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.

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.

Wildfires and heatwaves have recently affected the hydrological system in unprecedented ways due to climate change. In cold regions, these extremes cause rapid reductions in snow and ice albedo due to soot deposition and unseasonal melt. Snow and ice albedo dynamics control net shortwave radiation and the available energy for melt and runoff generation. Many albedo algorithms in hydrological models cannot accurately simulate albedo dynamics because they were developed or parameterised based on historical observations. Remotely sensed albedo data assimilation (DA) can potentially improve model performance by updating modelled albedo with observations. This study seeks to diagnose the effects of remotely sensed snow and ice albedo DA on the prediction of streamflow from glacierized basins during wildfires and heatwaves. Sentinel-2 20-m albedo estimates were assimilated into a glacio-hydrological model created using the Cold Regions Hydrological Modelling Platform (CRHM) in two Canadian Rockies glacierized basins, Athabasca Glacier Research Basin (AGRB) and Peyto Glacier Research Basin (PGRB). The study was conducted in 2018 (wildfires), 2019 (soot/algae), 2020 (normal), and 2021 (heatwaves). DA was employed to assimilate albedo into CRHM to simulate streamflow and was compared to a control run (CTRL) using off-the-shelf albedo parameters. Albedo DA benefited streamflow predictions during wildfires for both basins, with a KGE coefficient improvement of 0.18 and 0.20 in AGRB and PGRB, respectively. Four-year DA streamflow predictions were superior to CTRL in PGRB, but DA was slightly better in AGRB. DA was not beneficial to streamflow predictions during heatwaves. These results show that albedo DA can reveal otherwise unknown albedo and snowpack dynamics occurring in remote glacier accumulation zones that are not well simulated by model predictions alone. These findings corroborate the power of observational tools to incorporate near real-time information into hydrological models to better inform water managers of the streamflow response to wildfires and heatwaves.

A document by Christopher B Marsh. Click on the document to view its contents.

Vegetation structure is considered one of the most important factors shaping the spatial variation of snow accumulation under forest canopies. However, fine scale relationships between canopy density, snow interception, wind redistribution and sub-canopy accumulation are poorly understood and difficult to observe, and their influence governing stand-scale snow distributions that determine snow covered area depletion during melt is largely unknown. In this study, fine-scale observations of forest structure and sub-canopy snow accumulation were analyzed over two mid-winter snowfalls to a sub-alpine forest in Marmot Creek Research Basin, Canadian Rockies, Alberta, to identify the impact of snow-canopy interactions on spatial patterns of sub-canopy snow accumulation. High spatial resolution (5 cm and 25 cm) snow accumulation estimates and canopy structure metrics were calculated from the combination of repeated UAV-lidar observations with snow and photographic surveys, utilizing novel resampling methods including voxel ray sampling of lidar (VoxRS) to improve metric robustness and reduce bias. Over 50% of the spatial variance in forest snow accumulation was found at length scales less than 2 m, supporting the role of local scale canopy structure in governing variation in subcanopy snow accumulation. Additionally, subcanopy snow accumulation showed significant angular spread in relationships with overhead canopy structure; the vertical asymmetry coinciding with local windflow directions during snowfall. Detailed angular analysis showed nontrivial snow-vegetation relationships that likely reflect multiple snowfall-vegetation processes, including unloading and entrainment of intercepted snowfall during wind gusts and funneling of entrained particles by downwind vegetation. These fine-scale findings suggest several emergent processes which may influence snow accumulation at the scale of forest stands, with novel considerations for representing SWE distributions under dense evergreen canopies under varying environmental and canopy conditions. Similar studies over a broad range of conditions and forests will help refine and generalize the effects observed here for further snow hydrology and forestry applications.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

Excess nutrients in aquatic ecosystems is a major water quality problem globally. Worsening eutrophication issues are notable in cold temperate areas, with pervasive problems in many agriculturally dominated catchments. Predicting nutrient export to rivers and lakes is particularly difficult in cold agricultural environments because of challenges in modelling snow, soil, frozen ground, climate, and anthropogenic controls. Previous research has shown that the use of many popular small basin nutrient models can be problematic in cold regions due to poor representation of cold region hydrology. In this study, the Cold Regions Hydrological Modelling Platform (CRHM), a modular modelling system, which has been widely deployed across Canada and cold regions worldwide, was used to address this problem. CRHM was extended to simulate biogeochemical and transport processes for nitrogen and phosphorus through a complex of new process-based modules that represent physicochemical processes in snow, soil and freshwater. Agricultural practices such as tillage and fertilizer application, which strongly impact the availability and release of soil nutrients, can be explicitly represented in the model. A test case in an agricultural basin draining towards Lake Winnipeg shows that the model can capture the extreme hydrology and nutrient load variability of small agricultural basins at hourly time steps. It was demonstrated that fine temporal resolutions are an essential modelling requisite to capture strong concentration changes in agricultural tributaries in cold agricultural environments. Within these ephemeral and intermittent streams, on average, 30%, 31%, 20%, and 16% of the total annual load of NO3, NH4, SRP and partP occurred during the episodic snowmelt freshet ~9 days, accounting for 21% of the annual flow), but shows extreme temporal variation. The new nutrient modules are critical tools for predicting nutrient export from small agricultural drainage basins in cold climates via better representation of key hydrological processes, and a temporal resolution more suited to capture dynamics of ephemeral and intermittent streams.

Subalpine forests are hydrologically important to the function and health of mountain basins. Identifying the specific water sources and the proportions used by subalpine forests is necessary to understand potential impacts to these forests under a changing climate. The recent ‘Two Water Worlds’ hypothesis suggests that trees can favour tightly bound soil water instead of readily available free-flowing soil water. Little is known about the specific sources of water used by subalpine trees Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce) in the Canadian Rocky Mountains. In this study, stable water isotope (δ18O and δ2H) samples were obtained from Subalpine fir and Engelmann spruce trees at three points of the growing season in combination with water sources available at time of sampling (snow, bound soil water, saturated soil water, precipitation). Using the Bayesian Mixing Model, MixSIAR, relative source water proportions were calculated. In the drought summer examined, there was a net loss of water via evapotranspiration from the system. Results highlighted the importance of tightly bound soil water to subalpine forests, providing insights of future health under sustained years of drought and net loss in summer growing seasons. This work builds upon concepts from the ‘Two Water Worlds’ hypothesis, showing that subalpine trees can draw from different water sources depending on season and availability. In our case, water use was largely driven by a tension gradient within the soil allowing trees to utilize tightly bound soil water and saturated soil water at differing points of the growing season.