Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles

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.

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“…Bandini et al () describe a UAV‐borne system for retrieving orthometric water levels. The system comprises a GNSS and a ranging sensor.…”

Section: Methodsmentioning

confidence: 99%

“…Bandini, Jakobsen, Olesen, Reyna‐Gutierrez, and Bauer‐Gottwein () adopted a different approach, documenting the possibility of measuring accurate orthometric water levels from UAVs by using a system comprising a differential GNSS and a sensor to range water surfaces (radar).…”

Section: Introductionmentioning

confidence: 99%

Water level observations from unmanned aerial vehicles for improving estimates of surface water–groundwater interaction

Bandini

Butts

Jacobsen

et al. 2017

Hydrological Processes

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“…It pointed out that a RTK-GNSS survey may be sufficient to determine the requested tolerance [50]. Thus, in recent years, miniaturized components (GNSS receivers) have been adopted in field surveys [51]. Table 8.…”

Section: Uncertaintiesmentioning

confidence: 99%

Quantifying the Effects of Near-Bed Concentration on the Sediment Flux after the Operation of the Three Gorges Dam, Yangtze River

2017

Water

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Abstract:The regime of sediment transport in the Jingjiang Reach has significantly changed from quasi-equilibrium to sub-saturation since the impoundment of the Three Gorges Dam (TGD), and vertical profiles of suspended sediment concentration (SSC) have changed accordingly. Vertical profiles of SSC data measured at three hydrological stations in the Jingjiang Reach (Zhicheng, Shaishi, and Jianli), before and after the impoundment of TGD, were collected and analyzed. Analytic results indicate a remarkably large concentration in the near-bed zone (within 10% of water depth from the river-bed) in a sub-saturated channel. The maximum measured concentration was up to 15 times the vertical average concentration, while the ratio in quasi-equilibrium channel was less than four times that. Concentrations normalized with reference concentration at the same height, and may decrease with increasing values of suspension index (settling velocity over shear velocity). In addition, concentration near the water surface may be larger than concentration in the near-bed region when the suspension index is smaller than 0.01. Sediment flux transported in the near-bed zone may be up to 35% of the total sediment flux in unsaturated flows. The relationship between deviations of estimating sediment flux when ignoring the near-bed concentration and discharge in flood season and non-flood season are different in unsaturated and quasi-equilibrium channels. Analysis indicates that, in the quasi-equilibrium channel, more attention should be paid to near-bed concentration during non-flood season, the same as measurements during flood season with larger discharge.

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“…An overview of the wide variety of existing sensors can be found in Fraden [11]. Recently, there has been increasing interest in the development of image-based technologies for determining water level values (e.g., [12]). Griesbaum et al [13] developed a low-cost method for estimating flood elevation and inundation depth based on user-generated flood images.…”

Section: Introductionmentioning

confidence: 99%

“…The spreading use of unmanned aerial vehicles (UAVs) has paved the way for the integration of drone technology and optical sensing aimed at quantitatively estimating hydraulic data such as inundated areas (e.g., [16]), surface flow measurements (e.g., [17]), and water level estimations (e.g., [12]). Drones make possible the non-invasive monitoring of water bodies and are an efficient tool in difficult-to-access environments.…”

Section: Introductionmentioning

confidence: 99%

Water Level Measurements from Drones: A Pilot Case Study at a Dam Site

Ridolfi

Manciola

2018

Water

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Unmanned Aerial Vehicles (UAVs) are now filling in the gaps between spaceborne and ground-based observations and enhancing the spatial resolution and temporal coverage of data acquisition. In the realm of hydrological observations, UAVs play a key role in quantitatively characterizing the surface flow, allowing for remotely accessing the water body of interest. In this paper, we propose a technology that uses a sensing platform encompassing a drone and a camera to determine the water level. The images acquired by means of the sensing platform are then analyzed using the Canny method to detect the edges of water level and of Ground Control Points (GCPs) used as reference points. The water level is then retrieved from images and compared to a benchmark value obtained by a traditional device. The method is tested at four locations in an artificial lake in central Italy. Results are encouraging, as the overall mean error between estimated and true water level values is around 0.05 m. This technology is well suited to improve hydraulic modeling and thus provides reliable support to flood mitigation strategies.

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Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles

Cited by 67 publications

References 35 publications

Water level observations from unmanned aerial vehicles for improving estimates of surface water–groundwater interaction

Water level observations from unmanned aerial vehicles for improving estimates of surface water–groundwater interaction

Quantifying the Effects of Near-Bed Concentration on the Sediment Flux after the Operation of the Three Gorges Dam, Yangtze River

Water Level Measurements from Drones: A Pilot Case Study at a Dam Site

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