Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Observed Data
2.2.2. RS_ET Datasets
2.3. Methods
2.3.1. Cumulative Distribution Function (CDF)-Biased WB Method
2.3.2. Generalized CR (H2018) Method
2.3.3. Evaluation Criteria
2.3.4. The Method of Trends Detection
3. Results
3.1. Evaluation of RS_ET Datasets against Water Balance Estimates
3.2. Evaluation of CR Series against Water Balance Estimates
3.3. Spatial and Temporal Variation of ET at Multiple Spatial Scales
4. Discussion
5. Conclusions
- (1)
- The RS_ET datasets accurately simulated ET at the monthly scale in the HDM region, with mean cc and NSE values of 0.89 and 0.68, respectively. The SSEBop outperformed the others, with NSE and KGE values of 0.80 and 0.90, respectively. This was followed by GLASS and BESS. The NTSG and MOD16 performed worse, with mean RE values of more than 10.0% and mean NSE values below 0.50. In addition, the RS_ET datasets showed high accuracy in estimating the mean values in both the semiarid and humid regions. RT_ES showed relatively better performance in simulating monthly time series in semiarid regions than in humid regions, with the mean NSE value of the former being 0.78.
- (2)
- The CR model was also able to accurately simulate ET at the monthly scale in the HDM region, with mean cc, NSE, and RMSE values of 0.90, 0.81, and 9.07 mm mon−1, respectively. The simulation results of CR_ET in the wet season were better than those in the dry season, with cc and NSE values of 0.91 and 0.76, respectively. Moreover, the comprehensive performance of CR_ET in the Lancang River Basin was better, with mean cc and NSE of 0.92 and 0.83, respectively.
- (3)
- The spatial and temporal variations of the ET time series, including WB_ET, CR_ET, and RS_ET, were generally consistent in the HDM region. All ET time series showed a significant decreasing trend in the northern semiarid region of the HDM, with the lowest mean annual ET. Meanwhile, they exhibited an increasing trend in the humid southern region, with the highest mean annual ET.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hydrological Station | Longitude | Latitude | Catchment | Temporal | |
---|---|---|---|---|---|
Area (km2) | Coverage | ||||
Nu River Basin | Jiayuqiao | 96.24 | 30.87 | 68,384 | 1956–2000, 2007–2018 |
Gongshan | 98.68 | 27.73 | 101,146 | 1956–2000 | |
Daojieba | 98.88 | 24.98 | 110,224 | 1956–2000 | |
Lancang River Basin | Changdu | 97.17 | 31.15 | 53,800 | 1956–2000, 2007–2018 |
Jiuzhou | 99.22 | 25.79 | 88,051 | 1956–2000 | |
Yunjinghong | 100.78 | 22.03 | 115,894 | 1956–2000 | |
Jinsha River Basin | Zhimenda | 97.22 | 33.03 | 137,704 | 1956–1987, 2007–2018 |
Batang | 99.02 | 29.83 | 187,873 | 1956–1987, 2007–2018 | |
Shigu | 99.93 | 26.9 | 214,184 | 1956–1987, 2007–2018 |
Datasets | Method | Resolution | Temporal Coverage | |
---|---|---|---|---|
Spatial | Temporal | |||
BESS 1 | PM equation | 0.50° | 8 days | 2001–2015 |
ftp://147.46.64.183 (accessed on 15 September 2020) | ||||
GLEAM 3.5a | PT equation | 0.25° | 1 month | 1980–2020 |
https://www.gleam.eu/ (accessed on 20 July 2021) | ||||
SEBS 2 | Surface energy balance | 0.05° | 1 month | 2000–2017 |
https://data.tpdc.ac.cn/zh-hans/data/5a0d2e28-ebc6-4ea4-8ce4-a7f2897c8ee6/?q=%E8%92%B8%E6%95%A3 (accessed on 10 August 2020) | ||||
NTSG | Modified PM, PT equations | 0.083° | 1 month | 1982–2013 |
https://www.ntsg.umt.edu/project/global-et.php (accessed on 20 July 2021) | ||||
MOD16 | PM equation | 0.05° | 1 month | 2000–2014 |
http://files.ntsg.umt.edu/data/NTSG_Products/MOD16/MOD16A2_MONTHLY.MERRA_GMAO_1kmALB/GEOTIFF_0.05degree/ (accessed on 5 August 2020) | ||||
SSEBop 3 | Simplified surface energy balance | 0.096° | 1 month | 2003–2020 |
https://earlywarning.usgs.gov/fews/search (accessed on 1 November 2020) | ||||
PMLV2 4 | PML model | 0.05° | 8 days | 2002–2019 |
https://data.tpdc.ac.cn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/?q=%E8%92%B8%E6%95%A3 (accessed on 10 August 2020) | ||||
GLASS 5 | Bayesian model average | 0.05° | 8 days | 2001–2018 |
http://www.glass.umd.edu/ (accessed on 6 August 2020) |
Catchment/Weather Station/Dataset | Sample Size | Z-Value | p-Value | Catchment/Weather Station/Dataset | Sample Size | Z-Value | p-Value | ||
---|---|---|---|---|---|---|---|---|---|
WB_ET (Catchment) | NU | 57 | −2.51 | <0.05 | CR_ET (Weather station) | 56444 | 63 | 1.48 | 0.14 |
NM | 45 | −0.45 | 0.65 | 56548 | 0.57 | 0.57 | |||
ND | 45 | 2.33 | <0.05 | 56751 | 2.17 | <0.05 | |||
LCU | 57 | −1.36 | 0.17 | 56946 | 0.39 | 0.70 | |||
LCM | 45 | 0.39 | 0.7 | 56959 | 3.11 | <0.01 | |||
LCD | 45 | 2.88 | <0.01 | 56964 | 2.57 | <0.01 | |||
JSU | 44 | −0.21 | 0.83 | 56969 | 2.69 | <0.01 | |||
JSM | 44 | −1.45 | 0.15 | 56004 | −1.53 | 0.13 | |||
JSD | 44 | 1.74 | 0.08 | 56016 | −0.21 | 0.83 | |||
CR_ET (Weather station) | 55299 | 63 | −1.14 | 0.25 | 56021 | −0.33 | 0.74 | ||
56106 | −1.42 | 0.16 | 56029 | 0.03 | 0.98 | ||||
56109 | −1.24 | 0.22 | 56132 | −1.24 | 0.22 | ||||
56116 | −2.99 | <0.01 | 56144 | −1.06 | 0.29 | ||||
56223 | −2.27 | <0.05 | 56247 | 0.27 | 0.78 | ||||
56228 | −1.68 | 0.09 | 56342 | −0.33 | 0.74 | ||||
56331 | 0.58 | 0.59 | 56357 | 2.63 | <0.05 | ||||
56533 | −1.36 | 0.17 | 56441 | −1.51 | 0.13 | ||||
56643 | 0.15 | 0.88 | 56543 | 1.84 | 0.07 | ||||
56741 | 0.33 | 0.73 | RS_ET (Dataset) | BESS | 16 | −1.06 | 0.29 | ||
56748 | −1.17 | 0.24 | GLEAM | 19 | 0.17 | 0.86 | |||
56945 | 1.6 | 0.11 | SEBS | 18 | 1.45 | 0.15 | |||
56951 | 2.63 | <0.01 | NTSG | 14 | 1.75 | 0.08 | |||
56018 | −1.78 | 0.08 | MOD16 | 15 | −0.05 | 0.96 | |||
56125 | −2.81 | <0.01 | SSEBop | 18 | 0.51 | 0.61 | |||
56128 | −0.75 | 0.45 | PMLV2 | 17 | 1.10 | 0.27 | |||
56137 | −0.63 | 0.53 | GLASS | 18 | −0.09 | 0.92 |
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Jiang, Y.; Liu, Z. Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions. Remote Sens. 2021, 13, 5096. https://doi.org/10.3390/rs13245096
Jiang Y, Liu Z. Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions. Remote Sensing. 2021; 13(24):5096. https://doi.org/10.3390/rs13245096
Chicago/Turabian StyleJiang, Yongshan, and Zhaofei Liu. 2021. "Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions" Remote Sensing 13, no. 24: 5096. https://doi.org/10.3390/rs13245096