Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland
Abstract
:1. Introduction
2. Characterization of the Study Area
2.1. Geology
2.2. Soils
2.3. Environmental Values
3. Materials and Methods
3.1. Estimation of Meteorological Variables
3.2. UAV Photogrammetry
3.2.1. Data Acquisition
3.2.2. Photogrammetric Processing and Accuracy Assessment
3.2.3. Post-Processing
- Vi: volume between elevation i and i−1.
- Ai: number of pixels with elevation ≤ i (obtained from the histogram) multiplied by the area of each cell.
- Ai−1: number of pixels with elevation ≤ i − 1 (obtained from the histogram) multiplied by the area of each cell.
- h: Difference of elevation between i and i − 1.
3.3. Hydrological Monitoring Network
3.4. Budget Adjustment and Estimation of Pond–Aquifer Transfers
- Infi: Infiltration volume on day “i” (m3).
- Pi: Precipitation on day “i” (m).
- Ai: Flooded area on day “i” (m2).
- DVi: Volume stored on day “i” minus the volume stored on day “i − 1” (in m3).
- Evi: Evaporation on day “i” (m).
4. Results and Discussion
4.1. Validation of the Photogrammetry-Derived Flood Model
4.2. Analysis of the Tidal Influence on the Aquifer
- (i)
- Small oscillations of the piezometric level (<2 cm) caused by semi-diurnal tidal cycles. These oscillations display certain delay with respect to the low tide. Such delay varies depending on tide amplitude and the previous history, and may even occur during high tides, as happened on December 2 and 3 of 2015. The low amplitude of these oscillations is justified by the low transmissivity of the aquifer. Such low transmissivity is the result of the thin saturated zone (whose thickness was estimated at about 3.5 m through geotechnical surveys in the study area) and the low permeability of the materials, which was estimated in laboratory at 3.3 m/day for fine sands with silt and clay contents <7.5%. These data are consistent with the results of a low-flow injection test (0.08 l/s) under transient regime carried out by the authors on the piezometer P4, which displayed a transmissivity of 15 m/day.
- (ii)
- The stabilization of the piezometric levels owing to spring tides, which makes the aforementioned general downward trend to cease. This phenomenon produces a low frequency oscillation (twice a month approximately) with an estimated amplitude of around 3 cm on the piezometric level. This can be explained by the inversion of hydraulic gradients during the part of the tidal cycle when the water stage in the channel is above the piezometric level, forcing a flux of seawater towards the aquifer. These inputs would compensate over several days the aquifer´s discharge towards the tidal channel when the tide level is below the piezometric level. When neap tides occur again, the piezometric level shows a downward trend with certain delay that evidences discharge into the tidal channel. Therefore, there is a discontinuous entry of seawater that constitutes a saline input into the coastal sector of the aquifer when the piezometric levels are significantly below the high spring tides.
4.3. Estimation of Groundwater Recharge from the Pond
5. Applicability of the Method, Limitations and Future Research
6. Conclusions
- The understanding of the hydrogeological and topographical context is critical to define groundwater–surface water interactions and the dynamics of seasonal water bodies. In this regard, the DTM obtained through SfM and ground-filtering treatments provided an accurate representation of the basin morphology with a cell size of 6.9 cm and a RMSEz of 5.9 cm. This product combined with water stage records at 10-min intervals, enabled to calculate pond´s stage and volume stored, as well as estimating the balance between water inputs and outputs over a rainy period of 70 days.
- A detailed hydrological analysis requires in situ precipitation records over the studied system since rainfall episodes display high spatial heterogeneity in narrow time windows. In fact, the correlation between the daily rainfall collected in the pond´s rain gauge network and that from the meteorological station located 6 km from the study area was weak.
- In this case study, the variable “pond stage” determines the variability of the infiltration rate over time. An empirical law was established between the infiltration rate and the hydrological variable with which the correlation is better: the water stage. This relationship enables us to calculate the infiltration rate under hydrological conditions that do not allow its determination by means of a water balance.
- The application of the proposed methodology has enabled us to quantify the elements of the water budget. During the study period, inflows into the pond accounted for 6200 m3 approximately, of which 40% corresponded to direct precipitation over the pond and 60% to surface runoff. Outputs equalled the inputs, with 41% attributable to direct evaporation from water surface and 59% to transfers into the aquifer.
- The precise definition of the local datum is fundamental to define the marine influence on the aquifer–wetland system, underground flows and flooding during spring tides or storms.
- The proposed methodology constitutes an efficient and economical alternative for elucidating and monitoring the functioning of complex groundwater–surface water systems located in similar hydrological contexts, where high spatial and temporal resolutions are required.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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First Mission (17 April 2017) | Second Mission (20 March 2018) | |
---|---|---|
System | ATYGES FV-8 | Phantom 3 |
Number of images | 305 | 91 |
Average GSD (cm) | 1.38 | 3.26 |
Key points per image (median) | 88,241 | 45,897 |
Matches per calibrated images (median) | 43,077 | 24,242 |
Number of GCP | 11 | 0 |
Number of 2D key points observations for bundle block adjustment | 12,709,676 | 2,143,762 |
Number of 3D points for bundle block adjustment | 4,595,853 | 797,045 |
Mean reprojection error (pixels) | 0.133 | 0.179 |
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García-López, S.; Vélez-Nicolás, M.; Martínez-López, J.; Sánchez-Bellón, A.; Pacheco-Orellana, M.J.; Ruiz-Ortiz, V.; Muñoz-Pérez, J.J.; Barbero, L. Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland. Remote Sens. 2022, 14, 6185. https://doi.org/10.3390/rs14246185
García-López S, Vélez-Nicolás M, Martínez-López J, Sánchez-Bellón A, Pacheco-Orellana MJ, Ruiz-Ortiz V, Muñoz-Pérez JJ, Barbero L. Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland. Remote Sensing. 2022; 14(24):6185. https://doi.org/10.3390/rs14246185
Chicago/Turabian StyleGarcía-López, Santiago, Mercedes Vélez-Nicolás, Javier Martínez-López, Angel Sánchez-Bellón, María Jesús Pacheco-Orellana, Verónica Ruiz-Ortiz, Juan José Muñoz-Pérez, and Luis Barbero. 2022. "Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland" Remote Sensing 14, no. 24: 6185. https://doi.org/10.3390/rs14246185
APA StyleGarcía-López, S., Vélez-Nicolás, M., Martínez-López, J., Sánchez-Bellón, A., Pacheco-Orellana, M. J., Ruiz-Ortiz, V., Muñoz-Pérez, J. J., & Barbero, L. (2022). Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland. Remote Sensing, 14(24), 6185. https://doi.org/10.3390/rs14246185