Submit a paper to the Hydrological
Science Journal Special Issue following our workshop organized during the
2022 IAHS Assembly!
Objectives
For decades, hydrologists have been using time series of rainfall,
air temperature, water level and streamflow data to feed, calibrate and
evaluate statistical and deterministic hydrological models. These models
have many applications in hydrological engineering, such as estimating
the mean annual streamflow of an ungauged catchment, flood frequency
analysis at gauged and un-gauged sites, extrapolation of streamflow
response in changing climate (e.g. extreme floods and droughts), etc.
Unavoidably, the temporal series used within these methods are
characterized by epistemic and aleatory errors (e.g. incorrect
transcription of a handwritten value) and/or systematic inconstancies
(e.g. change of the gauging location). These errors raise the issue of
the (dis)informative content of the data used in hydrology and
specifically in rainfall-runoff modeling: are all periods equally
informative, are there seasonal trends? If some time series or periods
can be considered as disinformative, how is it possible to detect them
and what effects might they have on model identification and inference?
Are there criteria or methods (quantitative and/or qualitative) that
might be used to classify data in this way given different sources of
uncertainty, independent of the model being used? Might the
classification be different for catchments with more or less baseflow
for example? And if we can avoid using these series or periods in model
identification, will this have a significant impact on the performance
of the considered models and thus on their robustness?
We propose to organize a collaborative workshop during 2022 IASH
General Assembly to bring together a group of researchers around the
following question:
Can we quantify the (dis)information content of the uncertain
hydro-meteorological data series used in hydrology?
Participants will have to propose innovative solutions to identify
informative or disinformative data taking account of sources of data
uncertainty and to show how this classification might be used to produce
more robust hydrological calculations and models. The participants are
free to develop methods based on series analysis (e.g. analysis of
runoff coefficients), statistical tests, rainfall-runoff modeling or
machine learning. Contributions are welcomed in various domains: flood
frequency analysis, regionalization, rainfall-runoff modeling (both
lumped and semi-distributed), hydro-meteorological forecasting,
modelling of climate change impact on water resources, etc.
Potential road maps for the development of methods to identify
informative or disinformative data are given through the following types
of questions:
- If some periods of data are disinformative in model identification,
how can we validate or invalidate different potential model
structures?
- If some periods of data are disinformative in model identification
how can we assess predictability in flood frequency analyses or
simulations of future flow regime on a catchment?
- If some periods of data are disinformative for model identification,
how can we gain insight into the importance of different sources of
uncertainty in the modelling process?
To answer one or more of these questions, participants are invited to
test their methods on “their” catchment dataset or on catchment datasets
available online (e.g. CAMELS), and to discuss the obtained results in
terms of catchment characteristics (flashiness, baseflow or snow
influence, etc.)
The outputs of this workshop may be published in a dedicated
Hydrological Sciences Journal special issue!
Program
Workshop n°8: When are
hydrological data informative (or not)? Testing for information content
in the face of sources of uncertainty.
- Convener: Pierre Brigode | Co-conveners: Vazken
Andréassian, Stacey Archfield, Keith Beven, Louise Crochemore, Alexander
Gelfan, Julien Lerat & Ralf Merz.
- Orals: Wed, 01 Jun, 08:30–15:00 | Rooms Sully 1
and Sully 2
- Discussion and posters: Attendance Wed, 01 Jun, 08:30–15:00
| Rooms Sully 1 and Sully 2
- Abstracts
are available here
Orals (last update:
29/05/2022)
- 08:30 - 08:45: Workshop
introduction. P. Brigode & V. Andréassian.
- 08:45 - 09:00: Is
model calibration compensation for input errors? F.
Anwar & A. Bárdossy.
- 09:00 - 09:15: On
parameter instability in model building. P. Astagneau,
F. Bourgin, V. Andréassian & C. Perrin.
- 09:15 - 09:30:
Recalculation of historical streamflow series. Impact assessment
and valorization. A. Belleville & D. Sevrez.
- 09:30 - 09:45:
Dealing with uncertainty of rainfall-runoff parameters in a
stochastic simulation method for extreme flood estimation: milking the
most of a calibration dataset. E. Paquet.
- 09:45 - 10:00: Which
range of discharge data is most informative in the calibration of a
rainfall-runoff model? M. Saadi, C. Furusho-Percot &
S. Kollet.
Coffee break
- 10:30 - 10:45:
Hunting after shoals of red herrings - cataloguing
disinformative data and their causes. I. Westerberg
& J. Seibert.
- 10:45 - 11:00: Impact
of dubious data in streamflow time series on the efficiency and the
parameters of rainfall-runoff models. C. Thébault, C.
Perrin, V. Andréassian, G. Thirel & S. Legrand.
- 11:00 - 11:15: Value
of historical information for flood frequency estimation: case study on
the upper Rhine River. M. Lang, B. Renard, J. Le Coz
& M. Darienzo.
- 11:15 - 11:30:
Presentation of the posters and of the Hydrological Science
Journal Special Issue associated with the workshop.
P. Brigode.
- 11:30 - 12:00:
Discussions.
Lunch break
- 13:30 - 14:00:
Information and disinformation in data – can we do better as
a community? K. Beven.
- 14:00 - 15:00: Poster
tour.
Posters
- Choosing acceptable parameter
sets when calibrating a continental rainfall-runoff model (E-HYPE
v.4). C. Brendel, R. Capell, J. Musuuza, K. Isberg &
B. Arheimer.
- Why calibrate and average over
time if we can directly estimate parameters and their temporal evolution
from data? M. Hrachowitz
- Errors and uncertainties in
streamflow data. J. Le Coz, B. Renard, M. Darienzo, I.
Horner, F. Branger & M. Lang.
- Use of data assimilation to
improve rainfall-runoff model structure for climate change
projections. J. Lerat, F. Chiew, Zheng & D.
Robertson.
- Use of transfer entropy
between streamflow and forcing time series for the identification of
dominant rainfall-runoff dynamics and for the assessment of hydrological
similarity. M. Neri, Coulibaly & E. Toth.
- Evaluating the value of
crowd-based observations for hydrological modelling. J.
Seibert, F. Schwarzenbach, S. Blanco, M. Scheller, W. Ze & I. van
Meerveld.
- Can quantitative hydrological
models benefit from water quality measurements? Revisiting the baseflow
separation issue. G. Tallec, J. Tunqui Neira, V.
Andréassian & J.-M. Mouchel.
- Can observed information be
transferred from gauged to ungauged catchments? S. Tian,
J. Lerat, Renzullo, & Pipunic.
- On snow undercatch by
raingages: looking for the missing information. V.
Andréassian, Gevorgyan, Misakyan & Azizyan.
- Customizing large-scale
hydrological models for local applications: Is it a data information
extraction challenge? I. Pechlivanidis, & J.
Musuuza.
- Critical approach on the trend
analyses of groundwater levels time series: study case of Telangana
state, India. A. Selles, A. Paswan, S. Ferrant, B.
Dewandel & J-C. Marechal.
- Do hydrological model errors
spread across time and timescales? (withdrawn) P. Royer-Gaspard.
- On bad and good neighbors in a
hydrological regionalization perspective. (withdrawn) P. Brigode, F. Bourgin, V.
Andréassian, C. Perrin & L. Oudin.
- Using snow data to improve
realism of a semi-distributed hydrological model. (withdrawn) D. Ruelland.
- Efficiently exploiting the
information content of in-situ soil moisture measurements with machine
learning. (withdrawn)
R. Orth, S. O.