AGNR’s Associate Dean for Research Received the 2022 Best Technical Paper from the Journal of Hydrological Engineering

The honor recognizes research excellence in the development of a valuable tool for managing drought in coastal areas

August 12, 2022 Kimbra Cutlip

The Journal of Hydrologic Engineering selected a research paper by Puneet Srivastava, professor and associate dean for research and associate director of Maryland Agricultural Experiment Station at the University of Maryland, to receive the 2022 Best Technical Paper Award.

The award highlights the importance of AGNR faculty in leading research on critical issues such as water resource management and climate prediction.

“I was excited to hear that this paper won the best technical paper award,” Srivastava said. “The recognition really speaks to the quality of research that my students are doing, and I am very proud to be working with such successful young researchers.”

The paper, which was based on research led by Srivastava’s Ph.D. student Subhasis Mitra, now an assistant professor at Indian Institute of Technology Palakkad, outlines new, more comprehensive methods for identifying and characterizing drought conditions in coastal areas, helping better capture the progression of drought and recovery.

Droughts are among the most costly disasters in the world. They can lead to severe economic losses to agriculture and fisheries, threaten public health from waterborne pathogens and ignite political conflicts over freshwater supply.

Resource managers tasked with preparing for and guarding against droughts have limited options for predicting the onset and progression of droughts in coastal areas. The wide range of environments found in coastal regions, from small tidal ponds and streams to large rivers, marshes and bays, respond to drought in different and complex ways. So, indices that work for predicting and characterizing drought in upland areas–those that rely solely on things like rainfall, soil moisture and streamflow–can’t reliably characterize drought in a coastal environment.

For this reason, coastal resource managers tend to rely on the Coastal Drought Index (CDI), which is based on salinity data. But salinity can be affected by many things, like upstream damming, so salinity alone cannot adequately capture the complex dynamics that occur as drought conditions begin to appear and persist.

“Drought occurs on many different time scales,” Srivastava said, “It all starts with rainfall, but it can take a few weeks of low rainfall before you see it affecting soil moisture, and even longer before stream flows go down. And recovery doesn’t happen all at once either, a few heavy rains and it may appear that the drought is over. Streamflow may even look good, but groundwater may still be low.”

Drought indices based on any one variable cannot fully capture the story of a drought in progress. Recognizing the need for a more holistic approach, Srivastava and Mitra developed new methods for combining multiple types of data to more accurately predict the onset, duration and recovery from drought in coastal regions. The team developed two new coastal drought indices and applied them to historical data from the U.S. southeast coastal region. Both of their indices captured the major drought events that occurred in 2000, 2007, and 2011 through 2012, and did a better job of capturing the progression of drought and recovery.

“The indices described in the paper will be extremely helpful for resource managers considering things like how to advise on water use or fisheries management in these coastal areas,” said Srivastava who has worked closely with the National Integrated Drought Information System (NIDIS) to advise them on developing a drought early warning system for the southeastern US.

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The paper “Comprehensive Drought Assessment Tool for Coastal Areas, Bays, and Estuaries: Development of a Coastal Drought Index,” was published in the Journal of Hydrological Engineering. Research described in this paper was supported by the National Integrated Drought Information System.