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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.