On the value of mobile cosmic-ray neutron measurements for spatio-temporal soil moisture simulations

Eshrat Fatima^1,2*Rohini Kumar^1*Daniel Altdorff¹Sabine Attinger¹Friedrich Boeing¹Sascha Oswald²Oldrich Rakovec³Luis Samaniego¹Steffen Zacharias⁴Martin Schrön^5*

• ¹Dep. Computational Hydrosystems, UFZ - Helmholtz Centre for Environmental Research GmbH, Leipzig, Germany, Leipzig, Germany
• ²Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany, Potsdam, Germany
• ³Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic, Prague, Czechia
• ⁴Dep. Monitoring and Exploration Technologies, UFZ - Helmholtz Centre for Environmental Research GmbH, Leipzig, Germany, Leipzig, Germany
• ⁵Dep. Monitoring and Exploration Technologies, UFZ, Helmholtz Centre for Environmental Research, Helmholtz Association of German Research Centres (HZ), Leipzig, Germany

High-resolution soil moisture measurements are indispensable for advancing hydrological modeling and improving environmental risk assessments at regional scales. However, it remains an open question to what level do hydrological models are capable of representing spatio-temporal patterns of root-zone soil moisture. In this study, we present a novel integration of mobile Cosmic-Ray Neutron Sensor (CRNS) data collected via rail-based measurements into the mesoscale Hydrologic Model (mHM). Over ten months, daily CRNS observations had been acquired along a 9-km railway corridor and subsequently aggregated to a $\sim$ 200\,m, spatial resolution to align with the mHM resolution. Soil moisture related model parameters were optimized for distinct land cover types based on observed soil moisture dynamics, including dense forest, open forest, meadow, and railway shunting areas. Model simulations exhibited considerable improvements with Nash-Sutcliffe Efficiency (NSE) values increasing from -0.19 to 0.76 in the dense forest, and from 0.50 to 0.79 in the meadow with homogeneous land cover conditions. In contrast, areas characterized by mixed land use—such as half-open forests and railway yards exhibited lower performance, indicating areas of improvement in the model-data fusion scheme, including higher resolution that may be necessary to fully capture local variability. Further, results of the spatio-temporal analysis demonstrated the model ability to reproduce observed spatial patterns of CRNS-derived soil moisture with the spatial efficiency (SPAEF) score of 0.71 (1.0 being an ideal one). Finally, the transferability of the optimized parameters was evaluated by applying them to independent sites located 38-345 km away from the original measurement corridor. The reasonably good agreement between simulated and observed soil moisture at grassland sites further confirms the robustness and applicability of our model-data fusion approach, while substantial biases remain in forest sites. Overall, the integration of mobile CRNS measurements represents a new era for hydrological modeling by providing unprecedented spatial resolution and temporal coverage to facilitate more precise soil moisture estimations for effective water resource management and forecasting of floods and droughts.