Rainfall Estimation with a Geosensor Network of Cars - Theoretical Considerations and First Results

Fitzner, Daniel; Sester, Monika; Haberlandt, Uwe; Rabiei, Ehsan

doi:10.1127/1432-8364/2013/0161

Cited by 10 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the approach used in several recent studies (Ewen et al, 2008;Finger et al, 2015;Fitzner et al, 2013;Haberlandt and Sester, 2010;Seibert and Beven, 2009), we pretended that only a small subset of the data were available. In addition, various degrees of inaccuracy were assumed.…”

Section: Methodsmentioning

confidence: 99%

Value of uncertain streamflow observations for hydrological modelling

Etter¹,

Strobl²,

Seibert³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract.Previous studies have shown that a hydrological model can be parameterized using on a limited number of streamflow measurements for otherwise ungauged basins. Citizen science projects can collect such data but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We address the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We 10 pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The initial inaccuracy level was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error-distribution was divided by two and four, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic time series. 15These included scenarios with one observation each week or month and scenarios that are more realistic for crowdsourced datasets with irregular distributions throughout the year or a focus on spring or summer. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are 20 informative and useful for model parameterization. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration. 25 IntroductionThe application of hydrological models usually requires several years of precipitation, temperature and streamflow data for calibration, but these data are only available for a limited number of catchments. Therefore, several studies have addressed the question how much data are needed to calibrate a model for a catchment where continuous streamflow data are not available.Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Section: Methodsmentioning

confidence: 99%

Value of uncertain streamflow observations for hydrological modelling

Etter¹,

Strobl²,

Seibert³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Cars (e.g., connected vehicles) are equipped with many sensors to aid the driver and enhance safety and comfort. These sensors also capture the immediate environment of the car using front cameras, backwards cameras, ultrasonic (for parking assistance), GPS, radar, rain-sensing wipers (Fitzner et al 2013). The information is stored on local systems and can be transmitted to the available infrastructure or other vehicles.…”

Section: Collection Of Geospatial Big Datamentioning

confidence: 99%

Geospatial big data handling theory and methods: A review and research challenges

Dragićević

Castro

et al. 2016

ISPRS Journal of Photogrammetry and Remote Sensing

Self Cite

387

218

View full text Add to dashboard Cite

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.

show abstract

“…In order to make streamflow estimates useful, the standard deviation of the error distribution of the estimates needs to be reduced by a fac-tor of 2. Gibson and Bergman (1954) suggest that errors in distance estimates can be reduced from 33 % to 14 % with very little training. These findings are encouraging, although their tests covered distances larger than 365 m (400 yards) and the widths of the medium-sized rivers for which the streamflow was estimated were less than 40 m (Strobl et al, 2018).…”

Section: Recommendations For Citizen Science Projectsmentioning

confidence: 99%

Value of uncertain streamflow observations for hydrological modelling

Etter

Strobl

Seibert

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

show abstract

Rainfall Estimation with a Geosensor Network of Cars - Theoretical Considerations and First Results

Cited by 10 publications

References 1 publication

Value of uncertain streamflow observations for hydrological modelling

Value of uncertain streamflow observations for hydrological modelling

Geospatial big data handling theory and methods: A review and research challenges

Value of uncertain streamflow observations for hydrological modelling

Contact Info

Product

Resources

About