Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

Seibert, Mathias; Merz, Bruno; Apel, Heiko

doi:https://doi.org/10.5194/hess-21-1611-2017

Articles | Volume 21, issue 3

https://doi.org/10.5194/hess-21-1611-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Sub-seasonal to seasonal hydrological forecasting

https://doi.org/10.5194/hess-21-1611-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 3

Research article

|

17 Mar 2017

Research article |

| 17 Mar 2017

Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

Mathias Seibert, Bruno Merz, and Heiko Apel

Data sets

Southern Oscillation Index (SOI) Climate Prediction Center of NOAA ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/soi

Darwin sea level pressure Climate Prediction Center of NOAA http://www.cpc.ncep.noaa.gov/data/indices/darwin

Tahiti sea level pressure Climate Prediction Center of NOAA: http://www.cpc.ncep.noaa.gov/data/indices/tahiti

ENSO indices (ERSST) Climate Prediction Center of NOAA: http://www.cpc.ncep.noaa.gov/data/indices/

ENSO indices (OISST) Climate Prediction Center of NOAA: http://www.cpc.ncep.noaa.gov/data/indices/

North Atlantic Oscillation (NAO) Climate Prediction Center of NOAA ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/

Indian Ocean Dipole Mode Index (DMI) JAMSTEC http://www.jamstec.go.jp/frcgc/research/d1/iod/DATA/

Oceanic Nino Index (ONI) Climate Prediction Center of NOAA http://www.cpc.ncep.noaa.gov/products/analysis

Trans Nino Index (TNI) HadSST1.1 (until November 1981) and NCEP NOAA OI http://www.esrl.noaa.gov/psd/gcos wgsp/Timeseries/

NINO3.4 (HadSST) Physical Sciences Division of NOAA http://www.esrl.noaa.gov/psd/gcos wgsp/Timeseries/

Short summary

Seasonal early warning is vital for drought management in arid regions like the Limpopo Basin in southern Africa. This study shows that skilled seasonal forecasts can be achieved with statistical methods built upon driving factors for drought occurrence. These are the hydrological factors for current streamflow and meteorological drivers represented by anomalies in sea surface temperatures of the surrounding oceans, which combine to form unique combinations in the drought forecast models.