1. Water level observations from Unmanned Aerial Vehicles (UAVs) for improving probabilistic estimations of interaction between rivers and groundwater
Filippo Bandini1, Michael Butts2, Torsten Vammen Jacobsen2, and Peter Bauer-Gottwein1
1Department of Environmental Engineering, Technical University of Denmark, 2800 Lyngby,
2Water Resources Department, DHI, 2970 Hoersholm

2. Water level measurements: Ground-based VS Satellite VS Airborne
Pros:
Accurate
High temporal resolution
Cons:
Expensive
Need maintenance
Non uniform spatial distribution
Pros:
Very good spatial coverage (polar orbit satellites)
Often open source data
Measurements available for some years
Cons:
Temporal resolution depending on orbit configuration
Low spatial resolution
Influenced by atmosphere
Pros:
Low Cost and Flexibility of the payload
High Spatial Resolution
Flexible timing of the sampling
Cons:
Legislation
Repeatability

3. Water level measurements: Applications in hydrology
Water level measurements: Applications in hydrology
A variety of hydrological models could benefit from spatially distributed water level measurements, including:
Flood Propagation Mapping
Hydrologic monitoring of sinkholes, ephemeral lakes and other unconventional targets
Remote sensing of ice and snow depth
Urban flood in Copenhagen
Sacred Blue Cenote, Mexico.
Arctic region
All of these applications require cm accuracy of the orthometric water height

4. Application: calibration of a river model
Only few measurement stations record water level along river courses
Obtaining water level measurements with very high spatial resolution can help in retrieving accurate longitudinal water profiles. An accurate estimation of the river water level is essential for improving estimation of groundwater-surface water interaction.
Horizontal profile of profile of Mølleåen river (Denmark): in the shown branch there are 8 weirs which play an important role in regulating water level

5. Aim: improvement of groundwater surface water interaction estimation
Topic & aim:
Estimation of surface water-groundwater interaction in Mølleåen river (Denmark) by model calibration against both water level and discharge using DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm.
Model: Truly integrated model of groundwater, surface water, recharge and evapotranspiration (MIKE SHE-MIKE 11 model).

6. Flowchart of the river model calibration
Only discharge observations were available. Synthetic UAV water level observations have been extracted.
Two calibrations have been performed :
Against discharge only
Against both discharge and water level observations
Calibration parameters were river structures head loss coefficients and datum of some of the river cross sections
Probabilistic predictions of surface water–groundwater interaction have been produced to compare the two calibration methodologies.

7. Calibration against discharge
Calibration against discharge observations for a specific cross section:
Red dots are the discharge observations, dark grey region is the 95% confidence intervals of the output prediction due to parameter uncertainty, light grey region represents the remaining 95% prediction uncertainty due to uncertainty in forcing conditions and model structural errors

8. Calibration against water levels
Calibration against spatially distributed water level observations for a specific time step:
Water level is plotted as relative to datum. Green dots are water level observations, dark grey region is the 95% confidence intervals of the output prediction due to parameter uncertainty, light grey region represents the remaining 95% prediction uncertainty due to uncertainty in forcing conditions and model structural errors

9. Groundwater-SurfaceWater interaction (I), water level calibration
Plot of the 95% posterior uncertainty ranges for time-averaged baseflow over the entire branch:
Groundwater-SurfaceWater interaction (I), water level calibration
Baseflow after discharge calibration
Green dots are baseflow observations,
grey color region is the uncertainty
due to parameter uncertainty
Baseflow after water level calibration

10. Baseflow after discharge calibration after water level calibration
Groundwater-SurfaceWater interaction (II), Discharge vs Water level calibration
95% uncertainty ranges for the baseflow time series for a specific cross section:
Baseflow after discharge calibration
Green dots are baseflow observations,
grey color region is the uncertainty
due to parameter uncertainty
Baseflow
after water level calibration

11. Baseflow after discharge calibration after water level calibration
Groundwater-SurfaceWater interaction (III), Discharge vs Water level calibration
95% uncertainty ranges for the baseflow time series for a specific cross section:
Baseflow after discharge calibration
Green dots are baseflow observations,
grey color region is the uncertainty
due to parameter uncertainty
Baseflow
after water level calibration

12. Conclusions
When the model was calibrated against UAV spatially distributed water level measurements, estimations of surface water groundwater interaction drastically improved. In particular:
Sharpness of the predictions is approximately halved
RMSE (Root Mean Square Error) of the maximum a posteriori probability is reduced (approximately by two-thirds)
Interval Skill Score (ISS) is significantly decreased
Brier score of the binary outcome, either gaining or loosing river, has shown a slight decrease

13. Thanks for your attention
Poster board
A.117