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.

The concepts of resistance, recovery, and resilience are in diverse fields from behavioral psychology to planetary ecology. These “three Rs” describe some of the most important properties allowing complex systems to survive in dynamic environments. However, in many fields—including ecology—our ability to predict resistance, recovery and resilience remains limited. Here, we propose new disturbance terminology and describe a unifying definition of resistance, recovery, and resilience. We distinguish functional disturbances that affect short-term ecosystem processes from structural disturbances that alter the state factors of ecosystem development. We define resilience as the combination of resistance and recovery—i.e., the ability of a system to maintain its state by withstanding disturbance or rapidly recovering from it. In the Anthropocene, humans have become dominant drivers of many ecosystem processes and nearly all the state factors influencing ecosystem development. Consequently, the resilience of an individual ecological parameter is not an inherent attribute but a function of linkages with other biological, chemical, physical, and especially social parameters. Because every ecosystem experiences multiple, overlapping disturbances, a multidimensional resilience approach is needed that considers both ecosystem structure (configuration of linkages) and disturbance regime. We explore these concepts with a few case studies and recommend analytical tools and community-based approaches to strengthen ecosystem resilience. Disregarding cultural and social dimensions of disturbance regimes and ecosystem structures leads to undesirable outcomes, particularly in our current context of intensifying socioecological crises. Consequently, cultivating reciprocal relationships with natural disturbance regimes and ecosystem structures is crucial to Earth stewardship in the Anthropocene.

Little is currently known about the hydrochemistry of tropical glacierized mountain watersheds, which are among the most vulnerable systems in the world. Glacier retreat may impact their export of nutrients, with possible implications for downstream ecosystems. Solute export depends on dynamic and heterogeneous processes within the watershed, which calls for investigations of the different factors controlling hydrochemical variability. To examine these in a sub-humid glacierized watershed in Ecuador, we implemented a hydrological model that incorporates reactive transport, RT-Flux-PIHM. Our results demonstrate that calibrating the model to hydrochemical in addition to hydrological data is important for constraining groundwater fluxes, which we found to contribute 78% of stream discharge and to include 35% of the total glacial meltwater. Stream chemistry fluctuations are strongly controlled by varying contributions of groundwater, which contains high concentrations of reactive ions predominantly sourced from silicate mineral dissolution. The spatial variability in these concentrations, however, is driven more by heterogeneous evapotranspiration resulting from sharp montane vegetation gradients. With this concentrating effect, evapotranspiration also largely determines seasonal patterns in groundwater chemistry, with highest concentrations occurring in dry seasons, even when dissolution rates are low due to low soil moisture. While groundwater serves as a primary end-member source of streamwater, glacier melt-dominated surface runoff acts as a second source that imposes dilution events on an otherwise chemostatic concentration and discharge (C-Q) graph. Glacier melt overall decreases stream concentrations and increases discharge, with the latter effect dominating such that solute exports (C*Q) increase by 23% with melt.

Dissolved oxygen (DO) sustains aquatic life and is an essential water quality measure. Our capabilities of forecasting DO levels, however, remain elusive. Unlike the increasingly intensive earth surface and hydroclimatic data, water quality data often have large temporal gaps and sparse areal coverage. Here we ask the question: can a Long Short-Term Memory (LSTM) deep learning model learn the spatio-temporal dynamics of stream DO from intensive hydroclimatic and sparse DO observations at the continental scale? That is, can the model harvest the power of big hydroclimatic data and use them for water quality forecasting? Here we used data from CAMELS-chem, a new dataset that includes sparse DO concentrations from 236 minimally-disturbed watersheds. The trained model can generally learn the theory of DO solubility under specific temperature, pressure, and salinity conditions. It captures the bulk variability and seasonality of DO and exhibits the potential of forecasting water quality in ungauged basins without training data. It however often misses concentration peaks and troughs where DO level depends on complex biogeochemical processes. The model surprisingly does not perform better where data are more intensive. It performs better in basins with low streamflow variations, low DO variability, high runoff-ratio (> 0.45), and precipitation peaks in winter. This work suggests that more frequent data collection in anticipated DO peak and trough conditions are essential to help overcome the issue of sparse data, an outstanding challenge in the water quality community.