G. A. Artan

Developing a Flood Monitoring System From Remotely Sensed Data for the Limpopo Basin

This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administrations Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administrations infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting streamflow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information

Anthropogenic Enhancement of Moderate-to-Strong El Niño Events Likely Contributed to Drought and Poor Harvests in Southern Africa During 2016

Introduction. In December–February (DJF) of 2015/16, a strong El Niño (Niño‐3.4 SST >29°C) contributed to a severe drought over southern Africa (SA; Funk et al. 2016). A 9‐million ton cereal deficit resulted in 26 mil‐ lion people in need of humanitarian assistance (SADC 2016). While SA rainfall has a well‐documented nega‐ tive teleconnection with Niño‐3.4 SSTs (Hoell et al. 2015, 2017; Jury et al. 1994; Lindesay 1988; Misra 2003; Nicholson and Entekhabi 1987; Nicholson and Kim 1997; Reason et al. 2000; Rocha and Simmonds 1997), the link between climate change and El Niño remains unclear (Christensen et al. 2013) due to the large natural variability of ENSO SSTs (Wittenberg 2009), uncertainties surrounding measurements and trends (Solomon and Newman 2012), intermodel differences in ENSO representation and feedbacks (Guilyardi et al. 2012; Kim et al. 2014), and difficulties associated with quantifying ENSO strength (Cai et al. 2015). Figure 18.1a highlights observational uncertain‐ ties (Compo and Sardeshmukh 2010; Solomon and Newman 2012) using four datasets: ERSSTv4 (Huang et al. 2015), HadISST (Rayner et al. 2003), Kaplan SST (Kaplan et al. 1998), and Hurrell (Hurrell et al. 2008). These products differ substantially in their represen‐ tation of cool events and Niño‐3.4 variance. Two SST products indicate significant upward trends; two SST products do not. These data have been standardized to remove systematic differences in variance. Focusing just on the behavior of moderate–strong El Niño events (MSENEs), we can produce more ro‐ bust (first order) statistics by comparing the means of the top ten warmest Niño‐3.4 events between 1921–80 and the top six warmest events between 1981–2016. Rather than using a set SST threshold, MSENEs are defined as 1‐in‐6‐year warm events. This provides a simple nonparametric approach that takes advantage of the well understood quasi‐periodic nature of ENSO to identify MSENEs across multiple models and simulations. Modest changes in the number of events (say, 1‐in‐7 or 1‐in‐5) produced modest increases and decreases in El Niño temperatures, but did not sub‐ stantially change the results. We begin our analysis in 1921 (because ship data before 1921 is limited), and divide the remaining 96 years into two time periods with relatively weak and strong radiative forcing, respectively. Examining changes in MSENE means (horizontal lines in Fig. 18.1a), we find that all the observational datasets identify significant increases (Fig. ES18.1 examines ERSSTv4 errors). Note that we are not explicitly ex‐ amining changes in ENSO variance, ENSO means, or Niño‐3.4 SST trends, but only Niño‐3.4 magnitudes AFFILIATIONS: Funk—U.S. Geological Survey, Center for Earth Resources Observation and Science, and UC Santa Barbara Climate Hazards Group, Santa Barbara, California; Davenport, harrison, shukLa, pomposi, anD husak—UC Santa Barbara Climate Hazards Group, Santa Barbara, California; magaDzire, gaLu, anD koreCha—UC Santa Barbara Climate Hazards Group, Santa Barbara, California, and Famine Early Warning Systems Network; artan—Intergovernmental Authority on Development (IGAD) Climate Prediction & Applications Centre, Nairobi, Kenya; inDeje—IGAD USAID/Kenya and East Africa Planning for Resilience in East Africa Through Policy Adaptation, Research, and Economic Development, Nairobi, Kenya; maCharia—Regional Center for Mapping of Resources for Development, Nairobi, Kenya; nsaDisa—Director of the Southern African Development Community’s Climate Services Centre.