## 5 Conclusions  
This study introduces the _DIMONA_ PB model, which is a process-based model embedded into the namesake _DIMONA_ online modelling platform that facilitates model accessibility and application. The _DIMONA_ PB model, driven by meteorological and hydrological data and constrained by site, soil, and plant parameters, is shown to perform reasonably well for simulating the vascular, bryophyte, and ecosystem productivities in a northern peatland at hourly and daily time steps. The model relies on a set of simple yet process-oriented modifiers (equations) for water control on photosynthesis, plant growth processes, root/rhizoid respiration, and nutrient uptake. These modifiers show a potential for being generally applicable to wet and dry natural peatlands, peatlands used for agricultural purposes, drained peatlands, and peatland transition zones. Moreover, as they are directly related to soil water contents, these modifiers could be applicable not only to peatlands but also to mineral soil natural ecosystems and agricultural crops. Their simplicity circumvents the complicated and demanding multi-parameter equations for plant-water relations in many process-based models. Further, this study demonstrates that both WT (Hypothesis 1) and near-surface soil \(\theta\) (Hypothesis 2) are reliable hydrological model drivers for peatland GPP, but near-surface soil \(\theta\) performed slightly better than WT as a driver for hydrological control on productivity. The model results also demonstrated that the equations and algorithms of the _DIMONA_ PB model were able to capture the specifics of the peatland productivity response under varying hydrologic conditions (Hypothesis 3). With the recently introduced simplified methods and widely available near-surface soil \(\theta\) data extracted via satellite imagery and/or available on repository websites, the _DIMONA_ PB model embedded in the _DIMONA_ modelling platform is applicable on a global basis for any peatland at the site level or peatland polygons at the spatial level. Further, the _DIMONA_ platform accommodates model accessibility, data exploration, and model parameterization on a global basis. _DIMONA_ can obtain real-time estimates of productivity and hydrology by IoT modelling, where real-time meteorological and hydrological data are derived from websites with API endpoints. _DIMONA_ also allows for ML modelling of ecosystem productivities and other features, especially where predictability is the focus, rather than explanatory power of the models. Thus, the multifaceted study presented here proposes not only a process-based model and a new modelling approach for plant water control on productivities, as well as an online software tool for their facilitation and wide application, but also a broader concept for ecological modelling that involves Internet of Things and machine learning modelling, complementary to and benefiting from process-based modelling. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/w17020134/s1](https://www.mdpi.com/article/10.3390/w17020134/s1)]([https://www.mdpi.com/article/10.3390/w17020134/s1](https://www.mdpi.com/article/10.3390/w17020134/s1)). References [70-120] are cited in the supplementary materials. Conceptualization, D.D.D. ; Methodology, D.D.D. ; Software, D.D.D. ; Validation, D.D.D. ; Formal Analysis, D.D.D. ; Investigation, D.D.D. ; Resources, D.D.D. and P.M.L. ; Data Curation, P.M.L. ; Writing--Original Draft Preparation, D.D.D. ; Writing--Review and Editing, P.M.L. ; Visualization, D.D.D.D. ; Project Administration, D.D.D. ; Funding Acquisition, P.M.L. All authors have read and agreed to the published version of the manuscript. Data were collected with funding from the Canadian Carbon Program and the Fluxnet Canada Research Network (FCRN) through its major sponsors, the Natural Sciences and Engineering Council of Canada (NSERC), the Canadian Foundation for Climate and Atmospheric Sciences, and BIOCAP Canada.