Browsing by Author "Khouakhi, Abdou"
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Item Open Access Atmospheric rivers and associated extreme rainfall over Morocco(Wiley, 2022-05-13) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Chehbouni, Abdelghani; Raji, OtmaneAtmospheric 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.Item Open Access The burden of dengue in children by calculating spatial temperature: a methodological approach using remote sensing techniques(MDPI, 2021-04-16) Mendoza-Cano, Oliver; Rincón-Avalos, Pedro; Watson, Verity; Khouakhi, Abdou; López-de la Cruz, Jesús; Ruiz-Montero, Angelica Patricia; Nava-Garibaldi, Cynthia Monique; Lopez-Rojas, Mario; Murillo-Zamora, EfrénBackground: 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.Item Open Access Compound hydrometeorological extremes: drivers, mechanisms and methods(Frontiers, 2021-10-13) Zhang, Wei; Luo, Ming; Gao, Si; Chen, Weilin; Hari, Vittal; Khouakhi, AbdouCompound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.Item Open Access Detection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiers(MDPI, 2021-10-01) Zawadzka, Joanna Ewa; Truckell, Ian; Khouakhi, Abdou; Rivas Casado, MonicaTimely 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.Item Open Access Evaluation of satellite precipitation products over Mexico using Google Earth Engine(IWA Publishing, 2022-05-18) Rincón-Avalos, Pedro; Khouakhi, Abdou; Mendoza-Cano, Oliver; López-de la Cruz, Jesús; Paredes-Bonilla, Karla MichelleSatellite-based precipitation products and reanalysis precipitation products have the potential to overcome the lack of information in regions where there are no or insufficient rain gauges to achieve any hydrological study. The Google Earth Engine (GEE) data analysis platform has products in its repository with global coverage that offers different geospatial information capable of measuring the amount of precipitation. However, it is necessary to evaluate the reliability of the products. There are precipitation information biases in Mexico due to the scarce presence of gauging stations, failed operations, access difficulty, and data capture errors. This study evaluates the reliability of satellite and reanalysis precipitation products hosted in the GEE repository against rain gauge observation from 2001 to 2017 using data from 4,658 stations over Mexico. The evaluation was carried out using statistical indicators comparing the behavior across different topographic, climatic, and temporal conditions. The results exhibit that the performance of the products hosted in GEE seems to depend on elevation conditions for other climatic regions in Mexico. The results show that all products can capture the general precipitation patterns at annual, seasonal, and monthly scales; however, the accuracy of the product is clearly lower at a daily scale. All products are highly biased on low precipitation events.Item Open Access Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico(IWA Publishing, 2021-04-26) Mendoza-Cano, O.; Aquino-Santos, R.; López-de la Cruz, J.; Edwards, R. M.; Khouakhi, Abdou; Pattison, I.; Rangel-Licea, V.; Castellanos-Berjan, E.; Martinez-Preciado, M. A.; Rincón-Avalos, Pedro; Lepper, P.; Gutiérrez-Gómez, A.; Uribe-Ramos, J. M.; Ibarreche, J.; Perez, I.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.Item Open Access Extreme weather associated with atmospheric rivers over Morocco(EGU: European Geophysical Union, 2021-04-30) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Raji, OtmaneItem Open Access Geological mapping of carbonatites and related ores from the Oulad Dlim massif (Dakhla Province, Morocco) using remote sensing, portable X-ray fluorescence, and mineralogical data(EGU: European Geophysical Union, 2021-04-30) Malainine, Cheikh-Elwali; Raji, Otmane; Ouabid, Muhammad; Khouakhi, Abdou; Bodinier, Jean-Louis; Parat, Fleurice; El Messbahi, HichamItem Open Access Global changes in 20‐year, 50‐year, and 100‐year river floods(American Geophysical Union (AGU), 2021-03-18) Slater, L.; Villarini, G.; Archfield, S.; Faulkner, D.; Lamb, R.; Khouakhi, Abdou; Yin, J.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.Item Open Access Greater local cooling effects of trees across globally distributed urban green spaces(Elsevier, 2024-02-10) Kim, Jiyoung; Khouakhi, Abdou; Corstanje, Ronald; Johnston, Alice S. A.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.Item Open Access GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present(Copernicus Publications, 2023-12-08) Yin, Jiabo; Slater, Louise J.; Khouakhi, Abdou; Yu, Le; Liu, Pan; Li, Fupeng; Pokhrel, Yadu; Gentine, PierreTerrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).Item Open Access The impact of weather patterns on inter-annual crop yield variability(Elsevier, 2024-12-10) Knight, Chris; Khouakhi, Abdou; Waine, Toby W.Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions. Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990-2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling. The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.Item Open Access An integrated ASTER-based approach for mapping carbonatite and iron oxide-apatite deposits(Taylor & Francis, 2021-07-09) Malainine, Cheikh-Elwali; Raji, Otmane; Ouabid, Muhammad; Khouakhi, Abdou; Bodinier, Jean-Louis; Laamrani, Ahmed; Youbi, Nasrrddine; Boumehdi, Moulay AhmedMapping 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.Item Open Access The need for training and benchmark datasets for convolutional neural networks in flood applications(IWA Publishing, 2022-06-01) Khouakhi, Abdou; Zawadzka, Joanna Ewa; Truckell, IanFlood-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.Item Open Access Recent observed country‐wide climate trends in Morocco(Wiley, 2020-07-02) Driouech, Fatima; Stafi, Hafid; Khouakhi, Abdou; Moutia, Sara; Badi, Wafae; ElRhaz, Khalid; Chehbouni, AbdelghaniIn 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.Item Open Access Scaling up indigenous rainwater harvesting: a preliminary assessment in Rajasthan, India(MDPI, 2023-05-27) Rawat, Akanksha; Panigrahi, Niranjan; Yadav, Basant; Jadav, Kartik; Mohanty, Mohit Prakash; Khouakhi, Abdou; Knox, Jerry W.Rainwater harvesting (RWH) has the potential to enhance the sustainability of ground and surface water to meet increasing water demands and constrained supplies, even under a changing climate. Since arid and semi-arid regions frequently experience highly variable spatiotemporal rainfall patterns, rural communities have developed indigenous RWH techniques to capture and store rainwater for multiple uses. However, selecting appropriate sites for RWH, especially across large regions, remains challenging since the data required to evaluate suitability using critical criteria are often lacking. This study aimed to identify the essential criteria and develop a methodology to select potential RWH sites in Rajasthan (India). We combined GIS modeling (multicriteria decision analysis) with applied remote sensing techniques as it has the potential to assess land suitability for RWH. As assessment criteria, spatial datasets relating to land use/cover, rainfall, slope, soil texture, NDVI, and drainage density were considered. Later, weights were assigned to each criterion based on their relative importance to the RWH system, evidence from published literature, local expert advice, and field visits. GIS analyses were used to create RWH suitability maps (high, moderate, and unsuited maps). The sensitivity analysis was also carried out for identified weights to check the inadequacy and inconsistency among preferences. It was estimated that 3.6%, 8.2%, and 27.3% of the study area were highly, moderately, and unsuitable, respectively, for Chauka implementation. Further, sensitivity analysis results show that LULC is highly sensitive and NDVI is the least sensitive parameter in the selected study region, which suggests that changing the weight of these parameters is more likely to decide the outcome. Overall, this study shows the applicability of the GIS-based MCDA approach for up-scaling the traditional RWH systems and its suitability in other regions with similar field conditions, where RWH offers the potential to increase water resource availability and reliability to support rural communities and livelihoods.Item Open Access Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI(American Geophysical Union (AGU), 2024-05-01) Slater, Louise; Coxon, Gemma; Brunner, Manuela; McMillan, Hilary; Yu, Le; Zheng, Yanchen; Khouakhi, Abdou; Moulds, Simon; Berghuijs, WouterExplaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments.