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.

Abstract Although the importance of dynamic water storage and flowpath partitioning on discharge behavior has been well recognized within the critical zone community, there is still little consensus surrounding the question, “ How do climate factors from above and land characteristics from below dictate dynamic storage, flowpath partitioning, and ultimately regulate hydrological dynamics?” Answers to this question have been hindered by limited and inconsistent spatio-temporal data and arduous-to-measure subsurface data. Here we aim to answer this question above by using a semi-distributed hydrological model (HBV model) to simulate and understand the dynamics of water storage, groundwater flowpaths, and discharge in 15 headwater catchments across the contiguous United States. Results show that topography, precipitation falling as snow, and catchment soil texture all influence catchment dynamic storage, storage-discharge sensitivity, flowpath partitioning, and discharge flashiness. Flat, rain-dominated sites (< 30% precipitation as snow) with finer soils exhibited flashier discharge regimes than catchments with coarse soils and/or significant snowfall (>30% precipitation as snow). Rain-dominated sites with clay soils (indicative of chemical weathering) showed lower dynamic storage and discharge that was more sensitive to changes in dynamic storage than rainy sites with coarse soils. Steep, snowy sites with coarse soils (more mechanical weathering) had lowest dynamic storage and deep groundwater fed discharge that was less sensitive to changes in dynamic storage than fine-soil snowy or rainy catchments. These results highlight aridity and precipitation (snow versus rain) as the dominant climate controls from above and topography and soil texture as the dominant land controls from below. The study challenges the traditional view that climate controls water balance while subsurface structure dictates subsurface flow path. Rather, it shows that climate and land characteristics jointly regulate water balance, groundwater flowpath partitioning, and discharge responses. These findings have important implication for the projection of the future of water resources, especially as climate change and human activities continue to intensify.

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.