Jiexia Wu

Jiexia Wu is a Master of Science student in department of earth system science and policy at University of North Dakota. She holds a Master of Science Degree in water resource and environmental management, the Netherlands, and a Bachelor of Science degree in environmental science from Zhejiang University of Technology, China. Her recent research is Drought monitoring and prediction using NOAH land surface model and GRACE satellite observation

Email: jiexia.wu@my.und.edu       Phone: 7018859294

Drought monitoring and prediction using NOAH land surface model and GRACE satellite observation

Fellow: Jiexia Wu

Advisor: Dr. Xiaodong Zhang, Ph.D., Associate Professor, Department of Earth System Science & Policy, University of North Dakota

Degree Progress: Master of Science, Earth System Science & Policy, expected graduation 2013

Research:

Droughts usually occur after a long-term period with little precipitation or high temperature which causes high evapotranspiration [Dai, 2011; Lakshmi et al., 2004]. These processes cause soil moisture deficits and have severe impacts on agricultural production, economics and society [Clark et al., 2002; Marsh, 2007]. The North America is suffering a severe to extreme drought which began in the winter of 2010 and currently extends to the U.S., Mexico and part of Canada[Freedman, 2012]. In the U.S. the cost of drought is about 6-8 Billion per year [Witt, 1995]. The 2010 to 2012 drought brought an agricultural loss exceeded to 10 billion in 2011 in south [Walsh, 2011]. One of the possible reasons for the high cost is lack of recognition of drought events, because drought develops more slowly than other disaster like floods and hurricanes and it is hard to recognize drought until it becomes severe [Luo and Wood, 2007]. Therefore more accurately monitor drought progression and understand drought occurrence, development and recovery can inform drought mitigation plans and limit adverse effects [Sirdas and Sen, 2003].

For agricultural drought monitoring, soil moisture is the key indicator as such agricultural drought indices are often based on soil moisture deficit. For example, RSM[Thornthwaite and Mather, 1955], Soil Moisture Deficit Index (SMDI) was developed to quantify drought severity [Narasimhan and Srinivasan, 2005]. The SMDI was applied to drought monitoring for catchments in Texas, and it has never been used to large spatial scales. My research is applying SMDI to the entire continental U.S. to understand the spatial and temporal variability of 2010 to 2012 drought and develop drought prediction based on SMDI using soil moisture derived from Global Land Assimilation System (GLDAS) land surface model simulation and TSDI based on terrestrial water storage from (Gravity Recovery and Climate Experiment) GRACE.

Research Objectives:

Evaluate the ability of SMDI and TSDI on drought monitoring at large scale and develop a drought prediction method based on the indices

Progress:

Significance:

The North America is suffering a severe to extreme drought which began in the winter of 2010 and currently extends to the U.S., Mexico and part of Canada[Freedman, 2012]. In the U.S. the cost of drought is about 6-8 Billion per year [Witt, 1995]. The 2010 to 2012 drought brought an agricultural loss exceeded to 10 billion in 2011 in south [Walsh, 2011]. Understand drought occurrence, development and recovery can help on drought mitigation and improvement on drought prediction capability can help reducing cost on it. This research not only focuses on top layer soil moisture but also considers deeper water storage which sometimes is more persistent and severity. The initial time of drought prediction is 3 months and it helps on decision making.

Xiaodong Zhang

 

Dr. Xiaodong Zhang, Ph.D., Associate Professor, Department of Earth System Science & Policy, University