Apport du SIG et de la télédétection dans la modélisation spatiale de la susceptibilité aux mouvements de terrain dans la région d’Al Hoceima, Rif Oriental, Maroc

L’évaluation du degré de susceptibilité aux mouvements de terrains est devenue une préoccupation majeure dans les terrains montagneux, elle oriente les efforts à entreprendre pour prévenir les catastrophes, minimiser les risques et gérer les conséquences. Les techniques d’analyse spatialisée par le système d'information géographique (SIG) et télédétection sont de plus en plus utilisées pour évaluer la susceptibilité des versants aux mouvements de terrain. Dans cette étude, la modélisation par un modèle probabiliste bivarié (théorie d’évidence) a été utilisé pour cartographier les zones susceptibles aux mouvements de terrains dans la région d’Al Hoceima (NE du Maroc). Le SIG est également utilisé pour apprécier la relation entre : (i) les mouvements des terrains et (ii) la distribution spatiale des facteurs causatifs, l’information relative à ces deux composantes a été dérivée des données de terrain, traitements d’imageries satellitaires et les documents cartographiques disponibles. Ces données ont été intégrées dans une base de données SIG avec d’autres paramètres issus des cartes géologiques, topographiques et des stations météorologiques. Cette phase de préparation a été suivie par un test de l’indépendance conditionnelle des facteurs causatifs par rapport aux mouvements de terrain survenus dans la zone. Les paramètres indépendants ont servi aux calculs des poids positifs, négatifs (W+,W-), et leurs contrastes (C). Finalement, les cartes de pondérations obtenues des différentes combinaisons ont été évaluées pour retenir la meilleur simulation des facteurs causatifs, sur ce, une série des tests de validation des résultats obtenus par l’analyse de la courbe ROC a été effectuée. Les résultats ont montrés que la combinaison qui regroupe les facteurs prédictifs : pente, exposition des versants, lithologie et densité de fracturation s’avère être la meilleure combinaison possible. Cette combinaison permet de prédire environ 70% des instabilités existants.Mots-clés: mouvement de terrain, théorie de l’évidence, la région d’Al Hoceima, SIG et télédétection. The use of GIS and remote sensing in spatial modeling of susceptibility to landslides in the region of Al Hoceima (Eastern Rif, Morocco)The evaluation of the degree of susceptibility to landslides has become a major concern in mountainous terrain, it directs the efforts to be undertaken to prevent disasters, mitigate risk and manage the consequences. The spatial analysis using Geographic Information System (GIS) and remote sensing techniques are increasingly used to assess the susceptibility to landslides. In this study, a bivariate probability model (theory of evidence or 'weights of evidence') coupled with GIS for mapping areas susceptible to landslides in the region of Al Hoceima (NE of Morocco) was used. GIS is also used to assess the relationship between the landslides and the spatial distribution of causative factors. The Information on these factors was derived from observational field data and available cartographic and remote sensed data. These data were integrated in a GIS database with other parameters derived from geological maps, topographical and climate measures stations of the study area. This preparation stage was followed by a test of conditional independence of the causative factors in relation to landslides occurred in the study area. Independent parameters were used for calculations of positive weight, negative weight (W +, W -), and contrast (C). Finally, the maps obtained from different combinations were evaluated in order to retain the best simulation of causative factors, for this, a series of tests to validate the results obtained by the ROC curve analysis was performed. The results showed that the following combination of predictive factors: slope, exposure, lithology and density prove the best combination. This combination predicts about 70% of existing instabilities.Keywords: landslides, weights of evidence, region of Al Hoceima, GIS and remote sensing

Greater local cooling effects of trees across globally distributed urban green spaces

Urban green spaces (UGS) are an effective mitigation strategy for urban heat islands (UHIs) through their evapotranspiration and shading effects. Yet, the extent to which local UGS cooling effects vary across different background climates, plant characteristics and urban settings across global cities is not well understood. This study analysed 265 local air temperature (TA) measurements from 58 published studies across globally distributed sites to infer the potential influence of background climate, plant and urban variables among different UGS types (trees, grass, green roofs and walls). We show that trees were more effective at reducing local TA, with reductions 2–3 times greater than grass and green roofs and walls. We use a hierarchical linear mixed effects model to reveal that background climate (mean annual temperature) and plant characteristics (specific leaf area vegetation index) had the greatest influence on cooling effects across UGS types, while urban characteristics did not significantly influence the cooling effects of UGS. Notably, trees dominated the overall local cooling effects across global cities, indicating that greater tree growth in mild climates with lower mean annual temperatures has the greatest mitigation potential against UHIs. Our findings provide insights for urban heat mitigation using UGS interventions, particularly trees across cities worldwide with diverse climatic and environmental conditions and highlight the essential role of trees in creating healthy urban living environments for citizens under extreme weather conditions

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure

River channel conveyance capacity adjusts to modes of climate variability

River networks are typically treated as conduits of fixed discharge conveyance capacity in flood models and engineering design, despite knowledge that alluvial channel networks adjust their geometry, conveyance, planform, extent and drainage density over time in response to shifts in the magnitude and frequency of streamflows and sediment supply. Consistent relationships between modes of climate variability conducive to wetter-/drier-than-average conditions and changes in channel conveyance have never been established, hindering geomorphological prediction over interannual to multidecadal timescales. This paper explores the relationship between river channel conveyance/geometry and three modes of climate variability (the El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Arctic Oscillation) using two-, five- and ten-year medians of channel measurements, streamflow, precipitation and climate indices over seven decades in 67 United States rivers. We find that in two thirds of these rivers, channel capacity undergoes coherent phases of expansion/contraction in response to shifts in catchment precipitation and streamflow, driven by climate modes with different periodicities. Understanding the sensitivity of channel conveyance to climate modes would enable better river management, engineering design, and flood predictability over interannual to multidecadal timescales

Seasonal predictability of high sea level frequency using ENSO patterns along the U.S. West Coast

High sea levels can be conducive to coastal flooding, coastal erosion and inland salt-water intrusion, and thus pose a significant threat to coastal communities, ecosystems and coastal assets. Increases in high water levels have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate modes of variability such as the El Niño-Southern Oscillation (ENSO). Here, we examine the predictability of the seasonal frequency of high sea levels using the Niño3.4 index. Different high sea level quantities are considered at 23 tide gauges along the U.S. West Coast, including storm surge and nuisance (minor) floods. At each site, we develop a statistical probabilistic forecasting model for seasonal high sea level frequency during the cold period of October-March. As predictors, we compare the use of (1) seasonal Niño3.4 index observations over the warm antecedent period of July-September and (2) seasonal Niño3.4 index forecasts from the North American Multi-Model Ensemble (NMME) over the cold concurrent period of October-March. Results indicate that the Niño3.4 observations are a good predictor of seasonal high sea level frequency, especially for predicting the storm surge frequency. Correlation coefficients between the observed and modelled seasonal storm surge frequency range from 0.6 to 0.95 at most of the 23 tide gauges. In the predictive model, when using NMME Niño3.4 index, correlation coefficients range between approximately 0.4 and 0.7 at the southern gauges for Niño3.4 index forecasts initialized from October to June (the skill decreases with lead time). Our results provide insights into the seasonal predictability of high sea levels using ENSO patterns which is important for planning and coastal management

Contribution of Tropical Cyclones to Rainfall at the Global Scale

This study quantifies the relative contribution of tropical cyclones (TCs) to annual, seasonal, and extreme rainfall and examines the connection between El Niño–Southern Oscillation (ENSO) and the occurrence of extreme TC-induced rainfall across the globe. The authors use historical 6-h best-track TC datasets and daily precipitation data from 18 607 global rain gauges with at least 25 complete years of data between 1970 and 2014. The highest TC-induced rainfall totals occur in East Asia (>400 mm yr−1) and northeastern Australia (>200 mm yr−1), followed by the southeastern United States and along the coast of the Gulf of Mexico (100–150 mm yr−1). Fractionally, TCs account for 35%–50% of the mean annual rainfall in northwestern Australia, southeastern China, the northern Philippines, and Baja California, Mexico. Seasonally, between 40% and 50% of TC-induced rain is recorded along the western coast of Australia and in islands of the south Indian Ocean in the austral summer and in East Asia and Mexico in boreal summer and fall. In terms of extremes, using annual maximum and peak-over-threshold approaches, the highest proportions of TC-induced rainfall are found in East Asia, followed by Australia and North and Central America, with fractional contributions generally decreasing farther inland from the coast. The relationship between TC-induced extreme rainfall and ENSO reveals that TC-induced extreme rainfall tends to occur more frequently in Australia and along the U.S. East Coast during La Niña and in East Asia and the northwestern Pacific islands during El Niño

Seasonal predictability of high sea level frequency using ENSO patterns along the U.S. West Coast

High sea levels can be conducive to coastal flooding, coastal erosion and inland salt-water intrusion, and thus pose a significant threat to coastal communities, ecosystems and coastal assets. Increases in high water levels have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate modes of variability such as the El Niño-Southern Oscillation (ENSO). Here, we examine the predictability of the seasonal frequency of high sea levels using the Niño3.4 index. Different high sea level quantities are considered at 23 tide gauges along the U.S. West Coast, including storm surge and nuisance (minor) floods. At each site, we develop a statistical probabilistic forecasting model for seasonal high sea level frequency during the cold period of October-March. As predictors, we compare the use of (1) seasonal Niño3.4 index observations over the warm antecedent period of July-September and (2) seasonal Niño3.4 index forecasts from the North American Multi-Model Ensemble (NMME) over the cold concurrent period of October-March. Results indicate that the Niño3.4 observations are a good predictor of seasonal high sea level frequency, especially for predicting the storm surge frequency. Correlation coefficients between the observed and modelled seasonal storm surge frequency range from 0.6 to 0.95 at most of the 23 tide gauges. In the predictive model, when using NMME Niño3.4 index, correlation coefficients range between approximately 0.4 and 0.7 at the southern gauges for Niño3.4 index forecasts initialized from October to June (the skill decreases with lead time). Our results provide insights into the seasonal predictability of high sea levels using ENSO patterns which is important for planning and coastal management

Using R in hydrology: a review of recent developments and future directions

The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation, and vignettes (long-form guides that illustrate how to use packages); the role of integrated development environments (IDEs); and the challenges of big data and parallel computing in hydrology. Lastly, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, application programming interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events

Atmospheric rivers and associated extreme rainfall over Morocco

Atmospheric rivers (ARs) are long, narrow, and transient corridors of enhanced water vapour content in the lower troposphere, associated with strong low-level winds. These features play a key role in the global water cycle and drive weather extremes in many parts of the world. Here, we assessed the frequency and general characteristics of landfalling ARs over Morocco for the period 1979–2020. We used ECMWF ERA5 reanalysis data to detect and track landfalling ARs and then assessed AR association with rainfall at the annual and seasonal scales, as well as extreme rainfall events (defined as a daily precipitation amount exceeding the 99th percentile threshold of the wet days) at 30 gauging stations located across Morocco. Results indicate that about 36 ARs/year make landfall in Morocco. AR occurrence varies spatially and seasonally with highest occurrences in the autumn (SON) and Winter (DJF) in the northern part of the country and along the Atlantic across northern regions. AR rainfall climatology indicates up to 180 mm/year recorded in stations located in the north west. High fractional contributions (~28%) are recorded in the north and the Atlantic regions, with the driest regions of the south receiving about a third of their annual rainfall from ARs. For extreme rainfall, the highest AR contributions can attain over 50% in the southern dry regions and along the Atlantic north coast and Atlas highlands