Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 143-157, 2018
https://doi.org/10.5194/hess-22-143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 22, 143-157, 2018
https://doi.org/10.5194/hess-22-143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Jan 2018

Research article | 09 Jan 2018

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Ying Zhang1, Semu Moges2, and Paul Block1 Ying Zhang et al.
  • 1Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA
  • 2School of Civil and Environmental Engineering, Addis Ababa University, Addis Ababa, Ethiopia

Abstract. Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33%, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Publications Copernicus
Special issue
Download
Short summary
The study proposes advancing local-level seasonal rainfall predictions by first conditioning on regional-level predictions, as defined through cluster analysis. This statistical approach is applied to western Ethiopia, where lives and livelihoods are vulnerable to its high spatial–temporal rainfall variability, particularly given the high reliance on rain-fed agriculture. The statistical model improves in skills versus the non-clustered case or dynamical models for some critical regions.
The study proposes advancing local-level seasonal rainfall predictions by first conditioning on...
Citation
Share