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This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion (SfM) technology. To this end, the Flood-Level Marks (FLMs) were created from high-resolution UAV ortho-images and compared to the flood inundated areas simulated using the HEC-RAS hydraulic model. The flood quantiles for 25, 50, 100, and 200 return periods were then estimated by synthetic hydrographs using the Natural Resources Conservation Service (NRCS). The proposed method was applied to UAV image data collected from the Khosban village, in Taleghan County, Iran, in the ungauged sub-basin of the Khosban River. The study area is located along one kilometre of the river in the middle of the village. The results showed that the flood inundation areas modeled by the HEC-RAS were 33%, 19%, and 8% less than those estimated from the UAV’s FLMs for 25, 50, and 100 years return periods, respectively. For return periods of 200 years, this difference was overestimated by more than 6%, compared to the UAV’s FLM. The maximum flood depth in our four proposed scenarios of hydraulic models varied between 2.33 to 2.83 meters. These analyses showed that this method, based on the UAV imagery, is well suited to improve the hydraulic modeling for seasonal inundation in ungauged rivers, thus providing reliable support to flood mitigation strategies

Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

<p>The applicability of the Canadian Precipitation Analysis products known as the Regional Deterministic Precipitation Analysis (CaPA-RDPA) for hydrological modelling in boreal watersheds in Canada, which are constrained with shortage of precipitation information, has been the subject of a number of recent studies. The northern and mid-cordilleran alpine, sub-alpine, and boreal watersheds in Yukon, Canada, are prime examples of such Nordic regions where any hydrological modelling application is greatly scrambled due to lack of accurate precipitation information. In the course of the past few years, proper advancements were tailored to resolve these challenges and a forecasting system was designed at the operational level for short- to medium-range flow and inflow forecasting in major watersheds of interest to Yukon Energy. This forecasting system merges the precipitation products from the North American Ensemble forecasting System (NAEFS) and recorded flows or reconstructed reservoir inflows into the HYDROTEL distributed hydrological model, using the Ensemble Kalman Filtering (EnKF) data assimilation technique. In order to alleviate the adverse effects of scarce precipitation information, the forecasting system also enjoys a snow data assimilation routine in which simulated snowpack water content is updated through a distributed snow correction scheme. Together, both data assimilation schemes offer the system with a framework to accurately estimate flow magnitudes. This robust system not only mitigates the adverse effects of meteorological data constrains in Yukon, but also offers an opportunity to investigate the hydrological footprint of CaPA-RDPA products in Yukon, which is exactly the motivation behind this presentation. However, our overall goal is much more comprehensive as we are trying to elucidate whether assimilating snow monitoring information in a distributed hydrological model could meet the flow estimation accuracy in sparsely gauged basins to the same extent that would be achieved through either (i) the application of precipitation analysis products, or (ii) expanding the meteorological network. A proper answer to this question would provide us with valuable information with respect to the robustness of the snow data assimilation routine in HYDROTEL and the intrinsic added-value of using CaPA-RDPA products in sparsely gauged basins of Yukon.</p>

The increase in the frequency of floods, which is a projected consequence of climate change, can have wide-ranging health and economic impacts. To cope with these floods and to reduce their impacts, households can adopt some preventive behaviours. The main goal of this research was to compare the adoption of flood mitigation behaviours in three populations presenting distinctive characteristics with a valid and an invariant measure of behavioural adaptation, as well as a baseline measure (comparison group). The article also aims to test the moderated effect of having experienced a flood on the relation between the perception of risk of being flooded and the adoption of preventive behaviours. A survey was conducted in flood-prone areas and in some areas that were not at risk in Quebec, Canada, through phone interviews. Results confirmed that people who lived in an at-risk area and had experienced past flooding events are more inclined to adopt preventive behaviours than people who lived in an at-risk area but had never experienced such an event, and those who lived outside at-risk areas. In addition, our results indicate that the at-risk population who have never experienced a flood engage in few flood preventive behaviours. This is worrisome, as their rate of adopting adaptive behaviour is very similar to the one seen in populations living outside at-risk areas, despite the increased risk inherent to their situation. This could be partly explained by our data showing that around a quarter of the at-risk population did not know they were living in a flood-prone area. Our results show that communication efforts are necessary in order to better inform the population of the risk related to living in a flood-prone area and that incentives should be developed to help enhance the rate of preventive behaviours in at-risk populations having never experienced a flood.

Abstract The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree. , Article impact statement : The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability.