The Sebou watershed is the main receiver of rainwater contributions in the North of Morocco. The present study is interested in the knowledge of the global Physical characteristics on water dynamics in the Sefrou watershed at the level of the Sefrou sub-watershed which belongs to this large hydrological unit and which occupies its south-western part. The approach followed in this study consisted initially, in acquiring the data, organizing it, and processing it by a geographic information system (GIS), in order to obtain a global idea on the distribution of the different parameters in the entourage concerned by this study. The application of geographic information system tools makes it possible to establish a set of maps that will help develop an excellent descriptive analysis characteristic of the watershed. In this paper, we present the analysis results of the geological, climatic and hydrological characteristics of an important area of the Middle Atlas, with the notable importance of precipitation, runoff and rivers for irrigation and the supply of drinking water. of cities in the region. The hydrological study of the Sefrou watershed has shown a typical Mediterranean regime, the watershed receives an average annual rainfall of 454.22 mm, with a volume input of 183,96*10 ^6 ^3/year and an average annual temperature of 16.62°C. The actual evapotranspiration in the watershed is 389.22 mm/year which is 161,28.10^6 m^3/year.

Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes. To investigate the quality of these observations, we compared the water level class data for a number of locations where water levels were also measured and assessed when these observations were submitted. We analysed data for nine locations where citizen scientists reported multiple observations using a smartphone app and stream level data were also available. At twelve other locations, signposts were set up to ask citizens to record observations on a form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was higher than for the forms. A possible explanation is that for each app location, most contributions were made by a single person, whereas at the locations of the forms almost every observation was made by a new contributor. On average, more contributions were made between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the data collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application with its virtual staff gauge is a promising approach for crowd-based water level class observations.

A new flow for Canadian young hydrologists: Key scientific challenges addressed by research cultural shiftsCaroline Aubry-Wake1, Lauren D. Somers2,3, Hayley Alcock4, Aspen M. Anderson5, Amin Azarkhish6, Samuel Bansah7, Nicole M. Bell8, Kelly Biagi9, Mariana Castaneda-Gonzalez10, Olivier Champagne9, Anna Chesnokova10, Devin Coone6, Tasha-Leigh J. Gauthier11, Uttam Ghimire6, Nathan Glas6, Dylan M. Hrach11, Oi Yin Lai14, Pierrick Lamontagne-Halle3, Nicolas R. Leroux1, Laura Lyon3, Sohom Mandal12, Bouchra R. Nasri13, Nataša Popović11, Tracy. E. Rankin14, Kabir Rasouli15, Alexis Robinson16, Palash Sanyal17, Nadine J. Shatilla9, 18, Brandon Van Huizen11, Sophie Wilkinson9, Jessica Williamson11, Majid Zaremehrjardy191 Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada2 Civil and Environmental Engineering, Massachusetts Institute of Technology, MA, USA3 Department of Earth and Planetary Sciences, McGill University, Montreal QC4 Department of Natural Resource Science, McGill University, Montreal, QC, Canada5 Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada6 School of Engineering, University of Guelph, Ontario, ON, Canada7 Department of Geological Sciences, University of Manitoba, Winnipeg, Canada8 Centre for Water Resources Studies, Department of Civil & Resource Engineering, Dalhousie University, Halifax, NS, Canada9 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada.10 Department of Construction Engineering, École de technologie supérieure, Montreal, QC, Canada11 Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON, Canada12 Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada13 Department of Mathematics and Statistics, McGill University, Montréal, Qc, Canada14 Geography Department, McGill University, Montreal, QC, Canada15 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada16 Department of Geography and Planning, University of Toronto, Toronto, ON17 Global Institute for Water Security, University of Saskatchewan.18 Lorax Environmental Services Ltd, Vancouver, BC, Canada.19 Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB, Canada

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium sized trees and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception mass with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ -1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data from hazardous or remote terrain, and calibrating snow interception models to new forest types around the globe.