Bihang Fan

Earlier Vegetation Green-Up Has Reduced Spring Dust Storms

The observed decline of spring dust storms in Northeast Asia since the 1950s has been attributed to surface wind stilling. However, spring vegetation growth could also restrain dust storms through accumulating aboveground biomass and increasing surface roughness. To investigate the impacts of vegetation spring growth on dust storms, we examine the relationships between recorded spring dust storm outbreaks and satellite-derived vegetation green-up date in Inner Mongolia, Northern China from 1982 to 2008. We find a significant dampening effect of advanced vegetation growth on spring dust storms (r = 0.49, p = 0.01), with a one-day earlier green-up date corresponding to a decrease in annual spring dust storm outbreaks by 3%. Moreover, the higher correlation (r = 0.55, p < 0.01) between green-up date and dust storm outbreak ratio (the ratio of dust storm outbreaks to times of strong wind events) indicates that such effect is independent of changes in surface wind. Spatially, a negative correlation is detected between areas with advanced green-up dates and regional annual spring dust storms (r = −0.49, p = 0.01). This new insight is valuable for understanding dust storms dynamics under the changing climate. Our findings suggest that dust storms in Inner Mongolia will be further mitigated by the projected earlier vegetation green-up in the warming world.

Forward simulation of root’s ground penetrating radar signal: simulator development and validation

Background and aimsIt remains unclear how the limiting factors (e.g., root size, root water content, spacing between roots, and soil water content) affect root investigation using ground penetrating radar (GPR). The objective of this study is to develop a theoretical forward simulation protocol of synthesizing root’s GPR signal and test the feasibility of our proposed simulation protocol in evaluating the impacts of limiting factors on GPR-based root detection and quantification.MethodsThe proposed forward simulation protocol was developed by integrating several existing numerical models, such as the Root Composition Model, the Root Dielectric Constant Model, the Root Electrical Conductivity Model, the Soil Dielectric Constant Model, the Soil Electrical Conductivity Model, and a newly-established model (Root Length-Biomass Model). Resolution and GPR index obtained from both field collected radargrams and corresponding simulations were compared to validate the accuracy of simulation.ResultsSimulated radargrams exhibit similar resolution with that of the in situ collected. The same trends of root radar signals against different levels of root size, root water content, interval between roots, root depth, and antenna frequency were observed on both in situ radargrams and simulated radargrams. Strong correlations (correlation coefficients ranging from 0.87 to 0.96) were found between GPR indices extracted from the simulated data and those from the field collected data.ConclusionsOur proposed forward simulation is effective for assessing the impacts of limiting factors on root detection and quantification using GPR. This forward simulation protocol can be used to provide guidance for in situ GPR root investigation and can predict the accuracy of GPR-based root quantification under site-specific conditions.

A New Perspective on Understanding the Reduced Spring Dust Storm Frequency in Inner Mongolia, China

Spatiotemporal patterns of dust storms are affected by climate change through changes in convective instability, regional meteorological characteristics, and local sediment supply. Linking dust storm dynamics to climate change helps the understanding of what controls the initiation of dust storms, and assists the prediction of future dust storm occurrence. This study examines the temporal dynamics of spring dust storms in Inner Mongolia, a major dust source area in East Asia. We found that severe spring dust storms have significantly declined from 1954 to 2007. Four dust storm types showed similar decreasing trends from 2001 to 2012. This change in spring dust storm dynamics is attributed to the shift in vegetation green-up dates based on the analysis of a satellite derived vegetation index. Earlier vegetation green-up has a dampening effect on spring dust storms. Suitable environmental conditions for vegetation green-up hinder the emergence of dust storms. This study expands our understanding of the dynamics of spring dust storms in the changing climate through a new perspective on vegetation phenology in the spring.

Occurrence of subsurface lateral flow in the Shale Hills Catchment indicated by a soil water mass balance method: A simple method to detect subsurface lateral flow

Subsurface lateral flow (SLF) contributes substantially to hillslope runoff. However, because of the lack of appropriate methods, field investigation of SLF at the hillslope and catchment scales has been limited. Recently, high-frequency soil moisture monitoring has been tested to characterize SLF. This study presented a simple approach to determine SLF based on soil water mass balance and compared this method with an established approach that used the depth-specific soil moisture response time to precipitation to identify preferential flow. The new method defined the occurrence of SLF when the increase in soil water storage was greater than the accumulated quantity of effective precipitation during a rain event. We applied this method to a 10-minute resolution soil moisture dataset collected over 3 years from nine soil profiles along a concave hillslope. We found that (i) SLF frequency derived by the proposed method matched well with preferential flow frequency obtained by the established method (r > 0.9), (ii) precipitation and initial soil wetness together controlled the generation of SLF, precipitation intensity determined whether or not the hillslope produced SLF and the quantity of precipitation governed the spatial extent of SLF, and (iii) topographic and small-scale soil features led to spatially different frequencies of SLF during small storms. Identifying SLF from a soil moisture time series over an entire rain event complements the established method based on soil moisture dynamics at the beginning of an event. Applying both methods creates the potential to measure SLF frequency and vertical preferential flow frequency, respectively. Soil moisture sensor networks have improved the large-scale investigation of such preferential flows.

The Clustering of Severe Dust Storm Occurrence in China From 1958 to 2007

China is subjected to severe dust storms that deteriorate air quality and cause substantial damages to environment and socioeconomics. Although the annual frequency of severe dust storms in China has been declining since the 1950s, the variability of severe dust storm occurrence in time and space remains inadequately described under the changing climate. Based on the continuous observation at 368 meteorological observation sites across the mainland China over a 50-year period (1958 to 2007), we found that the temporal clustering of severe dust storm outbreak has intensified after 1985, which exacerbated the irregularity in the monthly distribution of severe dust storms. Moreover, the timing of the clustering has shown a higher interannual variation after 1985. Therefore, the variability of severe dust storm occurrence has increased significantly since the 1980s at the annual and monthly time scales. In addition, the relative probability of experiencing severe dust storms with a large influential range has risen since the 1980s. Spatially, severe dust storm outbreak has receded from spreading across the country in the 1950s to primarily affecting north and northwest China in the 2000s. These findings suggest a relatively higher risk of dust storm disaster in north and northwest China that is associated with the intensified clustering of severe dust storm activity in both time and space. Continuous studies are needed to identify the favorable synoptic system that stimulates the clustering of severe dust storm outbreak. We advocate more efforts to enhance severe dust storm prediction and warning dissemination in north and northwest China.