Assessing the potential impact of climate change and management practices on plant productivity, hydrological resources, and carbon fluxes using process-based models in the Mid-Atlantic, U.S.A.
Creators: name: Tijjani, Sadiya Baba
Publisher: name: No Publisher Supplied
Year: 2024

description: New remote sensing and modeling developments are advancing our ability to characterize complex relationships between terrestrial, aquatic, and atmospheric processes. This dissertation employs a data fusion approach that combines field measurement and remote sensing data along with future climate projections and the capability of the coupled soil water assessment tool (SWAT) and the Century (DayCent) process-based models- SWAT-C to simulate the interaction between the atmospheric, aquatic, and terrestrial ecosystems. SWAT-C includes the hydrologic components (e.g., surface runoff, baseflow, evapotranspiration, soil water, and water yield) and the biogeochemical components (e.g., net primary productivity (NPP), soil organic carbon (SOC), biomass, grain yield, nitrogen (N), phosphorus (P) and soil respiration), which are used to examine the relationship between climate and land use change on crop yield, hydrological resources, N, P, and carbon dynamics at selected locations in the Mid-Atlantic Region, USA. Specifically, this study examines the impact of climate change and regional management practices on corn and soybean yields, greenwater scarcity, irrigation demand, carbon, and nutrient (N and P) dynamics in Upper Maurice River Watershed (UMRW), southern New Jersey, using SWAT. The model result suggests that corn and soybean yield will decrease, resulting from heat stress and nutrient deficiency’s role in countering the effect of CO2 fertilization. This study examined the efficacy of two management scenarios to mitigate the projected loss in crop yield for the medium and high emission scenarios under the two Representative Concentration Pathways (i.e., RCP-4.5 and RCP-8.5). Scenario 1 involves future increases in the fertilizer amount, early planting, and the use of an improved cultivar. The result indicates that irrigation demand for corn is projected to decrease between -17% to -1%, while soybean irrigation demand is projected to decrease between -1% to +13%, resulting from the projected increase in precipitation and partial stomatal closure resulting from heat stress. Corn yield is expected to vary between -4% and +9%, while soybean yield is predicted to increase between +2% and +13% under the RCP-4.5. Similar changes are projected under RCP-8.5 but at a higher magnitude. Scenario 2 involves early planting and using improved cultivars to avoid heat stress and changing the timing of future fertilizer applications to coincide with the timing of corn nutrient demands (i.e., when the first and fifth leaves emerge) without increasing the amount of fertilizer. The result indicates a projected increase in corn of 39% (+21%) and soybean of +2% (+17%), respectively, under the 1.5°C (2.0°C) global warming scenario for the RCP-4.5. Additionally, scenario 2 indicates a decrease in the ensemble mean of annual loads to the stream of dissolved phosphorus, total nitrogen, and total phosphorus for RCP-4.5 and RCP-8.5. A novel outcome of this research is a modeling approach that allows for evaluating the timing of management actions that can satisfy crop nutrient demand while minimizing fertilizer runoff to waterbodies.To assess the carbon dynamics of Duke Farms watershed in central New Jersey, this study analyzed three scenarios: (1) the impact of current management practices on the carbon budget of the Duke Farm watershed, (2) the impact of land conversions on the carbon budget of the Duke Farm watershed, (3) the impact of projected climate change for the medium and high emission scenarios under the Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5) on soil organic carbon stock of the Duke Farms watershed. To achieve these objectives, this study employed a novel carbon budget calibration approach called distributed-calibration and independent verification (DCIV), coupled with the SWAT-C model. The DCIV framework provided robust validation of model behavior, promoting greater confidence in model predictions on carbon dynamics. The DCIV approach, as developed here, provides a model for implementation in other watersheds. The result of scenario 1 indicates that the Duke Farms’ property is an annual net CO2 sink rather than a source (i.e., negative net ecosystem exchange (-NEE)). Particularly, a large soil organic carbon (SOC) stock of at least 100 tons ha-1 is stored under mixed forest, deciduous forest, shrubland, and floodplain (grassland). The result of scenario 2 suggested that converting floodplain grassland to riparian forest has the potential to increase CO2 uptake from -0.6 kgC ha-1 to -11.2 kgC ha-1 (an increase of 1846%) and aboveground biomass by 10 tons ha-1 (77%), leading to an increased SOC stock of 33.4 tons ha-1 (23%). On the other hand, converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. The result of scenario 3 indicates a projected decrease in SOC for the medium and high emission scenarios: SSP2-4.5 and SS5-8.5. Specifically, the largest losses were estimated as 6 tons ha-1 (-6±6%) and 12 tons ha-1 (-12±3%) by 2100 under the SSP2-4.5 and SSP5-8.5, indicating that eCO2 fertilization is nutrient-limited. However, converting floodplain (grassland) to deciduous forest leads to higher SOC storage averaged between 33±4 and 34±4 tons ha-1 (33–34%) under SSP2-4.5. A similar increase was also observed under SSP5-8.5 but at a lower magnitude. Additionally, applying NPK fertilizer to all the land use/cover is predicted to increase SOC across all the LULC types, ranging between 7±2 and 31±22 tons ha-1 (8–36%). These SWAT-C model simulation results have potential implications for policymakers interested in management practices that promote carbon sequestration in the face of changing climate.