Geology topics

Sangchul Lee

Publications and source records attributed to Sangchul Lee.

4 recordsLinked to original sources

USGS2020

Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling

This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.

Delaware, Maryland↗

USGS2020

Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model

The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.

Maryland↗

USGS2018

Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model

Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO 2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 °C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO 2 , precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO 2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 °C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO 2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO 2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha −1 in nitrate yield relative to the watershed with a lower percent of croplands as a result of increased export of nitrate derived from fertilizer. The watershed dominated by poorly drained soils showed increased nitrate removal due do enhanced denitrification compared to the watershed dominated by well-drained soils. Our findings suggest that increased implementation of conservation practices would be necessary for this region to mitigate increased nitrate loads associated with predicted changes in future climate.

Greensboro Watershed, Tuckahoe Creek Watershed↗

USGS2017

Assessing the impacts of future climate conditions on the effectiveness of winter cover crops in reducing nitrate loads into the Chesapeake Bay Watershed using SWAT model

Winter cover crops (WCCs) have been widely implemented in the Coastal Plain of the Chesapeake Bay watershed (CBW) due to their high effectiveness at reducing nitrate loads. However, future climate conditions (FCCs) are expected to exacerbate water quality degradation in the CBW by increasing nitrate loads from agriculture. Accordingly, the question remains whether WCCs are sufficient to mitigate increased nutrient loads caused by FCCs. In this study, we assessed the impacts of FCCs on WCC nitrate reduction efficiency on the Coastal Plain of the CBW using Soil and Water Assessment Tool (SWAT) model. Three FCC scenarios (2085 – 2098) were prepared using General Circulation Models (GCMs), considering three Intergovernmnental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) greenhouse gas emission scenarios. We also developed six representative WCC implementation scenarios based on the most commonly used planting dates and species of WCCs in this region. Simulation results showed that WCC biomass increased by ~ 58 % under FCC scenarios, due to climate conditions conducive to the WCC growth. Prior to implementing WCCs, annual nitrate loads increased by ~ 43 % under FCC scenarios compared to the baseline scenario (2001 – 2014). When WCCs were planted, annual nitrate loads were substantially reduced by ~ 48 % and WCC nitrate reduction efficiency water ~ 5 % higher under FCC scenarios relative to the baseline. The increase rate of WCC nitrate reduction efficiency varied by FCC scenarios and WCC planting methods. As CO2 concentration was higher and winters were warmer under FCC scenarios, WCCs had greater biomass and therefore showed higher nitrate reduction efficiency. In response to FCC scenarios, the performance of less effective WCC practices (e.g., barley, wheat, and late planting) under the baseline indicated ~ 14 % higher increase rate of nitrate reduction efficiency compared to ones with better effectiveness under the baseline (e.g., rye and early planting), due to warmer temperatures. According to simulation results, WCCs were effective to mitigate nitrate loads accelerated by FCCs and therefore the role of WCCs in mitigating nitrate loads is even more important in the given FCCs.

Chesapeake Bay↗