Navarathnam Leelaruban 2015

Navaratnam Leelaruban (Leelan) is a Ph.D. Candidate in the Department of Civil and Environmental Engineering at North Dakota State University. He received a MS degree in Civil Engineering with water resources emphasis at North Dakota State University (2011), and a BS degree in Civil Engineering in University of Peradeniya, Sri Lanka (2007). His current research focuses on analysis and modeling of droughts and their impact.
Email: n.leelaruban@ndsu.edu

Fellow: Navaratnam Leelaruban
Advisor: G. Padmanabhan, Ph.D., Professor, Department of Civil and Environmental Engineering, North Dakota State University
Co-Advisor: Adnan Akuz. Ph.D., Professor of Climatological Practices, North Dakota State Climatologist, North Dakota State University
Degree Progress: Progress: Ph.D. in Civil and Environmental Engineering expected graduation in Spring 2016

A Study of Spatio-temporal Characteristics of North Dakota Droughts and their Impact

Drought is a complex phenomenon, and is difficult to define, analyze, and/or model. It is well known that drought is a spatially and temporally varying natural hazard. Understanding drought intensity, frequency, duration, spatial extent, and severity is critical in drought mitigation and planning. It is crucial to understand how drought propagates in space and time to effectively manage drought mitigating measures. Drought significantly impacts agriculture, environment, and society. A clear understanding of drought impact on these sectors will help address planning for future droughts. Quantifying drought impact is difficult because of the complex characteristics of drought and also the impacted sectors.

Various drought indices are used to identify and monitor drought situations, and to decide the timing and level of drought responses. The most commonly used indices include (i) Palmer Drought Severity Index (PDSI) (ii) Standardized Precipitation Index (SPI) (iii) Crop Moisture Index (CMI) and (iv) U.S Drought Monitor product. All except the last one are severity indicators and do not reflect the spatial extent of droughts. Each index has its own advantages and disadvantages from the users’ perspectives. A composite index which will reflect the severity and spatial coverage corresponding to that severity together, particularly at the county level will be useful for resource allocation for drought mitigation purposes.

It is estimated that drought costs the United States $6–8 billion annually. Drought creates stress on water sources (i.e., surface water, groundwater), and on soil moisture. This will impact water-dependent industries including agriculture, water supply, and recreation. Researchers have developed several techniques to understand the drought impact.

There is still a need for comprehensive drought study to understand the drought and its impact in North Dakota. Especially, the recent development and availability of computational tools can help develop better understanding of drought. The recent drought events in this region emphasize the need for a rigorous drought study.

Project Objectives:

1. To study the drought propagation mechanism based on the geospatial and temporal characteristics of droughts to gain a better understanding of the phenomenon
2. To study the impact of drought on water resources and agriculture in North Dakota

Progress:

A refined county-level drought Severity and Coverage index is developed for drought management based on U.S Drought Monitor (USDM) drought severity and coverage values. The spatial variation of drought severity and frequency within North Dakota are analyzed and mapped. The spatio-temporal characteristics of drought under different spatial scales are investigated. Based on the relationship between crop yield and USDM severity coverage values a crop specific county-wide drought Index is proposed. Transition probabilities are derived for crop yield categories from state of less severe drought year to more severe drought year using Markovian transitional probabilities. Impact of drought on barley yield is studied using Multiple Linear Regression and Artificial Neural Network. Responses of groundwater level to drought are being investigated based on drought severity and duration.

Significance:

North Dakota has experienced several drought events in the past. The impact of drought in this region has significant influence on the economy, social, and environmental sector of North Dakota. This study will analyze the characteristics and impact of drought in this region. The results of this study will be useful for state agencies, and water dependent industries to plan and manage the future drought events. In addition, this study will propose potential actions to be taken in order to improve the drought monitoring and mitigation in the state of North Dakota. Though this study focuses on the state of North Dakota, the methodologies used in this study can be adopted for other places. In general, this research will contribute to understand the propagation mechanism of drought, and assess the impact of drought on agriculture and groundwater resources using novel approaches.

Peer Reviewed Journal:

Odabas, M., Leelaruban, N., Halis Simsek, and G. Padmanabhan, 2014. Quantifying Impact of Droughts on Barley Yield in North Dakota, USA Using Multiple Linear Regression and Artificial Neural Network. Neural Network World, Vol. 24, No.4, pp. 343-356.

Leelaruban, N., P. Oduor, A. Akyuz, S. Shaik, and G. Padmanabhan., 2012. Leveraging a Spatio-Temporal Drought Severity and Coverage Index with Crop Yield Modeled as a Stochastic Process. International Journal of Hydrology Science and Technology (IJHST) Vol. 2, No. 3, pp.219 -236.

Conference/Presentation:

Leelaruban. N., and G. Padmanabhan, 2015. Droughts-Groundwater Relationship in Northern Great Plains Shallow Aquifers. World Environmental & Water Resources Congress, May 17-21, 2015, Austin, TX

Halis Simsek, Bilal Cemek, Leelaruban, N, G. Padmanabhan, 2014. An assessment of drought impact on barley yield using a county wide drought Severity and Coverage Index and Adaptive Network-based Fuzzy Inference System (ANFIS) Model. In proceeding of 2nd International Symposium on Innovative Technologies in Engineering and Science (ISITES), 18-20 Jun 2014, Karabuk, Turkey.

Leelaruban, N., Adnan Akyuz, Peter Oduor, G. Padmanabhan., 2010. Geospatial Analyses of Drought Impact and Severity in North Dakota, USA Using Remote Sensing and GIS. American Meteorological Society Annual Meeting, 18th Conference on Applied Climatology, 17–21 January 2010, Atlanta, GA.

Leelaruban. N., Odabas. M., Halis Simsek, and G. Padmanabhan, 2014. Application of Artificial Neural Network to study the Barley yield under different Drought Conditions. North Central ASABE/CSBE Intersectional Meeting, March 28-29, 2014, Brookings, SD [Podium Presentation]

Leelaruban, N., G. Padmanabhan., 2013. Groundwater Level Response to Droughts in North Dakota.Session 4-Groundwater Exploration, Development, and Management, 58th Annual Midwest Groundwater Conference, September, 23-25, 2013, Bismarck, ND [Podium Presentation]

Leelaruban, N., F. A. Akyüz, G. Padmanabhan., 2013. A Study of Drought Indices Performance in Reflecting Groundwater Responses to Drought in North Dakota, USA. NDSU/UND/SDSU Research Summit, April 23, 2013, Brookings, SD [Poster]

Leelaruban, N., F. A. Akyüz, G. Padmanabhan, S. Shaik., 2013. A Study of drought impact and severity based on developed county wide drought severity and coverage indices in North Dakota.5th Annual Graduate Student Research and Arts Forum, April 11, 2013, Fargo, ND [Poster]

Leelaruban, N., F. A. Akyüz, G. Padmanabhan, S. Shaik., 2012. A County-level Crop Specific Drought Severity-Coverage Index.H41A: Advancing Drought Monitoring and Prediction with Applications to Decision Making III Posters Session, AGU Fall Meeting. December 3-7, 2012, San Francisco, CA [Poster]

Leelaruban, N., A. Akyüz, G. Padmanabhan, and P. Oduor., 2010. An analysis of drought impact and severity in North Dakota, USA. ND EPSCoR State Conference, September 29, 2010, Grand Forks, ND [Poster]