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| Titel |
Water level observations from Unmanned Aerial Vehicles (UAVs) for improving probabilistic estimations of interaction between rivers and groundwater |
| VerfasserIn |
Filippo Bandini, Michael Butts, Torsten Vammen Jacobsen, Peter Bauer-Gottwein |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250123828
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| Publikation (Nr.) |
 EGU/EGU2016-3144.pdf |
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| Zusammenfassung |
| Integrated hydrological models are generally calibrated against observations
of river discharge and piezometric~head in groundwater aquifers. Integrated
hydrological models are rarely calibrated against spatially distributed
water level observations measured by either in-situ stations or spaceborne
platforms. Indeed in-situ observations derived from ground-based stations
are generally spaced too far apart to capture spatial patterns in the water
surface. On the other hand spaceborne observations have limited spatial
resolution. Additionally satellite observations have a temporal resolution
which is not ideal for observing the temporal patterns of the hydrological
variables during extreme events. UAVs (Unmanned Aerial Vehicles) offer
several advantages: i) high spatial resolution; ii) tracking of the water
body better than any satellite technology; iii) timing of the sampling
merely depending on the operators. In this case study the M{\o}lle{\aa}en
river (Denmark) and its catchment have been simulated through an integrated
hydrological model (MIKE 11-MIKE SHE). This model was initially calibrated
against observations of river discharge retrieved by in-situ stations and
against piezometric head of the aquifers. Subsequently the hydrological
model has been calibrated against dense spatially distributed water level
observations, which could potentially be retrieved by UAVs. Error
characteristics of synthetic UAV water level observations were taken from a
recent proof-of-concept study. Since the technology for ranging water level
is under development, UAV synthetic water level observations were extracted
from another model of the river with higher spatial resolution (cross
sections located every 10 m). This model with high resolution is assumed to
be absolute truth for the purpose of this work. The river model with the
coarser resolution has been calibrated against the synthetic water level
observations through Differential Evolution Adaptive Metropolis (DREAM)
algorithm, an efficient global Markov Chain Monte Carlo~(MCMC) in
high-dimensional spaces. Calibration against water level has demonstrated a
significant improvement of the estimation of the exchange flow between
groundwater and river branch. Groundwater flux and direction are now better
simulated. Reliability and sharpness of the probabilistic forecasts are
assessed with the sharpness, the interval skill score (ISS) of the 95{\%}
confidence interval, and with the root mean square error (RMSE) of the
maximum a posteriori probability (MAP). The binary outcome (either gaining
or loosing stream) of the flow direction is assessed with Brier score (BS).
After water level calibration the sharpness of the estimations is
approximately doubled with respect to the model calibrated only against
discharge, ISS has improved from 2.4$^{-7}$to 7.8$^{-8}$
$\frac{m^3}{s\cdot m}$, RMSE from 9.2$^{-8}$ to 2.4$^{-8
}\frac{m^3}{s\cdot m^}^{}$and BS is halved from 0.58 to 0.25. |
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