Dynamic modeling for regional climate adaptation

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

🌧️ 𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: 𝗚𝗜𝗦 𝗮𝗻𝗱 𝗥 𝗳𝗼𝗿 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗛𝘆𝗱𝗿𝗼𝗹𝗼𝗴𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 🌊 As professionals in flood risk management and climate adaptation, we understand the critical role of accurate extreme rainfall estimates in designing resilient infrastructure. In a recently published Q1 journal study, we developed a robust framework that combines R programming and GIS tools to assess rainfall variability and uncertainty for North Central Nigeria, a region highly vulnerable to extreme weather events. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝗣𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀 ✅ The Power of R: Using open-source tools, including R CRAN packages like mc2d and fitdistrplus, we quantified parameter uncertainties with the parametric bootstrap method and ran Monte Carlo simulations to evaluate variability in extreme rainfall quantiles. ✅ GIS Integration: Spatial analysis was critical to understanding regional variations in rainfall patterns and uncertainties. GIS enabled precise mapping and visualization of high-risk areas, supporting data-driven decision-making for regional adaptation. ✅ Return Period Insights: Quantile estimates showed significant variability at longer return periods (100+ years), highlighting the need for probabilistic approaches in long-term infrastructure planning. ✅ Regional Nuances: Our study revealed differences across locations. For example, GIS maps highlighted Abuja's more consistent moderate rainfall estimates versus Lokoja's heightened variability in extreme scenarios, providing actionable insights for tailored solutions. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 🌍 By combining R programming for statistical modeling with GIS for spatial analysis, this framework provides a powerful, replicable approach for: ✅ Flood risk modeling ✅Hydraulic infrastructure design ✅Climate resilience strategies The synergy of data science and spatial analysis not only improves the accuracy of extreme rainfall estimates but also empowers practitioners to visualize and adapt to spatial uncertainties effectively. 📚 Interested in applying these methods to your projects? Let’s collaborate to enhance resilience to extreme weather events. 🔗 Access the full study here: https://lnkd.in/dEqbg7a3 #GIS #RProgramming #FloodRiskManagement #ClimateAdaptation #Hydrology #InfrastructureResilience #DataScience

Regional Analysis Of The Dependence Of Peak-Flow Quantiles On Climate With Application To Adjustment To Climate Trends -- https://lnkd.in/eDjbYkmZ <-- shared paper -- H/T Karen Ryberg “Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model…” #GIS #spatial #mapping #floodfrequency #nonstationarity #fedscience #climatevariation #climatechange #extremeweather #regression #water #hydrology #flood #flooding #waterbalance #Midwest #USA #spatialanalysis #spatiotemporal #model #modeling #statistics #geostatistics #streamgage #gauge #basins #watersheds #precipitation #discharge U.S. Geological Survey (USGS)

🌊 How confident are you in the extent of your flood model results? In flood risk management, precision matters. Static models can give you a snapshot—but dynamic models like InfoWorks ICM from Autodesk Water Infrastructure give you the full story. With ICM, you're not just estimating flood extents—you’re simulating real-world behavior: ✅ Complex hydrodynamics ✅ Urban drainage interactions ✅ Real-time rainfall and runoff ✅ Infrastructure response under stress This means greater confidence in your results, better decision-making, and more resilient communities. Whether you're designing mitigation strategies or communicating risk to stakeholders, dynamic modeling ensures you're not just guessing—you’re planning with purpose. #FloodModeling #InfoWorksICM #WaterResilience #UrbanHydrology #DigitalTwins #HydraulicModeling #ClimateAdaptation