This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

Abstract. Airborne LiDAR is increasingly used in forest carbon, ecosystem, and resource monitoring. For practical design and manufacture reasons, the 1064 nm near-infrared (NIR) wavelength has been the most commonly adopted, and most literature in this field represents sampling characteristics in this wavelength. However, due to eye-safety and application-specific needs, other common wavelengths are 1550 nm and 532 nm. All provide canopy structure reconstructions that can be integrated or compared through space and time but the consistency or complementarity of 3D airborne LiDAR data sampled at multiple wavelengths is poorly understood. Here, we report on multispectral LiDAR missions carried out in 2013 and 2015 over a managed forest research site. The 1st used 3 independent sensors, and the 2nd used a single sensor carrying 3 lasers. The experiment revealed differences in proportions of returns at ground level, vertical foliage distributions, and gap probability across wavelengths. Canopy attenuation was greatest at 532 nm, presumably due to leaf tissue absorption. Relative to 1064 nm, foliage was undersampled at midheight percentiles at 1550 nm and 532 nm. Multisensor data demonstrated differences in foliage characterization due to combined influences of wavelength and acquisition configuration. Single-sensor multispectral data were more stable but demonstrated clear wavelength-dependent variation that could be exploited in intensity-based land cover classification without the aid of 3D derivatives. This work sets the stage for improvements in land surface classification and vertical foliage partitioning through the integration of active spectral and structural laser return information.

A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest attribute models from field data to intersecting airborne LiDAR data, and then to GLAS footprints. GLAS data were sequentially filtered and submitted to the k-nearest neighbour (k-NN) imputation algorithm to yield regional estimates of stand height and crown closure at a 30 m resolution. Resulting outputs were assessed against independent airborne LiDAR data to evaluate regional estimates of stand height (mean difference = −1 m, RMSE = 5 m) and crown closure (mean difference = −5%, RMSE = 9%). Additional assessments were performed as a function of dominant vegetation type and ecoregion to further evaluate regional products. These attributes form the primary descriptive structure attributes that are typical of forest inventory mapping programs, and provide insight into how they can be derived in northern boreal regions where field information and physical access is often limited.

This study examines the hydrological recovery of two regenerating boreal trembling aspen (Populus tremuloides Michx.) dominated stands and the sensitivity of that regeneration to drought within the first 5 years of establishment. The results indicate that evapotranspiration fluxes and water-use efficiency rebounded quickly as a result of new vegetation foliage growth and wet conditions found within the first 2 years following the harvest. However, a period of dry years had a significant influence on rates of postharvest growth, carbon dioxide (CO 2 ), and water fluxes at these sites. The northern study area (NSA) and southern study area (SSA) were harvested in the winters of 2007 and 2008, respectively. The first and second years of regeneration at the SSA and NSA, respectively, were marked by an early spring thaw and higher-than-normal precipitation, while air temperatures remained slightly above the 30-year normal. During this period, mean measured height of vegetation tripled at both sites, and cumulative evapotranspiration was approximately 60% of that prior to harvest by the end of the second year of growth. By the third year (2009), the NSA became a sink for atmospheric CO 2 during the snow free season (days of the year 128-238) despite low precipitation during the latter half of the summer. Volumetric soil moisture content in 2009 was the highest (on average) of the 5 years examined due to heavy snowfall and a late start to the growing season (where air temperatures consistently exceeded 0°C), resulting in sustained productivity. However, cumulative annual precipitation also declined to 79% and 57% in 2009 and 2010, respectively, of the 30-year normal for that region, leading to significant (lagged) declines in forest productivity at the NSA in 2010 and 2011. This resulted in the site becoming a source of CO 2 to the atmosphere during the 2010 and 2011 growing seasons (annual balance was not measured). Throughout the drought period (2009, 2010, and 2011), mean stand height increased by only 15%, 11%, and 14%, respectively, compared with the mean stand height in 2008. Water-use efficiency also declined in 2010 and 2011, whereas differences in light-use efficiency did not vary significantly because foliage was maintained (i.e., leaves did not abscise as a result of drought). The results of this study indicate that regenerating aspen stands are sensitive to drought and respond relatively quickly to changes in the soil moisture regime. This is important because regional drying as a result of predicted climatic changes combined with increased industrial activity may result in significant decline in productivity within these stands over broad regions. Résumé :Cette étude s'intéresse à la récupération hydrologique de deux peuplements boréaux en régénération dominés par le peuplier faux-tremble (Populus tremuloides Michx.) ainsi qu'à la sensibilité de la régénération à la sécheresse au cours des cinq années suivant son établissement. Les résultats de cette étude indiquent que le flux d'évapotranspiration (ET) et l...

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In this study, a Boolean classification was applied using novel methods to 3‐D vegetation structural and topographic attributes found within flux footprint source/sink areas measured by eddy covariance instrumentation. The purpose was to determine if the spatial frequency of 3‐D attributes, such as canopy height, effective leaf area index, etc., found within 1 km resolution Moderate Resolution Imaging Spectroradiometer (MODIS) pixels were significantly different from or similar to attributes sampled by flux footprints originating from prevailing wind directions. A Kolmogorov‐Smirnov test was used for the first time to apply confidence limits to individual MODIS pixels based on (1) the spatial distribution of cumulative frequencies of attributes representative of those sampled by eddy covariance and (2) temporal representation of MODIS pixels related to area sampling frequency by eddy covariance based on wind direction. Structural and topographic attributes at homogeneous Southern Old Aspen and heterogeneous Upland Aspen sites are representative of 56% and 69% of a 1 km radius area surrounding the tower and 21% and 47% of a 4 × 4 km area. Attributes found within the MODIS “tower” pixel compare well with attributes most frequently sampled by eddy covariance instruments at both sites. By classifying pixels using the Boolean approach, correspondence between MODIS pixels and eddy covariance estimates of gross primary production (GPP) explain up to 13% more variance than using pixels proximal to the tower. This study, therefore, provides a method for choosing MODIS pixels that have similar attributes to those found within footprints most frequently sampled by eddy covariance.

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