Rainfall‐Runoff Modeling Using Crowdsourced Water Level Data

Abstract

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-SR F 0.7, CS-SR F 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.

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

“…We aim to develop a methodology that is quick and easy to use for citizen scientists, while at the same time being robust and informative for the calibration of hydrological models and to thereby extend the knowledge on the potential of crowdsourced data with different qualities as proposed in Weeser et al (2019). We therefore investigated the potential value of discontinuous WL-class data as these can be obtained by citizens using synthetic data, which is a similar approach as in Etter et al (2018).…”

“…Water Resources Research on the potential of crowdsourced data with different qualities as proposed in Weeser et al (2019). We therefore investigated the potential value of discontinuous WL-class data as these can be obtained by citizens using synthetic data, which is a similar approach as in Etter et al (2018).…”

Advancing Process Representation in Hydrological Models: Integrating New Concepts, Knowledge, and Data

“…Weeser et al. (2019) highlighted that crowd‐sourced water level monitoring can shed light in process understanding of ungauged basins.…”

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

“…Many variables influence the yield of suspended sediments, and their partnerships with each other are extremely nonlinear and complex. Suspended sediment yields are therefore very difficult to estimate using traditional mathematical models such as multiple linear regression (MLR) and sediment rating curve (SRC) which do not provide satisfactory performance (Kiyoumars et al 2018;Weeser et al 2019). A typical feedforward neural network has its limitations when it comes to time-series data.…”