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Climate change is causing disruptions in Earth's weather patterns, leading to an increase in the frequency and severity of extreme weather events such as droughts, floods, frost, and heatwaves. These events can impact food production and lead to challenges in meeting the food needs of a growing population. Previous research has documented the role of temperature and precipitation during the growing season in explaining crop yield variability. For example, droughts and extreme heat can reduce cereal production by 9-10%.Current crop yield models use only a few meteorological variables to represent weather conditions. However, weather patterns or weather regimes, (i.e., persistent, and recurrent flow patterns of the large-scale atmospheric circulation) can provide a more comprehensive view of weather conditions, and can be used to predict and characterise extreme weather events and explain crop yield variation.In this study, we first conducted a literature review to examine the links between extreme weather events, such as heat waves, and droughts with weather patterns and regimes. One of the main findings of that review was the need to define what extreme weather is in the context of agriculture. The new definition is based on studies that identified optimal and terminal weather conditions for winter wheat at specific phenological stages. Using this definition of extreme weather, we analyse historic yields in East Anglia, UK, forming statistically based relationships between low yield years with a set of classified weather patterns from the UK Met Office. We focused on the weather patterns frequency of occurrence and persistence with additional consideration given to potential microclimates as we compare the effects weather patterns have on a specific farm with a long-term data set to the effects of the larger region. Preliminary analyses shows that a small number of these weather patterns are associated with high impact weather events that cause yield limiting conditions or physical damage to the crop such as wind lodging.It is hoped that further research will lead to the development of a next-generation crop yield variation model taking into account the weather patterns, which can provide longer-term predictions of regional crop yield variability to help agri-businesses, crop insurers and farmers to facilitate decision making, respond effectively to regional and global crop production shocks and food price spikes, and develop adaptation strategies to reduce the potential impact of extreme weather events.

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−1 recorded in stations located in the northwest. 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.

Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application of modern convolutional neural networks (CNNs) to specific flood-related problems such as flood extent detection and flood depth estimation. This review discusses the increasing role of CNNs in flood research with a growing number of published datasets, particularly since 2018. We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management. Such a library would provide benchmark datasets to advance CNN flood applications in general and serve as a resource, providing data scientists and the flood research community with the necessary data for model training and validation.

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.

Mapping of carbonatites and related mineral deposits has occupied prominent place in mineral resource exploration programs given their potential to host valuable concentrations of critical metals such as rare earth elements and niobium. Based on spectral characteristics of most indicative minerals for these rocks, a mapping approach was developed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The combination of band rationing outcomes with components from the principal component analysis and minimum noise fraction techniques highlighted the targeted rocks, with the excellent prospective zone representing ∼0.2% of the total investigated area. This approach was successfully applied to the Gleibat Lafhouda complex to rapidly delineate carbonatites and iron oxide-apatite ore outcrops. Results were validated through field observations and in-situ geochemical analysis using a portable X-ray fluorescence analyzer. Field data have also served as training data to perform a supervised classification, allowing further improvement of the mapping results.

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. HIGHLIGHTS IoT-based wireless sensor network for hydrometeorological data collection and flood monitoring has been developed for Colima-Villa de Álvarez in Mexico.; Real-time data is transferred over 3G cellular network and Wi-Fi.; Tests of the network on three separate tropical storm events showed robustness of the network during extreme events.

Background: Dengue fever is one of the most important arboviral diseases. Surface temperature versus dengue burden in tropical environments can provide valuable information that can be adapted in future measurements to improve health policies. Methods: A methodological approach using Daymet-V3 provided estimates of daily weather parameters. A Python code developed by us extracted the median temperature from the urban regions of Colima State (207.3 km2) in Mexico. JointPoint regression models computed the mean temperature-adjusted average annual percentage of change (AAPC) in disability-adjusted life years (DALY) rates (per 100,000) due to dengue in Colima State among school-aged (5–14 years old) children. Results: Primary outcomes were average temperature in urban areas and cumulative dengue burden in DALYs in the school-aged population. A model from 1990 to 2017 medium surface temperature with DALY rates was performed. The increase in DALYs rate was 64% (95% CI, 44–87%), and it seemed to depend on the 2000–2009 estimates (AAPC = 185%, 95% CI 18–588). Conclusion: From our knowledge, this is the first study to evaluate surface temperature and to model it through an extensive period with health economics calculations in a specific subset of the Latin-American endemic population for dengue epidemics.

Concepts like the 100-year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20-, 50-, and 100-year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20-, 50-, and 100-year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20-/50-year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100-year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.

During the last decades, carbonatites and associated rocks have received increased interest from mining companies and the scientific community. They represent a classic source of a variety of critical elements required by certain emerging technologies and industries such as niobium, rare earth elements (REE), and phosphorus. Morocco like many other countries have several Alkaline igneous complexes, however, their potential in terms of REE-P-rich carbonatites is poorly explored and needs to be investigated. This study is an attempt to develop an advanced exploration tool for the detection and mapping of these rocks using remote sensing. Preliminary investigations were focused on the Oulad Dlim massif at the western Reguibat Shield (Southeast of Dakhla province) where several carbonatite structures were reported, including Gleibat Lafhouda, Twihinate, Lamlaga, Lahjayra. Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) data were used to: (i) identify and map carbonatites and associated rocks liable to contain REE-P mineralization, (ii) investigate their spectral features, and produce predictive maps. Several image processing techniques, have been performed including band ratio, color composite image, principal component analysis and minimum noise fraction. The combination of these techniques appears to more effectively detect carbonatites and associated rocks. The effectiveness of this approach was verified using field investigation, in-situ geochemical analysis with portable X-ray fluorescence, and petrography. The field data were used to train classifiers to better delineate the spatial distribution of the different lithological facies. The results are generally consistent with available geological maps indicating that this approach can be satisfactorily applied in the early stages of geological exploration.

Atmospheric rivers (ARs) are long, narrow, and transient corridors of enhanced water vapour content in the lower troposphere, often connected to the warm sector of extratropical cyclones and associated with strong low-level winds. These features play a major role in the global water cycle and drive weather extremes in many parts of the world. Here, we investigated the characteristics of landfilling ARs, including their frequency and magnitude over Morocco for the period 1979–2020. We used ECMWF ERA5 reanalysis data to detect and track landfilling ARs, and compared different gridded precipitation products (i.e. Integrated Multi-satellite Retrievals for GPM (IMERG), ERA5 Land, and CHIRPS) with a set of gauging stations datasets distributed across Morocco. We assessed AR association with rainfall at the annual and seasonal scales, as well as for extreme rainfall events, in different datasets. Preliminary results indicate that around 20 ARs/year make landfall or have their centroids within 200 km from Morocco. AR occurrence varies spatially and seasonally with highest occurrences in winter (DJF) across northern regions and spring (MAM) in the southern part of country. Rainfall events of up to 250 mm/year are driven by ARs; with the southernmost and driest regions receiving most of their rainfall from ARs. This paper will provide an overview of extreme rainfall and wind associated with ARs across Morocco.

In the last few decades, remote sensing has played a major role in flood risk management strategies across the world. Geomatics products have been used to identify flood extents and impacts, to inform flood and evacuation models, and as part of emergency response activities. Recent technological advances have provided an opportunity to explore the use of remote sensing further within the context of flood risk management. For example, unmanned aircraft systems have been used to model evacuation routes and post-event to assess the impact of flooding at property level, identify households that would benefit from resilient and resistance measures, and identify the source of flooding. This chapter describes current applications of remote sensing within flood risk management and flood emergency response. The first section discusses the application of remote sensing in flood modeling, flood damage assessment, and vulnerability. The second section reviews how the development of remote sensing technologies has extended the range of their application in flood risk management. The last section captures key considerations for the use of standardized remote sensing data collection approaches to inform flood risk management activities.

In this study, we evaluate trends in precipitation and temperature and their related extreme indices in Morocco based on a set of National Climate Monitoring Products defined the by the commission for climatology of the WMO. We use daily precipitation, maximum and minimum temperature data from 30 meteorological stations distributed throughout the country and covering the period from 1960 to 2016. Statistically significant increasing trends in warm temperature events and a tendency towards decreasing cold extremes at both daytime and night are depicted across the country consistent with the generalized observed global warming. We found that the daily temperature in Morocco has risen with higher rates than the global scale. The depicted trend of 0.33°C per decade corresponds to a warming of approximately 1.1°C for the period 1984‐2016. The annual mean precipitation and the standardized drought index show less spatially consistent tendencies despite the predominance of negative trends. Considering the effect of the warming in the analysis of drought evolution using the Standardised Precipitation‐Evapotranspiration Index, we detected statistically significant trends towards dryer conditions in different regions of the northern half of the country. Analysis of the relationship between precipitation in Morocco and the large‐scale atmospheric circulation in the Atlantic area confirmed the effects of the North Atlantic Oscillation, especially for the winter season (with low influence at the annual scale). Moreover, we found that the NAO exerts significant influence on winter extreme temperatures during night time. However, such correlations alone may not explain the depicted significant generalized warming trends and the drying evolution.

Urban flooding is one of the major issues in many parts of the world and its management often challenging. Here we present Internet of Things (IoT) approach for monitoring urban flooding in the City of Colima, Mexico. A network of water level and weather sensors have been developed along with a web-based data platform integrated with IoT techniques to retrieve data using 3G/4G and Wi-Fi networks. The developed architecture uses the Message Queuing Telemetry Transport protocol to send real-time data packages from fixed nodes to a server that stores retrieved data in a non-relational database. Data can be accessed and displayed through different queries and graphical representations, allowing future use in flood analysis and prediction. Additionally, machine learning algorithms are integrated into the system for short-range water level predictions at different nodes of the network.

Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of ~ 10+ % when focusing on extremes and values above (below) high (low) thresholds. Moreover, the effects of the data processing method on trend estimation are generally small, even though the use of the daily minimum and maximum readings reduces the power of trend detection (~ 5–10% fewer trends detected in comparison with the reference data).

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.

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.

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 Nino-Southern Oscillation (ENSO). Here, we examine the predictability of the seasonal frequency of high sea levels using the Nino3.4 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 Nino3.4 index observations over the warm antecedent period of July-September and (2) seasonal Nino3.4 index forecasts from the North American Multi-Model Ensemble (NMME) over the cold concurrent period of October-March. Results indicate that the Nino3.4 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 Nino3.4 index, correlation coefficients range between approximately 0.4 and 0.7 at the southern gauges for Nino3.4 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.

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 important packages at every step of the hydrological workflow, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modelling, statistics, and the design of static and dynamic visualizations, presentations 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. Last, 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 (ARs) are narrow filaments of high moisture transported within extra-tropical systems. The role of ARs as drivers of heavy precipitation and flooding has been well documented; however, little is known about the contribution of landfalling ARs to high sea water levels, which are the leading cause of coastal flooding. Here we assess the relationship between ARs and extreme hourly sea level time series at 15 tide gauges along the continental U.S. Pacific Coast. Results indicate that ARs are associated with 15% to 50% of the annual sea level maxima before (and 22% to 65% after) removing tidal oscillations. Strong associations are also found when using other high sea level metrics. From a climatic perspective, the frequency of extreme hourly sea levels tends to increase during the negative phase of the Arctic Oscillation and during the positive phases of the Pacific-North American pattern and the El Nino–Southern Oscillation.

Khouakhi A., Snoussi M., Niazi S., Raji O., 2013. Vulnerability assessment of Al Hoceima bay (Moroccan Mediterranean coast): a coastal management tool to reduce potential impacts of sea-level rise and storm surges Projected increases in sea level rise (SLR) and in the magnitude and frequency of extreme weather events pose a major challenge for the management of low-lying coastal ecosystems and human settlements in the context of climate change. The bay of Al Hoceima is one of the least studied and largest low-lying coastal areas of the Moroccan Mediterranean coast, and is highly exposed to the effects of SLR and storms. The coast is also a touristic area and one of the most important economic assets in the region of Al Hoceima. Physical coastal vulnerability assessments are one of the principal tools for developing coastal management plans. Here we assess the vulnerability of the coastline to sea level rise and extreme weather events through a standard index methodology based on physical indices,...

As part of a broad assessment of climate change impacts in Morocco, an assessment of vulnerability and adaptation of coastal zones to sea-level rise was conducted. Tangier Bay which is the most important socio-economic pole in Northern Morocco represents one of the cases studies. Using a GIS-based inundation analysis and an erosion modelling approach, the potential physical vulnerability to accelerated sea-level rise was investigated, and the most vulnerable socio-economic sectors were assessed. Results indicate that 10% and 24% of the area will be at risk of flooding respectively for minimum (4 m) and maximum (11 m) inundation levels. The most severely impacted sectors are expected to be the coastal defences and the port, the urban area, tourist coastal infrastructures, the railway, and the industrial area. Shoreline erosion would affect nearly 20% and 45% of the total beach areas respectively in 2050 and 2100. Potential response strategies and adaptation options identified include: sand dune fixation, beach nourishment and building of seawalls to protect the urban and industrial areas of high value. It was also recommended that an Integrated Coastal Zone Management Plan for the region, including upgrading awareness, building regulation and urban growth planning should be the most appropriate tool to ensure a long-term sustainable development, while addressing the vulnerability of the coast to future sea-level rise.

Sea level rise is now a reality supported by scientific evidence, and coastal areas are already beginning to experience the consequences. The purpose of this work is to assess the vulnerability to accelerated sea level rise (SLR) and storm events in the barrier island of Nador lagoon, the largest lagoon (115 km2) of Morocco located along the Mediterranean coast. The vulnerability assessment was based on the combination of both a physical coastal vulnerability index and a socio-economic component, using a Geographic information system (GIS). The key results of this study are presented in vulnerability maps, and show that the Northwest sector of the barrier island is the most vulnerable area. Only the middle sector seems to be immune to the negative impacts of SLR and storm events. Human occupation of the area is mainly in the form of residential units, which represents only 3% of the total area. 20% of these units are located in highly vulnerable areas. Significant investments for future development projects are planned in an area identified as very vulnerable. This approach thus highlights the areas of the Nador lagoon where the effects of future SLR and storm events might be the greatest. Taking advantage of the currently low level of human occupation in the study zone, this information may have direct applications in coastal development programs and can assist decision-makers in the implementation of preventive management strategies in the most sensitive areas.