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

Etter, Simon; Strobl, Barbara; Seibert, Jan; Meerveld, H. J. Ilja van

doi:10.1029/2019wr026108

Cited by 28 publications

(28 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the other catchments with a high flashiness index (A6 and A9), the high flows were also captured well (10 and 13% of the observations at times when the water level was higher than the 90th percentile, respectively). A (small) bias in high flow observations can be beneficial for hydrological model calibration based on WL‐class data (Etter, Strobl, Seibert, & van Meerveld, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…As a first test, Strobl, Etter, van Meerveld, and Seibert (2019a) asked passers‐by at 10 river locations in Switzerland to estimate both the streamflow and the water level class based on the virtual staff gauge (hereafter shortened to WL‐class) and quantified the errors of these estimates. These errors were later used to create synthetic streamflow and WL‐class time series in two model studies to explore the potential value of such data for model calibration (Etter, Strobl, Seibert, & van Meerveld, 2020; Etter, Strobl, Seibert, & van Meerveld, 2018. These studies showed that the estimates of streamflow were not accurate enough to be informative for hydrological model calibration, but that WL‐class estimates significantly improved model performance compared to the situation without any data.…”

Section: Introductionmentioning

confidence: 99%

“…Citizen science data can be biased in time (Courter, Johnson, Stuyck, Lang, & Kaiser, 2013). This affects the value of these data for hydrological model calibration (Etter, Strobl, Seibert, & van Meerveld, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quality and timing of crowd‐based water level class observations

Etter

Strobl

Meerveld

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes (WL-classes). To investigate the quality of these observations, we compared the WL-class data with "real" (i.e., measured) water levels from the same stream at a nearby gauging station. We did this for nine locations where citizen scientists reported multiple observations using a smartphone app and at 12 locations where signposts were set up to ask citizens to record observations on a paper form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was better than for the forms. A possible explanation is that for each app location, a single person submitted the vast majority of the observations, whereas at the locations of the forms almost every observation was made by a different person. On average, there were more contributions between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the locations were data were collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application and the virtual staff gauge are a promising approach for crowd-based water level class observations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quality and timing of crowd‐based water level class observations

Etter

Strobl

Meerveld

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Validation data, obtained in a flood extent survey involving 44 community member's reporting on flood depths a few hours after the simulated rainfall event, proved to be valuable to validate flood modeling results. Earlier studies have shown that community observations provide a valuable source of information for flood risk analysis (Paul et al, 2018;Etter et al, 2019;Strobl et al, 2019). Even if the amount of data collected through flood surveys is small and the uncertainty of the flood depth classes cannot be quantified, the data still provides an effective form of validation.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, validation data to evaluate the performance of flood models is often missing. Previous research suggests that citizen observations offers an alternative data source in data-scarce environments to estimate water levels for model calibration (Etter et al, 2019;Strobl et al, 2019). Hence, we propose a framework for acquisition of drainage data and water levels required for flood model development and validation through community mapping.…”

Section: Introductionmentioning

confidence: 99%

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

et al. 2020

View full text Add to dashboard Cite

In this paper we demonstrate a framework for urban flood modeling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modeling on a level of detail that is currently inaccessible in many data-scarce environments. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.

show abstract

Improving calibration of groundwater flow models using headwater streamflow intermittence

Abhervé,

Roques,

de Dreuzy

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

Non‐perennial streams play a crucial role in ecological communities and the hydrological cycle. However, the key parameters and processes involved in stream intermittency remain poorly understood. While climatic conditions, geology and land use are well identified, the assessment and modelling of groundwater controls on streamflow intermittence remain a challenge. In this study, we explore new opportunities to calibrate process‐based 3D groundwater flow models designed to simulate hydrographic network dynamics in groundwater‐fed headwaters. Streamflow measurements and stream network maps are considered together to constrain the effective hydraulic properties of the aquifer in hydrogeological models. The simulations were then validated using visual observations of water presence/absence, provided by a national monitoring network in France (ONDE). We tested the methodology on two pilot unconfined shallow crystalline aquifer catchments, the Canut and Nançon catchments (Brittany, France). We found that both streamflow and stream network expansion/contraction dynamics are required to calibrate models that simultaneously estimate hydraulic conductivity and porosity with low uncertainties. The calibration allowed good prediction of stream intermittency, both in terms of flow and spatial extent. For the two catchments studied, Canut and Nançon, the hydraulic conductivity is close reaching 1.5 × 10−5 m/s and 4.5 × 10−5 m/s, respectively. However, they differ more in their storage capacity, with porosity estimated at 0.1% and 2.2%, respectively. Lower storage capacity leads to higher groundwater level fluctuations, shorter aquifer response times, an increase in the proportion of intermittent streams and a reduction in perennial flow. This new modelling framework for predicting headwater streamflow intermittence can be deployed to improve our understanding of groundwater controls in different geomorphological, geological and climatic contexts. It will benefit from advances in remote sensing and crowdsourcing approaches that generate new observational data products with high spatial and temporal resolution.

show abstract

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

Cited by 28 publications

References 56 publications

Quality and timing of crowd‐based water level class observations

Quality and timing of crowd‐based water level class observations

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

Improving calibration of groundwater flow models using headwater streamflow intermittence

Contact Info

Product

Resources

About