Modeling of potential evapotranspiration (PET) is important particularly in arid regions (such as Egypt). In previous works, I talked about the added value of calibrating the Hargreaves-Samani (HS) method as well as the linear scaling bias-correction method (here I attached these articles for your information): 1 - https://lnkd.in/dx7SRH7E 2 - https://lnkd.in/d5AbjGjk 3 - https://lnkd.in/dvHY2ePK Today, I present a work about bias-correction of the PET by means of the linear regression model (LRM) using the Climate Research Unit (CRU) as the observational dataset. In this work, I used the original version of the HS equation exploring the added value of the LRM using Taylor diagram. As you can see that the PET (on a point scale) can be accurately calculated using a LRM between the RegCM4's output and CRU data, so the calculated PET stays close to the CRU. Check this paper for more details: https://lnkd.in/dF3kHaCY Please let me know if you have any question.
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Important paper about modeling of potential evapotranspiration of Egypt using the RegCM4 regional climate model: Abstract: Potential Evapotranspiration (PET) is an important variable for monitoring daily agricultural activity as well as meteorological drought. Therefore, it is necessary to investigate the influence of different options of the physical dynamical downscaling and boundary layer schemes on the simulated PET. Using the RegCM4 regional climate model, four simulations were conducted (two for each case) in the period 1997 to 2017. In all simulations, the RegCM4 was configured with 25 km resolution and downscaled by the ERA-Interim reanalysis dataset. To ensure a reliable estimation of the PET, a calibrated version of the Hargreaves-Samani equation was adopted. A high-resolution product of the ERA5 was used as the observational dataset. Results showed that the simulated PET is insensitive either to the dynamical downscaling or the boundary layer options. Concerning the annual climatological cycle, the RegCM4's performance varies with month and location. Quantitatively, a root mean square error lies between 1 mm and 1.6 mm day-1 , the Nash-Sutcliffe efficiency between 0.2 and 0.6, and the coefficient of determination between 0.5 and 0.75. Additionally, the Linear Scaling (LS) method showed its added value in the evaluation/validation periods. In conclusion, the RegCM4 can be used to develop a regional PET map of Egypt using the LS either in the present climate or under different future scenarios.
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Modeling of potential evapotranspiration (PET) is a very important topic for meteorologists, hydrologists as well as the Agrometeorologists. However, it is not an easy task to do when station observation is not available for a long-time. However, using a high-resolution regional climate model, a simple empirical equation and high-resolution gridded product of the PET makes this point easy. In the post I refer to different trials of investigating of different physical configurations affecting the meteorological variables (involved in computing the PET) using different versions of the Hargreaves-Samani equation?(HS). As you will see that the HS version considerably affects the action of the physical configuration on the meteorological variables and eventually the PET. Here, I will leave you with these two papers to get more insights concerning this issue. Feel free to ask me any questions: https://lnkd.in/d5AbjGjk https://lnkd.in/dx7SRH7E
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Despite the remarkable advancements observed in recent years, our brains remain the most sophisticated and efficient supercomputers ever existed. Current understanding highlights that #ChangeDetection, #PatternRecognition, and #Tagging?are among the most critical features of this evolutionary biological machine. These capabilities enable it to maximize the utilization of finite resources while managing vast datasets?efficiently??. In marine data analysis, we often draw inspiration from nature's proven methodologies. A pertinent example is the article presented in the latest issue of #InternationalHydrographicReview, the official publication of the International Hydrographic Organization (IHO), in which I investigated the recurring patterns of surface circulation in the #BlackSea ?? using #SelfOrganizingMaps ??. This approach processes extensive datasets, in this specific example a decade's worth of observations, to assist scientists in uncovering meaningful insights by mimicking the features of our brains given above. The focus period predates the noticeable impacts of climate change, thus serving as a benchmark for evaluating the extent of changes attributed to #ClimateChange. Special thanks to the Chief Editor Patrick Westfeld, and the Editorial Board. The entire issue can be accessed online at: https://lnkd.in/eeeSz-bZ
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UNU-INWEH Science Talks (Online, Open to Public) From Threats to Opportunity: Towards a science-informed policy for addressing Earth’s Grand Challenges Recent advances in large-domain tracer-aided stable #isotope modelling Speaker: Prof. Tricia Stadnyk, UNU Hub at University of Calgary Time: 10 am EST, 2 October 2024 Event link: https://lnkd.in/gafAeUiR As global climate change alters hydroclimatic responses beyond the range of predictability based on historic hydrometeorological records, water resource practitioners are increasingly reliant on methods of modelling continental and global hydrology. Though local scale heterogeneity and connectivity between hydrologic storages and fluxes tend to be averaged out across large domains, it is precisely these process scale changes that remain crucial as early indicators of climate change. A lack of data at continental scales, particularly in high latitude or under-represented regions (e.g., the global south), presents a challenge for the accurate #calibration and evaluation of the very models necessary for predicting design flows required for climate adaptation and infrastructure policy. Efficient and accessible hydrologic #prediction tools capable of diagnosing and interpreting continental scale changes in water balance components and overall water supply, including #ecosystem changes and uncertainty methods for operational decision-making, are needed. In this Science Talk, recent advances in large-domain tracer-aided stable #isotope modelling and the contributions isotope tracers make to improving our knowledge of hydrologic processes across large-domains will be demonstrated. The webinar will close with a Q&A session to address any questions from attendees. #webinar #sciencetalk #unuinweh
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Here's my latest for the University of Arizona News - U of A hydrologists are integrating soil microbial data and artificial intelligence to make far-reaching changes in climate predictions. This gives a better understanding of how climate change has an effect on soil carbon. The project led by Yang Song, assistant professor of U of A's Hydrology and Atmospheric Sciences, is part of the $8 million initiative of the Department of Energy.
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Source: (Scientific reports) Study analyzes climate modeling in Panj River Basin, comparing CMIP5 and CMIP6 models for temperature and precipitation. Eight GCMs show high accuracy in Tmax (0.96) and Tmin (0.94). CMIP6 improves precipitation bias and reduces uncertainty. Projections indicate slight temperature rise and precipitation drop, with increased variability, highlighting complex climate dynamics. Further enhancements in hydrological modeling suggested.
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We are pleased to announce the publication of our latest scientific article, "Exploring Streamflow Dynamics: Trends and Abrupt Changes in Major European Rivers", in Stochastic Environmental Research and Risk Assessment. This study provides an in-depth examination of evolving hydrological patterns across 16 significant European rivers, illuminating how climate change is reshaping river flow dynamics with critical implications for water resource management and ecosystems. Employing advanced methodologies, including the Mann-Kendall test and the Bayesian Estimator of Abrupt Change (BEAST), this research identifies both long-term trends and abrupt shifts in streamflow, revealing: A general decline in flow rates for rivers such as the Danube and Drava, attributed to sustained climatic shifts. Contrasting upward trends in rivers like the Thames, highlighting the impact of localized climatic conditions. Another important result from the collaboration between the Università degli Studi di Cassino e del Lazio Meridionale and the Università di Pavia, this work underscores the necessity of adaptive strategies to sustainably manage water resources across Europe in an era of changing climate. Read the full article here: https://lnkd.in/dzdPqXn9 #ClimateChange #WaterResources #Hydrology #Streamflow #EuropeanRivers #EnvironmentalResearch #RicercaUnicas
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Summer moisture transport determines continental water availability, convective organization and precipitation over land. It is, therefore, important to understand the impact of atmospheric variability on different scales on the climatological mean moisture transport. In our new publication, we separate the climatological mean summer moisture transport over East Asia into its mean flow and turbulent components and analyze how these components are linked to the atmospheric water cycle as well as large-scale and mesoscale weather systems. This paper is the last publication of my PhD thesis where I have studied precipitation processes in the Tibetan Plateau region using both high-resolution climate models and observations: https://lnkd.in/gnw2H84x
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Today marks #ShareYourStripes Day, a global moment to share concern about how the climate is changing and the need for urgent action, organised by the University of Reading, England. https://lnkd.in/d_jiSgRt The graphic used in this post was generated here: https://lnkd.in/d9UV--Vs This graphic shows Region = Europe; Country = United Kingdom; Location = Ipswich. Date Range: 1850-2023 Graphics and lead scientist: Ed Hawkins, National Centre for Atmospheric Science, University of Reading., National Centre for Atmospheric Science, UoR. Data: Berkeley Earth & ERA5-Land, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR & ZAMG You can use the online tool to generate a graphic for where you are based, and share on your own social media using the hashtag #ShowYourStripes on LinkedIn tagging University of Reading (and on X @UniofReading). Online tool here: https://lnkd.in/dhTTsqVx on the website powered by Institute for Environmental Analytics #data #dataanalytics #visualisation #climatescience #climatechange #infographic
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