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Interaction between Soil Moisture and Air Temperature in the Mississippi River Basin
Citation:
Tang, C. AND D. Chen. Interaction between Soil Moisture and Air Temperature in the Mississippi River Basin. Journal of Water Resource and Protection. Scientific Research Publishing, Inc., Irvine, CA, 9(10):1119-1131, (2017).
Impact/Purpose:
The most likely and obvious application from the results is that the results provide insight for the land and atmosphere models fully coupling for climate change. Secondly, SM can sever as a potential predictor for T with a long land memory and strong land-atmosphere coupling, which is important for the future climate changes, especial global warming. Thirdly, the results help understanding the extreme climate because of SM’s drought index function.
Description:
Increasing air temperatures are expected to continue in the future. The relation between soil moisture and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relations between soil moisture and temperature was performed by developing a quantile regression model, a wavelet coherency model, and a Mann-Kendall correlation model from 1950 to 2010 in the Mississippi River Basin. The results indicate that first, anomaly air temperature is negatively correlated to anomaly soil moisture in the upper and lower basin, and however, the correlation between them are mixed in the middle basin. The correlation is stronger at the higher quantile (90th) of the two variables. Second, anomaly soil moisture and air temperature show strong coherency in annual frequency, indicating that the two variables are interannually correlated. Third, annual air temperature is significant negatively related to soil moisture, indicating that dry (wet) soil leads to warm (cool) weather in the basin. These results have potential application to future climate change research and water resource management. Also, the strong relationship between soil moisture and air temperature at annual scale could result in improved temperature predictability.
