Updating annual rainfall maxima statistics in a data-scarce region

The growing number of extreme hydrological events observed has raised the level of attention toward the impact of climate change on rainfall process, which is difficult to quantify given its strong spatial and temporal heterogeneity. Therefore, the impact of the climate cannot be determined on the individual hydrological series but must be assessed on a regional and/or district scale. With this objective, the present work aims at identifying the trends and dynamics of extreme sub-daily rainfall in southern Italy in the period 1970-2020. The database of annual maxima was assembled using all available rainfall data (provided by the National Hydrographic and Mareographic Service - SIMN, and the Regional Civil Protection). However, due to the numerous changes (location, type of sensor, managing agencies) experienced by the national monitoring network, the time-series were found to be extremely uneven and fragmented. Since the spatio-temporal discontinuity could invalidate any statistical analysis, gap-filling techniques (deterministic and/or geostatistical [Teegavarapu, 2009]) were applied to reconstruct the missing data. In particular, the “Spatially-Constrained Ordinary Kriging” (SC-OK) method [Avino et al., 2021] was used, namely a mixed procedure that adopts the Ordinary Kriging (OK) approach with the spatial constraints of the Inverse Distance Weighting (IDW) method. The SC-OK method allows to reconstruct only missing data for stations selected by the IDW method (those with a sufficient number of functioning neighbouring rain gauges). Then, the reconstructed dataset has been used to explore trends and regional patterns in annual maxima highlighting, how rainfall are evolving in the most recent years.

REFERENCES

Avino, A., Manfreda, S., Cimorelli, L., and Pianese, D. (2021). Trend of annual maximum rainfall in Campania region (Southern Italy). Hydrological Processes, 35.

Teegavarapu, R.S.V. (2009). Estimation of Missing Precipitation Records Integrating Surface Interpolation Techniques and Spatio-temporal Association Rules. Journal of Hydroinformatics, 11(2).