Patterns Of Soil Water Storage Research Articles

Scale-specific controls of soil water storage along a transect in a semiarid catchment

Spatiotemporal variability in soil water storage (SWS) is controlled by different factors operating at various intensities and scales. The traditional Pearson’s correlation analysis can be used to identify the linear correlations at the measurement scale only. In this study, wavelet coherency analysis was used to investigate scale-specific relationships between SWS and selected controlling factors. SWS of 135 sampling locations were calculated along a 1,340-m long sampling transect established in a typical catchment on the Loess Plateau, China. The selected controlling factors were soil saturated hydraulic conductivity (Ks); clay, silt, sand, and soil organic carbon (SOC) contents; elevation; and aboveground biomass (AGB). The spatial pattern of SWS measured in growing and nongrowing seasons and at different soil depths was similar. At all the sampling points, except for some locations in the depression area, SWS in the growing season was relatively greater than that in the nongrowing season. The influence of factors on SWS varied with scale, with soil Ks, clay and sand contents significantly correlated with SWS at large scales. Season and soil depth had no significant effect on scale-specific relationships between SWS and the controlling factors. The wavelet coherency analysis identified the type of correlation at different scales and locations. The outcomes of this study offer meaningful insights into the hydrological processes that can be observed in the Loess Plateau region and could have significant implications for future hydrological systems and water resources management.

Scale and location dependent time stability of soil water storage in a maize cropped field

Time stability of soil water storage (SWS) is the similarity of spatial patterns of soil water storage over time and is scale and location dependent due to multivariate effects of controlling factors. However, our understanding of scale and location dependency of the time stability of SWS and its seasonally dependent controls in cropped fields is inadequate. This study examined the scale and location dependent time stability of SWS at multiple depths, over different seasons in a cropped field in western Quebec, Canada. Soil water content was measured at 0.05, 0.10 and 0.20 m depths along six transects (128 locations per transect) using the actively heated fiber optic (AHFO) technique. Wavelet coherency analysis was used to examine the scale and location dependent time stability of SWS over different seasons. Results showed strong intra-seasonal time stability across all scales, locations and depths during the dry period in the summer due to a dominant control of evapotranspiration (ET). The weak inter-seasonal time stability of SWS showed the change in the dominant processes controlling the spatial patterns of SWS in different seasons. While the intra-seasonal time stability was also strong during the wet period of autumn, correlations were not significant across all scales, locations and depths, likely due to the dominant effects of smaller to medium scale processes. The results of the study clearly showed that the dominant hydrological processes controlling the time stability of SWS at different depths during a dry period in summer were different than that of a wet period in autumn. The change in the dominant hydrological processes affected the spatial scales and the locations of the similarity of the spatial patterns of SWS in the field. Therefore, the analysis outcome can be used to identify the change in the sampling domain as controlled by the hydrological processes operating at different scales and locations delivering the maximum information with minimum sampling effort.

Spatial and temporal variability of 0‐ to 5‐m soil–water storage at the watershed scale

AbstractDynamic relationships among rainfall patterns, soil water distribution, and plant growth are crucial for sustainable conservation of soil and water resources in water‐limited ecosystems. Spatial and temporal variation in deep soil water content at a watershed scale have not yet been characterized adequately due to the lack of deep soil water data. Deep soil–water storage (SWS) up to a depth of 5 m (n = 73) was measured at 19 sampling occasions at the LaoYeManQu watershed on the Chinese Loess Plateau (CLP). At a depth of 0–1.5 m, the annual mean SWS was highly correlated with rain intensity, and the correlation decreased with depth, but within the layers at 1.5–5.0 m, the changes in SWS indicated a lag between precipitation and the replenishment of soil water. Geostatistical parameters of SWS were also highly dependent on depth, and the mean SWS presented similar spatial structures in two adjacent layers. Temporal stability of SWS as indicated by mean relative difference, standard deviation of the relative difference (SDRD), and mean absolute bias error (MABE) was significantly weaker at the shallow than at deeper layers. Soil separates and organic carbon content controlled the spatial pattern of SWS at the watershed scale. One representative location (Site 57) was identified to estimate the mean SWS in the 1‐ to 5‐m layer of the watershed. Semivariograms of the SDRD and MABE were best fitted by an isotropic spherical model, and their spatial distributions were depth‐dependent. Both temporal stability and spatial variability of SWS increased over depth. This study is helpful for deep SWS estimation and sustainable management of soil and water on the CLP, and for other similar regions around the world.

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 and temporal patterns of soil water storage and vegetation water use in humid northern catchments

Using stable isotope data from soil and vegetation xylem samples across a range of landscape positions, this study provides preliminary insights into spatial patterns and temporal dynamics of soil-plant water interactions in a humid, low-energy northern environment. Our analysis showed that evaporative fractionation affected the isotopic signatures in soil water at shallow depths but was less marked than previously observed in other environments. By comparing the temporal dynamics of stable isotopes in soil water mainly held at suctions around and below field capacity, we found that these waters are not clearly separated. The study inferred that vegetation water sources at all sites were relatively constant, and most likely to be in the upper profile close to the soil/atmosphere interface. The data analyses also suggested that both vegetation type and landscape position, including soil type, may have a strong influence on local water uptake patterns, although more work is needed to explicitly identify water sources and understand the effect of plant physiological processes on xylem isotopic water signatures.

Estimation of spatial variability of soil water storage along the south–north transect on China’s Loess Plateau using the state-space approach

Purpose Soil water is a critical variable for hydrological and biological processes in arid and semi-arid ecosystems. Information on regional spatial pattern of soil water storage (SWS) and its relationship with environmental factors is important for optimal water management and vegetation restoration in China’s Loess Plateau (CLP) region. State-space approach and artificial neural network (ANN) were used to analyze spatial variability of SWS in the CLP region.

Spatiotemporal Characteristics of Soilwater Storage Along Regional Transect on the Loess Plateau, China

Knowledge of the spatiotemporal variability of soil water storage (SWS) is important for vegetation restoration and water resources optimization in semi‐arid areas. This study investigated the spatiotemporal characteristics of SWS in the 0–5 m profile and identified representative sites for reliably estimating the mean SWS along regional transect of the Loess Plateau. Mean relative difference and the associated standard deviation (SDRD) were used to assess temporal stability (TS) of profile SWS. The time‐averaged mean SWS in the 5‐m profile generally decreased from south to north following the decreasing rainfall gradient, with a mean value of 704 mm. Spatiotemporal analysis showed that temporal variations in SWS decreased with increasing soil depth, while the spatial variations increased. Comparisons of SDRD and the number of time‐stable locations with SDRD &lt;5% among various depths indicated that TS increased with increasing soil depth. The representative sites identified for the transect well reflected mean SWS of the five soil layers, and the accuracy of estimation increased with increasing soil depth. The TS of SWS patterns was controlled by precipitation, temperature, clay content, field capacity, soil organic carbon, elevation, slope gradient, and normalized difference vegetation index in the study area. This study demonstrates the feasibility of directly estimating the patterns of SWS using TS analysis on a large scale, which might be comprehensively controlled by the combined effects of climate, soil, topography, and vegetation.

Soil water storage prediction at high space–time resolution along an agricultural hillslope

Soil water storage (SWS) information at high space–time resolution is critical for understanding numerous hydrological, biological and chemical processes. However, obtaining such information is time- and cost-intensive due to the strong variability of SWS. We hypothesized that SWS information could be predicted accurately at high space–time resolution and low cost using the temporal stability (TS) concept. The water contents of different soil layers down to 1.0m in depth were measured along a 3.1ha slope from July 2013 to July 2015 at 4 locations using automatic measurement systems and at 103 locations manually. These values were multiplied by depth to convert them into SWS (0–0.2, 0.2–0.5, 0.5–1.0, and 0–1.0m). The spatial patterns of SWS were temporally stable. The SWS values were predicted at high space–time resolution by combining high-space and low-time resolution data and high-time and low-space resolution data using two methods. The first method (M1) was based on the most temporally stable locations (MTSLs) among four auto-measured locations. The second method (M2) identified the MTSLs from 107 locations including 103 manually measured locations of the four soil layers. The MTSLs of M2 were assigned high-time resolution data based on the relationships between the MTSLs of M1 and M2 at each soil layer. Once the MTSL and temporal stability relationship (TSR) of these two methods were identified, the SWS data for one auto-measured location (A4) were sufficient to predict the spatially averaged and spatially distributed SWS for the slope at any time. Although the predictive errors for M1 were generally acceptable, M2 was more accurate than M1 in most of the cases studied. The estimation errors for M2 were all less than 10% and were generally less than 5%. Among the four investigated soil layers, M2 outperformed M1 for the 0–0.2 and 0–1.0m soil layers, and the two methods yielded comparable results for the 0.5–1.0m soil layer. Meanwhile, although M1 slightly outperformed M2 for the 0.2–0.5m soil layer, both performed well. This method for predicting SWS at high accuracy and low cost could improve the prediction accuracy of early drought warnings and agricultural water resources management.

Use of a state-space approach to predict soil water storage at the hillslope scale on the Loess Plateau, China

Soil water storage is a critical variable controlling hydrological and biological processes. The precise estimation of soil water storage in diverse soil layers is fundamental to understanding hydro-biological processes and efficiently managing water resources. The objectives of this study were to evaluate the effects of topography (elevation) and soil properties (clay, silt, sand content, median grain size, and fractal dimension) on soil water storage and then to estimate soil water storage using a state-space approach. The soil water storage values of three soil layers (0–1, 1–2, and 2–3m) were measured from May to December 2014 at 70 locations along two 187m long transects on a hillslope of the Loess Plateau, China. Samples from various depths were also collected to determine soil properties. The best state-space approach explained 98.8% of the total variation in soil water storage, while the best classical linear regression equation only explained 64.2%. The state-space approach using any combination of variables described the spatial pattern of soil water storage much better than equivalent linear regression equations. Elevation and clay content were identified as the most effective combination for soil water storage estimation in the state-space approach, and were used to effectively predict the soil water storage spatial pattern along the second transect. The state-space approach is thus a useful tool that is recommended for predicting soil water storage spatial patterns at the hillslope scale using topography and soil properties.

Depth persistence of the spatial pattern of soil–water storage along a small transect in the Loess Plateau of China

Knowledge of the spatial patterns of soil water storage (SWS) in soil profiles is important for understanding the dynamics of soil water between surface and subsurface soil layers in semiarid area. We investigated the depth persistence of the overall and scale-specific spatial patterns of SWS for different soil layers during different seasons. Soil water contents were measured using a neutron probe on 22 occasions in 2012 and 2013 along a 1340-m transect over several sub-watersheds in the Liudaogou catchment on the Loess Plateau of China. Similarities in the spatial patterns of SWS were analyzed by Spearman's rank correlations and wavelet coherency. A spatiotemporal analysis indicated that the temporal evolution of the SWS profiles differed between the growing and non-growing seasons and that landscape position and soil texture determined the amount of SWS at each sampling location. Spearman's rank correlations were significant between any two layers within different seasons, and the correlation coefficients decreased as the distance between layers increased. Clay content controlled the spatial pattern of SWS between layers at large scales. The SWS spatial pattern had a higher depth persistence during the non-growing season than during the growing season, and the soil layer had a larger effect than season on the similarity in SWS spatial patterns. These results can improve our understanding of the hydrological processes in soil profiles and can be of considerable value in the application of hydrological models and in water management. (C) 2015 Elsevier B.V. All rights reserved.

Scaling analysis of soil water storage with missing measurements using the second‐generation continuous wavelet transform

SummaryInformation on the spatial variability of soil water storage (SWS) at different scales is important for understanding various hydrological, ecological and biogeochemical processes in the landscape. However, various obstructions such as roads or water bodies may result in missing measurements and create an irregular spatial series. The wavelet transform can quantify spatial variability at different scales and locations but is restricted to regular measurements. The objective of this study was to analyse the spatial variability of SWS with missing measurements using the second‐generation continuous wavelet transform (SGCWT). Soil water content (converted to SWS by multiplying with depth) was measured with a neutron probe and time‐domain reflectrometry along a transect of 128 points. Because there were missing measurements, I used SGCWT to partition the total variation into different scales and locations. Whilst there were some small‐scale variations (&lt; 20 m) along the transect, the medium scale variations (20–70 m with an average of about 30–45 m) were mainly concentrated within the depressions along the transect. The strongest variations were observed at around 90–110 m scale, representing the variations resulting from alternating knolls and depressions. Similar spatial patterns at different scales were observed during different seasons, indicating temporal stability in the spatial pattern of SWS. Among the controlling factors, the wavelet spectra of relative elevation (RE) and organic carbon (OC) were very similar to that of SWS. The wavelet covariance was also large between SWS and RE and OC at all seasons. As the OC reflects the long‐term history of water availability and might be controlled by topographic setting or elevation, it can be concluded that elevation is an important controlling factor of SWS irrespective of seasons in this type of landscape. The SGCWT provides a new way of analysing the spatial variability of regularly measured soil properties or those with missing measurements.

Landscape characteristics influence the spatial pattern of soil water storage: Similarity over times and at depths

Similarity in the spatial patterns of soil water storage (SWS) over time and at depths at multiple scales and locations reflects the similarity in the underlying hydrological processes. The objective of this study was to examine the similarity in the spatial patterns of SWS and its characteristic landscape positions for variable soil depths and over time at a field scale. Soil water content (further converted to SWS by multiplying with depth) was measured for five years (2007–2011) along a transect of 128 points at a study site that has representative hummocky landscape of the North American Prairie Pothole region. Surface (0–20cm) and subsurface (20–140cm at 20cm interval) soil water contents were measured using time domain reflectometry and a neutron probe, respectively. High rank correlation coefficient between the measurements over time and at any depth layers (surface=0–20cm, root zone=0–60cm and total active soil profile=0–120cm) indicated strong similarity of the spatial patterns of SWS and thus the underlying hydrological processes. The spatial patterns at large scales (>72m) were contributed by alternating knolls and depressions (dominant macro-topographical variations in this type of landscape) and were very similar between any measurement times and depth layers. Similarity over time was changed at medium scales (18–72m) due to the changes in the landform elements. However, changes in the small-scale (<18m) spatial patterns were not associated with any landscape characteristics. Similarity was increased at different scales with increase in soil depth owing to strong buffering capacity. Information on the similarity of the spatial patterns at different scales and locations can be used to identify change in sampling domain as controlled by hydrological processes operating at different scales and locations and thus can deliver maximum information with minimum sampling efforts.

Season- and depth-dependent time stability for characterising representative monitoring locations of soil water storage in a hummocky landscape

Characterizing the inherent spatial variability of soil water is intensive in terms of sampling effort and therefore costly. The objectives of this study were to examine time stability of the spatial pattern of soil water storage (SWS) at different seasons and depths, to identify representative monitoring locations (RMLs) that consistently reflect field average SWS, and to recommend efficient ways to identify these locations. Soil water was measured down to 1.4m at 0.20-m vertical depth intervals using time domain reflectometry and neutron backscattering along a hummocky transect in semiarid central Canada. Strong Spearman rank correlation coefficients between SWS spatial series indicated similarity of spatial patterns, which showed strong seasonal dependence: intra-season time stability was stronger than inter-annual time stability, which was stronger than inter-season time stability. The observed time stability was used to identify an RML. The RML for the surface layer (0–0.20m) was different from the root zone (0–0.60m) and the total active soil profile (0–1.20m), the latter two having adjacent RMLs. The bias (~3%–10% maximum) associated with using an RML to predict field-averaged SWS was examined using regression. However, the prediction of field-averaged SWS using an RML was better compared to the prediction using field extreme locations in terms of error associated with the prediction. Predictions using more than one RML yielded better results than when a single RML was used. Successful and rapid identification of RMLs greatly reduced the number of observations needed to characterize the average soil water behaviour of a field. The measurement of SWS through representative monitoring location(s) may be used for environmental monitoring and modelling, irrigation scheduling, nutrient recommendations, and predicting greenhouse gas emissions.

Application of multivariate empirical mode decomposition for revealing scale-and season-specific time stability of soil water storage

Spatial patterns of soil water storage (SWS), the total amount of water stored in soil at a given depth interval, tend to be similar if we measure at different times. This is characterized as time stability and can be used to optimize sampling design. The objective of this study was to examine the scale- and season-specific time stability of SWS spatial patterns at seven depth intervals (at every 20cm down to 140cm) in a hummocky landscape. Soil water content was measured 20 times using time domain reflectometry and a neutron probe along a transect of 128 points over a four-year period and converted to SWS by multiplying by the depth intervals. Multivariate empirical mode decomposition (MEMD) was used to decompose the spatial series of SWS into six or seven (depending on depth) components known as intrinsic mode functions (IMFs). Each IMF represents a specific scale of SWS. Spearman's rank correlation coefficients between IMFs from different measurement dates were used to characterize the time stability of SWS at different scales. The variance of each IMF and its ratio to the variance of the original SWS series was calculated. The dominant scale, which has the maximum ratio of variance to the original variances, was 104–128m (IMF5) for the depth intervals of 0–20cm to 60–80cm and 315–412m (IMF7) for the depth intervals of 80–100cm to 120–140cm. Time stability gradually increased with spatial scales and was the strongest at the dominant scale. At any scale, time stability was the strongest within the same season and the weakest between different seasons. This study indicates that MEMD combined with Spearman's rank correlation analysis has great potential for revealing the scale specific time stability of SWS.

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

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

Multifractal detrended fluctuation analysis in examining scaling properties of the spatial patterns of soil water storage

Abstract. Knowledge about the scaling properties of soil water storage is crucial in transferring locally measured fluctuations to larger scales and vice-versa. Studies based on remotely sensed data have shown that the variability in surface soil water has clear scaling properties (i.e., statistically self similar) over a wider range of spatial scales. However, the scaling property of soil water storage to a certain depth at a field scale is not well understood. The major challenges in scaling analysis for soil water are the presence of localized trends and nonstationarities in the spatial series. The objective of this study was to characterize scaling properties of soil water storage variability through multifractal detrended fluctuation analysis (MFDFA). A field experiment was conducted in a sub-humid climate at Alvena, Saskatchewan, Canada. A north-south transect of 624-m long was established on a rolling landscape. Soil water storage was monitored weekly between 2002 and 2005 at 104 locations along the transect. The spatial scaling property of the surface 0 to 40 cm depth was characterized using the MFDFA technique for six of the soil water content series (all gravimetrically determined) representing soil water storage after snowmelt, rainfall, and evapotranspiration. For the studied transect, scaling properties of soil water storage are different between drier periods and wet periods. It also appears that local controls such as site topography and texture (that dominantly control the pattern during wet states) results in multiscaling property. The nonlocal controls such as evapotranspiration results in the reduction of the degree of multiscaling and improvement in the simple scaling. Therefore, the scaling property of soil water storage is a function of both soil moisture status and the spatial extent considered.

Extracting soil water storage pattern using a self-organizing map

Soil water is one of the important controlling factors regulating plant growth in a semi-arid environment. It is distributed non-uniformly and exhibits spatial patterns in the landscape. Research has identified different controls of soil water in wet and dry state; however, there are no objective methods for differentiating the dry and wet states. The objective of this study was to extract the temporally persistent spatial patterns of soil water using a Self-Organizing Map (SOM) and identify benchmark sites for different patterns of soil water. Soil water storage along a transect of 576m at 4.5m intervals in a farm field in Saskatchewan, Canada was monitored from 2007 to 2009. A SOM was used to extract the patterns of soil water storage of the surface 0.2m depth compartment. Two patterns were identified: pattern 1 for dry states and pattern 2 for wet states, with the occurrence frequency of 52.9% and 47.1%, respectively. Pattern 1 was often found in summer and fall, and pattern 2 in early spring, and summer in wet years. Soil texture and organic carbon (OC) content showed strong correlation with soil water storage in dry states (pattern 1) and wet states (pattern 2). Based on the aforementioned finding, benchmark sites from each of the two patterns were identified, and they were not adjacent in terms of spatial location. Therefore, soil water states can be identified by using SOM and differentiation of wet and dry states enable identification of more representative benchmark sites.

On the relative role of upslope and downslope topography for describing water flow path and storage dynamics: a theoretical analysis

AbstractMany current conceptual rainfall‐runoff and shallow landslide stability models are based on the topographic index concept derived from the steady‐state assumption for subsurface water flow dynamics and the hypothesis that the surface gradient is a good approximation for the gradient of the total hydraulic head. However, increasing field evidence from sites around the world has shown poor correlations between the topographic index and the patterns of soil water storage. Here we present a new, smoothed, dynamic topographic index and test the ability of this index to reproduce spatial patterns of wetness areas and storage as provided by a distributed, physically based, Boussinesq equation (BEq) solver. Our results show that the new smoothed dynamic topographic index outperforms previous, locally computed indices in the estimation of storage dynamics, resulting in less fragmented and disconnected spatial patterns of storage. Our new dynamic index is able to capture both the upslope and downslope controls on water flow and approximates storage dynamics across scales. The new index is compatible with high‐resolution topographic data. We encourage the use of our smoothed dynamic topographic index to describe the lateral subsurface flow component in landslide generation models and conceptual rainfall‐runoff models, especially when high‐resolution digital elevation models are available. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Identifying scale specific controls of soil water storage in a hummocky landscape using wavelet coherency

High spatio-temporal variability of soil water is contributed from different ecohydrological and soil processes operating in different intensities at different scales. Traditional Pearson correlation analysis only examines linear correlation at the measurement scale. In this study, the correlation between soil water storage and its controlling factors was examined at different scales and locations in a hummocky landscape using wavelet coherency. Time domain reflectometry and neutron probe were used to measure soil water storage up to 1.4 m depth along a transect of 576 m long established in a hummocky landscape at St. Denis National Wildlife Area, Saskatchewan, Canada. In spite of visual similarity of the spatial pattern of soil water storage and elevation, the value of Pearson correlation coefficient was very small. However, wavelet coherency identified strong scale- and location-specific correlations between soil water storage and elevation. The total area of significant correlations as calculated from the total number of significant coherencies at different scales and locations was higher between soil water storage and elevation than between soil water storage and any other factors, which indicated a dominant control from elevation on soil water storage in the hummocky landscape. The largest area of significant correlation was observed at large scales (> 70 m), which can be attributed to the alternating knolls and depressions. The relationship between soil water storage and elevation at different scales was persistent at different times of the year or at different seasons with a slight reduction in the magnitude of correlation. The persistent relationship indicated the dominant control from elevation with slight change in the degree of the control. The scale-location specific correlation provides a complete picture on the controls of soil water storage, which was not possible with traditional correlation analysis.

Scales and locations of time stability of soil water storage in a hummocky landscape

Summary Different factors and processes operating in different intensities and at different space–time scales result in strong spatio-temporal variability in soil water storage. However, there is similarity between the overall spatial patterns of soil water storage measured at different times, which has been identified as time stability. The objective of this study was to examine the scales and locations of time stability of soil-water storage spatial patterns at different seasons and depths in a hummocky landscape. Soil water storage was measured up to 140 cm depth over a 4-year period using time domain reflectometry and a neutron probe along a transect in the St. Denis National Wildlife Area, Saskatchewan, Canada. The transect was 576 m long with 128 sampling points (4.5 m sampling interval) and traversed several knolls and depressions. There were high Spearman’s rank correlation coefficients between any two-measurement series, indicating strong time stability of the spatial pattern of soil water storage. The spatial patterns of soil-water storage from the same season (intra-season) had stronger time stability than those from different seasons (inter-season). Strong time stability was also observed between the measurement series from a season of 1 year and a measurement series from the same season of another year (inter-annual). Wavelet coherency analysis indicated that the large-scale (>72 m) spatial pattern was time stable irrespective of seasons due to the alternating knolls and depressions in the study area. There were also near replica spatial patterns at small (