Abstract

Abstract. 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 CO2 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 CO2, 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 CO2 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 CO2 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 CO2 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.

Highlights

  • Located in the Mid-Atlantic region, Chesapeake Bay (CB) is the largest and most productive estuary in the United States (US)

  • This underestimation was attributed to the inherent limitations of the Soil and Water Assessment Tool (SWAT) model and limited climate data to capture local storm effects as it does not account for the intensity and duration of the precipitation (Qiu et al, 2012)

  • The model performance measures for low flows (NSE for the natural logarithm of streamflow) indicated “satisfactory” to “very good” (Table 5). These results demonstrated that the calibrated model replicated actual conditions reasonably well (Moriasi et al, 2007; Arnold et al, 2012)

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Summary

Introduction

Located in the Mid-Atlantic region, Chesapeake Bay (CB) is the largest and most productive estuary in the United States (US). The Chesapeake Bay Watershed (CBW) covers an area. S. Lee et al.: Comparative analyses of hydrological responses of 166 000 km and is home to more than 18 million people and 3600 species of plants and animals (Chesapeake Bay Program, 2016). General circulation models (GCMs) have projected increases in temperature and precipitation of up to 5.0 ◦C and 21 %, respectively, by the end of this century in the CB region (Najjar et al, 2009), which could lead to substantial changes in hydrology and nitrogen (N) cycling. With precipitation and temperature changes, elevated CO2 concentrations affecting stomatal conductance have been viewed as one of the decisive factors modifying watershed hydrological processes (Chaplot, 2007; Wu et al, 2012a, b)

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