Open AccessImpacts of temperature effect removal on rainfall estimation from soil water content by using SM2RAIN algorithm
Author(s) -
Kim Oanh Hoang,
Minjiao Lu
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/344/1/012046
Subject(s) - environmental science , water content , clearance , scale (ratio) , hydrology (agriculture) , soil water , soil science , climatology , geography , geology , medicine , geotechnical engineering , cartography , urology
Rainfall data is the most basic and essential input for many studies in hydrology, climatology, or water management. However, rainfall data is quite limited in poorly equipped regions such as high mountain regions, developing countries, and savannahs. In recent years, a new approach named SM2RAIN that has been proposed to assess rainfall amount on a daily scale. This method uses the relative saturation of soil (Se) as the input to infer rainfall. Some of these studies showed promising results in evaluating rainfall by using SM2RAIN. Over past decades, however, some other studies have also found that the soil water content (SWC), which is used to calculate the Se, has errors associated with diurnal temperature fluctuation. As soil water content is the primary input variable of SM2RAIN, this error must have impacts on the performance of SM2RAIN. In this study, the authors have assessed and cleared the impact of temperature on SM2RAIN performance. Thus, an effective temperature correction method has been applied to remove the impact of temperature on soil water content at 19 sites over the United States, Africa (Senegal) and Europe (Romania) in varies climatic conditions to assess the impact of temperature effect removal on results of SM2RAIN algorithm. The criteria to evaluate the program such as Nash-Sutcliff has increased up to 53% depending on the initial results and climatic condition. The correlation coefficient has positive change as well. As a result, this removing makes the performance of the algorithm better.