Wind erosion in arid and semi-arid areas is an important global environmental issue, and changes in wind speed trends over time play a key role in wind erosion dynamics. In a warming climate, scientists have recently observed a widespread decline in wind speed, termed "stilling". Here, we apply the Revised Wind Erosion Equation Model (RWEQ) to simulate the variability of wind erosion and quantify the impact of wind speed changes on soil degradation dynamics over the eastern agro-pastoral transitional zone of Northern China from 1982 to 2016. Our results show that a significant (i.e., p<0.05) decrease (-0.007 m s-1 year-1) of near-surface wind speed was observed annually, with significant declining trends in spring (-0.010 m s-1 year-1)and autumn (-0.009 m s-1 year-1). At the same time, wind erosion simulations reveal a negative trend for the annual soil loss from wind erosion (-6.20 t hectare-2 year-1 , p<0.05; affecting 99.8% of the study region), with significant declining trends in all seasons, particularly in spring (-3.49 t hectare-2 year-1) and autumn (-1.26 hectare-2 year-1). Further, we isolate the effects of wind variability on wind erosion from 1982 to 2016 by the model variable control method. This shows that wind speed variability strongly weakens wind erosion at-8.14 t hectare-2 year-1 (p<0.05) annually, with the strongest stilling recorded in spring leading to major decreases of wind erosion in spring (-4.77 t hectare-2 year-1 , p<0.05). Meanwhile, the weakest stilling in summer had the opposite influence on wind erosion (+0.40 t hectare-2 year-1 , p<0.10). To summarize, our findings have shown a significant impact of wind stilling on the decline of soil erosion rates in Northern China.

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Abstract:Agriculture is a sector easily affected by meteorological conditions. Crop yield reduction, even regional conflicts, may occur during a drought. It is extremely important to improve the state of our knowledge on agricultural drought risk. This study has proposed a new method (vulnerability surfaces) for assessing vulnerability quantitatively and continuously by including the environmental variable as an additional perspective on exposure and assessed global maize drought risk based on these surfaces. In this research, based on the Environmental Policy Impact Climate (EPIC) model, irrigation scenarios were adopted to fit "Loss rate-Drought index-Environmental indicator (L-D-E)" vulnerability surfaces by constructing a database suitable for risk assessment on a large scale. Global maize drought risk was quantitatively assessed based on its optimal vulnerability surface. The results showed an R 2 for the optimal vulnerability surface of 0.9934, with coarse fragment content as the environmental indicator. The expected global average annual yield loss rate due to drought was 19.18%. The global average yield loss rate due to drought with different return periods (10a, 20a, 50a, and 100a) was 29.18%, 32.76%, 36.89%, and 38.26%, respectively. From a global perspective, Central Asia, the Iberian Peninsula, Eastern Africa, the Midwestern United States, Chile, and Brazil are the areas with the highest maize drought risk. The vulnerability surface is a further development of the vulnerability curve as a continuous expression of vulnerability and considers differences in environmental factors. It can reflect the spatial heterogeneity of crop vulnerability and can be applied in large-scale risk assessment research.

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