Downscaling a monthly ecosystem model to a daily time step: Implications and applications Zaixing Zhou, Scott Ollinger, Andrew Ouimette Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space University of New Hampshire, Durham, NH, USA Introduction Methods Results and Discussion Summary and Future Work References Figure 1. Schematic diagram of the role the terrestrial model PnET-CN plays in the NH EPSCoR Ecosystems & Society project. By downscaling from a monthly time step, the daily PnET-CN version uses climate data and land use provided by the land use scenario group to predict C fluxes (e.g., wood production and net ecosystem production), water runoff, and N loadings from terrestrial ecosystems. These variables are then integrated into the aquatic biogeochemistry model FrAMES and ultimately into ecosystem service analyses modeling. In order to validate our modeling results and evaluate the revisions to the PnET-CN model, the improved, daily time-step model was used to predict C cycling (GPP, NEE and NPP), water cycling (runoff and ET), and N cycling (N leaching, foliar N and N mineralization) in northern hardwood forests at Hubbard Brook Experimental Forest (HB) and Bartlett Experimental Forest (BEF), NH. The model was also applied regionally to the Merrimack- Great Bay watershed by being linked with GIS and FrAMES to investigate spatial patterns of C, water and N cycling under future climate change and land use scenarios. GPP and NEE estimates were validated with eddy flux tower data from BEF. Litterfall and wood growth collected as part of ongoing field campaigns at BEF and HB also served as validation data. HB Watershed 6 gauge runoff and N leaching were used to evaluate the water and N cycling at HB. Ecosystem models that run at daily or sub-daily time steps are needed to understand how climate change and climate variability affect seasonal patterns of ecosystem processes such as phenology, C and N cycling, and water use. Here, we present a modeling analysis that involved downscaling the PnET-CN forest ecosystem model (Aber et al., 1997) from a monthly to daily time step for improved understanding of ecosystem functions and tradeoffs. In the NH EPSCoR Ecosystems & Society project, PnET-CN plays two major roles for modeling terrestrial ecosystem response to changes in climate, land use, and atmospheric chemistry. First, it provides estimates of forest productivity and net C sequestration for ecosystem service analysis. Second, it generates terrestrial runoff and N leaching to parameterize the FrAMES aquatic biogeochemistry model (Wollheim et al., 2008). The results presented here demonstrate that temporal scale plays an important role when downscaling a monthly ecosystem model to a daily step version; simply applying existing algorithms with daily model inputs is insufficient. However, with a few specific revisions (Table 1), the downscaled model can provide extended and reliable predictions for terrestrial ecosystem functions. Aber JD, Ollinger SV, Driscoll CT (1997) Modeling nitrogen saturation in forest ecosystems in response to land use and atmospheric deposition. Ecological Modelling, 101, 61–78. Wollheim WM, Vörösmarty CJ, Bouwman AF et al. (2008) Global N removal by freshwater aquatic systems using a spatially distributed, within-basin approach. Global Biogeochemical Cycles, 22, GB2026. Figure 2. Validation and application of the revised PnET-CN model at BEF and HB hardwood forests. (a) Comparison of modeled and measured daily GPP from BEF (R-squared = 0.84). (b) Comparison of modeled and measured daily GPP from BEF (R-squared = 0.36). (c) Comparison of modeled and measured annual litter fall from BEF (R-squared = 0.55). (d) Comparison of modeled and measured average wood production from BEF and HB. (e) Comparison of modeled and measured annual runoff from HB watershed 6 (R-squared = 0.91). (f) Comparison of modeled and measured annual nitrate leaching from HB watershed 6. Figure 3. Application of the revised PnET-CN for Merrimack-Great Bay watershed. (a) Example of spatial distribution of wood production across Merrimack-Great Bay watershed in 2005; (b) Example of spatial distribution of C sequestration across Merrimack-Great Bay watershed in 2005; (c) Time series of wood production and NEP for Merrimack-Great Bay watershed from 2000 through 2013 (error bars are 1 standard deviation). In this study, by downscaling monthly PnET-CN to a daily time step, we found that the same algorithms that describe ecosystem function can generate statistically different predictions as a result of input climate data at different time scales (i.e., monthly vs. daily). With only a few revisions, the daily model can provide reliable predictions, including C fluxes (GPP, NEP and NPP), water cycling fluxes (runoff) and N cycling fluxes (N leaching) in northern hardwood forests. Regional application of the improved model shows promise for coupling with the FrAMES aquatic biogeochemistry model. In the future, more efforts should be focused on predicting wood production variability and N cycling. Disturbance/land-use history information will also be collected and incorporated into our regional simulation, given the effects of harvesting, agriculture, fire, etc. on stand age and long-term accumulation of soil organic matter. Results presented in Figure 2 demonstrate that the revised model captured well the seasonal pattern of photosynthesis (GPP) and net CO 2 exchange (NEE) (a-b), which were driven primarily by climate and phenology. Predicted litterfall from BEF sites were in line with field measurements, and showed little variability (c). Because measured wood production has large spatial variation, we used average values from BEF and HB. Wood growth is one of the least constrained variables in PnET-CN, and modeled wood production estimates were in line with field measured values (d). The measured nitrate leaching from HB Watershed 6 indicates a steady decline in N loss, currently representing only a small fraction of atmospheric N input. Because the mechanisms behind this “missing” sink remain unclear (e.g. accumulation in soil organic matter, losses in gaseous form), our modeled N leaching was overestimated in the past decade (f). Runoff from watershed 6, however, is in agreement with measurements (e). New mechanisms need to be tested and incorporated into our modelling framework. Still, the predicted runoff and N leaching provide reliable base estimates for the aquatic model FrAMES. ab c def Our analysis and validation shows that simply applying existing algorithms with daily model inputs was insufficient for downscaling because 1) temporal resolution affects modeling results by nonlinear relationships of variables (e.g., light response curve of photosynthesis) although the process equations remain the same; and 2) some processes work specifically for the monthly time step, e.g., photosynthesis at leaf out and leaf fall phases, C allocation to foliar NPP, and snow/water determination. For this reason, corresponding processes (described in Table 1 above) have been revised to facilitate our daily predictions. ProcessReason for revision Photosynthesis PnET-CN uses a leaf level photosynthesis process to scale canopy C assimilation, which theoretically does not depend on the temporal scale of input climate and is suitable for downscaling. However, the nonlinear relationships of variables (e.g., light response curve of photosynthesis) could result in large differences of predictions for different temporal input climate data although process equations remain the same. Adjusted scalar is necessary. E.g. AmaxFrac is 0.89 in daily version while it is 0.75 in monthly version. Foliage C allocation PnET-CN uses a full canopy concept to predict the foliage mass. The full canopy has the potential to harvest all light for maximization of its net photosynthesis at leaf level. The variable LightEffMin behaves differently in monthly and daily versions because the PAR fluxes are averaged and then smoothed in the monthly step so that they correspond with phenology. However, given the large variability in daily weather, even within a week, the potential light for foliage growth needs to be statistically averaged for the whole growing season. Phenology As with light (PAR), monthly temperatures are averaged, resulting in a smoothed seasonal pattern. However, daily temperature variability do not always follow the same pattern. Thus there should be a revision of the phenology starting and ending threshold values (i.e. the effective GDD). Photosynthetic capacity (Amax) also responds to phenology, which must be considered in the daily version. Rain/snow determinationPrecipitation in daily time step normally occurs as either rain or snow. It is not appropriate to split it as in monthly version because monthly precipitation is the average covering rain and/or snow. This improved prediction of snowpack and runoff in winter and early spring. Table 1. Processes in PnET-CN that need revision for downscaling a b c Fig 3 a-c show examples of results of the revised PnET-CN for Merrimack-Great Bay watershed. Spatial patterns of wood production vary markedly from NEP. Wood production patterns are controlled by land use; i.e. more hardwood forests result in more wood growth. Water bodies and impervious area are the primary drivers of the low values in the coastal region. NEP shows the combined effect of land use and climate. Coastal, relatively warm areas sequester less C than the western/mountain colder regions. Wood production has larger variation across the watershed than NEP. However, the annual variability of wood production is less than NEP. Both are correlated to temperature.