Citizen science pioneers in Kenya – A crowdsourced approach for hydrological monitoring

Weeser, Björn; Kroese, Jaqueline Stenfert; Jacobs, Suzanne; Njue, N.; Kemboi, Z.; Ran, Aobo; Rufino, Mariana C.; Breuer, Lutz

doi:10.1016/j.scitotenv.2018.03.130

Cited by 79 publications

(80 citation statements)

References 27 publications

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“…The different temporal observation scenarios with precise water level data represent the case when a physical staff gauge is placed in a stream and passers-by read the level and transmit their observation, as it is done in the CrowdHydrology project (Lowry & Fienen, 2013), Cithyd (www.cithyd. com;Balbo & Galimberti, 2016), and a project in Kenya (Weeser et al, 2018).…”

Section: Value Of Wl-class Data For Hydrological Model Calibrationmentioning

confidence: 99%

“…Some recent examples of water quantity‐focused projects are the EU‐funded citizen observatories that aim to complement data collection by authorities, such as WeSenseIt (http://www.wesenseit.com; Lanfranchi et al, ), GroundTruth2.0 (https://gt20.eu), and SCENT (https://scent-project.eu). Projects that specifically focus on streamflow or water levels are CrowdHydrology in the United States (Lowry et al, ; Lowry & Fienen, ), Smartphones4Water in Nepal (http://www.smartphones4water.org; Davids et al, ), a project in Kenya (http://www.uni-giessen.de/hydro/hydrocrowd_kenya; Weeser et al, ), Cithyd in Italy (http://www.cithyd.com; Balbo & Galimberti, ), and CrowdWater (http://www.crowdwater.ch; Seibert, Strobl, et al, ). The CrowdWater project aims to explore the value of citizen science data and to collect water level class (WL‐class) data (Seibert, Strobl, et al, ), as well as qualitative data on soil moisture and the state of temporary streams (Kampf et al, ; Seibert, van Meerveld, et al, ), and riverine export of macro plastic.…”

Section: Introductionmentioning

confidence: 99%

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

Etter

Strobl

Seibert

et al. 2020

Water Resources Research

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While hydrological models generally rely on continuous streamflow data for calibration, previous studies have shown that a few measurements can be sufficient to constrain model parameters. Other studies have shown that continuous water level or water level class (WL‐class) data can be informative for model calibration. In this study, we combined these approaches and explored the potential value of a limited number of WL‐class observations for calibration of a bucket‐type runoff model (HBV) for four catchments in Switzerland. We generated synthetic data to represent citizen science data and examined the effects of the temporal resolution of the observations, the numbers of WL‐classes, and the magnitude of the errors in the WL‐class observations on the model validation performance. Our results indicate that on average one observation per week for a 1‐year period can significantly improve model performance compared to the situation without any streamflow data. Furthermore, the validation performance for model parameters calibrated with WL‐class observations was similar to the performance of the calibration with precise water level measurements. The number of WL‐classes did not influence the validation performance noticeably when at least four WL‐classes were used. The impact of typical errors for citizen science‐based estimates of WL‐classes on the model performance was small. These results are encouraging for citizen science projects where citizens observe water levels for otherwise ungauged streams using virtual or physical staff gauges.

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Section: Value Of Wl-class Data For Hydrological Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Etter

Strobl

Seibert

et al. 2020

Water Resources Research

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“…Even though these early studies were very successful, similar approaches remained rare due to the logistical challenge to transmit and enter the collected data in a common database. However, recent developments in information and communication technology provide exciting new opportunities for citizen-science based approaches using text messages (Lowry and Fienen, 2013;Weeser et al, 2018), websites (e.g., Stream Tracker 1 ), apps (e.g., Teacher et al, 2013;Davids et al, 2018;Kampf et al, 2018; Photrack 2 ), data mining (Smith et al, 2015;Li et al, 2018) or custom-designed wearable sensors (e.g., Hut et al, 2016;smartfin 3 ). However, as stated by Jerad Bales, the Chief scientist for hydrology at the U.S. Geological Survey, "Crowdsourcing water-information is in its infancy [.…”

Section: Introductionmentioning

confidence: 99%

Virtual Staff Gauges for Crowd-Based Stream Level Observations

et al. 2019

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Hydrological observations are crucial for decision making for a wide range of water resource challenges. Citizen science is a potentially useful approach to complement existing observation networks to obtain this data. Previous projects, such as CrowdHydrology, have demonstrated that it is possible to engage the public in contributing hydrological observations. However, hydrological citizen science projects related to streamflow have, so far, been based on the use of different kinds of instruments or installations; in the case of stream level observations, this is usually a staff gauge. While it may be relatively easy to install a staff gauge at a few river sites, the need for a physical installation makes it difficult to scale this type of citizen science approach to a larger number of sites because these gauges cannot be installed everywhere or by everyone. Here, we present a smartphone app that allows collection of stream level information at any place without any physical installation as an alternative approach. The approach is similar to geocaching, with the difference that instead of finding treasurehunting sites, hydrological measurement sites can be generated by anyone and at any location and these sites can be found by the initiator or other citizen scientists to add another observation at another time. The app is based on a virtual staff gauge approach, where a picture of a staff gauge is digitally inserted into a photo of a stream bank or a bridge pillar, and the stream level during a subsequent field visit to that site is compared to the staff gauge on the first picture. The first experiences with the use of the app by citizen scientists were largely encouraging but also highlight a few challenges and possible improvements.

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“…However, citizen science projects that collect streamflow or stream level data in flowing waterbodies are 20 still rare. Two examples are the CrowdHydrology project (Lowry and Fienen, 2013) and a project in Kenya (Weeser et al, 2018), which both ask citizens to read stream levels at staff gauges and to send these as text messages to a central database.…”

Section: Introductionmentioning

confidence: 99%

Value of uncertain streamflow observations for hydrological modelling

Etter¹,

Strobl²,

Seibert³

et al. 2018

Preprint

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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

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Citizen science pioneers in Kenya – A crowdsourced approach for hydrological monitoring

Cited by 79 publications

References 27 publications

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

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

Virtual Staff Gauges for Crowd-Based Stream Level Observations

Value of uncertain streamflow observations for hydrological modelling

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