A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects
Abstract
:1. Introduction
Model | Name | Methodology | Input Variables | Strengths | Constrains | References |
---|---|---|---|---|---|---|
Simplified | Stephens–Stewart Makkink, Doorenbos–Pruitt, Jensen–Haise | Empirical relations between evaporation and solar radiation | Air temperature, solar radiation | Very low data demand If properly tuned, they can provide cost-effective evaporation estimates | Site-specific, they require some empirical coefficients to be calibrated | [36,37,38,39] |
Blaney–Criddle, | Empirical relations between evaporation and daylight hours | Air temperature, daylight hours | [16,36,40] | |||
Papadakis, Thornthwaite | ||||||
Mass balance | Calculates the water budget of the water body | Inflows, rainfall, runoff, outflows, water levels | Very accurate if the water budget is properly closed | Extremely data demanding Require continuous and accurate measurements of the input variables | e.g., [41] | |
Energy budget | BREB RS-based (SEBAL, METRIC, SEBS, AquaSEBS) | Calculate all the energy contributions entering and exiting the water body | Solar radiation, water temperature, air temperature, wind speed, relative humidity | Provide a comprehensive description of the energy balance. If RS-based, they provide evaporation maps | Data demanding | [13,17,31,42] [25,27,28,33] |
Mass transfer or bulk transfer | Dalton’s law (Dalton 1802) | Water temperature, air temperature, wind speed, relative humidity | Low data demand | Requires the tuning of the wind function | e.g., [43] | |
Combined | Priestly–Taylor, de Bruin, Brutsaert and Sticker, Penman | Combine radiative and aerodynamic terms | All variables required by the energy budget and mass transfer models | Most accurate Consider the energy stored by the water body | Most data demanding | [44,45,46,47,48] |
2. Materials and Methods
2.1. The LakeVap Tool
2.1.1. Formulation
2.1.2. Inputs and Outputs of the Model
- Water temperature , from now on referred to as Lake Surface Water Temperature (LSWT) from EO data at the satellite overpass time (i.e., between 9:30 and 10:30 UTC in the CCI-Lakes dataset). We took 10:00 UTC as a reference time;
- Downward shortwave radiation ;
- Air temperature ;
- Wind speed ;
- Relative humidity .
- Maps of instantaneous evaporation () at satellite overpass;
- Maps of daily evaporation () estimated during the 24 h including and after the satellite overpass (e.g., from 10:00 UTC to 09:00 UTC of the following day based on the CCI-Lakes dataset reference time). Daily evaporation is obtained by summing the hourly values estimated over the 24 h from the satellite overpass time.
2.2. Study Site
2.3. Data
2.3.1. Remote Sensing Imagery from CCI-Lakes Database
2.3.2. Meteorological Data
2.3.3. Delft3D Hydro-Thermodynamic Model
2.4. LakeVap Tool Runs and Evaluation Metrics
- LakeVap tool fed with WRF meteorological data (spatial average of grid points over the lake surface) from 2004 to 2018 (Section 3.1, Section 3.2 and Section 3.3);
- LakeVap tool fed with MET1 meteorological data (single-point values) from 1995 to 2019 (Section 3.2 and Section 3.3);
- LakeVap tool fed with MET2 meteorological data (single-point values) from 2012 to 2018 (Section 3.2 and Section 3.3);
- LakeVap tool fed with ERA5 meteorological data (spatial average of grid points over the lake surface) from 1995 to 2019 (Section 3.2 and Section 3.3).
- 5.
- LakeVap tool fed by WRF meteorological data spatially averaged over the lake surface (i.e., item 1. in the previous list, taken as reference);
- 6.
- Three sets where one of the three atmospheric variables is imposed as spatially varying at the time (e.g., air temperature), while the other two (e.g., wind speed and relative humidity) are kept as uniform over the lake (from a spatial average of WRF fields);
- 7.
- Three sets where pairs of atmospheric variables are given as spatially varying (air temperature and wind, relative humidity and wind, air temperature and relative humidity), while the remaining one is kept as uniform over the lake (from a spatial average of WRF fields);
- 8.
- LakeVap tool fed by all atmospheric fields from WRF.
3. Results
3.1. Long Term Evaporation Estimates
3.2. Validation of LakeVap Products with Delft3D Outputs
3.3. Implications of the Use of Different Meteorological Datasets
3.4. Spatial Variability of Evaporation
4. Discussion
4.1. Evaporation Estimates for Lake Garda
4.2. Sensitivity to Meteorological Forcing and Morphological Complexity of the Test Site
4.3. The LakeVap Tool and Applicability to Other Lakes
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Code Availability
References
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Type of Data | Time Availability | ID | Data Provider | Frequency | Spatial Resolution |
---|---|---|---|---|---|
LSWT maps | 1995–2019 | CCI-Lakes | ESA | Daily to weekly | 100 m |
In-situ weather data | 1990-date | MET1 | FEM | Hourly | Single point |
2012-date | MET2 | ARPA-Lombardia | Hourly | Single point | |
Global model weather data | 1950-date | ERA5 | ECMWF | Hourly | 0.1° (~11 km) |
Regional model weather data | 2004–2018 | WRF | [35,59] | Hourly | 2 km |
Simulated instantaneous evaporation maps | 2004–2018 | Delft3D | [35] | Daily | 100–400 m |
Instantaneous Evaporation | ||||||
---|---|---|---|---|---|---|
Meteorology Source | RMSD | BIAS | Corr | NSE | Mean ± Std LakeVap | Mean ± Std Delft3D |
(mm/h) | (mm/h) | (-) | (-) | (mm/h) | (mm/h) | |
WRF | 0.04 | 0.006 | 0.92 | 0.834 | 0.129 ± 0.081 | 0.128 ± 0.098 |
MET1 | 0.087 | −0.02 | 0.517 | 0.218 | 0.101 ± 0.058 | |
MET2 * | 0.081 | 0.001 | 0.614 | 0.308 | 0.128 ± 0.0858 | |
ERA5 | 0.091 | −0.045 | 0.606 | 0.129 | 0.078 ± 0.044 | |
Lake Surface Water Temperature | ||||||
LSWT source | RMSD | BIAS | Corr | NSE | Mean ± Std CCI-Lakes | Mean ± Std Delft3D |
(°C) | (°C) | (-) | (-) | (°C) | (°C) | |
CCI-Lakes database | 1.745 | 0.536 | 0.958 | 0.906 | 14.81 ± 5.75 | 14.27 ± 5.95 |
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Matta, E.; Amadori, M.; Free, G.; Giardino, C.; Bresciani, M. A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects. Remote Sens. 2022, 14, 2636. https://doi.org/10.3390/rs14112636
Matta E, Amadori M, Free G, Giardino C, Bresciani M. A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects. Remote Sensing. 2022; 14(11):2636. https://doi.org/10.3390/rs14112636
Chicago/Turabian StyleMatta, Erica, Marina Amadori, Gary Free, Claudia Giardino, and Mariano Bresciani. 2022. "A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects" Remote Sensing 14, no. 11: 2636. https://doi.org/10.3390/rs14112636