IMPROVING SWAT MODELING AND INTEGRATING OBSERVATIONAL ANALYSIS TO ASSESS THE IMPACTS OF CLIMATE AND LAND USE CHANGE ON HYDROLOGICAL PROCESSES IN THE UNITED STATES

Abstract

Climate and land use have been changing significantly. Climate and land use changes are intricately linked to the hydrological processes that have a significant impact on water resources. The availability of water resources is crucial for food and energy production. In the U.S., about 42% of water is used for agricultural irrigation to improve the productivity of grain crops, including corn and soybeans. About 30% of the corn produced in the U.S. is used for ethanol production, which raises serious concerns regarding the food-fuel competition and detrimental environmental impacts. The expansion of corn production competes for water resources, as corn requires irrigation. To address these issues, the cultivation of bioenergy crops on marginal lands is proposed as a solution that also alleviates water demand, as they do not require irrigation. This cultivation could involve complex interactions among climate, crops, and hydrological processes, which may lead to significant environmental consequences. This necessitates an understanding of how land use and climate changes will affect hydrological processes and, subsequently, food production.To understand these processes, I use the hydrological model, Soil and Water Assessment Tool (SWAT). SWAT has a distinct advantage in its capability to simulate water and sediment transport, crop growth, agricultural management, and land management practices, and has specialized extensions for groundwater simulation (SWAT-MODFLOW). In this study, I focus on large agricultural watersheds in the U.S. Northern High Plains (NHP) Aquifer region. The NHP is a vast and critical agricultural area that relies heavily on groundwater irrigation, which has resulted in streamflow and groundwater level decline. Although the SWAT model can quantify the environmental impacts of climate and land use changes, its utility is limited by: (1) deficiencies in the auto-irrigation algorithms in SWAT that continue irrigation during the non-growing season, (2) optimization of the SWAT model with streamflow data only, without considering other hydrological components such as evapotranspiration and soil moisture, (3) failure to explicitly account for shoot and root biomass development when simulating bioenergy crops, and (4) standalone climate and land use change simulation that do not consider the influence of climate-crop feedback on hydrological processes. To provide an accurate assessment of the impact of climate and land use changes on hydrological processes in the NHP, my first effort involved improving the irrigation algorithms in both SWAT and its extension SWAT-MODFLOW to provide an improved representation of irrigational water use conditions. The use of SWAT-MODFLOW provides an improved representation of hydrological processes, particularly when considering the significant role of groundwater dynamics in the overall water budget of the NHP. The modified SWAT and SWAT-MODFLOW were applied to the NHP which exhibited improved performance in simulating groundwater irrigation volume, groundwater level, and streamflow in the NHP. I also examined the effects of groundwater irrigation on the water cycle. Based on simulation results from SWAT-MODLFOW, historical irrigation has increased surface runoff, evapotranspiration, soil moisture, and groundwater recharge by 21.3%, 4.0%, 2.5%, and 1.5%, respectively. Irrigation also improved crop water productivity by nearly 27.2% for corn and 23.8% for soybean. To optimize the SWAT model for addressing climate and land use change issues in the NHP, I tried to identify the best approach to calibrate the SWAT. Traditionally, SWAT is calibrated using streamflow only. I hypothesize that calibrating SWAT with streamflow only, without considering broad hydrological processes like evapotranspiration and soil moisture causes model overfitting. I utilized the best available remotely sensed data, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE), to examine if multi-variate calibration could improve model performance. Contrary to my hypothesis, using remotely sensed data does not improve model performance because of two reasons: (a) remotely sensed data may lack physical constraints and has large uncertainty, and (b) SWAT may not provide an accurate representation of hydrological processes due to deficiencies in its model algorithms. Further investigation is required to address these issues. SWAT utilizes standalone climate change data without considering climate-crop feedback in environmental impact assessments of climate and land use change scenarios. To address this gap, I used another tool, the Climate-Weather and Research Forecasting (CWRF) model, both in standalone mode and coupled with the BioCro model. This approach incorporates the climate-crop feedback associated with the cultivation of perennial grasses on marginal lands. The climate data from these two scenarios: (i) Climate-only without climate-crop feedback, and (ii) Climate-crop feedback turned on, were used in SWAT to investigate hydrological responses to simulated climate-crop feedback in the NHP. The incorporation of climate-crop feedback produced cooler and wetter conditions in the NHP. These changes in temperature and precipitation have substantial environmental consequences. The cultivation of miscanthus on marginal lands increases evapotranspiration and decreases surface runoff, soil moisture, and percolation. At the watershed scale, surface runoff substantially increases during the growing season, both in the present (1985-2014) and the future (2031-2060). The differences in the extent of marginal land use for miscanthus cultivation between the Platte (4%) and Republican (20%) River basins result in different responses in streamflow and nitrogen loading. For example, in the future, annual streamflow and nitrogen loading increases by 5.5% and 2.3%, respectively, in the Platte River basin. In contrast, the Republican River basin exhibits negligible changes in annual streamflow and an 8.6% decrease in nitrogen loading during this period. These results highlight the importance of including climate-crop feedback in addition to the impact from land use change when assessing the impacts of climate and land use changes on hydrological processes. The SWAT model does not explicitly account for shoot and root biomass development. I integrated the grass growth sub-model from the DAYCENT into SWAT (SWAT–GRASSD) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASSM). Based on testing at eight sites in the U.S. Midwest, SWAT–GRASSM generally outperformed both SWAT and SWAT–GRASSD in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASSM can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. From these model experiments and analyses, I realized that there are still a lot of model and data gaps that need to be addressed. Consequently, the results of the impacts of the climate and land use changes on hydrological processes could be subjective due to inherent model deficiencies and calibration issues because of uncertainty in observed data. I, therefore, revisited the observed data screening approaches to identify near-natural streamflow data that are representative of actual watershed processes. Assuming that the hydrologic response of the watershed is primarily driven by precipitation and runoff processes, I applied lagged correlation analysis between precipitation and streamflow at various temporal scales, complemented by a systematic screening process using basin properties to select streamgages with near-natural streamflow. The screening method introduced here is objective and easy to implement in other data-sparse regions. The selected streamgages serve as valuable calibration and validation points for hydrological models, thereby it could help enhance the accuracy and confidence in model projections. In summary, my work improved the SWAT auto-irrigation algorithm for a better representation of irrigation water use and integrated the DAYCENT grass growth sub-model to enhance biomass yield predictions. The hydrological consequences of land use change for miscanthus cultivation highlighted the importance of incorporating climate-crop feedback into hydrological modeling. This integration had a substantial impact on the hydrological processes in the NHP. Despite the model improvements through changes in model algorithms and optimization, my work reveals that model and data gaps result in subjectivity in the study’s findings. To address this, I propose an objective screening process for streamgage selection that reflects natural watershed processes. The enhancements introduced to the modeling framework, coupled with the objective streamgage screening approach, will assist water resource managers and the regional hydrologic modeling community in the credible assessment of the hydrological impacts resulting from climate and land use changes.