## 3 Results and Discussion
### Control Capacity
#### 3.2.2 Limitations of the Modeling Framework  
The model framework and analyses presented here represent a step in our evolving understanding of how small catchments at HJA function. There are, however, a number of limitations to this work. The modeling focuses on an approximately monthly time series of input output data, which provides insight into seasonal dynamics. However, this time step is too coarse to understand the finer time dynamics that are often the focus of experimentalists working within the wide variety of contributing disciplines. At the same time, it may be too coarse to understand the longer term evolution of catchments, both in terms of nitrogen availability and changing climate. We envision significant potential to redevelop these modeling ideas to correspond to these different timescales--but the model as presented herein is not suited to either. Similarly, while we included a simple model of instream particulate retention and release, extreme events are not included. These events include for example large storms, mass movement, and fire, and in terms of nitrogen control, it is clear that over long time scales, these are at least as significant as the biotic controls and flushing that we explored here. These limitations simply mean that interpreting the results outside of the timeframe over which the model was run is not possible.  
The model outlined here includes a variety of parameters that cannot be measured directly. For example to separate net nitrogen mineralization into gross mineralization and gross immobilization requires a set of measurements that are very difficult to perform at point scales [50]. And further, the relationship between these point scale observations for catchment level simulations is not well-established. Yet competition between vegetation and microbes is a key feature of nitrogen cycling [51], and microbial immobilization and mobilization, as well as plant uptake must be included in a model designed to explore the effects of this competition. The parameters involved in this portion of the model (and others) are developed based solely upon educated guesses, and calibration procedures relying upon evaluation of the resulting rate terms. In the case of models developed for predictive purposes, simpler tends to be more effective, and incorporating net mineralization, which can be more readily measured, would make more sense. Nonetheless, while some of our decisions clearly reduce the predictive capability of the model through increased parameter uncertainty, the more complete structural definition provides a framework to outline both what is known, and what hypothesized about how these catchments function with respect to N cycling.  
Some of the most interesting aspects of watershed nitrogen work to emerge in the last two decades involve the role of spatially disaggregated watershed components in the processing of key stocks. This spatial dependence is evident throughout the literature, including terrestrial biogeochemistry, aquatic processes, catchment hydrologic processes, hyporheic zone interactions, climate science, and forest ecology. As constructed, HJA-N does include some quasi spatial distribution, but this distribution simply separates the upslope processes from the near stream processes. From both a biotic and abiotic standpoint, this is a large simplification. An important next step is the incorporation of similar mass balance equations within a spatially-distributed model that would better allow for the incorporation of key differences in spatial distribution of ecosystem processes.  
Lastly, a more complete evaluation of the model is needed to provide further guidance in terms of parameter sensitivity and associated model uncertainty. Sensitivity analyses also have the potential to provide insight into the degree of understanding we have regarding different model components, and as such assist in the prioritization of experimental studies designed to improve the both the general understanding of the system, and the predictive capability of the model.