Abstract:Previous research has shown that the temperature of underlying permafrost decreases after the ground surface is covered with sand. No significant conclusions have yet been drawn that explain why this happens, because the heat transfer mechanism effects of the sand layer on the underlying permafrost remain unclear. These mechanisms were studied in the present work. We found that the upward shortwave radiation flux of the Qinghai-Tibet Plateau ground surface with a sand layer covering was higher than that of the… Show more
“…These effects, however, are strongly influenced by the available burrow density of plateau pika; that is, plateau pika's disturbance would not benefit soil structure when there are over 544 burrows ha −1 (Guo et al, 2012a;Yu et al, 2017) because they will increase soil evaporation and the possibility of soil erosion. Temperature is another critical factor that controls patterns in regional ecosystems and permafrost dynamics on the Qinghai-Tibet Plateau (Xie et al, 2015). The plateau pika substantially modifies aboveground landscape characteristics and belowground soil properties (Guo et al, 2012a(Guo et al, , 2012bSun et al, 2015), and, in turn, will affect radiation partitioning at the ground surface as well as the belowground conduction of soil heat (Xie et al, 2015).…”
Summary
The plateau pika (Ochotona curzoniae) is one of the main native soil faunas on the Qinghai‐Tibet Plateau and plays a key role in the terrestrial ecosystem there. To understand how and why the soil microclimate changes after the plateau pika's disturbance, this study measured soil, vegetation, hydrologic and thermal properties and investigated soil moisture and soil temperature dynamics from 2014 to 2015 at the plot scale of four types of land surface: original grassland, new mound, old mound and bald patch. Our results showed that the average coefficients of surface runoff of original grassland, new mound, old mound and bald patch were 0.6, 3.0, 4.3 and 10.0%, respectively (P < 0.05). Evapotranspiration was largest for original grassland, especially under wet conditions, and was similar among the other three types of land surface during the growing season (P < 0.01). During varying precipitation events, the soil moisture content of new mound increased first, followed by old mound, original grassland and bald patch. Meanwhile, the increasing magnitude of soil moisture content had the same order. At the seasonal timescale, soil moisture content at 5‐cm depth was largest for old mound and smallest for bald patch (P < 0.01). The mean daily soil temperature at 5‐cm depth of new mound was approximately 0.8°C higher than that of old mound (P < 0.01) because of the smaller land surface reflectance of the former. The daily range of soil temperature at 5‐cm depth of original grassland and bald patch was about 2.7 and 4.7°C higher than the average value of new and old mounds, respectively, because of the larger soil thermal conductivity of the former two. A conceptual framework is suggested in this study to synthesize the evolution of soil microclimate under disturbance by the plateau pika. Overall, results indicated that new and old mounds accelerate soil hydrologic processes and have a better soil temperature buffer.
Highlights
Change in soil microclimate of different types of land surface under disturbance by the plateau pika.
New and old mounds accelerate soil hydrologic processes and have a better soil temperature buffer.
New and old mounds provide advantageous environments for plateau pika.
Bald patch had the smallest soil moisture content and largest daily range of soil temperature.
“…These effects, however, are strongly influenced by the available burrow density of plateau pika; that is, plateau pika's disturbance would not benefit soil structure when there are over 544 burrows ha −1 (Guo et al, 2012a;Yu et al, 2017) because they will increase soil evaporation and the possibility of soil erosion. Temperature is another critical factor that controls patterns in regional ecosystems and permafrost dynamics on the Qinghai-Tibet Plateau (Xie et al, 2015). The plateau pika substantially modifies aboveground landscape characteristics and belowground soil properties (Guo et al, 2012a(Guo et al, , 2012bSun et al, 2015), and, in turn, will affect radiation partitioning at the ground surface as well as the belowground conduction of soil heat (Xie et al, 2015).…”
Summary
The plateau pika (Ochotona curzoniae) is one of the main native soil faunas on the Qinghai‐Tibet Plateau and plays a key role in the terrestrial ecosystem there. To understand how and why the soil microclimate changes after the plateau pika's disturbance, this study measured soil, vegetation, hydrologic and thermal properties and investigated soil moisture and soil temperature dynamics from 2014 to 2015 at the plot scale of four types of land surface: original grassland, new mound, old mound and bald patch. Our results showed that the average coefficients of surface runoff of original grassland, new mound, old mound and bald patch were 0.6, 3.0, 4.3 and 10.0%, respectively (P < 0.05). Evapotranspiration was largest for original grassland, especially under wet conditions, and was similar among the other three types of land surface during the growing season (P < 0.01). During varying precipitation events, the soil moisture content of new mound increased first, followed by old mound, original grassland and bald patch. Meanwhile, the increasing magnitude of soil moisture content had the same order. At the seasonal timescale, soil moisture content at 5‐cm depth was largest for old mound and smallest for bald patch (P < 0.01). The mean daily soil temperature at 5‐cm depth of new mound was approximately 0.8°C higher than that of old mound (P < 0.01) because of the smaller land surface reflectance of the former. The daily range of soil temperature at 5‐cm depth of original grassland and bald patch was about 2.7 and 4.7°C higher than the average value of new and old mounds, respectively, because of the larger soil thermal conductivity of the former two. A conceptual framework is suggested in this study to synthesize the evolution of soil microclimate under disturbance by the plateau pika. Overall, results indicated that new and old mounds accelerate soil hydrologic processes and have a better soil temperature buffer.
Highlights
Change in soil microclimate of different types of land surface under disturbance by the plateau pika.
New and old mounds accelerate soil hydrologic processes and have a better soil temperature buffer.
New and old mounds provide advantageous environments for plateau pika.
Bald patch had the smallest soil moisture content and largest daily range of soil temperature.
“…This pattern is similar to the pattern observed by Xie et al . 28 but it does not explain how the sand layer (or sand veneer) protects the underlying permafrost from thermal change. In other words, conclusions cannot be drawn from ground temperatures observed at just one borehole.Figure 2Variations in soil temperatures at depths of 0.05 m ( a ), 0.2 m ( b ), 1.0 m ( c ), 2.0 m ( d ), 3.0 m ( e ), and 4.0 m ( f ) at QH-4 (30 cm sand layer) and QH-5 (115 cm sand layer) in the Honglianghe River Basin during the observation period from 1 December 2012 to 28 August 2015.Figure 3Mean annual soil temperature in 2013 and 2014 at 0.1 to 4.0 m depths at sites QH-4 (30 cm sand layer) and QH-5 (115 cm sand layer) in the Honglianghe River Basin.
…”
Section: Resultsmentioning
confidence: 99%
“…28 in the Hongliang River Basin. The data demonstrates that seasonal frozen ground can be found under a 3.0-m-thick sand cover at site Yu-4 and also under a 0.3-m-thick sand cover at site QH-4.…”
Section: Resultsmentioning
confidence: 99%
“…At the same time, the permafrost temperature under a sand dune with sparse vegetation at a depth of 14 m is −1.15 °C (site Yu-6) yet it is −0.74 °C under a 1.15-m-thick sand cover at site QH-5. When one compares the permafrost temperature at dune-free sites (site Yu-7, −0.64 °C and site Yu-6, −1.15 °C) with that at site Xie-1 (with a 120-cm-thick sand cover), the permafrost temperature at site Xie-1 is −0.65 °C 28 , clear, therefore, that a thick sand cover does not significantly influence the underlying permafrost temperature.Figure 4Soil temperature profiles at sites with ( a ) a 3.0-m-thick sand cover (Yu-4), ( b ) desertified land with sparse vegetation (Yu-6), ( c ) a 30-cm-thick sand cover (QH-4), and 1.15-m-thick sand cover (QH-5).
…”
Section: Resultsmentioning
confidence: 99%
“…In this situation, what is the role of the sand layer? According to Xie et al ., it acts as a thermal diode, having better heat removal properties but being less effective with respect to heat absorption 28 . In other words, it serves similarly to a coarse block field, as applied successfully to QTR embankments 46 .…”
Desertification of tundra regions may form an escalating cycle with permafrost degradation where more permafrost thaw leads to continued desertification. This traditional viewpoint has been challenged in recent reports that state desertification protects the underlying permafrost. However, our measurements of soil temperature from nine sites in the Honglianghe River Basin, interior QinghaiTibet Plateau, show that desertification can degrade permafrost. If one compares the permafrost temperatures at sites with thin sand covers (e.g. site Yu-7, permafrost temperature of −0.64 °C; site Yu-6, permafrost temperature of −1.15 °C) with that of site Xie-1 (−0.65 °C, with a 120-cm-thick sand cover), the permafrost temperature is not significantly different. It is clear that a thick sand cover does not influence the underlying permafrost temperature. Our observations support traditional geocryological knowledge which states that, under most circumstances, desertification does not protect, but rather degrades, permafrost.Permafrost is defined as ground that remains continuously at or below 0 °C for at least two consecutive years 1 . Permafrost is a product of long-term energy exchange between the ground surface and atmosphere and is therefore a condition of ground climate 2 . The difference in the macro-scale distribution of permafrost is controlled by the climate 3 ; however, the difference in the local conditions, such as the topography, vegetation, snow cover, soil and geological conditions, can significantly modify the thermal impacts of the climate 4-6 and result in local-scale anomalies in permafrost distribution 3 . One of the local-scale factors influencing permafrost distribution is dominantly surface cover, e.g. vegetation, snow cover, and water 7 . Many studies have presented the thermal effects of local-scale surface cover on the active layer processes, e.g., the hydrothermal regimes and ground freezing-thawing in the active layer, active layer thickness, and thermal states of permafrost 3,[8][9][10][11] . However, little focus has been placed on the hydrothermal impacts of desertification (sand layer or dune formation over the frozen ground) on the thermal regimes of soils in the active layer and the underlying permafrost.Permafrost underlies approximately 70% of the land area of the Qinghai-Tibet Plateau (QTP), the highest and most extensive plateau permafrost on Earth 12-14 . Observational evidence demonstrates that warming, thawing and degrading of the plateau permafrost has occurred during the past few decades 11,13,[15][16][17][18][19] . The effect of permafrost degradation in the Qinghai-Tibet Plateau dries the ground surfaces because the active layer usually thickens, the permafrost table lowers and vegetation becomes more fragmented and sparse. The result is an increase in wind action, enhanced deflation and Aeolian deposition, and increased potential risk of desertification 16,[20][21][22] . Since the 1960s, grassland deterioration and desertification have occurred in some parts of the QTP 23-25 and N...
Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
