Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models
Abstract
1. Introduction
2. Materials and Methods
2.1. Case Study Description (LRC) and Datasets
2.2. Formulation of the SPEI for the Study Area
2.3. Drought Trends over North-Eastern South Africa
2.4. SPEI Time Series Forecasting
2.4.1. The Generalized Additive Model without Auto-Correlated Errors
2.4.2. The Generalized Additive Model with Auto-Correlated Errors
3. Results
3.1. Spatial Variability of Drought in the Study Area
3.2. Exploratory Data Analysis
3.3. Variable Selection
3.4. Short- and Medium-Term Forecasting
3.5. Model Performance
3.6. Evaluation of Model Uncertainity
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Station Name | Station Code | Station Number | Data Span | Data Length | |
---|---|---|---|---|---|
1 | Mukumbani | Muk | 0766715 | 1956–2016 | 60 |
2 | Klein Australie | KA | 0723363 X | 1959–2016 | 57 |
3 | Matiwa | Mat | 0766509 9 | 1959–2016 | 57 |
4 | Nooitgedatch | Nooit | 0723334 X | 1959–2016 | 57 |
5 | Levubu | Lev | 0723485A | 1964–2016 | 54 |
6 | Vondo Bos | VB | 0766596 9 | 1963–2016 | 53 |
7 | Shefera | Shef | 0723182 6 | 1948–2016 | 68 |
8 | Tshivhase | Tshi | 0766628 W | 1986–2016 | 30 |
Station | Timescale | Mild (%) | Moderate (%) | Severe (%) | Extreme (%) |
---|---|---|---|---|---|
KA | 1 | 68.28 | 23.66 | 5.91 | 0.02 |
6 | 63.79 | 27.59 | 8.05 | 0.575 | |
12 | 65.68 | 17.16 | 15.98 | 1.18 | |
Lev | 1 | 65.91 | 22.35 | 2.24 | 0.56 |
6 | 66.67 | 16.67 | 16.67 | 0 | |
12 | 65.66 | 12.65 | 21.69 | 0 | |
Mat | 1 | 67.9 | 26.84 | 5.26 | 0 |
6 | 65.35 | 25.57 | 8.52 | 0.57 | |
12 | 69.14 | 14.2 | 10.49 | 6.14 | |
Muk | 1 | 68.42 | 23.68 | 7.37 | 0.53 |
6 | 65.36 | 25.7 | 6.7 | 2.23 | |
12 | 69.33 | 14.11 | 10.42 | 6.14 | |
Nooit | 1 | 66.86 | 24 | 7.43 | 1.14 |
6 | 63.28 | 25.42 | 10.72 | 0.57 | |
12 | 61.15 | 23.08 | 14.2 | 1.18 | |
Shef | 1 | 68.51 | 21.55 | 7.74 | 2.21 |
6 | 68.36 | 23.72 | 6.21 | 1.7 | |
12 | 70.88 | 21.43 | 6.05 | 1.65 | |
Tshi | 1 | 70.97 | 23.12 | 4.2 | 1.61 |
6 | 68.11 | 21.08 | 10.27 | 0.54 | |
12 | 65.06 | 19.88 | 15.06 | 0 | |
VB | 1 | 71.73 | 19.9 | 6.81 | 1.57 |
6 | 71.51 | 18.99 | 6.7 | 2.79 | |
12 | 71.88 | 15.63 | 6.65 | 6.65 |
Variable | 1-Month Timescale | 6-Month Timescale | 12-Month Timescale |
---|---|---|---|
SPEI | Rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | Rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | Non-linear trend, SPEIt−1 |
IMF 1 | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean |
IMF 2 | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean | SPEI, rain, non-linear trend, SPEIt−1 and SPEIt−2, Tmax, Tmin, Tmean |
IMF 3 | Non-linear trend | Non-linear trend | Non-linear trend |
IMF 4 | Non-linear trend | Non-linear trend | Non-linear trend |
IMF 5 | Non-linear trend | Non-linear trend, SPEIt−1 | Non-linear trend, SPEIt−1 |
IMF 6 | Non-linear trend, SPEIt−1 | Non-linear trend, SPEIt−1 | Non-linear trend, SPEIt−1 |
IMF 7 | Non-linear trend, SPEIt−1 and SPEIt−2 | Non-linear trend, SPEIt−1 and SPEIt−2 | Non-linear trend, SPEIt−1 |
Residual | Non-linear trend, SPEIt−1 and SPEIt−2 | Non-linear trend, SPEIt−1 and SPEIt−2 | Non-linear trend, SPEIt−1 and SPEIt−2, SPEI |
Timescale | Model | ME | RMSE | MAE | MPE | MAPE |
---|---|---|---|---|---|---|
1 | GAM | 0.0177 | 0.7676 | 0.6127 | −3.8647 | 231.728 |
EEMD-GAM | 0.6805 | 0.8829 | 0.7410 | −47.4685 | 275.233 | |
EEMD-ARIMA-GAM | 0.4718 | 0.481 | 0.4718 | −135.946 | 280.609 | |
fQRA | −0.0116 | 0.0599 | 0.03369 | 11.971 | 17.099 | |
6 | GAM | −0.0016 | 0.3644 | 0.2694 | 19.774 | 57.438 |
EEMD-GAM | −0.0563 | 0.3818 | 0.2833 | 13.330 | 57.293 | |
EEMD-ARIMA-GAM | −0.0599 | 0.3449 | 0.2595 | 10.227 | 51.763 | |
fQRA | 0.0030 | 0.2609 | 0.2057 | 8.053 | 37.699 | |
12 | GAM | 0.0021 | 0.1809 | 0.1199 | −63.013 | 123.075 |
EEMD-GAM | 0.0067 | 0.1978 | 0.1373 | −128.169 | 181.563 | |
EEMD-ARIMA-GAM | 0.0851 | 0.2221 | 0.162 | −77.719 | 183.636 | |
fQRA | 0.0032 | 0.1811 | 0.1194 | −67.49 | 127.262 |
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Mathivha, F.; Sigauke, C.; Chikoore, H.; Odiyo, J. Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models. Sustainability 2020, 12, 4006. https://doi.org/10.3390/su12104006
Mathivha F, Sigauke C, Chikoore H, Odiyo J. Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models. Sustainability. 2020; 12(10):4006. https://doi.org/10.3390/su12104006
Chicago/Turabian StyleMathivha, Fhumulani, Caston Sigauke, Hector Chikoore, and John Odiyo. 2020. "Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models" Sustainability 12, no. 10: 4006. https://doi.org/10.3390/su12104006
APA StyleMathivha, F., Sigauke, C., Chikoore, H., & Odiyo, J. (2020). Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models. Sustainability, 12(10), 4006. https://doi.org/10.3390/su12104006