Nitrogen mineralisation and its error of estimation under field conditions related to the light-fraction soil organic matter

Soil Research ◽  
1996 ◽  
Vol 34 (5) ◽  
pp. 755 ◽  
Author(s):  
J Sierra
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Soil Temperature ◽  
Light Fraction ◽  
Field Conditions ◽  
Optimum Level ◽  
N Mineralisation ◽  
Undisturbed Samples ◽  
Intact Soil Cores

In situ, incubations of intact soil cores were carried out to identify factors controlling nitrogen (N) mineralisation and its spatial variability under field conditions. The analysed factors were soil moisture, temperature, and the content of light-fraction (density ≤ 2 Mg/m3) organic carbon (LC) contained in the soil. The error associated with the estimate of in situ N mineralisation was analysed using undisturbed samples in laboratory incubations. The coefficient of variation of in situ N mineralisation ranged from 58 to 234%. Nitrogen and LC mineralisation in the field showed a similar temporal pattern. The major factor affecting this pattern was soil temperature, soil moisture being near the optimum level throughout the experiment. The rate of N mineralisation during an incubation period was correlated with the content of LC at the beginning of the period; this factor explained 40–50% of the variation in N mineralisation. At a low rate of N mineralisation, a large proportion of the spatial variability was attributed to the error of estimation. From the relationship between N mineralisation and LC content, we estimated the rate constant k which could be expressed as a function of soil temperature. Within the observed temperature range (daily mean average 11–17°C), the Q10 (temperature coefficient) of in situ N mineralisation was 1.5. Negative values of N mineralisation were associated with the lower LC content of each period, indicating the presence of an immobilisation process, or that a proportion of LC was not involved in N mineralisation.

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.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture under different climate conditions

2012 ◽  
Vol 9 (9) ◽  
pp. 10245-10276 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Warm Season ◽  
Weather Conditions ◽  
Data Sets ◽  
Scale Dependency ◽  
Convex Shape ◽  
Climate Conditions ◽  
Spatial Moments

Abstract. Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, 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 measurements (from a continuously monitored network across the US), modeled and satellite retrieved soil moisture obtained in a warm season (198 days) were examined at large extent scales (>100 km) over three different climate regions. The investigation on in situ measurements revealed that their spatial moments 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 across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1) the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2) the boundedness of soil moisture plays a pivoting role in the dependency of soil moisture spatial variability/skewness on its mean (and thus climate conditions); (3) the scale dependency of soil moisture spatial variability changes with climate conditions.

Evaluating the potential of Sentinel-1 images for the estimation of soil moisture on an alluvial Fan

2020 ◽  
Author(s):  
Abhilash Singh ◽  
Kumar Gaurav ◽  
Shashi Kumar
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Soil Temperature ◽  
Alluvial Fan ◽  
Soil Survey ◽  
Volumetric Soil Moisture ◽  
The North ◽  
S Model ◽  
Time Domain Reflectometer

<p>We evaluate the potential of Sentinel-1A & 1B satellite images to estimate the volumetric soil moisture content over an alluvial fan of the Kosi River in the North Bihar, India. Over this region, only dual polarised images (VH and VV) are available. However, the existing backscattering models (i.e., Dubois, Oh and IEM models) uses quad polarised (VV, VH, HH and HV) images for the estimation of soil permittivity and surface roughness over the bareland. To overcome the constraint of dual polarised data, we eliminated one of the unknown (i.e. surface roughness) by developing a regression model between the in-situ measured surface roughness and the ratio of backscatter values (VH/VV) in dB.  In a field campaign in the Kosi Fan from December 10-21, 2019, we have measured surface roughness, soil temperature, soil pH and soil moisture at 78 different location using the pin-meter, soil survey instrument (soil temperature and pH), and Time Domain Reflectometer (TDR) respectively. The average surface roughness and soil moisture varies between (0.61 - 5.45) cm and (0.12-0.53) m<sup>3</sup>/m<sup>3</sup> respectively in the study area.</p><p>Further, using the surface roughness we modify the Dubois, Oh and IEM models. This reduces the number of unknowns in the models from two to one; the soil permittivity. We compute the soil permittivity from the inversion of the existing backscattering models. Finally, we use the permittivity values in the Top’s model to estimate the volumetric soil moisture in the study area. Our initial results exhibit a good correlation (R<sup>2</sup> = 0.85) to the in-situ measured soil moisture.</p>

scholarly journals Measurements of soil respiration and simple models dependent on moisture and temperature for an Amazonian southwest tropical forest

2009 ◽  
Vol 6 (3) ◽  
pp. 6147-6177 ◽  
Author(s):  
F. B. Zanchi ◽  
H. R. da Rocha ◽  
H. C. de Freitas ◽  
B. Kruijt ◽  
M. J. Waterloo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Field Measurements ◽  
Dry Season ◽  
Wet Season ◽  
Hourly Data ◽  
Fresh Litter ◽  
Favorable Conditions

Abstract. Soil respiration plays a significant role in the carbon cycle of Amazonian tropical forests, although in situ measurements have only been poorly reported and the dependence of soil moisture and soil temperature also weakly understood. This work investigates the temporal variability of soil respiration using field measurements, which also included soil moisture, soil temperature and litterfall, from April 2003 to January 2004, in a southwest Brazilian tropical rainforest near Ji-Paraná, Rondônia. The experimental design deployed five automatic (static, semi-opened) soil chambers connected to an infra-red CO2 gas analyzer. The mean half-hourly soil respiration showed a large scattering from 0.6 to 18.9 μmol CO2 m−2 s−1 and the average was 8.0±3.4 μmol CO2 m−2 s−1. Soil respiration varied seasonally, being lower in the dry season and higher in the wet season, which generally responded positively to the variation of soil moisture and temperature year round. The peak was reached in the dry-to-wet season transition (September), this coincided with increasing sunlight, evapotranspiration and ecosystem productivity. Litterfall processes contributed to meet very favorable conditions for biomass decomposition in early wet season, especially the fresh litter on the forest floor accumulated during the dry season. We attempted to fit three models with the data: the exponential Q10 model, the Reichstein model, and the log-soil moisture model. The models do not contradict the scattering of observations, but poorly explain the variance of the half-hourly data, which is improved when the lag-time days averaging is longer. The observations suggested an optimum range of soil moisture, between 0.115

Effects of Soil Temperature and Drought on Root–Soil Respiration in Apple under Field Conditions

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 453a-453
Author(s):  
Liqin Wang ◽  
David M. Eissenstat ◽  
Dora E. Flores-Alva
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Root Respiration ◽  
Hot Water ◽  
Field Conditions ◽  
Drought Treatment ◽  
Dry Soil ◽  
Heated Soil ◽  
Soil Temperature And Moisture

Root respiration is very important to root efficiency, root lifespan, and carbon cycling in plant ecosystems. Yet, the effects of soil temperature and moisture on root respiration are poorly understood, especially under field conditions. In this study, we manipulated soil temperature and moisture by six bearing `Red Chief' Delicious/M26 trees near State College, Pa. Soil temperature was elevated 5 °C at 5-cm depth using circulating hot water and stainless steel grids. Soil temperature was monitored using thermocouples and a data logger, and soil moisture was monitored using TDR. Root–soil respiration was determined by static trapping at the soil surface. Heating was conducted from 8 May to 28 Oct. Drought was initiated on 21 Aug. and lasted 2 months. Root–soil respiration was lowest in spring and increased from June to late August. After September, respiration decreased until the experiment ended in November. Root-soil respiration was not correlated with root length density. Heating enhanced root–soil respiration about 15% to 20% in spring (May) and 10% in summer (June–August). After the drought treatment began, heating increased root-soil respiration about 42% in wet soil, but did not influence respiration in dry soil. Heating accentuated the effect of the drought treatment on soil moisture. After 2 months of no irrigation and no rain, soil moisture was reduced 5% in unheated soil and 10% in heated soil. Drought slowed root–soil respiration 17% in unheated soil and 36% in heated soil, mainly because heating increased respiration in wet soil, but compared to the unheated treatment, had no effect in dry soil.

Empirical modelling of plant and soil carbon flux drivers under field conditions

2020 ◽  
Author(s):  
Chris McCloskey ◽  
Guy Kirk ◽  
Wilfred Otten ◽  
Eric Paterson
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Soil Carbon ◽  
Field Conditions ◽  
Gas Flux ◽  
Soil C ◽  
C Dynamics ◽  
Plant Respiration ◽  
Soil Temperature And Moisture

<p>Our understanding of soil carbon (C) dynamics is limited; field measurements necessarily conflate fluxes from plant and soil sources and we therefore lack long-term field-scale data on soil C fluxes to use to test and improve soil C models. Furthermore, it is often unclear whether findings from lab-based studies, such as the presence of rhizosphere priming, apply to soil systems in the field. It is particularly important that we are able to understand the roles of soil temperature and moisture, and plant C inputs, as drivers of soil C dynamics in order to predict how changing climate and plant productivity may affect the net C balance of soils. We have developed a field laboratory with which to generate much-needed long-term C flux data under field conditions, giving near-continuous measurements of plant and soil C fluxes and their drivers.</p><p>The laboratory contains 24 0.8-m diameter, 1-m deep, naturally-structured soil monoliths of two contrasting C3 soils (a clay-loam and a sandy soil) in lysimeters. These are sown with a C4 grass (<em>Bouteloua dactyloides</em>), providing a large difference in C isotope signature between C4 plant respiration and C3-origin soil organic matter (SOM) decomposition, which enables clear partitioning of the net C flux. This species is used as a pasture grass in the United States, and regular trimming through the growing season simulates low-intensity grazing. The soil monoliths are fitted with gas flux chambers and connected via an automated sampling loop to a cavity ring-down spectrometer, which measures the concentration and <sup>12</sup>C:<sup>13</sup>C isotopic ratio of CO<sub>2</sub> during flux chamber closure. Depth-resolved measurements of soil temperature and moisture in each monolith are made near-continuously, along with measurements of incoming solar radiation, rainfall, and air temperature a the field site. The gas flux chambers are fitted with removable reflective backout covers allowing flux measurements both incorporating, and in the absence of, photosynthesis.</p><p>We have collected net ecosystem respiration data, measurements of photosynthesis, and recorded potential drivers of respiration over two growing seasons through 2018 and 2019. Through partitioning fluxes between plant respiration and SOM mineralisation we have revealed clear diurnal trends in both plant and soil C fluxes, along with overarching seasonal trends which modify both the magnitude of fluxes and their diurnal patterns. Rates of photosynthesis have been interpolated between measurement periods using machine learning to generate a predictive model, which has allowed us to investigate the effect of plant productivity on SOM mineralisation and assess whether rhizosphere priming can be detected in our system. Through regression analyses and linear mixed effects modelling we have evaluated the roles of soil temperature, soil moisture, and soil N content as drivers of variation in plant and soil respiration in our two contrasting soils. This has shown soil temperature to be the most important control on SOM mineralisation, with soil moisture content playing only a minor role. We have also used our empirical models to suggest how the carbon balance of pasture and grassland soils may respond to warming temperatures.</p>

Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability

2019 ◽  
Vol 64 (16) ◽  
pp. 1982-1996
Author(s):  
Jianhong Zhou ◽  
Zhiyong Wu ◽  
Hai He ◽  
Fang Wang ◽  
Zhengguang Xu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Vic Model

scholarly journals Interannual Variability of Atmospheric CH4 and Its Driver Over South Korea Captured by Integrated Data in 2019

2021 ◽  
Vol 13 (12) ◽  
pp. 2266
Author(s):  
Samuel Kenea ◽  
Hae-Young Lee ◽  
Sang-Won Joo ◽  
Shan-Lan Li ◽  
Lev Labzovskii ◽  
...  
Keyword(s):  
Growth Rate ◽  
Soil Moisture ◽  
Soil Temperature ◽  
Interannual Variability ◽  
Statistical Significance ◽  
Regional Distribution ◽  
High Growth ◽  
Surface Model ◽  
Annual Growth Rate

Understanding the temporal variability of atmospheric methane (CH4) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and spatial characteristics of anomalous CH4 mole fractions and its drivers, we used integrated data from different platforms such as in situ measurements and satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Greenhouse Gases Observing SATellite (GOSAT)) retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from −16.8 to 31.3 ppb yr−1 as captured in situ through 2015–2020 and 3.9 to 16.4 ppb yr−1 detected by GOSAT through 2014–2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr−1 and 13.4 ppb yr−1 from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr−1 and 16.4 ppb yr−1 from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according to the Noah Land Surface Model. The stable isotopic composition of 13C/12C in CH4 (δ13-CH4) collected by flask-air sampling at AMY during 2014–2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be −53.3‰ and −52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. We looked more closely at the variability of XCH4 and the relationship with soil properties. The result indicated a spatial distribution of interannual variability, as well as the captured elevated anomaly over the southwest of the domain in autumn 2019, up to 70 ppb, which was largely explained by the combined effect of soil temperature and soil moisture changes, indicating a pixel-wise correlation of XCH4 anomaly with those parameters in the range of 0.5–0.8 with a statistical significance (p < 0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH4 in Korea.

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