Announcements

Application for ISMC Awards 2026 now open

The application for the ISMC awards 2026 is now open. Canditates can apply directly for the: 

Rien van Genuchten Award

Harry Vereecken Lectureship Award

ISMC Early Career Award

ISMC Publication Award

For application details check our webisite here. Application deadline for all awards is 1^st of May 2026.

 Hand over of the 2024 van Genuchten Award to Paolo Nasta

Soil Intelligence working group has started

On January 14^th 2026 the Soil Intelligence working group had its Kick-off meeting. After a general introduction by Mehdi Rahmati and Sarem Norouzi Budiman Minasny presented a lecture entitled “Overcoming Black-Box Pitfalls Through Soil Science-Informed Machine Learning”. The lecture of Budiman can be found on the ISMC YouTube channel. The gerenl introduction can be also found on YouTube (klick here).

Webinar on Soil Structure

Monday 16^th of March 2026 from 3 PM to 5 PM (Berlin time) the working group pedotransfer functions and land-surface parameterization will held a webinar dedicated to Soil Structure. For the webinar three dedicated expers will present their work.

1. Nick Jarvis, SLU “A minimalist model of the effects of soil structure dynamics on soil hydraulic properties”
2. Efstathios Diamantopoulos, Uni Bayreuth “A modelling framework for temporal changes in soil structure and their effects on soil hydraulic properties”
3. John Köstel, Agroscope “Temporal evolution of soil macropore networks with respect to season, plants, macrofauna and land use and its expected impact on saturated hydraulic conductivity”

If you want to join the webinar use this link. The three presentations will be also recorded and made available via the ISMC YouTube channel after.

Kick-off of the Soil Pollution working group

The soil pollution transport and fate modelling working group as a cooperation between ISMC and the EU project SOILPROM will have its first meeting on March 10^_th 2026 3 PM to 5 PM (Berlin time) . After a general introduction of the aims of the working group, two presentations will be given by:

1. Giuseppe Brunetti, University of Calabria “Modeling the fate of contaminants in the soil-plant continuum”
2. Droge, Steven and Deligiannis, Michael, Wageningen University “SOILPROM lab data collection and modelling of sorption behaviour of ionizable pollutants ”
    

To join the meeting use this link. The presnetations will be also recorded and made available on the ISMC YouTube channel after.

Featured Paper

Do you want your paper featured?

Please share your recent paper if you want to be featured in the ISMC newsletter. With your contributions, we will select one paper to be featured in every newsletter. Submission can be done here. 

Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders

Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this setup across different fields and soil types. In this study, a pooled error covariance matrix was derived using soil moisture sensor measurements from three TEROS 10 (Meter Group, Inc., USA) sensors per MZ and soil moisture sampling campaigns conducted over three growing seasons, covering 93 cropping cycles in agricultural fields with diverse soil textures in Belgium. The MZ soil moisture estimated from a composite of nine soil samples with a small standard error (0.0038 m³ m⁻³) was considered the “true” MZ soil moisture. Based on these measurement data, we established a pooled linear recalibration of the TEROS 10 manufacturer's sensor calibration function. Then, for each individual sensor as well as for each MZ, we identified systematic offsets and temporally varying residual deviations between the calibrated sensor data and sampling data. Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. Since a systematic offset persists over time, it contributes to the temporal covariance of sensor observational errors. Therefore, we estimated the temporal covariance of observational errors of the individual and the MZ-averaged sensor measurements from the variance of the systematic offsets across all sensors and MZ averages, while the random error variance was derived from the variance of the pooled residual deviations. The total error variance was then obtained as the sum of these two components. Due to spatial soil moisture correlation, the variance and temporal covariance of MZ-averaged sensor observational errors could not be derived accurately from the individual sensor error variances and temporal covariances, assuming that the individual observational errors of the three sensors in a MZ were not correlated with each other. The pooled error covariance matrix of the MZ-averaged soil moisture measurements indicated a significant autocorrelation of sensor observational errors of 0.518, as the systematic error standard deviation (0.033 m³ m⁻³) was similar to the random error standard deviation (0.032 m³ m⁻³). To illustrate the impact of error covariance in probabilistic soil hydrological modeling, a case study was presented incorporating the pooled error covariance matrix in a Bayesian inverse modeling framework. These results demonstrate that the common assumption of uncorrelated random errors to determine parameter and model prediction uncertainty is not valid when measurements from sparse in situ soil moisture sensors are used to parameterize soil hydrological models. Further research is required to assess to what extent the error covariances found in this study can be transferred to other areas and how they impact parameter estimation in soil hydrological modeling. More information can be found here.

Advancing Soil Process Representation in Land–Surface Models

I am a postdoctoral researcher in the Department of Geography and Environmental Science at the University of Reading, UK. My research focuses on improving the representation of soil–plant hydraulic processes in land-surface models, with particular emphasis on ecLand. I work on developing and implementing physically consistent parameterisations of soil hydro-thermal processes to enhance the simulation of land–atmosphere interactions in weather and climate models.

Please tell us briefly about yourself and your research interest.

I began my academic journey with a bachelor’s degree in civil engineering, where I was first introduced to soil through geotechnical engineering, including concepts such as pore size distribution, soil texture, and soil water retention curve. I then completed a master’s degree in Hydraulic Engineering, during which my thesis focused on the numerical modelling of contaminant transport in unsaturated porous media. During my PhD, I was introduced to data assimilation, which combines model simulations and observational data in an optimal framework to improve estimates of the system state. Building on this background, my research interests centre on understanding the transport of water and heat in soils and on improving their representation in land-surface models to better capture land–atmosphere interactions.

How did you first become interested in soil modelling and learn about ISMC?

My interest in soil modelling began during my Master’s thesis, where I worked on the numerical modelling of contaminant transport in unsaturated porous media. This involved investigating how different transport parameters influence contaminant migration in soils and introduced me to physically based representations of flow and transport processes. During this period, I also became familiar with the HYDRUS modelling framework, which solves the coupled transport of water, heat, and vapour in soils.

During my PhD, my focus shifted towards the estimation of soil hydraulic and thermal parameters using data assimilation techniques, in particular the ensemble Kalman filter (EnKF). Through this work, I gained deeper exposure to Richards’ equation and commonly used soil hydraulic constitutive relationships, such as the Brooks–Corey and van Genuchten formulations. This experience strengthened my interest in understanding how water and heat transport are represented in soil models, and how these representations can be improved through parameter estimation, calibration, and the inclusion of more physically realistic soil processes.

I first became aware of ISMC through the paper “Modeling Soil Processes: Review, Key Challenges, and New Perspectives”, published in 2016, around the time I began my PhD. Since then, I have regularly followed ISMC activities through its website, which has been a valuable source of information on recent developments in soil modelling, including job opportunities, meeting reports, and relevant publications. Recently, I was part of the organizing team for the ISMC–GEWEX SoilWat meeting held at the University of Reading from 14–16 July 2025. It was a highly enriching experience to interact with leading experts in soil science, discuss my research with them, and learn from their presentations and discussions.

Can you share with us your current research focus?

I am currently a postdoctoral researcher at the University of Reading, working on the project “Towards a high-fidelity Integrated Forecasting System via ground-breaking data assimilation of the dynamic soil–vegetation hydraulic continuum.” My research focuses on developing and implementing a unified hydro-thermal framework that explicitly links soil hydraulic and thermal properties, and on integrating this framework within the ecLand land-surface model. As part of this effort, I am leading a working group under the ISMC–GEWEX SoilWat initiative. Working Group 1 focuses on applying the unified hydro-thermal framework across different land-surface models (LSMs), as well as on the intercomparison and evaluation of the resulting simulations using in situ observations.

In parallel, my research explores the implementation of a fully dynamic soil–vegetation system in ecLand using data assimilation, in which key hydraulic and vegetation parameters are treated as prognostic states rather than fixed variables. A central component of this work is the exploration of the Vegetation as a Soil Sensor (VaaSS) concept, which leverages satellite observations that capture essential processes of the soil–plant continuum and associated water, energy, and carbon exchanges to constrain the LSM parameters.

Please tell us briefly how your research could contribute to ISMC Science Panel’s activities? Or the other way around, how do you wish ISMC science panels help/support your research activities?

My research on the unified hydro-thermal framework aligns closely with the ISMC Science Panel activities, particularly within the thermal properties working group led by Anne Verhoef and Yijian Zeng. Through this work, we aim to apply the unified hydro-thermal framework across multiple land-surface models and to conduct systematic intercomparison and evaluation of the resulting simulations using in situ observations.

A key requirement of this effort is the availability of a global dataset linking soil thermal conductivity (λ), soil moisture (θ), and matric potential (ψ). Developing such a dataset requires a coordinated, community-driven effort to compile, harmonize, and share measurements from diverse regions and soil types, ensuring robust global representation rather than concentration in a few well-instrumented locations. In this context, ISMC provides an ideal platform to facilitate data sharing, establish common protocols, and foster collaboration across institutions.

What resources or skills would you recommend that early career members of ISMC should acquire? And how can ISMC help and support early career members in this regard?

One of the key challenges in soil science today is the gap in communication and mutual understanding between soil modellers and experimentalists. I strongly believe that early career researchers would benefit greatly from developing skills that bridge this divide. Gaining hands-on experience with both numerical modelling and field or laboratory soil experiments can provide valuable insight into how observations inform model development, and how models can, in turn, guide experimental design.

In addition to domain knowledge, strong computational skills are increasingly essential. Early career members should be comfortable running and modifying soil or land-surface models, working with large datasets, and developing reproducible workflows. Familiarity with coding practices and, where appropriate, exposure to machine learning and data-driven approaches can further enhance their ability to analyse complex soil–plant–atmosphere interactions.

ISMC can play an important role in supporting early career researchers by fostering interdisciplinary training opportunities and facilitating interaction between modellers and experimentalists. Regular conferences, workshops, and training schools organised under the ISMC science agenda provide an excellent platform for early career members to engage with leading experts, exchange ideas, and build collaborative networks. In addition, ISMC can help by promoting mentoring, sharing best practices, and encouraging open data and model development, thereby enabling early career researchers to develop the skills needed to address emerging challenges in soil modelling.