The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. This paper confronts these disciplinary barriers by advocating a synthesis of existing and missing links across the frontiers distinguishing hydrology from engineering, the social sciences and economics, Indigenous and place-based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. Specifically, we embrace ‘integrated modeling’, in both quantitative and qualitative senses, as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human-water systems. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: a) What is the modeling ‘purpose’? b) What constitutes a sound ‘boundary judgment’? and c) What are the ‘critical uncertainties’ and how do they propagate through interconnected subsystems? More broadly, we call for investigating what constitutes warranted ‘systems complexity’, as opposed to unjustified ‘computational complexity’ when representing complex natural and human-natural systems, with particular attention to interdependencies and feedbacks, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects.

In agricultural areas, Tile Drains (TDs) are often installed by farmers in order to drain any excess of water accumulating in crop fields. This anthropogenic modification to the land surface can have strong effects on streamflow in these areas. Here, a simple technique was employed in order to partly account for the effect that TDs can have on streamflow simulated with the GEM-Hydro physically-based and distributed hydrologic model developed at Environment and Climate Change Canada (ECCC). The technique consists in significantly increasing the horizontal hydraulic conductivity of the soil layer generally containing the TDs, in the land-surface scheme of GEM-Hydro, for the part of the grid-cell that contains TDs. To do so, a multiplying coefficient obtained through automatic calibration was used to increase the appropriate soil layer’s horizontal hydraulic conductivity. The part of the grid-cell containing Tile Drains was obtained from different databases depending on the country (i.e., US or Canada) or the province (Ontario or Quebec). Moreover, a similar strategy was followed to represent the effect that agricultural ploughing practices can have on streamflow, by increasing the model’s vertical hydraulic conductivity for the superficial soil layers. The methodology employed allowed to significantly increase the performance of GEM-Hydro streamflow simulations in the watershed of the Laurentian Great-Lakes when compared to the default (current) version of the model, while maintaining similar performances for other hydrologic variables simulated with GEM-Hydro, such as evapotranspiration, and soil moisture and surface temperature simulations, when comparing for example to the recent Global Land Evaporation Amsterdam Model (GLEAM version 3.5b) reference dataset. These findings are promising in the view of developing land-surface schemes that can be applied both for two-way coupling with atmospheric models and for environmental and hydrologic applications.

Catchment modelling has undergone tremendous developments during the past decades. In the 1970s, the focus was on simulation of catchment runoff with process descriptions and data inputs being lumped to the catchment scale. Later developments included spatially distributed models allowing data inputs and hydrological processes to be simulated at model grid scale, i.e. much finer than catchment scale. These models were able to explicitly simulate various processes such as soil moisture, evapotranspiration, groundwater and surface runoff. With the advancements in remote sensing technology and availability of high-resolution data, increased attention has in recent years been given to enhancing the capability of catchment models to reproduce spatial patterns and in this way improve our understanding of hydrological processes and the physical realism of catchment models. This development process has involved a wide spectrum of different aspects in the modelling process, reaching from an improved understanding of uncertainties in data, model parameters and model structures to new protocols for good modelling practices in water management. Recognizing the important role of biodiversity and social aspects, hydrologists are now extending the scope of their models to capture the interactions between water, biota and human social systems.