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Carbon storage capacity of semi-arid grassland soils and sequestration potentials in northern China
(2015)
Organic carbon (OC) sequestration in degraded semi-arid environments by improved soil management is assumed to contribute substantially to climate change mitigation. However, information about the soil organic carbon (SOC) sequestration potential in steppe soils and their current saturation status remains unknown. In this study, we estimated the OC storage capacity of semi-arid grassland soils on the basis of remote, natural steppe fragments in northern China. Based on the maximum OC saturation of silt and clay particles <20m, OC sequestration potentials of degraded steppe soils (grazing land, arable land, eroded areas) were estimated. The analysis of natural grassland soils revealed a strong linear regression between the proportion of the fine fraction and its OC content, confirming the importance of silt and clay particles for OC stabilization in steppe soils. This relationship was similar to derived regressions in temperate and tropical soils but on a lower level, probably due to a lower C input and different clay mineralogy. In relation to the estimated OC storage capacity, degraded steppe soils showed a high OC saturation of 78-85% despite massive SOC losses due to unsustainable land use. As a result, the potential of degraded grassland soils to sequester additional OC was generally low. This can be related to a relatively high contribution of labile SOC, which is preferentially lost in the course of soil degradation. Moreover, wind erosion leads to substantial loss of silt and clay particles and consequently results in a direct loss of the ability to stabilize additional OC. Our findings indicate that the SOC loss in semi-arid environments induced by intensive land use is largely irreversible. Observed SOC increases after improved land management mainly result in an accumulation of labile SOC prone to land use/climate changes and therefore cannot be regarded as contribution to long-term OC sequestration.
Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods.
So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap.
So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet.
However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC.
Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal.
Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling.
We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68.
It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution.
However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression.
Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site.
When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types.
Many semi arid savannas are prone to degradation, caused for example, by overgrazing or extreme climatic events, which often lead to shrub encroachment. Overgrazing by livestock affects vegetation and infiltration processes by directly altering plant composition (selective grazing) or by impacting soil physical properties (trampling). Water infiltration is controlled by several parameters, such as macropores (created by soil-burrowing animals or plant roots) and soil texture, but their effects have mostly been studied in isolation. Here we report on a study, in which we conducted infiltration experiments to analyze the interconnected effects of invertebrate-created macropores, shrubs and soil texture (sandy soil and loamy sand) on infiltration in two Namibian rangelands. Using structural equation modeling, we found a direct positive effect of shrub size on infiltration and indirectly via invertebrate macropores on both soil types. On loamy sands this effect was even stronger, but additionally, invertebrate-created macropores became relevant as a direct driver of infiltration. Our results provide new insights into the effects of vegetation and invertebrates on infiltration under different soil textures. Pastoralists should use management strategies that maintain a heterogeneous plant community that supports soil fauna to sustain healthy soil water dynamics, particularly on soils with higher loam content. Understanding the fundamental functioning of soil water dynamics in drylands is critical because these ecosystems are water-limited and support the livelihoods of many cultures worldwide.
Subterranean termites create tunnels (macropores) for foraging that can influence water infiltration and may lead to preferential flow to deeper soil layers. This is particularly important in water limited ecosystems such as semi-arid, agriculturally utilized savannas, which are particularly prone to land degradation and shrub-encroachment. Using termite activity has been suggested as a restoration measure, but their impact on hydrology is neither universal nor yet fully understood. Here, we used highly replicated, small-scale (50 x 50 cm) rain-simulation experiments to analyse the interacting effects of either vegetation (grass dominated vs. shrub dominated sites) or soil texture (sand vs. loamy sand) and termite foraging macropores on infiltration patterns. We used Brilliant Blue FCF as colour tracer to make the flow pathways in paired experiments visible, on either termite-disturbed soil or controls without surface macropores in two semi-arid Namibian savannas (with either heterogeneous soil texture or shrub cover). On highly shrub-encroached plots in the savanna site with heterogeneous soil texture, termite macropores increased maximum infiltration depth and total amount of infiltrated water on loamy sand, but not on sandy soil. In the sandy savanna with heterogeneous shrub cover, neither termite activity nor shrub density affected the infiltration. Termite's effect on infiltration depends on the soil's hydraulic conductivity and occurs mostly under ponded conditions, intercepting run-off. In semi-arid savanna soils with a considerable fraction of fine particles, termites are likely an important factor for soil water dynamics.