|PRADA, ANDRES - University Of Illinois|
|CHU, MARIA - University Of Illinois|
|GUZMAN, JORGE - University Of Oklahoma|
Submitted to: Journal of Environmental Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/17/2017
Publication Date: 2/24/2017
Citation: Prada, A.F., Chu, M.L., Guzman, J.A., Moriasi, D.N. 2017. Evaluating the impacts of agricultural land management practices: A probabilistic hydrologic modeling approach. Journal of Environmental Management. 193:512-523.
Interpretive Summary: Hydrologic and water quality models are used to assess water quality constituent losses from agricultural systems. A major problem during model building phase for a specific study site is the uncertainty involved with parameterization. Using a single calibrated set of parameters to represent baseline conditions of the study area processes limits the applicability and robustness of any given model to properly represent future or alternative scenarios. In this study, a framework was developed to facilitate model parameter selection while evaluating uncertainty to assess the impacts of land management practices on water quality at the watershed scale. The model framework was tested using the Agricultural Policy/Environmental eXtender (APEX) model for streamflow and nitrogen in Lake Creek watershed located in southwestern Oklahoma. Twenty-seven parameter combinations were found to adequately represent the study area flow and nitrogen transport processes and were considered as the baseline APEX model for Lake Creek. Converting cropped all area to pasture resulted in the highest nitrogen decrease of up to 30% when applied throughout the study area. Complete conversion to winter wheat cover increased nitrogen loss by up to 11%. Results from this study can be used to develop strategic decisions on the risks and tradeoffs associated with different management alternatives in order to increase productivity while minimizing environmental impacts. The framework can also be used for hydrologic and water in other study areas around the world.
Technical Abstract: The complexity of the hydrologic system challenges the development of models. One issue faced during the model development stage is the uncertainty involved in model parameterization. Using a single optimized set of parameters (one snapshot) to represent baseline conditions of the system limits the applicability and robustness of the model to properly represent future or alternative scenarios. The objective of this study was to develop a framework that facilitates model parameter selection while evaluating uncertainty to assess the impacts of land management practices at the watershed scale. The model framework was applied to the Lake Creek watershed located in southwestern Oklahoma, USA. First, a two-step probabilistic approach was implemented to parameterize the Agricultural Policy/Environmental eXtender (APEX) model using global uncertainty and sensitivity analysis; Second, the uncertainty due to model parameterization was quantified, and finally the model was used to estimate the full spectrum of total monthly water yield and total monthly Nitrogen loads in the watershed under different land management practices scenarios. Twenty-seven parameter combinations were found to adequately represent the response of the system and were considered as the baseline APEX model for Lake Creek. Using these 27 models, the total monthly water yield (WYLD) was estimated to vary from 0.17 to 17 mm while the total monthly Nitrogen load (N) can range from 0 to 0.5 Kg/ha. The most likely uncertainty for WYLD was 0.7 mm or approximately 10% of the mean WYLD value while for N load was 0.03 Kg/ha or approximately 38% of the monthly. However, uncertainty in WYLD can reach up to 29% of the average monthly total water yield while uncertainty in N can reach up to more than 400% of the average monthly total load. Changing the land cover to pasture manifested the highest decrease in Nitrogen load to up to 30% for a full pasture coverage while changing to full winter wheat cover can increase the Nitrogen load up to 11%. The methodology developed in this study was able to quantify the full spectrum of system responses, the uncertainty associated with them, and the most important parameters that drive their variability. The full spectrum of model outputs can provide robust information on the achievable responses of the watershed for different current and future land management practices. Similarly, by knowing the parameters that drive the variability of the outputs, future research can be prioritized to collect more information about these parameters resulting in a more efficient use of resources. Results from this study can be used to develop strategic decisions on the risks and tradeoffs associated with different management alternatives that aim to increase productivity while also minimizing their environmental impacts.