Multi-source data for climate-resilient water planning

🌍💧 𝗦𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗶𝗴 𝗣𝗶𝗰𝘁𝘂𝗿𝗲: 𝗛𝗼𝘄 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 + 𝗚𝗜𝗦 𝗮𝗿𝗲 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗪𝗮𝘁𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Water is life — but managing it has become one of the most pressing challenges of our time. With rising urban populations, climate extremes, and mounting pressure on ecosystems, I believe two powerful approaches are key to designing better solutions: 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗚𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗚𝗜𝗦). Here is how combining these tools leads to more holistic, sustainable outcomes — whether it's flood mitigation, urban drainage planning, or ecosystem restoration. 🔄 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 helps us understand the invisible threads — policies, feedback loops, behavior — that shape outcomes. 🗺️ 𝗚𝗜𝗦 provides spatial insight: where issues occur, why they matter, and what can be done — often in real time. Together, they empower us to: ✅ Model dynamic urban systems ✅ Simulate “what-if” climate and policy scenarios ✅ Design green infrastructure and nature-based solutions ✅ Engage communities and policymakers ✅ Plan smarter for the long term A traditional fix might be building higher levees. But I prefer asking deeper questions: What role did deforestation play? How did land-use change or waste disposal practices contribute? Can green roofs or upstream wetlands help? 📊 Tools like ArcGIS, QGIS, Google Earth Engine, InsightMaker, and Vensim have become my go-to for bridging data and decisions. 📈 The presented 𝗰𝗮𝘂𝘀𝗮𝗹 𝗹𝗼𝗼𝗽 𝗱𝗶𝗮𝗴𝗿𝗮𝗺 (𝗖𝗟𝗗), created using 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝗠𝗮𝗸𝗲𝗿, maps the feedback loops driving urban flood risk—from land use changes to infrastructure responses. 💡 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? Solving water challenges requires us to think both spatially and systemically. If you’re a fellow hydrologist, planner, engineer, or just passionate about sustainability, I’d love to connect. Let’s reimagine water resilience — together. #WaterManagement #GIS #SystemsThinking #ClimateResilience #UrbanPlanning #NatureBasedSolutions #Hydrology #Sustainability #FloodRisk #SpatialAnalysis #OluwatobiAiyelokun

📘 Downscaling CHIRPS Precipitation Data to 100m Resolution Using Sentinel-2 in Google Earth Engine Source Code = https://lnkd.in/dNc7NbjE 1. Introduction: Rainfall data at high spatial resolution is critical for precise hydrological analysis, drought monitoring, and agriculture planning. However, most global precipitation datasets, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), are available at coarser resolutions (~5 km). This project addresses this limitation by downscaling CHIRPS daily precipitation data to 100-meter spatial resolution using bilinear interpolation and Sentinel-2 as a high-resolution spatial reference. 2. Objective: To extract and sum CHIRPS precipitation data over a selected AOI (WMH District) for a specific 3-month period (October 2023 – January 2024). To downscale the CHIRPS raster data to a finer 100-meter resolution using Sentinel-2 spatial referencing. To visualize and compare the original and downscaled precipitation maps. To prepare refined precipitation layers for potential integration with NDVI, crop condition analysis, or drought indices. 3. Importance of the Study: Higher spatial resolution enables more localized analysis of rainfall, especially in heterogeneous landscapes. Improved input for climate models and agro-hydrological studies. Better decision-making for irrigation scheduling, water resource management, and drought preparedness. Supports integration with high-resolution datasets such as NDVI, land use, or soil moisture for multi-parameter environmental studies. 4. Benefits: Enhanced accuracy in rainfall data analysis at local and district levels. Scalable method applicable to any region globally. Supports policymakers and researchers with higher-resolution inputs for climate resilience, agricultural planning, and hydrological monitoring. Efficient use of cloud computing via Google Earth Engine for handling large spatiotemporal datasets. 5. Output: Original CHIRPS Precipitation Map (Oct 2023 – Jan 2024) clipped to WMH District. Downscaled Precipitation Map at 100m resolution, reprojected using Sentinel-2 reference. Color-coded visualization using a 5-class blue gradient, where: Light blue = Low precipitation Dark blue = High precipitation Ready-to-export raster layer of downscaled precipitation (if export added). Output maps can be further used for vegetation correlation (e.g., NDVI vs. rainfall) and SPI generation. #GEE #GoogleEarthEngine #BuildupAreaExpansion #GeospatialAnalytics #RemoteSensing #UrbanExpansion #Geospatial #GoogleEarthEngine #GIS #SustainableDevelopment #Sentinel2 #GeospatialTech #PhD #Agriculture #ClimateSmart #GIS #DeepLearning #ClimateSmartAgriculture #CropHealthMonitoring #DroughtMonitoring #SustainableFarming #Sentinel2 #GoogleEarthEngine #NDVI #LandsatData #GISMapping #GeospatialAnalysis #AIinAgriculture #EarthObservation #AgricultureMapping #RemoteSensin #SatelliteImagery

🌍 Exploring the Impact of MOD16A2 and CHIRPS Datasets on Water Resources and Vegetation Dynamics 🛰️ Have you heard about the MOD16A2 and CHIRPS datasets? 🌧️🌿 These powerful resources are revolutionizing how we understand and manage water resources and vegetation dynamics. 🌿 MOD16A2 (MODIS Evapotranspiration): This dataset, derived from NASA's MODIS satellites, provides crucial information on evapotranspiration (ET), a key component of the water cycle. By monitoring ET, we gain insights into plant water use, drought conditions, and overall ecosystem health. 🌧️ CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data): CHIRPS combines satellite data with ground observations to deliver high-resolution precipitation data. This enables us to track rainfall patterns, assess drought risks, and support agricultural planning and water resource management. ℹ️ These datasets are invaluable for researchers, policymakers, and environmental professionals working in fields such as hydrology, climate science, agriculture, and natural resource management. They empower us to make informed decisions for sustainable development and climate resilience. Let's harness the power of MOD16A2 and CHIRPS to drive positive change for our planet! 🌍💧 See code bellow: //Select area var roi = ee.FeatureCollection('users/paleomapas/NE_UF_2017_GCS_SIRGAS_2000') .filter(ee.Filter.eq('NM_ESTADO','MARANHTO')); //Outline var empty = ee.Image().byte(); var outline = empty.paint({ featureCollection: roi, color: 2, width: 2 }); Map.centerObject(roi); //====================================================================== //====================================================================== // Set start and end dates var startYear = 2002; var endYear = 2015; // Create date list var startDate = ee.Date.fromYMD(startYear, 1, 1); var endDate = ee.Date.fromYMD(endYear + 1, 1, 1); // Encompasses the 365 days of the year // Make years list var years = ee.List.sequence(startYear, endYear); // Make months list var months = ee.List.sequence(1, 12); //====================================================================== //====================================================================== // Import MOD16 evapotranspiration dataset. var mod16 = ee.ImageCollection('MODIS/006/MOD16A2').select('ET') .filterDate(startDate, endDate); // Precipitation var CHIRPS = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD') .filterDate(startDate, endDate); the other parts of the code down in the comments #EarthObservation #RemoteSensing #MODIS #ClimateScience #WaterResources #DataScience #GIS #EnvironmentalScience #NASA #SatelliteData #Hydrology #Agriculture #DroughtMonitoring #ClimateResilience #SustainableDevelopment #DataAnalytics #ScienceCommunication #LinkedInPost