Variability Of Soil Water Storage Research Articles

Variability analysis and the conservation capacity of soil water storage under different vegetation types in arid regions

Time-series analysis of soil water storage (SWS) can provide important information on critical hydrological variability related to soil water recharge, retention, and vegetation water consumption, all of which are highly non-linear and non-stationary, making mining and analysis extremely challenging. In this study, Multivariate Empirical Mode Decomposition (MEMD) was used to separate and analyze time-series data on soil moisture, soil temperature, and meteorological factors in three different land types (bare land [BL], grassland [GL], and shrubland [SL]) in the Mu Us Desert, Shaanxi Province, China. Path analysis was then used to reveal the driving force of periodic variability in SWS after decomposition. Using daily SWS data, two new indicators, evapotranspiration capacity (Ke) and rainfall interception capacity (Kp), were also proposed in order to improve the accuracy of soil water conservation capacity calculation. The results revealed strong time-persistent memory of SWS in all land types, with hydrological periodic variability mainly reflected on a monthly scale (34–45 d). The greatest memory period was observed in SL followed by BL and GL. Subsequent path analysis suggested that dry-wet alternation (R2=0.32–0.48) driven by interactions between precipitation, solar radiation, and saturated vapor pressure were the main causes of SWS variability in the summer. While, freeze–thaw cycles (R2=0.73–0.86) driven by the soil temperature and meteorological factors were the main causes of SWS variability in winter. In the root zone (0–150 cm), the water conservation capacity of BL, GL, and SL was 139.24, 38.24, and 24.76 mm/y, respectively. In addition, the Ke/Kp values and MEMD residual component revealed that SL has greater potential for water conservation than GL. Overall, these findings suggest that in order to conserve local groundwater aquifers, areas of BL require appropriate protection in arid regions. The results of this study provide a basis for future analyses of soil moisture variability and water conservation in arid regions.

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.

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Determining optimal nitrogen input rate on the base of fallow season precipitation to achieve higher crop water productivity and yield

Precipitation is the major cause of crop yield variation in rainfed agriculture production on the Loess Plateau. As overfertilization is economically and environmentally undesirable, and crop yield and the resulting returns for N input are uncertain when rainfall variability is high, optimizing N management according to the precipitation of fallow season is vital for efficient crop water use and high yield in dryland/rainfed farming systems. Thus, this study established a reference range describing wheat cultivation year types based on fallow season precipitation over 37 years. We conducted an 8-year field experiment using seven different N input rates, to focus on soil water content and its vertical distribution in the 0–300 cm soil layers and to assess the relationship between soil water utilization and yield formation. The final objective was to evaluate how the optimal N input affects crop water consumption, with the aim of maximizing yields. Eight experimental years (2009–2017) were classified as dry and normal years (four of each), with optimum N application rates of 150 and 180 kg Nha−1, respectively. Compared with that used by farmers (210 kg Nha−1), optimum N input saved 26.8% and 12.3% of N fertilizer, increased grain yield by 5.0% and 5.2%, and improved crop water productivity (CWP) by 6.1% and 8.5% in dry and normal years, respectively. Optimized N inputs reduced soil water consumption before the jointing stage and increased the soil water available for grain yield production. Soil water depletion occurred from deeper soil layers with crop development, and soil water storage (SWS) at 80–240 and 200–300 cm depth played an important role in increasing spike number and grain filling, respectively. Furthermore, soil water conservation treatment (increasing rainwater storage via deep plowing at start of the summer fallow season) was unable to eliminate the variability in soil water storage at sowing, and as “drought at sowing” effects on yield were more adverse than “drought in growing season” effects in dryland systems, our results indicate that adjusting N fertilizer inputs based on summer rainfall variation could enhance wheat yield and CWP in the rainfed farming system.

Soil Water Storage Temporal Stability With and Without Outliers

The knowledge on the temporal stability of spatial variability of soil water storage in the crops’ root zone is of fundamental importance for soil and water management. The objective of this work was to characterize the temporal distribution of water storage in a Latossolo vermelho amarelo and identify field locations with spatial patterns of high, intermediate and low soil water storage, in 13 samplings every 14 days. The assessed period included periods of drying and water recharge of the soil, along which soil water content was determined at 60 sampling points arranged in a 5 × 5 m grid covering an area of 1250 m2 (25 × 50 m). Soil water content was determined by means of a neutron probe, at soil depths of 0.2, 0.4, 0.6, 0.8 and 1.0 m. Soil water storage was calculated by Simpson’s rule and data were analyzed by the temporal persistence of the spatial pattern. Maximum values of soil water storage were obtained at the portion of the area with water flux concentration (sampling points 4, 28 and 57), with and without outliers, and low values of soil water storage were obtained at the highers levels of the site (sampling points 12, 18 and 19), with and without outliers. The sites representing the mean soil water storage were 32, 51 and 11, considering outliers, and 8, 11 and 53, without considering outliers.

Belowground soil water response in the afforestation-cropland interface under semi-arid conditions

Agroforestry is an effective measure to control soil erosion and maintain or increase productivity in semi-arid areas. However, the belowground soil water responses in the afforestation-cropland interface (ACI) is not well known. This study analyzed the variability of soil water storage (SWS) and deficit in three ACIs taking into account the distance between the forest and cropland. The variations of soil water content up to 4 m depth and at five distances from the interface (−5 m (in forest), 0 m (interface), 1 m, 3 m and 5 m (in cropland)) were estimated in three artificial forests (Salix matsudana, Sophora japonica, and Populus cathayana) and their adjacent croplands (maize). The results showed that soil water at the interfaces was significantly affected by forest. This effect was effective up to 160–170 cm of soil depth, and the ACI of S. matsudana had the greatest impact on the farmland soil water. There was no significant relative soil water deficit between 1 and 3 m length, and the ACI of P. cathayana showed the lowest changes in the lateral direction. The SWS of S. japonica in the ACI was clearly higher than the other two artificial forests (P < 0.05). Our findings indicated that S. japonica was the most suitable forest species for agriculture sustainability in the study area. The tree species and the distance between cropland and forest should be considered during the establishment of agroforestry systems. This study provided insights for water conservation and effective management of ACIs in semi-arid areas.

Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria

In this study, experiments were carried out for two seasons at the teaching and research farms, Obafemi Awolowo, Nigeria to evaluate the performance of FAO AquaCrop version 5.0 in simulating canopy cover, soil water storage, above ground biomass, evapotranspiration, grain yield and water productivity of soybeans (TGX 1448 2E) under rainfed conditions. The Experimental treatments consisted of conventional and six water conservation practices: Tied ridges; Mulching; Bund; Tied ridges plus mulch; Tied ridges plus bund and Bund plus mulch. The experimental treatments were arranged in randomised complete block design with four replicates. The 2012 and 2011 data sets were used for calibration and validation respectively. The performance of AquaCrop was tested using the regression coefficient (b), coefficient of determination (R2), Root mean square error (RMSE), Normalised root mean square error (NRMSE), Nash and Sutcliffe efficiency coefficient (EF), Willmott’s index of agreement (d-index) and percentage bias (PBIAS). AquaCrop simulated canopy cover adequately for the two seasons, 0.95 ≤ b ≤ 0.99; R2 ≥ 0.95 and RMSE ≤ 11.6%. The model captured well the variability in soil water storage with 0.95 ≤ b ≤ 1.01; R2 ≥ 0.74 and RMSE ≤ 10.2 mm. AquaCrop simulated evapotranspiration with R2 = 0.66 and RMSE = 64.6 mm. The model simulated dry above ground biomass with RMSE ≤ 0.25 t ha−1 and d-index ≥ 0.98. Grain yield was simulated with b = 1.01; R2 = 0.99; RMSE of 0.03 t ha−1d-index = 1.00 and the maximum deviation between the simulated and predicted grain yields is 3%. The water productivity was simulated with R2 = 0.78 and RMSE = 0.08 kg m-3. Considering the overall performance, the AquaCrop model is adequate in simulating canopy cover, dry above ground biomass and grain yield. However, it has low aptitude in simulating evapotranspiration and water productivity of rainfed soybeans under soil surfaces management practices.

Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle

Surface soil moisture is a critical but often neglected component of the hydrologic budget. Within mountain environments, surface soil moisture is highly heterogeneous and challenging to measure. Point measurements are often poorly representative of larger areas, while satellite pixels are generally too coarse in these topographically varied landscapes. In the Cordillera Blanca, Peru, rapid glacier recession is impacting downstream water supply in timing, quantity and quality. Recent research has shown that these proglacial valleys provide vital ecosystem services and store considerable amounts of water within the groundwater systems. However, the spatial and temporal variability of soil water storage is poorly understood. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared, thermal infrared) sensors to map sections of two proglacial valleys in the Cordillera Blanca, Peru at sub-metre resolution. We use the Temperature Vegetation Dryness Index (TVDI) (Sandholt et al., 2002), and apply it for the first time to UAV borne imagery to estimate absolute surface soil moisture through calibration with in-scene field measurements. Resulting surface soil moisture maps have 50 cm spatial resolution with an R2 value of 0.55 and 0.76 for the two study sites, Pachacoto and Llanganuco respectively. We analyse the multispectral orthomosaics and soil moisture maps to improve our understanding of the controls on spatial variability in surface soil moisture within the proglacial valleys of the Cordillera Blanca. The maps permit us to identify both important groundwater spring sources and areas where domestic grazing impacts observed soil moisture estimates. The high resolution observations provided by the UAV facilitate a greater understanding of fine scale heterogeneity within these environments.

The variability in soil water storage on the loess hillslopes in China and its estimation

Soil water storage (SWS) is a critical water resource for vegetation growth in the Loess Plateau. Changes in SWS during the growing season can reflect soil water depletion or recharge conditions in different hydrological years. This study investigated the soil water in three hillslopes covered with artificial forest, natural forest and natural grass in May and October of 2015–2017. Both path analysis and stepwise multiple linear regression were used to determine the direct and indirect effects of most important variables and develop a model for predicting changes in SWS. The results showed that compared with natural forestland and grassland, artificial forestland had low soil water, but deep SWS maintained a balance of inputs and outputs. In all soil layers, during the growing season, total precipitation (X4) was the most important variable affecting changes in SWS. The changes in SWS in surface soil layers were affected by more environmental factors than in deeper soil layers. The natural grassland (X3) and slope position (X12) have different degree of direct and indirect effects by means of antecedent soil water content (X5), even though their correlations were not significant. In addition, changes in SWS at the 0–100 cm, 100–200 cm and 0–400 cm soil layers were adequately predicted using the multiple regression equation, in which R2 reached values from 73.4–93.0%. The model simulating the changes in SWS during the growing season can improve understanding on whether rainfall infiltration can replenish soil water depletion.

Joint multifractal analysis for three variables: Characterizing the effect of topography and soil texture on soil water storage

Multifractal analysis describes heterogeneity in the distribution of a variable by characterizing and summarizing the variability across scales. Joint multifractal analysis has been widely employed to characterize scale relationships between two variables co-existing along a single geometric support. In this study, joint multifractal analysis was used for three variables coexisting in the same geometric support to describe the influence of topography (relative elevation) and soil texture (sand content) on water storage within a soil profile. Soil water storage, as well as sand content and relative elevation, were measured down to 1.4 m depth along a 576 m long transect in the hummocky landscape of central Saskatchewan, Canada. Joint multifractal analysis was conducted to consider both the strange attractor formalism and the method of moments. The variability in soil water storage, sand content and relative elevation was scale dependent and showed multifractal behavior. The spatial variability in relative elevation was strongly reflected on water storage across the analyzed spatial scales but the joint multifractal spectrum for sand content and water storage suggested a lower degree of correlation. The change in multifractality was also observed when there was high variability in relative elevation and texture. This clearly demonstrated the capability of joint multifractal analysis to completely characterize the scaling behavior among three variables.

The spatial variability of soil water storage and its controlling factors during dry and wet periods on loess hillslopes

Soil water storage (SWS), a critical parameter in hydrological processes, is an effective water source for vegetation growth in the semi-arid Loess Plateau of China. Its spatial pattern at various soil depths along transects and temporal changes in the dominant environmental factors that affect SWS are essential to ensure the sustainability of vegetation restoration efforts and achieve an accurate understanding of hydrological processes on the Loess hillslope. In this study, we investigated SWS at depths of 0–4m at a total of 54 points on three hillslopes covered with artificial forest, natural forest and natural grass during four observation periods. The results reflected clear seasonal trends in SWS. A substantial water deficit occurred during the severe drought year of 2015. SWS at depths of 0–1m increased and SWS at depths of 1–4m decreased from after the rainy season of 2015 to before the rainy season of 2016 (a near-normal drought year), and SWS at depths of 0–4m maintained its resemblance to conditions that occurred during the rainy season of 2016. These results may indicate that drought conditions affect variations in SWS. In addition, topography and vegetation type were the dominant factors controlling SWS in the different soil layers. SWS at shallow soil depths was mainly affected by topography, while SWS at deep soil depths was mainly controlled by vegetation type. During the dry season, slope aspect was the most important factor controlling SWS at shallow soil depths due to the effects of slope aspect on snowmelt and wind evaporation. On the other hand, during the wet season, the slope gradient was more important in terms of its effect on SWS than slope aspect at shallow soil depths due to the effects of slope gradient on infiltration and runoff.

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.

Evaluation of a Landscape‐Scale Approach to Cotton Modeling

The Texas Southern High Plains is a semiarid region for cotton (Gossypium hirsutum L.) production that uses irrigation water from the Ogallala Aquifer, where depletion exceeds recharge, prompting limits to pump water. Thus, there is a need for management strategies to optimize the diminishing water, and these could be modeled provided the spatiotemporal variability associated with irrigated cotton production is captured. The Precision Agricultural‐Landscape Modeling System (PALMS) is a grid‐based model that accounts for variability across complex landscapes but lacked a cotton model. Our objective was to integrate the Cotton2K model with PALMS to produce PALMScot and to compare calculated values of soil water storage obtained with PALMScot to field‐measured values for 2 yr and two irrigation treatments at two locations with contrasting soils, a fine sandy loam near Lamesa, TX, and a clay loam near Bushland, TX. Two statistics, mean absolute difference (MAD) and modified coefficient of efficiency (E1) were used to compare differences between measured and calculated values of soil water content. In both years, MAD was &lt;5% in the Amarillo and &lt;10% in the Pullman soils and E1 was 0.2 in the Amarillo and 0.4 in the Pullman soils, supporting the accuracy of the calculated values. Within the 1.4‐m profile of both soils, however, the model overestimated root water uptake in the surface horizon and underestimated it in deeper ones. Our evaluation implied that PALMScot is a tool that can be used to track spatial and temporal variability of soil water storage across a complex landscape.

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.

Water storage variability in a vineyard soil in the southern highlands of Santa Catarina state

In the subtropical regions of southern Brazil, rainfall distribution is uneven, which results in temporal variability of soil water storage. For grapes, water is generally available in excess and water deficiency occurs only occasionally. Furthermore, on the Southern Plateau of Santa Catarina, there are differences in soil properties, which results in high spatial variability. These two factors affect the composition of wine grapes. Spatio-temporal analyses are therefore useful in the selection of cultural practices as well as of adequate soils for vineyards. In this way, well-suited areas can produce grapes with a more appropriate composition for the production of quality wines. The aim of this study was to evaluate the spatio-temporal variability of water storage in a Cambisol during the growth cycle of a Cabernet Sauvignon vineyard and its relation to selected soil properties. The experimental area consisted of a commercial 8-year-old vineyard in São Joaquim, Santa Catarina, Brazil. A sampling grid with five rows and seven points per row, spaced 12 m apart, was outlined on an area of 3,456 m². Soil samples were collected with an auger at these points, 0.30 m away from the grapevines, in the 0.00-0.30 m layer, to determine gravimetric soil moisture. Measurements were taken once a week from December 2008 to April 2009, and every two weeks from December 2009 to March 2010. In December 2008, undisturbed soil samples were collected to determine bulk density, macro- and microporosity, and disturbed samples were used to quantify particle size distribution and organic carbon content. Results were subjected to descriptive analysis and semivariogram analysis, calculating the mean relative difference and the Pearson correlation. The average water storage in a Cambisol under grapevine on ridges had variable spatial dependence, i.e., the lower the average water storage, the higher the range of spatial dependence. Water storage had a stable spatial pattern during the trial period, indicating that the points with lower water storage or points with higher water storage during a certain period maintain these conditions throughout the experimental period. The relative difference is a simple method to identify positions that represent the average soil water storage more adequately at any time for a given area.

Estabilidade temporal e variabilidade espacial da distribuição da armazenagem de água no solo numa sucessão feijão/aveia-preta

A armazenagem de água no solo é muito variável no tempo e no espaço devido à influência de vários fatores ambientais e topográficos. Objetivou-se neste estudo: quantificar a armazenagem de água no solo; avaliar sua estabilidade temporal e sua variabilidade espacial em um local próximo e outro distante do sistema radicular numa sucessão feijão/aveia-preta; e constatar sua variabilidade espacial em função do relevo. Em área experimental de 1.500 m², situada em Piracicaba-SP (latitude de 22° 42' 30" S, longitude de 47° 38' 00" W e 546 m de altitude), estabeleceram-se 60 pontos de amostragem, distanciados entre si de 5 m, numa grade de 10 por 6 pontos (50 x 30 m). Os valores da armazenagem de água no solo apresentaram comportamento-padrão para o solo estudado, com valores maiores em profundidade do que na camada mais superficial. Houve maior estabilidade temporal da armazenagem de água no solo durante o período chuvoso, porém na fase de secagem do solo a estabilidade temporal também foi constatada, mas com valores de coeficiente de correlação mais elevados na camada de 0,0-0,80 m, mostrando que essa estabilidade foi claramente devida à posição topográfica desses pontos, os quais estão localizados na porção mais baixa do relevo. Assim, o ponto 52 foi escolhido como representativo da média na fase de recarga de água no solo em ambas as camadas estudadas, e o ponto 46, na camada superior durante a fase de secagem do solo. A dependência espacial da variabilidade da armazenagem de água no solo foi detectada em ambas as camadas de solo, porém com maior alcance na camada de 0,0-0,40 m, quando houve ocorrência de precipitação. Quando as chuvas cessaram, o alcance foi maior na camada de 0,0-0,80 m. Durante o período chuvoso, o padrão de variabilidade espacial foi muito semelhante em ambas as camadas de solo, com armazenagens maiores na faixa de maior inclinação e depressões do terreno.

Factors controlling soil water storage in the hummocky landscape of the Prairie Pothole Region of North America

Biswas, A., Chau, H. W., Bedard-Haughn, A. K. and Si, B. C. 2012. Factors controlling soil water storage in the hummocky landscape of the Prairie Pothole Region of North America. Can. J. Soil Sci. 92: 649–663. The Prairie Pothole Region (PPR) in North America is unique hummocky landscape containing hydrologically closed topographic depressions with no permanent inlet or outlet. Knowledge about the controls of soil water distribution in the landscape is important for understanding the hydrology in the PPR. In this study, we investigated the correlation between soil water storage and different controlling factors over time. Time domain reflectometry and neutron probe were used to measure soil water storage up to 1.4 m depth over 4 yr along a 576-m long transect at St. Denis National Wildlife Area, Saskatchewan, Canada, which represent a typical landscape of the PPR. Soil and vegetation properties were measured along the transect, and various terrain indices were calculated from the digital elevation map of the study area. Soil texture (e.g., correlation coefficient, r=−0.57 to −0.73 for sand) provided one of the best explanations for the variations in soil water storage by controlling the entry and transmission of water within soil in the semi-arid climate of study area. Bulk density (r=−0.22 to −0.56), depth of A horizon, (r=0.18 to 0.49), C horizon (r=0.29 to 0.69), and CaCO3 layer (r=0.31 to 0.79) influenced the water transmission through soil and were correlated to soil water storage. Beside soil properties, topographic wetness index (r=0.47 to 0.67), slope (r=−0.41 to −0.56), convergence index (r=−0.29 to −0.60), and flow connectivity (r=0.27 to 0.60) were also correlated to soil water storage. However, multiple linear regressions showed a consistent high contribution from soil properties such as sand, organic carbon, depth of CaCO3 layer, and bulk density in explaining the variability in soil water storage. A substantial contribution from topographic variables such as wetness index, gradient, and solar radiation was also observed. Therefore, unlike other geographic regions, the soil-water storage variations in the PPR are controlled by a combination of soil and terrain properties with dominant control from soil characteristics at the field scale.

Identifying effects of local and nonlocal factors of soil water storage using cyclical correlation analysis

AbstractThere are various factors governing the spatial and temporal variability of soil water storage including soil properties, topography and vegetation. Some factors act locally, whereas others act nonlocally, which means that a factor measured at one location has effect on soil water storage at another location. The objective of this study was to examine the effects of local and nonlocal controls of soil water storage in a hummocky landscape using cyclical correlation analysis. Soil water storage, soil properties and terrain indices were measured along a 128‐point transect of 576 m long from the semiarid, hummocky, prairie pothole region of North America. There are large coefficients of determination (r2) between soil water storage and sand content (r2 = 0.32–0.53), organic carbon content (r2 = 0.22–0.56), depth to carbonate layer (r2 = 0.13–0.63), wetness index (r2 = 0.25–0.45) and other variables at the measurement scale at different times, indicating strong local effects from these variables. The correlation coefficients were also calculated by physically shifting the spatial series of soil water storage with respect to that of controlling factors. The shifting improves the correlation between the spatial series, and the length of shifting indicated the difference in the response of soil water to its controlling factors. For example, the value of r2 increased more than eightfold (r2 = 0.47–0.64) after shifting the spatial series of soil water storage by 54 m, almost equal to the average length of existing slope, compared with the very weak correlation (r2 = 0.02–0.08) at the measurement scale. This indicated the nonlocal effect from the relative elevation. The identification of nonlocal effects from factors improves the prediction of soil water storage. Copyright © 2011 John Wiley &amp; Sons, Ltd.

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 (