Details

Autor(en) / Beteiligte

• Guse, Björn
• Fatichi, Simone
• Gharari, Shervan
• Melsen, Lieke A.

Titel

Advancing Process Representation in Hydrological Models: Integrating New Concepts, Knowledge, and Data

Ist Teil von

• Water resources research, 2021-11, Vol.57 (11), p.n/a

Ort / Verlag

Washington: John Wiley & Sons, Inc

Erscheinungsjahr

2021

Quelle

Wiley Online Library

Beschreibungen/Notizen

• Model fidelity and accuracy in process representations have been the crux of scientific hydrological modeling, creating a pressing need for a better linkage between the development of hydrological models and the growing number of data sources and measurement techniques. Improved representation of process dynamics in hydrological models can provide new insights into complex hydrological systems and point out less understood natural phenomena that need further investigation. This special issue includes contributions that offer potential solutions and strategies to improve and test the representation of hydrological processes. We have organized the special issue contributions into four topical categories: (a) Beyond streamflow, which looks into the power of complementary data sources in addition to traditionally used streamflow for process inference. (b) Challenge of subsurface hydrology, that reflects on lesser understood processes under the surface and their impact on the model structure. (c) Evaporation in hydrological modeling, linking ecological aspects to the hydrological functioning of the natural system. Finally, (d) top down vs. bottom up modeling approaches, relied upon for process representation analysis. The special issue and our reflection on the contributions present a snapshot of ongoing efforts for integrating new concepts, knowledge, and data in process representation in hydrological models. Key Points Data sources, such as isotopes or remote sensing, are increasingly used for enhancing model simulations and process representation The added value of including specific (sub‐)processes in models depends on the relevance of the process in the research area Both top‐down and bottom‐up approaches are essential for reliable transfer of process knowledge to model structures