The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)
Abstract
:1. Introduction
2. Materials and Methods
2.1. SMOS Soil Moisture Data
2.2. GloFAS Streamflow Predictions
2.2.1. H-TESSEL Surface and Subsurface Runoff Forecasts
2.2.2. LISFLOOD Channel Routing
2.3. Streamflow Observations
2.4. GloFAS Experiment Design
2.5. Streamflow Evaluation
2.5.1. Verification against In-Situ Observations
2.5.2. Global Impact upon GloFAS
3. Results
3.1. Verification against Observed Streamflow
3.1.1. United States
3.1.2. Australia
3.2. Global Impact upon GloFAS
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Forecast Time | Lead Time (hours) |
---|---|
d−1 at 18 UTC | 6–12 |
d0 at 06 UTC | 0–12 |
d0 at 18 UTC | 0–6 |
Dataset | Spatial Resolution (Degrees) | Temporal Resolution (Hours) |
---|---|---|
SMOS level 2 Soil Moisture (trained on ECMWF neural network) | 0.50° | Instantaneous |
H-TESSEL surface and subsurface runoff | 0.25° | 6 |
GloFAS Streamflow | 0.10° | 24 |
USGS streamflow observations | NA (point observations) | 24 |
BoM streamflow observations | NA (point observations) | 24 |
Simulation | R | Bias | KGEmod |
---|---|---|---|
Without SMOS DA | 0.428 | 0.840 | −0.504 |
With SMOS DA | 0.420 | 0.812 | −0.472 |
Simulation | R | Bias | KGEmod |
---|---|---|---|
Without SMOS DA | 0.410 | 2.466 | −1.248 |
With SMOS DA | 0.356 | 2.558 | −1.340 |
ESA CCI Land Cover Type | Number of Stations where KGEmodSS ≤ −0.05 (%) | Number of Stations where KGEmodSS ≥ 0.05 (%) |
---|---|---|
Grass | 16 (24%) | 28 (21%) |
Tree | 13 (20%) | 19 (14%) |
Urban | 7 (11%) | 12 (9%) |
Crop | 4 (6%) | 3 (2%) |
Vegetation | 0 (0%) | 8 (6%) |
Herbaceous | 2 (3%) | 11 (8%) |
Water | 11 (17%) | 24 (18%) |
Shrub | 13 (20%) | 29 (22%) |
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Baugh, C.; de Rosnay, P.; Lawrence, H.; Jurlina, T.; Drusch, M.; Zsoter, E.; Prudhomme, C. The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS). Remote Sens. 2020, 12, 1490. https://doi.org/10.3390/rs12091490
Baugh C, de Rosnay P, Lawrence H, Jurlina T, Drusch M, Zsoter E, Prudhomme C. The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS). Remote Sensing. 2020; 12(9):1490. https://doi.org/10.3390/rs12091490
Chicago/Turabian StyleBaugh, Calum, Patricia de Rosnay, Heather Lawrence, Toni Jurlina, Matthias Drusch, Ervin Zsoter, and Christel Prudhomme. 2020. "The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)" Remote Sensing 12, no. 9: 1490. https://doi.org/10.3390/rs12091490
APA StyleBaugh, C., de Rosnay, P., Lawrence, H., Jurlina, T., Drusch, M., Zsoter, E., & Prudhomme, C. (2020). The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS). Remote Sensing, 12(9), 1490. https://doi.org/10.3390/rs12091490