Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Precipitation and Runoff Data
2.2.2. Soil Moisture Data
2.3. Improved Xin’anjiang Model
3. Methods
3.1. Data Merging
3.2. Ensemble Kalman Filter (EnKF)
- Observation data update
3.3. Data Assimilation Setup
4. Results and Discussion
4.1. Data Merging
4.2. Data Assimilation
4.2.1. Small Flood
4.2.2. Medium Flood
4.2.3. Large Flood
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
NA | non-assimilation |
AF | assimilation of runoff data |
AFWR | assimilation of runoff and satellite-based soil moisture data |
AFWM | assimilation of runoff and merged soil moisture data |
RE | relative error |
SM | soil moisture |
KF | Kalman filter |
EKF | extended Kalman filter |
EnKF | ensemble Kalman filter |
DEM | digital elevation model |
ESA | European Space Agency |
CCI | Climate Change Initiative |
CDF | cumulative distribution function |
NSE | Nash–Sutcliffe efficiency coefficient |
AE | absolute error |
RMSE | root-mean-square error |
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Number | Parameter | Meaning | Lowe | Bound |
---|---|---|---|---|
1 | C | Ratio of potential evapotranspiration | 0 | 0.3 |
2 | IMP | Ratio of the impervious area to the total area | 0.02 | 0.7 |
3 | WUM | Tension water capacity of the upper layer | 5 | 100 |
4 | WLM | Tension water capacity of the lower layer | 40 | 200 |
5 | WDM | Tension water capacity of the deeper layer | 5 | 100 |
6 | B | Exponent of the distribution of the tension water capacity | 0.1 | 0.3 |
7 | SM | Free water capacity | 5 | 100 |
8 | EX | Exponent of distribution of free water capacity | 0.5 | 2 |
9 | KG | Outflow coefficient of free water storage to groundwater | 0.05 | 0.65 |
10 | KSS | Outflow coefficient of free water storage to interflow | 0.65 | 0.8 |
11 | KKSS | Recession constant of interflow storage | 0.05 | 0.95 |
12 | KKGF | Recession constant of fast groundwater | 0 | 1 |
13 | KKGS | Recession constant of slow groundwater | 0 | 1 |
14 | KD | Division value of groundwater | 0 | 1 |
15 | K | Ratio of pan evaporation | 0 | 1 |
16 | UH(1) | First coefficient of unit graph | 0 | 1 |
17 | UH(2) | Second coefficient of unit graph | 0 | 1 |
18 | UH(3) | Third coefficient of unit graph | 0 | 1 |
19 | Fm | Maximum infiltration rate | 0 | 10 |
20 | N1 | Empirical coefficient of infiltration curve | 0 | 1 |
21 | FC | Stable infiltration rate | 0 | 1 |
AE | RE | RMSE | |
---|---|---|---|
Before adjustment | −0.0186 | –0.0793 | 0.0391 |
After adjustment | −0.0031 | 0.0126 | 0.0350 |
Station | Average Error | NA | AF | AFWR | AFWM |
---|---|---|---|---|---|
Dage | AE | −0.524 | −0.018 | −0.193 | −0.145 |
RE | −0.292 | 0.143 | −0.021 | 0.011 | |
Gubeikou | AE | −2.438 | −0.47 | −0.417 | −0.43 |
RE | −0.449 | 0.165 | −0.09 | 0.032 | |
Xiahui | AE | −0.483 | −0.202 | −0.312 | −0.273 |
RE | 0.317 | 0.249 | 0.119 | 0.136 |
Station | Average Error | NA | AF | AFWR | AFWM |
---|---|---|---|---|---|
Dage | AE | −1.213 | 0.486 | −0.42 | −0.385 |
RE | −0.559 | 0.186 | −0.17 | −0.163 | |
Gubeikou | AE | 0.347 | –1.043 | −1.359 | −1.254 |
RE | 0.172 | −0.129 | −0.17 | −0.154 | |
Xiahui | AE | −3.313 | −2.145 | −3.752 | −3.679 |
RE | −0.201 | −0.113 | −0.298 | −0.298 |
Station | Average Error | NA | AF | AFWR | AFWM |
---|---|---|---|---|---|
Gubeikou | AE | −4.928 | 0.51 | −0.912 | −0.731 |
RE | −0.254 | 0.07 | −0.232 | −0.226 | |
Xiahui | AE | −8.551 | −3.817 | −4.116 | −4.547 |
RE | −0.453 | −0.168 | −0.203 | −0.227 |
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Zhang, C.; Cai, S.; Tong, J.; Liao, W.; Zhang, P. Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data. Water 2022, 14, 1555. https://doi.org/10.3390/w14101555
Zhang C, Cai S, Tong J, Liao W, Zhang P. Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data. Water. 2022; 14(10):1555. https://doi.org/10.3390/w14101555
Chicago/Turabian StyleZhang, Chen, Siyu Cai, Juxiu Tong, Weihong Liao, and Pingping Zhang. 2022. "Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data" Water 14, no. 10: 1555. https://doi.org/10.3390/w14101555
APA StyleZhang, C., Cai, S., Tong, J., Liao, W., & Zhang, P. (2022). Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data. Water, 14(10), 1555. https://doi.org/10.3390/w14101555