Improving design precipitation estimates by combining estimates from high-resolution adjusted radar data and long-term ombrographic measurements

Abstract Design precipitation estimates are crucial for hydrological modeling, flood risk assessment, and infrastructure planning. Traditional methods rely either on relatively sparse rain-gauge data or on areal but less reliable radar-derived precipitation data. We propose an innovative approach to improving the estimates by merging two design precipitation fields: (i) from adjusted high-resolution radar-derived intensities and (ii) from long-term ombrographic measurements. The proposed approach is demonstrated through application to Czechia on 1-h, 6-h, and 24-h design precipitation totals. Leave-one-out cross-validation identifies Empirical Bayesian Kriging of the ratios between ombrograph- and adjusted radar-derived design totals as the most effective merging method. The combined estimates offer enhanced spatial accuracy, better reflecting real spatial gradients of extreme precipitation. The resulting design precipitation fields reflect substantial spatial variability at shorter durations due to convective precipitation, whereas the highest 24-h design totals are concentrated in mountainous regions, consistent with large-scale circulation patterns driving prolonged stratiform rainfall. A comparison with previously developed datasets confirms the reliability of the new estimates, emphasizing the relevance of radar adjustments and regional frequency analysis. Moreover, the merging procedure increases underestimated radar-based short-term design totals, which highlights a key advantage of the proposed approach. The enhanced spatial resolution and reliability of the resulting design precipitation estimates enable more robust flood risk management, infrastructure design, and adaptation planning, particularly in this region with such diverse precipitation regimes. • We propose merging design precipitation values from radar and ombrographic data. • The merging procedure increases underestimated radar-based short-term design totals. • Resulting estimates better reflect real spatial gradients of extreme precipitation.