Abstract
As a method of detecting hydrological droughts in ungauged areas, we propose rule-based models using percentiles from remotely sensed key hydro-meteorological variables. Four rule-based models of the Decision Trees, Adaptive Boosting of Decision Trees (Adaboost), Random Forest, and Extremely Randomized Trees are used for their capabilities of modeling nonlinear relationships, and their results are compared to the multiple linear regression. The temporal information of month and the percentiles of key variables of water and energy balance including precipitation, actual evapotranspiration, Normalized Difference Vegetation Index (NDVI), land surface temperature, and soil moisture are used as input variables. Drought severity values are calculated from streamflow percentiles for 3-, 6-, 9-, and 12-month time scales as an indicator for hydrological droughts. Data from six basins of the case study area are used for tuning model parameters and training, and the remaining two basins are used for final evaluation. Models with an ensemble of trees successfully detect hydrological droughts despite the limited input variables (for Adaboost, correlation coefficients ≥ 0.85, mean absolute error ≤ 0.12, root-mean-square error–observations standard deviation ratio ≤ 0.53, and larger Nash–Sutcliffe efficiency of drought severity ≥ 0.72 for the test data set). The most important variable is precipitation, followed by soil moisture (3-month time scale) or NDVI (longer time scales). Hydrological droughts in various time scales are detected in ungauged areas of the case study area. Serious droughts in early 2002, from late 2006 to mid-2007, from early 2008 to 2009, and from mid-2013 to 2017 are detected.
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Abbreviations
- 7Q10:
-
The annual minimum 7-day mean streamflow with an annual exceedance probability of 90%
- ANNs:
-
Artificial neural networks
- BoM:
-
Bureau of meteorology
- CCA:
-
Canonical correlation analysis
- EANN:
-
Ensemble of ANNs
- ELM:
-
Extreme learning machine
- EMI:
-
El Nino-Southern Oscillation Modoki Index
- Full-tobit:
-
Type I tobit regression
- GAM:
-
Gaussian generalized additive model
- GBM:
-
Gradient boosting machine
- GEFS:
-
NOAA global ensemble forecasting system
- GEP:
-
Gene expression programming
- GLM:
-
Gaussian linear regression model
- GRNN:
-
Generalized regression neural networks
- IOD:
-
Indian Ocean Dipole Index
- KKNN:
-
Kernel-k-nearest neighbors
- LR:
-
Linear regression
- LSM:
-
Land surface model
- MARS:
-
Multivariate adaptive regression splines
- NLCCA:
-
Nonlinear ANN-based canonical correlation analysis
- OK:
-
Ordinary kriging
- OSELM:
-
Online sequential extreme learning machines
- OSMLR:
-
Online sequential multiple linear regression
- PDO:
-
Pacific Decadal Oscillation Index
- RBF:
-
Radial basis function
- ROI-tobit:
-
Region-of-influence type I tobit
- SOI:
-
Southern Oscillation Index
- SST:
-
Sea surface temperature
- SVMG:
-
Support vector machine with a Gaussian kernel
- SVMP:
-
Support vector machine with a polynomial kernel
- SVR:
-
Support vector regression
- WMDP:
-
Water Monitoring Data Portal
- WSC:
-
Water Survey of Canada
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This research was supported by the APEC Climate Center.
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Rhee, J., Park, K., Lee, S. et al. Detecting hydrological droughts in ungauged areas from remotely sensed hydro-meteorological variables using rule-based models. Nat Hazards 103, 2961–2988 (2020). https://doi.org/10.1007/s11069-020-04114-5
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DOI: https://doi.org/10.1007/s11069-020-04114-5