Internal processes in hydrological models: A glance at the Meuse basin from space

Research output: ThesisDissertation (TU Delft)

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Abstract

Contemplating the Meuse or any other river of the world, one may wonder about the journey of rain in becoming river. This fascinates hydrologists, as they develop theories to understand movement, storage and release of water through the landscape across climates. These theories are translated to hydrological models, which describe the complex reality in a simpler way. Models are then used to predict the hydrological cycle for the nearby or long-term future. This thesis aims to assist the Dutch Ministry of Infrastructure and Water Management in improving the reliability of hydrological modeling of the Meuse basin for operational and policy applications. Using in-situ and remote-sensing data, the value of representing additional processes in models is explored, as well as the creative use of additional data to improve hydrological predictions. First, water balance data is used to identify the potential presence of intercatchment groundwater flows (Chapter 3). These underground flow paths cross topographic catchment boundaries and mainly play a role in headwater catchments (< 500 km2) of the Meuse basin, which are underlain by productive aquifers. Representing this flux as a preferential threshold-initiated process improves low and high flow model performance and increases the consistency between modeled and remote-sensing estimates of actual evaporation. Besides the importance of quantifying the long-term hydrological partitioning of precipitation into streamflow, evaporation and potentially intercatchment groundwater flows, another key element of the hydrological response is the amount of water available in the root-zone of vegetation. The temporal dynamics of root-zone soil moisture control how much more water can be stored in the soil and how much water is available for transpiration. In Chapter 4, meaningful estimates of root-zone soil moisture are inferred from satellite observations of near-surface soil moisture, by establishing a link between the catchment-scale root-zone storage capacity and the Soil Water Index. Interestingly, hydrological models with different internal process representations of root-zone soil moisture, evaporation, snow and total storage at the catchment scale may lead to a similar aggregated streamflow response (Chapter 5). This discrepancy implies that models are not necessarily providing the right answers for the right reasons, as they cannot simultaneously be close to reality and different from each other. To circumvent the uncertainty of process representation, which is inherent to hydrological science, the use of multiple model structures is advocated for operational and policy applications. Nonetheless, testing the consistency between modeled hydrological behavior and independent remote-sensing data can foster model developments and lead to creating better models. Finally, we move beyond the use of historical in-situ and remote-sensing data to predict long-term hydrological behavior of the Meuse basin under projected global warming (Chapter 6). If environmental conditions change, it is likely to also assume ecosystem adaptation in response to climate change and a potential natural and/or anthropogenic shift in dominant species across the landscape. Non-stationarity in the representation of hydrological systems is introduced in a process-based model with three hydrological response units to account for the spatial variability of hydrological processes. More specifically, we adapt the root-zone storage capacity parameter using the information contained in the projected climate data. This is an important step forward in the great challenge of hydrological predictions under change. Despite data uncertainties and a lack of data at the required temporal and spatial resolutions, many possibilities are at hand with what is currently available to develop new theories, test and improve hydrological models. Requiring creativity, this is a beautiful challenge to further unravel the mysteries of the hydrological landscape.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Hrachowitz, M., Supervisor
  • Savenije, H.H.G., Supervisor
Award date17 Sep 2021
Print ISBNs978-94-6421-419-2
DOIs
Publication statusPublished - 2021

Keywords

  • hydrological modeling
  • Meuse basin
  • root-zone storage capacity
  • remote sensing
  • states and fluxes
  • intercatchment groundwater flow
  • Climate change
  • Soil Water Index

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