Unidentified human remains have historically been investigated nationally by law enforcement authorities. However, this approach is outdated in a globalized world with rapid transportation means, where humans easily move long distances across borders. Cross-border cooperation in solving cold-cases is rare due to political, administrative or technical challenges. It is fundamental to develop new tools to provide rapid and cost-effective leads for international cooperation. In this work, we demonstrate that isotopic measurements are effective screening tools to help identify cold-cases with potential international ramifications. We first complete existing databases of hydrogen and sulfur isotopes in human hair from residents across North America by compiling or analyzing hair from Canada, the United States (US) and Mexico. Using these databases, we develop maps predicting isotope variations in human hair across North America. We demonstrate that both δ2H and δ34S values of human hair are highly predictable and display strong spatial patterns. Multi-isotope analysis combined with dual δ2H and δ34S geographic probability maps provide evidence for international travel in two case studies. In the first, we demonstrate that multi-isotope analysis in bulk hair of deceased border crossers found in the US, close to the Mexico-US border, help trace their last place of residence or travel back to specific regions of Mexico. These findings were validated by the subsequent identification of these individuals through the Pima County Office of the Medical Examiner in Tucson, Arizona. In the second case study, we demonstrate that sequential multi-isotope analysis along the hair strands of an unidentified individual found in Canada provides detailed insights into the international mobility of this individual during the last year of life. In both cases, isotope data provide strong leads towards international travel.

With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.

With global warming and increasing water use, tap water resources need sustainable management. We used hydrogen and oxygen isotope analyses in tap water (i.e., δ2H and δ18O values) to identify issues associated with tap water resources in Canada. We analyzed 576 summer tap samples collected from across Canada and 76 tap samples from three cities during different seasons and years. We classified the samples based on their sources: groundwater (TapGroundwater), river (TapRiver) and lake (TapLake). δ2H values in tap water correlate strongly with values predicted for local precipitation across Canada with a stronger correlation for TapGroundwater and TapRiver than for TapLake. We then constructed water balance models to predict the δ2H of surface water across Canada, and validated them against Canadian stream δ2H data. δ2H values in tap water correlate strongly with values predicted for local surface water, however, the water balance models improved the predictability only for TapRiver and TapLake and not for TapGroundwater. TapGroundwater δ2H values reflect the δ2H values of annually averaged precipitation, whereas TapRiver and TapLake δ2H values reflect post-precipitation processes. We used the δ2H residuals between the observed and predicted δ2H values to assess regional processes influencing tap water δ2H values across Canada. Regionally, snow/glacier melt contributes to all tap sources around the Rockies. Tap waters are highly evaporated across Western Canada, irrespective of their sources. In the Great Lakes and East Coast regions, tap waters are evaporated in many localities, particularly those using surface reservoirs and lakes. We propose the use of these isotopic baselines as a way forward for the monitoring of tap water resources at different scales. These isotopic baselines also have valuable applications in human forensic studies in Canada.

Tap water supply is an essential resource for human societies. However with increasing water use and global warming, this resource needs to be monitored and managed sustainably. Here we use stable isotopes to identify potential issues associated with tap water resources in Canada. We analyze isotopes of 576 tap water samples collected from across Canada and classified them based on their supply sources including groundwater (Tap Groundwater ), river (Tap River ) and lake (Tap Lake ). We found, isotopic values in tap water correlate strongly with those predicted in local precipitation across Canada, suggesting precipitation is the parent source of tap water. However, this correlation is stronger for Tap Groundwater and Tap River than Tap Lake. To explain this difference, we constructed a series of water balance models to predict isotopic values of surface water across Canada validated against Canadian rivers isotopes data. We then compared the tap water isotopic

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