From global satellite water cycle products to field scale water states

M-GEO
WCC
Staff Involved
M-SE Core knowledge areas
Spatial Information Science (SIS)
Technical Engineering (TE)
Additional Remarks

This topic is supported by two on-going projects:

EcoExtreML project: Accelerating process understanding for ecosystem functioning under extreme climates with Physics-aware machine learning, https://research-software-directory.org/projects/ecoextreml, https://github.com/EcoExtreML, https://www.utoday.nl/science/70731/predicting-vegetation-health

WUNDER project: Water Use and Drought Ecohydrological Responses of Agricultural and Nature Ecosystems in the Netherlands: Towards Climate-Robust Production Systems and Water Management,

Topic description

Water resource management needs to consider a wide range of spatial scales and address a variety of problems, e.g. monitoring and managing droughts and water availability for different uses (root zone soil moisture, stream discharge as well as groundwater levels). Models provide an alternative to observations by bridging different scales and different processes, but the fidelity of model output strongly depends on the physical processes considered which in turn requires detailed information on the state of the soil and vegetation system and relevant forcings at the scale of interest. Therefore, there is a pressing need to harmonize the available information on the soil/vegetation system to reach a feasible approach for actual water management at field scales. A recent example is the 2022 summer drought, which posed challenges for water availability in vast regions in Europe, including some ill-prepared to cope with water scarcity. Climate change presents additional challenges regarding the preparedness and adaptation to future extremes, because e.g. similar or worse future events to that in 2022 may be expected more frequently.

Topic objectives and methodology

The objective is to deploy a data-driven approach, using existing in-situ measurement, satellite observations, model outputs (of STEMMUS-SCOPE), to develop a machine learning (ML)-based emulator (i.e., a surrogate of the original model), which can be used to produce a physically-consistent dataset for understanding

References for further reading

Wang, Y., Zeng, Y., Yu, L., Yang, P., Van Der Tol, C., Yu, Q., Lü, X., Cai, H., & Su, Z. (2021). Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0). Geoscientific Model Development, 14(3), 1379-1407. https://doi.org/10.5194/gmd-14-1379-2021

Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; Su, Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sens. 2021, 13, 4893. https://doi.org/10.3390/rs13234893

 

https://www.h2owaternetwerk.nl/h2o-actueel/wunder-project-onderzoekt-watergebruik-bij-extreme-droogte,

https://www.utwente.nl/en/research/themes/resilient/news-and-events/newsletter-v1/wunder-project/