A document by Wei Ji Leong. Click on the document to view its contents.

Seasonal streamflow prediction in snowmelt-driven basins is critical for hydrological applications, yet many regions lack sufficient ground-based observations to support traditional forecasting methods. This study introduces a satellite-based approach to estimate summer streamflow that tracks the areal progression of snowmelt based on remotely sensed runoff onset from Sentinel-1 (S1) and snow presence from the Moderate Resolution Imaging Spectroradiometer (MODIS). We classify snowmelt progression into three states: snow-on (dry snow), melting (wet snow), and snow-free, where the melt state (dry vs. wet) is provided by S1 and snow presence is provided by MODIS (before versus after snow disappearance date). The number of melting pixels over time within each basin is aggregated to calculate a “melt-area day” metric. To assess the hydrologic relevance, we compare melt-area day with streamflow volume during the water supply window. This analysis is conducted for the period 2017–2025, leveraging the availability of S1 data, and is evaluated both spatially and temporally across 35 CAMELS-US snow-dominated basins (>50% of annual precipitation as snow). Results show a strong and consistent relationship, with a Spearman’s correlation of 0.92 between melt-area day and streamflow volume across space. However, temporal correlations vary; some basins (e.g., in California) show strong temporal relationships, while others exhibit weak or even negative correlations, likely due to external influences such as summer precipitation, melt rate variability, and baseflow contributions. To further assess predictive skill, we apply multiple linear regression using melt-area day and precipitation as predictors. Results indicate that remotely sensed melt timing, combined with other predictors, has moderate skill in predicting summer streamflow. Notably, in basins with strong correlations during the Feb–June window, the relationship remains robust even when the melt-area metric leads streamflow by up to 45 days, suggesting its potential for early forecasting applications. This approach can be further exploited for estimating summer streamflow in ungauged basins using machine learning, supporting broader applications in water forecasting and climate adaptation.

Citizen science involves the public in scientific research, often through data collection and analysis using digital tools. It helps democratize science and increase the volume of meaningful environmental data. NASA’s GLOBE Observer app enables users to record land cover, cloud, and mosquito observations for public databases. However, as reported by NASA STEM Enhancement in Earth Sciences (SEES) interns, users of the land tool frequently face the loss of their observation data, issues with location accuracy, and low image resolution, all of which negatively impact classification results. To address these challenges, we are prototyping EarthLens, which is a drone and mobile app-based system designed to enhance citizen science land cover monitoring through real-time data collection and improved user experience. The project combines an open-sourced lightweight drone design, equipped with affordable high-resolution cameras and visible/NIR spectrometers (NASA STELLA devices), and an intuitive mobile application that allows users to upload and visualize their data. Through this, they can access geotagged photos, generate interactive maps, and use AI tools to analyze land cover data. Additionally, the app features a disaster overlay tool that utilizes satellite imagery to display before-and-after views of affected areas. Users may also utilize the app as a social platform where they can post their observations and participate in thematic challenges highlighting traits of their communities. EarthLens explores how advanced yet accessible technology can make citizen science more accurate, collaborative, and engaging. By addressing limitations in current platforms like the GLOBE Observer, this prototype aims to expand the role of everyday observers in environmental research and resilience efforts.

After the recent large earthquake disasters, Japan now emphasizes the role of universities in disaster prevention. Universities with expert knowledge of disasters and prevention are possibly able to play the role of linking the administration and citizens and supporting the collaboration of the many related agencies. Based on this recognition, since 2016, Nagoya University has actively built a framework for collaborating with the National University of Mongolia (MUIS) and the National Emergency Agency of Mongolia (NEMA). Nagoya University and the Open University of Japan intend to share Japan’s experience on “how to raise citizen’s disaster resilience” from the perspective of these universities. After the 1995 Kobe Earthquake, Japan has implemented many earthquake disaster prevention initiatives; however, in 2011, the Great East Japan Earthquake again caused substantial destructive damage. Although adequate measures had been taken, there is still a limit to governmental measures to deal with huge earthquakes with low occurring frequency. In countries where large earthquakes are possible, it is essential for each citizen to strengthen their own buildings or to endeavor to live in a place with good ground conditions and to ensure that this becomes the normal way of life. However, interest in disaster prevention often dissipates, and we are liable to repeat the same mistakes. How can we enhance citizens’ disaster resilience? As emphasized in the Sendai framework, one of the most important factors is “cooperation” and universities could become the focal point of this cooperation. Since 2002, Nagoya University, together with the administration and citizens, has begun to conduct various disaster prevention education projects in Japan. We introduce here the three projects based on Geography, Cultural Anthropology and Environmental Sciences, which were conducted within the period between 2014 and 2018. 1) Establishment of a Cooperative Center for Resilience Research (CCRR) between the National University of Mongolia and Nagoya University. 2) Organizing a public symposium for earthquake prevention by the Mongolian Government and CCRR. 3) Initiating the Grass-roots Joint Project: “Disaster Awareness Enlightenment Project for Large-scale Natural Disasters Caused by Global Environmental Change in Khovd Province, Mongolia” supported by Japan International Cooperation Agency (JICA). Through these projects, we want to determine disaster prevention methods suitable for Mongolia rather than just utilize Japanese methods of disaster prevention.

In the midst of frequent and multiple global extreme hydrological events (e.g., drought), numerous studies have focused on the effects of drought on soil microbial community composition, while few paying attention to the effects of post-drought rehydration on soil microbial communities and composition. This study used field-controlled experiments to compare the response changes of soil bacterial and fungal communities to rehydration processes after drought events of different intensities, analyzing the complexity of soil microbial community structure and their interspecies dispersion. Soil bacteria showed minimal changes in OTU number, phylum-level composition, α-diversity indices (Chao1 and Shannon), and community structure, soil bacteria exhibited smaller changes during post-drought rehydration. In contrast to soil bacterial communities, the post-drought rehydration process had a more significant impact on the composition and structure of soil fungal communities. Furthermore, the post-drought rehydration process after a drought had no dominant phyla of soil bacteria and fungi. Proteobacteria, Actinobacteriota, and Calditrichota remained the dominant phyla of soil bacteria. The three most prevalent phyla of soil bacteria were still Ascomycota, Mortierellomycota, and Basidiomycota. According to correlation network diagrams, post-drought rehydration had a more substantial and detrimental effect on the stability of both soil bacterial and fungal networks; however, soil fungal networks demonstrated greater stability than soil bacteria. In addition to offering significant scientific value for identifying drought-resistant microorganisms, this study enhances research on the mechanisms by which post-drought rehydration impacts soil microbial communities. It also reveals the ecological characteristics of soil microbial communities, and their recovery strategies under abiotic stress.

Belharra is a wave energy hotspot located on the French Basque coast. Here, unusually large waves occasionally break over a 15-meter deep seamount, producing world-class conditions for big wave surfing. This does not occur often, even though the region frequently encounters highly energetic swells, especially during winter. This work attempts to better understand the underlying processes leading to extreme waves at this location. A combination of spectral and phase-resolving wave models enables analysis of how the incident swell conditions and tide level affect the wave field at both regional and local scales. We carry out a detailed wave-by-wave investigation of the local conditions for a wide range of plausible scenarios. Qualitative comparisons are drawn with data from the trajectory of a surfer riding waves at Belharra during a big swell event. Regional and local features in the bathymetry appear to govern the concentration of swell energy at this spot. Long-period swells from directions around 300° - 315° generate the most energetic local conditions. Lower tide levels lead to greater breaking intensities, albeit with weaker wave amplifications.

Many iron meteorites sample the metallic cores of planetesimals, providing a unique opportunity to study early Solar System planetary processes. Modeling the chemical evolution during crystallization of these iron meteorite groups provides evidence that both S and P were present as light elements in planetesimal cores. As fractional crystallization of these cores proceeded, the concentrations of both S and P in the residual metallic liquid in the core increased, and eventually, liquid immiscibility in the Fe-Ni-S-P system would be expected to be encountered in many cases. For example, the IIAB and IIG irons may be related in this way. Considerable previous experimental work has determined element partitioning behaviors between solid metal and liquid metal as a function of the S and P concentrations of the metallic liquid, but few experimental studies have examined trace element partitioning behavior after the onset of liquid immiscibility. In this work, we present new experimental results that determine element partitioning behavior between two coexisting immiscible metallic liquids in the Fe-Ni-S-P system, as may be experienced during the later stages of the evolution of planetesimal cores.

The Southern Ocean (SO) provides a unique natural laboratory for studying cloud formation and cloud-aerosol interactions with minimal anthropogenic influence. The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), was an aircraft-based campaign conducted from Jan 15 to Feb 28, 2018, off the coast of Hobart, Tasmania, utilizing the NSF/NCAR GV research aircraft. The aircraft was equipped with in-situ probes and remote sensors to observe precipitation, cloud particles, and aerosols, providing detailed vertical profiles to characterize the marine boundary layer (MBL) and free troposphere.

Sustainable potato production depends on accurate, timely decisions about nitrogen (N) management and yield prediction that guide efficient resource use and reduce environmental impact. However, conventional approaches remain destructive, labor-intensive, and limited in spatial and temporal resolution. To address these limitations, precision agriculture increasingly relies on scalable, data-driven solutions to monitor in-season N status and predict yield across diverse cultivars and environments. Yet many existing models are still constrained by narrow-band indices, limited temporal scope, or incomplete integration of agronomic and environmental variables. This study addresses these gaps by applying artificial intelligence (AI) to high-resolution hyperspectral remote sensing data (400–2500 nm), genotype variations, N management practices, and environmental data collected across multiple growth stages and growing seasons. Using machine learning models (XGBoost and Random Forest), we integrated spectral vegetation indices, genotype, environmental, and management data to predict in-season leaf N concentration and final tuber yield. The models achieved high predictive accuracy for N status (R² = 0.918) and yield (R² = 0.834), with strong generalizability across growing seasons. Differences in spectral responses among N treatment levels enabled early prediction of yield outcomes, offering a powerful tool for improving N use efficiency and informing site-specific management decisions. These findings demonstrate how AI-driven hyperspectral analysis can enhance sustainability in potato cropping systems by reducing fertilizer waste, improving productivity, and mitigating environmental risks such as groundwater contamination.

Groundwater depletion in northern India, particularly in the intensively cultivated alluvial aquifers of the Indian Ganga Basin (IGB), poses a growing threat to agricultural sustainability and water security. This work aims to present a predictive assessment of groundwater balance and long-term resource sustainability in the IGB using a high-resolution numerical modelling approach. A four-layer aquifer model was developed at a 500-meter spatial resolution to simulate groundwater dynamics from 1997 to 2023. The model integrates refined estimates of recharge, evapotranspiration, groundwater abstraction, and river-aquifer interactions, and is implemented using MODFLOW 6 with a control-volume finite difference scheme. Uncertainties in the estimation of model parameters and resulting uncertainty in water balance components were quantified using the iterative ensemble simulation approach. The model was applied to assess groundwater use under varying climate and abstraction scenarios. Preliminary results showed historical storage depletion consistent with observed groundwater levels and regional trends identified using GRACE-based remote sensing approaches. Areas of significant storage depletion also overlapped with districts that have high estimated groundwater extraction. They also highlight spatial and temporal patterns of groundwater stress, identify critical zones of imbalance, and demonstrate the model’s utility in evaluating policy interventions. This modelling framework provides a robust tool for guiding sustainable groundwater management strategies at regional and sub-basin scales.

India is the most populous nation and among the most rapidly growing economies in the world. Agricultural lands cover ~53% of its geographical area, which together contribute to significant CO2 emissions. Additionally, biodiverse forests, grasslands and mangroves cover almost 22%, 19%, and 0.15% of the landmass with potentially large carbon sinks. For effective climate mitigation policy development and ecosystem health impact assessment in a changing climate, it is necessary to understand the carbon, water, and energy cycles in the region, which have remained elusive mainly due to a missing network of eddy covariance (EC) flux towers. This has resulted in a large uncertainties in the national, regional, continental, and global carbon budgets and impaired remotely sensed biophysical product development. To address this, we have systematically reviewed EC flux observations at twelve sites spread across different geoclimatic regions in India, representing its major land cover types for the first time since their inception. The Indian summer monsoon is a major planetary scale process that modulates the hydrometeorology of the surrounding region. Croplands absorb maximum CO2 during the monsoon, although it is not necessarily the favored season for carbon uptake across all ecosystems. Whereas some forests, croplands, and mangroves are found to act as well-watered ecosystems, others oscillate between well-watered and water-limited states, driven by temperature and moisture dynamics. Grasslands retain a large fraction of carbon. Water-limited ecosystems have the highest water use efficiency (WUE), whereas irrigated croplands have the lowest. Indian forests are predominantly tropical and subtropical, and have a smaller WUE than temperate and boreal forests; overall, their WUE lies in an intermediate range in the Indian region. The monsoon is shown to reverse the physiological control of the canopy-atmosphere coupling that is predominant during other times of the year. This information will be useful to scientists, economists, resource managers, and policymakers during the transition towards a sustainable and just global net-zero economy. Finally, we also identify the key areas for expanding the flux tower network in India and make the case for sustained measurements.

Airborne research and observations continue to be a part of a robust and resilient Earth Science Division program as defined in NASA’s decadal survey. As part of this, planning of the suborbital research flights involves much thought, time and effort in order to translate scientific sampling objectives to aircraft waypoints. We present the planning process and tools that have been used in 2 field campaigns operated by NASA in 2024; ARCSIX (Arctic Radiation-Cloud-Aerosol-Surface Interaction Experiment) and PACE-PAX (The Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment). Combined these campaigns have different guiding principles, regions, and 5 aircraft. To enable more efficient science operations using distributed observations, we developed an efficient tool for flight planning, which achieved numerous science objectives.

A document by Abhinav A. Click on the document to view its contents.

Variations in stable oxygen isotopic compositions in sea ice provide information on environmental conditions during sea ice formation, and also are important in understanding regional and temporal aspects of the freshwater budget of the Arctic Ocean. We analyzed the oxygen isotope fractionation between sea ice and seawater using ice core and surface ocean samples obtained in a field study in the Lincoln Sea/Switchyard region of the Arctic Ocean. Using the Sea Ice Tracking Utility (SITU) we track the sea ice backward in time along drift trajectories, and use a simple model to calculate ice growth rates. Our results indicate that sea ice at the bottom of the floes that we sampled in the Switchyard Region grew within the past winter along a trajectory extending back to the North Pole. The effective fractionation coefficients from the bottom ice layers and the parent water mass are close to 2.11 ‰ with a standard error of ± 0.06 ‰. Knowing this sea ice oxygen isotope fractionation coefficient for high Arctic drifting ice is critical for use of equations for mass balance, salinity, oxygen isotopes and nutrients to calculate water mass fractions and sources to understand freshwater balance.

The soil-borne disease Verticillium wilt causes significant yield losses in cotton. Developing resistant cotton varieties is a long-term solution to manage the disease. The phytopathological parameter used to assess variety resistance is the presence or absence of infection, where transverse sectioning of infected stems is discoloured/brown. The traditional method of selecting resistant varieties involves manual scoring of visual stem discolouration after cutting. However, this process is associated with high labour costs, delayed processing time and cognitive biases. Therefore, automatic detection of resistant varieties with scalable, cost-effective, and rapid phenotyping tools is needed in cotton breeding.

Urban road networks underpin human mobility, economic activity, and timely access to essential services. Yet these lifelines are increasingly susceptible to climate‑driven compound flooding, when fluvial, pluvial and coastal processes interact to create complex urban inundation scenarios. While resilience assessments have begun considering equity, many fail to account for the nuanced impacts of hazard driven infrastructure disruptions on sub-populations' access to critical facilities. Here we examine how compound flooding events reshape access to hospitals, shelter houses, and transportation centers across a representative coastal metropolis of Boston by combining a high-resolution 1D–2D hydrodynamic flood model with dynamic mesoscopic traffic simulation. This approach captures both street-level inundation and evolving traffic conditions, including rerouting and congestion during extreme events. We evaluate changes in travel time to critical facilities under both normal and flood conditions. Results reveal that even localized flooding can lead to widespread travel delays, significantly reducing or even eliminating access to emergency services. Further, these disruptions are spatially unequal, with socially vulnerable neighborhoods experiencing significantly longer delays. Our findings highlight the need for resilience strategies that not only maintain system functionality but also ensure equitable access. This includes distributed siting of critical services, adaptive traffic management, and nature-based flood mitigation to reduce future systemic risks.

Climate change is reshaping the agro-ecological suitability of crop-growing regions, with significant implications for non-staple, high-value commodities often cultivated in climatically sensitive and economically vulnerable areas where data coverage is limited. Here we present SPADE (Suitability Projections using Agro-ecological Dynamics and ECOCROP), a model designed to project changes in agro-ecological suitability over time, producing suitability maps and time series of change in suitable area across decades and Shared Socioeconomic Pathways. Developed as a lightweight Python package built on top of the SpatiaFi platform and the Google Earth Engine API, SPADE combines FAO ECOCROP parameters across a wide range of crops with global, high-resolution datasets on land use-land cover, soil pH, elevation, projected changes in Köppen-Geiger climate classifications, and projected urban expansion. This model can be applied to specialty crops such as vanilla, saffron, and vetiver to assess suitability dynamics across growing regions through 2080 for SSP2 and SSP5. Model outputs quantify the combined effects of biophysical constraints and socio-environmental pressures on the spatial redistribution of crop suitability, with implications for crop migration, market dynamics, and land-use transitions. The framework offers a scalable and reproducible approach for assessing climate-driven shifts in agro-ecological potential, particularly for underrepresented crops, supporting targeted adaptation strategies.

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