A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products
Abstract
:1. Introduction
- How does actual evapotranspiration dynamically evolve?
- What are the relevant and decisive factors that affect the AET changes?
- What kind of input and which machine learning algorithms can simulate the most intense future AET?
- How to integrate the results of multiple machine learning algorithms to generate an optimal set of AET future prediction products?
2. Materials and Methods
2.1. Case Study
2.2. Data
2.2.1. Meteorological Data (Temperature, Precipitation, and Potential Evapotranspiration)
2.2.2. Hydrological Data (Actual Evapotranspiration, Total Water Storage, and Runoff)
2.2.3. Vegetation Index (NDVI)
2.2.4. Future Climate Scenarios (CMIP6)
2.3. Methods
2.3.1. Man-Kendall Test
2.3.2. Boruta Algorithm
- Copy all features to build random shadow features. Shuffle all the features in the data randomly and rearrange the order of the features.
- Input the features and their copies into the random forest classifier to calculate the Z-scores.
- Remove features with lower Z-scores than the shadow attributes. The important variables, whose Z-scores are over the set of shadow features, are verified.
- Repeat steps 1–3 until all variables are identified.
2.3.3. Support Vector Regression (SVR)
2.3.4. Random Forest (RF)
2.3.5. Research Framework
3. Results
3.1. Assess the Evolution Tendency of AET
3.1.1. Temporal Evolution
3.1.2. Spatial Evolution
3.2. Assess the Relevant Factors and Determinant Order of AET
3.3. Projection of AET under Different Input Strategies
3.3.1. Accuracy of the Models under Different Input Strategies
3.3.2. Joint Optimal Prediction of Two Models
4. Discussion
4.1. The Framework of Trend Assessment
4.2. The Framework about the Relevant and Dominant Factor Assessment of AET
4.3. The Framework about the Joint Optimal Projection of AET
5. Conclusions
- In South America, AET increased significantly at a rate of 43.4 mm/10a recently and experienced an obvious decline in 2020 due to water shortage. In terms of spatial distribution, AET values tended to be lower on both sides while higher (>1000 mm) in the middle. AET in most areas of SA exhibited a significant ascending trend, especially in the Amazon area.
- With the P-coefficient exceeding 0.8, the correlation between AET and T, P, and NDVI was closer. The decisive factors obtained by Boruta algorithm were ranked as T > NDVI > P > PET > TWC > R, and NDVI controlled the largest area range. However, R was the primary determinant in the upper and middle reaches of the Amazon. It could be concluded that rapid runoff is the limiting factor for AET here due to the negative correlation.
- SVR paled in comparison to the RF in South America. By comparing the of the two models on the pixel scale and selecting the optimal model for simulation, the joint optimal prediction datasets were obtained. Furthermore, the S2 considering meteorological and vegetation data derived from MODIS simulated the most intensive future evaporation in the three input strategies.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Abbr. | Version | Spatial Resolutions | Time Span | Data Source (11 July 2021) |
---|---|---|---|---|---|
Temperature | T | CRU TS Version 4.05 | 0.5 degrees | 2001–2020 | https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/ge/ |
Precipitation | P | CRU TS Version 4.05 | 0.5 degrees | 2001–2020 | https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/ge/ |
Potential evapotranspiration | PET | CRU TS Version 4.05 | 0.5 degrees | 2001–2020 | https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/ge/ |
Actual evapotranspiration | AET | GLDAS Noah2.1 | 0.25 degrees | 2000–2020 | https://disc.gsfc.nasa.gov/datasets/ |
Surface runoff | Qs | GLDAS Noah2.1 | 0.25 degrees | 2000–2020 | https://disc.gsfc.nasa.gov/datasets/ |
Subface runoff | Qsb | GLDAS Noah2.1 | 0.25 degrees | 2000–2020 | https://disc.gsfc.nasa.gov/datasets/ |
Snowmelt runoff | Qsm | GLDAS Noah2.1 | 0.25 degrees | 2000–2020 | https://disc.gsfc.nasa.gov/datasets/ |
Total water storage changes | TWC | JPL GRACE(-FO) RL06 | 0.5 degrees | 2002–2020 | https://grace.jpl.nasa.gov/ |
Normalized Difference Vegetation Index | NDVI | MOD13C2 Version6 | 0.05 degrees | 2000–2020 | https://lpdaac.usgs.gov/products/mod13c2v006/ |
Temperature | T | CMIP6-NCAR SSP245 | 100 km | 2020–2090 | https://esgf-node.llnl.gov/projects/cmip6/ |
Precipitation | P | CMIP6-NCAR SSP245 | 100 km | 2020–2090 | https://esgf-node.llnl.gov/projects/cmip6/ |
Potential evapotranspiration | PET | IPSL-CM6A-LR SSP245 | 250 km | 2020–2090 | https://cmc.ipsl.fr/all-projects/ |
Month | Z-Value | Climate Change Rate (mm/10a) |
---|---|---|
January | 3.15 | 5.59 |
February | 3.43 | 5.31 |
March | 2.38 | 4.52 |
April | 3.15 | 4.97 |
May | 2.87 | 5.61 |
June | 2.59 | 4.78 |
July | 2.03 | 3.28 |
August | 1.68 | 2.72 |
September | 0.28 | 0.62 |
October | 0.77 | 0.15 |
November | 1.75 | 2.61 |
December | 2.03 | 3.23 |
T | P | PET | NDVI | R | TWC | |
---|---|---|---|---|---|---|
Correlation coefficient | 0.832 | 0.813 | 0.587 | 0.822 | 0.267 | 0.638 |
Boruta-order of importance | First | Third | Fourth | Second | Sixth | Fifth |
Order | T | P | PET | NDVI | R | TWC |
---|---|---|---|---|---|---|
First | 10.34 | 13.97 | 23.22 | 24.85 | 21.52 | 6.10 |
Second | 20.02 | 15.41 | 21.39 | 16.61 | 22.46 | 4.13 |
Third | 14.69 | 16.59 | 21.34 | 21.49 | 21.27 | 4.63 |
Fourth | 12.46 | 26.98 | 18.35 | 13.23 | 21.22 | 7.75 |
Fifth | 25.73 | 17.94 | 14.25 | 11.04 | 12.50 | 18.53 |
Sixth | 16.76 | 9.11 | 1.45 | 12.78 | 1.04 | 58.86 |
Input Strategies | Meteorological Factors | Vegetation Index | Hydrological Factors | Description |
---|---|---|---|---|
Input S1 | T, P, and PET From CMIP6 | Not input | Not input | Only Forced by the meteorological Factors during 2020–2090 |
Input S2 | T, P, and PET From CMIP6 | Monthly historical average NDVI | Not input | The future monthly NDVI roughly represented by the historical mean value |
Input S3 | T, P, and PET From CMIP6 | Monthly historical average NDVI | Monthly historical average R and TWC | The future monthly NDVI, R, and TWC roughly represented by the historical mean value |
Input Strategies | Model | |||
---|---|---|---|---|
Input S1 | RF | 4.73 | 42.09 | 59.17 |
SVR | 2.52 | 28.67 | 45.43 | |
Input S2 | RF | 16.95 | 51.61 | 64.84 |
SVR | 9.26 | 42.85 | 54.95 | |
Input S3 | RF | 16.57 | 53.94 | 70.70 |
SVR | 10.27 | 42.79 | 55.70 |
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Liu, Y.; Yue, Q.; Wang, Q.; Yu, J.; Zheng, Y.; Yao, X.; Xu, S. A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products. Remote Sens. 2021, 13, 3643. https://doi.org/10.3390/rs13183643
Liu Y, Yue Q, Wang Q, Yu J, Zheng Y, Yao X, Xu S. A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products. Remote Sensing. 2021; 13(18):3643. https://doi.org/10.3390/rs13183643
Chicago/Turabian StyleLiu, Yuan, Qimeng Yue, Qianyang Wang, Jingshan Yu, Yuexin Zheng, Xiaolei Yao, and Shugao Xu. 2021. "A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products" Remote Sensing 13, no. 18: 3643. https://doi.org/10.3390/rs13183643