Spatial dynamics of soil moisture and temperature in a black spruce boreal chronosequence

2006 ◽  
Vol 36 (11) ◽  
pp. 2794-2802 ◽  
Author(s):  
Ben Bond-Lamberty ◽  
Karen M Brown ◽  
Carol Goranson ◽  
Stith T Gower
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Sampling Design ◽  
Black Spruce ◽  
Structural Characteristics ◽  
Spatial Dynamics ◽  
Spherical Model ◽  
Local Scale ◽  
Spatial Dependencies ◽  
The Mean

This study analyzed the spatial dependencies of soil moisture and temperature in a six-stand chronosequence of boreal black spruce (Picea mariana (Mill.) BSP) stands. Spatial variability of soil temperature (TSOIL) was evaluated twice during the growing season using four transects in each stand, employing a cyclic sampling design with measurements spaced 2–92 m apart. Soil moisture (θg) was measured on one occasion. A spherical model was used to analyze the geostatistical correlation structure; θg and TSOIL at the 7- and 21-year-old stands did not exhibit stable ranges or sills. The fits with stable ranges and sills modeled the spatial patterns in the older stands reasonably well, although unexplained variability was high. Calculated ranges varied from 3 to 150 m for these stands, lengths probably related to structural characteristics influential in local-scale energy transfer. Transect-to-transect variability was significant and typically 5%–15% of the mean for TSOIL and 10%–70% for θg. TSOIL and θg were negatively correlated for most stands and depths, with TSOIL dropping 0.5–0.9 °C for every 1% rise in θg. The results reported here provide initial data to assess the spatial variability of TSOIL and θg in a variety of boreal forest stand ages.

scholarly journals Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux

2021 ◽  
Vol 18 (6) ◽  
pp. 2003-2025
Author(s):  
Elisa Vainio ◽  
Olli Peltola ◽  
Ville Kasurinen ◽  
Antti-Jussi Kieloaho ◽  
Eeva-Stiina Tuittila ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Boreal Forest ◽  
Forest Floor ◽  
Early Summer ◽  
Ground Vegetation ◽  
Ch4 Flux ◽  
Ch4 Uptake ◽  
The Mean ◽  
Sample Points

Abstract. Boreal forest soils are globally an important sink for methane (CH4), while these soils are also capable of emitting CH4 under favourable conditions. Soil wetness is a well-known driver of CH4 flux, and the wetness can be estimated with several terrain indices developed for the purpose. The aim of this study was to quantify the spatial variability of the forest floor CH4 flux with a topography-based upscaling method connecting the flux with its driving factors. We conducted spatially extensive forest floor CH4 flux and soil moisture measurements, complemented by ground vegetation classification, in a boreal pine forest. We then modelled the soil moisture with a random forest model using digital-elevation-model-derived topographic indices, based on which we upscaled the forest floor CH4 flux. The modelling was performed for two seasons: May–July and August–October. Additionally, we evaluated the number of flux measurement points needed to get an accurate estimate of the flux at the whole study site merely by averaging. Our results demonstrate high spatial heterogeneity in the forest floor CH4 flux resulting from the soil moisture variability as well as from the related ground vegetation. The mean measured CH4 flux at the sample points was −5.07 µmol m−2 h−1 in May–July and −8.67 µmol m−2 h−1 in August–October, while the modelled flux for the whole area was −7.42 and −9.91 µmol m−2 h−1 for the two seasons, respectively. The spatial variability in the soil moisture and consequently in the CH4 flux was higher in the early summer (modelled range from −12.3 to 6.19 µmol m−2 h−1) compared to the autumn period (range from −14.6 to −2.12 µmol m−2 h−1), and overall the CH4 uptake rate was higher in autumn compared to early summer. In the early summer there were patches emitting high amounts of CH4; however, these wet patches got drier and smaller in size towards the autumn, changing their dynamics to CH4 uptake. The mean values of the measured and modelled CH4 fluxes for the sample point locations were similar, indicating that the model was able to reproduce the results. For the whole site, upscaling predicted stronger CH4 uptake compared to simply averaging over the sample points. The results highlight the small-scale spatial variability of the boreal forest floor CH4 flux and the importance of soil chamber placement in order to obtain spatially representative CH4 flux results. To predict the CH4 fluxes over large areas more reliably, the locations of the sample points should be selected based on the spatial variability of the driving parameters, in addition to linking the measured fluxes with the parameters.

scholarly journals Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

2013 ◽  
Vol 26 (20) ◽  
pp. 7929-7937 ◽  
Author(s):  
Elsa Bernard ◽  
Philippe Naveau ◽  
Mathieu Vrac ◽  
Olivier Mestre
Keyword(s):  
Spatial Patterns ◽  
Extreme Value Theory ◽  
Heavy Rainfall ◽  
Spatial Clustering ◽  
Relevant Information ◽  
Value Theory ◽  
Research Field ◽  
Multivariate Extreme Value Theory ◽  
Spatial Dependencies ◽  
The Mean

Abstract One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classical k-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993–2011).

How landscape and climate affect the spatial variability of the Italian rainfall extremes? Some initial clues based on I2-RED

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Ilaria Butera ◽  
Pierluigi Claps
Keyword(s):  
Spatial Variability ◽  
Linear Regression Analysis ◽  
Alpine Region ◽  
Annual Rainfall ◽  
Mean Values ◽  
Rainfall Extremes ◽  
Geomorphological Parameters ◽  
The Mean ◽  
The Relationship ◽  
Italian Territory

<p>The intensity and the spatial distribution of precipitation depths are known to be highly dependent on relief and geomorphological parameters. Complex environments like mountainous regions are prone to intense and frequent precipitation events, especially if located near the coastline. Although the link between the mean annual rainfall and geomorphological parameters has received substantial attention, few literature studies investigate the relationship between the sub-daily maximum annual rainfall depth and geographical or morphological landscape features.<br>In this study, the mean of the rainfall extremes in Italy, recently revised in the so-called I<sup>2</sup>-RED dataset, are investigated in their spatial variability in comparison with some landscape and also some broad climatic characteristics. The database includes all sub-daily rainfall extremes recorded in Italy from 1916 until 2019 and this analysis considers their mean values (from 1 to 24 hours) in stations with at least 10 years of records, involving more than 3700 stations.<br>The geo-morpho-climatic factors considered range from latitude, longitude and minimum distance from the coastline on the geographic side, to elevation, slope, openness and obstruction morphological indices, and also include an often-neglected robust climatological information, as the local mean annual rainfall.<br>Obtained results highlight that the relationship between the annual maximum rainfall depths and the hydro-geomorphological parameters is not univocal over the entire Italian territory and over different time intervals. Considering the whole of Italy, the highest correlation is reached between the mean values of the 24-hours records and the mean annual precipitation (correlation coefficient greater than 0.75). This predominance remains also in sub-areas of the Italian territory (i.e., the Alpine region, the Apennines or the coastal areas) but correlation decreases as the time interval decreases, except for the Alpine region (0.73 for the 1-hour maximum). The other geomorphological parameters seem to act in conjunction, making it difficult to evaluate, with a simple linear regression analysis, their impact. As an example, the absolute value of the correlation coefficient between the elevation and the 1-hour extremes is greater than 0.35 for the Italian and the Alpine regions, while for the 24-hours interval it is greater than 0.35 over the coastal areas.<br>To further investigate the spatial variability of the relationship between rainfall and elevation, a spatial linear regression analysis has been undertaken. Local linear relationships have been fitted in circles centered on any of the 0.5-km size pixels in Italy, with 1 to 30 km radius and at least 5 stations included. Results indicate the need of more comprehensive terrain analysis to better understand the causes of local increasing or decreasing relations, poorly described in the available literature.</p>

scholarly journals Effects of micro-topography and vegetation type on soil moisture in a large gully on the Loess Plateau of China

2017 ◽  
Vol 49 (4) ◽  
pp. 1255-1270 ◽  
Author(s):  
Bowei Yu ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Jiuliang Feng ◽  
Xiaoping Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Loess Plateau ◽  
Root Zone ◽  
Plane Surface ◽  
Vegetation Type ◽  
Chinese Loess Plateau ◽  
Chinese Loess ◽  
Micro Topography ◽  
The Chinese Loess Plateau

Abstract Large gullies occur globally and can be classified into four main micro-topographic types: ridges, plane surfaces, pipes and cliffs. Afforestation is an effective method of controlling land degradation worldwide. However, the combined effects of afforestation and micro-topography on the variability of soil moisture remain poorly understood. The primary objectives of this study were to determine whether afforestation affects the spatial pattern of the root-zone (0–100 cm) soil moisture and whether soil moisture dynamics differ among the micro-topographic types in gully areas of the Chinese Loess Plateau. The results showed that in the woodland regions, the spatial mean moisture values decreased by an average of 6.2% and the spatial variability increased, as indicated by the standard deviation (17.1%) and the coefficient of variation (22.2%). In general, different micro-topographic types exerted different influences on soil moisture behavior. The plane surface presented the largest average soil moisture values and the smallest spatial variability. The lowest soil moisture values were observed in the ridge, mainly due to the rapid drainage of these areas. Although pipe woodland region can concentrate surface runoff during and after rainfall, the larger trees growing in these areas can lead to increased soil moisture evapotranspiration.

Statistical characterization of the spatial variability of soil moisture in a cutover peatland

2004 ◽  
Vol 18 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Richard M. Petrone ◽  
J. S. Price ◽  
S. K. Carey ◽  
J. M. Waddington
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Statistical Characterization

scholarly journals Changes of Variability in Response to Increasing Greenhouse Gases. Part II: Hydrology

2009 ◽  
Vol 22 (22) ◽  
pp. 6089-6103 ◽  
Author(s):  
Richard T. Wetherald
Keyword(s):  
Soil Moisture ◽  
Greenhouse Gases ◽  
General Circulation ◽  
Circulation Model ◽  
Early Spring ◽  
Precipitation Rate ◽  
Late Winter ◽  
Annual Data ◽  
Geophysical Fluid Dynamics Laboratory ◽  
The Mean

Abstract This paper examines hydrological variability and its changes in two different versions of a coupled ocean–atmosphere general circulation model developed at the National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory and forced with estimates of future increases of greenhouse gas and aerosol concentrations. This paper is the second part, documenting potential changes in variability as greenhouse gases increase. The variance changes are examined using an ensemble of 8 transient integrations for an older model version and 10 transient integrations for a newer model. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed on computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in the respective control integrations. The hydrologic cycle intensifies because of the increase of greenhouse gases. In general, precipitation variance increases in most places. This is the case virtually everywhere the mean precipitation rate increases and many places where the precipitation decreases. The precipitation rate variance decreases in the subtropics, where the mean precipitation rate also decreases. The increased precipitation rate and variance, in middle to higher latitudes during late fall, winter, and early spring leads to increased runoff and its variance during that period. On the other hand, the variance changes of soil moisture are more complicated, because soil moisture has both a lower and upper bound that tends to reduce its fluctuations. This is particularly true in middle to higher latitudes during winter and spring, when the soil moisture is close to its saturation value at many locations. Therefore, changes in its variance are limited. Soil moisture variance change is positive during the summer, when the mean soil moisture decreases and is close to the middle of its allowable range. In middle to high northern latitudes, an increase in runoff and its variance during late winter and spring plus the decrease in soil moisture and its variance during summer lend support to the hypothesis stated in other publications that a warmer climate can cause an increasing frequency of both excessive discharge and drier events, depending on season and latitude.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

2013 ◽  
Vol 17 (3) ◽  
pp. 1177-1188 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Scale Dependency ◽  
Convex Shape ◽  
Spatial Moments ◽  
Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

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