Tethys Geoscience Foundation

Natural hazards are interconnected—heavy rainfall triggers floods and landslides, while drought feeds wildfires. Discover how hydrological monitoring tools expand to predict compound environmental risks and build safer communities.

Read more: https://tethysgeoscience.org/2026/09/22/natural-hazards-beyond-water-landslides-earthquakes-and-wildfires/

From satellites orbiting hundreds of miles above Earth to sensors embedded deep in the soil—how do we bridge the gap in root-zone water monitoring? 🛰️🌱

A recent study published in Hydrology and Earth System Sciences, Retrieving root-zone soil moisture from land surface modelling and GRACE/-FO and validating its dynamics with in-situ data over West Africa, combines satellite observations, land surface modeling, and ground observations to better understand root-zone water storage dynamics across the region.

https://hess.copernicus.org/articles/30/5023/2026/

Retrieving root-zone soil moisture from land surface modelling and GRACE/-FO and validating its dynamics with in-situ data over West Africa Abstract. Rainfall variability in West Africa, driven by the West African Monsoon, poses significant challenges to agricultural productivity and livelihoods. In this context, understanding root-zone soil moisture (RZSM) dynamics is crucial since it serves as the primary water source for crops. While...

CNG Forum is a great opportunity for the Tethys community to explore the latest in open-source, cloud-native approaches to geospatial and Earth science data. Attendees can learn from and connect directly with engineers and leaders from top companies and organizations in the geospatial ecosystem, while exchanging ideas with others tackling similar technical challenges. Join us Oct 6-9, 2026 in Snowbird, UT. Grab your discounted ticket!
https://2026.cloudnativegeo.org/ #/buyTickets?promoCode=CNG26R7Q-08110915

This paper explores how conceptual hydrologic models can be reformulated as physically constrained, interpretable neural networks. The researchers tested their approach across 513 CAMELS-US basins and found that relatively compact neural-network representations could achieve strong predictive performance.

https://arxiv.org/abs/2607.26492?utm_source=chatgpt.com

From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks. Here, we develop a snow-water MCP network framework and evaluate it across 513 CAMELS-US basins. We fi...

What lies beneath our feet directly drives environmental stability.

Discover how innovative web applications built on Tethys Platform harness soil moisture, erosion assessment, and remote sensing data to transform hidden ground data into actionable decision-making tools.

Read the full insight: https://tethysgeoscience.org/2026/09/08/soil-science-applications-from-monitoring-to-decision-making/

Researchers created a large flood-mapping benchmark containing more than 14,000 image tiles from 219 flood events across 65 countries, combining Sentinel-1 SAR, Sentinel-2 optical imagery, DEM data, and manually validated flood labels. The researchers also provide the dataset and code.

Read the GEOID-Flood paper: https://arxiv.org/html/2608.02315v1

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This is a particularly good technical/research article. Researchers developed a network-based Streamflow Alteration Index to map how dams, reservoirs, and water-power facilities can alter streamflow throughout connected river networks. The work was tested across Ontario and produces a high-resolution database covering both gauged and ungauged locations.
https://arxiv.org/pdf/2608.02363

Discover how standardized hydrological monitoring via the HydroSOS framework delivers critical streamflow insights to strengthen early warning systems and disaster resilience across Eastern and Southern Africa.

Learn more: https://tethysgeoscience.org/2026/08/25/environmental-monitoring-at-scale-with-geoglows/

How can we improve streamflow predictions in watersheds where observations are limited? Researchers are exploring a hybrid approach that combines physical understanding, existing data, and machine learning.

https://www.usgs.gov/publications/a-hybrid-approach-revealing-headwater-hydrology?utm_source=chatgpt.com

A hybrid approach for revealing headwater hydrology Coordinated work to compile existing data and apply models pairing physical understanding with machine learning could substantially improve streamflow predictions for little-known headwater basins.