Reactive Transport Models (RTMs) are essential for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent RTMs integrate hydrological and biogeochemical interactions between land surface and subsurface. These emergent, watershed-scale RTMs are often spatially explicit and require large amount of data and extensive computational expertise. There is however a pressing need to create parsimonious models that require less data and are accessible to scientists with less computational background. Here we introduce BioRT-HBV 1.0 (hereafter BioRT), a watershed-scale, hydro-biogeochemical model that builds upon the widely used, bucket-type HBV model (Hydrologiska Bryåns Vattenavdelning), known for its simplicity and minimal data requirements. BioRT uses the conceptual structure and hydrology output of HBV to simulate processes including solute transport and biogeochemical reactions driven by reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. This paper presents the model structure and governing equations, demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, when constrained by data, BioRT can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous-to-measure subsurface, and their connections to observed solute export in streams and rivers. We posit that such parsimonious models increase model accessibility to users without in-depth computational training. It also can serve as an educational tool that promote pollination of ideas across different fields and foster a more diverse, equal, and inclusive user community.

Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes. To investigate the quality of these observations, we compared the water level class data for a number of locations where water levels were also measured and assessed when these observations were submitted. We analysed data for nine locations where citizen scientists reported multiple observations using a smartphone app and stream level data were also available. At twelve other locations, signposts were set up to ask citizens to record observations on a form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was higher than for the forms. A possible explanation is that for each app location, most contributions were made by a single person, whereas at the locations of the forms almost every observation was made by a new contributor. On average, more contributions were made between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the data collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application with its virtual staff gauge is a promising approach for crowd-based water level class observations.