Rainfall-Runoff Modeling Using Crowdsourced Water Level Data

Complex and costly discharge measurements are usually required to calibrate hydrological models. In contrast, water level measurements are straightforward, and practitioners can collect them using a crowdsourcing approach. Here we report how crowdsourced water levels were used to calibrate a lumped hydrological model. Using six different calibration schemes based on discharge or crowdsourced water levels, we assessed the value of crowdsourced data for hydrological modeling. As a benchmark, we used estimated discharge from automatically measured water levels and identified 2,500 parameter sets that resulted in the highest Nash-Sutcliffe-Efficiencies in a Monte Carlo-based uncertainty framework (Q-NSE). Spearman-Rank-Coefficients between crowdsourced water levels and modeled discharge (CS-SR) or observed discharge and modeled discharge (Q-SR) were used as an alternative way to calibrate the model. Additionally, we applied a filtering scheme (F), where we removed parameter sets, which resulted in a runoff that did not agree with the water balance derived from measured precipitation and publicly available remotely sensed evapotranspiration data. For the Q-NSE scheme, we achieved a mean NSE of 0.88, while NSEs of 0.43 and 0.36 were found for Q-SR and CS-SR, respectively. Within the filter schemes, NSEs approached the values achieved with the discharge calibrated model (Q-SRF 0.7, CS-SRF 0.69). Similar results were found for the validation period with slightly better efficiencies. With this study we demonstrate how crowdsourced water levels can be effectively used to calibrate a rainfall-runoff model, making this modeling approach a potential tool for ungauged catchments.