) coupled with Spearman rank correlation method are used to evaluate the dominant physiographic factors affecting soil moisture variability within the watershed. From a comprehensive set of 71,136 field measurements, this study identified 30 optimal sampling locations necessary to capture the spatial variability of soil moisture within a ± 2 % error band. Within these 30 locations, 8 randomly selected locations can adequately represent the temporal variability of the watershed mean soil moisture within a ± 1 % error band. Furthermore, EOFs coupled with Spearman rank correlation identified elevation and sand content as the dominant physical factors influencing soil moisture variability across the watershed. The insights derived from this study provide a robust framework for carrying out soil moisture studies, especially in an ungauged watershed.

Exploring the actual spatial resolution of 1 km satellite soil moisture products

High-resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil moisture products both at fine spatial (1 km) and temporal (daily) scales. Some of these products rely on several data sources as input (satellite, in situ, modelling), and therefore an evaluation of their actual spatial and temporal resolution is required. Nevertheless, the absence of appropriate ground monitoring networks poses a significant challenge for this assessment.In this study, five high-resolution (1 km) soil moisture products (S1-RT1, S1-COP, SMAP-Planet, SMAP-NSIDC, and ESACCI-Zheng) were analysed and evaluated throughout the Italian territory, together with a coarse resolution (12.5 km) dataset for comparison (ASCAT-HSAF). The main objective is to investigate their actual spatial and temporal resolution, and accuracy. Firstly, a cross-comparison of the products in space and time is carried out, including the use of triple collocation analysis. Secondly, an application-based assessment is implemented, considering irrigation, fire, drought, and precipitation case studies.The results clearly indicate the limitations and the potential of each product. Sentinel-1 based products (S1-COP and S1-RT1) are found able to reproduce high-resolution spatial patterns by detecting localised events for irrigation, fire, and precipitation. Their lower temporal resolution leads to accuracies lower than that of the SMAP-Planet product, and comparable with SMAP-NSIDC and ESACCI-Zheng products. However, SMAP-Planet is found to have an actual spatial resolution coarser than 1 km. The study highlights the need for further research to improve the high-resolution soil moisture products, and particularly to determine accurately the spatial resolution represented in soil moisture products. At the same time, the analysed products are found able to address high-resolution applications for the first time, opening promising activities for their operational use in hydrology and water resources management.

Spatiotemporal variability of soil moisture over Ethiopia and its teleconnections with remote and local drivers

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient r=0.95\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r=0.95$$\\end{document}, rmsd=0.04m3m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$rmsd=0.04{\\mathrm{m}}^{3}{\\mathrm{m}}^{-3}$$\\end{document} with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.

Factors leading to spatiotemporal variability of soil moisture and turfgrass quality within sand-capped golf course fairways

Factors leading to spatiotemporal variability of soil moisture and turfgrass quality within sand-capped golf course fairways

Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria

Abstract Soil moisture has continued to occupy the forefront of several interdisciplinary debates globally, particularly in agriculture, climatology, hydrology and civil engineering for several decades. Climate smart and precision farming depend extensively on knowledge of the volumetric pattern and trend in water content of the soil particularly at the root zone. This study evaluated the temporal and spatial variation of soil moisture (0–40 cm depth) in Etsako West Local Government Area, Edo State, Nigeria. It used time series soil moisture data (1982–2019) sourced from the Famine Early Warning Systems Network (FEWS NET) and Land Data Assimilation System (FLDAS). Descriptive and inferential statistics as well as geographical information system (GIS) were used in data analysis. Results showed that Nitisols (Iyuku) had mean soil moisture of 9.0285 m3/m3, Lixisols (Ugieda) recorded 9.0574 m3/m3 whereas Acrisols (Idegun) was 9.1307 m3/m3. September emerged as month with peak soil moisture of 0.9 m3/m3 in Lixisols and Acrisols while January and February were the months with the lowest value of 0.5 m3/m3 across the three soil classes. Soil moisture in all the soil classes also exhibited a rising trend in the climatic period with annual increase of 0.002 m3/m3 in Nitisols, 0.002 m3/m3 in Lixisols and 0.001 m3/m3 in Acrisols. Temporal pattern of soil moisture was not statistically significance while spatial variation was statistical significant across the soil classes. In a spatial data-driven society, this study has therefore unlocks the potentials of GIS and remote sensing resources in assessing global environmental change indicators in data sparse regions.

Soil moisture influences on Sierra Nevada dead fuel moisture content and fire risks

Dead fuel moisture influences the risk of fire ignition events, with implications for fire hazards, risk mitigation, and the design of prescribed burning activities. Because direct fuel moisture measurements are rarely available, fuel moisture must be estimated when evaluating fire risks. Most estimates rely primarily on atmospheric conditions and ignore the interaction of fuels with the soil surface with which they are in hydraulic contact. In this study we explore whether dead fuel moisture predictions can be improved with information about surface soil moisture. Despite the likelihood that dead fuels would exchange water with underlying soil, the influence of soil moisture on fuel moisture has been poorly studied. An analysis of 202 observations of co-located soil moisture, 1 and 10 h fuel moisture measurements, with environmental and meteorological covariates, showed that soil moisture had a small but significant effect on fuel moisture across all sampled conditions. The influence of soil moisture on fuel moisture was the most important among all other environmental factors for wet soil conditions and 10-h fuels, where a 1% increase in soil moisture content led to approximately a 0.6% increase in fuel moisture. The effect of soil moisture on 1-h fuel moisture although significant was small. Incorporating spatio-temporal variability of soil moisture into time-series or spatial predictions of fuel moisture tended to (i) reduce the seasonal duration in which fuels had a high probability of ignition by an average of 52–60 days across the tested years, and (ii) increased the heterogeneity of the probability of ignition through space, compared with similar models that did not incorporate soil moisture information.

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data

An approach to incorporate vegetation fraction into Modified Water Cloud Model (MWCM) and the evaluation of potential of multi-target random forest regression (MTFER) were done for the retrieval of spatio-temporal variability of soil moisture (SM) in Varanasi district of Uttar Pradesh, India. The Sentinel-1A SAR images were acquired for three different dates (19/12/2016, 05/02/2017 and 25/03/2017) for two types of spatial regions covered with vegetated and sparse vegetated soil field for SM retrieval. The vegetation fraction (fveg), computed from Landsat – 8 satellite data, was inserted into the modified water cloud model (MWCM) as a modification factor. Leaf area index (LAI) was used as a vegetation descriptor parameter (V) in the MWCM. Subsequently, a machine learning based MTRFR algorithm with a regularization routine was used for stable and optimum solution for complex problems related to the inversion of the MWCM for the accurate estimation of SM. The coefficient of determination (R2), root mean square error (RMSE) and nash sutcliffe efficiency (NSE) indicated significantly better results in the region-2 for all the temporal changes occurred than those of region-1. The results showed that incorporation of fveg to the MWCM provided high potential to retrieve spatio-temporal SM in the region-2 where soil fields were mostly covered with wheat and barley crops rather than in region-1 having sparse vegetated soil field. The overall accuracy of spatio-temporal retrieval of SM after incorporating vegetation factor to MWCM showed significantly better R2 = 0.82, RMSE = 3.18 (%) and NSE = 0.85. The inversion results proximate that the MTRFR techniques applied to the MWCM, including vegetation factor, has great a capability for an accurate SM retrieval in the vegetated soil field.

Spatio-temporal variability of soil moisture in a cropped agricultural plot within the Ganga Basin, India

Soil moisture dynamics in response to rainfall and irrigation events were examined using data obtained from continuous point measurements carried out under rice and wheat crops for an agricultural plot located within the Ganga Basin, India. Soil moisture data were collected using SM100 and SMEC300 sensors at 18 subplots and at four different depths (0−80 cm) during the period from 5 August 2018 to 31 March 2019. Soil moisture was decomposed into temporal mean and temporal anomalies components, and its variability was characterized considering both absolute soil moisture and temporal anomalies. They exhibited similar patterns at all the depths under rice crop cover. However, it varies with depth under wheat crop cover due to periodic wetting and drying conditions and temporally variable atmospheric demand. Similarly, the spatial variance of absolute soil moisture was decomposed into time-invariant and time-variant components. The results revealed that the time-invariant component contribution was dominant at all the depths (72.49–101.46 %) and the contribution of each component varies with soil wetness and land cover. In addition, temporal stability analysis of soil moisture was carried out. It was observed that the spatial pattern at surface depth cannot be preserved for subsurface depths, and similar subplots were found to be temporally stable at the surface and bottom depths under different crop covers. The results are expected to help improve the understanding of the nature of soil water dynamics in agricultural fields.

Temporal and spatial characteristics of soil moisture dynamics in fixed dune of the Horqin sandy land

The spatio-temporal variability of soil moisture (SM) in the fixed dune was analyzed based on the long-term monitoring data of SM at a depth of 1.5m in the fixed dune of the Horqin Sandy Land from May to September (that is growing season) for the 6th consecutive year (2009~2014) and the synchronize rainfall data. The results indicated that: (1) The changes of SM with the month generally showed a “W-shaped” curve in 2009, 2010 and 2011, and higher SM appeared in July. In 2012, the SM was lower in May and higher in June, and decreased slightly in the following three months. In 2013 and 2014, the changes of SM with the month showed a continuous downward trend. (2) Except for 2013, the SM was lower in the surface layer (0~10 cm), and the SM increased first, then decreased, and finally increased with soil depth. (3) As the soil depth increases, both SD and CV of SM showed a decreasing trend. According to the SD and CV method, the vertical variation layers of SM in fixed dunes of the study area were initially divided into the fast changing layer of 0~50 cm, the active layer of 50~90 cm and the second active layer of 90~150 cm.

Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China

Little measurement-based soil moisture information is available for the humid to semi-humid transitional zones in China. Anhui Province is a representative area of these zones and spans several hydroclimatic and geomorphological zones. To overcome shortage of observational soil moisture information in this area, we collected and compiled a multi-layer daily soil moisture record from 2012 to 2016 across Anhui Province based on the observations of a soil moisture-observing network composed of 85 sites. We then analyzed the vertical and spatiotemporal variability of soil moisture across this region. Primary findings are: (a) soil moisture in this region has apparent vertical gradients and increases with soil depth; (b) substantial soil moisture spatial variability appears in the shallow soil layers (depth ≤40 cm) with the highest values in the middle part of this region, but soil moisture in the deep layers (depth >40 cm) shows low spatial variability; (c) seasonality of soil moisture across this region is complicated and has fluctuations with July as the wettest month in most of the province; (d) inter-monthly fluctuation of soil moisture is larger in the upper soil layers than in the lower layers; and (e) antecedent accumulated rainfall is a major factor influencing soil moisture throughout this region with soil moisture in the upper layers more sensitive to immediate weather conditions and soil moisture in the lower layers showing lagged responses to the weather conditions. This study can not only reveal the spatiotemporal and vertical characteristics of soil moisture across this region, but also provide a useful reference dataset for the other soil moisture modeling and retrieval studies.

Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau

The Soil Moisture Active Passive (SMAP) satellite can no longer directly deliver high-resolution (9 km) soil moisture products with the failure of the onboard L-band radar. Thus, an appropriate replacement sensor and new algorithms are urgently needed to compensate for the lost radar and estimate soil moisture at high-resolution. This paper presents a new downscaling approach, the Downscaling basEd oN gradient boosting deciSion trEe (DENSE) method to solve this problem. We analyzed 26 soil moisture related indices, derived from MODIS and a digital elevation model, to identify proxy variables for soil moisture variability. A gradient boosting decision tree regression links the aggregated soil moisture proxies and SMAP observations at a coarse scale to express the nonlinear relationships between them. High-resolution soil moisture products were generated by applying this built regression model to the optimal soil moisture proxies at a fine scale over the entire Tibetan Plateau during the years 2015–2017. In situ measurements were collected from the Ngari, Naqu, and Maqu networks, which represent different climatic and vegetation conditions. We evaluated the downscaled soil moisture against ground observations at the daily, point, and network scales. The results indicated that the DENSE method effectively infers the spatio-temporal variability of soil moisture; and at the same time, preserves SMAP soil moisture accuracy. The performance of the proposed method failed in the Maqu network as this area is covered by dense vegetation. Although further improvements are still needed to correct vegetation effects, nevertheless, the DENSE method improved the spatial resolution of SMAP soil moisture estimates, from 36 km to 1 km over the Tibetan plateau where high-resolution soil moisture products are necessary to support local and global hydrological applications.

Spatiotemporal Analysis of Soil Moisture and Optimal Sampling Design for Regional‐Scale Soil Moisture Estimation in a Tropical Watershed of India

AbstractRegional‐scale soil moisture estimates are essential for several hydrological applications and for validating remote sensing‐based soil moisture products. Characterization of the regional‐scale soil moisture variability requires a robust in situ monitoring strategy at point scale to balance between representativeness and minimization of monitoring cost. In this study an optimal sampling design was determined to capture the spatiotemporal variability of soil moisture at watershed scale. The study was conducted for the typical Indian conditions of extreme seasonal variability that leads to very wet (during monsoon) to dry (during hot summer) in the eastern India. Soil moisture monitoring was done at 83 locations in an agricultural watershed of 500 km2 for 56 days of field campaigns across a year. Based on the analyses of 41,832 measurements collected during field campaigns, it was found that maximum numbers of required locations necessary to estimate watershed‐mean soil moisture within ±2% accuracy are 30. Moreover, five randomly selected locations were found to be sufficient for capturing the temporal variability of watershed‐mean soil moisture with an accuracy of ±3%. In addition, five most representative locations identified through time stability analysis can provide robust estimate of watershed‐mean soil moisture with accuracy of ±2%. Further, soil properties and topography are identified as significant physical parameters that jointly control the spatiotemporal persistence and variability of soil moisture in the Indian watershed. These findings will be quite useful to provide guidelines for optimizing short‐term soil moisture campaigns by sampling at few selected points representative of the watershed‐mean behavior.

Spatiotemporal variability of soil moisture in arid vegetation communities using MODIS vegetation and dryness indices

Among the methods devised to delineate soil and water information, optical/thermal remote sensing offers great capabilities by providing spatiotemporally multi-scale data and products like surface dryness indices. In this context, the present study utilized the MODIS-based TVDI, iTVDI, and VDI indices to scrutinize the effect of dryness on soil moisture in three vegetation communities: Artemisia spp. (sagebrush), Astragalus spp. (astragale) and grassland in central Iran during a wet (2004) and a dry year (2008). The MOD13A2, MOD11A2, and MOD09A1 products acquired on DOYs (day of the year) 97, 129, 161, and 193 were used to extract dryness and vegetation indices. The downscaled 32-day interval PERSIANN-CCS products and field-measured soil moisture were used to appraise the validity of indices. The results showed statistically significant relationships (P < .05) between iTVDI and cumulative precipitation and soil moisture in all vegetation communities and on most DOYs, suggesting iTVDI as an effective indicator to monitor dryness status. Foremost among the vegetation indices, NDVI was found as a good complementary indicator to estimate vegetation-soil moisture conditions due to exhibiting stronger linear associations with precipitation, particularly in grassland during the wet (R2 = 0.81) and the dry (R2 = 0.72) year. The lowest values of dryness indices were observed in western and southern parts of the study area. In general, it was concluded that side by side assessment of vegetation and dryness indices was a more promising approach to dryness assessment and management over arid and semi-arid rangelands.

Statistical analysis of estimated and observed soil moisture in sub-humid climate in north-western Jordan.

In this study, soil water balance model was implemented to estimation moisture in two sub-humid areas located in north-western part of Jordan namely Irbid and Ras Muneef. In addition, in situ observations of soil moisture were collected from 16 randomly distributed sampling locations and used for monitoring the spatiotemporal variability of soil moisture in the study area. Sampling was performed during the growing season in the study area with a total of seven sampling occasions from 11 March to 12 April 2017. The results showed that the estimated soil moisture in Ras Muneef was slightly higher than Irbid. This might be referred to variations of, for instance, Ras Muneef receiving higher precipitation and lower temperature values comparing to Irbid. Also, we noticed that the spatiotemporal variability of the observed soil moisture is directly linked with the precipitation in our study area. The coefficients of the spatial and temporal variabilities were in the range 3 to 40% and 8 to 15%, respectively. Due to the high Spearman's rank correlation values which range from 0.42 to 0.73, soil moisture is temporarily stable. Strong and positive relationship was found between the estimated and observed soil moisture with determination coefficients (r2) and root mean square error (RMSE) around 0.7 and 0.41, respectively, whereas negative relationship between evapotranspiration and observed soil moisture was shown with R3and RMSE are 0.34 and 0.24 respectively. This indicates that actual soil moisture can be predicted from soil water balance model.

How effective is information on soil-landscape units for determining spatio-temporal variability of near-surface soil moisture?

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within the landatmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil, and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. Therefore, we assume the soil-landscape units as hydrotopes or hydrological similar units. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. In this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the observed spatial variations in soil moisture along the forested hillslopes. The spatial structure explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices.

Applying fractal analysis to detect spatio-temporal variability of soil moisture content on two contrasting land use hillslopes

Soil moisture variations in space and time are critical in ecological, hydrological, pedological and environmental studies. This study used fractal analysis to detect the spatio-temporal variability of soil moisture on two contrasting land use hillslopes in the hilly area of Taihu Lake Basin of China. Surface (0–20cm) soil moisture data from January 2013 to September 2015 (a total of 37 sampling days) were analyzed at 39 and 38 sites on the tea garden and forest hillslopes, respectively, with a spatial resolution of about 8m. Results showed that the forest hillslope was significantly (P<0.05) wetter than the tea garden hillslope. The spatial mean soil moisture on both hillslopes had a significant negative linear correlation (R2=0.753 for tea garden (P<0.05) and R2=0.459 for forest (P<0.05)) with corresponding CV for 37 sampling dates. The fractal dimension (D) was found to be better than the nugget/sill ratio in describing the spatial dependence of soil moisture. The advantage of the D is that it does not require the modelling of the semivariogram since it can be calculated on the basis of the experimental semivariogram. Soil moisture on the forest hillslope showed stronger spatial dependence than that on the tea garden hillslope according to D. However, the soil moisture on both hillslopes showed similar temporal dependence and a low-to-moderate autocorrelation structure. In addition, the temporal variability of soil moisture content was spatially correlated on tea garden hillslope. If a location is temporally autocorrelated, the locations nearby tend also to be temporally autocorrelated. It would be possible to make more accurate moisture trend predictions for temporal autocorrelated locations with small D. These findings had important applications related to sampling design, simulation of soil water flow and agricultural water resources management.

Vegetation Controls on the Spatio-Temporal Heterogeneity of Deep Moisture in the Unsaturated Zone: A Hydrogeophysical Evaluation

Information on the spatio-temporal variability of soil moisture in the vadose zone is important to assess groundwater recharge and solute transport in unconsolidated substrate as influenced by biological processes. Time-lapse electrical resistivity imaging (ERI) was used to monitor soil moisture dynamics to a depth of 9 m in a grassland, a grassland encroached by a juniper species (eastern redcedar, Juniperus virginiana), a juniper woodland and an oak forest in the south-central Great Plains, Oklahoma, USA. A site-specific relationship between moisture content and electrical conductivity data was developed for the soil zone, and a perched water zone was monitored at two of the sites. Results showed that (a) change in soil moisture content was linearly correlated to change in electric conductivity in the soil zone; (b) vegetation cover type induced differences in vertical bulk electrical resistivity (ER) profiles and influenced the temporal evolution of soil moisture profiles; and (c) juniper encroachment lowered the water level in the perched groundwater aquifer. Our results suggest land use and vegetation cover type, as opposed to rock properties, controls deep water drainage for the vegetation transition zone. Methods used to measure hydrogeophysical changes, such as ERI, can be used for broader understanding of geological, physical, and biological processes and their links in Earth’s critical zones.

Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

Spatio-temporal variability of soil moisture (θ) is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time θ monitoring methods. This restricted the comprehensive and intensive examination of θ dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope θ dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict θ at 39 manual sites (collected 1–2 times per month) with θ collected at three real-time monitoring sites (collected every 5mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth of this site. Results showed that θ at the 39 manual sites can be reliably predicted (root mean square errors <0.028m3m−3) using both SMLR and SVM. The linear or non-linear relationship between θ at each manual site and at the three real-time monitoring sites was the main reason for choosing SMLR or SVM as the optimal prediction model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and θ temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope θ spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope θ dynamics can be acquired successfully.

On the observed hysteresis in field-scale soil moisture variability and its physical controls

The spatiotemporal variability of soil moisture (θ) has rarely been studied at the field scale across different seasons and sites. Here, we utilized 9 months of θ data in two semiarid ecosystems of North America to investigate the key relationship between the spatial mean (〈θ〉) and standard deviation (σθ) at the field-scale (∼100 m). Analyses revealed a strong seasonal control on the σθ versus 〈θ〉 relation and the existence of hysteretic cycles where wetting and dry-down phases have notably different behavior. Empirical orthogonal functions (EOFs) showed that θ variability depends on two dominant spatial patterns, with time-stable and seasonally varying contributions in time, respectively. Correlations between EOFs and land surface properties also indicated that θ patterns are linked to vegetation (terrain and soil) factors at the site with higher (lower) vegetation cover. These physical controls explained the observed hysteresis cycles, thus confirming interpretations from previous modeling studies for the first time.