Soil moisture drought detection and multi-temporal variability across China

Abstract

Soil moisture droughts can trigger abnormal changes of material and energy cycles in the soil-vegetation-atmosphere system, leading to important effects on local ecosystem, weather, and climate. Drought detection and understanding benefit disaster alleviation, as well as weather and climate predictions based on the understanding the land-atmosphere interactions. We thus simulated soil moisture using land surface model CLM3.5 driven with observed climate in China, and corrected wet bias in soil moisture simulations via introducing soil porosity parameter into soil water parameterization scheme. Then we defined soil moisture drought to quantify spatiotemporal variability of droughts. Over the period from 1951 to 2008, 40% of months (to the sum of 12×58) underwent droughts, with the average area of 54.6% of total land area of Mainland China. The annual monthly drought numbers presented a significant decrease in arid regions, but a significant increase in semi-arid and semi-humid regions, a decrease in humid regions but not significant. The Mainland as a whole experienced an increasing drought trend, with 77.3% of areal ratio of decrease to increase. The monthly droughts in winter were the strongest but the weakest in summer, impacting 54.3% and 8.4% total area of the Mainland, respectively. The drought lasting three months or more occurred mainly in the semi-arid and semi-humid regions, with probability >51.7%, even >77.6%, whereas those lasting 6 and 12 months or more impacted mainly across arid and semi-arid regions. soil moisture droughts, soil moisture, CLM3.5, land surface process modeling Citation:Li M X, Ma Z G. 2015. Soil moisture drought detection and multi-temporal variability

Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

“…Previous studies also showed that CLM3.5 produces too-high soil moisture with too-low variability compared to root zone soil moisture modeled by later CLM versions (4.0 and 4.5) (e.g., Lawrence et al, 2011;Niu et al, 2011). In order to reduce these biases, Li and Ma (2015) introduced a factor to describe soil porosity and increase recharge water from the soil column to the aquifer in the newer CLM, leading to improved estimates of soil moisture and biogeochemical processes. However, Lawrence et al (2011) showed that the differences between CLM3.5 and new versions of CLM with respect to soil moisture variability remained small when compared to observations.…”

Regional water budgets and hydroclimatic trend variations in Xinjiang from 1951 to 2000

“…Finally, the new observation-constrained atmospheric forcing data were constructed at 0.5°grid spacing, 3-h interval for a 50-year time span from 1951 to 2000. Several studies have already shown that this observation-constrained atmospheric forcing data can significantly improve the land surface simulations over China (Liu et al 2012;Li and Ma 2015).…”

“…However, according to Lawrence et al (2011), the soil moisture variability still shows negative bias compared with observations. Based upon the previous assessment of the applicability of CLM 3.5 over China (Li and Ma 2015;Li et al 2016), we opted for CLM 3.5 to simulate the long-term hydroclimatic trends over Xinjiang with enhanced hydrological parameterization in this study.…”

Variability of modeled runoff over China and its links to climate change

“…Nevertheless, CLM 3.5 produces higher soil moisture and lower variability than observations in the rooting zone. To reduce these biases, Li and Ma (2015) introduced a factor to describe soil porosity, increase the recharging water from the soil column to the aquifer, and reduce the flux in the opposite direction, as achieved by Lawrence et al (2011) and Niu et al (2011).…”

“…The modeled and observed soil moisture over China agreed well. For instance, the average correlation coefficient is up to 0.83 in Northeast China, where measurements are mostly available for summers during 1990-2000 (statistical details, see Li and Ma 2015).…”