All posts by Juliet Way-Henthorne

Declines in Ethiopia’s Belg Growing Season Rainfall and Length of Season

By Chris Funk, Diego Pedreros, and Diriba Korecha

 

Summary

An analysis of the best-available satellite and rainfall station data for Ethiopia confirms continued drying during the February-June Belg regions. Ninety percent of the area examined exhibited declining rainfall trends. The average decline for these areas appears to be about 50mm, or 20%. This 20% decline is enough to greatly increase the frequency of poor rainy seasons, which appear to be two-to-three times more frequent now than in the 1980s and early 1990s. An analysis of growing season lengths indicates more localized reductions, with the north-eastern Belg growing areas experiencing large 20-to-40 day declines in growing season length, and up to 60% reductions in seasonal rainfall totals. These substantial rainfall reductions, as well as the as the shrinking length of the growing season, hinder natural phenological vegetation growth over agropastoral and pastoral regions of northeastern, eastern, southern, and southeastern Ethiopia. 

 

Introduction

This brief analysis summarizes an analysis of trends in the Ethiopian Belg rains. The Belg season in Ethiopia extends from February to mid-June, and contributes 50-65% of the annual rainfall totals for the southern and southeastern parts of the country, and up to 40% for the northeastern and eastern sectors of the country. This study examines trends in Belg rainfall over the past 39 years (1981-2019), documenting changes in rainfall characteristics, including start, end, and duration of the season, spatial and temporal distribution, and rainfall totals. 

 

Methods

The rainfall analyzed combines the Climate Hazard Center InfraRed Precipitation with Stations (CHIRPS) product with a dense set of additional gauge observations provided by the Ethiopian National Meteorological Agency. Our analysis was restricted to pastoral and agro-pastoral regions that historically receive Belg season rainfall (Figure 1). Time series of dekadal (10-day) rainfall were extracted for each administrative zone. For agropastoral areas, the start of the Belg season was defined as when a dekad with 25 mm rainfall totals was followed by two consecutive dekads, with cumulative rainfall totals of 20 mm. For pastoral regions, the start of the Belg season was defined as when a dekad with 10 mm rainfall totals was followed by two consecutive dekads with cumulative rainfall totals of 5 mm. The end of the Belg season was identified as the last dekad before the second dekad of June, with rainfall totals of at least 10 mm for agro-pastoral, and 5 mm for pastoral regions. Length of the Belg rainfall season was calculated from these dates, and seasonal rainfall totals were calculated for the period from the first dekad of February to the second dekad of June. 

 

Figure 1. Agropastoral (blue) and pastoral (magenta) regions examined in this study.

 

Results – Changes in Belg rainfall totals

Changes in regional rainfall, expressed as a percent of the long-term median, are presented in Figure 2. Ninety percent of the area examined exhibited declining rainfall. Of the 47 zones examined, 79% (37 zones) exhibited declining rainfall trends. In south-central Ethiopia Guji, Liben, Afder, East Shewa, and Arsi all exhibit declines exceeding 20%. The central-eastern highlands (West and East Haraghe) exhibit large declines of 25 to 29%. Most zones in the eastern pastoral Somali region exhibit declines of 13 to 24%. The northern Belg regions tend to exhibit the largest percent declines, but the Belg rainfall totals in this region are generally lower on average, as well.

 

 

Figure 2. Trends in zonal Belg February-June rainfall, expressed as a percent of the long-term median rainfall.

We next present a time series of rainfall, averaged over the 37 zones with declining rainfall trends (Figure 3). Also shown on this plot are averages for 1981-1996 and 1997-2019. This region has experienced a large (50 mm or 20%) decline in rainfall. Dry seasons, with average rains equal to or less than ~227 mm have become the new normal. The FEWS NET/NMA data shown here indicate 17 such events since 1997 and 6 events since 2011, frequencies of 74 and 67%. In the 16 years between 1981 and 1996, there were only 4 such events. These data suggest that the frequency of dry Belg seasons has doubled or tripled in frequency. This has likely eroded farm and livestock productivity and contributed to increased food insecurity. These repetitive shocks may be eroding resilience. 

 

Figure 3. Time series of average Belg rainfall in eastern Ethiopia.

We can place the current dry conditions in deeper context by combining 1900-1980 values from the FEWS NET Centennial Trends data set with the 1981-2019 Belg totals presented above. Time series of Belg-region averages of the station-only Centennial Trends and the blended station CHIRPS data set are highly correlated (R=0.92), which allows us to combine 1900-1980 Centennial Trends data with the 1981-2019 presented in Figure 3. Figure 4 presents these combined data, smoothed with 20-year running averages. While there are some decadal variations, the recent decline appears unprecedented in the historical record. Our research (a, b) has suggested that this decline is associated with anthropogenic warming in the western Pacific.

Figure 4. A long-time series of Belg season rains, smoothed with 20-year running averages.

 

Results – changes in the length of the Belg season

We next present changes in the length of the Belg seasons (Figure 5). The number of zones with downward trends in the growing season is smaller (34), and the overall area exhibits declines in the length of the Belg season is smaller, ~65% of the area examined. These regions tend to appear in the northern parts of the Belg area. Changes in some of these northern areas appear to be very large: 20, 30, or even 40 days in some cases. For farmers, these decreases in the length of the rainy season may make it difficult to achieve substantial crop yields, especially in cooler areas with slow maturation rates. These decreases in season length may also impact important and productive “long cycle” crops, which are planted during the Belg, but harvested after the Kiremt rains come in the summer. For pastoralists, these much shorter Belg rainy seasons likely indicated longer lean times and reduced fodder and water availability.

 

Figure 5. Changes in the length of the Belg season (in days).

A time series of Belg season growing season length, averaged over the 34 zones with declining growing season lengths, is shown in Figure 6. The difference in the length of the season from year to year is notable. Good seasons average about 90 days. Poor seasons can have less than 70 days of rainfall. Overall, there appears to be a decline of about 10 days, on average. As noted above, however, this decline appears to preferentially be impacting areas in the northern Belg regions.

Figure 6. Average length of the Belg season in zones with negative length of season trends

Eastern East African March-to-May Rains Fail Again, Creating Severe Food Insecurity in Somalia

Eastern East African March-to-May Rains Fail Again, Creating Severe Food Insecurity in Somalia

Chris Funk, Juliet Way-Henthorne

This blog presents time series of March-to-May East African Standardized Precipitation Index (SPI) for eastern East Africa and Somalia. We also examine annual average maximum daily temperature data from the new CHIRTSmax data set. While the latter data only goes through 2016, it does provide a valuable assessment of the general temperature trends in these regions.

The intent is to place the region’s recent drought in a historic context, while also emphasizing the dangerous repetitive nature of these severe rainfall failures. According to FEWS NET, Drought, conflict, and macro-economic shocks in East Africa have placed millions of people in extreme food insecurity. In northern Somalia, the Famine Early Warning Systems Network anticipates emergency levels of food insecurity – conditions approaching potential famine – for some 2.2 million people due to consecutive poor rains in October-December of 2018 and March-May of 2019.  During the typical peak of the March-May season (April), exceptionally dry conditions prevailed over the northwest, northeast, and parts of the south of the country. Between January and April, the number of children admitted to health posts for treatment of severe malnutrition increased by 92%. The country faces substantial shortfalls in funding for humanitarian assistance, with only about 1.2 million out of a targeted 2.2 million currently receiving aid. This is in a situation where conditions are likely to deteriorate rapidly as stressors associated with the current dry season accumulate. 

Given this context, we begin by examining a map of March-May CHIRPS rainfall anomalies (Figure 1), calculated using a 1981-2010 baseline. As noted by many agencies, these rainfall totals were very poor over almost all of Kenya, most of eastern Ethiopia, and much of Somalia. While the most severely food insecure region is northern Somalia, here, we focus on time series of rainfall and temperature for all of Somalia and eastern East Africa (delineated with a light blue polygon in Figure 1), because i) the local food security is tied to regional food deficits, ii) the data in northern Somalia is somewhat uncertain, due to an almost complete lack of weather stations, and iii) the climatic patterns associated with these droughts tend to act on a regional spatial scale. 

Figure 1. March-to-May CHIRPS rainfall anomalies.

To produce long 1900-2019 time series of precipitation data, we use the 1900-2014 station-only Centennial Trends data set and the 1981-2019 CHIRPS precipitation archive. Time series of regional averages of the 1900-2014 and 1981-2019 Centennial Trends and CHIRPS data sets are highly correlated, with correlation values of approximately 0.9. This allows us to combine the two data sets to place the current drought in historic context. The combined time series use 1981-2019 CHIRPS values and bias-corrected 1900-1980 Centennial Trends rainfall data. The bias correction accounts for small mean differences between the two archives. Regional rainfall averages were calculated over the eastern East Africa region, outlined in light blue in Figure 1. This region has been used in numerous previous studies by the Climate Hazards Center. Regional averages were also calculated over Somalia, which was selected because of the very high levels of food insecurity in that country. These time series are expressed as SPI values, which have a mean of 0 and standard deviation of 1. 

Figure 2 shows the 1900-2019 March-May SPI time series for eastern East Africa. Since 1999, there have only been a few wet seasons: the very wet 2018, and 2010 and 2013. Conversely, 9 years had poor rainfall: 1999, 2000, 2001, 2004, 2008, 2009, 2011, 2017, and 2019. This means that the “new normal” in eastern East Africa has been a substantial or severe March-to-May drought about every other year. Such sequential rainfall deficits have had dangerous impacts, eroding resilience, economic reserves, and herd size and health.

Figure 2. Time series of eastern East Africa March-to-May SPI values.

 

 

Figure 3 shows a similar time series for Somalia. The 2016, 2017, and 2019 March-May Gu seasons were poor, as were the national averages for 2016 and 2018 during the October-December Deyr season. In Somaliland, the 2017 Deyr rains were also poor. Somalia has experienced poor Gu rains in 1999, 2000, 2001, 2004, 2008, 2009, 2011, 2012, 2017, and 2019—ten out of the last 21 years.

Averaging these time series using running 20-year averages allows us to visualize how the mean March-May rainfall conditions are changing over Somalia and East Africa (Figure 4). We see a large ~-1 standardized anomaly decline between the beginning of our data (the 1900-1919 time period), and the last 20-year time period (2000-2019). Dry has become the new normal in regions that struggle to produce enough food in the good years. Following the 1997/98 El Niño, the eastern Indian Ocean and Western Pacific saw dramatic increases in sea surface temperatures, and our research has suggested that this warming can describe the substantial post-1998 decline in East Africa precipitation. 

Figure 3. Somalia March-May Standardized Precipitation Index time series.

Figure 4. Eastern East Africa and Somalia SPI time series smoothed with running twenty-year averages.

We next briefly examine (Figure 5) annual averages from the new Climate Hazards InfraRed Temperatures with Stations Tmax product (CHIRTSmax).  The paper and data are available here. This product is uniquely suited to track temperatures in areas with poor station coverage (like Somalia). The Tmax values are based on the monthly averages of daily maximum temperatures. Here, we are examining the average of these 12 monthly values. While we are working on setting up operational updates for this data set, for now, the archive stops in 2016. What we can see (Figure 5), however, is a clear upward trend that has already increased average maximum temperatures from about 32.8 ° Celsius (91°F) to about 33.8 ° Celsius (92.8°F), just in the last 30 years or so. Very warm conditions have become substantially warmer.  These increased temperatures will exacerbate the impacts of droughts by increasing atmospheric water demand, damaging crops, and weakening the health of livestock. This means that when there is a drought, limited soil and plant moisture will return more quickly to the atmosphere through evaporation and transpiration. During grain filling, crops are likely to exhibit more temperature stress. And livestock herds will likely suffer from more health problems due to extremely warm temperatures. 

Figure 5. Average annual maximum daily temperatures for Somalia (red) and eastern East Africa (dark red), based on the new CHIRTSmax data set.

 

A Brief Analysis of March-May 2019 Climate Conditions

This blog concludes with a brief analysis of the observed March-May climate conditions. Sea surface temperature conditions in this season did not resemble those during the most recent drought years (not shown). Rather, mild El Niño-like conditions were present in the equatorial Pacific, accompanied by warm ocean waters in the southwest Indian Ocean. The latter likely contributed to the formation of cyclone Idai, a very dangerous cyclone which struck southern Africa in mid-March. Low-level geopotential height and wind anomalies (Figure 6) indicate a low-pressure anomaly in the southern Indian Ocean that helped draw moisture away from eastern Africa. Also notable were large positive high-pressure anomalies to the southeast of Australia, near 140°W and 45°S. This high-pressure cell helped drive anomalous winds and moisture transports westward into the equatorial west Pacific. 

Total precipitable anomalies measure the change in the total atmospheric moisture at a given location. Anomalies in total precipitable water track changes in moisture over time. What we see in March-May anomaly fields (Figure 7), is 1. a large reduction in moisture over eastern East Africa and the equatorial western Indian Ocean, and 2. a large increase in moisture near Indonesia. These moisture anomalies are due to the winds and pressure anomalies shown in Figure 5. Despite the absence of La Niña or strong Indian Ocean dipole conditions, conditions over the southern Indo-Pacific (Figure 6) set up a strong moisture dipole (Figure 7) that helped produce an eastern East African drought. 

Maps of reanalysis precipitation anomalies (Figure 8) reinforce the idea that even though the sea surface temperature conditions were different in 2019, the east African drought was once again associated with increased rainfall over the Maritime Continent region. The total precipitable water anomalies and precipitation anomalies over this region are striking in magnitude (~8 kg m-2 and 6 mm day-1) and quite similar to those that accompany typical La Niña-like conditions. The energy released by this precipitation is known to trigger subsidence and dry conditions to the west, over equatorial East Africa and the Indian Ocean. So while the sea surface conditions were different than those associated with La Niña-like droughts in 2011 or 2017 and most recent drought years, the equatorial rainfall dipole appears similar. In 2019, however, it appears likely to be driven by conditions in and over the southern Indian and southwestern Pacific Ocean basins. 

Figure 6. March-May low-level geopotential height anomalies and anomalous wind vectors.

Figure 7. Total Precipitable Water anomalies for March-May.

Figure 8. March-May Reanalysis rainfall anomalies.

Summary of GeoCOF Training, AGRHYMET 24-27 June 2019

Summary of GeoCOF Training, AGRHYMET 24-27 June 2019

Contributors:  Alkhalil Adoum, Tamuka Magadzire

Reinforcing partnership and capacity in seasonal forecasting in West Africa has been part of SERVIR/West Africa’s objective.  In collaboration with AGRHYMET and USGS/FEWS NET, GeoCOF training has been jointly planned and carried out to reinforce AGRHYMET and ACMAD climate scientists’ capacity in seasonal forecasting.

GeoCOF is a statistical software tool for seasonal forecasting developed by the FEWS NET/USGS activity in support of regional seasonal forecasting processes, with assistance from the SADC Climate Services Centre.  It uses multiple-linear regression modeling between climatic predictors (e.g. sea surface temperatures) and the predictant—usually seasonal rainfall totals. By implementing this training, SERVIR/WA and FEWS NET aim to improve the capacity of ACMAD and AGRHYMET climate scientists in seasonal forecasting.

The training took place at the AGRHYMET Regional Center from 24-27 June 2017, with about 15 participants from ACMAD, AGRHYMET, and FEWS NET.  The training was completed to the satisfaction of the participants, who were given attendance certificates. Looking toward the future, a social media communication group of participants was set up, and a number of proposals were put forward as a way to continue efforts to improve the accuracy of seasonal forecasts. These included:

  • regular technical information-sharing meetings involving AGRHYMET, ACMAD, FEWS NET, perhaps quarterly, covering themes such as short-term training on selected topics and sharing of results from forecast-related research and operational methodologies
  • Assess and validate the relative performance of various sources of satellite data (e.g. CHIRPS, RFE, TAMSAT, etc.) compared to rain gauge data
  • Investigate the use of alternative predictors regarding whether they can improve upon the accuracy of the SST-based forecasts
  • Propose a 2-3 day GeoCOF training (by the participants) of colleagues at AGRHYMET and ACMAD who did not manage to fully attend the original training

 

Our special thanks to SERVIR/West Africa for full financial support and AGRHYMET Regional Center for the logistic support.

Second Southern Africa Climate Webinar: Advance Warning of Extreme Precipitation and the Meteorology/Emergency-Response Interface

Proceedings of the Southern Africa Climate Webinar #2

Theme: Advance warning of extreme precipitation and the meteorology/emergency-response interface

Written by Tamuka Magadzire

The 2nd Southern Africa Climate Webinar was held on Thursday, 9 May 2019 under the theme Advance warning of extreme precipitation, and the meteorology/emergency-response interface. The webinar was organized by the SADC Climate Services Centre (CSC) and the UCSB Climate Hazards Center (CHC). Presentations and discussion focused on the production, dissemination, and application of advance warnings for extreme precipitation events.  Participants in the webinar included attendees from Botswana, Madagascar, Namibia, South Africa, Tanzania, Zimbabwe, SADC CSC, and the USA. Attendees sent in their questions during the presentations, and these were responded to by the participants.

In the first presentation, Dr. Nsadisa Faka of the SADC CSC described the disaster risk management processes in the SADC region, with the role of climate information. He outlined the early warnings that SADC CSC had issued during the development of Tropical Cyclone Idai, at least 10 days before the event took place. Dr. Nsadisa concluded with the challenges that SADC CSC and national meteorological agencies face in implementing their mandate to track and provide advisories on extreme precipitation events.

Dr. Chris Funk, from USGS and UCSB CHC, followed up with a presentation on combining satellite rainfall observations with weather model predictions to produce integrated monitoring products. In his talk, Dr. Funk described the process that the CHC has developed for bias-correcting precipitation forecasts to produce the CHIRPS-GEFS short-range forecasts. He further described how they operationally combine the CHIRPS-GEFS forecasts with observations to produce CHC early estimates that can potentially contribute to flood and drought forecasting efforts. The CHIRPS-GEFS estimates are available to view and download on the CHC website.

Mr. Clement Kalonga, a sustainable development expert with a focus on disaster risk reduction (DRR), closed out the presentations with a talk on the information needs of DRR agencies regarding extreme precipitation and flooding. Mr. Kalonga underlined the extent to which effective disaster risk management (DRM) relies on a comprehensive early warning approach in which meteorological information is integrated with hydrological, geological, and environmental analysis for comprehensive early warning that can inform preparedness and response by DRM agencies. As one possible way of meeting this need, Mr. Kalonga suggested the strengthening of existing national disaster committees and potential formation of technical multi-sectoral standing task forces that could be activated upon the early development of storm systems in the Indian Ocean. Additionally, Mr. Kalonga indicated that early warning messages can only be effective at the community level when the communities have already been sensitized to DRR through awareness and training, like simulation exercises that teach communities how to respond in a disaster situation.

In closing, participants converged on the need to initiate discussions on how climate and DRR communities can contribute to closing the identified gaps in the forecasting/early warning/disaster-response interface. The idea for a follow-up webinar exploring this was mooted.

The webinar was moderated by Dr. Tamuka Magadzire, FEWS NET regional scientist for southern Africa, with technical assistance from Ms. Juliet Way-Henthorne and Dr. Greg Husak from UCSB CHC.

 

The available abstracts for the presentations are provided below:

Presenter: Dr. Chris Funk

Title: Combining satellite rainfall observations with weather model predictions to produce integrated monitoring products

Abstract: In practice, there is a gap between weather observations and weather model predictions. While weather models often have high levels of skill, especially at approximately one-week time scales, it can be hard to combine this source of information with observations. Such combinations, however, can be very useful in disaster prediction and response. For example, when soils and saturated models predict heavy rains, floods may be a serious concern. Or when a poor start to the growing season is combined with a prediction of mid-season drought, we might be able to provide early warning of crop failures. Here, we describe a new set of bias-corrected precipitation forecasts (the CHIRPS-GEFS). We also show how these bias-corrected forecasts can be combined with observations to produce the Climate Hazard Center Early Estimates. Examples from southern and eastern Africa are provided.

Presenter: Mr. Clement Kalonga

Title: Information needs of Disaster Risk Reduction Agencies related to extreme precipitation and potential flooding

Abstract: The critical importance of early warning information to disaster risk management (DRM) cannot be overemphasized. Practically, there is very little effectiveness in DRM options in the absence of early warning information. Early warning information is not an end; rather is a critical input in the wider DRM system. This means the quality of DRM actions are highly dependent on the quality of early warning information in terms of clarity, accuracy, and timeliness, among other factors. Two critical aspects are key in this debate: namely, the conceptual understanding and application of early warning and inherent challenges in DRM systems that reduce impacts of early warning information. What constitutes early warning has often been narrowed to the dissemination of warning messages. For instance, consider communication-related to cyclone activity. This has limited handling of such information to climate scientists. However, early warning in its broadest sense should encompass risk awareness linked to emerging potential hazards triangulated with past, existing, and future risk information and vulnerability, effective monitoring and development of scenario-based warning messages, dissemination of warning with the inputs of the key stakeholders in the projected impact areas, and coordination of response options at various scales including at community level. Such inclusive stakeholder engagement improves quality of early warning information: for instance, a hydrologist can provide support with the projected impact of forecast precipitation in the area, thereby adding value to the cyclone warning for better preparedness and response. However, regardless of the quality of early warning information, most DRM systems in SADC Member States are weak. With inherently weak DRM systems, early warning impact will remain limited or negligible. Most communities have not done any risk and vulnerability mapping, to the extent that being told that a cyclone is coming their way does not mean anything in terms of what appropriate actions can be taken.

Recordings of the webinar can be found on the Climate Hazards Center YouTube account: 

https://www.youtube.com/channel/UCwOWPb5YxUfaU8QwwE4EnRQ

Average Upper Blue Nile River Flow Expected in 2019

Average Upper Blue Nile River Flow Expected in 2019

A guest blog by the Ad Hoc Blue Nile Forecast Group (listed alphabetically): Sarah Alexander (1), Paul Block (1), Annalise Blum (2), Shraddhanand Shukla (3), Shu Wu (1), Ben Zaitchik (2)*, and Ying Zhang (2).

  1. University of Wisconsin-Madison, Madison, WI, USA
  2. Johns Hopkins University, Baltimore, MD USA
  3. University of California Santa Barbara, Santa Barbara, CA, USA

*Correspondence can be addressed to zaitchik@jhu.edu

In 2018, we formed an ad hoc forecast group to provide annual outlooks for Blue Nile River flow at the beginning of each East Africa summertime rainy season. The motivation for this effort is the impending completion of the Grand Ethiopian Renaissance Dam (GERD; Figure 1). As we described in our original post on the issue, the GERD will be the largest hydropower dam in Africa. It is also the first major infrastructure project on the mainstem of the Ethiopian portion of the Blue Nile River, and the reservoir could potentially store on the order of 1.5 times the annual average flow of the river. The scope of the project and, in particular, its potential impacts on the downstream countries of Sudan and Egypt, have attracted interest and concern across the region and beyond. The reservoir filling period is expected to be particularly tense, given the potential for Ethiopia to curtail Blue Nile flows significantly in an effort to fill the reservoir and begin producing electricity as soon as possible. As recent power shortages have demonstrated, there is a pressing need for reliable electricity generation in Ethiopia, and the long-term economic impacts of the GERD are expected to be transformative.

Figure 1: The Nile Basin (yellow), including the GERD site (orange star) and GERD catchment (green).

When we produced our 2018 forecast last spring, there was some expectation that reservoir filling would commence in that year. It now appears that filling will not begin until 2020, on account of construction delays. Nevertheless, we are providing a 2019 forecast as an additional test of the forecast approach and of the communication process.

As with last year’s forecast, we have generated predictions for June-September total rainfall for the Upper Blue Nile basin, defined as the Blue Nile River basin within Ethiopia, which is upstream of the GERD site. These forecasts are then applied to a water balance model to estimate June-December Blue Nile River streamflow at the GERD site. The forecast for 2019 shows a high likelihood of near-normal conditions (Figure 2), with close to 70% of rainfall predictions and over 60% of streamflow predictions falling in the “near normal” category (i.e., the middle tercile of historical observations).

Figure 2. Percentage of forecasts in this study predicting below normal, near normal, and above normal June-September rainfall (left) and June-December streamflow at the GERD site (right) for 2019. Forecasts include eight NMME models (ensemble mean for each model with forecasts adjusted for mean biases) and eight statistical models. NMME models were adjusted based on each model’s historical mean and variability and rescaled to be consistent with the mean and variability of the climatology. The slightly higher chance of below normal flows is the result of one additional model (Blum 1) falling just below the near normal category for the streamflow predictions, but just within near-normal for rainfall.

Our precipitation forecasts comprise both statistical and dynamically based methods. Further information on these models is available in our 2018 forecast blog post. Statistical methods include previously published models, with coefficients refit to the available historical record and several original statistical forecasts developed by our group. The dynamically based forecasts are drawn from the North American Multi-Model Ensemble (NMME) seasonal forecasts. We use all ensemble members from all NMME models for which May-initialized forecasts were available at the time of writing. Figure 3 shows the results of statistical and dynamically based forecasts separately for rainfall and streamflow. In general, the NMME dynamically based forecasts are wetter and have a wider ensemble spread compared to the statistical forecasts, but the majority of predictions from both approaches are for near-average conditions. This forecast can be compared to our 2018 predictions, in which we forecast a higher probability of above-average flows (Figure 4).

Figure 3. (A) Boxplots of June-September rainfall climatology (1982-2017), according to CHIRPS rainfall estimates (grey), and of the 2019 forecasts for dynamically-based models participating in the North American Multimodel Ensemble (NMME; green) and for statistically-based models applied in this study (blue). Dashed lines show the upper and lower terciles of historical rainfall totals. (B) As in (A), but for the June-December forecast Blue Nile flow at the GERD site. NMME models are adjusted by model for variability and scaled to climatology mean and variability. Boxplots illustrate the interquartile range (IQR) with the horizontal line showing the mean value and points represent outliers (greater than  1.5*IQR away from the box.)

Figure 4. Same as Figure 2 but including a comparison to our forecast from 2018 (See http://blog.chg.ucsb.edu/?p=364) for details. Note that there were minor updates to methodology between 2018 and 2019, so this comparison is qualitative.

Examining each NMME model and statistical model separately (Figure 5), we see that there is considerable spread in the NMME multimodel ensemble, but that the median prediction of every NMME model is for near-normal or above-average rainfall. Spread is smaller between the statistical models, but two models do have median rainfall predictions that fall in the bottom tercile of the historical record.

Figure 5. Boxplots illustrating range of climatology (CHIRPS rainfall 1982-2017),  individual NMME ensemble predictions, and statistical models with re-sampled error added back in (10,000 resampled errors based on leave-one-out hindcasts for 1982-2017). Dashed lines show below/above normal conditions.

We are currently experiencing a weak-to-moderate El Niño (Figure 6). El Niño events are associated with drought in the Blue Nile basin, so in a sense, it is surprising that so many of the models—both statistical and dynamically-based—predict average or above average rainfall when there is an active El Niño. This forecast optimism stems from the fact that the current El Niño is weak. Assuming that it does not intensify—and projections are that it will persist but is not likely to intensify—we expect the low risk of an El Niño-induced drought in the Blue Nile in the coming months.

Figure 6: Probabilistic ENSO forecast, issued May 20, 2019, and available from the NOAA Climate Prediction Center.

We do note that the evolution of El Niño over the rainy season could have a significant impact on the performance of the prediction models included in our analysis. When we generated our original forecasts in May 2018, for example, the El Niño Southern Oscillation (ENSO) was in a neutral state, and intensification towards El Niño was not expected to occur for several months. But El Niño arrived in the middle of the summer rainy season, sooner than had been predicted. In the end, total June-September rainfall ended up being close to the long-term mean, while the forecasts that we issued in May included many members that forecast above-average conditions. We reported this shift in our July 2018 forecast update, which already showed signs that rainfall totals would be lower than had been expected in May. The arrival of El Niño likely tamped down rainfall totals, such that the beginning-of-season forecasts proved to be overly optimistic. As the 2019 rainy season evolves, it will be useful to keep an eye on the evolution of the current El Niño event and how it influences rainfall predictions in NMME and other operational forecast systems.

Climate Hazards Center Collaborates with RCMRD and SERVIR to Facilitate Climate-informed Decision Making in Eastern and Southern Africa

Climate Hazards Center Collaborates with RCMRD and SERVIR to Facilitate Climate-informed Decision Making in Eastern and Southern Africa

Contributors:
Shraddhanand Shukla, Greg Husak, Juliet Way-Henthorne, & Denis Macharia

Key Takeaways:

  • The Climate Hazards Center, in collaboration with RCMRD and SERVIR, moves into year-3 of the “Enhancing Eastern and Southern Africa Climate Services by Increasing Access to Remote Sensing and Model Data Sets” project.
  • The CHC and partners utilize three methods of capacity building—introduction to web services, hands-on training, and an “empower-the-trainers” approach to maximize impact.
  • This project benefits both the trainees and the CHC through the reciprocal nature of collaborative training. The trainees are introduced to advanced EOs and web services, and the CHC comprehends the existing needs and challenges in the application of EOs and web services.

Project History: Scope and Goals

Since July 2016, the University of California, Santa Barbara’s Climate Hazards Center (CHC) has worked with the Regional Centre for Mapping of Resources for Development (RCMRD) through support from the SERVIR-Applied Sciences Team (AST) program to empower technical professionals in key regions of southern and eastern Africa by utilizing various methods of capacity building, transfers of technology, and an “empower-the-trainers” approach.  To briefly summarize the project—which will end in June 2019—the primary outcome of employing a variety of techniques to best equip trainees to integrate Earth observation (EO) information and geospatial technologies into their climate services to regional decision makers has been largely successful. Additionally, the project allows the CHC to further its own mission of protecting the lives and livelihoods of at-risk communities through hands-on trainings and demonstrations of CHC tools and techniques that allow for early warning as it relates to food security and climate. Knowledge sharing builds a wider network of capable climate service providers, which, in turn, creates a ripple effect, as these trainees share new resources and skills with decision-making stakeholders in the region.

Obtaining publicly accessible, analysis-ready EO data is challenging for decision makers across the developing world, as the ability to transform data to workable information is often a critically missing link. Data sets and models can be applied to inform food security and water resource-related decisions, helping to improve monitoring and forecasting of droughts, water availability, and climatic conditions. However, without knowledge of how to access or understand this information, these valuable data sets and models go underused.

To combat this challenge within a SERVIR-AST project, the CHC collaborated with SERVIR eastern and southern Africa hub at RCMRD to co-host this series of regional workshops in Kenya, Tanzania, and Zambia. These trainings focused on enabling local and national climate service providers to confidently and effectively utilize such data sets and tools, ultimately allowing these key individuals to make better-informed decisions.

Regional Workshops:

The first workshop, which served primarily as a framing workshop through which to gauge climate service providers ’ needs, was held in Nairobi, Kenya between September 12th and 15th, 2017. The workshop’s location was strategically chosen as a base for local attendees who represented a variety of agencies focused on different sectors of agricultural development, relief effort, and crop insurance, including Kenya Forest Services (KFS Hqs), the World Food Programme (WFP), and IGAD Climate Prediction and Applications Centre (ICPAC). Attendees were drawn from RCMRD’s mandated countries—the parameters of which aided in selecting all workshop locations. By beginning with workshops focused on a local scale before gradually building outward to incorporate more regions, the CHC was able to give an overview of several web services and case studies to further refine training materials. This introductory workshop also demonstrated the needs of a typical user of data sets and models to best assess the needs of participating technical professionals.

The second workshop (held between January 30th and February 2nd, 2018, in Dar Es Salam, Tanzania) of year-2 of the AST project trained partners in web applications to access climate and hydrologic data sets and demonstrated how to apply this information to generate seasonal climate scenarios, agricultural drought monitoring, water-resource management, and index insurance. The next workshop (held between September 4th and 7th, 2018 in Lusaka, Zambia) focused on further enhancing climate services in the region.

“Connecting Space to Village” SERVIR Hydroclimate Training in Lusaka, Zambia. Day 1 comprised of an introduction to Climate Hazards Center data sets.

Participants in the 2018 SERVIR-AST training in Lusaka, Zambia access NASA and FEWS NET’s Land Data Assimilation System (FLDAS) simulations’ spatial and time series maps & data through NASA’s GIOVANNI.

Training events showcased how to access and use analysis-ready EOs, such as the CHC’s Infrared Precipitation with Station Data (CHIRPS), as well as tools like the Early Warning Explorer and ClimateSERV and modeled data from the Famine Early Warning Systems Network Land Data Assimilation System. Exposure to these workshop training materials resulted in use cases across countries and disciplines, with users applying these resources to their own decision-making needs.

A workshop participant noted that they had “been able to apply the skills gained. In January, together with colleagues, we were able to generate scenarios of seasonal rainfall for Malawi for the remainder of the season.”

These trainings then culminated in the enhanced capacity of over twenty agrometeorological technical professionals to better use EO data to address water resources and agricultural decisions in Africa. In addition, several participants discussed the future training of colleagues, which exemplifies the “empower-the-trainers” approach, as well as the increased impact and reach of these workshops.

One participant stated, “I am using some of the data sets introduced [during the training] in my day-to-day climate analysis, including forecasting. In addition, I have shared the tools with my colleague, who works in the agro-climate section, so that they can use the information from these tools to advise farmers and the ministry.”

CHC Project Expectations and Predicted Outcomes:

Through the continued collaboration of the CHC, RCMRD, and SERVIR-eastern and southern Africa, we are able to provide critical guidance to those with the direct, immediate ability to provide much-needed climate information to decision makers, often at crucial and timely moments. By offering such trainings, data becomes relevant, readily applicable information that better informs food security and water resource-related decisions. As one trainee wrote, “The training has equipped participants with new skills to process hydro-climate information for decision making, it is useful, relevant, and important.”

Working directly with partners in Africa has highlighted the need and potential for EOs in eastern and southern Africa. Throughout these trainings, we have seen technical professionals from national and regional met and hydrologic agencies using data and techniques acquired through this project, and we understand that the information is being used to inform decisions about water resources and agriculture. Helping those professionals acquire these tools and seeing monitoring techniques improve as they help to better assess the conditions on the ground is both invigorating and instructive, as such growth is the direct result of this project. As the project nears its final stage, we aim to incorporate and translate the feedback from all previous trainings into instructions to ensure that the framework best targets the needs of its participants. With this objective in mind, we believe that trainees will be better equipped, both in skills and resources, to access and apply these EOs with confidence.  

The value of these trainings cannot be overstated, as they benefit not only decision makers and key stakeholders, but also the CHC itself. Constructive feedback allows the CHC to evolve its training methods and content, creating a cycle of betterment that will ultimately reach a vast audience of eager and engaged climate service providers and decision makers. As such, the capabilities and tools of the CHC will have a greater impact, equipping people in the region with the skills necessary to access critical data sets and monitoring techniques. Additionally, the CHC gains invaluable face-to-face time with key stakeholders at regional and national levels. While currently in Year-3 of the project, continued feedback means that through the remainder of the project, participating technical professionals will receive training that has been tested and then altered to meet the trainees’ primary needs. Such activities that connect technical professionals to the CHC’s tools and techniques speaks to the lasting legacy that the Center intends to create.

CHC members Greg Husak and Shraddhanand Shukla, along with FEWS NET’s Tamuka Magadzire and SERVIR-E&SA’s Denis Macharia, with project participants in Lusaka, Zambia.

The benefits of such trainings span the breadth of the Climate Hazards Center’s widespread network of affiliates and partners. Denis Macharia, Weather and Climate Lead of SERVIR E&SA and CHC’s primary collaborator at RCMRD, states that “Data and skills are the two most critical needs of a hydrometeorologist in order to successfully accomplish his or her duties. In our region, accessing data can be a challenge, and when it is possible, still requires expertise.  Our partnership with UCSB and hydromet services agencies in the E&SA regions addresses these challenges quite remarkably. In the last 5 years, the UCSB team has produced a consistent rainfall data set and recently, an analogous temperature data set. Both data sets support analyses that are proving useful in addressing developmental challenges in the E&SA countries. The partnership is also providing key skills that are necessary to realizing maximum potential in the use of the data sets, training technical users in different applied skills like climate seasonal scenarios development and hydrological modeling. SERVIR will continue to ensure that data and skills gained so far are sustained over the long term and that agencies in the region continue to use these to inform decisions made by various stakeholders to address developmental challenges experienced in different sectors.”

For further reading on this project, please view the following articles courtesy of SERVIR GLOBAL, RCMRD, and NASA Applied Sciences Program:

https://www.servirglobal.net/Global/Articles/Article/2687/transforming-data-into-information-for-improved-food-security-decisions-in-east

https://rcmrd.org/regional-training-workshop-on-applied-hydro-climate-services-in-zambia

https://appliedsciences.nasa.gov/content/transforming-data-information-improved-food-security-decisions-eastern-and-southern-africa