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...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
American Geophysical Union
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/112510 |
| _version_ | 1855521412985389056 |
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| author | Weeser, B. Jacobs, S. Kraft, P. Rufino, Mariana C. Breuer, Lutz |
| author_browse | Breuer, Lutz Jacobs, S. Kraft, P. Rufino, Mariana C. Weeser, B. |
| author_facet | Weeser, B. Jacobs, S. Kraft, P. Rufino, Mariana C. Breuer, Lutz |
| author_sort | Weeser, B. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace112510 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | American Geophysical Union |
| publisherStr | American Geophysical Union |
| record_format | dspace |
| spelling | CGSpace1125102025-12-08T09:54:28Z Rainfall-Runoff Modeling Using Crowdsourced Water Level Data Weeser, B. Jacobs, S. Kraft, P. Rufino, Mariana C. Breuer, Lutz water management water resources models 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. 2019-12 2021-03-08T08:35:48Z 2021-03-08T08:35:48Z Journal Article https://hdl.handle.net/10568/112510 en Open Access American Geophysical Union Weeser, B. Jacobs, S. Kraft, P. Rufino, M.C. Breuer, L. 2019. Rainfall-Runoff Modeling Using Crowdsourced Water Level Data. Water Resources Research, 55 (12) : 10856-10871. https://doi.org/10.1029/2019WR025248 |
| spellingShingle | water management water resources models Weeser, B. Jacobs, S. Kraft, P. Rufino, Mariana C. Breuer, Lutz Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title | Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title_full | Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title_fullStr | Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title_full_unstemmed | Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title_short | Rainfall-Runoff Modeling Using Crowdsourced Water Level Data |
| title_sort | rainfall runoff modeling using crowdsourced water level data |
| topic | water management water resources models |
| url | https://hdl.handle.net/10568/112510 |
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