Analyzing Spatiotemporal Climate Event Patterns

Continuing on the #Urbanization and #DroughtRisk theme, one of the important questions comes up is - How should we do this analysis, when many cities globally may not have the data? We address this need by developing and adopting a data-fusion framework , which is now published as 🏭 "Huang, Shuzhe, et al. "Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets." #RemoteSensingofEnvironment 313 (2024): 114378." 📌 Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. 📍 From the text ....." we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets ... 📍 "comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities ..." 📍 "We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method.." #Droughts #IPCC #CityClimate Jackson School of Geosciences at The University of Texas at Austin Cockrell School of Engineering, The University of Texas at Austin University of Texas Center for Space Research #UTcityClimateCoLab

📜 Publication Alert 📜 Pleased to share our recent publication "Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon", published in the Journal of Hydrology: Regional Studies, Elsevier. I would like to appreciate the efforts of Mr. Sandipan Paul and guidance of Prof. Ramesh Teegavarapu in shaping this work. The manuscript can be accessed at: https://lnkd.in/dBmtnrXK 📒 This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinent during the monsoon season, critical for the region’s water resources and agriculture. Using observational (IMD, APHRODITE), reanalysis (IMDAA, GLDAS, ERA5-Land), satellite (CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) datasets, we assess their ability to reproduce EPE intensity, detectability, timing, trends, and statistical properties. Key outcomes: 🌍 The study reveals that EPE intensity and frequency are highest along India’s western coast and northeast, moderate in central regions, and lowest in arid western and peninsular areas. 🫧 Wet-to-wet, dry-to-dry, and wet-to-dry transitions follow similar regional patterns. 🛰️ Satellite datasets generally underestimate, while reanalysis datasets overestimate EPE intensities, introducing wet and dry biases in moderate-intensity event frequencies, respectively.  ⛈️ Results identify MSWEP as the most reliable alternative to IMD in data-scarce regions, providing valuable insights for hydrologic studies, climate impact assessments, disaster risk management and enhancing socio-economic resilience. Elsevier India

🌾 Spatio-Temporal Drought Assessment Using MODIS & ERA5 in Google Earth Engine Source Code = https://lnkd.in/dHd4kNrk 📌 Introduction Drought is one of the most significant environmental hazards affecting agriculture, water resources, and livelihoods. Monitoring drought using remote sensing provides consistent, objective, and timely insights across large areas. This study utilizes multi-source satellite data and geospatial analysis to assess spatio-temporal drought dynamics in the WMH District during 2023. 🎯 Objectives To integrate multi-source datasets (MODIS, CHIRPS, ERA5) for drought monitoring. To compute vegetation, temperature, and soil moisture indices for drought assessment. To develop composite drought indicators (VHI, NDDI) for hotspot identification. To generate spatially explicit drought risk maps for decision support in agriculture and water resource management. ⚙️ Methodology Data Sources: MODIS NDVI (250 m), MODIS LST (1 km) CHIRPS Rainfall (0.05°) ERA5 Soil Moisture (~11 km) Preprocessing: Clipping, scaling, temporal filtering in Google Earth Engine. Index Computation: Vegetation Condition Index (VCI) Temperature Condition Index (TCI) Soil Moisture Condition Index (SMCI) Normalized Drought Difference Index (NDDI) Integrated Vegetation Health Index (VHI) Analysis: Normalization of indices Identification of drought hotspots (VHI < 40) Visualization of spatio-temporal drought patterns 📈 Analysis & Results VCI detected vegetation stress zones, especially during late summer. TCI highlighted temperature-driven drought in central and southern parts. SMCI revealed persistent soil moisture deficits. VHI & NDDI provided integrated drought severity classification. Hotspot mapping revealed severe drought clusters in agricultural zones, supporting early warning systems. 📦 Outputs Raster layers (GeoTIFF): VCI, TCI, SMCI, VHI, NDDI Drought hotspot maps (VHI < 40) Time-series drought trend visualization in GEE Exportable datasets for GIS-based planning 🔑 Keywords Drought Monitoring, Remote Sensing, MODIS, ERA5, CHIRPS, Vegetation Health Index (VHI), Google Earth Engine, WMH District, Spatio-Temporal Analysis #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

Recently my collaborators have published a paper "XAI4EXTREMES: AN INTERPRETABLE MACHINE LEARNING FRAMEWORK FOR UNDERSTANDING EXTREME-WEATHER PRECURSORS UNDER CLIMATE CHANGE" The paper explores the growing prevalence and intensity of extreme weather events driven by climate change, which are causing significant global impacts. Despite progress in numerical weather prediction and artificial intelligence (AI), challenges persist in identifying early indicators of these events and understanding their behavior in the context of a changing climate. To address these challenges, the authors introduce post-hoc interpretability methods to generate relevance weather maps. These maps highlight critical precursors of extreme weather as identified by deep learning models. By comparing machine-generated insights with existing domain knowledge, the study aims to deepen our understanding of the factors contributing to extreme weather. The research focuses on heatwaves in Indochina, employing a novel binary classification dataset composed of 720 samples. The dataset is divided into training, validation, and testing subsets. A Transformer model is trained to classify heatwaves, and four interpretability methods are applied to ensure the accuracy of relevance maps. A notable discovery is that temperature at 200 hPa has emerged as a vital precursor for Indochina heatwaves, particularly in recent decades. This pattern may reflect a "fingerprint" of climate change, as warming in the upper troposphere correlates with these heatwave events. The paper introduces the XAI4Extremes framework, which integrates predictive deep learning with interpretability techniques. This framework seeks to uncover the data deemed important by AI models for predicting true positive samples (correctly identified heatwaves). By comparing this "machine perspective" with expert human knowledge, it opens avenues for new discoveries. The authors highlight ongoing challenges in this field, including enhancing the robustness of interpretability methods and developing self-explainable machine learning approaches tailored for spatio-temporal data. These challenges are framed as opportunities for future research and innovation. This study makes significant contributions to understanding extreme weather events and their precursors by leveraging cutting-edge AI techniques to improve predictive accuracy and advance knowledge in the era of climate change. Gianmarco Mengaldo George Karniadakis, Yang Juntao, Chengyu Dong and others. https://lnkd.in/grue5jpt