Afid Nur Kholis
Department of Environmental Informatics
Helmholtz Centre for Environmental Research - UFZ
Permoserstr. 15
04318 Leipzig, Germany
2025 (2)
- Kholis, A., Kalbacher, T., Boeing, F., Cuntz, M., Samaniego, L. (2025):
1-D Richards equation or infiltration capacity approaches? A comparative assessment in mesoscale hydrologic modelling across 201 German basins
EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025
EGUsphere
Copernicus Publications, EGU25-7161 10.5194/egusphere-egu25-7161 - Kholis, A., Kalbacher, T., Rakovec, O., Boeing, F., Cuntz, M., Samaniego, L. (2025):
Evaluating Richards equation and infiltration capacity approaches in mesoscale hydrologic modelling
Water Resour. Res. 61 (8), e2024WR039625 10.1029/2024WR039625
LISA PhD College
The goal of the PhD college LISA is to create a consistent scaling framework for LandTrans by exploring model interfaces and providing a scientific basis for spatial and temporal scaling between the coupled models covering the water, carbon, and nutrient cycle and their parametrization at regional landscapes. For this purpose, the model interfaces and scaling strategies developed in LISA will be examined at various use-cases (i.e. demonstrator catchments).
PhD Topic: Groundwater recharge and soil moisture, two interacting key parameters at the interface between soil and groundwater hydrology
Groundwater recharge and soil moisture are both key variables within the water balance and their alteration not only directly impacts the hydrological cycle, but also significantly affects the carbon and nutrient cycles. Therefore, it is important to understand where and how their interplay is changing due to climate change and land use, and the implications of changing distribution and dynamics on water availability for humans and nature. Scenario analysis, which use the interconnected models that simulate parts of the hydrologic cycle (i.e., OGS, mHM, FORMIND) are needed to investigate this. Therefore, a new interface between these models is needed that allows feedback's in both directions, and is SMART in doing so. This will allow to summarize the influencing factors of each other's model across scales and transfer them back and forth between the models as effective quantities. Scaling through this interface is of great interest in two respects.