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dc.contributor.authorSridhar, Venkataramanaen
dc.contributor.authorSehgal, Viniten
dc.date.accessioned2018-12-19T11:29:03Zen
dc.date.available2018-12-19T11:29:03Zen
dc.date.issued2019-03en
dc.identifier.urihttp://hdl.handle.net/10919/86447en
dc.description.abstractDrought assessment at local scales needs a reliable framework capturing both local landscape processes and the watershed-scale hydrological responses. Any such tool, implementable in a near real-time, semi-automated framework, is largely unavailable for the South Atlantic-Gulf (SAG) region in the Southeastern US (SEUS). In this study, we evaluate a drought monitoring and forecasting approach using multi-layer, high resolution, simulated soil moisture for 50 watersheds in the SEUS. Soil and Water Assessment Tool (SWAT) is integrated with meteorological drivers (precipitation and temperature) from Climate Forecast System Reanalysis data (CFSR) for retrospective simulations (January 1982–December 2013), and Climate forecast system model version-2 (CFSv2) models to obtain the near real-time estimates (January 2014 through mid-March 2017) and 9-month lead forecasts (mid-March through December 2017) of the hydrologic variables at 12-digit Hydrologic Unit Code (HUC-12) resolution. Drought assessment is carried out by combining the drought severity estimates of the weekly percentiles of the surface and total column (TC) soil moisture, aggregated to 4-week and 8-week respectively, following a different severity classification scheme for each layer. Several drought indices and observed drought maps from the U.S. Drought Monitor (USDM) are used to compare the agreement (or disagreement) of the proposed approach with the observed drought conditions in the region. The results show promising application of the proposed approach for near real-time estimation as the drought estimates show high (∼80–90%) Index of Agreement (IOA) with the Palmer Drought Severity Index (PDSI) for all drought categories (mild-exceptional). The retrospective assessment shows that TC soil moisture percentiles have high correlation (>0.7) with long-term drought indices. The surface soil moisture percentiles show high correlation (∼0.6) with Palmer Z Index (ZNDX) and 1-month Standardized Precipitation Index (SPI-1), while longer aggregations increase the association with the long-term drought indices. While the SWAT-CFSv2 based drought estimation is useful in near real-time mode, higher disagreement between the forecasted drought maps and the observed drought severity from the USDM is noted for a forecast window of greater than ∼ 4–6 weeks.en
dc.description.sponsorshipThis project was funded, in part, by the Virginia Agricultural Experiment Station (Blacksburg) and the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture (Washington, D.C.). We are grateful that the Advanced Research Computing facility at Virginia Tech was made available for our simulation analysis. We are also thankful for the financial support from Virginia Tech's Open Access Subvention Fund.en
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleWatershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.Sen
dc.typeArticle - Refereeden
dc.date.updated2018-12-19T11:29:01Zen
dc.description.versionPublished online (Publication status)en
dc.contributor.departmentBiological Systems Engineeringen
dc.title.serialWeather and Climate Extremesen
dc.identifier.doihttps://doi.org/10.1016/j.wace.2018.100191en
dc.type.otherArticleen
dc.identifier.volume23en
dc.identifier.orcidSridhar, Venkataramana [0000-0002-1003-2247]en
dcterms.dateAccepted2018-12-12en
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Biological Systems Engineeringen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Fralin Affiliated Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/University Research Institutesen


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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International