HESSD

Hydrology and Earth System Sciences Discussions

HESSD

Hydrol. Earth Syst. Sci. Discuss.

1812-2116

Göttingen, Germany

10.5194/hess-2019-683

On the value of high density rain gauge observations for small Alpine headwater catchments

Michelon

Anthony

https://orcid.org/0000-0002-6839-5593

¹ Benoit

Lionel

¹ Beria

Harsh

https://orcid.org/0000-0003-2597-9449

¹ Ceperley

Natalie

https://orcid.org/0000-0002-2260-8426

¹ Schaefli

Bettina

https://orcid.org/0000-0003-1140-6244

¹ ²

Institute of Earth Surface Dynamics (IDYST), Faculty of Geosciences and Environment, University of Lausanne, Lausanne, 1015, Switzerland

now at: Institute of Geography (GIUB), Faculty of Science, University of Berne, Switzerland

PP00P2\_157611

21 01 2020

2020 1 31

2020

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://hess.copernicus.org/preprints/hess-2019-683/

The full text article is available as a PDF file from https://hess.copernicus.org/preprints/hess-2019-683/hess-2019-683.pdf

Spatial rainfall patterns exert a key control on the catchment scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps where summer rainfall events were monitored during 3 months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help constrain hydrologic processes. The framework is based on two steps, the identification of key hydro-meteorological metrics that explain the runoff coefficient and lag times (e.g. total event rainfall, center of mass of the precipitation field) and the identification of the optimal rain gauge network density to reliably reproduce the value of these metrics. The hydrological metrics are evaluated through correlation and regression analysis, resulting in the identification of three main drivers for the runoff coefficient and for runoff response lag times: the areal rainfall, the spatial asymmetry of the rainfall field and the antecedent rainfall over the three days preceding an event. The relationships between these measures and the optimal network density gives insights into the importance of reliably observing the localisation of incoming rainfall. Even at the small spatial scale of this case study, the results show that an accurate representation of the rainfall field, with at least two rain gauges, is of prime importance to understand the hydrologic response. The largely data-based analysis framework developed here is readily transferable to other settings. Given the low cost of the deployed rainfall sensor network, the approach has potential for future detailed studies in to-date sparsely observed catchments. Future work could in particular also refine the presented analysis by improving the design of the rain gauge deployment to ensure a good representation of geomorphological units and of the relative distances to the stream network.