Temperature sensitivity of soil respiration is essential to predict possible changes in terrestrial carbon budget on various scenarios about atmospheric and soil climates. Although it is often evaluated by using respiratory quotient “Q 10”, Q 10 values of soil respiration seem to vary depending on methods or scales of evaluation. Aiming at probing how Q 10 values of soil respiration are evaluated differently for a field, this study used a model of soil respiration rate, and numerically evaluated soil respiration rates along depth by fitting the model to depth distributions of CO 2 concentration measured in a field. And temperature sensitivity of soil respiration rate was evaluated by comparing the determined soil respiration rates with atmospheric and soil temperatures measured in the field. The results showed that the relation between surface CO 2 emission rates and atmospheric temperatures was represented by lower Q 10 values than that between soil respiration rates and soil temperatures, presumably because the top soil layers had acclimatized in more extent to the existing thermal regime than the underlying deeper layers. Thus, for evaluating effects of long-term rise in atmospheric temperature on soil respiration, it is necessary to precisely predict the long-term change in depth distribution of soil temperature as well as to quantify temperature sensitivity of soil respiration along depth. The evaluated sensitivity of surface CO 2 emission rate to atmospheric temperature showed hysteresis, implying the needs for more knowledge about temperature sensitivity of soil respiration evaluated in both warming and cooling processes for better understandings and predictions about terrestrial carbon cycling.
Soil respiration is the sum of respiratory activities in a soil. Since respiratory activities in a soil must involve soil microbes or plant roots taking in free oxygen (O2), decomposing organic substances, and producing metabolic energy, water, and carbon dioxide (CO2), the intensity of the total of them is one of direct measures of soil health and can be quantified by measuring the amount of CO2 released from the soil in a certain period.
Emission of CO2 from a soil body attracts attention as an essential quantity concerning about carbon budgets between lands and atmosphere. This is because soils can be primary pool of carbon in terrestrial ecosystems. Past studies had estimated the size of global soil organic carbon to be up to 1395 [PgC] (Post et al., 1982) or 1576 [PgC] (Eswaran et al., 1993) . These kinds of estimations have been oddly updated with larger values year by year. For instance, Batjes (1996) evaluated the amount of global soil carbon within 1-meter depth as 2157 - 2293 [PgC]. Kochy et al. (2015) estimated it as nearly 3000 [PgC]. At the same time, the global soil respiration has been thought to emit 87 to 95 [PgC yr−1] (Hashimoto et al., 2015) , which was equivalent to nearly ten times more amount of carbon emission than fossil-fuel burning and cement manufacture of 36.1 ± 0.3 [PgCO2 yr−1] (Liu et al., 2023) . These values suggest that changes in flow and storage of soil carbon may give large effects on the global carbon cycling.
Soil respiration is well related to regimes of ambient temperature and is commonly more promoted under warmer conditions in an ordinary temperature range. And the sensitivity of soil respiration to temperature has often been analyzed with the concept of respiratory quotient “Q10” which is defined as a respiration rate at T + 10 [K] divided by that at T [K]. However, Q10 values of soil respiration are likely to vary depending on methods or scales of evaluation. Relatively small Q10 values of around 2 or less have been found mainly in global- or regional-scale studies of CO2 emission from land surfaces, portion of which includes 1.5 by Knorr & Heimann (1995) , 1 to 2 by Kaminski et al. (2002) , 1.37 by Ise & Moorcroft (2006) , 1.43 to 2.03 by Zhou et al. (2009) , 1.5 by Bond-Lamberty & Thompson (2010) , 1.4 by Mahecha et al. (2010) , 1.41 to 1.43 by Zhan et al. (2017) .
However, CO2 emission from soils in site-specific or soil-sample scales seems to be characterized with relatively larger Q10 values of more than 2. For example, Raich & Schlesinger (1992) compiled the Q10 values from 15 literatures and estimated the median value of 2.4. Wang et al. (2010) also collected 185 data sets from 69 publications, and obtained the average of 2.67. Wang et al. (2018) measured soil respiration rates in a meadow steppe for two years, and their seasonal and annual Q10 values ranged from 2 to 4. Kim et al. (2020) also measured soil respiration rates throughout a year in an urban and a well reserved forest, and reported Q10 values of more than 3.
These apparent differences among the estimated Q10 values has not yet been explained well, and may cause such a hypothesis that a Q10 value of soil respiration is estimated higher when soil temperatures are related to respiration rates in the soil layer than when atmospheric temperatures are adopted to explain CO2 emission rates from the surface of the soil layer, since the studies about soil respiration in larger spatial scales often monitor above-ground meteorology while the studies in soil-sample scales or local-site scales had often correlated respiration rates with temperatures of their soils.
One promising way to examine this hypothesis is to use a mathematical model that allows to evaluate both a surface CO2 emission rate from a soil layer and a depth distribution of soil respiration rates in the soil layer for given sets of measured data including gaseous CO2 concentration in soil and temperatures of both the soil layer and the atmosphere. Thus, this study proposed a model of soil respiration rate, and determined depth distribution of soil respiration rates by making the model’s outputs best reproduce the dynamics of soil gaseous CO2 measured in a field. And, for examining the hypothesis mentioned above, the determined soil respiration rates were compared with both atmospheric and soil temperatures monitored in the field.
The inputs for the model of soil respiration rate were the data sets observed by Iiyama (2023) in a meadow field. The field was underlaid with volcanic ash soil layers which consisted of a topsoil layer with the thickness of about 0.25 [m], followed by a transitional layer of about 0.3 [m] in thick, underlain by a subsoil layer. Two study sites were set to represent a tree-standing area and a harvesting area in the field, and named the sites A and B, respectively. The range of rooting depth in the site A reached at least 1 [m] in depth, while that in the site B was found mainly within 0.25 [m] from the soil surface.
The data sets consisted of the depth profiles of state variables and physical properties of the soil layers in the field. The group of the state variables was comprised of CO2 concentration of soil gas c [kg·m−3], soil temperature Ts [K], and volumetric water content θ [m3·m−3], which were measured at several depths each as shown in
| Depth range [m] | Site A | Site B | ||||
|---|---|---|---|---|---|---|
| 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | |
| Bulk density ρd [kg·m−3] | 533 (36) | 612 (11) | 519 (6) | 601 (62) | 600 (23) | 602 (23) |
| Particle density ρs [kg·m−3] | 2547 (82) | 2838 (18) | 2805 (19) | 2463 (287) | 2808 (38) | 2784 (23) |
| Saturated water content θs [m3·m−3] | 0.791 (0.075) | 0.784 (0.019) | 0.815 (0.013) | 0.756 (0.156) | 0.786 (0.041) | 0.784 (0.039) |
physical quantities, saturated volumetric water content θs [m3·m−3] was evaluated as 1 − ρd/ρs. The details about the measurements of these state variables with the physical properties were explained in Iiyama (2023) . In parallel with the state variables, atmospheric temperature Ta [K] at each site was also measured by a temperature logger (HOBO U23 Pro v2 external temperature/relative humidity data logger (U23-002); Onset Computer Corp; Bourne, MA, USA), which was contained in a solar radiation shield (RS1; Onset Computer Corp; Bourne, MA, USA).
When the respiratory activity in a soil is represented by an amount of CO2 produced in the soil, some optimum condition can be supposed, in which a unit mass of soil emits CO2 in an optimum rate so [kg·kg−1·s−1]. When the soil has a bulk density of ρd [kg·m−3], the quantity so can be converted into the optimum rate of CO2 production per unit volume of the soil ρdso [kg·m−3·s−1].
Since the respiratory activity requires sure supply of oxygen, it should be restricted when soil moisture content is too high to allow soil air to be smoothly exchanged. At the same time, the respiratory activity weakens under an extremely dry condition because of the lowering of metabolic activities of soil microbes or plant roots with the lack of soil moisture causing the inhibition of flows of substrate supply to the activities. Thus, for modeling the soil respiration rate under a field condition, the term ρdso should be multiplied by some inhibiting factor ι [non-dim] which takes a value between 0 and 1, depending on water content θ [m3·m−3] or air-filled porosity a [m3·m−3] of the soil. Therefore, by using the expressions so, ρd, and ι, the soil respiration rate S [kg·m−3·s−1] can be modeled with the following expression:
S = ι ρ d s o (1)
According to past studies for soil respiration in fields (Liu et al., 2002; Xu et al., 2004; Jassal et al., 2008; Zhang et al, 2010) and in laboratories (Guntinas et al, 2013; Zhang et al, 2015) , soil respiration is likely to be limited in the lowest and highest extremities of the entire moisture range. Thus, this study modeled ι as below:
ι = a a + κ 0 θ θ + κ 0 κ 1 = θ s − θ θ s − θ + κ 0 θ θ + κ 0 κ 1 (2)
where κ0 [m3·m−3] is a constant that regulates the ways of increase and decrease in ι with the changes in θ. The other parameter κ1 can be mathematically derived when it is set in order for making ι take 1 as its maximum as follows:
κ 1 = ( θ s θ s + 2 κ 0 ) − 2 (3)
The optimum CO2 production rate so in Equation (1) should vary along depth due to the differences in population densities of microbes or plant roots in a soil layer, meaning that so can be modeled as a function of the vertical location. As a first approximation, it was assumed that there is a depth at which so takes the maximum along depth. As an example of smooth curve that satisfies the above assumption, this study used such an expression as:
s o = { 0 ( z u ≤ z ≤ 0 or z ≤ z l ) s o max 2 ( 1 + cos ( π z − z max z u − z max ) ) ( z max ≤ z ≤ z u ) s o max 2 ( 1 + cos ( π − π z − z l z max − z l ) ) ( z l ≤ z ≤ z max ) (4)
where z [m] indicates the upward-positive vertical location and takes 0 [m] at the level of a soil surface, zu [m] and zl [m] are the upper most and the lower most levels of the domain of soil respiration, so max [kg·kg−1·s−1] is the potentially maximum rate of CO2 production in the domain z l ≤ z ≤ z u , and zmax [m] is the vertical location where so takes so max.
The model described as Equations (1) to (4) means that the depth profile of soil respiration rate S is explained by the soil physical state that the depth distributions of ρd, θ, and θs represent and by such respiratory characteristics of the soil layer as so max, zl, zmax, zu, and κ0. Among these expressions, so max is the only expression that can be related to soil temperature Ts [K]. Therefore, for probing the temperature sensitivity of S, so max and zmax were set as unknown variables to be identified for given sets of the other terms. And the identified series of so max was compared with a series of Ts obtained at corresponding times and depths.
The rest of the parameters were evaluated by using the measured data sets. ρd and θs in Equations (1) to (3) were given by citing the values tabulated on
To set a problem for identifying so max and zmax in Equation (4) is to set up a series of equations that involves S. Thus, the mass balance about CO2 in a soil layer was formulated for the development of the equations. As the first step of the application, a soil layer to be analyzed was discretized with a series of calculation nodes arranged along depth. The nodes were numbered from 0 to n to point any of vertical locations in the soil layer from the bottom z0 [m] to the surface zn [m]. Then, gaseous CO2 concentrations at z = zi ( 0 ≤ i ≤ n ) can be denoted as ci [kg·m−3] on the node i. When a certain ci is needed to be compared with a measured CO2 concentration, the measured data of CO2 concentration in
A segment was defined for every node so that ci represents the value of CO2 concentration for the segment that includes the node i. Any of the segment boundaries was placed at the center of two adjacent nodes so that a segment around z = zi spans between (zi + zi−1)/2 and (zi+1 + zi)/2 for 0 ≤ i ≤ n − 1 . For i = 0 and i = n, the segments cover the domains z 0 ≤ z ≤ ( z 1 + z 0 ) / 2 and ( z n + z n − 1 ) / 2 ≤ z ≤ z n , respectively. In this study, n = 20, z0 = −1 [m], and zn = z20 = 0 [m] were assigned, and all of zi for 1 ≤ i ≤ n − 1 were arranged every 0.05 [m] along depth, for the soil layers of analysis was 1 [m] in thick and most of the measured data sets were obtained with the spatial resolution of more than 0.1 [m] as shown in
Then, the flow of gaseous CO2 in the soil layer was formulated. Since gas flow in soils owes almost solely on gaseous diffusion driven by difference in concentration of the gas species along depth, a value of CO2 gas flux qi [kg·m−2·s−1] can be evaluated on a segment boundary between z = zi and z = zi+1 such that:
q i = − D s i c i + 1 − c i z i + 1 − z i (5)
where Ds i [m2·s−1] is the soil gas diffusion coefficient for CO2 assigned to the domain covering between the centers of two adjacent segments i and i + 1. The formulation of Ds will be described in Section 2.4.
By using the terms defined above, the mass balance equations of CO2 for the segments i ( 1 ≤ i ≤ n − 1 ) and two boundary conditions can be expressed as follows:
{ M i , j + 1 − M i , j t j + 1 − t j z i + 1 − z i 2 = q b − q i + S i z i + 1 − z i 2 ( i = 0 ) M i , j + 1 − M i , j t j + 1 − t j z i + 1 − z i − 1 2 = q i − 1 − q i + S i z i + 1 − z i − 1 2 ( 1 ≤ i ≤ n − 1 ) c i , j + 1 = c a ( i = n ) (6)
where Mi,j [kg·m−3] is the mass of CO2 per unit volume of soil stored in the segment i at a time tj [s], being called the storage term in this study. The subscript j denotes a time-step number (j ≥ 0) so that the left sides of the first and second sub-equations in Equation (6) denote the rates of increase in the mass of CO2 stored in a segment i for a time interval Δt = tj+1 − tj [s]. In this study, 1800[s] was assigned to Δt as an increment sufficiently small compared with the measurement intervals for the state variables c, Ts, and θ in the field (
Soil gas diffusion coefficient Ds i [m2·s−1] in Equation (5) was evaluated as follows:
D s i = ξ i D a i (7)
where ξi [non-dim] is the pore tortuosity factor while Da i [m2·s−1] is the diffusion coefficient of gaseous CO2 in the atmosphere. These two terms were both assigned to each of the domains of z i ≤ z ≤ z i + 1 ( 0 ≤ i ≤ n − 1 ).
The tortuosity factor ξi in Equation (7) is a function of air-filled porosity of a soil, and was expressed by the following model (Iiyama et al., 2012) :
ξ i = { 0 ( a ¯ < a 0 ) λ ( a ¯ − a 0 ) m 1 / θ ¯ s m 2 ( a ¯ ≥ a 0 ) (8)
where a ¯ [m3·m−3], θ ¯ [m3·m−3], and θ ¯ s [m3·m−3] are the spatially-averaged air-filled porosity, volumetric water content, and saturated volumetric water content of the soil which are evaluated by citing θ and θs at z = zi and zi+1, a0 [m3·m−3] is the minimum air-filled porosity for gaseous diffusion practically taking place in the soil, λ [non-dim], m1 [non-dim], and m2 [non-dim] are the constants that vary with type of soil. The values of the parameters applied in this study are tabulated in
The diffusion coefficient of gaseous CO2 in the atmosphere, Da i in Equation (7), was expressed as a function of pressure and temperature around the place of interest as follows (Freijer & Leffelaar, 1996; Osozawa & Hasegawa, 1995; Makita, 1988) :
D a i = D a s t d ( T ¯ s T s s t d ) ν p s t d p ¯ (9)
where Da std = 1.38 × 10−5 [m2·s−1], Ts std = 273.15 [K], ν = 1.75, and pstd = 101.325 [kPa]. T ¯ s [K] is the soil temperature in the domain of z i ≤ z ≤ z i + 1 which is evaluated by citing Ts at z = zi and zi+1. For the pressure of the soil air, pstd was assigned to p ¯ with the assumption that most of the air-filled pores in the entire soil layer are opened to the atmosphere.
The storage term Mi, j [kg·m−3] in Equation (6) was expressed as the sum of the amount of CO2 contained in both the liquid and gaseous phases in a segment i,
| Depth range [m] | Site A | Site B | ||||
|---|---|---|---|---|---|---|
| 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | |
| a0 [m3·m−3] | 0.050 | 0.050 | 0.000 | 0.050 | 0.050 | 0.025 |
| θs [m3·m−3] | 0.791 | 0.784 | 0.815 | 0.756 | 0.786 | 0.784 |
| λ [-] | 0.064 | 0.059 | 0.060 | 0.066 | 0.062 | 0.080 |
| m1 [-] | 1.296 | 1.139 | 1.002 | 1.585 | 1.352 | 1.926 |
| m2 [-] | 8.07 | 7.448 | 5.342 | 8.535 | 7.454 | 13.288 |
since the solubility of CO2 is relatively high among gas species in soil air. When the dissolved fraction of CO2 is considered, the storage term can be expressed as:
M i , j = ( a i , j + θ i , j K H ) c i , j = ( θ s i − θ i , j + θ i , j K H ) c i , j (10)
where ai, j and θi, j are air-filled porosity and volumetric water content of soil in the segment i at a time t = tj. The Henry’s coefficient for CO2, KH, was introduced with the assumption that gas-liquid equilibrium can be achieved instantly. For KH, the following polynomial function was used for considering its temperature dependence:
K H = k 0 + k 1 T s i , j + k 2 T s i , j 2 + k 3 T s i , j 3 (11)
where Ts i,j is soil temperature in the segment i at a time t = tj. The parameters k0 = 217.8098, k1 = −1.990097, k2 = 6.096563 × 10−3, and k3 = −6.250000 × 10−6 were obtained by fitting the third-order polynomial function to the reference values tabulated on National Astronomical Observatory of Japan (2020) .
The numerical scheme made of the series of equations from (1) to (11) can be used for obtaining a time-series of CO2 concentrations ci along depth for a given set of state variables, physical properties, and parameters including so max and zmax. This study used this numerical scheme inversely to identify an optimum pair of so max and zmax by fitting the numerically-determined ci to the values of CO2 concentration obtained in the field in the following steps (
Step 1 Select a date of measurement of soil gaseous CO2 concentrations in the field from the time domain plotted in
Step 2 Select the date of measurement next to t0, and name it t1. Since the interval of CO2 concentration measurements was 7 days, t1 - t0 was equivalent to 7 days.
Step 3 Assign the measured values of soil gaseous CO2 concentrations at the two consecutive measurements to the sets of variables cmeas(z, t0) and cmeas(z, t1).
Step 4 Let cmeas(z, t0) be the initial condition for the numerical scheme.
Step 5 Set up the input data sets for the numerical scheme. The inputs are comprised of the state variables other than CO2 concentrations, the physical properties, and the parameters except for so max and zmax. The values of θ and Ts for any time and any location were evaluated by linearly interpolating the measured values obtained between t0 and t1 at two neighboring measurement depths.
Step 6 Define the domains and increments about so max and zmax for finding out an optimum pair s o max ∗ and z max ∗ that minimizes the error between a numerically-determined ci, namely ccalc(z, t1), to the measured values cmeas(z, t1).
Step 7 Prepare a tentative pair of so max and zmax from the domains defined in Step 6.
Step 8 Run the numerical scheme and obtain a time-series of ci as the output for the time domain of analysis being 0 ≤ t [ s ] ≤ t max , where tmax = 7 × 86400 [s].
Step 9 Assign the calculated values of soil gaseous CO2 concentrations at t = tmax to the set of variables ccalc(z, t1).
Step 10 Evaluate the error between cmeas(z, t1) and ccalc(z, t1) with the concept of least squares.
Step 11 If the error is smaller than the minimum of ever-recorded errors, then update a best-ever pair s o max ∗ and z max ∗ with the currently-tried so max and zmax, and the minimum-ever error is also updated with the error just evaluated in Step 10.
Step 12 If the domain defined in step 6 has not yet entirely been scanned, return to step 7. If it has been done, exit the iteration of the series of steps from Step 7 to this Step 12 and obtain the optimum pair of so max and zmax as s o max ∗ and z max ∗ .
By applying the series of these steps to every measurement interval of soil gaseous CO2 concentration in the field, a time-series of so max and zmax were determined.
After the determination of a time-series of so max and zmax, time-series of CO2 emission rates from the soil surfaces qsur [kg·m−2·s−1] were also evaluated for all the measurement intervals by applying Equation (5) to the top-5-cm layer and using ccalc(z, t) for the identified pairs of so max and zmax. Thus, qsur is expressed as follows:
q s u r = 1 J ∑ j = 1 J ( − D s c calc ( z n , t j ) − c calc ( z n − 1 , t j ) z n − z n − 1 ) (12)
where J is the maximum of the time-step number j such that tJ − t0 is equivalent to a measurement interval of CO2 concentration profile, being 7 days in this study. The values of ccalc(zn, tj) and ccalc(zn−1, tj) are the CO2 concentrations calculated for the time tj and the vertical locations of z = 0 [m] and z = −0.05 [m], respectively. Ds was evaluated in the same manner as explained in section 2.4.
The seasonal trends of zmax indicated that the most active depth ranges of soil respiration stayed in the top soil layers from May to September, while they went down to the subsoil layers during the winter term. These evaluations can largely be explained by the seasonal change in depth distribution of soil temperature,
implying that the depth range of soil respiration would follow the seasonal transition of the warmest depth in a soil profile. However, there was also two-month difference in the commencement of deepening in zmax between the two sites. This difference was presumably because the respiratory activities of the larch tree roots in the site A have more significant tolerance to the lowering of soil temperature than those of the grasses and forbs in the site B.
Some local minimum values were found in the midst of June and in the end of September while zmax stayed in the top soil layers. Each of them was presumably due to sudden momentary lowering of atmospheric temperature (
On the other hand, in August, momentary reduction in surface CO2 emission coincided with zmax deepening into the subsoil layers. In the midst of August, there was a dry period lasting more than two weeks, and soil water contents in the top soil layers dropped to its lowest level of the year (
In both sites, the values for the top soil layers were likely to be higher than those for the other deeper layers for a given temperature, reflecting the denser populations of plant roots and soil microbes in the shallower depth ranges.
The rates of increase in so max tended to increase with rising temperature regardless of depth range, suggesting that the relationship between soil temperature Ts [K] and so max [ngCO2 g−1 s−1] can be described as follows:
s o max = α s exp ( λ s ( T s − 273.15 ) ) (13)
Thus, Equation (13) was fitted to each of the data sets on
Equation (13) reproduced well the obtained Ts − so max relations. Since the soil respiration model in this study had no a-priori assumption about temperature sensitivity, it was inferred that the relation between soil temperature and potentially maximum CO2 production rate in the study field can be commonly characterized by the exponential-type function.
The temperature sensitivity of soil respiration rate differed among the depth ranges. In both sites, the top soil layers showed the lowest Q10 values in the entire soil layers. This means that respiratory activities in the deeper soil layers can be more easily stimulated by the change in soil thermal regime, while the absolute amounts of CO2 production were larger in the top soil layers than in the deeper layers.
In summation, the relatively simple model of soil respiration combined with in-situ monitoring of CO2 concentration profile served as an effective measure for both identifying the depth range of the most active layer of soil respiration and characterizing the temperature sensitivity of soil respiration in each soil layer.
The Ta − qsur relations in both sites were also characterized as follows:
q s u r = α q exp ( λ q ( T a − 273.15 ) ) (14)
| Depth range [m] | Site A | Site B | ||||
|---|---|---|---|---|---|---|
| 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | 0 - 0.25 | 0.25 - 0.55 | 0.55 - 1.00 | |
| αs [ngCO2 g−1 s−1] | 0.410 | 0.192 | 0.068 | 0.105 | 0.035 | 0.039 |
| λs [K−1] | 0.054 | 0.089 | 0.121 | 0.073 | 0.115 | 0.094 |
| R2 | 0.636 | 0.713 | 0.914 | 0.755 | 0.971 | 0.889 |
| Q10 [-] | 1.719 | 2.440 | 3.337 | 2.071 | 3.152 | 2.570 |
And fitting Equation (14) to the values on
The obtained Q10 values for Ta − qsur relations were 1.968 and 2.153 for the sites A and B, respectively. The comparisons of these values with the Q10 values for the transitional and the subsoil layers listed on
On the other hand, these values were similar in size to the Q10 values for the top soil layers of the two sites, implying that the respiratory activities in the top soil layers were main source of the surface CO2 emission during the field measurement period, and had acclimatized in more extent to the existing thermal regime than the other deeper layers so that they were relatively insensitive to change in soil temperature. These implications mean that the seasonal variation of surface CO2 emission rate from a soil layer can be approximately predicted by evaluating the temperature sensitivity of the surface CO2 emission rate itself or of the CO2 production rate near the surface of the soil layer. At the same time, however, it is also clarified that for evaluating possible effects on soil respiration of long-term rise in atmospheric temperature through which a yearly-mean soil temperature can change, it is necessary to precisely predict the long-term change in depth distribution of soil temperature as well as to quantify temperature sensitivity of soil respiration at each depth.
The Ta − qsur relations showed seasonal hysteretic behaviors (
| Site A | Site B | |
|---|---|---|
| αq [MgCO2 ha−1 yr−1] | 19.271 | 6.091 |
| λq [K−1] | 0.068 | 0.077 |
| R2 | 0.855 | 0.901 |
| Q10 [-] | 1.968 | 2.153 |
values for a given temperature than in the cooling season. On
This study proposed a model of soil respiration rate for determining depth distribution of soil respiration rates based on the dynamics of soil gaseous CO2 measured in a field. Then, temperature sensitivity of soil respiration rate was evaluated by using both atmospheric and soil temperatures in the field, with such a hypothesis that a Q10 value of soil respiration is estimated higher when respiration rates in a soil layer are related to soil temperatures than when surface CO2 emission rates from the soil layer are explained by atmospheric temperature.
The numerical scheme in this study clarified the seasonal behavior of the most active depth range of soil respiration, and implied that the depth range of soil respiration follows the seasonal transition of the warmest depth in a soil profile, though it seems also necessary to consider some plant physiological features like the difference in temperature-tolerance among plant species.
The numerical scheme in this study reproduced annual trends of surface CO2 emission rate with acceptable values, suggesting that a sudden momentary change in atmospheric temperature directly affects surface CO2 emission rate without changing the most active depth range of soil respiration, while an extremely dry condition can change, though tentatively, most active depth range of soil respiration itself.
The relation between soil temperature and potentially maximum CO2 production rate obtained in this study indicated that the temperature sensitivity of soil respiration rate can be reproduced well by the exponential-type function. According to the obtained relation, the top soil layers had the lowest Q10 values in the entire soil layers, meaning that respiratory activities in deeper soil layers can be more sensitive to the change in soil thermal regime, even though the absolute amounts of CO2 production are larger in the top soil layers so far.
The sensitivity of surface CO2 emission rate to atmospheric temperature was characterized by lower Q10 values compared with that of soil respiration rate to soil temperature, supporting the hypothesis of this study. Therefore, it was inferred that for evaluating possible effects on soil respiration of long-term rise in atmospheric temperature through which a yearly-mean soil temperature can change, it is necessary to precisely predict the long-term change in depth distribution of soil temperature as well as to quantify temperature sensitivity of soil respiration at each depth.
However, it was also noticed that the main source of surface CO2 emission in a field is often in the top soil layer of the field, where respiratory activities may have acclimatized to the existing thermal regime. Thus, seasonal variation of surface CO2 emission rate from a soil layer can be approximately predicted by evaluating the temperature sensitivity of the surface CO2 emission rate itself or of the CO2 production rate near the surface of the soil layer.
The evaluated sensitivity of surface CO2 emission rate to atmospheric temperature showed hysteresis. And the tendency was more significant for a soil layer with larger potentially maximum CO2 production rate. This means that when the temperature sensitivity of soil respiration is modeled by a single-valued function, the model may over- or under-estimate actual soil respiration rates. Therefore, further studies are required to obtain more knowledge about temperature sensitivity of soil respiration in both warming and cooling processes for better understandings and predictions about terrestrial carbon cycling.
The author thanks Mr. T. Shiozawa, Mr. E. Saito and Mr. N. Yamaguchi in the Utsunomiya University Farm for their supports in the managements of the study field. The author also thanks Mr. Suto in the Graduate School of Agriculture, Utsunomiya University, and Ms. M. Anzai, Mr. K. Ogasawara, Mr. Y. Sakanishi, and Mr. Y. Usui in the School of Agriculture, Utsunomiya University, for their measurement works about the soils of the study field.
The author declares no conflicts of interest regarding the publication of this paper.
Iiyama, I. (2023). Temperature Sensitivity of Soil Respiration Probed by Numerical Analysis of Field-Ob- served Data Sets. Journal of Geoscience and Environment Protection, 11, 65-84. https://doi.org/10.4236/gep.2023.118005