On the interrelations between topography, soil depth, soil moisture, transpiration rates and species distribution at the hillslope scale

Abstract. A better understanding of the controls on subsurface stormflow generation has been the focus of numerous experimental and modelling studies. However, the effect of the spatial variability of throughfall on soil moisture patterns and subsurface stormflow (SSF) generation has not yet been studied in detail. The objectives of this study are three-fold: (1) to investigate the influence of spatially variable throughfall on soil moisture; (2) to investigate if soil moisture patterns reflect a balance between throughfall and bedrock topography patterns; and (3) to investigate how this balance changes when soil depth, storm size and slope angle are varied. Virtual experiments are used to address these questions. A virtual experiment is a numerical experiment driven by collective field intelligence. It provides a learning tool to investigate the effect of separated processes in a complex system. In our virtual experiment we combined spatial throughfall data from the Huewelerbach catchment in Luxembourg with the topography characteristics of a well-studied hillslope within the Panola Mountain Research Watershed, Georgia, USA. We used HYDRUS-3D as a modeling platform. The virtual experiment shows that throughfall patterns influence soil moisture patterns, but only during and shortly after a storm. With a semi-variogram analysis we showed how the effective range of the soil moisture pattern (i.e. the main descriptor of a spatial pattern in case of a small nugget to sill ratio), has a similar effective range as the throughfall pattern during the storm and gradually returns to the effective range of the bedrock topography pattern after throughfall has ceased. The same analysis was carried out to investigate how this balance changes due to changes in storm size and hillslope controls. The analysis showed that the throughfall pattern is more important during large storms on gentle slopes. For steeper slopes the bedrock topography becomes more important.

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The effect of spatial throughfall patterns on soil moisture patterns at the hillslope scale

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1749-2013 ◽

2013 ◽

Vol 17 (5) ◽

pp. 1749-1763 ◽

Cited By ~ 28

Author(s):

A. M. J. Coenders-Gerrits ◽

L. Hopp ◽

H. H. G. Savenije ◽

L. Pfister

Keyword(s):

Complex System ◽

Soil Moisture ◽

Numerical Experiment ◽

Soil Depth ◽

Slope Angle ◽

Effective Range ◽

Virtual Experiment ◽

Virtual Experiments ◽

Bedrock Topography ◽

Hillslope Scale

Abstract. Improving the understanding of the controls on subsurface stormflow generation has been the goal of numerous experimental and modeling studies. However, the effect of the spatial variability of throughfall on soil moisture patterns and subsurface stormflow (SSF) generation has not yet been studied in detail. The objectives of this study are three-fold: (1) to investigate the influence of a spatially variable throughfall pattern on soil moisture; (2) to investigate if soil moisture patterns reflect a balance between a throughfall and bedrock topography patterns; and (3) to investigate how this balance changes when soil depth, storm size and slope angle are varied. Virtual experiments are used to address these questions. A virtual experiment is a numerical experiment driven by collective field intelligence. It provides a learning tool to investigate the effect of individual processes in a complex system. In our virtual experiment we combined spatial throughfall data from the Huewelerbach catchment in Luxembourg with the topography of a well-studied hillslope within the Panola Mountain Research Watershed, Georgia, USA. We used HYDRUS-3D as a modeling platform. The virtual experiment shows that throughfall patterns influence soil moisture patterns, but only during and shortly after a storm. With a semi-variogram analysis we showed how the effective range of the soil moisture pattern (i.e., the main descriptor of a spatial pattern in case of a small nugget to sill ratio), is similar to the effective range of the throughfall pattern during the storm and gradually returns to the effective range of the bedrock topography after throughfall has ceased. The same analysis was carried out to investigate how this balance changes due to changes in storm size, soil depth, and slope. The analysis showed that the throughfall pattern is more important during large storms on gentle slopes. For steeper slopes the bedrock topography becomes more important.

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Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

Silva Gandavensis ◽

10.21825/sg.v66i0.817 ◽

2001 ◽

Vol 66 ◽

Author(s):

M. Aslanidou ◽

P. Smiris

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Soil Depth ◽

Moisture Distribution ◽

Taxus Baccata ◽

Mixed Stands ◽

Potential Growth ◽

North East ◽

Soil Moisture Distribution

This study deals with the soil moisture distribution and its effect on the potential growth and adaptation of the over-story species in north-east Chalkidiki. These species are: Quercus dalechampii Ten, Quercus conferta Kit, Quercus pubescens Willd, Castanea sativa Mill, Fagus moesiaca Maly-Domin and also Taxus baccata L. in mixed stands with Fagus moesiaca. Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content of each sample was measured in order to determine soil moisture distribution and its contribution to the growth of the forest species. The most important results are: i) available water is influenced by the soil depth. During the summer, at a soil depth of 10 cm a significant restriction was observed. ii) the large duration of the dry period in the deep soil layers has less adverse effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out at a soil depth of 40 cm iii) in the beginning of the growing season, the soil moisture content is greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew stands, is 10-15 % in the Q. pubescens stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii stands.

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Vegetation of the Witbank Nature Reserve and its importance for conservation of threatened Rocky Highveld Grassland

Koedoe ◽

10.4102/koedoe.v40i2.275 ◽

1997 ◽

Vol 40 (2) ◽

Cited By ~ 10

Author(s):

C.M. Smit ◽

G.J. Bredenkamp ◽

N. Van Rooyen ◽

A.E. Van Wyk ◽

J.M. Combrinck

Keyword(s):

Soil Moisture ◽

Plant Communities ◽

Nature Reserve ◽

Soil Depth ◽

Floristic Composition ◽

Diversity Indices ◽

Moisture Gradient ◽

Negative Effects ◽

Soil Moisture Gradient ◽

Great Escarpment

A vegetation survey of the Witbank Nature Reserve, comprising 847 hectares, was conducted. Phytosociological data were used to identify plant communities, as well as to determine alpha and beta diversities. Eleven plant communities were recognised, two of these are subdivided into sub- communities, resulting in 14 vegetation units. These communities represent four main vegetation types, namely grassland, woodland, wetland and disturbed vegetation. Grassland communities have the highest plant diversity and wetland vegetation the lowest. Floristic composition indicates that the vegetation of the Rocky Highveld Grassland has affinities to the grassland and savanna biomes and also to the Afromontane vegetation of the Great Escarpment. An ordination scatter diagram shows the distribution of the 14 plant communities or sub-communities along a soil moisture gradient, as well as along a soil depth/surface rock gradient. The sequence of communities along the soil moisture gradient is used for calculating beta-diversity indices. It is concluded that the relatively small size of the Witbank Nature Reserve is unlikely to have significant negative effects on the phytodiversity of the various plant communities. This nature reserve is therefore of considerable importance in conserving a representative sample of the Rocky Highveld Grassland.

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An integral-balance nonlinear model to simulate changes in soil moisture, groundwater and surface runoff dynamics at the hillslope scale

Advances in Water Resources ◽

10.1016/j.advwatres.2014.06.003 ◽

2014 ◽

Vol 71 ◽

pp. 125-139 ◽

Cited By ~ 9

Author(s):

Rodica Curtu ◽

Ricardo Mantilla ◽

Morgan Fonley ◽

Luciana K. Cunha ◽

Scott J. Small ◽

...

Keyword(s):

Soil Moisture ◽

Nonlinear Model ◽

Surface Runoff ◽

Hillslope Scale ◽

Runoff Dynamics

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Modified Richards’ Equation to Improve Estimates of Soil Moisture in Two-Layered Soils after Infiltration

Water ◽

10.3390/w10091174 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1174 ◽

Cited By ~ 5

Author(s):

Honglin Zhu ◽

Tingxi Liu ◽

Baolin Xue ◽

Yinglan A. ◽

Guoqiang Wang

Keyword(s):

Soil Moisture ◽

Overland Flow ◽

Hydrological Cycle ◽

Soil Depth ◽

Controlling Factors ◽

Moisture Distribution ◽

Richards Equation ◽

Infiltration Process ◽

Soil Moisture Distribution

Soil moisture distribution plays a significant role in soil erosion, evapotranspiration, and overland flow. Infiltration is a main component of the hydrological cycle, and simulations of soil moisture can improve infiltration process modeling. Different environmental factors affect soil moisture distribution in different soil layers. Soil moisture distribution is influenced mainly by soil properties (e.g., porosity) in the upper layer (10 cm), but by gravity-related factors (e.g., slope) in the deeper layer (50 cm). Richards’ equation is a widely used infiltration equation in hydrological models, but its homogeneous assumptions simplify the pattern of soil moisture distribution, leading to overestimates. Here, we present a modified Richards’ equation to predict soil moisture distribution in different layers along vertical infiltration. Two formulae considering different controlling factors were used to estimate soil moisture distribution at a given time and depth. Data for factors including slope, soil depth, porosity, and hydraulic conductivity were obtained from the literature and in situ measurements and used as prior information. Simulations were compared between the modified and the original Richards’ equations and with measurements taken at different times and depths. Comparisons with soil moisture data measured in situ indicated that the modified Richards’ equation still had limitations in terms of reproducing soil moisture in different slope positions and rainfall periods. However, compared with the original Richards’ equation, the modified equation estimated soil moisture with spatial diversity in the infiltration process more accurately. The equation may benefit from further solutions that consider various controlling factors in layers. Our results show that the proposed modified Richards’ equation provides a more effective approach to predict soil moisture in the vertical infiltration process.

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Large-scale lateral saturated soil hydraulic conductivity as metric for the connectivity of the subsurface flow paths at hillslope scale

10.22541/au.163896913.32177523/v1 ◽

2021 ◽

Author(s):

Mario Pirastru ◽

Massimo Iovino ◽

Hassan Awada ◽

Roberto Marrosu ◽

Simone Di Prima ◽

...

Keyword(s):

Soil Moisture ◽

Hydraulic Conductivity ◽

Spatial Scale ◽

Water Table ◽

Subsurface Flow ◽

Lateral Flow ◽

Saturated Soil ◽

Flow Paths ◽

Soil Hydraulic Conductivity ◽

Hillslope Scale

Lateral saturated soil hydraulic conductivity, Ks,l, is the soil property governing subsurface water transfer in hillslopes, and the key parameter in many numerical models simulating hydrological processes both at the hillslope and catchment scales. Likewise, the hydrological connectivity of lateral flow paths plays a significant role in determining the intensity of the subsurface flow at various spatial scales. The objective of the study is to investigate the relationship between Ks,l and hydraulic connectivity at the hillslope spatial scale. Ks,l was determined by the subsurface flow rates intercepted by drains, and by water table depths observed in a well network. Hydraulic connectivity of the lateral flow paths was evaluated by the synchronicity among piezometric peaks, and between the latter and the peaks of drained flow. Soil moisture and precipitation data were used to investigate the influence of the transient hydrological soil condition on connectivity and Ks,l. It was found that the higher was the synchronicity of the water table response between wells, the lower was the time lag between the peaks of water levels and those of the drained subsurface flow. Moreover, the most synchronic water table rises determined the highest drainage rates. The relationships between Ks,l and water table depths were highly non-linear, with a sharp increase of the values for water table levels close to the soil surface. Estimated Ks,l values for the full saturated soil were in the order of thousands of mm h-1, suggesting the activation of macropores in the root zone. The Ks,l values determined at the peak of the drainage events were correlated with the indicators of synchronicity. The sum of the antecedent soil moisture and of the precipitation was correlated with the indicators of connectivity and with Ks,l. We suggest that, for simulating realistic processes at the hillslope scale, the hydraulic connectivity could be implicitly considered in hydrological modelling through an evaluation of Ks,l at the same spatial scale.

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Effect of Soil Depth on the Quantification of Soil Moisture Content Value Estimated from NOAA Satellite Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.567.705 ◽

2014 ◽

Vol 567 ◽

pp. 705-710

Author(s):

Abdalhaleem A. Hassaballa ◽

Abdul Nasir Matori ◽

Helmi Z.M. Shafri

Keyword(s):

Soil Moisture ◽

Critical Parameter ◽

Satellite Images ◽

Soil Moisture Content ◽

Soil Depth ◽

Vegetation Indices ◽

Field Measurements ◽

Spatial Estimation ◽

Optical Images

Soil moisture (MC) is considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes especially over humid areas. However, MC is very critical parameter to measure because of its variability in both space and time. The fluctuation of MC along the soil depth in turn, makes it so difficult to assess from optical satellite techniques. The study aims to produce a rectified satellite’s surface temperature (Ts) in order to enhance the spatial estimation of MC. The study also aims to produce MC estimates from three variable depths of the soil using optical images from NOAA 17 in order to examine the potential of satellite techniques in assessing the MC along the soil depths. The universal triangle (UT) algorithm was used for MC assessment based on Ts, vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths. The study area was divided into three classes according to the nature of surface cover. The resultant MC extracted from the UT method with rectified Ts, produced accuracies of MC ranging from 0.65 to 0.89 when validated with in-situ measured MC at depths 5cm and 10 cm respectively.

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A regionally explicit, global SWI calibration based on ISMN observations

10.5194/egusphere-egu21-496 ◽

2021 ◽

Author(s):

Manolis G. Grillakis

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Large Scale ◽

Root Zone ◽

Soil Depth ◽

European Space Agency ◽

Soil Layer ◽

Added Value ◽

Water Index ◽

Soil Moisture Data

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 &#8211; 2018.The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

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A Multilayer Soil Moisture Dataset Based on the Gravimetric Method in China and Its Characteristics

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0035.1 ◽

2019 ◽

Vol 20 (8) ◽

pp. 1721-1736 ◽

Cited By ~ 4

Author(s):

Aihui Wang ◽

Xueli Shi

Keyword(s):

Soil Moisture ◽

Soil Depth ◽

Linear Trend ◽

Standardized Precipitation Index ◽

Gravimetric Method ◽

Northwest China ◽

Soil Layers ◽

Significance Level ◽

The Mean ◽

The Standardized Precipitation Index

Abstract Based on the gravimetric-technique-measured soil relative wetness and the observed soil characteristic parameters from 1992 to 2013 in China, this study derives a user-convenient monthly volumetric soil moisture (SM) dataset from 732 stations for five soil layers (10, 20, 50, 70, and 100 cm). The temporal–spatial variations in SM and its relationship with precipitation (Pr) in different subregions are then explored. The magnitude of SM is relatively large in south China and is low in northwest China, and it generally increases with soil depth in each region. The maximum SM appears in spring and/or autumn and the minimum in summer, and the SM seasonality does not vary as distinctly as that of Pr. For the top three soil layers (10-, 20-, and 50-cm levels), the linear trend analysis indicates an overall increasing SM tendency, and the mean trends (averaged across stations with trends passing a 95% significance level test) are 9.35 × 10−7, 7.37 × 10−3, and 2.45 × 10−3 cm3 cm−3 yr−1, respectively. SM memory depends on the soil depth and regions, and it has longer retention time in the deeper layers. Furthermore, the correlation between SM and antecedent Pr varies with soil depth and lag time. The antecedent Pr anomaly (1 or 2 months in advance) can be used to some extent as a surrogate SM anomaly in most regions except for in arid regions. This result is further demonstrated by the relationships between the SM anomaly and the standardized precipitation index. The current SM dataset can be used in various applications, such as validating satellite-retrieved products and model outputs.

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