Estimates Of Rs Research Articles

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean {149}_{-23}^{+29} Pg C yr−1) are significantly higher than most GPP estimates reported in the literature ({113}_{-18}^{+18} Pg C yr−1). Similarly, historical GPP estimates imply a soil respiration flux (RsGPP, bootstrap mean of {68}_{-8}^{+10} Pg C yr−1) statistically inconsistent with most published RS values ({87}_{-8}^{+9} Pg C yr−1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global RS:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

Reliability Estimation in Multicomponent Stress-Strength for Generalized Inverted Exponential Distribution Based on Ranked Set Sampling

Stress-strength models are considered of great significance due to their applicability in varied fields. We address the estimation of the system reliability of a multicomponent stress-strength model, say Rs,k, of an s out of k system when the pair stress and strengths are drawn from a generalized inverted exponential distribution. The system is deemed as working if at least s out of k strengths be more than its stress. We obtain the reliability estimators when the data of strength and stress distributions are collected from three sampling schemes, specifically; simple random sampling, ranked set sampling, and median ranked set sampling. We obtain four estimators of Rs,k out from median ranked set sampling. The behavior of different estimates is examined via a simulation study based on mean squared errors and efficiencies. The simulation studies point out that the reliability estimates of Rs,k, from the ranked set sampling scheme are preferred than other estimates picked from the simple random sample and median ranked set sampling in a majority of the situations. The theoretical studies are explained with the aid of real data analysis.

Homogenized century-long surface incident solar radiation over Japan

Abstract. Surface incident solar radiation (Rs) plays a key role in climate change on Earth. Rs can be directly measured, and it shows substantial variability on decadal scales, i.e. global dimming and brightening. Rs can also be derived from the observed sunshine duration (SunDu) with reliable accuracy. The SunDu-derived Rs has been used as a reference to detect and adjust inhomogeneity in the observed Rs. However, both the observed Rs and SunDu-derived Rs may have inhomogeneity. In Japan, SunDu has been measured since 1890, and Rs has been measured since 1961 at ∼100 stations. In this study, the observed Rs and SunDu-derived Rs were first checked for inhomogeneity independently using the statistical software RHtests. If confirmed by the metadata of these observations, the detected inhomogeneity was adjusted based on the RHtests quantile-matching method. Second, the two homogenized time series were compared to detect further possible inhomogeneity. If confirmed by the independent ground-based manual observations of cloud cover fraction, the detected inhomogeneity was adjusted based on the reference dataset. As a result, a sharp decrease of more than 20 W m−2 in the observed Rs from 1961 to 1975 caused by instrument displacement was detected and adjusted. Similarly, a decline of about 20 W m−2 in SunDu-derived Rs due to steady instrument replacement from 1985 to 1990 was detected and adjusted too. After homogenization, the two estimates of Rs agree well. The homogenized SunDu-derived Rs show an increased at a rate of 0.9 W m−2 per decade (p<0.01) from 1961 to 2014, which was caused by a positive aerosol-related radiative effect (2.2 W m−2 per decade) and a negative cloud cover radiative effect (−1.4 W m−2 per decade). The brightening over Japan was the strongest in spring, likely due to a significant decline in aerosol transported from Asian dust storms. The observed raw Rs data and their homogenized time series used in this study are available at https://doi.org/10.11888/Meteoro.tpdc.271524 (Ma et al., 2021).

Comparison of sire breeding values for milk yield traits based on daughters milked once or twice daily in New Zealand

Abstract Context In New Zealand, cows are usually milked twice a day (TAD), but in ~8% of herds, cows are milked once a day (OAD) for the entire lactation. If a genetic correlation (rg) of the same trait expressed in two environments (such as TAD and OAD) is substantially <1.0, then the genetic merit assessed from TAD herds may not be reliable for predicting genetic merit in OAD herds. Initial evaluation of sires has been undertaken from progeny test herds with TAD milking, and the best sires have then been made widely available for use in TAD or OAD herds. Aim The study was designed to test formally whether sire re-rankings occur in widely used sires at different milking frequencies. Methods Regression coefficients and rank correlations (rS) for estimated breeding values (EBVs) of 242 widely used sires (86 Holstein-Friesian, 96 Jersey and 60 crossbred) were calculated for yields of milk, fat and protein, and for somatic cell score (SCS). The rS values were contrasted with expected rank correlations (rE) between TAD and OAD EBVs assuming rg = 1.0 between true BVs expressed at the two milking frequencies and accounting for the fact that sires were highly selected. Key results Within and across breeds, regression coefficients of OAD on TAD EBVs for the same traits were <1.0, but rS values between TAD and OAD EBVs of the same sires were strong (>0.75) for milk yield, moderate–strong for fat and protein yield (0.55–0.77), and moderate for SCS (0.41–0.65). Estimates of rS were higher than their critical rE values, indicating no significant sire re-ranking across milking frequencies. Conclusions On the basis of the results, a separate selection program to generate sires for use in OAD herds is not justified. Implications Farmers operating under OAD systems can rely on genetic evaluation of sires evaluated in TAD systems and used in the OAD population. However, producers should recognise that the realised productive and economic advantage of daughters of elite sires born in OAD herds is diminished relative to that expected in TAD herds.

Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n=5173 and v5.0 n=10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6PgCyear-1 , and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation)PgCyear-1 , whereas parameterization using SRDBv5 yielded 96.5PgCyear-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation)PgCyear-1 . Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4)PgCyear-1 , respectively. SRDBv5's inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4±21.4PgCyear-1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.

Improvement of ERA5 over ERA-Interim in Simulating Surface Incident Solar Radiation throughout China

AbstractSurface incident solar radiation (Rs) is important for providing essential information on climate change. Existing studies have shown that the Rs values from current reanalyses are significantly overestimated throughout China. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the fifth generation of atmospheric reanalysis (i.e., ERA5) with a much higher spatiotemporal resolution and a major upgrade compared to its predecessor, ERA-Interim. This study is to verify whether ERA5 can improve the Rs simulation using sunshine duration–derived Rs values at ~2200 stations over China from 1979 to 2014 as reference data. Compared with the observed multiyear national mean, the Rs overestimation is reduced from 15.88 W m−2 in ERA-Interim to 10.07 W m−2 in ERA5. From 1979 to 1993, ERA-Interim (−1.99 W m−2 decade−1; p < 0.05) and ERA5 (−2.42 W m−2 decade−1; p < 0.05) estimates of Rs in China continued to decrease and the decline of the latter is closer to the observed. After 1993, they both show a strong brightening (i.e., 2.26 W m−2 decade−1 in ERA-Interim and 1.49 W m−2 decade−1 in ERA5) but observations show a nonsignificant increase by 0.30 W m−2 decade−1. Due to the improvement of total cloud cover (TCC) simulation by ERA5, its Rs trend bias induced by the TCC trend bias is smaller than that in ERA-Interim. In addition, the reason why the simulation trend in ERA5 remains biased might be that ERA5 still ignores aerosol changes on interannual or decadal time scales. Therefore, subsequent reanalysis products still need to improve their simulation of clouds, water vapor, and aerosol, especially in aerosol direct and indirect effects on Rs.

Spatial variability of soil respiration (Rs) and its controls are subjected to strong seasonality in an even‐aged European beech (Fagus sylvatica L.) stand

AbstractUncertainties arising from the so‐far poorly explained spatial variability of soil respiration (Rs) remain large. This is partly due to the limited understanding about how spatially variable Rs actually is, but also on how environmental controls determine Rs's spatial variability and how these controls vary in time (e.g., seasonally). Our study was designed to look more deeply into the complexity of Rs's spatial variability in a European beech even‐aged stand, covering both phenologically and climatically contrasting periods (spring, summer, autumn and winter). Although we studied a relatively homogeneous stand, we found a large spatial variability of Rs (coefficients of variation > 30%) characterized by strong seasonality. This large spatial variability of Rs suggests that even in relatively homogeneous stands there is a large potential source of error when estimating Rs. This was also reflected by the sampling effort needed to obtain seasonally robust estimates of Rs, which may actually require a number of samples above that used in Rs studies. We further postulate that the effect of seasonality on the spatial variability and environmental controls of Rs was determined by the seasonal shifts of its microclimatic controls: during winter, low temperatures constrain plant and soil metabolic activities and hence reduce Rs variability (temperature‐controlled processes), whereas during summer, water demand by vegetation and changes in water availability due to the microtopography of the terrain (i.e., slope) increase Rs variability (water‐controlled processes). This study provides novel information on the spatiotemporal variability of Rs and looks more deeply into the seasonality of its environmental controls and the architecture of their causal‐effect relationships controlling Rs's spatial variability. Our study further shows that improving current estimates of Rs at local and regional levels might be necessary in order to reduce uncertainties and improve CO2 estimates at larger spatial scales.Highlights The spatial variability of soil respiration (Rs) and its environmental controls vary seasonally. Seasonal shifts from temperature‐ to water‐controlled processes determine Rs's spatial variability. Besides microclimate, slope and grass cover explain the spatiotemporal variability of Rs. An intense sampling effort is needed to obtain robust Rs estimates even in homogeneous forests.

Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products

This study assesses the accuracy of estimating daily grass reference evapotranspiration (PM-ETo) using daily shortwave radiation (Rs) and reference evapotranspiration (ETREF) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (Rs ERA5) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study's use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ETo estimation. The first assessment focused on comparing LSA-SAF estimates of Rs (Rs LSA-SAF) against ground stations (Rs ground). The results showed a good matching between the two Rs data sets for continental Portugal but a tendency for Rs LSA-SAF to under-estimate Rs ground in the cloudy islands of Azores. ETo values computed using Rs LSA-SAF data and observed temperature, humidity and wind speed (ETo LSA-SAF) were then compared with PM-ETo estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ETo were quality checked for surface aridity effects. It was observed that ETo LSA-SAF is strongly correlated with PM-ETo (R2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d−1, and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ETo LSA-SAF over-estimated PM-ETo. This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ETREF product (ETREF LSA-SAF) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ETo derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the Rs LSA-SAF and Rs ERA5 data, which showed a superiority of the LSA-SAF product for ETo estimations (ETo PMT LSA-SAF). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ETo PMT and ETREF LSA-SAF. Differently, results showed that using the Rs LSA-SAF in the PMT approach (ETo PMT LSA-SAF) produces significantly better RMSE results than ETo PMT and ETREF LSA-SAF. Overall, the performed assessment allows concluding that the use of Rs LSA-SAF, and to a lesser extent the use of the Rs ERA5, highly improves the accuracy of computation of ETo when Rs observations are not available, including when only temperature data are accessible. The use of the ETREF LSA-SAF product is a good alternative when observed weather data are not available.

MODIS-Derived Estimation of Soil Respiration within Five Cold Temperate Coniferous Forest Sites in the Eastern Loess Plateau, China

Soil respiration (Rs) is seldom analyzed using remotely sensed data because satellite technology has difficulty monitoring various respiratory processes in the soil. We investigated the potential of remote sensing data products to estimate Rs, including land surface temperature (LST) and spectral vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), using a nine-year (2007–2015) field measurement dataset of Rs and soil temperature (Ts) at five forest sites at the eastern Loess Plateau, China. The results indicate that soil temperature is the primary factor influencing the seasonal variation of Rs at the five sites. The accuracy of the model based on the observed data is not significantly different from the model based on MODIS-derived nighttime LST values. There was a significant difference with the model based on MODIS-derived daytime LST values. Therefore, nighttime LST was the optimum LST for estimation of Rs. The normalized difference vegetation index (NDVI) consistently exhibited a stronger correlation with Rs when compared to the green edge chlorophyll index and enhanced vegetation index. Further analysis showed that adding the NDVI into the model considering only Ts or nighttime LST could significantly improve the simulation accuracy of Rs. The models depending on nighttime LST and NDVI showed comparable accuracy with the models based on the in situ Ts and NDVI. These results suggest that models based entirely on remote sensing data from MODIS have the potential to estimate Rs at the cold temperate coniferous forest sites. The performance of the model in other vegetation types or regions has also been proved. Our conclusions further confirmed that it is feasible for large-scale estimates of Rs by means of MODIS data in temperate coniferous forest ecosystems.

New Techniques and Data for Understanding the Global Soil Respiration Flux

AbstractSoil respiration (Rs; the soil surface‐to‐atmosphere CO2 flux) has been measured in the field for decades, but only recently have we begun to assemble and leverage these small‐scale but extensive data. Recently, Zhao et al. (2017, https://doi.org/10.1002/2016ef000480) applied a novel artificial neural network model to the problem of estimating the global Rs flux and understanding its variations between regions and biomes. Their results point to a convergence in estimates of global Rs, and the power of leveraging the long record of observed Rs in global ecosystems, but also to uncertainties about soils' response to climate change. It will take a combination of long‐term studies, data syntheses, modeling intercomparisons, and probably a new generation of sampling networks and experiments to fully resolve these questions.

Underestimation of soil respiration in a desert ecosystem

Soil respiration (Rs), conventionally considered as net soil CO2 flux, is an important link in the terrestrial carbon cycle. However, existing estimates of Rs in drylands may be erroneous, because of the abiotic soil CO2 flux. This may seriously hamper our understanding of the carbon cycle of desert ecosystems. In this study, we monitored CO2 flux of natural and sterilized soils, and obtained the net and abiotic soil CO2 flux in a desert ecosystem in Ningxia, China, and then computed Rs by using these data. Daily Rs was much larger than the net soil CO2 flux both in the growing and non-growing season. Further analysis indicated that Rs was controlled by soil temperature (Ts) over the annual cycle, and an exponential model could provide a good prediction of Rs. During the growing season, Rs increased significantly with rising soil water content (VWC) (P<0.05). A bivariate (Ts and VWC) exponential–power model had a better performance for predicting Rs in the growing season than a Ts-only model. However, during the non-growing season, this model failed to simulate Rs. Our results suggest that Rs is greatly underestimated, and the exponential model is more applicable for annual Rs prediction.

Daily reference crop evapotranspiration in the humid environments of Azores islands using reduced data sets: accuracy of FAO-PM temperature and Hargreaves-Samani methods

Reference crop evapotranspiration (ETo) estimations using the FAO Penman-Monteith equation (PM-ETo) require several weather variables that are often not available. Then, ETo may be computed with procedures proposed in FAO56, either using the PM-ETo equation with temperature estimates of actual vapor pressure (e a) and solar radiation (R s), and default wind speed values (u 2), the PMT method, or using the Hargreaves-Samani equation (HS). The accuracy of estimates of daily e a, R s, and u 2 is provided in a companion paper (Paredes et al. 2017) applied to data of 20 locations distributed through eight islands of Azores, thus focusing on humid environments. Both estimation procedures using the PMT method (ETo PMT) and the HS equation (ETo HS) were assessed by statistically comparing their results with those obtained for the PM-ETo with data of the same 20 locations. Results show that both approaches provide for accurate ETo estimations, with RMSE for PMT ranging 0.48–0.73 mm day−1 and for HS varying 0.47–0.86 mm day−1. It was observed that ETo PMT is linearly related with PM-ETo, while non-linearity was observed for ETo HS in weather stations located at high elevation. Impacts of wind were not important for HS but required proper adjustments in the case of PMT. Results show that the PMT approach is more accurate than HS. Moreover, PMT allows the use of observed variables together with estimators of the missing ones, which improves the accuracy of the PMT approach. The preference for the PMT method, fully based upon the PM-ETo equation, is therefore obvious.

The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale.

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108Pgyr-1 , but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.

Analysis of surface incident shortwave radiation from four satellite products

Incident solar radiation (Rs) over the Earth's surface is important for studying our climate and environment. Global observation networks have been established, but many land surfaces are under-represented. Satellite remote sensing is the only way to estimate Rs at both global and regional scales. Many efforts have been made to evaluate the accuracy of current Rs products generated from satellite observations, but only a limited amount of ground measurements was generally used and the individual satellite products were used for analyzing Rs variability. In this study, four satellite estimates of Rs, including the Global Energy and Water Cycle Experiment — Surface Radiation Budget (GEWEX-SRB V3.0), the International Satellite Cloud Climatology Project — Flux Data (ISCCP-FD), the University of Maryland (UMD)/Shortwave Radiation Budget (SRB) (UMD-SRB V3.3.3) product, and the Earth's Radiant Energy System (CERES) EBAF, were evaluated using comprehensive ground measurements at 1151 sites around the world from the Global Energy Balance Archive (GEBA) and the China Meteorological Administration (CMA). It was found that these satellite estimates of Rs agree better with surface measurements at monthly than at daily time scale and can capture the seasonal variation of Rs very well, but these satellite products overestimated Rs by approximately 10wm−2. The mean bias and the root mean square error (RMSE) of the monthly mean estimates from these four data sets were 10.2wm−2 and 24.8wm−2 respectively. The global annual mean values of Rs were 186.7wm−2, 185.4wm−2, and 188.6wm−2 for CERES-EBAF, ISCCP-FD, and GEWEX-SRB V3.0 respectively. The averaged global annual mean Rs value from ground-measured-calibrated three satellite derived Rs products was 180.6wm−2, which is smaller than that estimated from individual satellite-derived products. The CERES-EBAF product shows the best accuracy among these four data sets, which indicates that including more accurate cloud information from active instruments can improve the accuracy of Rs. These satellite products show different temporal trends. Both GEWEX-SRB V3.0 and ISCCP-FD showed similar trends at the global scale but with different magnitudes. A significant dimming was found between 1984 and 1991, followed by brightening from 1992 to 2000, and then by a significant dimming over 2001–2007. The CERES-EBAF product showed a brightening trend, but not significantly since 2000. The variability from satellite estimates at pixel level was also analyzed. The results are comparable with previous studies based on observed Rs at the surface for specific regions, although some inconsistencies still exist and the magnitudes of the variations should be further quantified. We also found that clouds contribute more to the long-term variations of Rs derived from satellite observations than aerosols.

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

Initial soil respiration response to biomass harvesting and green-tree retention in aspen-dominated forests of the Great Lakes region

Contemporary forest management practices are increasingly designed to optimize novel objectives, such as maximizing biomass feedstocks and/or maintaining ecological legacies, but many uncertainties exist regarding how these practices influence forest carbon (C) cycling. We examined the responses of soil respiration (Rs) to biomass harvesting and green-tree retention in an effort to empirically assess their impacts on C cycling. We measured Rs and soil microclimatic variables over four growing seasons following implementation of these management practices using a fully replicated, operational-scale experiment in aspen-dominated forests in northern Minnesota. Treatments included three levels of biomass removal within harvested areas: whole-tree harvest (no slash deliberately retained), 20% slash retained, and stem-only harvest (all slash retained), and two levels of green-tree retention: 0.1ha aggregate or none. The relative amount of biomass removed had a negligible effect on Rs in harvested areas, but treatment effects were probably obscured by heterogeneous slash configurations and rapid post-harvest regeneration of aspen in all of the treatments. Discrete measurements of Rs and soil temperature within green-tree aggregates were not discernible from surrounding harvested areas or unharvested control stands until the fourth year following harvest, when Rs was higher in unharvested controls than in aggregates and harvested stands. Growing season estimates of Rs showed that unharvested control stands had higher Rs than both harvested stands and aggregates in the first and third years following harvest. Our results suggest that retention of larger forest aggregates may be necessary to maintain ecosystem-level responses similar to those in unharvested stands. Moreover, they highlight the innate complexity of operational-scale research and suggest that the initial impacts of biomass harvest on Rs may be indiscernible from traditional harvest in systems where incidental breakage is high.

Estimating soil respiration using spatial data products: A case study in a deciduous broadleaf forest in the Midwest USA

AbstractThis study aimed to investigate the potential of spatially distributed data products in estimating soil respiration (Rs), including land surface temperature (LST) and spectral vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) and root zone soil moisture derived from the assimilation of the NASA Advanced Microwave Scanning Radiometer‐EOS and a land surface model, at a deciduous broadleaf forest site in the Midwest USA. Several statistical models were used to examine the dependencies of Rs on these spatial data products, and accuracy of these models was compared to the models based on in situ measurements. The models based on mean LST (i.e., averaging nighttime and daytime LST from MODIS) and root zone soil moisture explained 82% and 72% of seasonal variations in Rs for spring and winter dormant periods, respectively. In the growing season, the models depending on mean LST, root zone soil moisture, and photosynthesis‐related enhanced vegetation index showed comparable accuracy with the models entirely based on in situ measured data, except for the midgrowing period. Drought stress led to a relatively low explanation capacity for the Rs model based on spatial data products during the midgrowing period. However, this model still explained 76% of temporal dynamics of Rs over the midgrowing period. Our results suggested that simple models based entirely on spatial data products have the potential to estimate Rs at the temperate deciduous forest site. The conclusions drawn from the present study provided valuable information for large‐scale estimates of Rs in temperate deciduous forest ecosystems.

Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan, Canada

Soil respiration (Rs) is of great importance to the global carbon balance. Remote sensing of Rs is challenging because of (1) the lack of long-term Rs data for model development and (2) limited knowledge of using satellite-based products to estimate Rs. Using 8-years (2002–2009) of continuous Rs measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that Rs was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between Rs and NDVI×LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled Rs closely tracked the seasonal patterns of measured Rs and explained 74–92% of the variance in Rs with a root mean square error (RMSE) less than 1.0gC/m2/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of Rs with an overall R2 of 0.78 (p<0.001) for data of these two sites. Consequently, we mapped Rs of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of Rs were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in Rs. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of Rs.

Effect of clear-cutting silviculture on soil respiration in a subtropical forest of China

Aims Clear-cutting is a common forest management practice, especially in subtropical China. However, the potential ecological consequences of clear-cutting remain unclear. In particular, the effect of clear-cutting on soil processes, such as the carbon cycle, has not been quantified in subtropical forests. Here, we investigated the response of soil respiration ( rs) to clear-cutting during a 12-month period in a subtropical forest in eastern China. Methods We randomly selected four clear-cut (CC) plots and four corresponding undisturbed forest ( uF) plots. measurements of rs were made at monthly time points and were combined with continuous climatic measurements in both CC and uF. Daily rs was estimated by interpolating data with an exponential model dependent on soil temperature. Daily rs was cumulated to annual rs estimates. Important Findings In the first year after clear-cutting, annual estimates of rs in CC (508 ± 23 g C m −2 yr −1 ) showed no significant difference to uF plots (480 ± 12 g C m −2 yr −1 ). During the summer, soil temperatures were usually higher, whereas the soil volumetric water content was lower in CC than in uF plots. The long-term effects of clear-cutting on rs are not significant, although there might be effects during the first several months after clear-cutting. Compared with previous work, this pattern was more pronounced in our subtropical forest than in the temperate and boreal forests that have been studied by others. With aboveground residuals off-site after clear-cutting, our results indicate that the stimulation of increasing root debris, as well as environmental changes, will not lead to a significant increase in rs. In addition, long-term rs will not show a significant decrease from the termination of root respiration, and this observation might be because of the influence of fast-growing vegetation after clear-cutting in situ.

Temperature response of soil respiration in a Chinese pine plantation: hysteresis and seasonal vs. diel Q10.

Although the temperature response of soil respiration (Rs) has been studied extensively, several issues remain unresolved, including hysteresis in the Rs–temperature relationship and differences in the long- vs. short-term Rs sensitivity to temperature. Progress on these issues will contribute to reduced uncertainties in carbon cycle modeling. We monitored soil CO2 efflux with an automated chamber system in a Pinus tabulaeformis plantation near Beijing throughout 2011. Soil temperature at 10-cm depth (Ts) exerted a strong control over Rs, with the annual temperature sensitivity (Q 10) and basal rate at 10°C (Rs 10) being 2.76 and 1.40 µmol m−2 s−1, respectively. Both Rs and short-term (i.e., daily) estimates of Rs 10 showed pronounced seasonal hysteresis with respect to Ts, with the efflux in the second half of the year being larger than that early in the season for a given temperature. The hysteresis may be associated with the confounding effects of microbial population dynamics and/or litter input. As a result, all of the applied regression models failed to yield unbiased estimates of Rs over the entire annual cycle. Lags between Rs and Ts were observed at the diel scale in the early and late growing season, but not in summer. The seasonality in these lags may be due to the use of a single Ts measurement depth, which failed to represent seasonal changes in the depth of CO2 production. Daily estimates of Q 10 averaged 2.04, smaller than the value obtained from the seasonal relationship. In addition, daily Q 10 decreased with increasing Ts, which may contribute feedback to the climate system under global warming scenarios. The use of a fixed, universal Q 10 is considered adequate when modeling annual carbon budgets across large spatial extents. In contrast, a seasonally-varying, environmentally-controlled Q 10 should be used when short-term accuracy is required.