How Spatial Patterns of Soil Moisture Dynamics Can Explain Field‐Scale Soil Moisture Variability: Observations From a Sodic Landscape

2019 ◽  
Vol 55 (5) ◽  
pp. 4410-4426 ◽  
Author(s):  
A. M. Peterson ◽  
W. H. Helgason ◽  
A. M. Ireson
Keyword(s):  
Soil Moisture ◽  
Spatial Patterns ◽  
Field Scale ◽  
Soil Moisture Dynamics ◽  
Soil Moisture Variability ◽  
Moisture Dynamics ◽  
Moisture Variability

scholarly journals Soil moisture: variable in space but redundant in time

2020 ◽  
Vol 24 (5) ◽  
pp. 2633-2653 ◽  
Author(s):  
Mirko Mälicke ◽  
Sibylle K. Hassler ◽  
Theresa Blume ◽  
Markus Weiler ◽  
Erwin Zehe
Keyword(s):  
Soil Moisture ◽  
Spatial Patterns ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Soil Conditions ◽  
Catchment Scale ◽  
Significant Information ◽  
Soil Moisture Variability ◽  
Over Time ◽  
Moisture Variability

Abstract. Soil moisture at the catchment scale exhibits a huge spatial variability. This suggests that even a large amount of observation points would not be able to capture soil moisture variability. We present a measure to capture the spatial dissimilarity and its change over time. Statistical dispersion among observation points is related to their distance to describe spatial patterns. We analyzed the temporal evolution and emergence of these patterns and used the mean shift clustering algorithm to identify and analyze clusters. We found that soil moisture observations from the 19.4 km2 Colpach catchment in Luxembourg cluster in two fundamentally different states. On the one hand, we found rainfall-driven data clusters, usually characterized by strong relationships between dispersion and distance. Their spatial extent roughly matches the average hillslope length in the study area of about 500 m. On the other hand, we found clusters covering the vegetation period. In drying and then dry soil conditions there is no particular spatial dependence in soil moisture patterns, and the values are highly similar beyond hillslope scale. By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty (when are patterns different outside of uncertainty margins?). We were able to prove that the spatial information contained in soil moisture observations is highly redundant (differences in spatial patterns over time are within the error margins). Thus, they can be compressed (all cluster members can be substituted by one representative member) to only a fragment of the original data volume without significant information loss. Our most interesting finding is that even a few soil moisture time series bear a considerable amount of information about dynamic changes in soil moisture. We argue that distributed soil moisture sampling reflects an organized catchment state, where soil moisture variability is not random. Thus, only a small amount of observation points is necessary to capture soil moisture dynamics.

Understanding soil moisture dynamics through cosmic rays: a global analysis

2021 ◽  
Author(s):  
Daniel Power ◽  
Rafael Rosolem ◽  
Miguel Rico-Ramirez ◽  
Darin Desilets ◽  
Sharon Desilets
Keyword(s):  
Soil Moisture ◽  
Cosmic Rays ◽  
Cosmic Ray ◽  
Fast Neutrons ◽  
Field Scale ◽  
Hydrogen Atoms ◽  
Soil Moisture Dynamics ◽  
Large Sample ◽  
Wide Range ◽  
Moisture Dynamics

<p>Despite its importance in many hydrological and environmental applications, direct estimates of soil moisture at the field-scale is still challenging. The spatial gap between point scale sensors and satellite derived products is becoming increasingly important to consider in the push for hyper-resolution (sub)kilometre-hydrometeorological models. Cosmic-Ray Neutron Sensors (CRNS) can help to bridge this spatial gap. CRNS provide estimates of field-scale (sub-kilometre) root-zone integrated soil moisture typically at hourly intervals. They achieve this by counting fast neutrons which are produced in the atmosphere from incoming cosmic rays. Fast neutrons are mitigated primarily by hydrogen atoms, and it is this relationship that allows us to estimate field averaged soil moisture. National networks of CRNS are available in the USA, Australia, the UK, and Germany, along with individual sites across the globe. As these networks have expanded, so has our knowledge on best practices for calibration and correction of the sensor measurements. However, there continues to be a divergence and lack of harmonization in some processing data methods leading to an additional uncertainty when comparing sensors in different networks. This can undermine efforts to employ large-sample hydrological analysis of CRNS across a wide range of climate and biomes. To provide an easily accessible platform for multi-site comparison worldwide, we developed the Cosmic Ray Sensor Python tool (crspy). Crspy is an open-source Python package which is designed to process CRNS data from global networks in a uniform and harmonized way (https://www.github.com/danpower101/crspy). Additionally, crspy has been developed for multi-site ‘big-data’ analysis in hydrology. Our crspy tool produces detailed information in the form of metadata for each site, using both site specific data as well as global data products to give information on soil properties (SoilGridsv2), land cover/aboveground biomass (ESA CCI) and climate data (ERA5-land). Our preliminary analysis and tool development was carried out using data from more than 100 sites globally from the public domain. We will present an analysis of this large sample of data, utilising the harmonized soil moisture readings along with detailed metadata for each site. We aim to increase our understanding of the dominant mechanisms controlling soil moisture dynamics which will undoubtedly be useful in multiple areas of research such as catchment classification, agriculture and irrigation, and hydrological model development.</p>

scholarly journals Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore Mountains

2009 ◽  
Vol 6 (6) ◽  
pp. 7503-7537 ◽  
Author(s):  
E. Zehe ◽  
T. Graeff ◽  
M. Morgner ◽  
A. Bauer ◽  
A. Bronstert
Keyword(s):  
Soil Moisture ◽  
Sampling Strategy ◽  
Runoff Generation ◽  
Field Scale ◽  
Soil Moisture Dynamics ◽  
Long Term Monitoring ◽  
Grassland Site ◽  
Term Monitoring ◽  
Moisture Dynamics

Abstract. This study presents an application of an innovative sampling strategy to assess soil moisture dynamics in a headwater of the Weißeritz in the German eastern Ore Mountains. A grassland site and a forested site were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. Distributed time series of vertically averaged soil moisture data from both sites/ensembles were analyzed by statistical and geostatistical methods. Spatial variability and the spatial mean at the forested site were larger than at the grassland site. Furthermore, clustering of TDR probes in combination with long-term monitoring allowed identification of average spatial covariance structures at the small field scale for different wetness states. The correlation length of soil water content as well as the sill to nugget ratio at the grassland site increased with increasing average wetness and but, in contrast, were constant at the forested site. As soil properties at both the forested and grassland sites are extremely variable, this suggests that the correlation structure at the forested site is dominated by the pattern of throughfall and interception. We also found a strong correlation between average soil moisture dynamics and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld, which explains almost as much variability in the runoff coefficients as pre-event discharge. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, long term simulations with a physically based hydrological model were in good/acceptable accordance with the time series of spatial average soil water content observed at the forested site and the grassland site, respectively. Both simulations used a homogeneous soil setup that closely reproduces observed average soil conditions observed at the field sites. This corroborates the proposed sampling strategy of clustering TDR probes in typical functional units is a promising technique to explore the soil moisture control on runoff generation. Long term monitoring of such sites could maybe yield valuable information for flood warning. The sampling strategy helps furthermore to unravel different types of soil moisture variability.

scholarly journals Wireless Sensor Network Deployment for Monitoring Soil Moisture Dynamics at the Field Scale

2013 ◽  
Vol 19 ◽  
pp. 426-435 ◽  
Author(s):  
B. Majone ◽  
F. Viani ◽  
E. Filippi ◽  
A. Bellin ◽  
A. Massa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Field Scale ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics

A study of field-scale soil moisture variability using the COsmic-ray Soil Moisture Observing System (COSMOS) at IITM Pune site

2021 ◽  
Vol 597 ◽  
pp. 126102
Author(s):  
Milind Mujumdar ◽  
Mangesh M. Goswami ◽  
Ross Morrison ◽  
Jonathan G Evans ◽  
Naresh Ganeshi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cosmic Ray ◽  
Field Scale ◽  
Soil Moisture Variability ◽  
Moisture Variability

Soil moisture dynamics of different land-cover types in the blacksoil regions of China

2009 ◽  
Vol 17 (2) ◽  
pp. 256-260 ◽  
Author(s):  
Feng WANG ◽  
Shu-Qi WANG ◽  
Xiao-Zeng HAN ◽  
Feng-Xian WANG ◽  
Ke-Qiang ZHANG
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Soil Moisture Dynamics ◽  
Land Cover Types ◽  
Moisture Dynamics

scholarly journals A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220457 ◽  
Author(s):  
Andrew Gillreath-Brown ◽  
Lisa Nagaoka ◽  
Steve Wolverton
Keyword(s):  
Soil Moisture ◽  
Agricultural Landscapes ◽  
Soil Moisture Variability ◽  
Moisture Variability
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