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

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.

Project Objectives:

General objective:

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

Specific Objectives

Drought monitoring

• Examine the temporal distribution of the two indices (compare with precipitation and PDSI)
• Examine the spatial distribution of the two indices (compare with U.S. drought monitor)
• Understand the evolution of 2010 to 2012 drought in top layer soil (SMDI) and the entire soil column (TSDI)

Drought prediction

• Find an appropriate model to predict climate variables
• Develop drought prediction using the indices and climate variables.

Progress:

• The performances of the two indices were evaluated temporally and spatially. The two indices can used for understand drought evolution for the current drought events.
• Two methods were applied to predict climate variables and the drought prediction based on predicted climate variables matches with drought observation.

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.