Rainfall runoff and leaching are the main driving forces that nitrogen, an important non-point source (NPS) pollutant, enters streams, lakes and groundwater. Hydrological processes thus play a pivotal role in NPS pollutant transport. However, existing environmental models often use oversimplified hydrological components and do not properly account for overland flow process. To better track the pollutant transport at a watershed scale, a new model is presented by integrating nitrogen-related processes into a comprehensive hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). This new model, called DHSVM-N, features a nitrate transport process at a fine resolution, incorporates landscape connectivity, and enables proper investigations of the interactions between hydrological and biogeochemical processes. Results from the new model are compared with those based on Soil & Water Assessment Tool (SWAT). The new model is shown capable of capturing the “hot spots” and spatial distribution patterns of denitrification, reflecting the important role in which heterogeneity of the watershed characteristics plays. In addition, a set of control experiments are designed using DHSVM-N and its variant to study the respective role of hydrology and nitrate transport process in modeling the denitrification process. Our results highlight the importance of adequately representing hydrological processes in modeling denitrification. Results also manifest the importance of having a good transport model with accurate flow pathways that considers realistic landscape connectivity and topology in identifying the denitrification hot spots and in properly estimating the amount of nitrate removed by denitrification.

A modeling framework is presented for hydrological modeling to more accurately describe the water, energy, and carbon cycles and their interactions with participating processes. This framework extends the modeling strategy presented in Luo et al. (2013) by simultaneously using multiple plausible expressions, derived from different perspectives, in representing the same processes, and enforcing them together with an optimality rule and a semi-empirical expression for plant CO2 uptake. The objectives are to reduce unconstrained free variables, mitigate parameter or variable equifinality, reduce result uncertainties, and ultimately increase the model robustness and predictability. For demonstration, the least cost optimality theory from Prentice et al. (2014), after extended to include water-limited conditions, is combined with the updated semi-empirical Ball-Berry-Leuning formulation (Tuzet et al., 2003). These two expressions are combined with other multiple expressions adopted for hydrological modeling. This framework is incorporated into both VIC+ and a modified DHSVM hydrological models with each applied to two different sites. Numerical studies are performed that using three approaches which only differ in the stomatal conductance modeling, namely, one uses the extended Prentice, one the semi-empirical, and the new framework that uses both. Results show that although all three approaches give reasonable estimates of limited measured fluxes, the present modeling framework gives much more reasonable estimates in the stomatal conductance and in other major model variables, and it also results in giving a relationship between carboxylation and transpiration that is consistent with observations. This modeling framework is general and can be adopted for other fields of study.

A modeling framework is presented for hydrological modeling to more accurately describe the water, energy, and carbon cycles and their interactions with participating processes. This framework extends the modeling strategy presented in Luo et al. (2013) by simultaneously using multiple plausible expressions, derived from different perspectives, in representing the same processes, and enforcing them together with an optimality rule and a semi-empirical expression for plant CO2 uptake. The objectives are to reduce unconstrained free variables, mitigate parameter or variable equifinality, reduce result uncertainties, and ultimately increase the model robustness and predictability. For demonstration, the least cost optimality theory from Prentice et al. (2014), after extended to include water-limited conditions, is combined with the updated semi-empirical Ball-Berry-Leuning formulation (Tuzet et al., 2003). These two expressions are combined with other multiple expressions adopted for hydrological modeling. This framework is incorporated into both VIC+ and a modified DHSVM hydrological models with each applied to two different sites. Numerical studies are performed that using three approaches which only differ in the stomatal conductance modeling, namely, one uses the extended Prentice, one the semi-empirical, and the new framework that uses both. Results show that although all three approaches give reasonable estimates of limited measured fluxes, the present modeling framework gives much more reasonable estimates in the stomatal conductance and in other major model variables, and it also results in giving a relationship between carboxylation and transpiration that is consistent with observations. This modeling framework is general and can be adopted for other fields of study.