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ACCESSION NO: 1005991 [Full Record]
PROJ NO: SC.W-2014-09392 AGENCY: NIFA SC.W
PROJ TYPE: AFRI COMPETITIVE GRANT PROJ STATUS: EXTENDED
CONTRACT/GRANT/AGREEMENT NO: 2015-68007-23210 PROPOSAL NO: 2014-09392
START: 15 MAR 2015 TERM: 14 MAR 2018 FY: 2018
GRANT AMT: $150,000 GRANT YR: 2015
AWARD TOTAL: $150,000
INITIAL AWARD YEAR: 2015
INVESTIGATOR: Mishra, A.
PERFORMING INSTITUTION:
CLEMSON UNIVERSITY
CLEMSON, SOUTH CAROLINA 29634
TOWARDS A NEAR REAL-TIME AGRICULTURAL DROUGHT MONITORING AND FORECASTING
NON-TECHNICAL SUMMARY: Our research objectives, which align with the NIFA objectives is quite relevant due to recent drought situations nationwide in that it will help to improve agricultural water management under drought scenarios. The funding will be used to improve technologies to provide near real-time drought forecast information for farmers, ranchers, forest owners and managers, public policy experts, public and private managers and citizens to improve water resource quantity. Currently two major limitations that exists in agricultural drought monitoring/forecasting are: (i) soil moisture derived from hydrologic models or remote sensing products provide aggregated information at coarse resolution and often witness larger uncertainty, however, the agricultural drought relies on finer/local scale information; (ii) the agricultural drought is monitored and
forecasted using available soil moisture at a uniform (constant) depth, which may not be suitable in real world scenarios.
OBJECTIVES: Agricultural drought, usually, refers to a period with declining soil moisture and consequent crop failure without any reference to surface water resources. A decline of soil moisture depends on several climate-catchment variables; therefore by incorporating high resolution real time soil moisture into drought monitor will improve predicting agricultural drought at near real-time conditions. This is important as farmers/growers require real-time information on status of soil moisture availability to decide 'when to irrigate and how much to irrigate'. Additionally, the drought forecasted information will indicate whether the ongoing drought will be progressive or recessive in nature. So far, advancement is made in agriculture drought monitoring, however limited success was observed for developing near realtime forecasting capabilities. The models,
interactive analysis and results will be disseminated for use by different stakeholders. These tools/models will be designed for application and deployment anywhere in the U.S.
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