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Steven Quiring  - - - 
Top co-authors See all
Rohini Kumar

65 shared publications

UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany

Fernando Domínguez-Castro

53 shared publications

Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Zaragoza, Spain

Benjamin Zaitchik

42 shared publications

Department of Earth and Planatery Sciences, Johns Hopkins University, Baltimore, MD, USA

Mark Svoboda

41 shared publications

National Drought Mitigation Center, University of Nebraska–Lincoln, Lincoln, Nebraska

Roshanak Nateghi

37 shared publications

School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

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Distribution of Articles published per year 
(2017 - 2019)
Total number of journals
published in
 
4
 
Publications
PREPRINT-CONTENT 0 Reads 0 Citations Leveraging statistical learning theory to characterize the U.S. water consumption Ellen Wongso, Roshanak Nateghi, Benjamin Zaitchik, Steven Qu... Published: 15 February 2019
doi: 10.1002/essoar.10500810.1
DOI See at publisher website ABS Show/hide abstract
Access to accurate estimates of water withdrawal is requisite for urban planners as well as operators of critical infrastructure systems to make optimal operational decisions and investment plans to ensure reliable and affordable provisioning of water. Furthermore, identifying the key predictors of water withdrawal is important to regulators for promoting sustainable development policies to reduce water use. In this paper, we developed a rigorously evaluated predictive model, using statistical learning theory, to estimate state-level, per-capita water withdrawal as a function of various geographic, climatic and socio-economic variables. We then harnessed the data-driven predictive model to identify the key factors associated with high water-usage intensity among different sectors in the U.S. We analyzed the predictive accuracy of a range of parametric models (e.g., generalized linear models) and non-parametric, flexible learning algorithms (e.g., generalized additive models, multivariate adaptive regression splines and random forest). Our results identified irrigated farming, thermo-electric energy generation and urbanization as the most water-intensive anthropogenic activities, on a per-capita basis. Among the climate factors, precipitation was also found to be a key predictor of per-capita water withdrawal, with drier conditions associated with higher water withdrawals. Results of the first-order sensitivity analysis indicated changes between +/-10% in the future water withdrawal across the U.S., in response to precipitation changes, by the end of the 21st Century under the business-as-usual scenario. Overall, our study highlights the utility of leveraging statistical learning theory in developing data-driven models that can yield valuable insights related to the water withdrawal patterns across expansive geographical areas.
Article 0 Reads 0 Citations Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring Trent W. Ford, Steven M. Quiring Published: 05 February 2019
Water Resources Research, doi: 10.1029/2018wr024039
DOI See at publisher website
Article 1 Read 0 Citations Response of crop yield to different time-scales of drought in the United States: Spatio-temporal patterns and climatic a... Marina Peña-Gallardo, Sergio M. Vicente-Serrano, Steven Quir... Published: 01 January 2019
Agricultural and Forest Meteorology, doi: 10.1016/j.agrformet.2018.09.019
DOI See at publisher website
Article 0 Reads 1 Citation Effectiveness of drought indices in identifying impacts on major crops over the USA Marina Peña-Gallardo, Sergio M. Vicente-Serrano, Fernando Do... Published: 01 January 2018
Climate Research, doi: 10.3354/cr01519
DOI See at publisher website
Conference 12 Reads 0 Citations Complex spatial and temporal influences of climatic drought time-scales on hydrological droughts in natural basins of U.... Sergio Vicente-Serrano, Marina Peña-Gallardo, Jamie Hannafor... Published: 05 November 2017
First International Electronic Conference on the Hydrological Cycle, doi: 10.3390/chycle-2017-04835
DOI See at publisher website
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