Forecasting droughts in East Africa

109

108

Abstract. In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers implement disaster risk reduction measures while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent East African droughts to a stronger Walker circulation, resulting from warming in the Indo-Pacific warm pool and an increased east-to-west sea surface temperature (SST) gradient in the western Pacific, we show that the two dominant modes of East African boreal spring rainfall variability are tied to SST fluctuations in the western central Pacific and central Indian Ocean, respectively. Variations in these two rainfall modes can thus be predicted using two SST indicesthe western Pacific gradient (WPG) and central Indian Ocean index (CIO), with our statistical forecasts exhibiting reasonable cross-validated skill (r cv ≈ 0.6). In contrast, the current generation of coupled forecast models show no skill during the long rains. Our SST indices also appear to capture most of the major recent drought events such as 2000, 2009 and 2011. Predictions based on these simple indices can be used to support regional forecasting efforts and land surface data assimilations to help inform early warning and guide climate outlooks.

Section: How Understanding Trends Can Lead To Better Drought Predictionsmentioning

confidence: 99%

Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

Funk

Hoell

Shukla

et al. 2014

109

108

“…For example, disaster managers regularly participate in Regional Climate Outlook forums and climate service partnerships (Hewitt et al, 2012;ICPAC, 2016;Mwangi et al, 2014). While many early warning systems focus on short-term hydrological flood warnings, these climate service initiatives promote the use of forecasts of seasonal total rainfall.…”

Section: Introductionmentioning

confidence: 99%

Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Self Cite

Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

“…Droughts are classified as agricultural (soil moisture deficit), meteorological (precipitation deficit), and hydrological (streamflow/groundwater deficit), and various drought indicators based on soil moisture, precipitation and runoff have been developed to describe different aspects of droughts (Heim, 2002;Wood et al, 2002;Wood and Lettenmaier, 2006;Mo, 2008;Hao and AghaKouchak, 2013). Most drought prediction studies are based on the standardized precipitation index (SPI; McKee et al, 1993) with the input precipitation derived from dynamical weather/climate models (Yoon et al, 2012;Mwangi et al, 2014;Dutra et al, 2013Dutra et al, , 2014a. While dynamic models provide valuable information, precipitation forecasts are subject to high uncertainty and models exhibit very low skill in predicting precipitation with a few months lead time (Goddard et al, 2003;National Research Council, 2006;Livezey and Timofeyeva, 2008;Lavers et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

AghaKouchak

2014

145

Abstract. The 2012 drought was one of the most extensive drought events in half a century, resulting in over USD 12 billion in economic loss in the United States and substantial indirect impacts on global food security and commodity prices. An important feature of the 2012 drought was rapid development and intensification in late spring/early summer, a critical time for crop development and investment planning. Drought prediction remains a major challenge because dynamical precipitation forecasts are highly uncertain, and their prediction skill is low. Using a probabilistic framework for drought forecasting based on the persistence property of accumulated soil moisture, this paper shows that the US drought of summer 2012 was predictable several months in advance. The presented drought forecasting framework provides the probability occurrence of drought based on climatology and near-past observations of soil moisture. The results indicate that soil moisture exhibits higher persistence than precipitation, and hence improves drought predictability.