Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.2.1. MODIS LST Product
2.2.2. The CLDAS Climate Reanalysis Dataset
2.2.3. Ground Observation at the Langtang Valley
2.3. Methods
2.3.1. The LST Gap-Filling Method
2.3.2. The Air Temperature Estimation Method
2.3.3. Statistical Metrics for Accuracy Assessment
3. Results and Analysis
3.1. The MODIS LST Reconstrution Result
3.2. Spatial Distribution Pattern of Estimated Ta
3.3. Accuracy Assessment of the Estimated Ta
4. Discussion
4.1. Impact of Weather Condition on the Ta Estimation Accuracy
4.2. Lapse Rate Derived from the Station Observed, CLDAS and Remote Sensing Based Ta
4.3. Relationship between Ta and Runoff at Glacierized Basins
5. Summary and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Maximum | Mean | Minimum | ||||||
---|---|---|---|---|---|---|---|---|
RMSE (K) | Bias (K) | RMSE (K) | Bias (K) | RMSE (K) | Bias (K) | |||
Kyanging | Whole Year | CLDAS | 2.07 | −0.90 | 2.87 | −2.14 | 5.13 | −4.19 |
SML | 2.21 | 0.17 | 2.55 | −1.22 | 4.77 | −3.83 | ||
PML | 2.05 | 0.42 | 1.88 | −0.68 | 3.63 | −2.86 | ||
summer | CLDAS | 1.27 | 0.46 | 0.94 | −0.74 | 2.23 | −2.09 | |
SML | 2.23 | −0.75 | 3.20 | −2.46 | 5.72 | −5.09 | ||
PML | 2.01 | 1.16 | 1.40 | −0.40 | 2.84 | −2.39 | ||
winter | CLDAS | 2.40 | −1.66 | 3.51 | −2.92 | 6.18 | −5.36 | |
SML | 2.19 | 0.70 | 2.09 | −0.51 | 4.13 | −3.11 | ||
PML | 2.07 | −0.01 | 2.11 | −0.85 | 4.01 | −3.13 | ||
Yala | Whole Year | CLDAS | 7.26 | 7.03 | 5.23 | 4.95 | 3.65 | 2.64 |
SML | 4.81 | 4.30 | 2.93 | 2.12 | 2.84 | −0.37 | ||
PML | 4.53 | 4.03 | 2.68 | 1.96 | 2.36 | −0.35 | ||
summer | CLDAS | 7.28 | 7.15 | 5.29 | 5.24 | 3.71 | 3.59 | |
SML | 5.07 | 4.84 | 2.83 | 2.45 | 1.85 | 0.28 | ||
PML | 4.26 | 4.05 | 2.39 | 2.11 | 1.36 | 0.43 | ||
winter | CLDAS | 6.09 | 4.67 | 4.82 | 2.60 | 5.00 | −0.08 | |
SML | 4.61 | 3.90 | 3.01 | 1.87 | 3.39 | −0.85 | ||
PML | 4.72 | 4.02 | 2.87 | 1.85 | 2.89 | −0.93 |
Elevation (km) | Lapse Rate (K/km) |
---|---|
<2.5 | −3.35 |
2.5–3.5 | −4.50 |
3.5–4.5 | −5.12 |
4.5–5.5 | −5.25 |
5.5–6.5 | −5.27 |
>6.5 | −5.67 |
Aspect | Lapse Rate (K/km) |
---|---|
North | −4.7 |
Northeast | −5.5 |
East | −5.4 |
Southeast | −5.3 |
South | −5.1 |
Southwest | −5.3 |
West | −5.3 |
Northwest | −5.0 |
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Zhou, W.; Peng, B.; Shi, J.; Wang, T.; Dhital, Y.P.; Yao, R.; Yu, Y.; Lei, Z.; Zhao, R. Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal. Remote Sens. 2017, 9, 959. https://doi.org/10.3390/rs9090959
Zhou W, Peng B, Shi J, Wang T, Dhital YP, Yao R, Yu Y, Lei Z, Zhao R. Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal. Remote Sensing. 2017; 9(9):959. https://doi.org/10.3390/rs9090959
Chicago/Turabian StyleZhou, Wang, Bin Peng, Jiancheng Shi, Tianxing Wang, Yam Prasad Dhital, Ruzhen Yao, Yuechi Yu, Zhongteng Lei, and Rui Zhao. 2017. "Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal" Remote Sensing 9, no. 9: 959. https://doi.org/10.3390/rs9090959