Article on the evaluation of gridded soil moisture products published in HESS

On January 12th, 2026, Hydrology and Earth System Sciences (HESS) published our article entitled From grid to ground: how well do gridded products represent soil moisture dynamics in natural ecosystems during precipitation events?. This study investigates how spatial patterns, temporal trends, and record length in hourly precipitation data affect annual maximum intensities estimated with stationary and non-stationary models across a climatically and topographically diverse region.

Motivation

Soil moisture is a key variable controlling how water moves through landscapes, supports vegetation, and interacts with the atmosphere. It plays a central role in drought monitoring, ecosystem management, and hydrological modelling. In many regions—particularly natural or remote ecosystems—direct soil moisture measurements are scarce. As a result, scientists and practitioners often rely on large-scale datasets derived from satellites or land surface models. This study evaluates how accurately these datasets represent soil moisture dynamics across Chile’s wide range of climates, from arid northern zones to humid southern forests.

What was the novelty?

The study assessed four widely used soil moisture datasets, ERA5, ERA5-Land, SMAP-L4, and GLDAS-Noah, against detailed field observations collected every three hours from the Kimün-Ko monitoring network. The monitoring sites span ten near-natural ecosystems along Chile’s hydroclimatic gradient.

In addition to standard statistical indicators, the researchers applied an event-based diagnostic method that examines how soil moisture responds to individual rainfall events. This approach evaluates both the magnitude of the response and how quickly the soil becomes wetter after rainfall.

What we found

The evaluation revealed consistent patterns with direct implications for environmental monitoring and modelling:

• ERA5 and ERA5-Land showed the most reliable overall performance. These datasets reproduced seasonal soil moisture dynamics reasonably well across most regions, particularly in the wetter southern ecosystems.

• Deeper soil layers were simulated more accurately than surface layers. Root-zone soil moisture changes more slowly and is less sensitive to short-term fluctuations, making it easier for large-scale models to represent.

• Arid regions remain difficult to simulate. In northern ecosystems, all datasets struggled to reproduce the first rainfall response after long dry periods, typically overestimating how much and how quickly soil moisture increased.

• Performance varies by product and location. Some datasets performed well under specific conditions—for example, one showed relatively strong skill for surface soil moisture in selected arid sites—while others systematically underestimated soil moisture in wetter environments.

Why the new diagnostic approach is important

A key contribution of the study is the demonstration that traditional performance metrics can overlook important timing and response errors. A dataset may appear accurate when evaluated over long periods but still fail to capture the rapid changes that occur during individual storms. Event-based diagnostics provide a clearer understanding of how models represent real hydrological processes, especially during extreme or short-lived events.

Why this matters for practice and decision-making

The findings provide practical guidance for selecting soil moisture datasets in regions where field measurements are limited. In particular, identifying the most reliable products supports better drought monitoring, improved hydrological simulations, and more informed ecosystem and water resource management.

Our study also highlights the importance of evaluating not only average performance, but also the dynamic response of soils to rainfall—an aspect that becomes increasingly critical under changing climate conditions.

The full article can be found here: https://doi.org/10.5194/hess-30-1813-2026.