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Water level observations from unmanned aerial vehicles for improving estimates of surface water–groundwater interaction
Author(s) -
Bandini Filippo,
Butts Michael,
Jacobsen Torsten Vammen,
BauerGottwein Peter
Publication year - 2017
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.11366
Subject(s) - environmental science , aquifer , hydrology (agriculture) , groundwater , surface water , calibration , sampling (signal processing) , temporal resolution , water level , remote sensing , groundwater model , water table , geology , groundwater flow , computer science , geography , environmental engineering , statistics , physics , geotechnical engineering , mathematics , cartography , filter (signal processing) , quantum mechanics , computer vision
Integrated hydrological models are usually calibrated against observations of river discharge and piezometric head in groundwater aquifers. Calibration of such models against spatially distributed observations of river water level can potentially improve their reliability and predictive skill. However, traditional river gauging stations are normally spaced too far apart to capture spatial patterns in the water surface, whereas spaceborne observations have limited spatial and temporal resolution. Unmanned aerial vehicles can retrieve river water level measurements, providing (a) high spatial resolution; (b) spatially continuous profiles along or across the water body, and (c) flexible timing of sampling. A semisynthetic study was conducted to analyse the value of the new unmanned aerial vehicle‐borne datatype for improving hydrological models, in particular estimates of groundwater–surface water (GW–SW) interaction. Mølleåen River (Denmark) and its catchment were simulated using an integrated hydrological model (MIKE 11–MIKE SHE). Calibration against distributed surface water levels using the Differential Evolution Adaptive Metropolis algorithm demonstrated a significant improvement in estimating spatial patterns and time series of GW–SW interaction. After water level calibration, the sharpness of the estimates of GW–SW time series improves by ~50% and root mean square error decreases by ~75% compared with those of a model calibrated against discharge only.