Soil Moisture | Dr. Mauricio Zambrano-Bigiarini

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

Sat, 04 Apr 2026 00:00:00 +0000

On January 12th, 2026, published our article entitled . 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.

Study area: (a) catchment location (CAMELS-CL; Alvarez-Garreton et al., 2018); (b) elevation (SRTMv4.1; Jarvis et al., 2008); (c) land cover classification (CLDynamicLandCover.V2; Galleguillos et al., 2024); (d) soil properties (CLSoilMaps; Dinamarca et al., 2023); and (e) aridity index (AI=P/PET) 1970–2000 (Global-AI-PET-v3; Zomer et al., 2022).

Locations of in situ TEROS 10 and TEROS 12 sensors. (a) Example of TEROS 10 and TEROS 12 sensors installed across various land cover types; (b) northern arid sites in the Petorca (PRB) and Mapocho (MRB) river basins; and (c) southern humid sites in the Cauquenes (CRB) and Trancura (TRB) river basins. Red triangles indicate the locations of in situ SM monitoring sites. Grid cell boundaries of each gridded SM product are shown for ERA5 (green), ERA5-Land (purple), SPL4SMAU (blue), and GLDAS-Noah (lightblue).

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.

Schematic summary of the main conclusions.

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: .

Infographic summary, created by Google NotebookLM (23-Apr-2026)

Pulliko: Gridded soil moisture for Chile

Fri, 30 Jan 2026 00:00:00 +0000

Context and motivation

Reliable monitoring of soil moisture is essential for understanding water availability, managing drought risk, and supporting sustainable water resources management. Soil moisture regulates key hydrological processes such as infiltration, runoff, evaporation, and plant water uptake, and plays a central role in land–atmosphere interactions. Conditions in the surface soil layer (SSM; 0–10 cm) respond rapidly to rainfall events, while moisture in the deeper root zone (RZSM; 0–100 cm) evolves more slowly and sustains vegetation during dry periods, influencing the onset and persistence of extreme events such as droughts and intense rainfall.

Accurate representation of these dynamics requires spatially continuous information derived from multiple data sources. Ground-based measurements provide high-quality observations but are geographically sparse, particularly in the Southern Hemisphere. Satellite observations offer broad and frequent coverage, yet they primarily capture near-surface conditions and can be affected by vegetation and environmental factors. Therefore, integrating diverse datasets within a unified monitoring framework is therefore critical for delivering timely, reliable information on soil moisture conditions across large and climatically diverse regions such as Chile, where environmental conditions range from the hyper-arid north to the humid south.

Description

In response to this need, during her undergraduate thesis, Rocío Muñoz Neira developed under my supervision an operational web platform designed to provide near real-time monitoring of soil moisture and its anomalies across continental Chile. The platform integrates multiple high-quality datasets to deliver timely, spatially consistent information that can support decision-making in agriculture, water resources management, environmental monitoring, and scientific research.

Four state-of-the-art gridded soil moisture products were selected based on their long-term data availability, spatial and temporal resolution, and operational reliability. These products provide volumetric soil moisture estimates for both the surface soil layer (0–10 cm) and the root zone soil layer (0–100 cm). The four available gridded soil moisture products are:

ERA5 (0.25° spatial resolution, hourly updates, approximately 6-day latency),
ERA5-Land (0.1°, hourly, approximately 6-day latency),
GLDAS-Noah (0.25°, three-hourly, approximately 4-month latency), and
SMAP-L4 (9 km resolution, three-hourly, approximately 2.5-day latency).

The following article evalautes the four previous soil moisture datasets against in situ measurements:

Núñez-Ibarra, D. A.; Zambrano-Bigiarini, M.; Galleguillos, M. (2026). . Hydrology and Earth System Sciences, 30, 1813–1847. .

Together, these complementary soil moisture datasets offer robust coverage across a wide range of climatic and hydrological conditions.

To enhance the interpretation of soil moisture conditions, the platform also computes two standardised drought indicators of soil moisture anomalies:

The Standardized Soil Moisture Index (SSMI) is a parametric indicator based on the gamma probability distribution, while
the Empirical Standardized Soil Moisture Index (ESSMI) is a non-parametric indicator derived using kernel density estimation techniques.

These indices are calculated automatically on a daily basis at multiple temporal aggregation scales (1, 3, 6, 12, and 24 months), allowing users to assess short-term variability as well as longer-term hydrological trends.

The system operates through a fully automated data pipeline. External data servers are queried regularly to identify the most recent observations, which are then downloaded, processed, and stored on the internal infrastructure of the of the Department of Civil Engineering at the Universidad de La Frontera. The processed soil moisture fields and derived anomaly indicators are subsequently displayed through the , meaning “water in the soil” in mapuzungun, interactive web interface, enabling users to explore current conditions and historical patterns in an intuitive and accessible manner.

Main screen of web platform

By combining reliable data sources, automated processing, and interactive visualization tools, this platform provides a practical and scientifically robust resource for monitoring soil moisture dynamics across Chile. Its near real-time capabilities support informed decision-making, improve situational awareness during hydrological extremes, and contribute to a better understanding of the country’s evolving water and climate conditions.

Additional information about the development of this platform can be found in the .

Article on hydropedological clustering published in JoH

Fri, 19 Dec 2025 00:00:00 +0000

On December 19th, 2025, published our article entitled . This study investigates how different soil datasets and classification approaches affect the performance of the SWAT+ hydrological model in simulating low streamflows and soil water content (SWC).

Motivation

In Mediterranean climates, such as central Chile, rivers often experience very low flows during long dry seasons. These low flows are critical for agriculture, drinking water supply, and ecosystem health. Yet they remain difficult to be reliably simulated because the way soils store and release water is complex and varies substantially across the landscape. Many hydrological models rely on global soil databases that do not fully capture local soil behavior. This study evaluates a new method for organizing soil information, called hydropedological clustering, to improve the simulation of low streamflows in the Cauquenes catchment.

What is new in this study

The researchers compared four different soil datasets, including widely used global maps and locally developed soil information. They introduced a new clustering strategy that groups soils according to key soil hydraulic properties that directly control water movement and storage:

Saturated hydraulic conductivity, which governs how quickly water can move through soil
Available water capacity, which determines how much water soil can retain for plants
The van Genuchten α parameter, which reflects soil pore structure

Rather than classifying soils only by texture (sand, silt, and clay), this method focuses on how soils actually function hydrologically. The result is a more meaningful representation of soil processes within the model.

What we found

The hydropedological clustering method produced consistently better results than conventional soil classifications. It improved the accuracy of low-flow simulations, reproduced key hydrological indicators more realistically, and reduced model calibration time. The approach also provided more reliable estimates of soil moisture across the root zone, avoiding the large overestimations often associated with coarse global datasets. A central conclusion is that how soils are classified can matter more than how detailed the map resolution is!.

Why this study is important for water management

Reliable low-flow simulations are essential for managing water during droughts, when supply is limited and demand is high. Improved modelling supports better decisions on water allocation, irrigation planning, environmental flow protection, and energy production. The study demonstrates a practical and transferable framework for integrating locally relevant soil knowledge into hydrological models. This capability is particularly valuable for regions facing increasing water stress under climate variability and prolonged drought conditions.

The full article can be found here: .

Graphical abstract

Infographic summary, created by Google NotebookLM (23-Apr-2026)

The catchment’s memory: understanding how hydrological extremes are modulated by antecedent soil moisture conditions in a warmer climate

Tue, 02 Mar 2021 00:00:00 +0000

Context and motivation

Global warming is reshaping the hydrological cycle, not only through gradual shifts in mean precipitation but also by intensifying precipitation extremes. These extremes cascade through river basins, potentially triggering floods or prolonged hydrological droughts, with far-reaching impacts on communities, infrastructure, and ecosystems. A critical regulator of this cascade is antecedent soil moisture, which governs runoff generation and reflects the “memory” of the catchment and modulating how atmospheric anomalies translate into hydrological responses.

Chile’s 2010–2019 megadrought offers a unique large-scale natural experiment to examine how sustained warming and drying alter the transformation of meteorological extremes into hydrological extremes. Understanding this transformation is essential for anticipating future risks under a changing climate.

Project description

This four-year research project (April 2021–March 2025) is funded by the Chilean National Agency for Research and Development ( ) under the Concurso Fondecyt Regular 2021 call. The project investigates four representative Chilean catchments (Petorca en Longotoma, Mapocho en Los Almendros, Cauquenes en Desembocadura y Trancura antes de Llafenco) over the 1980–2019 period, integrating statistical extreme-event analysis with process-based hydrological modelling. Meteorological and hydrological extremes will be systematically characterized using standardised indices (e.g., SPI, SSI/SRI) and complementary metrics of duration, volume, and intensity to assess multi-decadal changes in frequency and severity.

To explore the mechanisms underlying these changes, two hydrological models with contrasting structural representations of catchment processes will be implemented (TUWmodel, SWAT+). Model calibration will rely on a multi-variable, multi-objective framework, jointly assimilating in-situ observations and advanced remote-sensing products, including soil moisture, total water storage, evapotranspiration, and snow cover.

Sub-daily simulations (2001–2019) will further enable a detailed assessment of how antecedent soil moisture conditions influence peak discharge generation and storm-event dynamics, providing process-level insight into the amplification or attenuation of extremes.

The project (ANID-Fondecyt 1212071: The catchment’s memory: understanding how hydrological extremes are modulated by antecedent soil moisture conditions in a warmer climate) is led by me, and have Dr. Mauricio Galleguillos (U. de Chile and U. Adolfo Ibañez, Chile), Dra. Camila Alvarez-Garreton (CR2, Chile) and Dr. Oscar Baez-Villanueva (U. Ghent, Belgium) as co-investigators.

Expected contribution to decision-making

By elucidating the role of soil moisture as a mediator between atmospheric forcing and hydrological response, the project will advance understanding of how warming climates reshape flood and drought dynamics. The results will provide quantitative evidence on the potential amplification of flood peaks and the persistence of hydrological droughts under changing baseline conditions.

These findings will directly inform water resources planning, risk assessment, and early-warning system design. Beyond Chile, the methodological framework and analytical tools developed in this project will offer transferable approaches for operational hydrological assessment and climate resilience planning in regions facing increasing hydroclimatic stress.