Long-Term Ecological Forecasting

Overshoot or not, forest ecosystems face irreversible damage at 1.5°C warming. Forest impacts under Paris-compliant scenarios remain poorly quantified, leaving critical knowledge gaps around ecosystem stability thresholds. Research shows that the Amazon forest dieback risk lurks at just 1.3°C global warming (long-term) or 2.1°C (short-term). Even with aggressive mitigation, 55% of simulations breach dieback thresholds by 2300, creating potential losses up to 130,000 km². Merely stabilizing temperatures isn't enough—reducing warming below thresholds seems unavoidable for preventing irreversible ecosystem transformations. By Gregory Munday, Chris Jones, Norman J. Steinert, Camilla Mathison, Eleanor Burke, Christopher Smith, Chris Huntingford, Rebecca Varney & Andy Wiltshire.

𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗙𝗿𝗼𝗺 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗼𝗻 𝗠𝗮𝗺𝗺𝗮𝗹𝘀 Long-term studies are the backbone of ecological understanding – provide insights we simply can’t gain any other way – and yet remain rare. In my new article recently published with Mammal Review, I share key insights from decades of monitoring mammals and other terrestrial biodiversity across south-eastern Australia, to reflect on what makes these long-term efforts successful and required for biodiversity conservation. The work highlights how sustained data collection can: • Quantify cumulative impacts of landscape change on mammal communities • Distinguish genuine population shifts from background variability (like weather) • Reveal how time-varying habitat factors drive species persistence and decline It also distils ten practical lessons learned from running long-term research, such as to analyse data frequently, to plan well ahead to maintain funding continuity, and to deeply consider the key questions being asked. 📖 Read the paper here: https://lnkd.in/gmqsWKjw #BiodiversityConservation #ConservationScience #BiodiversityMonitoring #EcologicalMonitoring #Ecology #LongTermEcology #MammalEcology #Mammals #MammalReview

Excited to share that our manuscript, “Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment”, led by my PhD student Surendra Maharjan has been published in Ecological Informatics (Impact Factor – 7.3, CiteScore – 11.4). This study focuses on the Upper West Walker River Watershed in the eastern Sierra Nevada, California, a mountainous snow-fed region where seasonal snowpack acts as a natural reservoir, storing water in winter and releasing it gradually to sustain streamflow and ecological systems downstream. However, climate warming is shifting precipitation from snow to rain and accelerating melt, increasing vulnerability and creating an urgent need for advanced tools that can forecast ecological water risks. To address this challenge, this study evaluates three modeling approaches: the process-based SWAT hydrological model, a data-driven Long Short-Term Memory (LSTM) deep learning model, and a Physics-Informed LSTM (PIML) that integrates melt physics and precipitation-phase constraints. Key Findings: The PIML model demonstrated the most robust and well-balanced performance across key hydrologic metrics (NSE, KGE, RMSE). Future climate projections indicate that peak SWE may decline by up to 60%. Peak discharge may decrease by about 33% under warming conditions. Snowmelt and runoff may shift 10–19 days earlier, shortening the hydrologic season. These changes compress the hydrologic season, threaten summer ecological water availability, and heighten drought risk across snow-fed systems. The results underscore the growing challenges of managing water resources in snow-dominated basins under climate change. Coupling physics with deep learning offers a promising path toward more reliable forecasting of snowpack dynamics and streamflow in mountain watersheds. EssDs Chapman Chapman University Schmid College of Science and Technology Surendra Maharjan Wenzhao Li Rejoice Thomas Shahryar Fazli Hesham Morgan Mohamed Allali Ali Elgendy Link : https://lnkd.in/g4f6VpiK

“Deep learning to predict long-term erosion and deposition patterns in reservoirs”   The Yellow River Basin contains nearly 3,000 reservoirs, with river embankments stretching over 300,000 km.  These reservoirs have significantly improved water resources management for many user requirements including the reduction of high-drought and high-flood-risk areas in the Yellow River Basin.   The erosion-deposition process in reservoirs is a natural geological phenomenon that governs sediment accumulation.  This process involves the settling of suspended sediments resulting in continuous changes in reservoir bathymetry. The intensity of sediment transport and deposition is primarily regulated by water flow, sediment load, along with varied external environmental factors and typically peaks during the flood season.  Such processes are highly dynamic and directly affect the reservoir's water storage capacity, dam stability, and overall operational efficiency.   A recent study investigated a 130 km section of the Yellow River between the Sanmenxia and Xiaolangdi dams, using deep learning to predict long-term erosion and deposition patterns. From 2009 to 2023, water depth data from 56 sites (840 measurements) with unmanned survey boats and drone-based LiDAR (Light Detection and Ranging), along with flow and sediment records were collected.   Key drivers of sediment dynamics included sediment load, maximum sediment concentration, and maximum flow.     Data from 2009 to 2023 revealed elevation shifts from −0.21 m near the dam to +1.158 m at the reservoir's tail.  Predictions for 2024 to 2050 suggest varied riverbed changes, with the Guxian Reservoir's operation in 2036 expanding elevation ranges from −0.625 to 0.875 m.     These findings highlight the potential for deep learning to enhance efficient sediment management in reservoirs and offer insights for sustainable future hydraulic engineering practice.  The results reveal that integrated modeling approaches relied as shown here can offer a robust framework for regulating reservoir morpho-dynamics in sediment-rich basins, with promising implications for similar dammed reservoirs worldwide.   Study details are provided in Sun et al. (2025) in WRR, “Long-Term Prediction Model for Erosion-Deposition Topographic Evolution in the Sanmenxia-To-Xiaolangdi Reach of the Yellow River Based on Deep Learning”

From static maps to dynamic forecasts: AI transforms land cover prediction Land cover change drives erosion, water quality, fire regimes, and biodiversity loss—yet forecasting these dynamics has remained a major scientific challenge. A new study in Journal of Remote Sensing (DOI: 10.34133/remotesensing.0780 ) presents Themeda, a deep learning framework that predicts annual land cover across Australia’s savannas with 93.4% accuracy, outperforming baseline persistence models. Key innovations: 🔹 Temporal AI architecture: ConvLSTM + novel Temporal U-Net 🔹 33 years of data: Satellite imagery + rainfall, temperature, soil, and fire records 🔹 Probabilistic outputs: Capturing uncertainty and ecological shifts at multiple scales 🔹 Global potential: Adaptable for biodiversity protection, carbon accounting, and sustainable resource use Developed by Robert Turnbull, Damien Mannion, Jessie A. Wells, Kabir Manandhar Shrestha, Attila Balogh, and Rebecca Runting at University of Melbourne, Themeda demonstrates how AI can move ecological science toward predictive, actionable insights. #RemoteSensing #DeepLearning #EcologicalForecasting #Sustainability #ClimateChange

🌍 A Decade of NDVI, EVI, and NDWI Analysis in Comilla Region! 📊 Over the last 10 years, I have been analyzing vegetation and water index trends in Comilla using Google Earth Engine for data processing and ArcGIS for spatial visualization. This study helps understand ecological changes, seasonal variations, and long-term trends in vegetation health. Key Findings: • The highest NDVI was recorded in 2023 (0.484), indicating significant vegetation growth. • The lowest NDVI was observed in 2020 (0.338), possibly linked to environmental stress factors. Tools & Techniques: • Google Earth Engine: Used to generate NDVI, EVI, and NDWI trends over the years. • ArcGIS: Created a time-series NDVI map to visualize spatial changes. The insights from this study can help in agricultural planning, environmental monitoring, and sustainable land management. 🌱🚀 #GIS #RemoteSensing #GoogleEarthEngine #ArcGIS #NDVI #EVI #NDWI #EnvironmentalMonitoring #SustainableLandManagement #Agriculture #GeospatialAnalysis #ClimateChange #LandUse #EarthObservation #SpatialData #GISMapping

Over the past weeks, I’ve been working on a project for the Time Series course in the Earth System Data Science and Remote Sensing Master’s program at University of Leipzig . We explored how modern forecasting models, such as TimeGPT, can be applied to long-horizon vegetation forecasting using satellite-derived time series. Although TimeGPT was originally developed for financial time series, it showed promising results when adapted to the environmental domain, especially when fine tuned and provided with long historical context. The project involved forecasting over 220,000 spatial time series, requiring careful data preparation, batching, and format transformation to work within the constraints of the API. One of the takeaways was recognizing the potential of these models not just for forecasting, but also for gap filling and interpolation in satellite time series, where missing data is common. It also raised interesting possibilities for future work, such as integrating spatial context and improving how models handle gridded data structures. This work was developed with support from Nixtla, who provided access to the TimeGPT API for experimentation and scientific exploration. Their openness to academic and scientific use of AI tools made this exploration possible. Thank you! 💫 Sample result, TimeGPT predictions (dashed green line) compared to actual kNDVI values (solid green line) for selected spatial points. This configuration includes all the context, exogenous variables, and fine tuning. 💫 Project repository, https://lnkd.in/e8dtUYvZ 💫 Project report, https://lnkd.in/eJY49_7g #DataScience #TimeSeries #AI

The detrimental effects of droughts on water resources and agriculture can lead to significant economic losses and risk to lives. Using key climatic factors to analyze changes in a relevant index, this study aims to forecast droughts. The study is structured into three distinct phases. First, the computation of the Standardized Precipitation Evapotranspiration Index (SPEI) for the Chitral and Swat River basins was carried out using data from 1981 to 2022. This index is designed to predict both short-term and long-term droughts. Second, the dataset was split into training and testing sets, with 80% designated for training and 20% for testing the models, employing algorithms such as XGBoost, Decision Tree, AdaBoost, and Linear Regression, along with various climate variables. Finally, the models were evaluated using statistical metrics like R² (Coefficient of Determination), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and MSE (Mean Squared Error), and future predictions from 2023 to 2045 were made based on the well-trained and tested models. The results demonstrate promising performance, with R² values of 0.968, 0.906, 0.901, and 0.287, and RMSE values of 0.265, 0.291, 0.302, and 0.837 for XGBoost, AdaBoost, Decision Tree, and Linear Regression, respectively. The SPEI shows potential as a useful tool for drought prediction, and spatial distribution mapping in ArcMap using the Inverse Distance Weighting method reveals persistent moderate droughts in both basins. Additional research using a larger dataset or combining data from different areas could enhance the applicability of the findings and lead to a deeper understanding.

🌍 What can tree rings tell us about the jet stream and extreme weather? A new study published in AGU Advances (Broadman et al., 2025) uses 1,000 years of tree-ring data to reconstruct the behavior of a key jet stream pattern — the so-called wave-5 configuration — which is known to drive simultaneous climate extremes across the Northern Hemisphere during summer. ✅ This wave-5 pattern occurs when the polar jet stream forms five large ridges and troughs around the hemisphere. When this pattern locks in place during May–July, it often leads to concurrent heatwaves and droughts in places like southern Europe, the southeastern U.S., and China, while other regions (like Spain or western Canada) may become unusually wet. What’s fascinating is that the study shows no significant increase in the occurrence of wave5 events over the past millennium — even in recent decades, values remain within the range of natural variability. ✅ However, there’s a critical twist: while the frequency of wave5 events hasn’t changed much, their impacts have intensified due to anthropogenic warming. Today’s wave5-driven events occur in a globally warmer climate, which means hotter heatwaves, deeper droughts, and more stress on crops and ecosystems. The study also uncovers a strong and persistent link between La Niña winters and wave5 patterns the following summer — a relationship that holds both in modern and pre-industrial centuries. This connection could improve long-term forecasting of compound extremes by several months. ✅ By combining tree-ring-based drought reconstructions with modern reanalysis data and ENSO indices, the researchers created a new “wave5 phase count” index (wave5-PC) that captures the configuration’s frequency and intensity. This interdisciplinary work bridges the gap between short-term observations and long-term climate variability, showing how natural cycles like ENSO interact with atmospheric dynamics and how climate change amplifies the consequences, even without clear changes in the underlying patterns. ✅ Takeaway: Even in a highly uncertain atmospheric system, proxy records like tree rings can extend our understanding of climate dynamics far beyond the instrumental era. This kind of research bridges the gap between natural variability and anthropogenic influence — and gives us tools to prepare for what’s next. 📖 Reference: Broadman, E., Kornhuber, K., Dorado-Liñán, I., Xu, G., & Trouet, V. (2025). A Millennium of ENSO Influence on Jet Stream Driven Summer Climate Extremes. 👉 https://lnkd.in/dSJ2XGDW #ClimateScience #JetStream #ENSO #ExtremeWeather #TreeRings #Paleoclimatology #ClimateChange #LaNina #Heatwaves #Drought #ScienceCommunication #AGU #ReconstructionScience

Time passing can be cruel to forecasts - or conversely offer powerful vindication. Climate, energy and demography are three essential issues where proper forecasting is vital for policymakers, corporate leaders and our societies in general. 4 years ago I wrote a book, Mégavagues (Dunod, 2021 - only available in French at this stage) with a set of forecasts on these issues based on extensive research: no magic here, just megatrend analysis with a fundamental questioning of key drivers. Excerpts of the book are attached to this post. The full book itself is available on Amazon and other online bookstores. 3 key forecasts seemed outlandish just a few years back: - Climate change is out of control with a 4.2°C global warming soon after 2100 (p46), likely to cause a massive rise in sea levels - contrary to the benign 1.5°C forecast which was common wisdom at the time of writing; - Global solar PV installed volumes are on a sustainable exponential trend with a 25% annual growth of cumulated capacity (p72), contrary to IEA, BNEF and other forecasts of stagnant volumes; - We are past peak birth and have entered a global demographic crash (p112) contrary to persistent expectations of continued population explosion. Recent data offers strong vindication of these 3 forecasts: - The UN now anticipates 3.1°C global warming by 2100 (https://lnkd.in/e-T4S5mc) - Global solar PV installations are expected to exceed 600 GW this year, multiplying a factor of 5 in just 5 years, bringing annual cumulated installation growth from 2019 to 2024 to 29% (https://lnkd.in/eFgHxaQ9.) - A demographic crash is taking shape around the world with a spectacular collapse in fertility rates and the number of births in Europe, Asia, Latin America - and even Africa (https://on.ft.com/4fjTPkA) Mégavagues contains a another key forecast (p75-90): green hydrogen will soon be competitive with fossil fuels, giving credibility to a scenario of accelerated full decarbonization by 2040. Expect announcements soon to powerfully back up that forecast (hint: it is about an industry-wide competitive hydrogen marketplace).