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Estimating evapotranspiration at high spatio-temporal resolution based on the Bayesian model averaging method in Baoding Plain, China
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Abstract: Evapotranspiration (ET) is a core parameter of the hydrology and carbon cycles, and its accurate estimation is crucial for water resource management. Satellite-based ET products provide an effective means for large-scale monitoring. However, due to limitations in the spatial and temporal resolution, the use of these products at regional and field scales is limited. In this study, daily ET in the Baoding Plain—a key groundwater resource recharge area—was estimated at a 500 m spatial resolution using the Bayesian Model Averaging (BMA) method. The model was driven by a synthesis of remote sensing datasets, reanalysis products, and interpolated data from meteorological stations. Validation results from in-situ observations indicated that the BMA ET had better performance (R=0.83, RMSE=1.25 mm/d) than each model in the BMA scheme. The spatiotemporal analysis revealed that the average annual BMA ET in the Baoding Plain was 683 mm/year from 2000 to 2019. Seasonal and monthly variations in the BMA ET captured the irrigation and water consumption patterns of the local crop rotation systems. A significant increasing trend of BMA ET (2.40 mm/year2) was observed in the Baoding Plain over the study period. At the regional scale, ET over more than 50% of the plain exhibited a significant positive trend. Further analysis identified water availability, solar radiation, and temperature as the primary drivers of ET variation. The BMA ET product generated in this study is characterized by high spatiotemporal resolution and accuracy. This reliable, high-resolution dataset offers valuable support for precision agricultural water management and hydrological studies, including groundwater investigations, in this predominantly agricultural region.
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Key words:
- Evapotranspiration /
- Satellite-based model /
- Random Forest model /
- Climate change /
- Water resources
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Figure 1. Overview of the Baoding Plain
Notes: (a) Location of the Baoding Plain (i.e. the study area) within Hebei Province and the distribution of the flux sites. (b) The digital elevation model (DEM) of the Baoding Plain; (c) The land cover types in the Baoding Plain; (d) Overview of the water systems and the location of Baiyangdian Lake within the Baoding Plain
Figure 2. Observed evapotranspiration (ET) at eddy-covariance sites versus predicted ET from BMA and the four models. The red solid lines are the regression lines
Figure 3. Comparison of ET observations and simulations of the BMA and four single models at 6 EC sites: (a) mean values; (b) RMSE; (c) R; (d) bias and (e) NSE
Figure 4. The spatial distribution of ET estimates from the BMA method in the Baoding Plain from 2000 to 2019
Figure 5. Spatial distributions of seasonal ET values in the Baoding Plain from 2000 to 2019
Figure 7. Temporal variability of the mean BMA ET in the Baoding Plain: (a) Interannual variability and (b) the seasonal variability
Figure 9. Spatial patterns of environmental variables including (a) Rn, (b) Tm, (c) VPD, (d) Rh, (e) Pre, (f) Ws, (g) NDVI and (h) LAI
Figure 10. Spatial patterns of trends in (a) Rn, (b) Tm, (c) VPD, (d) Rh, (e) Pre, (f) Ws, (g) NDVI and (h) LAI
Figure 11. Interannual variations of (a) Rn, (b) Tm, (c) VPD, (d) Rh, (e) Pre, (f) Ws, (g) NDVI, and (h) LAI
Figure 12. Pearson's correlation coefficients between environmental variables and ET. The grids in dark gray indicate P>0.05 (non-significant correlations)
Figure 13. The area percentage of land use and land cover types of the total area
Notes: The inset shows the area percentages of the land use types with small proportions
Figure 14. Changes in the percentage share of each ecosystem type relative to the total terrestrial ecosystem area during 2000—2019
Table 1. Details of flux sites
Site name Sources Location information Time period Vegetational type CN-Bed LaThuile Flux (39.53°N,116.25°E) 2005—2006 Forest DX2 The National Tibetan Plateau Data Center (39.62°N,116.43°E) 2008—2010 Cropland GT The National Tibetan Plateau Data Center (36.52°N,115.13°E) 2008—2010 Cropland HL The National Tibetan Plateau Data Center (40.35°N,115.79°E) 2013—2017 Cropland MY The National Tibetan Plateau Data Center (40.63°N,117.32°E) 2008—2010 Cropland YC China Flux (36.95°N,116.60°E) 2003—2010 Cropland 
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Table 2. Details of regional datasets
Type Dataset Time span Temporal resolution Spatial resolution Remote sensing datasets MCD12Q1 2000—2019 yearly 500 m MOD13Q1 2000—2019 16-day 250 m MOD15A2 2000—2019 8-day 500 m SRTMv4.1 2008 yearly 90 m Reanalysis dataset GLDAS 2.1 2000—2019 3-hour 0.25°
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Table 3. Validation of regional interpolation datasets using flux site data.
Rn (W/m2) Tm (°C) Rh (%) Ps (hPa) R 0.87 0.99 0.93 0.82 Bias 15.17 −0.06 −0.23 −3.76 RMSE 32.90 1.60 7.62 14.86 NSE 0.60 0.98 0.84 0.65
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Table 4. Uncertainty analysis of BMA ET at each site.
Site Mean Standard Deviation 90% conf.int Containing Ratio (CR) Average Relative Deviation Amplitude (RD) CN-Bed 3.50 0.97 (1.98, 5.18) 88.73% 3.45 DX2 1.41 0.51 (0.61, 2.28) 100% 0.16 GT 0.67 0.09 (0.53, 0.83) 95.45% 0.35 HL 0.82 0.34 (0.34, 1.45) 93.42% 0.57 YC 1.45 0.22 (1.10, 1.86) 95.23% 0.51 * Due to the insufficient data volume after data quality control, the MY station was excluded from the Monte Carlo uncertainty quantification.
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