Speaker
Description
Accurate hydrological simulations, particularly those aimed at reproducing extreme floods, require rainfall data with fine spatiotemporal resolution. Collecting such data through conventional monitoring networks involves considerable installation and maintenance costs, which substantially increase the overall cost of hydrological modelling. Opportunistic sensors, such as commercial microwave links (CMLs), have long been recognised as a promising alternative for obtaining high-quality rainfall information. However, their application in hydrological modelling has largely been restricted to small, urbanised catchments and to fully-distributed hydrological models. To enable their usage in larger catchments and/or with semi-distributed models, specific guidelines are required to inform key modelling- and data-processing decisions.
This study investigates the implications of selecting: (1) a specific approach to CML signal processing for rainfall estimation, (2) alternative rainfall data sources, and (3) a spatial interpolation method. The first modelling decision is examined by comparing hydrological simulations derived from different CML calibration methods. The second decision is assessed by analysing model performance when forced with CML rainfall as a standalone input, and in a hybrid configuration that merges CML estimates with conventional rain gauge observations. The third decision is examined by applying multiple variants of the inverse distance weighting (IDW) method, as well as the nearest neighbour method to calculate subcatchment-averaged rainfall as the input for the semi-distributed model.
The impact of each modelling decision is evaluated based on model performance in reproducing 12 flood events at the outlet of the pre-alpine, peri-urban Lambro catchment in the north of Italy. The results show that CML calibration using local ground truth data, and merging with rain gauge data can improve model performance. Model performance can also be improved by applying the IDW method with an exponent of three and a short allowable distance between a subcatchment centroid and a link (e.g., 5 km). The greatest added value of using CML rainfall data for hydrological modelling is noted in simulations of the most extreme floods, when CML rainfall can even outperform the model forced by the conventional rain gauge data. These improvements are most evident in reproducing flood dynamics, which is represented by the Nash-Sutcliffe efficiency coefficients, and peak flows, as opposed to model performance in reproduction of hydrograph volume. These findings strongly encourage further research on integrating CML rainfall data into operational hydrological modelling, especially in flood flow ranges.