Biology and Environmental Sciences at ORNL’s Post

Dear Network, Here's my 2024 LinkedIn Rewind! 🌍 2024: In the Sahel, we proved that smarter data can transform water management – one algorithm, one watershed at a time. This year, our research at 2iE demonstrated how cutting-edge technology can help communities facing increasingly unpredictable water resources. By combining machine learning, hydrology, and deep local knowledge, we're not just studying water – we're creating solutions. ✅ Machine Learning Breakthrough: Developed ML models achieving 99% accuracy in evapotranspiration estimation, critical for sustainable agriculture in water-scarce regions. Our XGBoost approach isn't just a technical achievement – it's a lifeline for farmers battling climate uncertainty. https://lnkd.in/ewJppcj2 ✅ Rainfall Estimation Innovation: Compared satellite and soil moisture-based precipitation products, revealing new pathways for more accurate drought detection. RFE2 and CHIRPS products showed remarkable potential for early warning systems. https://lnkd.in/eai45xC4 ✅ Flood Prediction Progress: Enhanced rainfall-runoff modeling for the Sirba River Basin, demonstrating how precise event-based simulations can improve infrastructure planning and community resilience. https://lnkd.in/exYGCfiD Beyond the numbers, this work represents our commitment to training the next generation of water resource managers. Each research project is a collaborative journey with students and colleagues who believe technology can solve real-world challenges. Looking ahead to 2025: We'll continue pushing the boundaries of machine learning in hydrology, always centering our work on the communities who need innovative solutions most. To my fellow friends, colleagues, students, and the incredible Sahelian communities we serve – thank you for another year of meaningful research. Thanks Prof Luca Brocca for letting me discover Coauthor.studio! #SahelHydrology #WaterResourceManagement #ClimateAdaptation #MachineLearningHydrology #LinkedInRewind #Coauthor #2024wrapped

Friendly reminder to consider submitting an abstract to the Flood Forecasting session at AGU24! The 2024 Annual Meeting of the American Geophysical Union will take place December 9-23 in Washington DC. Check out: Flood Forecasting @ AGU24 https://lnkd.in/gj2yrWYP Session Description Floods are the most common type of natural disaster and the rate of flood-related disasters has more than doubled since the start of the 21st century. Warning systems can mitigate the human impacts from flooding by reducing fatalities and economic damage. Populations in low- and middle-income countries remain the most vulnerable. Providing actionable flood warnings requires solving problems across an extended workflow involving observation, modeling, forecasting, warning, impact evaluation, and decision making. This session is dedicated to sharing work related to all aspects of this workflow. Relevant work includes, but is not limited to, the following: - Modeling and forecasting - Observations to support forecasts - Research and technological innovation supporting effective early warning systems, including last mile delivery - Impact-based forecasting - Human geography, social dynamics and economic impact of flood forecasting, alerts and response - Transition of Research to Operations (R2O) and Operations to Research (O2R) - Policy and governance frameworks Conveners: Grey Nearing (Google Research) Shaun Harrigan & Florian Pappenberger (Copernicus ECMWF) Steve Burian (Alabama Water Institute, Cooperative Institute for Research to Operations in Hydrology (CIROH) at The University of Alabama)

Consider submitting an abstract to the Flood Forecasting session at AGU24! The 2024 Annual Meeting of the American Geophysical Union will take place December 9-23 in Washington DC. Abstract submissions are open here: https://lnkd.in/gBGxdjbM Session Description Floods are the most common type of natural disaster and the rate of flood-related disasters has more than doubled since the start of the 21st century. Warning systems can mitigate the human impacts from flooding by reducing fatalities and economic damage. Populations in low- and middle-income countries remain the most vulnerable. Providing actionable flood warnings requires solving problems across an extended workflow involving observation, modeling, forecasting, warning, impact evaluation, and decision making. This session is dedicated to sharing work related to all aspects of this workflow. Relevant work includes, but is not limited to, the following: - Modeling and forecasting - Observations to support forecasts - Research and technological innovation supporting effective early warning systems, including last mile delivery - Impact-based forecasting - Human geography, social dynamics and economic impact of flood forecasting, alerts and response - Transition of Research to Operations (R2O) and Operations to Research (O2R) - Policy and governance frameworks Conveners: Grey Nearing (Google Research) Shaun Harrigan & Florian Pappenberger (Copernicus ECMWF) Steve Burian (Alabama Water Institute, Cooperative Institute for Research to Operations in Hydrology (CIROH) at The University of Alabama)

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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.

University of Queensland researchers are developing technology to enable insects like beetles and cockroaches to search for victims of earthquakes and other disasters.

Curious about a new machine learning method to investigate atmospheric drivers of predictability in the climate system? * Happy to share my first PhD paper published a couple of days ago in Environmental Data Science https://lnkd.in/deagzm8S The short summary: Large-scale atmospheric dynamics modulate the occurrence of extreme events and act as sources of predictability on timescales ranging from days to decades. However, identifying dynamical drivers that are informative of a particular extreme, as well as predictable themselves, remains a challenge. In this paper, we introduce a novel machine learning approach to identify regional dynamical drivers of local extremes based on a regularized variational autoencoder architecture. Applying the method to study extreme precipitation over Morocco, we are able to identify regional dynamical drivers that are more predictive of the impact compared to three other baseline methods, while maintaining their physical consistency. A big thank you to my PhD supervisors and co-authors Marlene Kretschmer, Ted Shepherd, Magdalena Alonso Balmaseda and Yevgeniya Kovalchuk! *or maybe you are not curious, maybe you just are just wondering what I am doing with my time at the moment..

Ever wondered how mountains help regulate our planet's water supply? The European Commission's Joint Research Centre (JRC) shares some new insights here! https://lnkd.in/dmmqTxGj   As it turns out, mountains play a crucial role in mitigating the impacts of climate change. Depending on local topography and geology, mountain groundwater helps buffer climate variability and reduce ecological and anthropic impacts downstream. But what about the vulnerability of these groundwater resources? A recent study helped quantify the impact of geomorfological conditions on mountain environments. What do you think is the most important aspect of mountain water management? Share your thoughts in the comments below! #mountainwater #climatechange #groundwater #sustainability #JRCexplains"

For those who missed the General Assembly of the European Geosciences Union (EGU) in Vienna last month. You can now access the posters presented by Reask on our website! Have a look at our Insight section to access the following documents:  https://lnkd.in/dzKCA96U    𝐌𝐞𝐭𝐫𝐲𝐜 𝐚𝐧𝐝 𝐃𝐞𝐞𝐩𝐂𝐲𝐜: 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 𝐟𝐫𝐨𝐦 𝐑𝐞𝐚𝐬𝐤 𝐢𝐧 𝐃𝐢𝐬𝐚𝐬𝐭𝐞𝐫 𝐑𝐢𝐬𝐤 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐧𝐠 𝐚𝐧𝐝 𝐇𝐮𝐦𝐚𝐧𝐢𝐭𝐚𝐫𝐢𝐚𝐧 𝐈𝐦𝐩𝐚𝐜𝐭 𝐌𝐢𝐭𝐢𝐠𝐚𝐭𝐢𝐨𝐧  Authors: David Schmid and Thomas Loridan. 𝐀𝐬𝐬𝐞𝐬𝐬𝐢𝐧𝐠 𝐃𝐫𝐨𝐮𝐠𝐡𝐭-𝐈𝐧𝐝𝐮𝐜𝐞𝐝 𝐒𝐮𝐛𝐬𝐢𝐝𝐞𝐧𝐜𝐞 𝐑𝐢𝐬𝐤 𝐢𝐧 𝐅𝐫𝐚𝐧𝐜𝐞 𝐔𝐧𝐝𝐞𝐫 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐚𝐧𝐝 𝐅𝐮𝐭𝐮𝐫𝐞 𝐂𝐥𝐢𝐦𝐚𝐭𝐞   Authors: Mathis Joffrain from AXA, Nicolas Bruneau from Reask.    𝐓𝐫𝐨𝐩𝐢𝐜𝐚𝐥 𝐂𝐲𝐜𝐥𝐨𝐧𝐞-𝐈𝐧𝐝𝐮𝐜𝐞𝐝 𝐂𝐨𝐚𝐬𝐭𝐚𝐥 𝐅𝐥𝐨𝐨𝐝𝐢𝐧𝐠 𝐔𝐧𝐝𝐞𝐫 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐚𝐧𝐝 𝐅𝐮𝐭𝐮𝐫𝐞 𝐂𝐥𝐢𝐦𝐚𝐭𝐞𝐬: A Novel Model Framework for Continental Scale Applications   Authors: Niall Quinn, Tom Collings, Hamish Wilkinson, and Malcolm Haylock from Fathom; Ivan Haigh from the University of Southampton; and Ignatius Ryan P., Nicolas Bruneau from Reask. 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 𝐢𝐧𝐥𝐚𝐧𝐝 𝐟𝐥𝐨𝐨𝐝𝐢𝐧𝐠 𝐜𝐚𝐮𝐬𝐞𝐝 𝐛𝐲 𝐭𝐫𝐨𝐩𝐢𝐜𝐚𝐥 𝐜𝐲𝐜𝐥𝐨𝐧𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐔𝐒 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈-𝐛𝐚𝐬𝐞𝐝 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐞𝐯𝐞𝐧𝐭𝐬   Authors: Nans Addor (+ University of Exeter, Natalie Lord, Jannis Hoch, and Malcolm Haylock from Fathom, Balaji Mani, Thomas Loridan, and Naoki Mizukami from the NSF NCAR - The National Center for Atmospheric Research.   Contact us at info@reask.earth to discuss these topics with the team!    #climatechange #climaterisk #natcat #catrisk #hurricane #droughtrisk #subsidence #mitigation #climate #catastrophe #insurance #tropicalcyclone #machinelearning #ai #climatescience #parametricinsurance 

🎉Exciting News!🎉 I'm thrilled to announce that our latest research article has been published in the journal #Water! 📄 Title: "Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?" This study investigates the impact of climate change on water resources in the Ruzizi River Basin, using a combination of historical data and SWAT (Soil and Water Assessment Tool) modeling. By leveraging this powerful hydrological model, we were able to simulate future water dynamics under different climate scenarios (SSP2-4.5 and SSP5-8.5), revealing significant declines in precipitation and surface runoff. These insights are critical for developing adaptive water management strategies for agriculture, domestic use, and ecosystem protection. Check out the article here: https://lnkd.in/dcNW9r_s A big thank you to everyone who has supported us throughout this journey. Feel free to read and share! #SWATmodel #Climatemodeling #waterresources #hydrology #climatechange #research #Science #teamwork #adaptation #Sustainability