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Browsing by Author "Sehgal, Vinit"

Now showing 1 - 3 of 3
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    Integrating Climate Forecasts with the Soil and Water Assessment Tool (SWAT) for High-Resolution Hydrologic Simulations and Forecasts in the Southeastern U.S.
    Sehgal, Vinit; Sridhar, Venkataramana; Juran, Luke; Ogejo, Jactone Arogo (MDPI, 2018-08-29)
    This study provides high-resolution modeling of daily water budget components at Hydrologic Unit Code (HUC)-12 resolution for 50 watersheds of the South Atlantic Gulf (SAG) region in the southeastern U.S. (SEUS) by implementing the Soil and Water Assessment Tool (SWAT) model in the form of a near real-time, semi-automated framework. A near real-time hydrologic simulation framework is implemented with a lead time of nine months (March–December 2017) by integrating the calibrated SWAT model with National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) weather data to forecast daily water balance components. The modeling exercise is conducted as a precursor for various future hydrologic studies (retrospective or forecasting) for the region by providing a calibrated hydrological dataset at high spatial (HUC-12) and temporal (1-day) resolution. The models are calibrated (January 2003–December 2010) and validated (January 2011–December 2013) for each watershed using the observed streamflow data from 50 United States Geological Survey (USGS) gauging stations. The water balance analysis for the region shows that the implemented models satisfactorily represent the hydrology of the region across different sub-regions (Appalachian highlands, plains, and coastal wetlands) and seasons. While CFSv2-driven SWAT models are able to provide reasonable performance in near real-time and can be used for decision making in the region, caution is advised for using model outputs as the streamflow forecasts display significant deviation from observed streamflow for all watersheds for lead times greater than a month.
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    Near Real-time Seasonal Drought Forecasting and Retrospective Drought Analysis using Simulated Multi- layer Soil Moisture from Hydrological Models at Sub- Watershed Scales
    Sehgal, Vinit (Virginia Tech, 2017-07-28)
    This study proposes a stratified approach of drought severity assessment using multi-layer simulated soil moisture. SWAT (Soil and Water Assessment Tool) models are calibrated for 50 watersheds in the South-Atlantic Gulf region of the Southeastern US and a high-resolution daily soil moisture dataset is obtained at Hydrologic Unit Code (HUC-12) resolution for a period of January 1982 through December 2013. A near real-time hydrologic simulation framework by coupling the calibrated SWAT models with the National Centers for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) weather data is developed to forecast various water balance components including soil moisture (SM), actual evapotranspiration (ET), potential evapotranspiration ET (PET), and runoff (SURQ) for near-real time drought severity assessment, and drought forecasting for a lead of 9-months. A combination of the surface and total rooting depth soil moisture percentiles proves to be an effective increment over conventional drought assessment approaches in capturing both, transient and long-term drought impacts. The proposed real-time drought monitoring approach shows high accuracy in capturing drought onset and propagation and shows a high degree of similarity with the U.S. Drought Monitor (USDM), the long-term (PDSI, PHDI, SPI-9 and SPI-12), and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices.
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    Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.S
    Sridhar, Venkataramana; Sehgal, Vinit (2019-03)
    Drought 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.