@WLF6 - Sixth World Landslide Forum 2023

Published on 16 November 2023 at 12:05


6th World Landslide Forum - WLF6, Florence (Italy), 14 - 17 November 2023.

Radar-based rainfall estimation is a useful tool for processes monitoring from remote, in domaines including hydrological and environmental modelling, until applied geomorphology and landslides. However, such data may include systemic and natural perturbations that need to be corrected before using these data. To encompass this problem, adjustments based on raingauge observations are frequently adopted.
In this research, we compared the performance of different radar-raingauge merging procedures on a regional raingauge-radar network focusing on a selected number of rainfalls events.
The methods we studied are of 1) Kriging with External Drift (KED) interpolation (Wackernagel 1998), 2) Probability-Matching-Method (PMM, Rosenfeld et al., 1994), and 3) an Adjusted kriging mixed method exploiting the conditional merging (ADj) process (Sinclair-Pegram, 2005). The latter was made available by DPCN (Italian National Civil Protection Department), while methods 1) and 2) were applied on recorded raingauge rainfalls over the Tuscany Regional Territory at 15’ time-step, and CAPPI (Constant Altitude Plan Position Indicator) reflectivity data from the Italian radar network at 2000/3000/5000 m at 5’ and 10’. Relationships describing precipitation VS radar reflectivity were integrated and analysed as part of the development of the merging procedures. The comparisons between the three rainfall fields were based on the analyses of variance, Cumulative Distribution Function (CDF), and explicative coefficients such as BIAS, RMSE (Root Mean Square Error), MAD (Median Absolute Deviation).
In general, average rainfalls showed slight variability between the methods. Comparing CDFs, little differences were detected between KED and ADj with bias mostly pronounced in lower quantiles, while more marked differences are observed in higher quantiles for the ADj-PMM methods. The analyses showed different precipitation spatial patterns depending on the applied procedure, closer to the radar data when using ADj, and more reflecting the gauge’s data structure when adopting KED. The probabilistic method (PMM) had the advantage to account for gauge data while preserving the spatial radar patterns, thus confirming interesting perspectives. Comparing the performance of different radar-raingauge merging methodologies was useful in order to better understand the specificity of the available radar-data-elaborations at the regional scale that can be adopted for modelling procedures. It was possible to obtain a clearer picture of which methodology is the most suitable in terms of spatial and temporal representativeness, variance, and image patterns.

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