Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. PT-JPL Algorithm
2.2. PT-SM Algorithm
2.3. Evaluation
2.4. Eddy Covariance (EC) Flux Data
2.5. Observed Meteorological and Hydrological Data
2.6. Remote Sensing NDVI, EVI, and LAI Data
2.7. Canopy Height and Soil Moisture Data
3. Results
3.1. Spatial Distribution Characteristics of Soil Moisture
3.2. Comparison of Algorithm Parameters
3.3. Comparison of Incorporating Soil Moisture into Soil Evaporation and Transpiration
3.4. Model Evaluation with In Situ Forcing
3.5. Evapotranspiration Partitioning
3.6. Spatial Distributions of Mean Annual Evapotranspiration and Its Components
4. Discussion
4.1. Soil Water Availability Constraint on Evaporation and Transpiration
4.2. Contribution of Each ET Component to the Total ET
4.3. Inter-Annual Variability of ET for 2003–2015
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tower | SITE_NAME | IGBP | LAT (°N) | LON (°E) | Time Period | NETWORK |
---|---|---|---|---|---|---|
CN-Din | Dinghushan | EBF | 23.17 | 112.54 | 2004–2005 | ChinaFLUX |
CN-Qia | Qianyanzhou | ENF | 26.74 | 115.06 | 2003–2005 | ChinaFLUX |
XSBN | Xishuangbanna | EBF | 21.90 | 101.27 | 2003–2005 | ChinaFLUX |
CN-Cha | Changbaishan | MF | 42.40 | 128.10 | 2003–2005 | ChinaFLUX |
DX | Daxing | Crop | 39.62 | 116.43 | 2008–2010 | WATER |
MY | Miyun | Crop | 40.63 | 117.32 | 2008–2010 | WATER |
QH | Qinghai | GRA | 37.60 | 101.33 | 2002–2004 | Asiaflux |
CN-Ha2 | Haibei Shrubland | OSH | 37.61 | 101.33 | 2003–2005 | ChinaFLUX |
YC | Yucheng | Crop | 36.96 | 116.63 | 2003–2005 | ChinaFLUX |
GT-1 | Guantao | Crop | 36.52 | 115.13 | 2008–2010 | WATER |
AR | Arou | GRA | 38.09 | 100.52 | 2009–2011 | WATER |
CN-Du2 | Duolun_grassland (D01) | GRA | 42.05 | 116.28 | 2006–2008 | USCCC |
CN-Cng | Changling | GRA | 44.59 | 123.51 | 2007–2010 | USCCC |
CN-Dan | Dangxiong | GRA | 30.50 | 91.07 | 2003–2005 | ChinaFLUX |
CN-Sw2 | Siziwang Grazed (SZWG) | GRA | 41.79 | 111.90 | 2011 | USCCC |
GT-2 | Guantan | DNF | 38.53 | 100.25 | 2010–2011 | WATER |
NMG | Neimenggu | GRA | 44.13 | 116.30 | 2004–2005 | ChinaFLUX |
Data | Designation | Time-Period | Resolution | Source | |
---|---|---|---|---|---|
Temporal Spatial | |||||
Total precipitation (mm) | P | January 2003–December 2015 | monthly | 0.1 × 0.1° | China Meteorological Forcing Dataset |
GRACE-derived land water storage changes (cm) | ∆TWS | January 2003–December 2015 | monthly | 1.0 × 1.0° | GRACE satellite images |
Soil relative humidity (%) | SRH | January 2003–December 2015 | 10-days | stationly | China Meteorological Forcing Dataset |
GLDAS-2 Noah Land Surface Model (LSM) L4 model outputs (kg m−2) | SM | January 2003–December 2015 | monthly | 0.25 × 0.25° | GLDAS-2 |
Soil-Moisture_from_ITPLDAS | SM | January 2002–December 2011 | daily | 0.25 × 0.25° | National Tibetan Plateau/Third Pole Environment Data Center |
Tower | PT-JPL | PT_Es | PT_T | PT-SM | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | NSE | RMSE | Bias | R2 | NSE | RMSE | Bias | R2 | NSE | RMSE | Bias | R2 | NSE | RMSE | Bias | ||
CN-Din | 0.87 | 0.54 | 12.68 | −10.33 | 0.89 | 0.65 | 9.85 | −5.68 | 0.90 | 0.89 | 6.58 | −3.52 | 0.94 | 0.93 | 4.76 | −1.10 | |
CN-Qia | 0.96 | 0.96 | 6.30 | −2.29 | 0.96 | 0.96 | 5.77 | −0.17 | 0.96 | 0.94 | 7.38 | 4.43 | 0.96 | 0.91 | 8.87 | 6.40 | |
XSBN | 0.90 | 0.78 | 7.98 | −5.76 | 0.90 | 0.88 | 6.65 | −1.45 | 0.90 | 0.89 | 5.13 | 1.89 | 0.93 | 0.92 | 4.82 | 1.59 | |
CN-Cha | 0.98 | 0.91 | 8.50 | −6.28 | 0.98 | 0.92 | 6.57 | −3.18 | 0.98 | 0.94 | 5.27 | 1.58 | 0.98 | 0.97 | 4.87 | 2.46 | |
DX | 0.93 | 0.89 | 10.33 | −6.47 | 0.93 | 0.91 | 9.85 | −2.30 | 0.94 | 0.93 | 8.42 | −1.05 | 0.95 | 0.95 | 7.03 | 0.14 | |
MY | 0.95 | 0.94 | 8.41 | −3.00 | 0.95 | 0.95 | 7.57 | −1.05 | 0.96 | 0.96 | 7.80 | −0.62 | 0.97 | 0.96 | 7.00 | 1.33 | |
QH | 0.98 | 0.93 | 7.77 | −5.26 | 0.98 | 0.94 | 6.57 | −3.61 | 0.97 | 0.95 | 5.29 | −0.81 | 0.98 | 0.97 | 4.86 | 1.17 | |
CN-Ha2 | 0.98 | 0.90 | 10.20 | −7.68 | 0.99 | 0.95 | 7.15 | −5.10 | 0.99 | 0.99 | 3.48 | 0.66 | 0.99 | 0.98 | 4.59 | 2.90 | |
YC | 0.93 | 0.85 | 11.29 | −8.09 | 0.95 | 0.91 | 9.50 | −6.01 | 0.97 | 0.98 | 5.20 | −1.07 | 0.98 | 0.98 | 5.02 | 1.17 | |
GT-1 | 0.87 | 0.77 | 6.34 | −3.05 | 0.89 | 0.82 | 4.51 | −1.99 | 0.96 | 0.95 | 4.46 | 0.71 | 0.99 | 0.98 | 4.41 | 1.90 | |
AR | 0.86 | 0.79 | 13.01 | −7.70 | 0.90 | 0.84 | 8.17 | −5.46 | 0.97 | 0.94 | 7.83 | 0.88 | 0.98 | 0.97 | 6.96 | 2.35 | |
CN-Du2 | 0.91 | 0.82 | 7.29 | −5.15 | 0.93 | 0.86 | 5.32 | −3.54 | 0.99 | 0.99 | 3.06 | 0.75 | 0.98 | 0.96 | 5.42 | 3.77 | |
CN-Cng | 0.93 | 0.73 | 12.20 | −9.23 | 0.96 | 0.80 | 11.45 | −6.57 | 0.95 | 0.87 | 9.54 | 1.80 | 0.95 | 0.91 | 7.93 | 3.71 | |
CN-Dan | 0.91 | 0.69 | 17.48 | −8.69 | 0.93 | 0.76 | 10.87 | −5.67 | 0.96 | 0.95 | 7.81 | −2.64 | 0.98 | 0.97 | 6.92 | 2.03 | |
CN-Sw2 | 0.90 | 0.72 | 6.93 | −4.72 | 0.94 | 0.83 | 5.41 | −3.25 | 0.98 | 0.96 | 4.03 | 1.52 | 0.99 | 0.96 | 3.40 | 2.68 | |
GT-2 | 0.92 | 0.82 | 7.12 | −3.48 | 0.93 | 0.90 | 6.57 | −1.58 | 0.97 | 0.95 | 6.18 | 2.85 | 0.98 | 0.95 | 5.81 | 4.57 | |
NMG | 0.94 | 0.76 | 7.41 | −5.67 | 0.95 | 0.83 | 5.44 | −2.57 | 0.96 | 0.95 | 4.97 | 1.55 | 0.96 | 0.95 | 4.58 | 2.03 | |
Change rate (%) | PET/P < 1.7 | 0 | 6 | −16 | 3 | 1 | 12 | −29 | 6 | 2 | 14 | −31 | 8 | ||||
PET/P > 1.7 | 3 | 9 | −23 | 2 | 7 | 23 | −39 | 7 | 8 | 25 | −40 | 9 | |||||
All towers | 2 | 7 | −20 | 3 | 4 | 18 | −34 | 7 | 5 | 20 | −36 | 8 |
Landcover | Interception Ratio | |
---|---|---|
Current Study | Literature | |
Crop | 0.18 | 0.17 (Vinukollu et al. 2011); 0.12 (Gu et al. 2018) |
DNF | 0.14 | 0.12 (Vinukollu et al. 2011); 0.23 (Miralles et al. 2010) |
EBF | 0.15 | 0.14 (Vinukollu et al. 2011); 0.17 (Miralles et al. 2010); 0.16 (Gu et al. 2018) |
ENF | 0.19 | 0.19 (Vinukollu et al. 2011); 0.23 (Miralles et al. 2010); 0.12 (Gu et al. 2018) |
GRA | 0.16 | 0.14 (Vinukollu et al. 2011); 0.13 (Gu et al. 2018) |
MF | 0.19 | 0.19 (Vinukollu et al. 2011); 0.13 (Gu et al. 2018) |
OSH | 0.11 | 0.09 (Gu et al. 2018) |
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Xing, W.; Wang, W.; Shao, Q.; Song, L.; Cao, M. Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data. Remote Sens. 2021, 13, 3118. https://doi.org/10.3390/rs13163118
Xing W, Wang W, Shao Q, Song L, Cao M. Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data. Remote Sensing. 2021; 13(16):3118. https://doi.org/10.3390/rs13163118
Chicago/Turabian StyleXing, Wanqiu, Weiguang Wang, Quanxi Shao, Linye Song, and Mingzhu Cao. 2021. "Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data" Remote Sensing 13, no. 16: 3118. https://doi.org/10.3390/rs13163118