Reliable precipitation forcing is essential for calculating the water balance, seasonal snowpack, glacier mass balance, streamflow, and other hydrological variables. However, satellite precipitation is often the only forcing available to run hydrological models in data-scarce regions, compromising hydrological calculations when unreliable. The IMERG product estimates precipitation quasi-globally from a combination of passive microwave and infrared satellites, which are intercalibrated based on GPM’s DPR and GMI instruments. Current GPM-DPR radar algorithms have satisfactorily estimated rainfall, but a limited consideration of PSD, attenuation correction, and ground clutter have degraded snowfall estimation, especially in mountain regions. This study aims to improve satellite radar snowfall estimates for this situation. Nearly two years (between 2019 and 2022) of aloft precipitation concentration, surface hydrometeor size, number and fall velocity, and surface precipitation rate from a high elevation site in the Canadian Rockies and collocated GPM-DPR reflectivities were used to develop a new snowfall estimation algorithm. Snowfall estimates using the new algorithm and measured GPM-DPR reflectivities were compared to other GPM-DPR-based products, including CORRA, which is employed to intercalibrate IMERG. Snowfall rates estimated with measured Ka reflectivities, and from CORRA were compared to MRR-2 observations, and had correlation, bias, and RMSE of 0.58 and 0.07, 0.43 and -0.38 mm h-1, and 0.83 and 0.85 mm h-1, respectively. Predictions using measured Ka reflectivity suggest that enhanced satellite radar snowfall estimates can be achieved using a simple measured reflectivity algorithm. These improved snowfall estimates can be adopted to intercalibrate IMERG in cold mountain regions, thereby improving regional precipitation estimates.

Clouds are crucial to Earth’s climate system, influencing radiation and contributing to climate projection uncertainties. Here, the simulated cloud fraction by the sixth version of the Canadian Regional Climate Model (CRCM6/GEM5) was evaluated using CALIPSO lidar retrievals and the second version of the Cloud Feedback Intercomparison Project (CFMIP) Observation Simulator Package (COSP2) for the years 2014 and 2015. Horizontal and vertical distributions of clouds in the CRCM6/GEM5 model were evaluated using cloud profiles and four cloud categories (total, high-, mid- and low-level clouds) derived directly from the CRCM6/GEM5 model and treated using the COSP2 satellite simulator. A seasonal analysis was conducted across specific regions in North America. Results showed that the use of COSP2 is essential for comparing CRCM6/GEM5 outputs against satellite data to account for variable definitions and signal attenuation of active instruments (e.g., Cloud-Aerosol Lidar with Orthogonal Polarization: CALIOP). Spatial and vertical cloud distributions and seasonal patterns were generally well represented by the CRCM6/GEM5 for both winter (December-February) and summer (June-August). High- and low-level clouds were particularly well-represented, especially in winter. The CRCM6/GEM5 model demonstrated some difficulty producing enough clouds to accurately represent those at mid-level. Cloud fraction representation was systematically better during winter than summer. The CRCM6/GEM5 generally performed well over the whole North American domain for the four cloud categories and COSP2 was confirmed to help mitigate discrepancies in variable definitions. These results contribute to a better understanding of the CRCM6/GEM5 cloud representations and the use of COSP2 with high-resolution models.

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.