Rainfall data retrieved from Commercial Microwave Links (CMLs) have been already used to predict the runoff from urbanized or natural catchment in preliminary studies. In the recent publication, Jaroslav Pastorek and his colleagues from Czech Technical University in Prague quantify the reliability of discharge predictions in the small urbanized catchment with 3 years of runoff and CML rainfall observations. The study suggest how to reduce systematic errors in CML rainfall observations and assesses the potential of such precipitation estimates for discharge predictions in small urban catchments. CML rainfall retrieval is optimized using flow data observed at the catchment outlet or hourly rain rates from rain gauges located at different distances from the catchment. To quantify uncertainties of runoff predictions, the deterministic hydrodynamic model is extended by a stochastic error explicitly accounting for model bias. Resulting runoff prediction intervals, namely their width and reliability, show that optimized CML rainfall data predict discharges only slightly worse than those based on benchmark rain gauges located in the catchment.
The figure shows an example of a predicted hydrograph at the outlet from the catchment using rainfall data from municipal (top B), CMLs optimized by these gauges (middle A2), and local „benchmark“ rain gauges (bottom C). The measured reference hydrograph is displayed by circles. Below each graph, the performance metrics quantify the agreement with measured reference and the reliability of simulated hydrograph within the predicted uncertainty bounds.
For further information, have a look at the article: https://doi.org/10.1016/j.jhydrol.2022.129051