Multi-source data for climate-resilient water planning

Flood resilience is not just an infrastructure challenge. It is a data coordination challenge. We have been working with Ordnance Survey on an Intelligent Flood Readiness Model to explore how existing datasets can better inform national and local decision-making. With England experiencing well-above-average rainfall in early 2026, including record levels in some regions, the limitations of static planning cycles are becoming increasingly visible. Using Snowflake as the intelligence layer, we brought together building-level data, deprivation indices, and flood risk policy datasets to create a more integrated view of exposure and vulnerability. The findings highlight important considerations for policy: ➡️ Up to 1.2 million buildings may sit outside current flood defenses ➡️ 68% are in the most deprived communities, raising questions of equity and resilience ➡️ 85% are exposed to surface water flooding, which remains underrepresented in planning discussions ➡️ 84% were built before flood risk was systematically embedded into planning policy. This is not about identifying gaps in any single dataset. It is about what becomes visible when data held across institutions is connected and analyzed collectively. There is a clear opportunity to complement existing Flood Risk Management Plans with more dynamic, data-driven approaches. This can support better prioritization of interventions, more targeted investment, and improved long-term resilience. The same principle applies beyond flooding. Many complex policy challenges depend on fragmented datasets owned by different organizations. Connecting them can materially improve decision-making. Data sources used in the solution include: 1️⃣ Buildings Data from Ordnance Survey 2️⃣ Indices of Multiple Deprivation from the Ministry of Housing, Communities and Local Government 3️⃣ Flood Risk Management Policy Documents 2021-2027 from the Environment Agency 4️⃣ Flood Defenses from the Department for Environment, Food and Rural Affairs Further details: https://lnkd.in/e6UYfFAZ Rebecca O'Connor | Camilla Dowson | Daniel Reeves | Tim Chilton | Abs Gandhi | Katherine James |

💧 “𝗡𝗼 𝗱𝗮𝘁𝗮” 𝗱𝗼𝗲𝘀 𝗡𝗢𝗧 𝗺𝗲𝗮𝗻 “𝗻𝗼 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀.” One of the biggest myths in water resources engineering is that you need long-term streamflow records to do meaningful hydrological modelling. In reality, many of the most critical water decisions happen in 𝘂𝗻𝗴𝗮𝘂𝗴𝗲𝗱 𝗯𝗮𝘀𝗶𝗻𝘀. Across several catchments I’ve worked on, I’ve seen this challenge repeatedly: No gauges. No discharge data. Yet, decisions still need to be made on abstraction, recharge, and water security. So what actually works? 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗜’𝘃𝗲 𝗳𝗼𝘂𝗻𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲: 🔹 Combine 𝗿𝗲𝗺𝗼𝘁𝗲 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 + 𝗴𝗹𝗼𝗯𝗮𝗹 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 (CHIRPS, ERA5, DEMs) 🔹 Use 𝗽𝗿𝗼𝗰𝗲𝘀𝘀-𝗯𝗮𝘀𝗲𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 as much as possible (e.g., SWAT) to simulate system behaviour 🔹 Apply 𝗿𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 & 𝗹𝗶𝘁𝗲𝗿𝗮𝘁𝘂𝗿𝗲-𝗯𝗮𝘀𝗲𝗱 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀 🔹 Validate indirectly using:       Seasonal flow patterns       Runoff coefficients       Hydroclimatic consistency The goal is not perfection; it’s 𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. Because in data-scarce regions, hydrology is not about having perfect data… It’s about 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗶𝗺𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 🌍 This is where innovation in digital hydrology becomes critical for water security. 👉 If you work in ungauged basins, what methods have worked for you? #Hydrology #WaterResources #DataScarcity #SWATModel #HydrologicalModelling #WaterSecurity #ClimateResilience #GIS #RemoteSensing #EngineeringInsights

Top 10 Free Remote Sensing Data Sources for Irrigation & Climate Change Assessment A practical guide for early-career researchers and students In my opinion, if you understand satellite data properly, you can design smarter irrigation systems, assess climate risks, and support policy decisions without expensive datasets. Here are the 10 free platforms I consistently rely on for irrigation water management and climate assessments. 1. NASA Earthdata: https://lnkd.in/dqjMGG63 Datasets: MODIS (ET, LST, NDVI), SMAP soil moisture, GPM precipitation Applications: ✓Crop water stress assessment ✓Evapotranspiration estimation ✓Irrigation scheduling ✓Rainfall variability analysis 2. USGS EarthExplorer: https://lnkd.in/dMBMVc92 Datasets: Landsat archive (1972–present), SRTM DEM Applications: ✓Canal network mapping ✓Command area monitoring ✓Long-term land use change ✓Watershed delineation 3. Copernicus Open Access Hub: https://lnkd.in/du3jTmTx Datasets: Sentinel-1 (SAR), Sentinel-2 (optical), Sentinel-3 Applications: ✓Flood irrigation monitoring ✓Crop classification ✓Soil moisture proxy mapping ✓Surface water extent Sentinel-1 SAR is especially powerful for cloudy monsoon regions. 4. Google Earth Engine: https://lnkd.in/dRqSBubq Datasets: Integrated Landsat, Sentinel, CHIRPS, GLDAS, ERA5 Applications: ✓Large-scale irrigation performance analysis ✓Drought indices (SPI, VCI, TCI) ✓Climate trend mapping ✓Automated water productivity mapping 5. FAO WaPOR: https://lnkd.in/dpG_dScM Datasets: Actual evapotranspiration (AET), biomass production, water productivity Applications: ✓Irrigation efficiency assessment ✓Crop water productivity ✓Benchmarking irrigation schemes 6. CHIRPS: https://lnkd.in/ddJkkBFs Datasets: High-resolution precipitation (1981–present) Applications: ✓Rainfall trend analysis ✓Drought monitoring ✓Rainfall-runoff modeling ✓Irrigation demand planning 7. GLDAS: https://lnkd.in/diVuMcPY Datasets: Soil moisture, runoff, evapotranspiration, land surface fluxes Applications: ✓Basin-scale water balance ✓Model calibration support ✓Irrigation demand modeling 8. ESA Climate Change Initiative: http://cci.esa.int/data Datasets: Soil moisture, land cover, fire, glacier mass balance Applications: ✓Climate impact assessments ✓Land cover transition analysis ✓Soil moisture trend evaluation 9. OpenTopography: https://lnkd.in/dfAhtyer Datasets: High-resolution DEMs, LiDAR Applications: ✓Canal alignment design ✓Irrigation system planning ✓Floodplain mapping 10. NOAA National Centers for Environmental Information: https://www.ncei.noaa.gov/ Datasets: Climate normals, temperature, drought indices Applications: ✓Climate change trend detection ✓Extreme temperature assessment ✓Irrigation water requirement projections #RemoteSensing #Hydrology #ClimateChange #Irrigation #WaterResources #EarthObservation #ClimateResearch #WaterSecurity #SustainableDevelopment #SatelliteData #ClimateResilience #OpenData

Water scarcity, flooding, and shifting agricultural conditions are reshaping how communities plan for the future. NASA - National Aeronautics and Space Administration and Microsoft’s latest collaboration is bringing cutting‑edge hydrology science together with Azure AI to make critical water insights more accessible, enabling easier exploration, interpretation, and application of the data. NASA’s new multi‑agent hydrology copilot, powered by Azure OpenAI Service, Azure AI Search, and Microsoft Foundry, helps broaden access to complex datasets like NLDAS‑3 by allowing users to ask plain‑language questions and receive clear, science‑grounded answers. From drought monitoring to flood preparedness, this work is lowering barriers for state agencies, researchers, and planners who rely on timely and accurate environmental intelligence. Proud to see how we are unlocking new ways to turn decades of Earth science into actionable insights for climate resilience, water management, and environmental analysis: https://lnkd.in/gMqPvBBn #AI #Azure #AzureAI #MicrosoftFoundry