This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.

Hydrologic and water quality models are often used to understand and simulate non-point source nutrient inputs to receiving waterbodies afflicted by eutrophication. The most widely used hydrologic-water quality model for estimating non-point source nutrient loads from agricultural uplands is the Soil and Water Assessment Tool (SWAT). SWAT uses the QUAL-2E 1-dimensional steady-state model to simulate in-stream processes that govern the transport of nutrients through channels and rivers. However, the instream-solute transport routine within SWAT is limited in predicting phosphorus cycling and algal dynamics. In this study, we improve the in-stream module of SWAT+, a restructured version of SWAT. We apply the modified SWAT+ to the Western Lake Erie Basin to examine how improved representation of the in-stream module influences nutrient dynamics from the edge-of-field through streams and to the watershed outlet. Our source code modifications focus on improving the representation of phosphorus exchange between the stream bed and the water column. This phosphorus exchange is governed by the equilibrium phosphorus concentration (EPC), which determines whether the stream bed is a phosphorus source or sink, and a phosphorus transformation coefficient which determines the rate of P exchange. These improvements to the in-stream routine within SWAT+ will aid decision-makers in understanding the time lags and management levers needed to achieve water quality targets for large basins.

Many waterbodies around the world are adversely impacted by harmful algal blooms (HABs). One primary driver of these blooms is often high concentrations of anthropogenic phosphorus loading. The phosphorus mitigation plans require accurate information on nutrient sources and transport, to and through water bodies, including the stream network. Diffuse sources, are particularly difficult to quantify due to the cost of in situ monitoring, and is often supplemented using various water quality models. SWAT, a comprehensive watershed-scale model, is widely used to assess and improve downstream water quality using QUAL2E equations. EPA developed QUAL2E can model phytoplankton growth but has a limited capacity to model benthic algae. Although SWAT requires a lesser number of parameters while simulating water quality outputs, unlike, HSPF, INCA, SPARROW, WASP, and MIKE-SHE, the water quality algorithm within SWAT needs modifications for simulating phosphorus legacy within the waterbodies. This study reviews the existing water quality models to improve the water quality algorithm within SWAT. Most of the water quality models can simulate processes, including the proliferation of fixed and floating algal biomass and phosphorus cycling (QUAL2E/K, WASP, HSPF). Some water quality models are better in simulating the time-dependent factors, such as light attenuation, form and concentration of nutrients, and water temperature (HSPF, INCA). There are a few water quality algorithms that can simulate both horizontal stream flow and shallow flow (SHETRAN, INCA). Both horizontal and shallow flow takes into account the anisotropy and variable biogeochemistry impacts on the turbulence of water, thus, the water quality. Some water quality models simulate the non-linear relationship between nutrient concentration and discharge timing and magnitude (SPARROW). There are some commercialized models like MIKE-SHE that simulate reasonably good results, but the water quality algorithm/equation/process is not publically available. Our review of the existing water quality models will help in identifying, modifying, and implementing the SWAT source code revisions required to improve and mitigate water quality degradation from a finer spatial scale, including small ditches and streams, to the large-scaled watershed over time.

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (FAIR Principles - GO FAIR (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provides more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICONs) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.